Data Wrangling with pandas

8. Data Wrangling with `pandas`#

8.1. Introduction: Why It’s So Important to Get Data Management Right #

8.1.1. The Case of “Genomic Signatures to Guide the Use of Chemotherapeutics”#

In 2006, a study “Genomic Signatures to Guide the Use of Chemotherapeutics” was published in Nature Medicine. This study tackles the question of why chemotherapy works well for some cancer patients and less well for others. The authors claimed that they could predict whether and how treatments would work for a patient based on the sequence of chemicals in the DNA in the patient’s cancer cells. This study got a lot of attention as a major breakthrough in cancer treatments. The hope was to use genomic data to tailor a treatment regime for individual patients that maximizes the likelihod that the treatment would be effective.

Unfortunately, as you can see on the journal’s website for this article, the study has been retracted. The story of how this study came into question, and the damage it did before it was retracted, is described by Keith Baggerly in his talk “The Importance of Reproducibility in High-Throughput Biology: Case Studies in Forensic Bioinformatics”:

from IPython.display import IFrame
IFrame(src="https://www.youtube.com/embed/7gYIs7uYbMo", width="560", height="315")

The slides for this talk are available here. In short, Baggerly and his coauthor Kevin R. Coombes attempted to reproduce the findings in the Potti et al study, and they were unable to do so. There is a difference between reproduction and replication: reproducing a study involves using the same data used in the original study and writing code that yields the exact same results; replication involves repeating the study using new data. While replication is an essential practice for establishing the validity of a study, replication is also a higher standard than reproduction. If a study cannot be reproduced, then there must have been errors in the process collecting and cleaning the raw data that led to the publication of incorrect results.

After deciding to replicate the Potti et al article, Baggerly and Coombes were able to get a copy of the raw data that were used in the study, but they did not find scripts or documentation that described the steps the original study took to clean the raw data. That’s because there were no scripts and no documentation: the data were cleaned on the fly, probably with a point-and-click interface like Microsoft Excel, with no records of what was done to the data.

Without having good documentation available that explains how research was done, Baggerly and Coombes had to resort to “forensics”. They started with the results and the raw data, and they inferred what the researchers must have done to obtain the reported results from the raw data. They were alerted to potential problems with the study when they tried to replicate the finding that there is a clear difference between two types of drugs, as shown in the graphic on slide 13 of Baggerly’s slide deck. It should have been an easy finding to replicate. Instead they found significant overlap between the two drugs, as shown in the graphic on slide 14. They responded by trying to reproduce exactly what was done to the raw data in the original study to produce the first finding. After exhausting all possible valid data management steps, they started making mistakes on purpose to see if one of those mistakes would replicate the published results.

Baggerly and Coombes found that the authors of the original paper made an error in which the codes for each reported gene were off by one. That is, the data for each gene is matched to the label for the gene whose data exists one row above. This error happened, according to Baggerly’s best guess, because the software that the authors used requires the inputted data file to NOT have a header row for column names. But they seemed to just copy and paste the data, along with the header row. A second result that Baggerly says should “bother you” is that, on the heat map on slide 29, Baggery and Coombs matched 6 of the 7 heat maps despite the fact that they only matched the reported gene names for 3 of the 7 drugs. Therefore they would expect to reproduce the heat map for only these 3 drugs. Baggerly doesn’t know why they matched the heat maps for the other 3 drugs despite the fact that they are using the wrong data.

A second discrepancy that raises a red flag for Baggerly is the plot showing the sensitive and resistant patients, as compared to the analogous plot from the paper that introduced the test dataset. The plot shows 13 sensitive and 11 resistant samples, whereas the paper that introduced the data shows 11 sensitive and 13 resistant samples. Baggery believes that the authors mixed up the 0 and 1, indicating sensitive and resistant, in the spreadsheet.

In short, Baggerly and Coombes were only able to reproduce the findings in the Potti et al study by making “small” mistakes on purpose. When I say small, I mean that these sorts of errors can occur frequently when using a point-and-click interface for data management and a copy-and-paste method for transfering data. These errors also occur without informing the user of any errors. But in practice these small errors have profound impacts on the results of the study. If those results are taken seriously and applied outside of the specific study, these mistakes can do a lot of harm in the world.

In the case of the Potti et al study, while Baggerly and Coombes were investigating the paper, drug trials began at Duke University based on the results of the study. The treatment was administered to the patients with childhood leukemia. Because of the mistake that swaped the 0s and 1s, the treatment was administered to patients who are resistant to it, will not benefit from it, and may in fact be harmed by it.

Baggerly and Coombes wrote a report detailing these inconsistencies and a few others - there’s evidence that some samples were reused 2, 3, or 4 times, and 4 genes appear in the results despite the fact that they are not included in the test data - and sent the report to Nature Medicine. Their report caused Duke to stop the clinical trials of these drugs pending an investigation. But a short while later the trials were restarted, with the statement that the investigation had “strengthened confidence in this approach.” The investigators refused to share their findings. Baggery and Coombs has to issue a freedom of informatiom act (FOIA) request to see the results of the investigation, only to find that no mention was made of the problematic drugs. Writing directly to Duke yielded no response. They had to get many prominent biostatisticians to sign a joint letter of concern. The letter got some attention in the media, but mostly because it was revealed that the principal investigator of the flawed studies lied on his CV about being a Rhodes scholar! Only after the letter generated a serious amount of negative media attention for Duke did the trials get suspended.

8.1.2. The Reproducible Research Movement #

There are four lessons we can learn from that Baggerly and Coombes’s work to reassess the Potti et al. First, mistakes at the data management stage happen all the time, and when the environment uses a point-and-click interface the errors are easier to miss because of a lack of a sufficient error system. Second, small errors can have big effects. It’s easy to confuse categories when they are labeled with arbitrary numbers instead of words, but if that confusion results in mixing up the treatment and control groups then every effect reported in the study will have the incorrect sign, and all of the conclusions we draw will be the exact opposite of the truth. Third, it is common for people who do work with data to fail to keep records of their code and the steps they used to manipulate the raw data prior to an analysis, and without documentation, it can be extremely difficult to impossible to catch errors in the data management process. Fourth, while most scientists are hopefully open to seeing and admitting their mistakes, there aren’t a lot of incentives in academia and other fields to make the research stream more transparent.

In “The Reproducible Research Movement in Statistics”, Victoria Stodden describes the problem:

The research system must offer rewards for reproducible research at every level from departmental decisions to grant funding and journal publication, incorporating notions of code and data sharing into institutional promotion and hiring and grant proposal review. The current academic research system places the primary emphasis on publication and little on reproducibility. This has the effect of penalizing those researchers that produce reproducible computational research. Software development has been characterized as support of science rather than doing real science. The result is that scientists are discouraged from spending time writing, testing, or releasing code. With the ever-increasing pervasiveness of computation and software across the research landscape, such attitudes and practices must change (p.1069-1070).

As a result of these issues, the last decade has seen a remarkable cultural shift in fields that use data towards making data management easier and more transparent. These efforts are often called part of the “reproducible research movement”. The movement involves two efforts: first, a greater push to develop software that makes it easier to clean data and to share and communicate code; second, a drive to change standards of best practice in fields that use data. Since Victoria Stodden’s article in 2013, data science has incorporated both parts of the reproducible research movement. In Python, the pandas package makes it much easier to clean data using code that is intuitive and relatively easy to follow, and Jupyter notebooks combine code, results, and text that explains the code in one document. Modern data science requires practioners to share or be ready to share their code, often in a notebook.

8.1.3. The Origin of `pandas`#

The pandas package in Python is written and maintained by Wes McKinney, who also wrote Python for Data Analysis. On the pandas team page, under governance, McKinney is listed as the pandas “Benevolent Dictator for Life”. The phrase “Benevolent Dictator for Life” is given to leaders of important open-source projects, and originated with Guido van Rossum, the creator of Python.

McKinney developed pandas in 2008 and first released the project publicly in 2009. Though not explicitly presented as a contribution to the reproducible research movement, the goal of pandas was to replace existing tools for data manipulation that had led to major mistakes in research, as was the case for the Potti et al article. According to this profile of Wes McKinney in Quartz Magazine, pandas “makes it so that data analysis tasks that would have taken 50 complex lines of code in the past now only take 5 simple lines, because McKinney already did the heavy lifting.” Specifically:

Python was missing some key features that would make it a good language for data analysis. For example, it was challenging to import CSV files (one of the most common formats for storing datasets). It also didn’t have an intuitive way of dealing with spreadsheet-like datasets with rows and columns, or a simple way to create a new column based on existing columns.

Pandas addressed these problems. David Robinson, a data scientist at Stack Overflow, explained the importance of it in technical terms. “The idea of treating in memory data like you would a SQL table is incredibly powerful,” he says. “By introducing the ‘DataFrame,’ Pandas made it possible to do intuitive analysis and exploration in Python that wasn’t possible in other languages like Java. And is still not possible.”

That is, pandas, like SQL, is a declarative language, not a procedural one. Simple functions and methods in pandas call underlying imperative code that performs the requested action by operating on vectors, matrices, and arrays. pandas as much as possible uses numpy functions to perform actions as quickly as possible (by using numpy’s vectorized code). In short, the motivation for the development of pandas is to make data manipulation as straightforward as possible and for those functions to run as quickly as possible.

pandas is an extremely popular package for data science, and the Quartz article asserts that much of the growth in popularity that Python has experienced on Stack Overflow can be attibuted to pandas. While pandas is currently the 34th most frequently downloaded Python package, it is certainly one of the most frequently downloaded for data science applications in Python.

8.1.4. Tidy Data #

In Module 7, we discussed the concept of Tidy Data as described by Hadley Wickham, founder of RStudio and creator of the tidyverse packages in R, in the article “Tidy Data”. Tidy Data is an organizational schema for data that makes most kinds of data analysis possible. To qualify as tidy, data must follow these rules:

Each [feature] forms a column.

Each observation forms a row.

Each type of observational unit forms a table (p. 2).

In addition, even when data are arranged in a single table in which records are rows, features are columns, and all records are of like units, there are many steps a researcher should take to manipulate the data to make a dataframe easier to work with. Hadley Wickham defines four essential “verbs” of data manipulation:

Filter: subsetting or removing observations based on some condition.

Transform: adding or modifying variables. These modifications can involve either a single variable (e.g., log-transformation), or multiple variables (e.g., computing density from weight and volume).

Aggregate: collapsing multiple values into a single value (e.g., by summing or taking means).

Sort: changing the order of observations (p. 13).

This philosophy of data management is built into the tidyverse packages for R, but it also forms a guiding principle for data manipulation with pandas because pandas includes tools to accomplish all of these tasks. In fact, Hadley Wickham and Wes McKinney are currently working together on a project called https://ursalabs.org/, which aims to bridge the R-Python divide and to develop platform-independent data science tools.

8.2. Example: The American National Election Study #

The American National Election Study (ANES) is a massive public opinion survey conducted after every national election. It is one of the greatest sources of data available about the voting population of the United States. It contains far more information than a typical public opinion poll. Iterations of the survey contain thousands of features from thousands of respondents, and examines people’s attitudes on the election, the candidates, the parties, it collects massive amounts of demographic information and other characteristics from voters, and it records people’s opinions on a myriad of political and social issues.

Prior to each election the ANES conducts a “pilot study” that asks many of the questions that will be asked on the post-election survey. The idea is to capture a snapshot of the American electorate prior to the election and to get a sense of how the survey instrument is working so that adjustments can be made in time. Here we will work with the 2019 ANES pilot data. To understand the features and the values used to code responses, the data have an associated questionnaire and codebook. The pilot data were collected in December 2019 and contain 900 features collected from 3,165 respondents.

Despite a great dfeal of pre-processing on the part of the data authors, the data is not currently in form we can analyze. We will use the tools available in pandas to manipulate the data and prepare it for analysis. The data are available in CSV, SAS, and Stata formats. I put the CSV version on my Github repository so that we can have easier access to it, but I encourage you to access the data yourself by registering for a free account on electionstudies.org.

We start by loading the numpy and pandas packages, along with some additional packages and modules to perform some specialized tasks. Please use pip install or conda install to install these packages if you have not yet done so:

import numpy as np
import pandas as pd
import sidetable
import warnings
warnings.filterwarnings('ignore')

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[2], line 3
      1 import numpy as np
      2 import pandas as pd
----> 3 import sidetable
      4 import warnings
      5 warnings.filterwarnings('ignore')

ModuleNotFoundError: No module named 'sidetable'

Then we load the data with pd.read_csv():

anes = pd.read_csv("https://github.com/jkropko/DS-6001/raw/master/localdata/anes_pilot_2019.csv")

These data are technically in tidy format because the records are stored in the rows, the features are stored in the columns, and the rows all represent individual people. But there are still problems that will keep us from being able to use the data in analyses. There are more features here than we can manage in a feasible amount of time. Many of the features have names that we might want to change to be more descriptive, and use arbitrary numeric codes to stand in for categories. If we look at the codebook, we will also see that negative numbers in the cells represent missing datapoints, and unless we take steps to replace these datapoints they will be read as valid numeric data. We will use the tools in pandas to manipulate the data so that we keep only the features of interest, labels throughout the data are intuitive and readable, columns have the correct data type, and missing datapoints are coded as missing.

8.3. Tools for Understanding the Data and Diagnosing Problems #

Before we can start manipulating the data, we need to be able to effectively look at the data to catch the problems that we need to address. All of these tools exist as methods or attributes of a pandas dataframe or of a specific column of a dataframe. This discussion reviews some of the material covered in Module 2, because these methods are useful for assessing both whether a data file loaded properly and for seeing the problems that still exist after the data have been loaded.

First, to see the dimensions of a dataframe and the dataframe’s memory usage, we can use the .info() method:

anes.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3165 entries, 0 to 3164
Columns: 900 entries, version to flag_state
dtypes: float64(223), int64(600), object(77)
memory usage: 21.7+ MB

In this case, the anes data has 3,165 rows and 900 columns, and takes up a little bit more than 21.7 MB of memory. The .info() output also tells us that of the 900 columns, 223 have float (numeric with decimals) valued data, 600 have integer valued data, and 77 have object values, which refers to strings or categories.

Viewing the data table itself can be extremely useful. As we discussed in module 2, however, the default behavior limits the number of rows and columns that can be displayed in a dataframe. I find it useful to turn off the limit on the number of columns that can be displayed:

pd.set_option('display.max_columns', None) 

Now we can see all of the columns in any dataframe we call. With 900 columns, there will be quite a lot of sidescrolling, and it will be difficult to find any one column in particular. Viewing the data will become more useful once we limit the columns we will include in the data. To see the first few rows, use the .head(n) method, where n is the number of rows from the top to display. The first three rows, for example, are

anes.head(3)

	version	caseid	weight	weight_spss	form	follow	reg1a	reg1b	liveurban	youthurban	placeid1a	placeid1a_t	placeid1b	placeidimport	turnout16a	turnout16a1	turnout16b	turnout16c	vote16	turnout18a	turnout18a1	particip_1	particip_2	particip_3	particip_4	particip_5	particip_6	particip_7	particip_8	particip_9	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftimmig1	ftimmig2	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales	vote20dem	vote20cand	vote20cand2	electable	vote20jb	vote20ew	vote20bs	cvote2020	tsplit1	contact1a	contact1b	contact2a	contact2b	contact3	contact4m_1	contact4m_2	contact4m_3	apppres5	frnpres5	immpres5	econpres5	apppres7	frnpres7	immpres7	econpres7	mip	econnow	finworry	confecon	improve1	national1	national2	conspire1	conspire2	conspire3	taxecon	billtax	trade1	trade2	trade3	trade4	richpoor2	guarinc	lcself	lcd	lcr	pop1	pop2	pop3	corrupt	immignum	refugees	dreamstr	dreamer	dreamstr1	dreamstr2	wall5	wall7	pathway	preturn	popen	release1	release2	famsep	tchina	trussia	tiran	tmexico	tjapan	hlthcare1	hlthcare2	abortion1	abortion2	freecol	loans	diversity5	diversity7	language	buyback	gw1	gw2	knowopioid1	knowopioid2	opioiddo	sentence	prek	demo4	experts	science	exphelp	elite1	elite2	elite3	elite4	ukraine1	ukraine2	excessive	rural1alt1	rural1alt2	rural2alt1	rural2alt2	rural3alt1	rural3alt2	rural4alt1	rural4alt2	conf_unemp	unemp	conf_interfere	interfere	conf_autism	autism1	autism2	conf_gmo	gmo1	gmo2	conf_warm	warm	conf_illegal	illegal	impeach1	impeach2	pk_cjus	pk_cjus_correct	pk_germ	pk_germ_correct	pk_sen	pk_spend	pk_geer	cheat	pid7x	pid1d	pid2d	pid1r	pid2r	pidstr	pidlean	ngun	shooting	dem_activduty	milyears	milyr1	milyr2	milyr3	milyr4	milyr5	milyr6	combat	harass1a	harass1b	rr1	rr2	rr3	rr4	health1a	health1b	hospital	feet	inches	nweight	bmi	disable1	disable2	disable3	disable4	disable5	disable6	smoker1	smoker2	exercise	relig1a	relig1a_txt	relig2a	att1	att2	att3	relig1b	relig1b_t	relig2b	relig3b	relig3b_t	attother	exptravel_ever	exphomesch	expfarm	expffood	expconvert	expholiday	explie	expshark	expdivorce	exparrest	expoverdose	expdefault	exppubasst	exphybrid	expmistake	explightning	exptravel_year	expindian	exphunt	expflag	exppublib	explottery	expshoponline	exppubtrans	expfight	expavoid	expknowimmig	expknowtrans	expbuyusa	expretire	expcolldebt	expknowpris	socmed_1	socmed_2	socmed_3	socmed_4	socmed_5	socmed_6	socmed_7	socmed_8	socmed_9	socmed_t	facebook1	facebook2	facebook3	twitter1	twitter2	twitter3	instagram1	instagram2	instagram3	reddit1	reddit2	reddit3	youtube1	youtube2	youtube3	snapchat1	snapchat2	snapchat3	tiktok1	tiktok2	tiktok3	raceid	racework	whitejob	race_sub1	race_sub2	voterid1	voterid2	serious	photo1	photo2	photo3	photo4	reinterview	birthyr	gender	educ	marstat	child18	race	employ	employ_t	faminc_new	votereg	ideo5	pid7	newsint	presvote16post	presvote16post_t	pew_bornagain	pew_religimp	pew_churatd	pew_prayer	religpew	religpew_t	religpew_protestant	religpew_protestant_t	inputstate	zipCode	FIPCounty	region	EnrollmentDate	CompletedSurveys	qualityControl_overall_scale	tsmart_P2012	tsmart_G2012	tsmart_P2016	tsmart_G2016	tsmart_P2018	tsmart_G2018	illegal_skp	pk_geer_skp	rr_grid_skp	follow_page_timing	reg1a_page_timing	liveurban_page_timing	youthurban_page_timing	placeid1a_page_timing	placeidimport_page_timing	turnout16a_page_timing	vote16_page_timing	turnout18a_page_timing	particip_page_timing	fttrump_page_timing	ftobama_page_timing	ftbiden_page_timing	ftwarren_page_timing	ftsanders_page_timing	ftbuttigieg_page_timing	ftharris_page_timing	ftblack_page_timing	ftwhite_page_timing	fthisp_page_timing	ftasian_page_timing	ftmuslim_page_timing	ftillegal_page_timing	ftimmig1_page_timing	ftjournal_page_timing	ftnato_page_timing	ftun_page_timing	ftice_page_timing	ftnra_page_timing	ftchina_page_timing	ftnkorea_page_timing	ftmexico_page_timing	ftsaudi_page_timing	ftukraine_page_timing	ftiran_page_timing	ftbritain_page_timing	ftgermany_page_timing	ftjapan_page_timing	ftisrael_page_timing	ftfrance_page_timing	ftcanada_page_timing	ftturkey_page_timing	ftrussia_page_timing	ftpales_page_timing	vote20dem_page_timing	vote20cand_page_timing	vote20cand2_page_timing	electable_page_timing	vote20jb_page_timing	vote20ew_page_timing	vote20bs_page_timing	cvote2020_page_timing	tsplit1_page_timing	contact1a_page_timing	contact3_page_timing	apppres7_page_timing	frnpres7_page_timing	immpres7_page_timing	econpres7_page_timing	mip_page_timing	econnow_page_timing	finworry_page_timing	confecon_page_timing	improve1_page_timing	national1_page_timing	national2_page_timing	conspire1_page_timing	conspire2_page_timing	conspire3_page_timing	taxecon_page_timing	billtax_page_timing	trade1_page_timing	trade2_page_timing	trade3_page_timing	trade4_page_timing	richpoor2_page_timing	guarinc_page_timing	lc_grid_page_timing	pop_grid_page_timing	corrupt_page_timing	immignum_page_timing	refugees_page_timing	dreamer_page_timing	wall7_page_timing	path_grid_page_timing	threat_grid_page_timing	abortion1_page_timing	abortion2_page_timing	freecol_page_timing	loans_page_timing	diversity7_page_timing	language_page_timing	gw_grid_page_timing	knowopioid1_page_timing	knowopioid2_page_timing	opioiddo_page_timing	prek_page_timing	demo4_page_timing	experts_page_timing	science_page_timing	exphelp_page_timing	elite1_page_timing	elite2_page_timing	elite3_page_timing	elite4_page_timing	ukraine1_page_timing	ukraine2_page_timing	rural1alt1_page_timing	rural2alt1_page_timing	rural3alt1_page_timing	rural4alt1_page_timing	unemp_page_timing	interfere_page_timing	autism1_page_timing	gmo1_page_timing	warm_page_timing	illegal_page_timing	impeach1_page_timing	impeach2_page_timing	pk_cjus_page_timing	pk_germ_page_timing	pk_sen_page_timing	pk_spend_page_timing	pk_geer_page_timing	cheat_page_timing	pid1d_page_timing	pid1r_page_timing	pidstr_page_timing	dem_activduty_page_timing	harass1a_page_timing	rr_grid_page_timing	health1a_page_timing	health1b_page_timing	tall_page_timing	nweight_page_timing	relig1a_page_timing	att1_page_timing	att2_page_timing	att3_page_timing	attother_page_timing	exp1_grid_page_timing	rexp1_grid_page_timing	cexp1_grid_page_timing	socmed_page_timing	facebook1_page_timing	facebook2_page_timing	facebook3_page_timing	youtube1_page_timing	youtube2_page_timing	youtube3_page_timing	snapchat1_page_timing	snapchat2_page_timing	snapchat3_page_timing	raceid_page_timing	racework_page_timing	whitejob_page_timing	voterid1_page_timing	serious_page_timing	reinterview_page_timing	ord_fttrump	ord_ftobama	ord_ftbiden	ord_ftwarren	ord_ftsanders	ord_ftbuttigieg	ord_ftharris	ord_ftblack	ord_ftwhite	ord_fthisp	ord_ftasian	ord_ftmuslim	ord_ftimmig1	ord_ftjournal	ord_ftnato	ord_ftun	ord_ftice	ord_ftnra	ord_ftchina	ord_ftnkorea	ord_ftmexico	ord_ftsaudi	ord_ftukraine	ord_ftiran	ord_ftbritain	ord_ftgermany	ord_ftjapan	ord_ftisrael	ord_ftfrance	ord_ftcanada	ord_ftturkey	ord_ftrussia	ord_ftpales	ord_electable_1	ord_electable_2	ord_conspire1	ord_conspire2	ord_conspire3	ord_lcself	ord_lcd	ord_lcr	ord_pathway	ord_preturn	ord_popen	ord_release1	ord_release2	ord_famsep	ord_tchina	ord_trussia	ord_tiran	ord_tmexico	ord_tjapan	ord_gw1	ord_gw2	ord_elite1	ord_elite2	ord_elite3	ord_elite4	ord_exptravel_ever	ord_exphomesch	ord_expfarm	ord_expffood	ord_expconvert	ord_expholiday	ord_explie	ord_expshark	ord_exptravel_year	ord_expindian	ord_exphunt	ord_expflag	ord_exppublib	ord_expknowimmig	ord_expknowtrans	ord_expbuyusa	ord_att2_reverse	starttime	endtime	duration	pop_density_public
0	ANES 2019 Pilot Study version 20200204	1	1.34719693063187	1.10160293017768	1	2	2	-1	3	4	1	__NA__	-1	1	1	-1	-1	-1	3	2	-1	2	2	2	2	2	2	2	2	1	47	90	52	52	49	997	50	99	99	99	100	88	79	97	-1	99	82	71	86	88	90	66	89	88	81	77	98	94	89	88	99	99	92	89	86	1	1	-1	1	2	3	4	1	2	2	-1	-1	-1	2	-1	-1	-1	-1	-1	-1	-1	3	5	6	2	Health Care	3	2	2	3	2	4	4	3	2	4	2	1	2	2	2	1	2	2	3	6	2	1	4	3	5	3	1	2	-1	1	-1	6	4	4	4	4	2	5	2	2	4	1	1	-1	-1	3	6	2	3	-1	2	1	-1	1	1	1	5	2	-1	1	2	4	3	4	3	4	1	3	4	1	-1	2	-1	6	-1	4	-1	4	-1	2	2	2	1	2	1	-1	1	2	-1	5	1	2	2	2	2	supreme court	0.5	chancellor of germany	1	6	1	1896	1	2	-1	__NA__	1	__NA__	2	-1	-1	-1	3	-1	-1	-1	-1	-1	-1	-1	-1	2	-1	2	5	3	2	-1	3	-1	5	7.0	167	26.1530407663177	-1	-1	-1	-1	-1	-1	-1	-1	-1	11	-3, restricted access	-1	1	4	-1	-1	-3, restricted access	-1	-1	-3, restricted access	2	1	2	1	2	1	1	1	2	-1	-1	-1	-1	-1	-1	-1	-1	2	2	2	1	1	-1	-1	-1	-1	-1	1	2	1	-1	-1	-1	1	2	2	2	1	1	2	2	2	__NA__	4	4	5	-1	-1	-1	-1	-1	-1	-1	-1	-1	6	4	5	4	5	5	-1	-1	-1	4	4	2	3	3	1	-1	4	-1	-1	-1	-1	1	1969	1	2	6	1	3	1	__NA__	9	1	4	2	3	1	__NA__	1	2	4	2	1	__NA__	3	-3, restricted access	48	-3	-3, restricted acces	3	952	8	6							1	1	1	16.589	9.859	10.154	4.701	14.499	5.820	20.567	9.181	8.710	21.348	18.756	8.893	10.590	8.145	6.211	5.852	13.662	8.236	4.700	2.560	3.830	8.659	1.679	3.532	5.137	3.904	3.249	5.800	7.497	10.249	4.214	5.650	3.823	3.182	2.453	4.631	4.221	3.878	3.864	13.908	2.109	2.791	4.533	4.073	7.851	2.061	0.000	1.581	10.187	10.604	10.280	7.675	11.450	9.791	7.406	11.550	12.644	9.552	4.790	24.282	9.484	7.464	7.862	8.550	8.196	12.529	10.322	26.776	16.807	12.225	13.644	6.283	7.447	9.175	7.214	16.818	13.215	26.492	23.471	23.151	14.269	8.762	25.964	8.449	81.175	27.595	10.938	15.648	23.579	8.986	10.292	10.475	18.166	9.738	9.304	7.880	9.317	11.263	10.350	8.196	8.431	9.988	7.999	7.395	7.857	21.232	7.656	9.238	17.678	9.944	11.431	16.626	15.182	13.521	22.262	16.507	15.448	13.736	9.093	148.197	43.565	19.686	13.172	63.358	4.055	0.000	4.712	3.661	6.431	6.640	5.461	0.000	12.976	11.782	11.122	27.980	10.877	11.099	0.000	11.208	42.442	20.666	15.841	12.346	6.977	7.873	6.404	4.989	5.937	3.575	6.285	8.925	4.66	7.330	8.978	7.691	6.749	11.122	8.620	1	2	3	5	7	6	4	8	11	12	10	9	14	15	19	18	17	16	27	22	23	25	24	33	32	20	21	30	26	34	31	29	28	1	2	1	3	2	1	3	2	1	2	3	4	5	6	1	2	5	4	3	2	1	3	1	4	2	1	7	4	8	5	6	2	3	1	5	2	4	3	1	3	2	1	12/31/2019 18:57:33	12/31/2019 19:39:49	2536	1520
1	ANES 2019 Pilot Study version 20200204	2	.780822076219216	.638478211724453	1	1	1	-1	3	3	2	__NA__	-1	2	1	-1	-1	-1	1	1	-1	2	2	2	2	2	2	2	2	1	41	30	41	17	31	30	29	91	96	92	93	93	25	94	-1	67	86	78	91	93	44	19	93	25	82	22	89	91	94	71	66	100	20	25	77	2	-1	2	-1	1	1	1	2	3	2	-1	-1	-1	2	-1	-1	-1	-1	-1	-1	-1	5	3	2	1	Working together	1	2	2	3	2	4	2	5	3	2	5	2	2	2	2	4	6	6	1	7	2	2	4	3	2	3	1	2	-1	1	-1	2	2	2	2	4	4	4	5	4	3	1	1	-1	-1	3	1	5	6	-1	1	1	-1	1	1	2	-1	2	-1	3	2	4	4	4	3	3	1	2	1	1	-1	2	-1	5	-1	4	-1	4	-1	5	2	4	1	5	2	-1	2	1	-1	5	1	4	2	7	7	chief justice supremer court	1.0	germany chanceller	1	6	4	-7	2	6	2	__NA__	-1	__NA__	2	-1	-1	-1	3	-1	-1	-1	-1	-1	-1	-1	-1	3	-1	2	2	4	2	3	-1	-1	5	10.0	235	33.715306122449	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	-3, restricted access	-1	1	1	1	-1	-3, restricted access	-1	-1	-3, restricted access	1	1	2	2	2	2	1	1	2	-1	-1	-1	-1	-1	-1	-1	-1	1	1	1	1	1	-1	-1	-1	-1	-1	1	2	1	-1	-1	-1	1	2	2	2	1	2	2	2	2	__NA__	4	2	5	-1	-1	-1	-1	-1	-1	-1	-1	-1	6	5	5	-1	-1	-1	-1	-1	-1	2	4	2	1	1	1	-1	5	-1	-1	-1	-1	1	1942	1	6	1	2	1	5	__NA__	10	1	4	6	1	2	__NA__	2	1	2	2	2	__NA__	-7	-3, restricted access	1	-3	-3, restricted acces	3	1851	102	8	1	1	1	1	1	1	0	1	0	18.260	4.491	4.347	4.402	7.792	7.883	9.701	4.269	4.675	13.778	12.653	4.982	3.735	3.534	2.999	4.752	3.720	5.888	3.343	3.709	3.461	3.497	2.850	2.800	4.368	3.392	4.289	5.103	3.714	3.830	3.684	3.034	3.203	4.249	3.204	4.261	2.965	3.921	3.944	3.005	3.957	2.563	2.851	5.058	10.103	0.000	16.162	0.000	5.488	2.545	4.166	3.917	7.401	15.224	9.504	8.313	9.936	18.458	2.612	17.717	14.047	12.074	8.786	16.296	8.308	14.036	10.536	11.471	41.226	17.611	12.546	15.419	9.987	11.953	9.101	47.424	23.917	23.035	22.959	19.200	14.184	17.988	28.093	9.900	59.269	31.066	9.629	8.167	26.090	5.929	12.640	8.760	19.786	12.204	0.000	11.883	9.104	20.069	13.238	9.484	8.421	7.949	10.471	9.505	12.731	9.809	10.753	10.894	17.091	14.769	22.217	16.001	10.516	6.040	10.127	7.370	12.535	12.185	13.065	41.833	19.554	12.117	18.485	21.098	16.150	6.339	0.000	2.188	4.077	8.858	48.761	4.232	0.000	8.812	7.203	2.755	14.926	12.028	7.928	8.769	27.447	16.748	35.828	7.674	7.823	9.306	6.101	6.035	9.364	8.051	0.000	0.000	0.00	8.698	13.225	14.061	5.518	14.695	7.927	1	2	3	6	5	4	7	8	9	10	12	11	14	15	17	16	18	19	21	27	24	28	22	32	34	23	20	31	30	26	33	29	25			2	3	1	1	3	2	3	2	1	4	5	6	1	3	2	5	4	1	2	3	1	2	4	8	5	7	4	3	1	6	2	5	2	1	4	3	1	2	3	1	12/21/2019 4:19:56	12/21/2019 4:53:19	2003	1800
2	ANES 2019 Pilot Study version 20200204	3	.966366930694957	.790198239229266	1	1	1	-1	1	4	1	__NA__	-1	4	1	-1	-1	-1	2	1	-1	1	2	2	2	2	1	2	1	2	0	91	88	15	60	70	68	48	49	49	49	50	39	69	-1	63	66	51	40	2	2	2	1	3	59	1	50	1	1	1	51	87	50	1	3	1	1	-1	1	2	3	2	1	2	2	-1	-1	-1	2	-1	-1	-1	-1	-1	-1	-1	7	7	7	7	health care	4	5	5	7	1	1	5	4	1	7	1	1	3	3	1	1	4	4	4	7	2	2	5	5	4	4	2	2	-1	2	-1	7	3	1	5	5	5	5	5	5	5	5	5	-1	-1	3	7	6	7	-1	1	5	-1	1	1	2	-1	1	-1	1	6	3	3	5	5	1	1	5	5	1	-1	2	-1	4	-1	4	-1	4	-1	2	2	5	1	1	2	-1	3	1	-1	5	1	5	2	1	1	don't know	0.0	don't know	0	4	4	1960	2	1	1	__NA__	-1	__NA__	1	-1	-1	-1	3	-1	-1	-1	-1	-1	-1	-1	-1	3	-1	1	5	4	1	2	-1	-1	5	4.0	160	27.4609375	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	-3, restricted access	-1	1	1	1	-1	-3, restricted access	-1	-1	-3, restricted access	1	2	2	2	2	2	1	1	2	-1	-1	-1	-1	-1	-1	-1	-1	2	2	2	2	1	-1	-1	-1	-1	-1	1	2	1	-1	-1	-1	1	2	2	2	2	2	2	2	2	__NA__	1	1	5	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	3	5	3	1	1	1	-1	5	-1	-1	-1	-1	1	1954	2	2	1	2	1	1	__NA__	8	1	3	1	2	1	__NA__	2	1	2	2	2	__NA__	-7	-3, restricted access	37	-3	-3, restricted acces	3	1576	249	9							0	0	0	10.393	4.406	5.095	5.183	9.068	7.562	20.654	3.642	8.004	52.884	14.342	8.833	5.336	6.344	9.386	7.735	7.817	4.727	4.530	6.723	4.873	6.401	11.542	7.602	5.969	6.955	10.404	20.956	9.733	4.223	5.717	5.417	6.487	9.220	3.874	13.394	4.204	8.374	4.453	5.695	4.635	4.992	6.135	5.758	5.716	6.970	0.000	33.173	7.789	12.756	9.381	10.139	5.447	19.189	10.866	8.407	5.273	4.474	4.124	19.337	17.748	6.833	5.940	30.722	20.596	13.606	12.951	9.781	37.086	11.142	11.334	15.668	17.238	25.370	13.978	10.143	72.549	15.953	34.345	15.078	21.909	18.511	29.479	8.484	88.518	19.246	28.604	9.095	26.145	11.639	24.933	12.824	22.079	24.131	0.000	7.846	15.807	24.690	36.809	17.871	14.538	9.213	21.222	6.331	6.392	7.599	8.802	26.009	11.535	9.976	12.721	28.735	8.838	12.885	15.169	11.453	17.480	12.401	8.518	24.951	14.769	21.653	19.182	19.827	6.654	0.000	0.000	0.000	7.439	12.501	47.529	7.273	0.000	5.697	5.141	3.550	9.917	3.977	6.014	4.070	30.007	18.690	20.602	4.427	4.098	7.478	5.348	0.000	0.000	0.000	0.000	0.000	0.00	20.608	39.129	16.251	7.152	10.665	9.366	1	2	4	6	5	3	7	10	12	8	9	11	14	15	17	16	19	18	27	30	23	22	25	32	20	34	29	26	21	24	33	31	28	1	2	2	3	1	1	2	3	2	3	1	4	5	6	4	5	1	3	2	1	2	2	1	4	3	4	5	6	3	2	8	7	1	2	5	1	3	4	3	2	1	0	12/22/2019 23:03:28	12/22/2019 23:41:43	2295	70

To see the last several rows, use the .tail(m) method where m is the number of rows from the bottom. The last three rows are:

anes.tail(3)

	version	caseid	weight	weight_spss	form	follow	reg1a	reg1b	liveurban	youthurban	placeid1a	placeid1a_t	placeid1b	placeidimport	turnout16a	turnout16a1	turnout16b	turnout16c	vote16	turnout18a	turnout18a1	particip_1	particip_2	particip_3	particip_4	particip_5	particip_6	particip_7	particip_8	particip_9	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftimmig1	ftimmig2	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales	vote20dem	vote20cand	vote20cand2	electable	vote20jb	vote20ew	vote20bs	cvote2020	tsplit1	contact1a	contact1b	contact2a	contact2b	contact3	contact4m_1	contact4m_2	contact4m_3	apppres5	frnpres5	immpres5	econpres5	apppres7	frnpres7	immpres7	econpres7	mip	econnow	finworry	confecon	improve1	national1	national2	conspire1	conspire2	conspire3	taxecon	billtax	trade1	trade2	trade3	trade4	richpoor2	guarinc	lcself	lcd	lcr	pop1	pop2	pop3	corrupt	immignum	refugees	dreamstr	dreamer	dreamstr1	dreamstr2	wall5	wall7	pathway	preturn	popen	release1	release2	famsep	tchina	trussia	tiran	tmexico	tjapan	hlthcare1	hlthcare2	abortion1	abortion2	freecol	loans	diversity5	diversity7	language	buyback	gw1	gw2	knowopioid1	knowopioid2	opioiddo	sentence	prek	demo4	experts	science	exphelp	elite1	elite2	elite3	elite4	ukraine1	ukraine2	excessive	rural1alt1	rural1alt2	rural2alt1	rural2alt2	rural3alt1	rural3alt2	rural4alt1	rural4alt2	conf_unemp	unemp	conf_interfere	interfere	conf_autism	autism1	autism2	conf_gmo	gmo1	gmo2	conf_warm	warm	conf_illegal	illegal	impeach1	impeach2	pk_cjus	pk_germ	pk_germ_correct	pk_sen	pk_spend	pk_geer	cheat	pid7x	pid1d	pid2d	pid1r	pid2r	pidstr	pidlean	ngun	shooting	dem_activduty	milyears	milyr1	milyr2	milyr3	milyr4	milyr5	milyr6	combat	harass1a	harass1b	rr1	rr2	rr3	rr4	health1a	health1b	hospital	feet	inches	nweight	bmi	disable1	disable2	disable3	disable4	disable5	disable6	smoker1	smoker2	exercise	relig1a	relig1a_txt	relig2a	att1	att2	att3	relig1b	relig1b_t	relig2b	relig3b	relig3b_t	attother	exptravel_ever	exphomesch	expfarm	expffood	expconvert	expholiday	explie	expshark	expdivorce	exparrest	expoverdose	expdefault	exppubasst	exphybrid	expmistake	explightning	exptravel_year	expindian	exphunt	expflag	exppublib	explottery	expshoponline	exppubtrans	expfight	expavoid	expknowimmig	expknowtrans	expbuyusa	expretire	expcolldebt	expknowpris	socmed_1	socmed_2	socmed_3	socmed_4	socmed_5	socmed_6	socmed_7	socmed_8	socmed_9	socmed_t	facebook1	facebook2	facebook3	twitter1	twitter2	twitter3	instagram1	instagram2	instagram3	reddit1	reddit2	reddit3	youtube1	youtube2	youtube3	snapchat1	snapchat2	snapchat3	tiktok1	tiktok2	tiktok3	raceid	racework	whitejob	race_sub1	race_sub2	voterid1	voterid2	serious	photo1	photo2	photo3	photo4	reinterview	birthyr	gender	educ	marstat	child18	race	employ	employ_t	faminc_new	votereg	ideo5	pid7	newsint	presvote16post	presvote16post_t	pew_bornagain	pew_religimp	pew_churatd	pew_prayer	religpew	religpew_t	religpew_protestant	religpew_protestant_t	inputstate	zipCode	FIPCounty	region	EnrollmentDate	CompletedSurveys	qualityControl_overall_scale	tsmart_P2012	tsmart_G2012	tsmart_P2016	tsmart_G2016	frnpres5_skp	ftpales_skp	pk_cjus_skp	refugees_skp	rural3alt1_skp	follow_page_timing	reg1a_page_timing	reg1b_page_timing	liveurban_page_timing	youthurban_page_timing	placeid1a_page_timing	placeid1b_page_timing	placeidimport_page_timing	turnout16a_page_timing	turnout16b_page_timing	vote16_page_timing	turnout18a_page_timing	particip_page_timing	fttrump_page_timing	ftobama_page_timing	ftbiden_page_timing	ftwarren_page_timing	ftsanders_page_timing	ftbuttigieg_page_timing	ftharris_page_timing	ftblack_page_timing	ftwhite_page_timing	fthisp_page_timing	ftasian_page_timing	ftmuslim_page_timing	ftillegal_page_timing	ftimmig1_page_timing	ftimmig2_page_timing	ftjournal_page_timing	ftnato_page_timing	ftun_page_timing	ftice_page_timing	ftnra_page_timing	ftchina_page_timing	ftnkorea_page_timing	ftmexico_page_timing	ftsaudi_page_timing	ftukraine_page_timing	ftiran_page_timing	ftbritain_page_timing	ftgermany_page_timing	ftjapan_page_timing	ftisrael_page_timing	ftfrance_page_timing	ftcanada_page_timing	ftturkey_page_timing	ftrussia_page_timing	ftpales_page_timing	vote20dem_page_timing	vote20cand_page_timing	electable_page_timing	vote20jb_page_timing	vote20ew_page_timing	vote20bs_page_timing	cvote2020_page_timing	tsplit1_page_timing	contact1a_page_timing	contact1b_page_timing	contact3_page_timing	apppres5_page_timing	frnpres5_page_timing	immpres5_page_timing	econpres5_page_timing	apppres7_page_timing	frnpres7_page_timing	immpres7_page_timing	econpres7_page_timing	mip_page_timing	econnow_page_timing	finworry_page_timing	confecon_page_timing	improve1_page_timing	national1_page_timing	national2_page_timing	conspire1_page_timing	conspire2_page_timing	conspire3_page_timing	taxecon_page_timing	billtax_page_timing	trade1_page_timing	trade2_page_timing	trade3_page_timing	trade4_page_timing	richpoor2_page_timing	guarinc_page_timing	lc_grid_page_timing	pop_grid_page_timing	corrupt_page_timing	immignum_page_timing	refugees_page_timing	dreamer_page_timing	wall_page_timing	wall7_page_timing	path_grid_page_timing	threat_grid_page_timing	hlthcare1_page_timing	hlthcare2_page_timing	abortion1_page_timing	abortion2_page_timing	freecol_page_timing	loans_page_timing	diversity5_page_timing	diversity7_page_timing	language_page_timing	buyback_page_timing	gw_grid_page_timing	knowopioid1_page_timing	opioiddo_page_timing	sentence_page_timing	prek_page_timing	demo4_page_timing	experts_page_timing	science_page_timing	exphelp_page_timing	elite1_page_timing	elite2_page_timing	elite3_page_timing	elite4_page_timing	ukraine1_page_timing	ukraine2_page_timing	excessive_page_timing	rural1alt1_page_timing	rural1alt2_page_timing	rural2alt1_page_timing	rural2alt2_page_timing	rural3alt1_page_timing	rural3alt2_page_timing	rural4alt1_page_timing	rural4alt2_page_timing	unemp_page_timing	interfere_page_timing	autism1_page_timing	autism2_page_timing	gmo1_page_timing	gmo2_page_timing	warm_page_timing	illegal_page_timing	impeach1_page_timing	impeach2_page_timing	pk2_intro_page_timing	pk_cjus_page_timing	pk_germ_page_timing	pk_sen_page_timing	pk_spend_page_timing	pk_geer_page_timing	cheat_page_timing	ngun_page_timing	shooting_page_timing	dem_activduty_page_timing	harass1a_page_timing	harass1b_page_timing	rr_grid_page_timing	health1a_page_timing	health1b_page_timing	hospital_page_timing	tall_page_timing	nweight_page_timing	disable_grid_page_timing	smoker1_page_timing	smoker2_page_timing	exercise_page_timing	relig1a_page_timing	att1_page_timing	att2_page_timing	relig1b_page_timing	relig2b_page_timing	exp1_grid_page_timing	exp2_grid_page_timing	rexp1_grid_page_timing	rexp2_grid_page_timing	cexp1_grid_page_timing	cexp2_grid_page_timing	socmed_page_timing	facebook1_page_timing	facebook2_page_timing	facebook3_page_timing	twitter1_page_timing	twitter2_page_timing	twitter3_page_timing	instagram1_page_timing	instagram2_page_timing	instagram3_page_timing	youtube1_page_timing	youtube2_page_timing	youtube3_page_timing	raceid_page_timing	racework_page_timing	whitejob_page_timing	voterid1_page_timing	voterid2_page_timing	serious_page_timing	reinterview_page_timing	ord_fttrump	ord_ftobama	ord_ftbiden	ord_ftwarren	ord_ftsanders	ord_ftbuttigieg	ord_ftharris	ord_ftblack	ord_ftwhite	ord_fthisp	ord_ftasian	ord_ftmuslim	ord_ftimmig1	ord_ftimmig2	ord_ftjournal	ord_ftnato	ord_ftun	ord_ftice	ord_ftnra	ord_ftchina	ord_ftnkorea	ord_ftmexico	ord_ftsaudi	ord_ftukraine	ord_ftiran	ord_ftbritain	ord_ftgermany	ord_ftjapan	ord_ftisrael	ord_ftfrance	ord_ftcanada	ord_ftturkey	ord_ftrussia	ord_ftpales	ord_electable_1	ord_electable_2	ord_conspire1	ord_conspire2	ord_conspire3	ord_lcself	ord_lcd	ord_lcr	ord_pathway	ord_preturn	ord_popen	ord_release1	ord_release2	ord_famsep	ord_tchina	ord_trussia	ord_tiran	ord_tmexico	ord_tjapan	ord_gw1	ord_gw2	ord_elite1	ord_elite2	ord_elite3	ord_elite4	ord_disable1	ord_disable2	ord_disable3	ord_disable4	ord_disable5	ord_disable6	ord_exptravel_ever	ord_exphomesch	ord_expfarm	ord_expffood	ord_expconvert	ord_expholiday	ord_explie	ord_expshark	ord_expdivorce	ord_exparrest	ord_expoverdose	ord_expdefault	ord_exppubasst	ord_exphybrid	ord_expmistake	ord_explightning	ord_exptravel_year	ord_expindian	ord_exphunt	ord_expflag	ord_exppublib	ord_explottery	ord_expshoponline	ord_exppubtrans	ord_expfight	ord_expavoid	ord_expknowimmig	ord_expknowtrans	ord_expbuyusa	ord_expretire	ord_expcolldebt	ord_expknowpris	ord_att2_reverse	starttime	endtime	duration	pop_density_public
3162	ANES 2019 Pilot Study version 20200204	3163	.892833236147303	.73006973719765	2	3	-1	3	4	1	-1	__NA__	4	3	-1	-1	2	-1	-1	2	-1	2	2	2	2	2	2	2	2	1	6	31	50	2	59	0	31	88	100	100	100	60	100	-1	100	51	3	60	94	41	100	60	100	78	40	59	100	72	99	99	99	100	51	41	87	3	-1	-1	-1	4	4	4	4	4	-1	2	-1	-1	-1	-1	-1	-1	5	5	5	5	-1	-1	-1	-1	donald trump	2	5	4	6	1	5	5	1	1	1	7	5	4	2	2	1	7	7	4	4	5	5	5	1	7	7	1	2	-1	1	5	-1	5	5	5	5	1	5	-1	-1	-1	-1	-1	1	5	-1	-1	7	7	1	-1	1	7	5	5	2	-1	1	1	1	4	5	5	5	1	5	1	5	5	1	2	-1	1	-1	1	-1	1	-1	1	1	2	3	1	5	-1	2	5	-1	2	1	2	3	1	7	7	no	chancellor of europe	1	2	1	1444	2	4	-1	__NA__	-1	__NA__	-1	3	0	3	3	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	1	1	3	3	3	-1	1	7	5.0	175	15.5315	2	2	1	2	1	1	2	-1	2	-1	-3, restricted access	-1	1	2	-1	3	-3, restricted access	-1	-1	-3, restricted access	-1	-1	-1	-1	-1	-1	-1	-1	-1	1	1	2	1	1	2	1	2	-1	-1	-1	-1	-1	2	2	1	2	2	-1	-1	-1	2	1	2	1	1	1	2	2	2	2	2	2	__NA__	1	1	5	7	5	5	7	5	5	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	5	5	3	3	3	-1	2	5	-1	-1	-1	-1	1	1980	1	4	1	2	3	2	__NA__	4	2	2	4	7	7	__NA__	2	1	1	1	1	__NA__	3	-3, restricted access	47	-3	-3, restricted acces	3	179	156	9					1	0	0	0	0	6.171	0.000	4.744	5.487	9.117	0.000	11.729	17.977	0.000	4.232	0.000	4.955	4.118	13.230	7.743	6.538	5.253	6.740	7.512	3.692	7.074	8.041	6.904	8.615	7.851	5.253	0.000	4.181	3.884	4.136	4.484	6.832	5.209	3.727	9.289	5.675	4.046	4.884	5.471	4.010	6.924	4.154	4.677	4.877	4.444	5.091	3.690	4.879	3.157	0.000	0.000	3.565	2.815	2.423	3.292	3.182	0.000	3.111	0.000	4.075	3.729	3.661	2.817	0.00	0.000	0.000	0.000	14.868	4.227	3.807	4.957	4.420	3.858	12.512	3.739	5.764	7.959	7.423	4.858	4.506	4.239	6.822	3.527	3.324	4.559	8.038	6.835	3.517	13.014	6.245	13.085	5.254	0.000	14.771	0.000	3.734	2.392	0.000	0.000	2.039	4.682	7.654	0.000	12.898	7.020	5.208	4.205	5.766	4.442	8.525	5.126	3.792	3.755	3.969	4.736	5.764	4.824	9.553	6.415	4.100	7.151	0.000	3.328	0.00	3.266	0.00	7.566	0.000	3.913	10.925	10.081	0.000	8.310	0.000	9.105	6.381	7.238	4.501	2.297	1.720	10.167	22.402	5.258	11.603	9.610	3.496	9.293	5.065	4.192	0.000	10.153	23.828	3.930	0.000	4.101	13.219	6.197	15.415	4.411	0.000	6.875	0.000	6.199	5.176	4.704	0.000	0.000	25.929	0.000	12.167	0.000	10.375	4.302	3.099	2.531	3.725	3.716	4.002	2.135	3.862	3.258	1.885	0.000	0.000	0.000	4.102	5.759	11.928	0.00	5.281	15.987	5.334	1	2	6	4	5	3	7	10	12	8	9	11	0	14	15	16	19	17	18	26	23	28	27	31	34	30	24	25	20	21	32	29	33	22			1	2	3	1	3	2	2	1	3	4	5	6						2	1	2	3	4	1	4	6	3	5	1	2									7	3	4	5	1	8	2	6						3	2	5	4	1				3	1	2	1	12/31/2019 20:10:04	12/31/2019 20:29:15	1151	200
3163	ANES 2019 Pilot Study version 20200204	3164	1.58161278448241	1.29328477387127	2	1	-1	3	3	4	-1	__NA__	2	4	-1	-1	2	-1	-1	2	-1	2	2	2	2	2	2	2	2	1	1	100	95	62	79	59	51	100	65	50	52	50	51	-1	49	91	84	72	59	0	54	0	62	0	60	0	73	55	50	1	56	89	0	0	1	1	1	-1	2	2	3	2	1	2	-1	2	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	7	7	7	4	Donald Trump	3	4	3	5	1	3	4	5	5	6	2	1	4	3	4	3	2	2	2	4	1	1	4	5	4	3	2	2	-1	2	-1	7	2	3	2	4	2	4	-1	-1	-1	-1	-1	3	5	-1	-1	4	5	-1	4	5	1	1	3	2	-1	1	5	2	1	4	5	4	5	5	2	5	5	1	3	-1	3	-1	5	-1	5	-1	5	2	2	5	1	5	-1	2	5	-1	2	5	1	5	2	1	1	republican party	not sure	0	4	4	1970	2	2	-1	__NA__	1	__NA__	2	-1	0	3	3	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	1	3	3	2	3	-1	1	5	11.0	190	26.4967	2	2	2	2	2	2	1	1	3	-1	-3, restricted access	-1	2	-1	-1	-1	-3, restricted access	2	-1	-3, restricted access	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	1	2	2	2	2	1	2	-1	-1	-1	-1	-1	1	2	2	2	2	-1	-1	-1	2	2	2	2	2	2	2	1	2	2	2	2	__NA__	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	5	4	5	-1	-1	-1	-1	-1	-1	5	5	1	2	2	-1	2	5	-1	-1	-1	-1	1	1960	1	1	1	2	2	4	__NA__	1	2	2	2	1	7	__NA__	2	2	6	7	11	__NA__	-7	-3, restricted access	6	-3	-3, restricted acces	4	1072	1117	9					0	1	0	0	0	22.776	0.000	5.979	7.954	19.452	0.000	12.340	10.267	0.000	23.684	0.000	15.141	17.413	13.859	10.821	4.535	7.973	4.811	7.034	4.996	4.527	3.683	4.917	9.405	5.766	6.478	0.000	8.614	5.032	5.439	4.327	8.170	4.455	3.603	5.639	3.643	10.550	4.175	8.272	5.764	3.426	4.688	4.580	5.099	4.287	4.478	4.883	4.066	16.823	19.913	34.058	9.328	9.508	3.291	8.991	44.269	0.000	21.831	0.000	0.000	0.000	0.000	0.000	11.71	8.499	7.077	6.332	51.329	14.182	13.553	8.190	17.121	10.773	18.920	11.721	13.302	17.977	17.030	11.952	8.882	14.055	7.388	15.926	11.318	40.049	39.888	29.432	10.023	18.666	12.519	13.439	0.000	8.737	54.015	0.000	18.292	17.948	0.000	0.000	18.384	13.031	0.000	18.828	9.921	13.785	17.596	7.415	9.176	18.835	13.171	10.482	19.440	9.917	15.687	9.472	7.964	9.106	8.164	13.930	10.141	11.132	0.000	12.841	0.00	26.118	0.00	11.323	0.000	9.259	44.698	10.561	0.000	32.948	0.000	13.943	9.923	10.111	11.093	11.843	7.698	135.410	19.693	6.403	34.352	16.268	10.749	8.387	7.313	5.852	0.000	20.717	33.046	7.973	0.000	16.569	9.735	6.780	23.313	3.822	4.375	6.345	0.000	11.195	0.000	0.000	10.963	0.000	34.899	0.000	15.704	0.000	9.907	5.757	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	5.548	14.599	5.226	4.902	9.533	14.596	0.00	6.922	18.059	13.644	1	2	6	3	4	7	5	10	12	9	11	8	0	14	15	19	16	18	17	34	20	30	26	32	23	24	31	33	29	22	25	21	28	27	1	2	2	3	1	1	3	2	2	3	1	4	5	6						2	1	1	3	2	4	2	5	6	3	1	4									1	4	2	6	7	3	5	8						4	1	2	3	5				2	1	3	0	12/31/2019 22:10:05	12/31/2019 22:52:37	2552	6600
3164	ANES 2019 Pilot Study version 20200204	3165	.809576969671362	.661991088100273	1	2	1	-1	2	4	2	__NA__	-1	1	1	-1	-1	-1	2	1	-1	2	2	2	2	2	2	2	2	1	0	100	70	51	100	100	39	100	0	100	100	99	100	100	-1	98	100	98	2	0	99	98	98	99	96	100	100	99	100	97	99	98	98	98	99	1	3	-1	2	2	2	2	1	2	2	-1	-1	-1	2	-1	-1	-1	5	5	5	5	-1	-1	-1	-1	trump	5	5	5	7	5	3	3	3	1	7	7	3	3	3	1	1	7	6	6	2	1	1	1	3	2	2	1	2	-1	1	5	-1	1	1	1	1	1	1	1	1	1	1	1	-1	-1	3	4	7	7	5	-1	1	-1	1	1	2	-1	4	-1	4	4	3	3	3	5	5	5	5	5	2	-1	2	-1	7	-1	7	-1	7	-1	5	1	5	1	5	1	-1	5	1	-1	5	1	5	1	7	7		/////////////	0	4	4	1950	2	1	-1	__NA__	1	__NA__	1	-1	-1	-1	3	-1	-1	-1	-1	-1	-1	-1	-1	2	-1	5	5	1	5	-1	3	-1	6	0.0	310	42.039	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	-3, restricted access	-1	2	-1	-1	-1	-3, restricted access	-1	-1	-3, restricted access	-1	2	2	2	1	2	2	2	2	-1	-1	-1	-1	-1	-1	-1	-1	2	2	1	2	1	-1	-1	-1	-1	-1	1	1	1	-1	-1	-1	2	2	2	2	2	2	2	2	1	__NA__	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	5	5	1	5	5	1	-1	5	-1	-1	-1	-1	1	1960	1	2	1	2	5	1	__NA__	5	1	3	1	2	1	__NA__	2	1	6	2	2	__NA__	-7	-3, restricted access	15	-3	-3, restricted acces	4	716	455	9	1	1	1	1	0	0	1	1	1	11.733	6.713	0.000	10.621	7.110	18.052	0.000	12.675	21.724	0.000	2.929	8.263	19.129	12.883	12.828	7.378	12.400	7.377	5.027	6.363	4.359	12.839	8.849	6.745	6.412	3.484	6.198	0.000	6.742	6.963	8.125	7.621	6.460	5.808	4.202	3.431	3.106	13.730	5.581	5.026	5.597	7.950	3.580	3.297	6.614	10.652	3.413	3.623	3.663	14.878	20.579	7.179	4.245	2.677	3.247	4.174	16.213	0.000	21.869	9.980	8.292	3.435	3.672	0.00	0.000	0.000	0.000	15.404	13.541	8.883	15.064	12.369	7.302	19.684	15.286	14.684	30.504	19.788	6.883	23.589	7.179	3.066	25.757	8.636	9.898	26.573	28.218	6.090	44.205	20.003	11.859	23.348	0.000	18.054	21.005	0.000	0.000	9.468	15.818	18.156	7.710	7.826	0.000	10.682	0.000	18.799	11.448	11.778	0.000	20.925	4.175	23.073	13.272	12.735	7.339	17.971	16.864	20.221	9.399	3.971	0.000	18.039	0.000	8.35	0.000	8.39	0.000	3.772	0.000	24.187	9.203	8.986	0.000	5.161	0.000	4.762	7.808	11.134	5.327	0.000	1.740	9.498	17.707	13.880	21.359	6.560	0.000	0.000	6.391	14.481	0.000	1348.991	0.000	10.312	0.000	15.887	8.462	0.000	0.000	0.000	0.000	5.547	10.946	0.000	0.000	0.000	25.084	0.000	18.232	0.000	20.694	0.000	5.296	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	9.516	5.433	15.367	6.79	0.000	12.530	11.353	1	2	3	4	5	6	7	11	8	12	9	10	14	0	15	19	18	17	16	22	34	28	21	26	24	29	32	33	31	30	20	23	27	25	2	1	3	2	1	1	2	3	3	2	1	4	5	6	4	2	1	3	5	2	1	4	3	1	2							8	3	1	4	2	7	5	6									3	4	2	5	1						2	1	3				1	12/31/2019 23:27:51	1/1/2020 0:21:59	3248	1

To see a random selection of rows, use the .sample(p, replace=False) method, where p is the sample size and replace=False indicates sampling without replacement. To take a sample with replacement (for bootstrapping, for example), write replace=True. Here are five randomly drawn rows from anes:

anes.sample(5, replace=False)

	version	caseid	weight	weight_spss	form	follow	reg1a	reg1b	liveurban	youthurban	placeid1a	placeid1a_t	placeid1b	placeidimport	turnout16a	turnout16a1	turnout16b	turnout16c	vote16	turnout18a	turnout18a1	particip_1	particip_2	particip_3	particip_4	particip_5	particip_6	particip_7	particip_8	particip_9	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftimmig1	ftimmig2	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales	vote20dem	vote20cand	vote20cand2	electable	vote20jb	vote20ew	vote20bs	cvote2020	tsplit1	contact1a	contact1b	contact2a	contact2b	contact3	contact4m_1	contact4m_2	contact4m_3	apppres5	frnpres5	immpres5	econpres5	apppres7	frnpres7	immpres7	econpres7	mip	econnow	finworry	confecon	improve1	national1	national2	conspire1	conspire2	conspire3	taxecon	billtax	trade1	trade2	trade3	trade4	richpoor2	guarinc	lcself	lcd	lcr	pop1	pop2	pop3	corrupt	immignum	refugees	dreamstr	dreamer	dreamstr1	dreamstr2	wall5	wall7	pathway	preturn	popen	release1	release2	famsep	tchina	trussia	tiran	tmexico	tjapan	hlthcare1	hlthcare2	abortion1	abortion2	freecol	loans	diversity5	diversity7	language	buyback	gw1	gw2	knowopioid1	knowopioid2	opioiddo	sentence	prek	demo4	experts	science	exphelp	elite1	elite2	elite3	elite4	ukraine1	ukraine2	excessive	rural1alt1	rural1alt2	rural2alt1	rural2alt2	rural3alt1	rural3alt2	rural4alt1	rural4alt2	conf_unemp	unemp	conf_interfere	interfere	conf_autism	autism1	autism2	conf_gmo	gmo1	gmo2	conf_warm	warm	conf_illegal	illegal	impeach1	impeach2	pk_cjus	pk_cjus_correct	pk_germ	pk_germ_correct	pk_sen	pk_spend	pk_geer	cheat	pid7x	pid1d	pid2d	pid1r	pid2r	pidstr	pidlean	ngun	shooting	dem_activduty	milyears	milyr1	milyr2	milyr3	milyr4	milyr5	milyr6	combat	harass1a	harass1b	rr1	rr2	rr3	rr4	health1a	health1b	hospital	feet	inches	nweight	bmi	disable1	disable2	disable3	disable4	disable5	disable6	smoker1	smoker2	exercise	relig1a	relig1a_txt	relig2a	att1	att2	att3	relig1b	relig1b_t	relig2b	relig3b	relig3b_t	attother	exptravel_ever	exphomesch	expfarm	expffood	expconvert	expholiday	explie	expshark	expdivorce	exparrest	expoverdose	expdefault	exppubasst	exphybrid	expmistake	explightning	exptravel_year	expindian	exphunt	expflag	exppublib	explottery	expshoponline	exppubtrans	expfight	expavoid	expknowimmig	expknowtrans	expbuyusa	expretire	expcolldebt	expknowpris	socmed_1	socmed_2	socmed_3	socmed_4	socmed_5	socmed_6	socmed_7	socmed_8	socmed_9	socmed_t	facebook1	facebook2	facebook3	twitter1	twitter2	twitter3	instagram1	instagram2	instagram3	reddit1	reddit2	reddit3	youtube1	youtube2	youtube3	snapchat1	snapchat2	snapchat3	tiktok1	tiktok2	tiktok3	raceid	racework	whitejob	race_sub1	race_sub2	voterid1	voterid2	serious	photo1	photo2	photo3	photo4	reinterview	birthyr	gender	educ	marstat	child18	race	employ	employ_t	faminc_new	votereg	ideo5	pid7	newsint	presvote16post	presvote16post_t	pew_bornagain	pew_religimp	pew_churatd	pew_prayer	religpew	religpew_t	religpew_protestant	religpew_protestant_t	inputstate	zipCode	FIPCounty	region	EnrollmentDate	CompletedSurveys	qualityControl_overall_scale	tsmart_P2012	tsmart_G2012	tsmart_P2016	tsmart_G2016	tsmart_P2018	tsmart_G2018	ftnra_skp	fttrump_skp	pk_geer_skp	pk_spend_skp	rr_grid_skp	vote20bs_skp	youtube3_skp	follow_page_timing	reg1a_page_timing	reg1b_page_timing	liveurban_page_timing	youthurban_page_timing	placeid1a_page_timing	placeid1b_page_timing	placeidimport_page_timing	turnout16a_page_timing	turnout16c_page_timing	vote16_page_timing	turnout18a_page_timing	particip_page_timing	fttrump_page_timing	ftobama_page_timing	ftbiden_page_timing	ftwarren_page_timing	ftsanders_page_timing	ftbuttigieg_page_timing	ftharris_page_timing	ftblack_page_timing	ftwhite_page_timing	fthisp_page_timing	ftasian_page_timing	ftmuslim_page_timing	ftillegal_page_timing	ftimmig1_page_timing	ftimmig2_page_timing	ftjournal_page_timing	ftnato_page_timing	ftun_page_timing	ftice_page_timing	ftnra_page_timing	ftchina_page_timing	ftnkorea_page_timing	ftmexico_page_timing	ftsaudi_page_timing	ftukraine_page_timing	ftiran_page_timing	ftbritain_page_timing	ftgermany_page_timing	ftjapan_page_timing	ftisrael_page_timing	ftfrance_page_timing	ftcanada_page_timing	ftturkey_page_timing	ftrussia_page_timing	ftpales_page_timing	vote20dem_page_timing	vote20cand_page_timing	vote20cand2_page_timing	electable_page_timing	vote20jb_page_timing	vote20ew_page_timing	vote20bs_page_timing	cvote2020_page_timing	tsplit1_page_timing	contact1a_page_timing	contact1b_page_timing	contact3_page_timing	apppres5_page_timing	frnpres5_page_timing	immpres5_page_timing	econpres5_page_timing	apppres7_page_timing	frnpres7_page_timing	immpres7_page_timing	econpres7_page_timing	mip_page_timing	econnow_page_timing	finworry_page_timing	confecon_page_timing	improve1_page_timing	national1_page_timing	national2_page_timing	conspire1_page_timing	conspire2_page_timing	conspire3_page_timing	taxecon_page_timing	billtax_page_timing	trade1_page_timing	trade2_page_timing	trade3_page_timing	trade4_page_timing	richpoor2_page_timing	guarinc_page_timing	lc_grid_page_timing	pop_grid_page_timing	corrupt_page_timing	immignum_page_timing	refugees_page_timing	dreamer_page_timing	wall_page_timing	wall7_page_timing	path_grid_page_timing	threat_grid_page_timing	hlthcare1_page_timing	hlthcare2_page_timing	abortion1_page_timing	abortion2_page_timing	freecol_page_timing	loans_page_timing	diversity5_page_timing	diversity7_page_timing	language_page_timing	buyback_page_timing	gw_grid_page_timing	knowopioid1_page_timing	knowopioid2_page_timing	opioiddo_page_timing	sentence_page_timing	prek_page_timing	demo4_page_timing	experts_page_timing	science_page_timing	exphelp_page_timing	elite1_page_timing	elite2_page_timing	elite3_page_timing	elite4_page_timing	ukraine1_page_timing	ukraine2_page_timing	excessive_page_timing	rural1alt1_page_timing	rural1alt2_page_timing	rural2alt1_page_timing	rural2alt2_page_timing	rural3alt1_page_timing	rural3alt2_page_timing	rural4alt1_page_timing	rural4alt2_page_timing	unemp_page_timing	interfere_page_timing	autism1_page_timing	autism2_page_timing	gmo1_page_timing	gmo2_page_timing	warm_page_timing	illegal_page_timing	impeach1_page_timing	impeach2_page_timing	pk2_intro_page_timing	pk_cjus_page_timing	pk_germ_page_timing	pk_sen_page_timing	pk_spend_page_timing	pk_geer_page_timing	cheat_page_timing	pid1d_page_timing	pid1r_page_timing	pidstr_page_timing	pidlean_page_timing	ngun_page_timing	shooting_page_timing	dem_activduty_page_timing	harass1a_page_timing	harass1b_page_timing	rr_grid_page_timing	health1a_page_timing	health1b_page_timing	hospital_page_timing	tall_page_timing	nweight_page_timing	disable_grid_page_timing	smoker1_page_timing	smoker2_page_timing	exercise_page_timing	relig1a_page_timing	relig2a_page_timing	att1_page_timing	att2_page_timing	att3_page_timing	relig1b_page_timing	relig2b_page_timing	relig3b_page_timing	attother_page_timing	exp1_grid_page_timing	exp2_grid_page_timing	rexp1_grid_page_timing	rexp2_grid_page_timing	cexp1_grid_page_timing	cexp2_grid_page_timing	socmed_page_timing	facebook1_page_timing	facebook2_page_timing	facebook3_page_timing	twitter1_page_timing	twitter2_page_timing	twitter3_page_timing	instagram1_page_timing	instagram2_page_timing	instagram3_page_timing	reddit1_page_timing	reddit2_page_timing	reddit3_page_timing	youtube1_page_timing	youtube2_page_timing	youtube3_page_timing	snapchat1_page_timing	snapchat2_page_timing	snapchat3_page_timing	raceid_page_timing	racework_page_timing	whitejob_page_timing	voterid1_page_timing	voterid2_page_timing	serious_page_timing	photo1_page_timing	photo2_page_timing	photo3_page_timing	photo4_page_timing	reinterview_page_timing	ord_fttrump	ord_ftobama	ord_ftbiden	ord_ftwarren	ord_ftsanders	ord_ftbuttigieg	ord_ftharris	ord_ftblack	ord_ftwhite	ord_fthisp	ord_ftasian	ord_ftmuslim	ord_ftimmig1	ord_ftimmig2	ord_ftjournal	ord_ftnato	ord_ftun	ord_ftice	ord_ftnra	ord_ftchina	ord_ftnkorea	ord_ftmexico	ord_ftsaudi	ord_ftukraine	ord_ftiran	ord_ftbritain	ord_ftgermany	ord_ftjapan	ord_ftisrael	ord_ftfrance	ord_ftcanada	ord_ftturkey	ord_ftrussia	ord_ftpales	ord_electable_1	ord_electable_2	ord_conspire1	ord_conspire2	ord_conspire3	ord_lcself	ord_lcd	ord_lcr	ord_pathway	ord_preturn	ord_popen	ord_release1	ord_release2	ord_famsep	ord_tchina	ord_trussia	ord_tiran	ord_tmexico	ord_tjapan	ord_gw1	ord_gw2	ord_elite1	ord_elite2	ord_elite3	ord_elite4	ord_disable1	ord_disable2	ord_disable3	ord_disable4	ord_disable5	ord_disable6	ord_exptravel_ever	ord_exphomesch	ord_expfarm	ord_expffood	ord_expconvert	ord_expholiday	ord_explie	ord_expshark	ord_expdivorce	ord_exparrest	ord_expoverdose	ord_expdefault	ord_exppubasst	ord_exphybrid	ord_expmistake	ord_explightning	ord_exptravel_year	ord_expindian	ord_exphunt	ord_expflag	ord_exppublib	ord_explottery	ord_expshoponline	ord_exppubtrans	ord_expfight	ord_expavoid	ord_expknowimmig	ord_expknowtrans	ord_expbuyusa	ord_expretire	ord_expcolldebt	ord_expknowpris	ord_att2_reverse	starttime	endtime	duration	pop_density_public
67	ANES 2019 Pilot Study version 20200204	68	.929783697332345	.760284129308659	1	1	1	-1	1	1	4	__NA__	-1	4	1	-1	-1	-1	1	1	-1	2	2	2	2	2	2	2	2	1	98	4	7	0	0	1	1	90	98	89	94	10	20	96	-1	11	50	17	77	100	43	12	15	43	24	9	73	38	75	100	61	82	19	22	66	2	-1	2	-1	1	1	1	2	3	2	-1	-1	-1	2	-1	-1	-1	1	1	1	1	-1	-1	-1	-1	The way the democrats are dividing the country...	1	1	3	2	1	3	5	2	3	2	7	1	2	3	1	5	7	6	1	6	1	1	5	5	6	4	2	1	2	-1	1	-1	3	1	5	2	4	4	4	3	4	3	2	-1	-1	1	1	7	7	5	-1	5	-1	4	1	1	1	2	-1	7	2	2	2	3	2	3	1	3	2	2	-1	4	-1	4	-1	5	-1	4	-1	5	2	3	2	3	2	-1	4	1	-1	5	2	3	2	7	7	supreme court judge	0.5	none	0	6	4	1960	2	5	3	__NA__	-1	__NA__	-1	1	-1	-1	3	-1	-1	-1	-1	-1	-1	-1	-1	2	-1	1	3	4	3	-1	3	-1	5	3.0	115	20.3691106072058	-1	-1	-1	-1	-1	-1	-1	-1	-1	1	-3, restricted access	-1	1	1	1	-1	-3, restricted access	-1	-1	-3, restricted access	1	1	2	1	1	2	2	1	2	-1	-1	-1	-1	-1	-1	-1	-1	2	2	2	2	2	-1	-1	-1	-1	-1	1	2	1	-1	-1	-1	2	2	2	2	2	2	2	1	2	internet	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	3	4	3	1	1	1	-1	4	3	3	2	1	1	1939	2	2	1	2	1	5	__NA__	97	1	4	5	1	2	__NA__	1	1	2	1	1	__NA__	3	-3, restricted access	18	-3	-3, restricted acces	2	1465	107	9							0	0	1	1	0	0	0	12.605	8.561	0.000	4.676	6.086	8.623	0.000	6.783	13.154	0.000	5.859	8.943	22.593	13.085	6.119	4.728	5.953	4.762	4.915	4.268	6.341	6.195	8.563	5.304	5.709	4.892	7.929	0.000	4.738	4.315	3.870	7.847	4.711	5.231	3.433	4.471	4.100	3.030	3.804	4.352	4.421	4.910	5.988	3.732	5.127	3.462	3.176	7.250	13.088	0.000	10.181	0.000	8.010	7.790	6.176	7.504	18.578	12.275	0.000	8.962	7.102	7.993	6.607	5.440	0.000	0.000	0.000	0.000	157.504	20.323	7.883	7.132	10.428	8.030	11.614	9.143	19.182	12.333	14.244	14.694	7.818	10.659	8.603	10.045	14.982	15.821	20.262	18.713	8.648	11.748	9.797	22.721	14.641	0.000	59.721	21.400	0.000	0.000	8.785	15.041	13.196	10.385	9.419	0.000	9.518	0.000	23.314	10.642	11.350	10.034	0.000	16.009	9.661	10.949	9.864	13.077	8.927	7.401	9.461	7.076	8.240	7.476	0.000	9.272	0.000	8.648	0.000	11.764	0.000	9.422	0.000	13.480	13.033	10.491	0.000	12.082	0.000	9.893	16.968	12.699	14.246	0.000	36.350	19.847	11.796	14.944	26.245	8.036	7.71	0.00	0.0	5.925	0.000	0.000	8.265	12.022	0.000	40.868	0.000	7.345	0.000	12.139	8.783	0.000	0.000	0.000	0.000	6.009	0.000	19.316	7.016	6.071	0.000	0.000	0.00	12.014	32.912	0.000	23.145	0.000	17.793	0.000	23.788	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	8.760	9.573	6.610	5.050	0.000	14.295	11.154	11.232	14.603	8.415	13.064	1	2	3	5	6	4	7	11	9	8	12	10	14	0	15	17	16	19	18	20	25	23	22	32	30	29	27	28	34	31	24	21	26	33			3	1	2	1	2	3	3	1	2	4	5	6	4	2	3	5	1	1	2	1	4	2	3							7	8	6	4	5	3	1	2									3	5	4	1	2						3	2	1				0	12/21/2019 17:07:44	12/21/2019 17:42:29	2085	70
663	ANES 2019 Pilot Study version 20200204	664	1.00210010152631	.819417253018064	2	1	-1	1	4	4	-1	__NA__	3	2	-1	-1	-1	1	1	1	-1	2	2	2	1	2	2	2	2	2	93	9	13	5	4	3	12	77	70	51	70	25	7	-1	70	38	51	38	72	70	57	41	29	21	50	28	70	62	60	70	60	60	51	41	42	2	-1	2	-1	1	1	1	5	3	-1	2	-1	-1	-1	-1	-1	-1	2	2	1	2	-1	-1	-1	-1	The divisiveness being led by the Democrats	1	1	1	2	1	3	3	2	3	1	6	2	2	2	2	5	7	7	1	6	4	2	4	4	5	5	1	1	1	-1	1	-1	4	2	5	2	4	3	-1	-1	-1	-1	-1	7	2	-1	-1	6	6	3	-1	2	7	3	3	1	1	3	4	4	2	3	2	2	2	2	2	2	3	2	2	-1	3	-1	5	-1	5	-1	5	4	2	3	2	4	-1	2	2	-1	1	3	2	3	1	7	7	chief justice us supreme court	1.0	german chancellor	1	6	3	1000	2	7	-1	__NA__	2	__NA__	1	-1	6	2	3	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	3	5	4	3	2	-1	2	5	8.0	285	43.3293685121107	2	2	2	2	2	2	2	-1	1	-1	-3, restricted access	-1	1	1	1	1	-3, restricted access	-1	-1	-3, restricted access	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	2	1	2	2	2	1	2	-1	-1	-1	-1	-1	2	1	2	2	1	-1	-1	-1	1	2	2	1	2	1	2	1	2	2	2	2	__NA__	1	4	4	-1	-1	-1	3	4	5	-1	-1	-1	5	5	5	-1	-1	-1	-1	-1	-1	2	2	2	1	1	-1	1	5	-1	-1	-1	-1	2	1954	2	3	1	2	1	5	__NA__	97	1	5	7	1	2	__NA__	1	1	1	1	1	__NA__	10	-3, restricted access	45	-3	-3, restricted acces	3	1600	65	5	1	1	1	1			0	0	1	0	0	0	0	10.575	0.000	3.943	5.080	5.256	0.000	8.086	7.508	0.000	12.626	4.227	9.367	25.599	11.180	7.155	10.986	3.892	7.709	3.036	9.219	12.620	13.901	17.284	10.959	13.165	4.969	0.000	5.072	5.953	7.669	5.590	10.398	8.173	4.119	8.830	4.507	8.311	5.106	6.248	4.399	5.938	4.015	5.927	4.001	7.234	4.500	5.501	11.055	7.024	0.000	7.076	0.000	6.410	3.201	3.034	8.745	10.334	0.000	10.342	0.000	7.916	7.655	4.827	3.789	0.000	0.000	0.000	0.000	93.275	9.801	12.050	4.766	10.754	7.442	12.423	75.957	7.313	19.533	12.353	11.694	8.132	8.404	12.716	6.249	13.565	15.216	25.858	46.299	7.933	24.508	12.597	21.479	4.266	0.000	127.041	0.000	51.281	9.120	0.000	0.000	17.544	7.115	60.696	0.000	11.166	13.541	26.676	10.324	7.117	10.681	23.546	15.486	20.892	10.092	10.674	19.447	4.115	7.083	6.467	6.931	6.770	9.389	7.662	0.000	34.422	0.000	10.148	0.000	12.281	0.000	6.939	14.188	8.670	0.000	8.154	0.000	64.669	11.075	12.863	8.662	11.917	8.820	20.142	10.619	11.990	21.414	12.633	4.604	0.00	0.00	0.0	0.000	14.504	9.606	5.555	0.000	11.338	99.253	3.440	0.000	5.324	9.485	8.825	26.068	2.612	0.000	5.602	0.000	0.000	7.959	2.967	4.716	3.254	0.000	0.00	0.000	0.000	30.114	0.000	20.753	0.000	12.525	9.278	4.799	5.514	5.240	0.000	0.000	0.000	5.097	9.285	10.413	0.000	0.000	0.000	6.452	5.424	3.429	0.000	0.000	0.000	10.101	8.297	7.479	0.000	4.745	10.159	0.000	0.000	0.000	0.000	5.665	1	2	3	5	7	6	4	10	11	9	12	8	0	14	15	17	19	16	18	29	32	22	20	23	30	28	24	26	31	25	33	34	21	27			1	3	2	1	2	3	2	3	1	4	5	6						2	1	4	2	3	1	3	5	4	2	6	1									8	7	1	4	5	2	6	3						4	5	1	2	3				2	3	1	0	12/21/2019 3:02:57	12/21/2019 3:41:11	2294	700
1807	ANES 2019 Pilot Study version 20200204	1808	1.9039498655159	1.55685980503235	2	1	-1	1	3	3	-1	__NA__	2	4	-1	-1	-1	1	2	2	-1	2	2	2	2	1	1	2	1	2	40	46	40	60	65	45	30	65	60	60	75	65	60	-1	75	60	70	60	10	15	20	20	45	25	50	25	60	75	80	35	70	70	25	20	40	1	8	-1	1	1	2	2	1	3	-1	2	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	6	5	6	3	Cost of living increasing at a rate faster tha...	2	2	2	3	2	4	4	3	3	3	3	2	4	1	2	2	3	3	3	7	3	2	4	3	5	3	2	2	-1	2	-1	6	1	3	4	5	4	5	-1	-1	-1	-1	-1	2	3	-1	-1	5	5	-1	2	1	3	2	2	2	-1	1	1	1	6	3	4	4	3	3	2	3	5	1	3	-1	3	-1	4	-1	4	-1	4	4	2	4	1	5	-1	2	2	-1	1	4	1	3	2	3	3	board member	0.0	chancellor	1	6	1	1980	2	2	-1	__NA__	1	__NA__	2	-1	1	2	3	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	5	2	2	4	4	-1	2	5	11.0	186	25.938901011704	2	2	2	2	2	2	2	-1	4	-1	-3, restricted access	-1	2	-1	-1	-1	-3, restricted access	2	-1	-3, restricted access	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	2	2	2	2	2	1	2	-1	-1	-1	-1	-1	2	1	1	2	1	-1	-1	-1	1	2	2	1	1	1	1	1	1	2	2	2	__NA__	5	4	5	1	2	5	5	5	5	1	2	5	2	3	5	3	5	5	-1	-1	-1	3	3	3	3	3	-1	1	5	2	2	2	1	1	1995	1	5	5	2	3	1	__NA__	8	1	3	2	1	1	__NA__	2	4	6	7	11	__NA__	-7	-3, restricted access	24	-3	-3, restricted acces	3	666	21	7			1	1			0	0	0	0	0	0	1	17.862	0.000	2.532	2.447	3.598	0.000	8.000	5.905	0.000	9.003	2.828	21.783	17.034	8.345	8.621	3.805	6.521	5.645	3.505	4.597	4.031	8.369	3.430	4.040	3.916	4.467	0.000	2.520	2.975	4.381	6.075	5.744	5.110	2.961	9.189	5.423	4.553	4.439	3.033	6.742	4.339	2.354	5.153	3.564	3.557	4.188	4.967	4.700	4.086	10.521	0.000	13.584	27.039	4.418	1.242	5.575	20.167	0.000	17.044	0.000	0.000	0.000	0.000	0.000	7.302	3.075	5.578	2.979	48.065	6.153	5.462	3.362	13.932	4.167	7.396	5.586	34.032	15.292	8.026	6.999	29.071	8.544	10.065	4.478	35.276	15.546	43.561	19.886	7.566	17.165	18.270	30.201	0.000	8.953	86.339	0.000	35.766	13.557	0.000	0.000	17.655	8.190	0.000	15.471	7.292	7.685	10.952	5.588	0.000	26.345	19.742	9.879	15.825	7.609	5.424	10.693	2.323	4.178	5.244	3.312	15.754	8.948	3.795	0.000	20.216	0.000	18.101	0.000	3.733	0.000	9.238	21.077	9.497	0.000	9.023	0.000	13.497	7.498	9.300	4.721	6.797	10.567	23.992	15.487	9.984	21.284	11.008	8.935	0.00	3.39	2.1	0.000	3.731	3.206	5.544	0.000	5.337	33.968	4.899	0.000	5.017	4.242	4.828	25.389	3.181	0.000	3.381	0.000	0.000	4.845	0.000	0.000	0.000	3.909	0.00	0.000	0.000	23.766	0.000	10.310	0.000	10.473	5.912	4.587	4.119	3.609	2.577	1.564	2.944	3.540	5.446	1.157	11.605	6.447	3.249	14.591	3.848	1.437	4.341	2.536	1.879	2.592	4.759	6.874	0.000	6.128	10.347	4.343	5.241	4.402	2.303	4.595	1	2	4	3	7	5	6	10	8	9	11	12	0	14	15	17	16	18	19	32	27	33	24	28	31	22	34	23	25	30	26	29	20	21	1	2	3	2	1	1	3	2	1	2	3	4	5	6						1	2	4	1	3	2	5	3	4	6	1	2									5	2	1	6	3	4	8	7						3	4	2	5	1				1	2	3	1	12/22/2019 3:23:41	12/22/2019 3:56:16	1955	1700
1179	ANES 2019 Pilot Study version 20200204	1180	1.03170391458116	.843624290953015	2	1	-1	1	3	4	-1	__NA__	4	1	-1	-1	-1	1	2	1	-1	2	1	2	1	1	1	1	1	2	0	100	84	79	59	79	86	91	68	93	84	94	65	-1	100	89	91	94	0	0	66	3	91	1	98	17	90	91	69	73	92	100	3	12	81	1	5	-1	2	2	2	2	1	2	-1	2	-1	-1	-1	-1	-1	-1	5	5	5	5	-1	-1	-1	-1	Donald Trump	5	5	4	7	4	5	4	4	3	7	1	3	5	1	4	1	1	1	2	7	1	1	4	4	3	1	1	2	-1	1	5	-1	1	4	1	5	2	5	-1	-1	-1	-1	-1	1	5	-1	-1	3	4	1	-1	1	1	1	2	1	1	1	1	2	7	3	5	5	5	5	1	5	5	1	4	-1	4	-1	1	-1	3	-1	4	5	2	5	1	5	-1	2	2	-1	2	5	1	5	2	1	1	supreme court	0.5	president of germany	1	6	1	2017	2	1	1	__NA__	-1	__NA__	1	-1	0	5	3	-1	-1	-1	-1	-1	-1	-1	-1	-1	4	5	1	1	5	-1	1	1	5	0.0	148	28.9011111111111	2	2	1	1	2	1	1	1	1	-1	-3, restricted access	-1	2	-1	-1	-1	-3, restricted access	1	2	-3, restricted access	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	1	1	1	1	2	1	2	-1	-1	-1	-1	-1	1	1	2	2	2	-1	-1	-1	2	2	2	1	1	1	2	1	1	2	2	2	__NA__	1	1	5	1	1	1	3	5	5	-1	-1	-1	4	5	5	2	5	5	-1	-1	-1	2	1	2	1	1	-1	1	5	2	5	1	2	1	1963	2	3	5	2	1	6	__NA__	2	1	1	1	1	1	__NA__	2	3	5	5	2	__NA__	-7	-3, restricted access	26	-3	-3, restricted acces	2	246	180	9	1	1		1	1	1	1	1	0	0	0	0	0	12.663	0.000	3.283	5.844	5.179	0.000	11.159	4.454	0.000	16.330	3.013	11.565	18.756	17.642	14.286	4.856	2.908	3.527	3.505	3.511	5.499	6.019	4.087	4.629	5.340	5.644	0.000	4.914	3.547	7.482	3.310	4.461	5.008	4.030	4.777	3.143	4.891	3.693	3.630	3.332	3.237	3.736	3.196	3.015	3.206	3.169	4.423	4.809	9.257	11.589	0.000	7.762	5.583	3.237	2.977	4.247	3.295	0.000	6.854	0.000	8.870	4.642	2.633	2.617	0.000	0.000	0.000	0.000	17.371	8.997	5.191	4.445	7.504	6.043	10.847	12.979	7.074	16.876	10.038	6.419	6.497	9.217	7.414	6.822	5.700	12.585	19.849	14.666	6.654	10.214	7.887	14.017	5.195	0.000	42.204	0.000	10.312	5.994	0.000	0.000	9.965	9.984	6.782	0.000	7.051	9.088	14.188	12.163	14.158	5.748	21.570	12.802	7.844	7.827	5.198	7.225	3.286	5.672	3.764	8.894	6.533	5.962	7.621	0.000	8.332	0.000	8.508	0.000	13.972	0.000	8.600	12.624	7.428	0.000	10.564	0.000	11.722	10.496	7.223	8.163	7.076	9.030	25.343	20.133	11.591	14.067	12.623	5.172	0.00	0.00	0.0	0.000	7.931	5.124	4.510	0.000	6.616	35.591	0.000	4.727	8.790	9.799	6.757	29.264	4.057	3.634	3.125	0.000	0.000	8.839	0.000	0.000	0.000	6.680	3.05	0.000	0.000	26.732	0.000	27.923	0.000	8.634	8.412	3.564	4.856	4.972	2.502	3.723	3.317	4.086	5.551	3.198	0.000	0.000	0.000	4.684	3.480	2.473	2.954	5.293	1.911	10.452	8.917	5.565	0.000	4.649	10.106	8.299	5.735	5.963	5.182	6.683	1	2	3	7	5	4	6	12	10	9	11	8	0	14	15	18	16	19	17	21	25	31	26	32	29	30	24	27	33	28	23	34	22	20	2	1	1	2	3	1	2	3	1	2	3	4	5	6						2	1	4	3	2	1	2	6	1	5	3	4									5	3	8	4	1	2	6	7						5	3	1	4	2				1	2	3	1	12/21/2019 14:06:10	12/21/2019 14:32:56	1606	5400
612	ANES 2019 Pilot Study version 20200204	613	1.02431222398371	.83758010555148	1	1	1	-1	3	4	2	__NA__	-1	3	1	-1	-1	-1	2	1	-1	2	1	2	1	2	2	2	2	2	6	84	64	30	22	32	35	72	72	55	86	47	31	68	-1	94	81	86	22	7	36	9	47	22	50	9	84	55	52	92	81	95	12	7	44	1	1	-1	2	2	2	2	1	2	2	-1	-1	-1	2	-1	-1	-1	5	5	4	3	-1	-1	-1	-1	Divisions between political parties	2	2	2	6	2	4	3	3	1	6	2	2	4	4	4	4	7	4	3	7	4	4	3	4	3	4	1	2	-1	1	5	-1	2	3	2	5	1	5	4	5	4	1	1	-1	-1	4	7	6	6	1	-1	1	-1	1	1	2	-1	2	-1	1	7	4	5	5	3	3	1	3	5	1	-1	2	-1	5	-1	4	-1	4	-1	2	2	5	1	5	2	-1	5	1	-1	5	1	5	2	1	1	chief justice of the supreme court	1.0	germany’s chancellor	1	6	1	1900	2	3	3	__NA__	-1	__NA__	-1	2	-1	-1	3	-1	-1	-1	-1	-1	-1	-1	-1	3	-1	4	1	1	5	4	-1	-1	5	5.0	150	24.9585798816568	-1	-1	-1	-1	-1	-1	-1	-1	-1	12	-3, restricted access	3	2	-1	-1	-1	-3, restricted access	-1	-1	-3, restricted access	-1	1	2	2	2	2	1	1	2	-1	-1	-1	-1	-1	-1	-1	-1	2	1	2	2	1	-1	-1	-1	-1	-1	1	1	1	-1	-1	-1	1	1	1	2	1	2	2	2	2	__NA__	1	4	5	2	2	4	6	5	5	-1	-1	-1	7	5	5	-1	-1	-1	-1	-1	-1	3	1	1	1	1	2	-1	5	-1	-1	-1	-1	1	1956	2	5	6	2	1	5	__NA__	8	1	3	3	1	1	__NA__	2	4	6	7	11	__NA__	-7	-3, restricted access	9	-3	-3, restricted acces	1	465	235	9		1		1		1	0	0	1	0	1	1	0	6.243	3.020	0.000	3.433	7.511	4.965	0.000	3.581	6.209	0.000	2.925	4.003	25.662	13.037	4.463	3.753	5.600	3.381	6.252	4.518	2.812	9.894	4.929	2.778	5.832	5.727	4.280	0.000	3.867	3.478	3.981	3.025	3.286	2.592	2.281	2.555	2.242	2.648	2.226	3.077	2.471	2.854	2.669	3.061	2.852	5.107	2.574	4.639	4.481	6.531	0.000	5.708	7.900	4.943	7.870	3.926	7.024	5.924	0.000	3.238	4.684	4.924	5.501	9.809	0.000	0.000	0.000	0.000	31.713	4.181	5.273	3.075	7.229	5.289	6.673	7.706	7.426	5.979	8.905	4.617	7.412	5.539	5.063	5.251	5.593	8.633	18.134	17.326	5.016	7.104	5.791	8.768	4.127	0.000	36.514	29.273	0.000	0.000	4.784	6.217	10.501	5.440	6.374	0.000	3.519	0.000	7.381	4.392	0.000	7.148	0.000	3.936	13.038	9.676	4.330	4.904	3.679	3.798	3.528	4.469	6.102	5.554	0.000	7.300	0.000	5.332	0.000	5.190	0.000	3.610	0.000	7.910	6.348	6.007	0.000	5.542	0.000	6.402	6.346	6.125	7.072	0.000	26.005	14.638	8.166	18.556	9.838	4.741	0.00	0.00	0.0	0.000	0.000	0.000	6.946	8.284	0.000	6.478	3.581	0.000	0.000	9.086	6.066	0.000	0.000	0.000	0.000	4.296	6.292	5.919	0.000	0.000	0.000	0.000	0.00	0.000	15.869	0.000	14.369	0.000	11.323	0.000	7.745	4.216	4.151	3.852	2.921	3.461	3.777	3.105	3.208	2.620	0.000	0.000	0.000	5.218	2.276	2.375	0.000	0.000	0.000	3.437	4.377	5.111	3.906	0.000	6.466	0.000	0.000	0.000	0.000	4.235	1	2	3	4	7	5	6	12	8	10	11	9	14	0	15	18	17	16	19	25	26	31	34	24	32	28	22	21	33	29	30	20	23	27	1	2	1	3	2	1	2	3	3	1	2	4	5	6	2	5	4	3	1	1	2	4	2	3	1							8	4	1	2	3	5	6	7									2	4	3	1	5						1	3	2				0	12/21/2019 2:32:49	12/21/2019 2:55:45	1376	800

To see the count of non-missing observations and summary statistics such as the mean, standard deviation, minimum, maximum, and quantiles for the numeric columns, use the .describe() method:

anes.describe()

	caseid	form	follow	reg1a	reg1b	liveurban	youthurban	placeid1a	placeid1b	placeidimport	turnout16a	turnout16a1	turnout16b	turnout16c	vote16	turnout18a	turnout18a1	particip_1	particip_2	particip_3	particip_4	particip_5	particip_6	particip_7	particip_8	particip_9	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftimmig1	ftimmig2	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales	vote20dem	vote20cand	vote20cand2	electable	vote20jb	vote20ew	vote20bs	cvote2020	tsplit1	contact1a	contact1b	contact2a	contact2b	contact3	contact4m_1	contact4m_2	contact4m_3	apppres5	frnpres5	immpres5	econpres5	apppres7	frnpres7	immpres7	econpres7	econnow	finworry	confecon	improve1	national1	national2	conspire1	conspire2	conspire3	taxecon	billtax	trade1	trade2	trade3	trade4	richpoor2	guarinc	lcself	lcd	lcr	pop1	pop2	pop3	corrupt	immignum	refugees	dreamstr	dreamer	dreamstr1	dreamstr2	wall5	wall7	pathway	preturn	popen	release1	release2	famsep	tchina	trussia	tiran	tmexico	tjapan	hlthcare1	hlthcare2	abortion1	abortion2	freecol	loans	diversity5	diversity7	language	buyback	gw1	gw2	knowopioid1	knowopioid2	opioiddo	sentence	prek	demo4	experts	science	exphelp	elite1	elite2	elite3	elite4	ukraine1	ukraine2	excessive	rural1alt1	rural1alt2	rural2alt1	rural2alt2	rural3alt1	rural3alt2	rural4alt1	rural4alt2	conf_unemp	unemp	conf_interfere	interfere	conf_autism	autism1	autism2	conf_gmo	gmo1	gmo2	conf_warm	warm	conf_illegal	illegal	impeach1	impeach2	pk_cjus_correct	pk_germ_correct	pk_sen	pk_spend	pk_geer	cheat	pid7x	pid1d	pid1r	pidstr	pidlean	ngun	shooting	dem_activduty	milyears	milyr1	milyr2	milyr3	milyr4	milyr5	milyr6	combat	harass1a	harass1b	rr1	rr2	rr3	rr4	health1a	health1b	hospital	feet	inches	nweight	disable1	disable2	disable3	disable4	disable5	disable6	smoker1	smoker2	exercise	relig1a	relig2a	att1	att2	att3	relig1b	relig2b	relig3b	attother	exptravel_ever	exphomesch	expfarm	expffood	expconvert	expholiday	explie	expshark	expdivorce	exparrest	expoverdose	expdefault	exppubasst	exphybrid	expmistake	explightning	exptravel_year	expindian	exphunt	expflag	exppublib	explottery	expshoponline	exppubtrans	expfight	expavoid	expknowimmig	expknowtrans	expbuyusa	expretire	expcolldebt	expknowpris	socmed_1	socmed_2	socmed_3	socmed_4	socmed_5	socmed_6	socmed_7	socmed_8	socmed_9	facebook1	facebook2	facebook3	twitter1	twitter2	twitter3	instagram1	instagram2	instagram3	reddit1	reddit2	reddit3	youtube1	youtube2	youtube3	snapchat1	snapchat2	snapchat3	tiktok1	tiktok2	tiktok3	raceid	racework	whitejob	race_sub1	race_sub2	voterid1	voterid2	serious	photo1	photo2	photo3	photo4	reinterview	birthyr	gender	educ	marstat	child18	race	employ	faminc_new	votereg	ideo5	pid7	newsint	presvote16post	pew_bornagain	pew_religimp	pew_churatd	pew_prayer	religpew	religpew_protestant	inputstate	zipCode	region	qualityControl_overall_scale	abortion1_skp	abortion2_skp	apppres5_skp	apppres7_skp	att1_skp	att2_skp	att3_skp	attother_skp	autism1_skp	autism2_skp	billtax_skp	buyback_skp	cexp1_grid_skp	cexp2_grid_skp	cheat_skp	combat_skp	confecon_skp	conspire1_skp	conspire2_skp	conspire3_skp	contact1a_skp	contact1b_skp	contact2a_skp	contact2b_skp	contact3_skp	corrupt_skp	cvote2020_skp	dem_activduty_skp	demo4_skp	disable_grid_skp	diversity5_skp	diversity7_skp	dreamer_skp	econnow_skp	econpres5_skp	econpres7_skp	electable_skp	elite1_skp	elite2_skp	elite3_skp	elite4_skp	excessive_skp	exercise_skp	exp1_grid_skp	exp2_grid_skp	experts_skp	exphelp_skp	facebook1_skp	facebook2_skp	facebook3_skp	finworry_skp	follow_skp	freecol_skp	frnpres5_skp	frnpres7_skp	ftasian_skp	ftbiden_skp	ftblack_skp	ftbritain_skp	ftbuttigieg_skp	ftcanada_skp	ftchina_skp	ftfrance_skp	ftgermany_skp	ftharris_skp	fthisp_skp	ftice_skp	ftillegal_skp	ftimmig1_skp	ftimmig2_skp	ftiran_skp	ftisrael_skp	ftjapan_skp	ftjournal_skp	ftmexico_skp	ftmuslim_skp	ftnato_skp	ftnkorea_skp	ftnra_skp	ftobama_skp	ftpales_skp	ftrussia_skp	ftsanders_skp	ftsaudi_skp	fttrump_skp	ftturkey_skp	ftukraine_skp	ftun_skp	ftwarren_skp	ftwhite_skp	gmo1_skp	gmo2_skp	guarinc_skp	gw_grid_skp	harass1a_skp	harass1b_skp	health1a_skp	health1b_skp	hlthcare1_skp	hlthcare2_skp	hospital_skp	illegal_skp	immignum_skp	immpres5_skp	immpres7_skp	impeach1_skp	impeach2_skp	improve1_skp	instagram1_skp	instagram2_skp	instagram3_skp	interfere_skp	knowopioid1_skp	knowopioid2_skp	language_skp	lc_grid_skp	liveurban_skp	loans_skp	milyears_skp	milyr_skp	mip_skp	national1_skp	national2_skp	ngun_skp	opioiddo_skp	particip_skp	path_grid_skp	pid1d_skp	pid1r_skp	pidlean_skp	pidstr_skp	pk_cjus_skp	pk_geer_skp	pk_germ_skp	pk_sen_skp	pk_spend_skp	placeid1a_skp	placeid1b_skp	placeidimport_skp	pop_grid_skp	prek_skp	raceid_skp	racework_skp	reddit1_skp	reddit2_skp	reddit3_skp	refugees_skp	reg1a_skp	reg1b_skp	reinterivew_skp	relig1a_skp	relig1b_skp	relig2a_skp	relig2b_skp	relig3b_skp	rexp1_grid_skp	rexp2_grid_skp	richpoor2_skp	rr_grid_skp	rural1alt1_skp	rural1alt2_skp	rural2alt1_skp	rural2alt2_skp	rural3alt1_skp	rural4alt1_skp	rural4alt2_skp	science_skp	sentence_skp	serious_skp	shooting_skp	smoker1_skp	smoker2_skp	snapchat1_skp	snapchat2_skp	snapchat3_skp	socmed_skp	tall_skp	taxecon_skp	threat_grid_skp	tiktok1_skp	tiktok2_skp	tiktok3_skp	trade1_skp	trade2_skp	trade3_skp	trade4_skp	tsplit1_skp	turnout16a_skp	turnout16b_skp	turnout16c_skp	turnout18a_skp	twitter1_skp	twitter2_skp	twitter3_skp	ukraine1_skp	ukraine2_skp	unemp_skp	vote16_skp	vote20bs_skp	vote20cand2_skp	vote20cand_skp	vote20dem_skp	vote20ew_skp	vote20jb_skp	voterid1_skp	voterid2_skp	wall7_skp	wall5_skp	warm_skp	nweight_skp	whitejob_skp	youthurban_skp	youtube1_skp	youtube2_skp	youtube3_skp	rural3alt2_skp	follow_page_timing	reg1a_page_timing	reg1b_page_timing	liveurban_page_timing	youthurban_page_timing	placeid1a_page_timing	placeid1b_page_timing	placeidimport_page_timing	turnout16a_page_timing	turnout16b_page_timing	turnout16c_page_timing	vote16_page_timing	turnout18a_page_timing	particip_page_timing	fttrump_page_timing	ftobama_page_timing	ftbiden_page_timing	ftwarren_page_timing	ftsanders_page_timing	ftbuttigieg_page_timing	ftharris_page_timing	ftblack_page_timing	ftwhite_page_timing	fthisp_page_timing	ftasian_page_timing	ftmuslim_page_timing	ftillegal_page_timing	ftimmig1_page_timing	ftimmig2_page_timing	ftjournal_page_timing	ftnato_page_timing	ftun_page_timing	ftice_page_timing	ftnra_page_timing	ftchina_page_timing	ftnkorea_page_timing	ftmexico_page_timing	ftsaudi_page_timing	ftukraine_page_timing	ftiran_page_timing	ftbritain_page_timing	ftgermany_page_timing	ftjapan_page_timing	ftisrael_page_timing	ftfrance_page_timing	ftcanada_page_timing	ftturkey_page_timing	ftrussia_page_timing	ftpales_page_timing	vote20dem_page_timing	vote20cand_page_timing	vote20cand2_page_timing	electable_page_timing	vote20jb_page_timing	vote20ew_page_timing	vote20bs_page_timing	cvote2020_page_timing	tsplit1_page_timing	contact1a_page_timing	contact1b_page_timing	contact2a_page_timing	contact2b_page_timing	contact3_page_timing	apppres5_page_timing	frnpres5_page_timing	immpres5_page_timing	econpres5_page_timing	apppres7_page_timing	frnpres7_page_timing	immpres7_page_timing	econpres7_page_timing	mip_page_timing	econnow_page_timing	finworry_page_timing	confecon_page_timing	improve1_page_timing	national1_page_timing	national2_page_timing	conspire1_page_timing	conspire2_page_timing	conspire3_page_timing	taxecon_page_timing	billtax_page_timing	trade1_page_timing	trade2_page_timing	trade3_page_timing	trade4_page_timing	richpoor2_page_timing	guarinc_page_timing	lc_grid_page_timing	pop_grid_page_timing	corrupt_page_timing	immignum_page_timing	refugees_page_timing	dreamer_page_timing	wall_page_timing	wall7_page_timing	path_grid_page_timing	threat_grid_page_timing	hlthcare1_page_timing	hlthcare2_page_timing	abortion1_page_timing	abortion2_page_timing	freecol_page_timing	loans_page_timing	diversity5_page_timing	diversity7_page_timing	language_page_timing	buyback_page_timing	gw_grid_page_timing	knowopioid1_page_timing	knowopioid2_page_timing	opioiddo_page_timing	sentence_page_timing	prek_page_timing	demo4_page_timing	experts_page_timing	science_page_timing	exphelp_page_timing	elite1_page_timing	elite2_page_timing	elite3_page_timing	elite4_page_timing	ukraine1_page_timing	ukraine2_page_timing	excessive_page_timing	rural1alt1_page_timing	rural1alt2_page_timing	rural2alt1_page_timing	rural2alt2_page_timing	rural3alt1_page_timing	rural3alt2_page_timing	rural4alt1_page_timing	rural4alt2_page_timing	unemp_page_timing	interfere_page_timing	autism1_page_timing	autism2_page_timing	gmo1_page_timing	gmo2_page_timing	warm_page_timing	illegal_page_timing	impeach1_page_timing	impeach2_page_timing	pk2_intro_page_timing	pk_cjus_page_timing	pk_germ_page_timing	pk_sen_page_timing	pk_spend_page_timing	pk_geer_page_timing	cheat_page_timing	pid1d_page_timing	pid1r_page_timing	pidstr_page_timing	pidlean_page_timing	ngun_page_timing	shooting_page_timing	dem_activduty_page_timing	milyears_page_timing	milyr_page_timing	combat_page_timing	harass1a_page_timing	harass1b_page_timing	rr_grid_page_timing	health1a_page_timing	health1b_page_timing	hospital_page_timing	tall_page_timing	nweight_page_timing	disable_grid_page_timing	smoker1_page_timing	smoker2_page_timing	exercise_page_timing	relig1a_page_timing	relig2a_page_timing	att1_page_timing	att2_page_timing	att3_page_timing	relig1b_page_timing	relig2b_page_timing	relig3b_page_timing	attother_page_timing	exp1_grid_page_timing	exp2_grid_page_timing	rexp1_grid_page_timing	rexp2_grid_page_timing	cexp1_grid_page_timing	cexp2_grid_page_timing	socmed_page_timing	facebook1_page_timing	facebook2_page_timing	facebook3_page_timing	twitter1_page_timing	twitter2_page_timing	twitter3_page_timing	instagram1_page_timing	instagram2_page_timing	instagram3_page_timing	reddit1_page_timing	reddit2_page_timing	reddit3_page_timing	youtube1_page_timing	youtube2_page_timing	youtube3_page_timing	snapchat1_page_timing	snapchat2_page_timing	snapchat3_page_timing	tiktok1_page_timing	tiktok2_page_timing	tiktok3_page_timing	raceid_page_timing	racework_page_timing	whitejob_page_timing	voterid1_page_timing	voterid2_page_timing	serious_page_timing	photo1_page_timing	photo2_page_timing	photo3_page_timing	photo4_page_timing	reinterview_page_timing	ord_fttrump	ord_ftobama	ord_ftbiden	ord_ftwarren	ord_ftsanders	ord_ftbuttigieg	ord_ftharris	ord_ftblack	ord_ftwhite	ord_fthisp	ord_ftasian	ord_ftmuslim	ord_ftimmig1	ord_ftimmig2	ord_ftjournal	ord_ftnato	ord_ftun	ord_ftice	ord_ftnra	ord_ftchina	ord_ftnkorea	ord_ftmexico	ord_ftsaudi	ord_ftukraine	ord_ftiran	ord_ftbritain	ord_ftgermany	ord_ftjapan	ord_ftisrael	ord_ftfrance	ord_ftcanada	ord_ftturkey	ord_ftrussia	ord_ftpales	ord_conspire1	ord_conspire2	ord_conspire3	ord_lcself	ord_lcd	ord_lcr	ord_pathway	ord_preturn	ord_popen	ord_release1	ord_release2	ord_famsep	ord_gw1	ord_gw2	ord_elite1	ord_elite2	ord_elite3	ord_elite4	ord_lc_reverse	ord_att2_reverse	duration
count	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.00000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.00000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.00000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.00000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.00000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.00000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.00000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.00000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.00000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.0	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000
mean	1583.000000	1.498894	1.672038	0.117220	0.140284	2.665403	2.641390	0.699842	0.740284	2.357662	0.111216	-0.974408	-0.429068	-0.46951	1.146603	1.318799	-0.875197	1.880885	1.802528	1.93049	1.659716	1.790205	1.869510	1.812322	1.663191	1.572828	49.350711	57.589573	56.269194	78.185150	55.091311	124.552923	95.901738	70.738073	70.762717	68.907425	69.159242	51.828120	42.086888	33.718167	35.901422	49.866667	96.762717	70.290995	69.216430	63.884992	37.933965	20.754818	53.505845	32.030648	48.959874	26.515640	69.140284	60.447077	64.789889	59.599052	58.558610	72.419589	37.601264	31.415166	39.97030	1.743760	1.376303	0.117852	0.232859	1.878041	1.882148	1.862559	1.989889	2.216430	0.402212	0.417062	-0.706793	-0.783886	0.496367	-0.911216	-0.896051	-0.890679	1.093523	1.101738	1.073618	0.900790	1.723855	1.717852	1.691943	1.430648	2.547235	2.394945	2.545024	4.408531	2.426224	3.007583	3.175671	2.932070	3.010742	3.770616	3.777883	2.112480	2.911216	2.533017	2.250553	2.906161	4.480885	4.109321	2.310269	5.544708	2.213902	2.040126	3.823697	3.617378	4.011058	3.407583	1.587678	1.666983	-0.321643	0.848025	0.993365	1.559558	2.402528	2.712480	3.378831	3.442338	3.149131	3.816114	1.213270	1.336809	1.291627	0.618957	0.370300	1.535861	0.860348	0.87267	1.687520	4.393681	4.454660	0.721011	1.154818	2.242022	1.506477	2.475513	2.327962	1.604423	0.674882	2.706793	1.153555	3.622749	4.952923	3.009479	3.272354	3.230016	2.983886	3.503002	1.792733	3.063823	3.668562	1.306477	0.958610	1.056872	1.167457	1.772196	1.696682	1.770616	1.747235	1.854344	1.903318	3.534913	1.764929	3.880253	1.248025	3.804423	0.400948	0.400000	3.306477	0.248025	0.181043	3.779463	1.249605	3.616430	1.637599	3.950079	3.956082	0.465403	0.610742	5.828120	2.326698	1671.986414	1.838863	3.481517	0.495419	0.450237	0.340600	0.194945	1.831596	0.528910	2.835703	17.943128	-0.575671	-0.58357	-0.633175	-0.635387	-0.607899	-0.593997	-0.599368	0.839810	0.786098	2.560506	2.957030	3.135861	2.998420	1.109321	0.971248	0.340600	5.071090	5.406975	183.066035	0.412006	0.450553	0.423065	0.404423	0.452449	0.428436	0.274882	-0.245182	1.002844	2.697630	-0.556714	1.592733	0.351975	-0.642970	-0.415482	-0.206319	-0.610111	-0.542496	0.152291	0.461295	0.332070	0.327962	0.402528	0.257820	0.037915	0.485308	0.334913	0.377567	0.326698	0.395261	0.313744	0.448973	0.020221	0.472354	0.310585	0.328910	0.300790	0.254028	0.212322	0.178831	0.036967	0.233491	0.385782	0.278673	0.144392	0.337757	0.182306	0.215482	0.408531	0.372196	1.198736	1.597472	1.602844	1.826856	1.284676	1.827488	1.947235	1.974724	1.922907	1.943760	2.068246	3.043602	1.055608	0.533017	1.028436	0.919115	1.024329	1.206951	-0.035071	-0.190521	-0.049605	2.404107	2.520695	3.040126	-0.126066	-0.071406	-0.037599	-0.690047	-0.715008	-0.701106	2.556398	2.773460	2.273302	1.416114	1.416114	0.342496	0.344392	4.567773	0.286572	0.253081	0.207583	0.218641	1.054344	1967.966509	1.516272	3.496367	2.636651	1.791469	1.667615	3.492575	18.533333	1.156082	3.388310	4.031596	1.925118	3.222433	1.705845	2.248341	4.283096	3.931122	5.137125	0.030016	28.057820	-3.0	2.682464	7.612006	0.001264	0.001896	0.001896	0.001264	0.002528	0.000948	0.0	0.000316	0.005371	0.012954	0.002844	0.000316	0.005687	0.006635	0.007267	0.001580	0.003476	0.006635	0.003476	0.004107	0.001264	0.003791	0.000316	0.000632	0.001264	0.005687	0.001580	0.003791	0.005687	0.016114	0.000948	0.000948	0.036019	0.003476	0.002844	0.004739	0.000316	0.005371	0.007267	0.006319	0.004423	0.000632	0.000632	0.013586	0.015166	0.003791	0.004423	0.000316	0.002212	0.006319	0.001896	0.001580	0.000948	0.004107	0.005055	0.012954	0.015798	0.005687	0.005055	0.033807	0.002528	0.006635	0.004107	0.004423	0.027172	0.008215	0.009795	0.007583	0.007267	0.009479	0.008847	0.006951	0.00316	0.006951	0.004107	0.012322	0.009795	0.008531	0.016746	0.042338	0.012638	0.005371	0.018641	0.007899	0.098578	0.009163	0.010427	0.007899	0.021485	0.008215	0.006003	0.012638	0.000316	0.006003	0.002528	0.000948	0.000316	0.000316	0.000948	0.001264	0.0	0.014534	0.003476	0.001580	0.002528	0.002212	0.007583	0.002212	0.001580	0.002212	0.004107	0.022433	0.011374	0.000948	0.001264	0.023697	0.00316	0.008847	0.000316	0.000316	0.072038	0.001896	0.001264	0.0	0.004107	0.004739	0.020221	0.000316	0.0	0.000632	0.000316	0.090047	0.211374	0.051185	0.029068	0.007583	0.000316	0.001580	0.004107	0.015798	0.006003	0.006635	0.008215	0.000948	0.000948	0.001580	0.007267	0.001580	0.000948	0.004423	0.002844	0.000632	0.000316	0.001580	0.0	0.009479	0.009479	0.005687	0.012954	0.001580	0.000948	0.007267	0.004107	0.002212	0.002844	0.005371	0.005687	0.000632	0.002212	0.0	0.001896	0.000632	0.000632	0.000948	0.001580	0.002212	0.012954	0.005371	0.010427	0.000316	0.000316	0.000948	0.000632	0.011690	0.014218	0.003791	0.004107	0.001264	0.000632	0.000632	0.003476	0.001264	0.001580	0.002844	0.004739	0.016746	0.024961	0.001896	0.006319	0.003791	0.001264	0.003476	0.006635	0.005371	0.000948	0.001264	0.001264	0.000948	0.014218	0.018325	0.006003	0.005687	0.000948	0.00316	0.005687	0.002528	16.249831	2.851289	4.758153	9.947926	9.746972	5.948907	6.459573	10.551217	7.434285	7.914338	3.746515	4.370908	16.349738	26.844717	14.821811	9.187120	6.666985	9.203342	6.349779	10.455729	6.368160	8.764240	8.407842	9.570333	6.737906	7.946085	7.463983	3.537166	3.961809	6.674058	8.099450	6.692616	13.294170	7.196664	6.297405	5.605068	6.167858	8.368036	5.799524	6.397577	5.627565	5.504396	7.314327	7.127235	5.342872	5.601698	7.028444	10.067284	6.142951	9.488965	8.053627	2.994213	5.99733	10.199777	6.486621	6.119887	7.631131	11.593834	6.120466	8.947167	0.397838	0.472865	4.374793	4.534931	3.620925	2.905165	2.821052	6.751886	4.862747	3.070789	3.232060	48.462781	14.782748	12.893939	7.614476	26.695615	11.381638	14.058791	13.468457	14.334284	16.524850	17.648784	14.067368	14.669283	12.939733	11.314433	11.419157	14.676886	34.775299	22.727071	31.546775	12.257626	16.569494	20.682562	20.941951	4.080805	16.403611	83.353785	18.758895	11.695224	6.509795	6.051318	7.318181	16.792928	15.692625	6.401828	7.377582	10.994774	9.668506	28.472640	63.464335	4.374284	14.293756	28.085451	15.632500	31.944563	13.800104	10.805658	18.735078	9.756997	9.406772	10.210200	11.382238	13.971114	10.983584	5.463366	7.046347	9.188105	6.174507	6.588835	5.950917	8.755212	5.610690	7.194316	16.579748	15.251651	4.975384	6.640365	5.351997	6.936025	15.165952	12.869905	13.785718	13.804272	5.174459	76.840578	21.269621	14.060265	17.778390	20.862071	8.696251	0.627698	0.474750	0.389372	0.341393	4.223396	4.235764	8.487581	0.113170	1.547438	0.962805	5.052195	4.617948	44.516415	3.460289	3.956396	5.144718	11.258700	7.711925	14.459429	2.299373	1.370671	3.840478	5.087858	0.797151	12.819019	3.932843	0.980799	2.208268	2.328449	0.906647	2.492929	16.092442	14.840867	9.559169	12.830056	9.584312	7.607143	12.263852	9.575728	6.777868	5.412755	2.243678	2.985571	2.739002	2.054936	2.432392	1.481347	8.649955	0.766930	0.623996	4.825806	4.369771	3.329777	0.882941	0.742097	0.523940	0.207807	0.163375	0.130342	8.343833	10.489896	11.550905	3.259258	7.736379	18.957856	5.420974	3.359211	3.170164	3.117318	7.152483	1.0	2.0	5.006319	4.959558	4.985466	5.012322	5.036335	10.013270	10.008847	10.000000	10.000000	9.977883	7.015482	6.984518	15.0	17.534281	17.490679	17.454976	17.520063	26.947235	27.053081	27.027488	27.082148	27.173144	27.053397	26.917536	26.979147	27.073934	26.939021	27.024329	26.875829	27.012006	27.012954	26.828752	1.992101	1.995577	2.012322	1.0	2.503633	2.496367	2.002844	2.023381	1.973776	4.0	5.0	6.0	1.512480	1.487520	2.488784	2.515640	2.496367	2.499210	0.0	0.506161	8663.017694
std	913.801127	0.500078	0.926089	1.195428	1.247464	1.070029	1.096192	1.880001	1.906350	1.306302	1.158824	0.364741	0.980422	0.96956	1.202344	0.576437	0.632481	0.323975	0.398155	0.25436	0.473880	0.407226	0.336895	0.390516	0.472694	0.494746	84.472485	73.002606	120.196311	189.367529	115.863403	276.040047	235.892477	23.964258	23.046131	24.333374	23.753420	29.722614	32.037712	39.488803	40.827825	31.566846	196.277400	138.611353	129.933928	134.687957	24.924698	23.160735	26.513570	24.203703	24.480570	24.801873	23.691241	24.822482	23.868048	29.137909	25.092663	23.950280	23.866562	24.985195	27.53408	0.785695	3.227412	1.503372	1.482449	0.920157	0.930424	0.906015	1.224961	1.039782	1.427890	1.443793	0.919295	0.791903	1.525718	0.454661	0.528816	0.552598	2.426304	2.430919	2.417922	2.236128	3.242645	3.232316	3.232296	2.951463	1.190750	1.230450	1.217087	1.984036	1.256340	1.196790	1.138945	1.037598	1.288336	1.992199	2.186822	0.958025	1.095626	1.004384	0.961705	1.513856	2.134250	2.006010	1.486062	1.611915	1.110544	1.038133	1.146316	1.005617	1.695779	1.905662	0.799201	0.850188	1.295056	1.470947	2.365528	3.122146	1.480934	1.557206	1.599698	1.574602	1.511817	1.503996	2.386954	2.495949	2.457080	1.827282	1.565067	3.014146	2.102935	2.04003	3.125481	2.265519	2.253859	1.940376	2.475170	1.413388	3.017706	1.471103	1.340742	0.489051	2.287246	1.601586	2.532555	2.135247	1.934449	1.166553	1.239014	1.087009	1.235559	1.157237	0.965923	1.362203	1.450328	0.857907	2.086891	2.224431	2.340165	2.908679	2.856271	2.967346	2.953912	3.000624	3.058243	1.352924	0.620221	1.269303	0.607137	1.309541	1.473984	1.508258	1.218110	1.303537	1.338110	1.222687	0.548764	1.302573	0.642797	2.628259	2.633112	0.445501	0.487659	6.347812	1.355817	671.902905	0.425120	2.961733	1.682924	1.681818	1.184448	1.620499	39.321173	1.715605	0.429832	193.708767	1.042572	1.02669	0.919159	0.914008	0.975749	1.005246	0.965917	1.986028	1.891677	1.459922	1.511874	1.461211	1.461797	2.164109	2.155637	1.383124	0.984232	3.517829	63.705234	1.486639	1.514105	1.494672	1.488721	1.515417	1.498529	1.325245	1.471359	2.214977	4.999799	1.239248	0.514036	1.845648	0.868132	1.307694	1.226328	1.119147	0.977874	1.213675	1.486827	1.387591	1.384122	1.443979	1.321515	1.083646	1.503322	1.419009	1.460803	1.420256	1.466496	1.409451	1.490277	1.076221	1.514034	1.360878	1.376685	1.343812	1.317940	1.259266	1.265958	1.109190	1.329245	1.451487	1.370888	1.195363	1.384164	1.255306	1.294382	1.460908	1.433035	0.399112	0.490485	0.489386	0.378431	0.451331	0.377884	0.223599	0.156988	0.266781	2.140158	1.957453	2.295352	2.848149	2.088270	2.609150	2.721554	2.595528	2.787311	2.291847	1.878594	2.135665	2.678055	2.445997	2.663485	2.119435	2.085549	2.149187	1.401072	1.230273	1.283618	1.621324	1.725155	1.281567	1.025432	1.025432	1.449828	1.475957	1.010175	1.453350	1.376188	1.321396	1.412950	0.272329	17.149547	0.499814	1.492629	1.867967	0.406322	1.272128	2.409492	31.326407	0.426262	1.464257	2.277164	1.451628	2.487606	0.480051	1.185511	1.755823	2.502905	4.504054	15.766871	15.948919	0.0	1.026802	2.499655	0.035533	0.043506	0.043506	0.035533	0.050220	0.030778	0.0	0.017775	0.073103	0.113095	0.053258	0.017775	0.075211	0.081198	0.084950	0.039721	0.058860	0.081198	0.058860	0.063968	0.035533	0.061468	0.017775	0.025134	0.035533	0.075211	0.039721	0.061468	0.075211	0.125933	0.030778	0.030778	0.186367	0.058860	0.053258	0.068690	0.017775	0.073103	0.084950	0.079254	0.066372	0.025134	0.025134	0.115783	0.122232	0.061468	0.066372	0.017775	0.046984	0.079254	0.043506	0.039721	0.030778	0.063968	0.070932	0.113095	0.124712	0.075211	0.070932	0.180761	0.050220	0.081198	0.063968	0.066372	0.162611	0.090277	0.098498	0.086763	0.084950	0.096911	0.093655	0.083096	0.05613	0.083096	0.063968	0.110337	0.098498	0.091982	0.128337	0.201391	0.111725	0.073103	0.135276	0.088538	0.298142	0.095298	0.101593	0.088538	0.145017	0.090277	0.077259	0.111725	0.017775	0.077259	0.050220	0.030778	0.017775	0.017775	0.030778	0.035533	0.0	0.119697	0.058860	0.039721	0.050220	0.046984	0.086763	0.046984	0.039721	0.046984	0.063968	0.148110	0.106059	0.030778	0.035533	0.152126	0.05613	0.093655	0.017775	0.017775	0.258592	0.043506	0.035533	0.0	0.063968	0.068690	0.140778	0.017775	0.0	0.025134	0.017775	0.286295	0.408348	0.220409	0.168024	0.086763	0.017775	0.039721	0.063968	0.124712	0.077259	0.081198	0.090277	0.030778	0.030778	0.039721	0.084950	0.039721	0.030778	0.066372	0.053258	0.025134	0.017775	0.039721	0.0	0.096911	0.096911	0.075211	0.113095	0.039721	0.030778	0.084950	0.063968	0.046984	0.053258	0.073103	0.075211	0.025134	0.046984	0.0	0.043506	0.025134	0.025134	0.030778	0.039721	0.046984	0.113095	0.073103	0.101593	0.017775	0.017775	0.030778	0.025134	0.107505	0.118407	0.061468	0.063968	0.035533	0.025134	0.025134	0.058860	0.035533	0.039721	0.053258	0.068690	0.128337	0.156029	0.043506	0.079254	0.061468	0.035533	0.058860	0.081198	0.073103	0.030778	0.035533	0.035533	0.030778	0.118407	0.134147	0.077259	0.075211	0.030778	0.05613	0.075211	0.050220	91.689491	8.013374	75.269861	89.293214	98.002761	13.326773	31.622833	77.918708	74.763266	306.564583	11.010138	25.512346	358.175432	123.517520	56.211355	13.970517	10.154124	97.829508	8.604871	189.550416	11.777612	94.479839	26.059846	147.990913	7.492682	51.463324	14.962254	9.763798	18.387004	19.348970	48.523896	25.024014	254.182999	20.081360	25.769919	6.804976	25.557934	87.159584	8.803043	31.211370	8.772881	14.073176	60.280534	63.342451	8.978854	8.979428	51.363497	240.697989	13.561271	26.876109	203.621035	7.233232	22.61214	52.163298	30.063915	56.660079	12.409541	33.913834	26.821527	91.273237	1.411344	2.944575	12.159694	14.317243	9.163497	9.957565	7.366386	91.724847	37.105394	9.114192	16.300898	107.099793	92.655169	194.082786	45.561832	721.571430	46.519450	43.163617	33.620619	20.000704	37.026307	93.475135	20.568615	79.109682	40.477215	45.763967	43.374388	42.251312	898.058434	22.214917	143.545418	23.637567	45.784958	267.363429	31.037061	10.980474	488.503496	999.359831	169.557500	141.053492	81.488826	16.638078	115.163808	77.248099	130.874965	12.516527	16.382132	23.934882	122.050078	262.044279	2996.102211	10.764148	65.549654	976.066908	53.151902	865.064211	28.589297	31.986785	397.158687	58.267161	25.350807	33.802799	47.396083	108.038556	15.474524	30.568121	39.538143	104.069768	42.793900	15.981852	19.768094	96.167439	22.457806	78.979356	33.456917	128.393785	9.028735	17.827140	7.909739	16.491784	63.505379	40.691826	95.848241	121.228974	25.408713	2905.712060	35.065594	29.237517	29.337386	28.634091	97.000902	5.705071	1.816856	1.419760	2.504516	6.802250	10.172237	19.951727	1.336745	5.052157	3.423748	7.744789	9.349799	102.914053	10.108189	11.249815	13.926650	21.377974	11.329708	97.707176	4.153955	12.476203	15.827777	14.087427	3.560941	54.941761	60.732149	2.785403	21.435226	13.708400	3.780030	7.317347	67.277601	36.232032	18.145739	92.075579	21.143147	24.664814	111.209349	230.160421	20.793832	11.490907	6.467550	23.008330	42.175293	6.105756	15.858954	3.577448	417.484339	4.379992	5.055334	15.946097	12.096853	10.209333	5.843517	6.763745	2.938336	1.081654	1.045189	1.036706	22.916155	29.939871	48.979658	5.583838	145.072732	146.792561	15.338817	5.809931	4.962913	21.673566	6.071246	0.0	0.0	1.421778	1.413188	1.422162	1.401024	1.412740	1.387073	1.426868	1.390322	1.437928	1.428937	7.001089	7.001089	0.0	1.121496	1.109517	1.115463	1.124654	4.340741	4.337860	4.274798	4.287866	4.363288	4.331476	4.313910	4.269546	4.361824	4.316758	4.336696	4.291975	4.344284	4.356668	4.275706	0.813291	0.826997	0.809340	0.0	0.500066	0.500066	0.818360	0.810072	0.820645	0.0	0.0	0.0	0.499923	0.499923	1.113481	1.111154	1.129034	1.118917	0.0	0.500041	36096.187009
min	1.000000	1.000000	1.000000	-1.000000	-1.000000	1.000000	1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-7.000000	-1.000000	-1.00000	-1.000000	1.000000	-7.000000	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	0.000000	-7.000000	-7.000000	-7.000000	-7.000000	0.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	0.000000	0.000000	0.000000	-7.000000	-7.000000	0.000000	-7.000000	-7.000000	-7.00000	-7.000000	-7.000000	-7.000000	-1.000000	1.000000	1.000000	1.000000	1.000000	-7.000000	-1.000000	-1.000000	-1.000000	-7.000000	-7.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-7.000000	-7.000000	-1.000000	-7.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-7.000000	1.000000	-7.000000	-7.000000	1.000000	1.000000	-7.000000	-7.000000	1.000000	-7.000000	1.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-1.000000	-7.000000	-7.000000	-7.000000	-1.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-1.000000	-7.000000	-1.000000	-1.000000	-1.00000	-7.000000	-7.000000	-7.000000	-1.000000	-7.000000	1.000000	-1.000000	-7.000000	-7.000000	1.000000	-7.000000	-7.000000	-1.000000	1.000000	-7.000000	-7.000000	-7.000000	1.000000	1.000000	-7.000000	-7.000000	1.000000	-7.000000	-7.000000	-7.000000	-1.000000	-7.000000	-7.000000	-7.000000	-7.000000	-1.000000	-7.000000	-7.000000	-1.000000	-7.000000	-1.000000	-7.000000	-1.000000	-7.000000	-7.000000	-1.000000	-7.000000	-7.000000	-1.000000	-7.000000	-1.000000	-7.000000	1.000000	1.000000	0.000000	0.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-1.000000	-1.000000	-7.000000	-7.000000	-1.000000	-7.000000	-7.000000	-1.000000	-1.00000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-1.000000	-1.000000	-1.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-1.000000	-1.000000	-7.000000	-7.000000	-1.000000	-7.000000	-7.000000	-1.000000	-1.000000	-1.000000	-1.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-1.000000	-7.000000	-1.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-1.000000	-1.000000	-7.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-7.000000	-7.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-7.000000	-7.000000	-7.000000	-1.000000	-1.000000	-1.000000	-7.000000	1.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	1926.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-7.000000	1.000000	1.000000	-7.000000	1.000000	-7.000000	1.000000	-7.000000	1.000000	1.000000	1.000000	-7.000000	-7.000000	1.000000	-3.0	1.000000	-7.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.649000	0.000000	0.000000	0.629000	0.117000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.738000	1.137000	1.291000	0.414000	0.388000	0.278000	0.486000	0.496000	0.468000	0.473000	0.540000	0.631000	0.527000	0.488000	0.341000	0.000000	0.000000	0.549000	0.595000	0.473000	0.557000	0.555000	0.060000	0.271000	0.105000	0.463000	0.122000	0.218000	0.190000	0.215000	0.306000	0.108000	0.118000	0.623000	0.185000	0.072000	0.137000	0.902000	0.000000	0.000000	0.00000	0.719000	0.383000	0.585000	0.604000	0.607000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.812000	0.671000	0.629000	0.649000	0.717000	0.693000	0.624000	0.512000	0.544000	0.496000	0.603000	0.303000	0.563000	0.304000	0.539000	0.505000	0.588000	0.561000	1.114000	0.900000	0.780000	0.663000	0.657000	0.727000	0.000000	0.000000	1.350000	0.000000	0.000000	0.000000	0.000000	0.000000	0.533000	0.237000	0.000000	0.000000	0.607000	0.000000	1.349000	0.964000	0.000000	0.513000	0.000000	0.672000	0.593000	0.668000	0.595000	0.633000	0.413000	0.575000	0.481000	0.636000	0.567000	0.756000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.846000	0.751000	0.000000	0.000000	0.000000	0.000000	0.615000	0.640000	0.716000	0.243000	0.000000	0.540000	0.458000	0.455000	0.752000	0.473000	0.345000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.765000	0.000000	0.000000	0.000000	0.000000	0.000000	1.286000	0.000000	0.000000	0.000000	0.533000	0.471000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.913000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.788000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.402000	0.000000	0.000000	0.736000	0.000000	0.000000	0.000000	0.000000	0.795000	1.0	2.0	3.000000	3.000000	3.000000	3.000000	3.000000	8.000000	8.000000	8.000000	8.000000	8.000000	0.000000	0.000000	15.0	16.000000	16.000000	16.000000	16.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	1.000000	1.000000	1.000000	1.0	2.000000	2.000000	1.000000	1.000000	1.000000	4.0	5.0	6.0	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.0	0.000000	492.000000
25%	792.000000	1.000000	1.000000	-1.000000	-1.000000	2.000000	2.000000	-1.000000	-1.000000	1.000000	-1.000000	-1.000000	-1.000000	-1.00000	1.000000	1.000000	-1.000000	2.000000	2.000000	2.00000	1.000000	2.000000	2.000000	2.000000	1.000000	1.000000	2.000000	11.000000	7.000000	4.000000	5.000000	9.000000	5.000000	51.000000	51.000000	50.000000	51.000000	31.000000	9.000000	-1.000000	-1.000000	21.000000	42.000000	26.000000	24.000000	6.000000	17.000000	2.000000	38.000000	10.000000	35.000000	4.000000	52.000000	49.000000	50.000000	42.000000	47.000000	56.000000	17.000000	8.000000	13.00000	1.000000	-1.000000	-1.000000	-1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	2.000000	1.000000	2.000000	3.000000	1.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	1.000000	2.000000	2.000000	2.000000	2.000000	3.000000	2.000000	1.000000	5.000000	1.000000	1.000000	3.000000	3.000000	3.000000	2.000000	1.000000	1.000000	-1.000000	-1.000000	-1.000000	-1.000000	1.000000	1.000000	2.000000	2.000000	2.000000	3.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.00000	-1.000000	2.000000	2.000000	-1.000000	-1.000000	1.000000	-1.000000	1.000000	1.000000	1.000000	-1.000000	1.000000	-1.000000	2.000000	4.000000	2.000000	2.000000	3.000000	2.000000	3.000000	1.000000	2.000000	3.000000	1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	3.000000	2.000000	3.000000	1.000000	3.000000	-1.000000	-1.000000	3.000000	-1.000000	-1.000000	3.000000	1.000000	3.000000	1.000000	1.000000	1.000000	0.000000	0.000000	4.000000	1.000000	1896.000000	2.000000	1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	3.000000	-1.000000	-1.000000	-1.00000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	1.000000	2.000000	2.000000	2.000000	-1.000000	-1.000000	-1.000000	5.000000	2.500000	146.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	1.000000	1.000000	1.000000	2.000000	1.000000	2.000000	2.000000	2.000000	2.000000	1.000000	1.000000	2.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-1.000000	-1.000000	5.000000	-1.000000	-1.000000	-1.000000	-1.000000	1.000000	1955.000000	1.000000	2.000000	1.000000	2.000000	1.000000	1.000000	4.000000	1.000000	2.000000	2.000000	1.000000	1.000000	1.000000	1.000000	3.000000	1.000000	1.000000	-7.000000	12.000000	-3.0	2.000000	7.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	5.476000	0.000000	0.000000	3.515000	3.647000	0.000000	0.000000	4.747000	0.000000	0.000000	0.000000	1.797000	4.598000	9.838000	6.772000	4.698000	3.457000	3.311000	3.380000	3.419000	3.352000	3.461000	3.646000	3.322000	3.447000	3.504000	3.672000	0.000000	0.000000	3.341000	3.263000	3.361000	4.198000	3.611000	2.837000	2.910000	2.954000	2.894000	2.979000	2.913000	2.974000	2.792000	2.859000	2.963000	2.802000	2.900000	2.855000	2.886000	3.000000	3.696000	0.000000	0.000000	0.00000	4.360000	2.595000	2.010000	3.391000	3.970000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	12.829000	5.257000	4.640000	3.502000	6.281000	4.789000	6.328000	5.929000	6.502000	7.408000	7.088000	7.012000	5.757000	5.297000	3.854000	4.683000	5.831000	8.858000	12.148000	13.359000	6.270000	7.097000	6.055000	10.575000	0.000000	0.000000	30.919000	0.000000	0.000000	0.000000	0.000000	0.000000	6.918000	5.173000	0.000000	0.000000	5.631000	0.000000	9.924000	4.896000	0.000000	5.874000	0.000000	6.857000	6.707000	6.702000	4.917000	5.537000	3.566000	4.211000	4.569000	4.469000	5.747000	5.275000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	8.812000	6.685000	0.000000	0.000000	0.000000	0.000000	6.545000	6.337000	6.242000	5.144000	0.000000	10.493000	8.273000	6.425000	8.426000	8.000000	4.150000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.959000	0.000000	0.000000	0.000000	0.000000	0.000000	20.150000	0.000000	0.000000	0.000000	6.232000	4.608000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	6.272000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	5.862000	2.406000	2.812000	1.777000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.963000	4.780000	5.364000	0.000000	0.000000	8.305000	0.000000	0.000000	0.000000	0.000000	4.347000	1.0	2.0	4.000000	4.000000	4.000000	4.000000	4.000000	9.000000	9.000000	9.000000	9.000000	9.000000	0.000000	0.000000	15.0	17.000000	17.000000	16.000000	17.000000	23.000000	23.000000	23.000000	23.000000	23.000000	23.000000	23.000000	23.000000	23.000000	23.000000	23.000000	23.000000	23.000000	23.000000	23.000000	1.000000	1.000000	1.000000	1.0	2.000000	2.000000	1.000000	1.000000	1.000000	4.0	5.0	6.0	1.000000	1.000000	1.000000	2.000000	1.000000	2.000000	0.0	0.000000	1486.000000
50%	1583.000000	1.000000	1.000000	1.000000	-1.000000	3.000000	3.000000	1.000000	-1.000000	2.000000	1.000000	-1.000000	-1.000000	-1.00000	1.000000	1.000000	-1.000000	2.000000	2.000000	2.00000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	38.000000	59.000000	44.000000	45.000000	44.000000	48.000000	41.000000	73.000000	73.000000	71.000000	71.000000	51.000000	45.000000	0.000000	-1.000000	50.000000	55.000000	52.000000	51.000000	50.000000	40.000000	11.000000	52.000000	31.000000	50.000000	20.000000	72.000000	60.000000	67.000000	58.000000	59.000000	78.000000	41.000000	30.000000	46.00000	2.000000	-1.000000	-1.000000	-1.000000	2.000000	2.000000	2.000000	2.000000	2.000000	1.000000	-1.000000	-1.000000	-1.000000	1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	1.000000	1.000000	1.000000	1.000000	3.000000	2.000000	2.000000	5.000000	2.000000	3.000000	3.000000	3.000000	3.000000	4.000000	4.000000	2.000000	3.000000	3.000000	2.000000	3.000000	4.000000	4.000000	2.000000	6.000000	2.000000	2.000000	4.000000	4.000000	4.000000	3.000000	1.000000	2.000000	-1.000000	1.000000	-1.000000	1.000000	2.000000	3.000000	3.000000	4.000000	3.000000	4.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-1.000000	-1.000000	1.00000	1.000000	4.000000	4.000000	-1.000000	1.000000	2.000000	-1.000000	2.000000	2.000000	2.000000	-1.000000	2.000000	-1.000000	4.000000	5.000000	3.000000	3.000000	3.000000	3.000000	3.000000	2.000000	3.000000	4.000000	1.000000	-1.000000	1.000000	-1.000000	1.000000	-1.000000	1.000000	-1.000000	1.000000	-1.000000	4.000000	2.000000	4.000000	1.000000	4.000000	-1.000000	-1.000000	3.000000	1.000000	-1.000000	4.000000	1.000000	4.000000	2.000000	4.000000	4.000000	0.500000	1.000000	6.000000	2.000000	1965.000000	2.000000	4.000000	-1.000000	-1.000000	1.000000	-1.000000	-1.000000	-1.000000	3.000000	-1.000000	-1.000000	-1.00000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	2.000000	3.000000	3.000000	3.000000	1.000000	-1.000000	-1.000000	5.000000	5.000000	180.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	2.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	1.000000	1.000000	1.000000	-1.000000	-1.000000	-1.000000	1.000000	2.000000	2.000000	2.000000	1.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	4.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	3.000000	4.000000	5.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	3.000000	3.000000	2.000000	1.000000	1.000000	1.000000	-1.000000	5.000000	-1.000000	1.000000	1.000000	-1.000000	1.000000	1964.000000	2.000000	3.000000	1.000000	2.000000	1.000000	4.000000	7.000000	1.000000	3.000000	4.000000	1.000000	2.000000	2.000000	2.000000	5.000000	3.000000	2.000000	-7.000000	29.000000	-3.0	3.000000	8.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	8.425000	1.148000	0.000000	5.173000	5.419000	1.055000	0.000000	6.665000	0.988000	0.000000	0.000000	3.122000	7.032000	17.468000	10.154000	6.835000	4.846000	4.691000	4.730000	4.899000	4.736000	5.131000	5.423000	4.980000	5.039000	5.022000	5.257000	0.884000	0.000000	4.727000	4.669000	4.536000	5.931000	4.934000	4.060000	4.188000	4.194000	3.994000	4.344000	4.132000	4.121000	3.938000	4.015000	4.219000	3.898000	3.999000	3.999000	4.055000	4.311000	6.254000	0.000000	0.000000	0.00000	6.760000	4.132000	3.146000	5.861000	8.135000	0.960000	0.000000	0.000000	0.000000	0.864000	0.000000	0.000000	0.000000	0.000000	1.424000	1.094000	0.951000	0.908000	23.544000	7.876000	6.621000	4.963000	9.121000	6.922000	9.405000	9.161000	10.419000	11.273000	10.243000	10.553000	9.038000	8.668000	7.159000	6.989000	9.117000	13.730000	18.257000	20.634000	8.629000	10.355000	9.629000	15.612000	0.000000	1.342000	47.008000	2.779000	0.000000	0.000000	1.074000	0.893000	10.864000	8.215000	0.000000	1.037000	8.268000	0.000000	15.747000	7.792000	0.000000	8.816000	0.000000	10.769000	11.015000	10.214000	7.299000	8.282000	5.555000	6.621000	6.894000	6.961000	8.453000	8.151000	0.000000	0.879000	0.000000	0.761000	0.000000	0.687000	0.000000	0.734000	0.000000	12.303000	9.403000	1.663000	0.000000	1.349000	0.000000	9.579000	9.006000	8.817000	7.981000	0.000000	17.346000	14.350000	9.257000	12.174000	12.731000	5.634000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	5.898000	0.000000	0.000000	0.000000	1.053000	0.000000	31.996000	1.959000	0.000000	0.000000	8.325000	6.065000	0.000000	0.000000	0.000000	0.000000	1.461000	0.000000	9.008000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.186000	0.000000	1.470000	0.000000	1.481000	0.000000	7.987000	4.057000	5.367000	4.373000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.714000	3.097000	2.158000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	5.898000	7.279000	7.799000	0.984000	0.000000	11.471000	0.833000	0.825000	1.095000	1.133000	6.219000	1.0	2.0	5.000000	5.000000	5.000000	5.000000	5.000000	10.000000	10.000000	10.000000	10.000000	10.000000	14.000000	0.000000	15.0	18.000000	17.000000	17.000000	18.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	2.000000	2.000000	2.000000	1.0	3.000000	2.000000	2.000000	2.000000	2.000000	4.0	5.0	6.0	2.000000	1.000000	2.000000	3.000000	2.000000	2.000000	0.0	1.000000	2049.000000
75%	2374.000000	2.000000	2.000000	1.000000	1.000000	4.000000	4.000000	2.000000	2.000000	3.000000	1.000000	-1.000000	1.000000	-1.00000	2.000000	2.000000	-1.000000	2.000000	2.000000	2.00000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	91.000000	91.000000	71.000000	75.000000	75.000000	70.000000	63.000000	91.000000	90.000000	90.000000	90.000000	75.000000	68.000000	72.000000	79.000000	77.000000	80.000000	77.000000	86.000000	85.000000	52.000000	35.000000	72.000000	50.000000	64.000000	47.000000	89.000000	80.000000	84.000000	86.000000	78.000000	92.000000	51.000000	50.000000	55.00000	2.000000	4.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	3.000000	2.000000	2.000000	-1.000000	-1.000000	2.000000	-1.000000	-1.000000	-1.000000	3.000000	3.000000	3.000000	3.000000	5.000000	5.000000	5.000000	4.000000	3.000000	3.000000	3.000000	6.000000	3.000000	4.000000	4.000000	4.000000	4.000000	5.000000	6.000000	3.000000	4.000000	3.000000	3.000000	4.000000	7.000000	6.000000	3.000000	7.000000	3.000000	3.000000	5.000000	4.000000	5.000000	4.000000	2.000000	2.000000	1.000000	2.000000	3.000000	4.000000	3.000000	4.000000	5.000000	5.000000	5.000000	5.000000	3.000000	4.000000	4.000000	2.000000	1.000000	4.000000	3.000000	3.00000	4.000000	7.000000	7.000000	3.000000	4.000000	3.000000	4.000000	3.000000	3.000000	2.000000	3.000000	4.000000	3.000000	5.000000	7.000000	4.000000	4.000000	4.000000	4.000000	4.000000	2.000000	4.000000	5.000000	2.000000	3.000000	3.000000	4.000000	4.000000	4.000000	4.000000	4.000000	4.000000	4.000000	5.000000	2.000000	5.000000	2.000000	5.000000	2.000000	2.000000	4.000000	1.000000	1.000000	5.000000	2.000000	5.000000	2.000000	7.000000	7.000000	1.000000	1.000000	6.000000	4.000000	1999.000000	2.000000	6.000000	2.000000	2.000000	1.000000	2.000000	0.000000	2.000000	3.000000	-1.000000	-1.000000	-1.00000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	2.000000	2.000000	4.000000	4.000000	4.000000	4.000000	3.000000	3.000000	2.000000	5.000000	8.000000	212.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	-1.000000	3.000000	7.000000	-1.000000	2.000000	2.000000	-1.000000	-1.000000	1.000000	-1.000000	-1.000000	1.000000	2.000000	2.000000	2.000000	2.000000	2.000000	1.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	1.000000	2.000000	2.000000	2.000000	2.000000	2.000000	1.000000	1.000000	1.000000	1.000000	2.000000	2.000000	1.000000	2.000000	1.000000	1.000000	2.000000	2.000000	1.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	3.000000	4.000000	5.000000	3.000000	2.000000	4.000000	3.000000	4.000000	5.000000	-1.000000	-1.000000	-1.000000	4.000000	5.000000	5.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	4.000000	4.000000	3.000000	2.000000	2.000000	1.000000	1.000000	5.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1983.000000	2.000000	5.000000	5.000000	2.000000	2.000000	5.000000	11.000000	1.000000	4.000000	6.000000	2.000000	7.000000	2.000000	3.000000	6.000000	7.000000	11.000000	3.000000	42.000000	-3.0	3.000000	9.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	12.970000	4.006000	4.179000	7.845000	7.997000	8.863000	8.851000	9.440000	9.003000	1.864000	0.000000	4.677000	10.263000	25.749000	15.585000	10.271000	7.070000	7.050000	6.940000	7.238000	6.841000	8.141000	8.811000	7.770000	7.608000	7.915000	7.962000	5.092000	5.167000	6.806000	6.861000	6.422000	8.626000	7.242000	5.965000	6.223000	6.109000	5.897000	6.455000	6.095000	5.962000	5.653000	6.048000	6.179000	5.747000	5.822000	5.906000	6.060000	6.499000	9.921000	6.446000	4.792000	8.15900	9.382000	6.428000	4.955000	8.820000	13.771000	9.288000	11.001000	0.000000	0.000000	6.502000	6.541000	5.557000	4.281000	3.875000	7.407000	5.921000	4.520000	4.234000	46.174000	11.196000	9.378000	7.003000	13.148000	10.218000	13.525000	14.150000	16.557000	17.284000	14.588000	15.548000	13.520000	13.264000	10.595000	10.460000	14.702000	20.942000	26.777000	30.162000	12.255000	15.662000	14.434000	23.128000	6.458000	6.884000	67.336000	21.600000	10.610000	6.701000	9.012000	6.832000	16.285000	12.223000	9.700000	10.629000	11.728000	9.856000	23.400000	11.603000	7.415000	13.529000	14.551000	15.814000	17.600000	15.071000	10.675000	12.492000	8.421000	10.345000	10.159000	10.391000	12.460000	12.426000	7.528000	9.232000	10.277000	8.489000	9.642000	8.386000	9.426000	7.290000	7.899000	17.726000	12.893000	8.352000	10.207000	9.109000	10.534000	13.722000	13.007000	12.360000	11.725000	6.266000	28.775000	24.665000	14.010000	18.452000	23.318000	7.680000	0.000000	0.000000	0.000000	0.000000	6.706000	6.509000	9.021000	0.000000	0.000000	0.000000	8.307000	7.172000	46.409000	4.974000	6.086000	8.155000	11.544000	8.196000	19.915000	3.649000	0.000000	5.569000	6.046000	0.000000	12.621000	4.865000	0.000000	0.000000	3.268000	0.000000	0.000000	25.601000	24.335000	15.722000	16.944000	15.299000	11.298000	11.079000	6.101000	7.553000	6.517000	3.716000	3.698000	2.952000	3.350000	3.077000	2.175000	0.000000	0.000000	0.000000	5.991000	5.583000	4.329000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	8.948000	10.826000	11.297000	5.255000	7.586000	15.388000	8.509000	5.366000	5.120000	4.491000	8.406000	1.0	2.0	6.000000	6.000000	6.000000	6.000000	6.000000	11.000000	11.000000	11.000000	11.000000	11.000000	14.000000	14.000000	15.0	19.000000	18.000000	18.000000	19.000000	31.000000	31.000000	31.000000	31.000000	31.000000	31.000000	31.000000	31.000000	31.000000	31.000000	31.000000	31.000000	31.000000	31.000000	31.000000	3.000000	3.000000	3.000000	1.0	3.000000	3.000000	3.000000	3.000000	3.000000	4.0	5.0	6.0	2.000000	2.000000	3.000000	3.000000	4.000000	4.000000	0.0	1.000000	2977.000000
max	3165.000000	2.000000	4.000000	3.000000	4.000000	4.000000	4.000000	5.000000	4.000000	5.000000	3.000000	2.000000	2.000000	2.00000	3.000000	3.000000	2.000000	2.000000	2.000000	2.00000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	997.000000	997.000000	997.000000	997.000000	997.000000	997.000000	997.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	997.000000	997.000000	997.000000	997.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.00000	3.000000	9.000000	4.000000	4.000000	4.000000	4.000000	4.000000	5.000000	4.000000	2.000000	2.000000	4.000000	4.000000	3.000000	2.000000	2.000000	2.000000	5.000000	5.000000	5.000000	5.000000	7.000000	7.000000	7.000000	7.000000	5.000000	5.000000	5.000000	7.000000	5.000000	5.000000	5.000000	5.000000	5.000000	7.000000	7.000000	5.000000	5.000000	5.000000	5.000000	5.000000	7.000000	7.000000	7.000000	7.000000	5.000000	5.000000	5.000000	5.000000	7.000000	7.000000	3.000000	2.000000	3.000000	3.000000	5.000000	7.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	7.000000	5.000000	5.00000	7.000000	7.000000	7.000000	5.000000	7.000000	5.000000	7.000000	5.000000	5.000000	2.000000	5.000000	7.000000	7.000000	7.000000	7.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	2.000000	5.000000	7.000000	7.000000	7.000000	7.000000	7.000000	7.000000	7.000000	7.000000	5.000000	2.000000	5.000000	2.000000	5.000000	2.000000	2.000000	5.000000	2.000000	2.000000	5.000000	2.000000	5.000000	2.000000	7.000000	7.000000	1.000000	1.000000	99.000000	4.000000	2018.000000	2.000000	7.000000	4.000000	4.000000	2.000000	3.000000	999.000000	5.000000	3.000000	2019.000000	2.000000	2.00000	2.000000	2.000000	2.000000	2.000000	2.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	2.000000	9.000000	11.500000	999.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	3.000000	5.000000	12.000000	3.000000	2.000000	5.000000	2.000000	4.000000	2.000000	4.000000	3.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	7.000000	5.000000	5.000000	7.000000	5.000000	5.000000	7.000000	5.000000	5.000000	7.000000	5.000000	5.000000	7.000000	5.000000	5.000000	7.000000	5.000000	5.000000	7.000000	5.000000	5.000000	5.000000	5.000000	5.000000	6.000000	6.000000	3.000000	3.000000	5.000000	5.000000	5.000000	5.000000	5.000000	2.000000	2000.000000	2.000000	6.000000	6.000000	2.000000	8.000000	9.000000	97.000000	3.000000	6.000000	8.000000	7.000000	7.000000	2.000000	4.000000	7.000000	8.000000	12.000000	90.000000	56.000000	-3.0	4.000000	9.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.0	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.0	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.0	1.000000	1.000000	1.000000	1.000000	0.0	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.0	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.0	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000	3329.142000	283.030000	3716.698000	4510.024000	5353.798000	294.913000	1612.191000	4173.815000	3979.179000	17243.753000	212.733000	1329.599000	20128.321000	4797.269000	2161.685000	474.717000	300.425000	4478.240000	256.690000	10614.993000	505.686000	5286.402000	907.872000	8316.339000	193.480000	2832.327000	432.521000	308.329000	762.215000	816.590000	2022.514000	1170.860000	14131.280000	756.190000	1284.965000	174.731000	1107.227000	4204.543000	283.518000	1678.225000	244.462000	587.067000	2853.474000	3496.967000	302.005000	208.710000	2165.035000	13528.502000	565.111000	1000.423000	11443.881000	221.362000	817.92500	2166.168000	1324.991000	2755.001000	394.123000	1640.298000	1294.523000	5033.181000	19.199000	107.309000	509.857000	516.104000	277.502000	340.382000	167.083000	5113.233000	1571.900000	419.809000	652.383000	3572.141000	3697.854000	10871.468000	2361.832000	40511.681000	1619.251000	1930.682000	1440.703000	409.381000	1051.996000	4116.227000	518.480000	4046.200000	1814.829000	1659.970000	1734.092000	1587.520000	50514.468000	429.053000	7444.931000	589.019000	1347.613000	13738.832000	658.068000	273.400000	24606.442000	55770.827000	8411.571000	7600.416000	4391.346000	601.745000	6182.909000	3900.135000	4658.247000	227.618000	366.238000	716.259000	5675.307000	11441.165000	168563.584000	384.680000	3402.048000	54876.037000	2508.591000	48464.985000	1108.529000	1190.970000	22251.219000	2800.366000	1211.201000	1530.032000	2238.145000	5893.071000	522.649000	1460.527000	1826.907000	5556.395000	2308.030000	444.203000	670.029000	5189.545000	667.093000	3986.526000	1367.509000	5985.957000	329.162000	709.513000	115.925000	529.633000	2360.807000	1848.990000	5081.053000	5628.526000	780.263000	163485.708000	1274.305000	1052.558000	531.474000	700.991000	5431.972000	288.646000	25.872000	31.343000	110.287000	177.980000	355.788000	814.179000	38.299000	75.818000	98.169000	152.853000	310.773000	2610.304000	300.601000	460.057000	667.837000	626.714000	346.873000	4418.358000	86.403000	684.437000	716.407000	377.896000	85.989000	1856.375000	3398.365000	36.171000	902.372000	700.214000	73.700000	184.015000	3517.603000	1235.324000	618.634000	4620.500000	576.556000	1170.608000	6213.150000	12914.558000	649.053000	355.542000	220.059000	1041.387000	2340.553000	205.250000	824.052000	82.856000	23442.337000	159.664000	220.371000	683.150000	533.287000	442.793000	280.159000	357.771000	84.795000	23.890000	34.589000	44.077000	980.717000	892.568000	1804.309000	100.479000	8120.092000	5315.118000	662.626000	127.858000	80.219000	1193.837000	146.586000	1.0	2.0	7.000000	7.000000	7.000000	7.000000	7.000000	12.000000	12.000000	12.000000	12.000000	12.000000	14.000000	14.000000	15.0	19.000000	19.000000	19.000000	19.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	3.000000	3.000000	3.000000	1.0	3.000000	3.000000	3.000000	3.000000	3.000000	4.0	5.0	6.0	2.000000	2.000000	4.000000	4.000000	4.000000	4.000000	0.0	1.000000	489139.000000

To change the quantiles that are displayed, use the percentiles argument, set equal to a list of the desired quantiles. To see the 33rd and 67th quantiles, type

anes.describe(percentiles = [.33, .67])

	caseid	form	follow	reg1a	reg1b	liveurban	youthurban	placeid1a	placeid1b	placeidimport	turnout16a	turnout16a1	turnout16b	turnout16c	vote16	turnout18a	turnout18a1	particip_1	particip_2	particip_3	particip_4	particip_5	particip_6	particip_7	particip_8	particip_9	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftimmig1	ftimmig2	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales	vote20dem	vote20cand	vote20cand2	electable	vote20jb	vote20ew	vote20bs	cvote2020	tsplit1	contact1a	contact1b	contact2a	contact2b	contact3	contact4m_1	contact4m_2	contact4m_3	apppres5	frnpres5	immpres5	econpres5	apppres7	frnpres7	immpres7	econpres7	econnow	finworry	confecon	improve1	national1	national2	conspire1	conspire2	conspire3	taxecon	billtax	trade1	trade2	trade3	trade4	richpoor2	guarinc	lcself	lcd	lcr	pop1	pop2	pop3	corrupt	immignum	refugees	dreamstr	dreamer	dreamstr1	dreamstr2	wall5	wall7	pathway	preturn	popen	release1	release2	famsep	tchina	trussia	tiran	tmexico	tjapan	hlthcare1	hlthcare2	abortion1	abortion2	freecol	loans	diversity5	diversity7	language	buyback	gw1	gw2	knowopioid1	knowopioid2	opioiddo	sentence	prek	demo4	experts	science	exphelp	elite1	elite2	elite3	elite4	ukraine1	ukraine2	excessive	rural1alt1	rural1alt2	rural2alt1	rural2alt2	rural3alt1	rural3alt2	rural4alt1	rural4alt2	conf_unemp	unemp	conf_interfere	interfere	conf_autism	autism1	autism2	conf_gmo	gmo1	gmo2	conf_warm	warm	conf_illegal	illegal	impeach1	impeach2	pk_cjus_correct	pk_germ_correct	pk_sen	pk_spend	pk_geer	cheat	pid7x	pid1d	pid1r	pidstr	pidlean	ngun	shooting	dem_activduty	milyears	milyr1	milyr2	milyr3	milyr4	milyr5	milyr6	combat	harass1a	harass1b	rr1	rr2	rr3	rr4	health1a	health1b	hospital	feet	inches	nweight	disable1	disable2	disable3	disable4	disable5	disable6	smoker1	smoker2	exercise	relig1a	relig2a	att1	att2	att3	relig1b	relig2b	relig3b	attother	exptravel_ever	exphomesch	expfarm	expffood	expconvert	expholiday	explie	expshark	expdivorce	exparrest	expoverdose	expdefault	exppubasst	exphybrid	expmistake	explightning	exptravel_year	expindian	exphunt	expflag	exppublib	explottery	expshoponline	exppubtrans	expfight	expavoid	expknowimmig	expknowtrans	expbuyusa	expretire	expcolldebt	expknowpris	socmed_1	socmed_2	socmed_3	socmed_4	socmed_5	socmed_6	socmed_7	socmed_8	socmed_9	facebook1	facebook2	facebook3	twitter1	twitter2	twitter3	instagram1	instagram2	instagram3	reddit1	reddit2	reddit3	youtube1	youtube2	youtube3	snapchat1	snapchat2	snapchat3	tiktok1	tiktok2	tiktok3	raceid	racework	whitejob	race_sub1	race_sub2	voterid1	voterid2	serious	photo1	photo2	photo3	photo4	reinterview	birthyr	gender	educ	marstat	child18	race	employ	faminc_new	votereg	ideo5	pid7	newsint	presvote16post	pew_bornagain	pew_religimp	pew_churatd	pew_prayer	religpew	religpew_protestant	inputstate	zipCode	region	qualityControl_overall_scale	abortion1_skp	abortion2_skp	apppres5_skp	apppres7_skp	att1_skp	att2_skp	att3_skp	attother_skp	autism1_skp	autism2_skp	billtax_skp	buyback_skp	cexp1_grid_skp	cexp2_grid_skp	cheat_skp	combat_skp	confecon_skp	conspire1_skp	conspire2_skp	conspire3_skp	contact1a_skp	contact1b_skp	contact2a_skp	contact2b_skp	contact3_skp	corrupt_skp	cvote2020_skp	dem_activduty_skp	demo4_skp	disable_grid_skp	diversity5_skp	diversity7_skp	dreamer_skp	econnow_skp	econpres5_skp	econpres7_skp	electable_skp	elite1_skp	elite2_skp	elite3_skp	elite4_skp	excessive_skp	exercise_skp	exp1_grid_skp	exp2_grid_skp	experts_skp	exphelp_skp	facebook1_skp	facebook2_skp	facebook3_skp	finworry_skp	follow_skp	freecol_skp	frnpres5_skp	frnpres7_skp	ftasian_skp	ftbiden_skp	ftblack_skp	ftbritain_skp	ftbuttigieg_skp	ftcanada_skp	ftchina_skp	ftfrance_skp	ftgermany_skp	ftharris_skp	fthisp_skp	ftice_skp	ftillegal_skp	ftimmig1_skp	ftimmig2_skp	ftiran_skp	ftisrael_skp	ftjapan_skp	ftjournal_skp	ftmexico_skp	ftmuslim_skp	ftnato_skp	ftnkorea_skp	ftnra_skp	ftobama_skp	ftpales_skp	ftrussia_skp	ftsanders_skp	ftsaudi_skp	fttrump_skp	ftturkey_skp	ftukraine_skp	ftun_skp	ftwarren_skp	ftwhite_skp	gmo1_skp	gmo2_skp	guarinc_skp	gw_grid_skp	harass1a_skp	harass1b_skp	health1a_skp	health1b_skp	hlthcare1_skp	hlthcare2_skp	hospital_skp	illegal_skp	immignum_skp	immpres5_skp	immpres7_skp	impeach1_skp	impeach2_skp	improve1_skp	instagram1_skp	instagram2_skp	instagram3_skp	interfere_skp	knowopioid1_skp	knowopioid2_skp	language_skp	lc_grid_skp	liveurban_skp	loans_skp	milyears_skp	milyr_skp	mip_skp	national1_skp	national2_skp	ngun_skp	opioiddo_skp	particip_skp	path_grid_skp	pid1d_skp	pid1r_skp	pidlean_skp	pidstr_skp	pk_cjus_skp	pk_geer_skp	pk_germ_skp	pk_sen_skp	pk_spend_skp	placeid1a_skp	placeid1b_skp	placeidimport_skp	pop_grid_skp	prek_skp	raceid_skp	racework_skp	reddit1_skp	reddit2_skp	reddit3_skp	refugees_skp	reg1a_skp	reg1b_skp	reinterivew_skp	relig1a_skp	relig1b_skp	relig2a_skp	relig2b_skp	relig3b_skp	rexp1_grid_skp	rexp2_grid_skp	richpoor2_skp	rr_grid_skp	rural1alt1_skp	rural1alt2_skp	rural2alt1_skp	rural2alt2_skp	rural3alt1_skp	rural4alt1_skp	rural4alt2_skp	science_skp	sentence_skp	serious_skp	shooting_skp	smoker1_skp	smoker2_skp	snapchat1_skp	snapchat2_skp	snapchat3_skp	socmed_skp	tall_skp	taxecon_skp	threat_grid_skp	tiktok1_skp	tiktok2_skp	tiktok3_skp	trade1_skp	trade2_skp	trade3_skp	trade4_skp	tsplit1_skp	turnout16a_skp	turnout16b_skp	turnout16c_skp	turnout18a_skp	twitter1_skp	twitter2_skp	twitter3_skp	ukraine1_skp	ukraine2_skp	unemp_skp	vote16_skp	vote20bs_skp	vote20cand2_skp	vote20cand_skp	vote20dem_skp	vote20ew_skp	vote20jb_skp	voterid1_skp	voterid2_skp	wall7_skp	wall5_skp	warm_skp	nweight_skp	whitejob_skp	youthurban_skp	youtube1_skp	youtube2_skp	youtube3_skp	rural3alt2_skp	follow_page_timing	reg1a_page_timing	reg1b_page_timing	liveurban_page_timing	youthurban_page_timing	placeid1a_page_timing	placeid1b_page_timing	placeidimport_page_timing	turnout16a_page_timing	turnout16b_page_timing	turnout16c_page_timing	vote16_page_timing	turnout18a_page_timing	particip_page_timing	fttrump_page_timing	ftobama_page_timing	ftbiden_page_timing	ftwarren_page_timing	ftsanders_page_timing	ftbuttigieg_page_timing	ftharris_page_timing	ftblack_page_timing	ftwhite_page_timing	fthisp_page_timing	ftasian_page_timing	ftmuslim_page_timing	ftillegal_page_timing	ftimmig1_page_timing	ftimmig2_page_timing	ftjournal_page_timing	ftnato_page_timing	ftun_page_timing	ftice_page_timing	ftnra_page_timing	ftchina_page_timing	ftnkorea_page_timing	ftmexico_page_timing	ftsaudi_page_timing	ftukraine_page_timing	ftiran_page_timing	ftbritain_page_timing	ftgermany_page_timing	ftjapan_page_timing	ftisrael_page_timing	ftfrance_page_timing	ftcanada_page_timing	ftturkey_page_timing	ftrussia_page_timing	ftpales_page_timing	vote20dem_page_timing	vote20cand_page_timing	vote20cand2_page_timing	electable_page_timing	vote20jb_page_timing	vote20ew_page_timing	vote20bs_page_timing	cvote2020_page_timing	tsplit1_page_timing	contact1a_page_timing	contact1b_page_timing	contact2a_page_timing	contact2b_page_timing	contact3_page_timing	apppres5_page_timing	frnpres5_page_timing	immpres5_page_timing	econpres5_page_timing	apppres7_page_timing	frnpres7_page_timing	immpres7_page_timing	econpres7_page_timing	mip_page_timing	econnow_page_timing	finworry_page_timing	confecon_page_timing	improve1_page_timing	national1_page_timing	national2_page_timing	conspire1_page_timing	conspire2_page_timing	conspire3_page_timing	taxecon_page_timing	billtax_page_timing	trade1_page_timing	trade2_page_timing	trade3_page_timing	trade4_page_timing	richpoor2_page_timing	guarinc_page_timing	lc_grid_page_timing	pop_grid_page_timing	corrupt_page_timing	immignum_page_timing	refugees_page_timing	dreamer_page_timing	wall_page_timing	wall7_page_timing	path_grid_page_timing	threat_grid_page_timing	hlthcare1_page_timing	hlthcare2_page_timing	abortion1_page_timing	abortion2_page_timing	freecol_page_timing	loans_page_timing	diversity5_page_timing	diversity7_page_timing	language_page_timing	buyback_page_timing	gw_grid_page_timing	knowopioid1_page_timing	knowopioid2_page_timing	opioiddo_page_timing	sentence_page_timing	prek_page_timing	demo4_page_timing	experts_page_timing	science_page_timing	exphelp_page_timing	elite1_page_timing	elite2_page_timing	elite3_page_timing	elite4_page_timing	ukraine1_page_timing	ukraine2_page_timing	excessive_page_timing	rural1alt1_page_timing	rural1alt2_page_timing	rural2alt1_page_timing	rural2alt2_page_timing	rural3alt1_page_timing	rural3alt2_page_timing	rural4alt1_page_timing	rural4alt2_page_timing	unemp_page_timing	interfere_page_timing	autism1_page_timing	autism2_page_timing	gmo1_page_timing	gmo2_page_timing	warm_page_timing	illegal_page_timing	impeach1_page_timing	impeach2_page_timing	pk2_intro_page_timing	pk_cjus_page_timing	pk_germ_page_timing	pk_sen_page_timing	pk_spend_page_timing	pk_geer_page_timing	cheat_page_timing	pid1d_page_timing	pid1r_page_timing	pidstr_page_timing	pidlean_page_timing	ngun_page_timing	shooting_page_timing	dem_activduty_page_timing	milyears_page_timing	milyr_page_timing	combat_page_timing	harass1a_page_timing	harass1b_page_timing	rr_grid_page_timing	health1a_page_timing	health1b_page_timing	hospital_page_timing	tall_page_timing	nweight_page_timing	disable_grid_page_timing	smoker1_page_timing	smoker2_page_timing	exercise_page_timing	relig1a_page_timing	relig2a_page_timing	att1_page_timing	att2_page_timing	att3_page_timing	relig1b_page_timing	relig2b_page_timing	relig3b_page_timing	attother_page_timing	exp1_grid_page_timing	exp2_grid_page_timing	rexp1_grid_page_timing	rexp2_grid_page_timing	cexp1_grid_page_timing	cexp2_grid_page_timing	socmed_page_timing	facebook1_page_timing	facebook2_page_timing	facebook3_page_timing	twitter1_page_timing	twitter2_page_timing	twitter3_page_timing	instagram1_page_timing	instagram2_page_timing	instagram3_page_timing	reddit1_page_timing	reddit2_page_timing	reddit3_page_timing	youtube1_page_timing	youtube2_page_timing	youtube3_page_timing	snapchat1_page_timing	snapchat2_page_timing	snapchat3_page_timing	tiktok1_page_timing	tiktok2_page_timing	tiktok3_page_timing	raceid_page_timing	racework_page_timing	whitejob_page_timing	voterid1_page_timing	voterid2_page_timing	serious_page_timing	photo1_page_timing	photo2_page_timing	photo3_page_timing	photo4_page_timing	reinterview_page_timing	ord_fttrump	ord_ftobama	ord_ftbiden	ord_ftwarren	ord_ftsanders	ord_ftbuttigieg	ord_ftharris	ord_ftblack	ord_ftwhite	ord_fthisp	ord_ftasian	ord_ftmuslim	ord_ftimmig1	ord_ftimmig2	ord_ftjournal	ord_ftnato	ord_ftun	ord_ftice	ord_ftnra	ord_ftchina	ord_ftnkorea	ord_ftmexico	ord_ftsaudi	ord_ftukraine	ord_ftiran	ord_ftbritain	ord_ftgermany	ord_ftjapan	ord_ftisrael	ord_ftfrance	ord_ftcanada	ord_ftturkey	ord_ftrussia	ord_ftpales	ord_conspire1	ord_conspire2	ord_conspire3	ord_lcself	ord_lcd	ord_lcr	ord_pathway	ord_preturn	ord_popen	ord_release1	ord_release2	ord_famsep	ord_gw1	ord_gw2	ord_elite1	ord_elite2	ord_elite3	ord_elite4	ord_lc_reverse	ord_att2_reverse	duration
count	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.00000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.00000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.00000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.00000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.00000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.00000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.00000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.00000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.00000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.0	3165.0	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.000000	3165.0	3165.000000	3165.000000
mean	1583.000000	1.498894	1.672038	0.117220	0.140284	2.665403	2.641390	0.699842	0.740284	2.357662	0.111216	-0.974408	-0.429068	-0.46951	1.146603	1.318799	-0.875197	1.880885	1.802528	1.93049	1.659716	1.790205	1.869510	1.812322	1.663191	1.572828	49.350711	57.589573	56.269194	78.185150	55.091311	124.552923	95.901738	70.738073	70.762717	68.907425	69.159242	51.828120	42.086888	33.718167	35.901422	49.866667	96.762717	70.290995	69.216430	63.884992	37.933965	20.754818	53.505845	32.030648	48.959874	26.515640	69.140284	60.447077	64.789889	59.599052	58.558610	72.419589	37.601264	31.415166	39.97030	1.743760	1.376303	0.117852	0.232859	1.878041	1.882148	1.862559	1.989889	2.216430	0.402212	0.417062	-0.706793	-0.783886	0.496367	-0.911216	-0.896051	-0.890679	1.093523	1.101738	1.073618	0.900790	1.723855	1.717852	1.691943	1.430648	2.547235	2.394945	2.545024	4.408531	2.426224	3.007583	3.175671	2.932070	3.010742	3.770616	3.777883	2.112480	2.911216	2.533017	2.250553	2.906161	4.480885	4.109321	2.310269	5.544708	2.213902	2.040126	3.823697	3.617378	4.011058	3.407583	1.587678	1.666983	-0.321643	0.848025	0.993365	1.559558	2.402528	2.712480	3.378831	3.442338	3.149131	3.816114	1.213270	1.336809	1.291627	0.618957	0.370300	1.535861	0.860348	0.87267	1.687520	4.393681	4.454660	0.721011	1.154818	2.242022	1.506477	2.475513	2.327962	1.604423	0.674882	2.706793	1.153555	3.622749	4.952923	3.009479	3.272354	3.230016	2.983886	3.503002	1.792733	3.063823	3.668562	1.306477	0.958610	1.056872	1.167457	1.772196	1.696682	1.770616	1.747235	1.854344	1.903318	3.534913	1.764929	3.880253	1.248025	3.804423	0.400948	0.400000	3.306477	0.248025	0.181043	3.779463	1.249605	3.616430	1.637599	3.950079	3.956082	0.465403	0.610742	5.828120	2.326698	1671.986414	1.838863	3.481517	0.495419	0.450237	0.340600	0.194945	1.831596	0.528910	2.835703	17.943128	-0.575671	-0.58357	-0.633175	-0.635387	-0.607899	-0.593997	-0.599368	0.839810	0.786098	2.560506	2.957030	3.135861	2.998420	1.109321	0.971248	0.340600	5.071090	5.406975	183.066035	0.412006	0.450553	0.423065	0.404423	0.452449	0.428436	0.274882	-0.245182	1.002844	2.697630	-0.556714	1.592733	0.351975	-0.642970	-0.415482	-0.206319	-0.610111	-0.542496	0.152291	0.461295	0.332070	0.327962	0.402528	0.257820	0.037915	0.485308	0.334913	0.377567	0.326698	0.395261	0.313744	0.448973	0.020221	0.472354	0.310585	0.328910	0.300790	0.254028	0.212322	0.178831	0.036967	0.233491	0.385782	0.278673	0.144392	0.337757	0.182306	0.215482	0.408531	0.372196	1.198736	1.597472	1.602844	1.826856	1.284676	1.827488	1.947235	1.974724	1.922907	1.943760	2.068246	3.043602	1.055608	0.533017	1.028436	0.919115	1.024329	1.206951	-0.035071	-0.190521	-0.049605	2.404107	2.520695	3.040126	-0.126066	-0.071406	-0.037599	-0.690047	-0.715008	-0.701106	2.556398	2.773460	2.273302	1.416114	1.416114	0.342496	0.344392	4.567773	0.286572	0.253081	0.207583	0.218641	1.054344	1967.966509	1.516272	3.496367	2.636651	1.791469	1.667615	3.492575	18.533333	1.156082	3.388310	4.031596	1.925118	3.222433	1.705845	2.248341	4.283096	3.931122	5.137125	0.030016	28.057820	-3.0	2.682464	7.612006	0.001264	0.001896	0.001896	0.001264	0.002528	0.000948	0.0	0.000316	0.005371	0.012954	0.002844	0.000316	0.005687	0.006635	0.007267	0.001580	0.003476	0.006635	0.003476	0.004107	0.001264	0.003791	0.000316	0.000632	0.001264	0.005687	0.001580	0.003791	0.005687	0.016114	0.000948	0.000948	0.036019	0.003476	0.002844	0.004739	0.000316	0.005371	0.007267	0.006319	0.004423	0.000632	0.000632	0.013586	0.015166	0.003791	0.004423	0.000316	0.002212	0.006319	0.001896	0.001580	0.000948	0.004107	0.005055	0.012954	0.015798	0.005687	0.005055	0.033807	0.002528	0.006635	0.004107	0.004423	0.027172	0.008215	0.009795	0.007583	0.007267	0.009479	0.008847	0.006951	0.00316	0.006951	0.004107	0.012322	0.009795	0.008531	0.016746	0.042338	0.012638	0.005371	0.018641	0.007899	0.098578	0.009163	0.010427	0.007899	0.021485	0.008215	0.006003	0.012638	0.000316	0.006003	0.002528	0.000948	0.000316	0.000316	0.000948	0.001264	0.0	0.014534	0.003476	0.001580	0.002528	0.002212	0.007583	0.002212	0.001580	0.002212	0.004107	0.022433	0.011374	0.000948	0.001264	0.023697	0.00316	0.008847	0.000316	0.000316	0.072038	0.001896	0.001264	0.0	0.004107	0.004739	0.020221	0.000316	0.0	0.000632	0.000316	0.090047	0.211374	0.051185	0.029068	0.007583	0.000316	0.001580	0.004107	0.015798	0.006003	0.006635	0.008215	0.000948	0.000948	0.001580	0.007267	0.001580	0.000948	0.004423	0.002844	0.000632	0.000316	0.001580	0.0	0.009479	0.009479	0.005687	0.012954	0.001580	0.000948	0.007267	0.004107	0.002212	0.002844	0.005371	0.005687	0.000632	0.002212	0.0	0.001896	0.000632	0.000632	0.000948	0.001580	0.002212	0.012954	0.005371	0.010427	0.000316	0.000316	0.000948	0.000632	0.011690	0.014218	0.003791	0.004107	0.001264	0.000632	0.000632	0.003476	0.001264	0.001580	0.002844	0.004739	0.016746	0.024961	0.001896	0.006319	0.003791	0.001264	0.003476	0.006635	0.005371	0.000948	0.001264	0.001264	0.000948	0.014218	0.018325	0.006003	0.005687	0.000948	0.00316	0.005687	0.002528	16.249831	2.851289	4.758153	9.947926	9.746972	5.948907	6.459573	10.551217	7.434285	7.914338	3.746515	4.370908	16.349738	26.844717	14.821811	9.187120	6.666985	9.203342	6.349779	10.455729	6.368160	8.764240	8.407842	9.570333	6.737906	7.946085	7.463983	3.537166	3.961809	6.674058	8.099450	6.692616	13.294170	7.196664	6.297405	5.605068	6.167858	8.368036	5.799524	6.397577	5.627565	5.504396	7.314327	7.127235	5.342872	5.601698	7.028444	10.067284	6.142951	9.488965	8.053627	2.994213	5.99733	10.199777	6.486621	6.119887	7.631131	11.593834	6.120466	8.947167	0.397838	0.472865	4.374793	4.534931	3.620925	2.905165	2.821052	6.751886	4.862747	3.070789	3.232060	48.462781	14.782748	12.893939	7.614476	26.695615	11.381638	14.058791	13.468457	14.334284	16.524850	17.648784	14.067368	14.669283	12.939733	11.314433	11.419157	14.676886	34.775299	22.727071	31.546775	12.257626	16.569494	20.682562	20.941951	4.080805	16.403611	83.353785	18.758895	11.695224	6.509795	6.051318	7.318181	16.792928	15.692625	6.401828	7.377582	10.994774	9.668506	28.472640	63.464335	4.374284	14.293756	28.085451	15.632500	31.944563	13.800104	10.805658	18.735078	9.756997	9.406772	10.210200	11.382238	13.971114	10.983584	5.463366	7.046347	9.188105	6.174507	6.588835	5.950917	8.755212	5.610690	7.194316	16.579748	15.251651	4.975384	6.640365	5.351997	6.936025	15.165952	12.869905	13.785718	13.804272	5.174459	76.840578	21.269621	14.060265	17.778390	20.862071	8.696251	0.627698	0.474750	0.389372	0.341393	4.223396	4.235764	8.487581	0.113170	1.547438	0.962805	5.052195	4.617948	44.516415	3.460289	3.956396	5.144718	11.258700	7.711925	14.459429	2.299373	1.370671	3.840478	5.087858	0.797151	12.819019	3.932843	0.980799	2.208268	2.328449	0.906647	2.492929	16.092442	14.840867	9.559169	12.830056	9.584312	7.607143	12.263852	9.575728	6.777868	5.412755	2.243678	2.985571	2.739002	2.054936	2.432392	1.481347	8.649955	0.766930	0.623996	4.825806	4.369771	3.329777	0.882941	0.742097	0.523940	0.207807	0.163375	0.130342	8.343833	10.489896	11.550905	3.259258	7.736379	18.957856	5.420974	3.359211	3.170164	3.117318	7.152483	1.0	2.0	5.006319	4.959558	4.985466	5.012322	5.036335	10.013270	10.008847	10.000000	10.000000	9.977883	7.015482	6.984518	15.0	17.534281	17.490679	17.454976	17.520063	26.947235	27.053081	27.027488	27.082148	27.173144	27.053397	26.917536	26.979147	27.073934	26.939021	27.024329	26.875829	27.012006	27.012954	26.828752	1.992101	1.995577	2.012322	1.0	2.503633	2.496367	2.002844	2.023381	1.973776	4.0	5.0	6.0	1.512480	1.487520	2.488784	2.515640	2.496367	2.499210	0.0	0.506161	8663.017694
std	913.801127	0.500078	0.926089	1.195428	1.247464	1.070029	1.096192	1.880001	1.906350	1.306302	1.158824	0.364741	0.980422	0.96956	1.202344	0.576437	0.632481	0.323975	0.398155	0.25436	0.473880	0.407226	0.336895	0.390516	0.472694	0.494746	84.472485	73.002606	120.196311	189.367529	115.863403	276.040047	235.892477	23.964258	23.046131	24.333374	23.753420	29.722614	32.037712	39.488803	40.827825	31.566846	196.277400	138.611353	129.933928	134.687957	24.924698	23.160735	26.513570	24.203703	24.480570	24.801873	23.691241	24.822482	23.868048	29.137909	25.092663	23.950280	23.866562	24.985195	27.53408	0.785695	3.227412	1.503372	1.482449	0.920157	0.930424	0.906015	1.224961	1.039782	1.427890	1.443793	0.919295	0.791903	1.525718	0.454661	0.528816	0.552598	2.426304	2.430919	2.417922	2.236128	3.242645	3.232316	3.232296	2.951463	1.190750	1.230450	1.217087	1.984036	1.256340	1.196790	1.138945	1.037598	1.288336	1.992199	2.186822	0.958025	1.095626	1.004384	0.961705	1.513856	2.134250	2.006010	1.486062	1.611915	1.110544	1.038133	1.146316	1.005617	1.695779	1.905662	0.799201	0.850188	1.295056	1.470947	2.365528	3.122146	1.480934	1.557206	1.599698	1.574602	1.511817	1.503996	2.386954	2.495949	2.457080	1.827282	1.565067	3.014146	2.102935	2.04003	3.125481	2.265519	2.253859	1.940376	2.475170	1.413388	3.017706	1.471103	1.340742	0.489051	2.287246	1.601586	2.532555	2.135247	1.934449	1.166553	1.239014	1.087009	1.235559	1.157237	0.965923	1.362203	1.450328	0.857907	2.086891	2.224431	2.340165	2.908679	2.856271	2.967346	2.953912	3.000624	3.058243	1.352924	0.620221	1.269303	0.607137	1.309541	1.473984	1.508258	1.218110	1.303537	1.338110	1.222687	0.548764	1.302573	0.642797	2.628259	2.633112	0.445501	0.487659	6.347812	1.355817	671.902905	0.425120	2.961733	1.682924	1.681818	1.184448	1.620499	39.321173	1.715605	0.429832	193.708767	1.042572	1.02669	0.919159	0.914008	0.975749	1.005246	0.965917	1.986028	1.891677	1.459922	1.511874	1.461211	1.461797	2.164109	2.155637	1.383124	0.984232	3.517829	63.705234	1.486639	1.514105	1.494672	1.488721	1.515417	1.498529	1.325245	1.471359	2.214977	4.999799	1.239248	0.514036	1.845648	0.868132	1.307694	1.226328	1.119147	0.977874	1.213675	1.486827	1.387591	1.384122	1.443979	1.321515	1.083646	1.503322	1.419009	1.460803	1.420256	1.466496	1.409451	1.490277	1.076221	1.514034	1.360878	1.376685	1.343812	1.317940	1.259266	1.265958	1.109190	1.329245	1.451487	1.370888	1.195363	1.384164	1.255306	1.294382	1.460908	1.433035	0.399112	0.490485	0.489386	0.378431	0.451331	0.377884	0.223599	0.156988	0.266781	2.140158	1.957453	2.295352	2.848149	2.088270	2.609150	2.721554	2.595528	2.787311	2.291847	1.878594	2.135665	2.678055	2.445997	2.663485	2.119435	2.085549	2.149187	1.401072	1.230273	1.283618	1.621324	1.725155	1.281567	1.025432	1.025432	1.449828	1.475957	1.010175	1.453350	1.376188	1.321396	1.412950	0.272329	17.149547	0.499814	1.492629	1.867967	0.406322	1.272128	2.409492	31.326407	0.426262	1.464257	2.277164	1.451628	2.487606	0.480051	1.185511	1.755823	2.502905	4.504054	15.766871	15.948919	0.0	1.026802	2.499655	0.035533	0.043506	0.043506	0.035533	0.050220	0.030778	0.0	0.017775	0.073103	0.113095	0.053258	0.017775	0.075211	0.081198	0.084950	0.039721	0.058860	0.081198	0.058860	0.063968	0.035533	0.061468	0.017775	0.025134	0.035533	0.075211	0.039721	0.061468	0.075211	0.125933	0.030778	0.030778	0.186367	0.058860	0.053258	0.068690	0.017775	0.073103	0.084950	0.079254	0.066372	0.025134	0.025134	0.115783	0.122232	0.061468	0.066372	0.017775	0.046984	0.079254	0.043506	0.039721	0.030778	0.063968	0.070932	0.113095	0.124712	0.075211	0.070932	0.180761	0.050220	0.081198	0.063968	0.066372	0.162611	0.090277	0.098498	0.086763	0.084950	0.096911	0.093655	0.083096	0.05613	0.083096	0.063968	0.110337	0.098498	0.091982	0.128337	0.201391	0.111725	0.073103	0.135276	0.088538	0.298142	0.095298	0.101593	0.088538	0.145017	0.090277	0.077259	0.111725	0.017775	0.077259	0.050220	0.030778	0.017775	0.017775	0.030778	0.035533	0.0	0.119697	0.058860	0.039721	0.050220	0.046984	0.086763	0.046984	0.039721	0.046984	0.063968	0.148110	0.106059	0.030778	0.035533	0.152126	0.05613	0.093655	0.017775	0.017775	0.258592	0.043506	0.035533	0.0	0.063968	0.068690	0.140778	0.017775	0.0	0.025134	0.017775	0.286295	0.408348	0.220409	0.168024	0.086763	0.017775	0.039721	0.063968	0.124712	0.077259	0.081198	0.090277	0.030778	0.030778	0.039721	0.084950	0.039721	0.030778	0.066372	0.053258	0.025134	0.017775	0.039721	0.0	0.096911	0.096911	0.075211	0.113095	0.039721	0.030778	0.084950	0.063968	0.046984	0.053258	0.073103	0.075211	0.025134	0.046984	0.0	0.043506	0.025134	0.025134	0.030778	0.039721	0.046984	0.113095	0.073103	0.101593	0.017775	0.017775	0.030778	0.025134	0.107505	0.118407	0.061468	0.063968	0.035533	0.025134	0.025134	0.058860	0.035533	0.039721	0.053258	0.068690	0.128337	0.156029	0.043506	0.079254	0.061468	0.035533	0.058860	0.081198	0.073103	0.030778	0.035533	0.035533	0.030778	0.118407	0.134147	0.077259	0.075211	0.030778	0.05613	0.075211	0.050220	91.689491	8.013374	75.269861	89.293214	98.002761	13.326773	31.622833	77.918708	74.763266	306.564583	11.010138	25.512346	358.175432	123.517520	56.211355	13.970517	10.154124	97.829508	8.604871	189.550416	11.777612	94.479839	26.059846	147.990913	7.492682	51.463324	14.962254	9.763798	18.387004	19.348970	48.523896	25.024014	254.182999	20.081360	25.769919	6.804976	25.557934	87.159584	8.803043	31.211370	8.772881	14.073176	60.280534	63.342451	8.978854	8.979428	51.363497	240.697989	13.561271	26.876109	203.621035	7.233232	22.61214	52.163298	30.063915	56.660079	12.409541	33.913834	26.821527	91.273237	1.411344	2.944575	12.159694	14.317243	9.163497	9.957565	7.366386	91.724847	37.105394	9.114192	16.300898	107.099793	92.655169	194.082786	45.561832	721.571430	46.519450	43.163617	33.620619	20.000704	37.026307	93.475135	20.568615	79.109682	40.477215	45.763967	43.374388	42.251312	898.058434	22.214917	143.545418	23.637567	45.784958	267.363429	31.037061	10.980474	488.503496	999.359831	169.557500	141.053492	81.488826	16.638078	115.163808	77.248099	130.874965	12.516527	16.382132	23.934882	122.050078	262.044279	2996.102211	10.764148	65.549654	976.066908	53.151902	865.064211	28.589297	31.986785	397.158687	58.267161	25.350807	33.802799	47.396083	108.038556	15.474524	30.568121	39.538143	104.069768	42.793900	15.981852	19.768094	96.167439	22.457806	78.979356	33.456917	128.393785	9.028735	17.827140	7.909739	16.491784	63.505379	40.691826	95.848241	121.228974	25.408713	2905.712060	35.065594	29.237517	29.337386	28.634091	97.000902	5.705071	1.816856	1.419760	2.504516	6.802250	10.172237	19.951727	1.336745	5.052157	3.423748	7.744789	9.349799	102.914053	10.108189	11.249815	13.926650	21.377974	11.329708	97.707176	4.153955	12.476203	15.827777	14.087427	3.560941	54.941761	60.732149	2.785403	21.435226	13.708400	3.780030	7.317347	67.277601	36.232032	18.145739	92.075579	21.143147	24.664814	111.209349	230.160421	20.793832	11.490907	6.467550	23.008330	42.175293	6.105756	15.858954	3.577448	417.484339	4.379992	5.055334	15.946097	12.096853	10.209333	5.843517	6.763745	2.938336	1.081654	1.045189	1.036706	22.916155	29.939871	48.979658	5.583838	145.072732	146.792561	15.338817	5.809931	4.962913	21.673566	6.071246	0.0	0.0	1.421778	1.413188	1.422162	1.401024	1.412740	1.387073	1.426868	1.390322	1.437928	1.428937	7.001089	7.001089	0.0	1.121496	1.109517	1.115463	1.124654	4.340741	4.337860	4.274798	4.287866	4.363288	4.331476	4.313910	4.269546	4.361824	4.316758	4.336696	4.291975	4.344284	4.356668	4.275706	0.813291	0.826997	0.809340	0.0	0.500066	0.500066	0.818360	0.810072	0.820645	0.0	0.0	0.0	0.499923	0.499923	1.113481	1.111154	1.129034	1.118917	0.0	0.500041	36096.187009
min	1.000000	1.000000	1.000000	-1.000000	-1.000000	1.000000	1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-7.000000	-1.000000	-1.00000	-1.000000	1.000000	-7.000000	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	0.000000	-7.000000	-7.000000	-7.000000	-7.000000	0.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	0.000000	0.000000	0.000000	-7.000000	-7.000000	0.000000	-7.000000	-7.000000	-7.00000	-7.000000	-7.000000	-7.000000	-1.000000	1.000000	1.000000	1.000000	1.000000	-7.000000	-1.000000	-1.000000	-1.000000	-7.000000	-7.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-7.000000	-7.000000	-1.000000	-7.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-7.000000	1.000000	-7.000000	-7.000000	1.000000	1.000000	-7.000000	-7.000000	1.000000	-7.000000	1.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-1.000000	-7.000000	-7.000000	-7.000000	-1.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-1.000000	-7.000000	-1.000000	-1.000000	-1.00000	-7.000000	-7.000000	-7.000000	-1.000000	-7.000000	1.000000	-1.000000	-7.000000	-7.000000	1.000000	-7.000000	-7.000000	-1.000000	1.000000	-7.000000	-7.000000	-7.000000	1.000000	1.000000	-7.000000	-7.000000	1.000000	-7.000000	-7.000000	-7.000000	-1.000000	-7.000000	-7.000000	-7.000000	-7.000000	-1.000000	-7.000000	-7.000000	-1.000000	-7.000000	-1.000000	-7.000000	-1.000000	-7.000000	-7.000000	-1.000000	-7.000000	-7.000000	-1.000000	-7.000000	-1.000000	-7.000000	1.000000	1.000000	0.000000	0.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-1.000000	-1.000000	-7.000000	-7.000000	-1.000000	-7.000000	-7.000000	-1.000000	-1.00000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-1.000000	-1.000000	-1.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-1.000000	-1.000000	-7.000000	-7.000000	-1.000000	-7.000000	-7.000000	-1.000000	-1.000000	-1.000000	-1.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-1.000000	-7.000000	-1.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-1.000000	-1.000000	-7.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-7.000000	-7.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-7.000000	-7.000000	-7.000000	-1.000000	-1.000000	-1.000000	-7.000000	1.000000	-7.000000	-7.000000	-7.000000	-7.000000	-7.000000	1926.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-7.000000	1.000000	1.000000	-7.000000	1.000000	-7.000000	1.000000	-7.000000	1.000000	1.000000	1.000000	-7.000000	-7.000000	1.000000	-3.0	1.000000	-7.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.649000	0.000000	0.000000	0.629000	0.117000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.738000	1.137000	1.291000	0.414000	0.388000	0.278000	0.486000	0.496000	0.468000	0.473000	0.540000	0.631000	0.527000	0.488000	0.341000	0.000000	0.000000	0.549000	0.595000	0.473000	0.557000	0.555000	0.060000	0.271000	0.105000	0.463000	0.122000	0.218000	0.190000	0.215000	0.306000	0.108000	0.118000	0.623000	0.185000	0.072000	0.137000	0.902000	0.000000	0.000000	0.00000	0.719000	0.383000	0.585000	0.604000	0.607000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.812000	0.671000	0.629000	0.649000	0.717000	0.693000	0.624000	0.512000	0.544000	0.496000	0.603000	0.303000	0.563000	0.304000	0.539000	0.505000	0.588000	0.561000	1.114000	0.900000	0.780000	0.663000	0.657000	0.727000	0.000000	0.000000	1.350000	0.000000	0.000000	0.000000	0.000000	0.000000	0.533000	0.237000	0.000000	0.000000	0.607000	0.000000	1.349000	0.964000	0.000000	0.513000	0.000000	0.672000	0.593000	0.668000	0.595000	0.633000	0.413000	0.575000	0.481000	0.636000	0.567000	0.756000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.846000	0.751000	0.000000	0.000000	0.000000	0.000000	0.615000	0.640000	0.716000	0.243000	0.000000	0.540000	0.458000	0.455000	0.752000	0.473000	0.345000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.765000	0.000000	0.000000	0.000000	0.000000	0.000000	1.286000	0.000000	0.000000	0.000000	0.533000	0.471000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.913000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.788000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.402000	0.000000	0.000000	0.736000	0.000000	0.000000	0.000000	0.000000	0.795000	1.0	2.0	3.000000	3.000000	3.000000	3.000000	3.000000	8.000000	8.000000	8.000000	8.000000	8.000000	0.000000	0.000000	15.0	16.000000	16.000000	16.000000	16.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	1.000000	1.000000	1.000000	1.0	2.000000	2.000000	1.000000	1.000000	1.000000	4.0	5.0	6.0	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.0	0.000000	492.000000
33%	1045.120000	1.000000	1.000000	-1.000000	-1.000000	2.000000	2.000000	-1.000000	-1.000000	1.000000	-1.000000	-1.000000	-1.000000	-1.00000	1.000000	1.000000	-1.000000	2.000000	2.000000	2.00000	1.000000	2.000000	2.000000	2.000000	1.000000	1.000000	3.000000	30.000000	16.000000	11.000000	12.120000	19.000000	11.000000	58.000000	59.000000	55.000000	55.000000	43.120000	19.120000	-1.000000	-1.000000	33.000000	50.000000	41.000000	37.000000	16.000000	25.000000	4.000000	46.120000	16.000000	44.000000	7.000000	60.000000	50.000000	53.000000	50.000000	50.000000	63.000000	25.000000	14.000000	25.00000	1.000000	-1.000000	-1.000000	-1.000000	1.000000	1.000000	1.000000	1.000000	2.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	2.000000	2.000000	2.000000	4.000000	2.000000	3.000000	3.000000	3.000000	2.000000	3.000000	2.000000	2.000000	3.000000	2.000000	2.000000	2.000000	3.000000	3.000000	1.000000	5.000000	2.000000	1.000000	3.000000	3.000000	4.000000	2.000000	1.000000	2.000000	-1.000000	1.000000	-1.000000	-1.000000	1.000000	2.000000	3.000000	3.000000	2.000000	3.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.00000	-1.000000	3.000000	3.000000	-1.000000	-1.000000	1.000000	-1.000000	1.000000	1.000000	1.000000	-1.000000	2.000000	-1.000000	2.000000	4.000000	3.000000	3.000000	3.000000	2.000000	3.000000	1.000000	2.000000	3.000000	1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	3.000000	2.000000	3.000000	1.000000	3.000000	-1.000000	-1.000000	3.000000	-1.000000	-1.000000	3.000000	1.000000	3.000000	1.000000	1.000000	1.000000	0.000000	0.000000	6.000000	1.000000	1896.000000	2.000000	2.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	3.000000	-1.000000	-1.000000	-1.00000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	2.000000	2.000000	3.000000	2.000000	-1.000000	-1.000000	-1.000000	5.000000	3.000000	155.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	1.000000	1.000000	1.000000	2.000000	1.000000	2.000000	2.000000	2.000000	2.000000	1.000000	1.000000	3.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	1.000000	1.000000	3.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	2.000000	2.000000	1.000000	1.000000	1.000000	-1.000000	-1.000000	5.000000	-1.000000	-1.000000	-1.000000	-1.000000	1.000000	1957.000000	1.000000	2.000000	1.000000	2.000000	1.000000	1.000000	4.000000	1.000000	3.000000	3.000000	1.000000	2.000000	2.000000	1.000000	4.000000	2.000000	1.000000	-7.000000	17.000000	-3.0	2.000000	8.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	6.304240	0.000000	0.000000	3.980240	4.217000	0.000000	0.000000	5.320840	0.000000	0.000000	0.000000	2.288360	5.341720	12.384240	7.750840	5.347600	3.856240	3.726120	3.794360	3.870240	3.759720	3.920120	4.129000	3.761120	3.897120	3.991240	4.122120	0.000000	0.000000	3.737240	3.702120	3.697120	4.749480	3.982120	3.216120	3.263480	3.308240	3.211120	3.360120	3.312000	3.337120	3.140240	3.258000	3.328240	3.166120	3.234120	3.215120	3.205360	3.391000	4.478360	0.000000	0.000000	0.00000	5.118120	3.040840	2.349000	4.177000	5.030760	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	15.602000	6.150000	5.260240	3.942240	7.272360	5.464120	7.263360	7.002120	7.665120	8.584120	8.062240	8.307240	6.825720	6.378960	5.154720	5.476480	6.933360	10.495000	14.085320	15.576080	6.964000	8.173920	7.337080	12.181600	0.000000	0.000000	36.317760	0.000000	0.000000	0.000000	0.000000	0.000000	8.365000	6.207600	0.000000	0.000000	6.511360	0.000000	11.829960	5.823120	0.000000	6.849000	0.000000	8.161120	8.167080	7.954120	5.693000	6.427000	4.160480	4.897360	5.262120	5.284000	6.717240	6.206480	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	9.983080	7.613000	0.000000	0.000000	0.000000	0.000000	7.436360	7.223840	7.120360	6.099240	0.000000	12.655840	10.037360	7.291360	9.570000	9.220120	4.636000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	4.579000	0.000000	0.000000	0.000000	0.000000	0.000000	24.288040	0.000000	0.000000	0.000000	6.882120	5.046000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	7.091320	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	6.468000	3.027360	3.941240	2.963920	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2.168120	1.507360	1.115000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	4.574120	5.518000	6.209840	0.000000	0.000000	9.393120	0.000000	0.000000	0.000000	0.000000	4.970120	1.0	2.0	4.000000	4.000000	4.000000	4.000000	4.000000	9.000000	9.000000	9.000000	9.000000	9.000000	0.000000	0.000000	15.0	17.000000	17.000000	17.000000	17.000000	24.000000	25.000000	25.000000	25.000000	25.000000	25.000000	24.000000	24.000000	24.000000	24.000000	25.000000	24.000000	24.000000	24.000000	24.000000	1.000000	1.000000	2.000000	1.0	2.000000	2.000000	1.000000	2.000000	1.000000	4.0	5.0	6.0	1.000000	1.000000	2.000000	2.000000	2.000000	2.000000	0.0	0.000000	1653.120000
50%	1583.000000	1.000000	1.000000	1.000000	-1.000000	3.000000	3.000000	1.000000	-1.000000	2.000000	1.000000	-1.000000	-1.000000	-1.00000	1.000000	1.000000	-1.000000	2.000000	2.000000	2.00000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	38.000000	59.000000	44.000000	45.000000	44.000000	48.000000	41.000000	73.000000	73.000000	71.000000	71.000000	51.000000	45.000000	0.000000	-1.000000	50.000000	55.000000	52.000000	51.000000	50.000000	40.000000	11.000000	52.000000	31.000000	50.000000	20.000000	72.000000	60.000000	67.000000	58.000000	59.000000	78.000000	41.000000	30.000000	46.00000	2.000000	-1.000000	-1.000000	-1.000000	2.000000	2.000000	2.000000	2.000000	2.000000	1.000000	-1.000000	-1.000000	-1.000000	1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	1.000000	1.000000	1.000000	1.000000	3.000000	2.000000	2.000000	5.000000	2.000000	3.000000	3.000000	3.000000	3.000000	4.000000	4.000000	2.000000	3.000000	3.000000	2.000000	3.000000	4.000000	4.000000	2.000000	6.000000	2.000000	2.000000	4.000000	4.000000	4.000000	3.000000	1.000000	2.000000	-1.000000	1.000000	-1.000000	1.000000	2.000000	3.000000	3.000000	4.000000	3.000000	4.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-1.000000	-1.000000	1.00000	1.000000	4.000000	4.000000	-1.000000	1.000000	2.000000	-1.000000	2.000000	2.000000	2.000000	-1.000000	2.000000	-1.000000	4.000000	5.000000	3.000000	3.000000	3.000000	3.000000	3.000000	2.000000	3.000000	4.000000	1.000000	-1.000000	1.000000	-1.000000	1.000000	-1.000000	1.000000	-1.000000	1.000000	-1.000000	4.000000	2.000000	4.000000	1.000000	4.000000	-1.000000	-1.000000	3.000000	1.000000	-1.000000	4.000000	1.000000	4.000000	2.000000	4.000000	4.000000	0.500000	1.000000	6.000000	2.000000	1965.000000	2.000000	4.000000	-1.000000	-1.000000	1.000000	-1.000000	-1.000000	-1.000000	3.000000	-1.000000	-1.000000	-1.00000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	2.000000	3.000000	3.000000	3.000000	1.000000	-1.000000	-1.000000	5.000000	5.000000	180.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	2.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	1.000000	1.000000	1.000000	-1.000000	-1.000000	-1.000000	1.000000	2.000000	2.000000	2.000000	1.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	4.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	3.000000	4.000000	5.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	3.000000	3.000000	2.000000	1.000000	1.000000	1.000000	-1.000000	5.000000	-1.000000	1.000000	1.000000	-1.000000	1.000000	1964.000000	2.000000	3.000000	1.000000	2.000000	1.000000	4.000000	7.000000	1.000000	3.000000	4.000000	1.000000	2.000000	2.000000	2.000000	5.000000	3.000000	2.000000	-7.000000	29.000000	-3.0	3.000000	8.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	8.425000	1.148000	0.000000	5.173000	5.419000	1.055000	0.000000	6.665000	0.988000	0.000000	0.000000	3.122000	7.032000	17.468000	10.154000	6.835000	4.846000	4.691000	4.730000	4.899000	4.736000	5.131000	5.423000	4.980000	5.039000	5.022000	5.257000	0.884000	0.000000	4.727000	4.669000	4.536000	5.931000	4.934000	4.060000	4.188000	4.194000	3.994000	4.344000	4.132000	4.121000	3.938000	4.015000	4.219000	3.898000	3.999000	3.999000	4.055000	4.311000	6.254000	0.000000	0.000000	0.00000	6.760000	4.132000	3.146000	5.861000	8.135000	0.960000	0.000000	0.000000	0.000000	0.864000	0.000000	0.000000	0.000000	0.000000	1.424000	1.094000	0.951000	0.908000	23.544000	7.876000	6.621000	4.963000	9.121000	6.922000	9.405000	9.161000	10.419000	11.273000	10.243000	10.553000	9.038000	8.668000	7.159000	6.989000	9.117000	13.730000	18.257000	20.634000	8.629000	10.355000	9.629000	15.612000	0.000000	1.342000	47.008000	2.779000	0.000000	0.000000	1.074000	0.893000	10.864000	8.215000	0.000000	1.037000	8.268000	0.000000	15.747000	7.792000	0.000000	8.816000	0.000000	10.769000	11.015000	10.214000	7.299000	8.282000	5.555000	6.621000	6.894000	6.961000	8.453000	8.151000	0.000000	0.879000	0.000000	0.761000	0.000000	0.687000	0.000000	0.734000	0.000000	12.303000	9.403000	1.663000	0.000000	1.349000	0.000000	9.579000	9.006000	8.817000	7.981000	0.000000	17.346000	14.350000	9.257000	12.174000	12.731000	5.634000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	5.898000	0.000000	0.000000	0.000000	1.053000	0.000000	31.996000	1.959000	0.000000	0.000000	8.325000	6.065000	0.000000	0.000000	0.000000	0.000000	1.461000	0.000000	9.008000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.186000	0.000000	1.470000	0.000000	1.481000	0.000000	7.987000	4.057000	5.367000	4.373000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.714000	3.097000	2.158000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	5.898000	7.279000	7.799000	0.984000	0.000000	11.471000	0.833000	0.825000	1.095000	1.133000	6.219000	1.0	2.0	5.000000	5.000000	5.000000	5.000000	5.000000	10.000000	10.000000	10.000000	10.000000	10.000000	14.000000	0.000000	15.0	18.000000	17.000000	17.000000	18.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	2.000000	2.000000	2.000000	1.0	3.000000	2.000000	2.000000	2.000000	2.000000	4.0	5.0	6.0	2.000000	1.000000	2.000000	3.000000	2.000000	2.000000	0.0	1.000000	2049.000000
67%	2120.880000	2.000000	2.000000	1.000000	1.000000	3.000000	3.000000	2.000000	2.000000	3.000000	1.000000	-1.000000	-1.000000	-1.00000	2.000000	1.000000	-1.000000	2.000000	2.000000	2.00000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	79.880000	84.880000	61.000000	63.000000	64.000000	59.000000	54.000000	87.000000	86.000000	85.000000	85.000000	66.000000	56.000000	59.000000	65.000000	68.000000	71.000000	69.000000	74.000000	71.000000	50.000000	25.880000	67.000000	46.000000	57.000000	39.000000	84.000000	73.000000	79.000000	77.000000	70.000000	89.000000	50.000000	45.000000	51.00000	2.000000	1.000000	2.000000	1.000000	2.000000	2.000000	2.000000	2.000000	3.000000	2.000000	2.000000	-1.000000	-1.000000	2.000000	-1.000000	-1.000000	-1.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	3.000000	3.000000	3.000000	6.000000	3.000000	3.000000	4.000000	3.000000	4.000000	4.000000	5.000000	3.000000	3.000000	3.000000	3.000000	4.000000	6.000000	5.000000	3.000000	7.000000	3.000000	2.000000	4.000000	4.000000	4.000000	4.000000	2.000000	2.000000	-1.000000	2.000000	1.000000	2.000000	3.000000	3.000000	5.000000	5.000000	4.000000	5.000000	3.000000	3.000000	3.000000	1.000000	1.000000	3.000000	2.000000	2.00000	4.000000	6.000000	6.000000	2.000000	2.000000	3.000000	2.000000	3.000000	3.000000	2.000000	1.000000	3.000000	2.000000	4.000000	7.000000	4.000000	4.000000	4.000000	3.000000	4.000000	2.000000	4.000000	5.000000	2.000000	2.000000	3.000000	3.000000	4.000000	4.000000	4.000000	4.000000	4.000000	4.000000	5.000000	2.000000	5.000000	1.000000	5.000000	2.000000	2.000000	4.000000	1.000000	1.000000	5.000000	1.000000	5.000000	2.000000	7.000000	7.000000	1.000000	1.000000	6.000000	3.000000	1989.000000	2.000000	5.000000	1.000000	1.000000	1.000000	1.000000	0.000000	1.000000	3.000000	-1.000000	-1.000000	-1.00000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	2.000000	2.000000	3.000000	4.000000	4.000000	4.000000	3.000000	3.000000	2.000000	5.000000	7.000000	200.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	1.000000	-1.000000	2.000000	2.000000	-1.000000	2.000000	1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	1.000000	2.000000	2.000000	2.000000	2.000000	1.000000	1.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	1.000000	2.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	2.000000	1.000000	1.000000	2.000000	1.000000	1.000000	2.000000	2.000000	1.000000	2.000000	2.000000	2.000000	1.000000	2.000000	2.000000	2.000000	2.000000	3.000000	3.000000	5.000000	2.000000	1.000000	3.000000	1.000000	3.000000	4.000000	-1.000000	-1.000000	-1.000000	4.000000	4.000000	5.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	3.000000	4.000000	3.000000	1.000000	1.000000	1.000000	1.000000	5.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1979.000000	2.000000	4.000000	4.000000	2.000000	1.000000	5.000000	9.000000	1.000000	4.000000	5.000000	2.000000	3.000000	2.000000	3.000000	6.000000	6.000000	9.000000	1.000000	37.000000	-3.0	3.000000	9.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	11.219160	3.299760	3.537400	6.692160	6.928400	6.826880	7.052880	8.317280	6.792000	0.000000	0.000000	4.132760	9.037520	22.723160	13.299320	8.907680	6.139640	6.126280	5.987000	6.398880	5.960520	6.809840	7.420640	6.663760	6.561880	6.777760	6.947000	3.989640	4.090880	5.957880	5.944000	5.661640	7.518400	6.290640	5.168880	5.429040	5.304760	5.075680	5.585280	5.334760	5.208880	4.979640	5.266160	5.370880	4.991760	5.092520	5.101880	5.203760	5.574640	8.455760	4.477880	2.676520	5.56980	8.377280	5.590000	4.256400	7.748920	11.616040	7.376760	8.722000	0.000000	0.000000	5.270880	5.145000	4.213160	2.983000	2.864200	5.919440	4.561760	3.369520	3.160160	36.396160	9.894880	8.306280	6.204000	11.530640	8.748760	11.966480	11.911520	14.155280	14.920720	12.780760	13.545680	11.756760	11.240520	9.160640	9.087320	12.243040	17.834160	23.634200	26.352240	10.725880	13.367400	12.515040	20.043880	5.249400	5.703880	58.774480	17.406640	8.542920	5.469400	7.179600	5.457000	14.178320	10.625760	7.689640	8.332080	10.402000	7.851280	20.144040	10.151760	4.489880	11.678760	11.470440	13.852680	14.996880	13.077400	9.287400	10.650160	7.311520	8.894760	8.910400	8.999400	10.803520	10.643640	6.148280	7.681880	8.120280	6.703040	7.675080	6.769800	7.441640	5.655000	6.140400	15.534960	11.560880	6.894760	8.317920	7.278880	8.553760	12.057760	11.439400	11.031880	10.157880	3.253120	23.971160	20.130840	12.010880	15.939080	18.316000	6.805760	0.000000	0.000000	0.000000	0.000000	5.568280	5.340880	7.724880	0.000000	0.000000	0.000000	6.834880	5.963640	40.950720	4.199640	5.014640	6.530880	10.151920	7.378640	16.018680	3.121640	0.000000	4.612880	4.599920	0.000000	11.295680	3.364360	0.000000	0.000000	0.000000	0.000000	0.000000	21.456520	19.658760	13.389880	13.781880	12.434920	9.229600	9.715920	5.304760	6.722160	5.696400	2.717800	2.562000	1.913680	2.444680	1.948000	1.378880	0.000000	0.000000	0.000000	5.091880	4.712680	3.568760	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	7.740760	9.372760	10.003760	4.438880	6.273280	13.807160	6.967920	4.286760	4.047400	3.595280	7.559640	1.0	2.0	6.000000	6.000000	6.000000	6.000000	6.000000	11.000000	11.000000	11.000000	11.000000	11.000000	14.000000	14.000000	15.0	18.000000	18.000000	18.000000	18.000000	29.000000	30.000000	29.880000	30.000000	30.000000	30.000000	29.000000	29.000000	30.000000	30.000000	30.000000	29.000000	30.000000	30.000000	29.000000	2.000000	3.000000	3.000000	1.0	3.000000	3.000000	3.000000	3.000000	2.000000	4.0	5.0	6.0	2.000000	2.000000	3.000000	3.000000	3.000000	3.000000	0.0	1.000000	2552.880000
max	3165.000000	2.000000	4.000000	3.000000	4.000000	4.000000	4.000000	5.000000	4.000000	5.000000	3.000000	2.000000	2.000000	2.00000	3.000000	3.000000	2.000000	2.000000	2.000000	2.00000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	997.000000	997.000000	997.000000	997.000000	997.000000	997.000000	997.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	997.000000	997.000000	997.000000	997.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.00000	3.000000	9.000000	4.000000	4.000000	4.000000	4.000000	4.000000	5.000000	4.000000	2.000000	2.000000	4.000000	4.000000	3.000000	2.000000	2.000000	2.000000	5.000000	5.000000	5.000000	5.000000	7.000000	7.000000	7.000000	7.000000	5.000000	5.000000	5.000000	7.000000	5.000000	5.000000	5.000000	5.000000	5.000000	7.000000	7.000000	5.000000	5.000000	5.000000	5.000000	5.000000	7.000000	7.000000	7.000000	7.000000	5.000000	5.000000	5.000000	5.000000	7.000000	7.000000	3.000000	2.000000	3.000000	3.000000	5.000000	7.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	7.000000	5.000000	5.00000	7.000000	7.000000	7.000000	5.000000	7.000000	5.000000	7.000000	5.000000	5.000000	2.000000	5.000000	7.000000	7.000000	7.000000	7.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	2.000000	5.000000	7.000000	7.000000	7.000000	7.000000	7.000000	7.000000	7.000000	7.000000	5.000000	2.000000	5.000000	2.000000	5.000000	2.000000	2.000000	5.000000	2.000000	2.000000	5.000000	2.000000	5.000000	2.000000	7.000000	7.000000	1.000000	1.000000	99.000000	4.000000	2018.000000	2.000000	7.000000	4.000000	4.000000	2.000000	3.000000	999.000000	5.000000	3.000000	2019.000000	2.000000	2.00000	2.000000	2.000000	2.000000	2.000000	2.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	2.000000	9.000000	11.500000	999.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	3.000000	5.000000	12.000000	3.000000	2.000000	5.000000	2.000000	4.000000	2.000000	4.000000	3.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	7.000000	5.000000	5.000000	7.000000	5.000000	5.000000	7.000000	5.000000	5.000000	7.000000	5.000000	5.000000	7.000000	5.000000	5.000000	7.000000	5.000000	5.000000	7.000000	5.000000	5.000000	5.000000	5.000000	5.000000	6.000000	6.000000	3.000000	3.000000	5.000000	5.000000	5.000000	5.000000	5.000000	2.000000	2000.000000	2.000000	6.000000	6.000000	2.000000	8.000000	9.000000	97.000000	3.000000	6.000000	8.000000	7.000000	7.000000	2.000000	4.000000	7.000000	8.000000	12.000000	90.000000	56.000000	-3.0	4.000000	9.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.0	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.0	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.0	1.000000	1.000000	1.000000	1.000000	0.0	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.0	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.0	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000	3329.142000	283.030000	3716.698000	4510.024000	5353.798000	294.913000	1612.191000	4173.815000	3979.179000	17243.753000	212.733000	1329.599000	20128.321000	4797.269000	2161.685000	474.717000	300.425000	4478.240000	256.690000	10614.993000	505.686000	5286.402000	907.872000	8316.339000	193.480000	2832.327000	432.521000	308.329000	762.215000	816.590000	2022.514000	1170.860000	14131.280000	756.190000	1284.965000	174.731000	1107.227000	4204.543000	283.518000	1678.225000	244.462000	587.067000	2853.474000	3496.967000	302.005000	208.710000	2165.035000	13528.502000	565.111000	1000.423000	11443.881000	221.362000	817.92500	2166.168000	1324.991000	2755.001000	394.123000	1640.298000	1294.523000	5033.181000	19.199000	107.309000	509.857000	516.104000	277.502000	340.382000	167.083000	5113.233000	1571.900000	419.809000	652.383000	3572.141000	3697.854000	10871.468000	2361.832000	40511.681000	1619.251000	1930.682000	1440.703000	409.381000	1051.996000	4116.227000	518.480000	4046.200000	1814.829000	1659.970000	1734.092000	1587.520000	50514.468000	429.053000	7444.931000	589.019000	1347.613000	13738.832000	658.068000	273.400000	24606.442000	55770.827000	8411.571000	7600.416000	4391.346000	601.745000	6182.909000	3900.135000	4658.247000	227.618000	366.238000	716.259000	5675.307000	11441.165000	168563.584000	384.680000	3402.048000	54876.037000	2508.591000	48464.985000	1108.529000	1190.970000	22251.219000	2800.366000	1211.201000	1530.032000	2238.145000	5893.071000	522.649000	1460.527000	1826.907000	5556.395000	2308.030000	444.203000	670.029000	5189.545000	667.093000	3986.526000	1367.509000	5985.957000	329.162000	709.513000	115.925000	529.633000	2360.807000	1848.990000	5081.053000	5628.526000	780.263000	163485.708000	1274.305000	1052.558000	531.474000	700.991000	5431.972000	288.646000	25.872000	31.343000	110.287000	177.980000	355.788000	814.179000	38.299000	75.818000	98.169000	152.853000	310.773000	2610.304000	300.601000	460.057000	667.837000	626.714000	346.873000	4418.358000	86.403000	684.437000	716.407000	377.896000	85.989000	1856.375000	3398.365000	36.171000	902.372000	700.214000	73.700000	184.015000	3517.603000	1235.324000	618.634000	4620.500000	576.556000	1170.608000	6213.150000	12914.558000	649.053000	355.542000	220.059000	1041.387000	2340.553000	205.250000	824.052000	82.856000	23442.337000	159.664000	220.371000	683.150000	533.287000	442.793000	280.159000	357.771000	84.795000	23.890000	34.589000	44.077000	980.717000	892.568000	1804.309000	100.479000	8120.092000	5315.118000	662.626000	127.858000	80.219000	1193.837000	146.586000	1.0	2.0	7.000000	7.000000	7.000000	7.000000	7.000000	12.000000	12.000000	12.000000	12.000000	12.000000	14.000000	14.000000	15.0	19.000000	19.000000	19.000000	19.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	3.000000	3.000000	3.000000	1.0	3.000000	3.000000	3.000000	3.000000	3.000000	4.0	5.0	6.0	2.000000	2.000000	4.000000	4.000000	4.000000	4.000000	0.0	1.000000	489139.000000

To see the count of non-missing observations, number of unique values, and the most frequent value and its frequency for string or categorical features, use the include='object' argument:

anes.describe(include='object')

	version	weight	weight_spss	placeid1a_t	mip	pk_cjus	pk_germ	pid2d	pid2r	bmi	relig1a_txt	relig1b_t	relig3b_t	socmed_t	employ_t	presvote16post_t	religpew_t	religpew_protestant_t	FIPCounty	EnrollmentDate	CompletedSurveys	tsmart_P2012	tsmart_G2012	tsmart_P2016	tsmart_G2016	tsmart_P2018	tsmart_G2018	ord_ftillegal	ord_electable_1	ord_electable_2	ord_tchina	ord_trussia	ord_tiran	ord_tmexico	ord_tjapan	ord_disable1	ord_disable2	ord_disable3	ord_disable4	ord_disable5	ord_disable6	ord_exptravel_ever	ord_exphomesch	ord_expfarm	ord_expffood	ord_expconvert	ord_expholiday	ord_explie	ord_expshark	ord_expdivorce	ord_exparrest	ord_expoverdose	ord_expdefault	ord_exppubasst	ord_exphybrid	ord_expmistake	ord_explightning	ord_exptravel_year	ord_expindian	ord_exphunt	ord_expflag	ord_exppublib	ord_explottery	ord_expshoponline	ord_exppubtrans	ord_expfight	ord_expavoid	ord_expknowimmig	ord_expknowtrans	ord_expbuyusa	ord_expretire	ord_expcolldebt	ord_expknowpris	starttime	endtime	pop_density_public	flag_state
count	3165	3165	3165	3165	3158	3143	3143	3165	3164	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165	3165
unique	1	825	825	43	2073	511	599	22	12	1381	1	1	1	49	4	1	4	1	1	2027	818	2	2	2	2	2	2	1	3	3	6	6	6	6	6	7	7	7	7	7	7	9	9	9	9	9	9	9	9	9	9	9	9	9	9	9	9	6	6	6	6	6	6	6	6	6	6	4	4	4	4	4	4	3132	3146	259	4
top	ANES 2019 Pilot Study version 20200204			__NA__	Donald Trump	chief justice	chancellor of germany	__NA__	__NA__		-3, restricted access	-3, restricted access	-3, restricted access	__NA__	__NA__	__NA__	__NA__	-3, restricted access	-3, restricted acces	250	108		1		1																																																	12/31/2019 19:41:26	12/21/2019 22:50:57		0
freq	3165	165	165	3121	77	313	473	3140	3148	42	3165	3165	3165	3085	3162	3165	3159	3165	3165	13	17	2394	1633	2166	1773	2164	1587	3165	1769	1769	1579	1579	1579	1579	1579	1586	1586	1586	1586	1586	1586	1579	1579	1579	1579	1579	1579	1579	1579	1586	1586	1586	1586	1586	1586	1586	1586	1579	1579	1579	1579	1579	1586	1586	1586	1586	1586	1579	1579	1579	1586	1586	1586	2	2	188	2901

To see the name and data type of each column, use the .dtypes attribute. To avoid truncation, type pd.set_option('display.max_rows', None). Because there are 900 columns to report in this case, to save space I won’t turn off the truncation:

anes.dtypes

version               object
caseid                 int64
weight                object
weight_spss           object
form                   int64
                       ...  
starttime             object
endtime               object
duration               int64
pop_density_public    object
flag_state            object
Length: 900, dtype: object

To find rows in the data that are duplicated, use the .duplicated() method. This method outputs a list of values that are True or False indicating whether or not each row is a duplicate of another row in the dataframe. To view the duplicated rows, use the .duplicated() output to filter the rows when using .loc to subset the data (more on .loc below). By default, .duplicated() marks all duplicate rows as True except for the first occurrence in the dataframe. In the case of the ANES data, there are no duplicated rows:

anes.loc[anes.duplicated(),]

	version	caseid	weight	weight_spss	form	follow	reg1a	reg1b	liveurban	youthurban	placeid1a	placeid1a_t	placeid1b	placeidimport	turnout16a	turnout16a1	turnout16b	turnout16c	vote16	turnout18a	turnout18a1	particip_1	particip_2	particip_3	particip_4	particip_5	particip_6	particip_7	particip_8	particip_9	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftimmig1	ftimmig2	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales	vote20dem	vote20cand	vote20cand2	electable	vote20jb	vote20ew	vote20bs	cvote2020	tsplit1	contact1a	contact1b	contact2a	contact2b	contact3	contact4m_1	contact4m_2	contact4m_3	apppres5	frnpres5	immpres5	econpres5	apppres7	frnpres7	immpres7	econpres7	mip	econnow	finworry	confecon	improve1	national1	national2	conspire1	conspire2	conspire3	taxecon	billtax	trade1	trade2	trade3	trade4	richpoor2	guarinc	lcself	lcd	lcr	pop1	pop2	pop3	corrupt	immignum	refugees	dreamstr	dreamer	dreamstr1	dreamstr2	wall5	wall7	pathway	preturn	popen	release1	release2	famsep	tchina	trussia	tiran	tmexico	tjapan	hlthcare1	hlthcare2	abortion1	abortion2	freecol	loans	diversity5	diversity7	language	buyback	gw1	gw2	knowopioid1	knowopioid2	opioiddo	sentence	prek	demo4	experts	science	exphelp	elite1	elite2	elite3	elite4	ukraine1	ukraine2	excessive	rural1alt1	rural1alt2	rural2alt1	rural2alt2	rural3alt1	rural3alt2	rural4alt1	rural4alt2	conf_unemp	unemp	conf_interfere	interfere	conf_autism	autism1	autism2	conf_gmo	gmo1	gmo2	conf_warm	warm	conf_illegal	illegal	impeach1	impeach2	pk_cjus	pk_cjus_correct	pk_germ	pk_germ_correct	pk_sen	pk_spend	pk_geer	cheat	pid7x	pid1d	pid2d	pid1r	pid2r	pidstr	pidlean	ngun	shooting	dem_activduty	milyears	milyr1	milyr2	milyr3	milyr4	milyr5	milyr6	combat	harass1a	harass1b	rr1	rr2	rr3	rr4	health1a	health1b	hospital	feet	inches	nweight	bmi	disable1	disable2	disable3	disable4	disable5	disable6	smoker1	smoker2	exercise	relig1a	relig1a_txt	relig2a	att1	att2	att3	relig1b	relig1b_t	relig2b	relig3b	relig3b_t	attother	exptravel_ever	exphomesch	expfarm	expffood	expconvert	expholiday	explie	expshark	expdivorce	exparrest	expoverdose	expdefault	exppubasst	exphybrid	expmistake	explightning	exptravel_year	expindian	exphunt	expflag	exppublib	explottery	expshoponline	exppubtrans	expfight	expavoid	expknowimmig	expknowtrans	expbuyusa	expretire	expcolldebt	expknowpris	socmed_1	socmed_2	socmed_3	socmed_4	socmed_5	socmed_6	socmed_7	socmed_8	socmed_9	socmed_t	facebook1	facebook2	facebook3	twitter1	twitter2	twitter3	instagram1	instagram2	instagram3	reddit1	reddit2	reddit3	youtube1	youtube2	youtube3	snapchat1	snapchat2	snapchat3	tiktok1	tiktok2	tiktok3	raceid	racework	whitejob	race_sub1	race_sub2	voterid1	voterid2	serious	photo1	photo2	photo3	photo4	reinterview	birthyr	gender	educ	marstat	child18	race	employ	employ_t	faminc_new	votereg	ideo5	pid7	newsint	presvote16post	presvote16post_t	pew_bornagain	pew_religimp	pew_churatd	pew_prayer	religpew	religpew_t	religpew_protestant	religpew_protestant_t	inputstate	zipCode	FIPCounty	region	EnrollmentDate	CompletedSurveys	qualityControl_overall_scale	tsmart_P2012	tsmart_G2012	tsmart_P2016	tsmart_G2016	tsmart_P2018	tsmart_G2018	abortion1_skp	abortion2_skp	apppres5_skp	apppres7_skp	att1_skp	att2_skp	att3_skp	attother_skp	autism1_skp	autism2_skp	billtax_skp	buyback_skp	cexp1_grid_skp	cexp2_grid_skp	cheat_skp	combat_skp	confecon_skp	conspire1_skp	conspire2_skp	conspire3_skp	contact1a_skp	contact1b_skp	contact2a_skp	contact2b_skp	contact3_skp	corrupt_skp	cvote2020_skp	dem_activduty_skp	demo4_skp	disable_grid_skp	diversity5_skp	diversity7_skp	dreamer_skp	econnow_skp	econpres5_skp	econpres7_skp	electable_skp	elite1_skp	elite2_skp	elite3_skp	elite4_skp	excessive_skp	exercise_skp	exp1_grid_skp	exp2_grid_skp	experts_skp	exphelp_skp	facebook1_skp	facebook2_skp	facebook3_skp	finworry_skp	follow_skp	freecol_skp	frnpres5_skp	frnpres7_skp	ftasian_skp	ftbiden_skp	ftblack_skp	ftbritain_skp	ftbuttigieg_skp	ftcanada_skp	ftchina_skp	ftfrance_skp	ftgermany_skp	ftharris_skp	fthisp_skp	ftice_skp	ftillegal_skp	ftimmig1_skp	ftimmig2_skp	ftiran_skp	ftisrael_skp	ftjapan_skp	ftjournal_skp	ftmexico_skp	ftmuslim_skp	ftnato_skp	ftnkorea_skp	ftnra_skp	ftobama_skp	ftpales_skp	ftrussia_skp	ftsanders_skp	ftsaudi_skp	fttrump_skp	ftturkey_skp	ftukraine_skp	ftun_skp	ftwarren_skp	ftwhite_skp	gmo1_skp	gmo2_skp	guarinc_skp	gw_grid_skp	harass1a_skp	harass1b_skp	health1a_skp	health1b_skp	hlthcare1_skp	hlthcare2_skp	hospital_skp	illegal_skp	immignum_skp	immpres5_skp	immpres7_skp	impeach1_skp	impeach2_skp	improve1_skp	instagram1_skp	instagram2_skp	instagram3_skp	interfere_skp	knowopioid1_skp	knowopioid2_skp	language_skp	lc_grid_skp	liveurban_skp	loans_skp	milyears_skp	milyr_skp	mip_skp	national1_skp	national2_skp	ngun_skp	opioiddo_skp	particip_skp	path_grid_skp	pid1d_skp	pid1r_skp	pidlean_skp	pidstr_skp	pk_cjus_skp	pk_geer_skp	pk_germ_skp	pk_sen_skp	pk_spend_skp	placeid1a_skp	placeid1b_skp	placeidimport_skp	pop_grid_skp	prek_skp	raceid_skp	racework_skp	reddit1_skp	reddit2_skp	reddit3_skp	refugees_skp	reg1a_skp	reg1b_skp	reinterivew_skp	relig1a_skp	relig1b_skp	relig2a_skp	relig2b_skp	relig3b_skp	rexp1_grid_skp	rexp2_grid_skp	richpoor2_skp	rr_grid_skp	rural1alt1_skp	rural1alt2_skp	rural2alt1_skp	rural2alt2_skp	rural3alt1_skp	rural4alt1_skp	rural4alt2_skp	science_skp	sentence_skp	serious_skp	shooting_skp	smoker1_skp	smoker2_skp	snapchat1_skp	snapchat2_skp	snapchat3_skp	socmed_skp	tall_skp	taxecon_skp	threat_grid_skp	tiktok1_skp	tiktok2_skp	tiktok3_skp	trade1_skp	trade2_skp	trade3_skp	trade4_skp	tsplit1_skp	turnout16a_skp	turnout16b_skp	turnout16c_skp	turnout18a_skp	twitter1_skp	twitter2_skp	twitter3_skp	ukraine1_skp	ukraine2_skp	unemp_skp	vote16_skp	vote20bs_skp	vote20cand2_skp	vote20cand_skp	vote20dem_skp	vote20ew_skp	vote20jb_skp	voterid1_skp	voterid2_skp	wall7_skp	wall5_skp	warm_skp	nweight_skp	whitejob_skp	youthurban_skp	youtube1_skp	youtube2_skp	youtube3_skp	rural3alt2_skp	follow_page_timing	reg1a_page_timing	reg1b_page_timing	liveurban_page_timing	youthurban_page_timing	placeid1a_page_timing	placeid1b_page_timing	placeidimport_page_timing	turnout16a_page_timing	turnout16b_page_timing	turnout16c_page_timing	vote16_page_timing	turnout18a_page_timing	particip_page_timing	fttrump_page_timing	ftobama_page_timing	ftbiden_page_timing	ftwarren_page_timing	ftsanders_page_timing	ftbuttigieg_page_timing	ftharris_page_timing	ftblack_page_timing	ftwhite_page_timing	fthisp_page_timing	ftasian_page_timing	ftmuslim_page_timing	ftillegal_page_timing	ftimmig1_page_timing	ftimmig2_page_timing	ftjournal_page_timing	ftnato_page_timing	ftun_page_timing	ftice_page_timing	ftnra_page_timing	ftchina_page_timing	ftnkorea_page_timing	ftmexico_page_timing	ftsaudi_page_timing	ftukraine_page_timing	ftiran_page_timing	ftbritain_page_timing	ftgermany_page_timing	ftjapan_page_timing	ftisrael_page_timing	ftfrance_page_timing	ftcanada_page_timing	ftturkey_page_timing	ftrussia_page_timing	ftpales_page_timing	vote20dem_page_timing	vote20cand_page_timing	vote20cand2_page_timing	electable_page_timing	vote20jb_page_timing	vote20ew_page_timing	vote20bs_page_timing	cvote2020_page_timing	tsplit1_page_timing	contact1a_page_timing	contact1b_page_timing	contact2a_page_timing	contact2b_page_timing	contact3_page_timing	apppres5_page_timing	frnpres5_page_timing	immpres5_page_timing	econpres5_page_timing	apppres7_page_timing	frnpres7_page_timing	immpres7_page_timing	econpres7_page_timing	mip_page_timing	econnow_page_timing	finworry_page_timing	confecon_page_timing	improve1_page_timing	national1_page_timing	national2_page_timing	conspire1_page_timing	conspire2_page_timing	conspire3_page_timing	taxecon_page_timing	billtax_page_timing	trade1_page_timing	trade2_page_timing	trade3_page_timing	trade4_page_timing	richpoor2_page_timing	guarinc_page_timing	lc_grid_page_timing	pop_grid_page_timing	corrupt_page_timing	immignum_page_timing	refugees_page_timing	dreamer_page_timing	wall_page_timing	wall7_page_timing	path_grid_page_timing	threat_grid_page_timing	hlthcare1_page_timing	hlthcare2_page_timing	abortion1_page_timing	abortion2_page_timing	freecol_page_timing	loans_page_timing	diversity5_page_timing	diversity7_page_timing	language_page_timing	buyback_page_timing	gw_grid_page_timing	knowopioid1_page_timing	knowopioid2_page_timing	opioiddo_page_timing	sentence_page_timing	prek_page_timing	demo4_page_timing	experts_page_timing	science_page_timing	exphelp_page_timing	elite1_page_timing	elite2_page_timing	elite3_page_timing	elite4_page_timing	ukraine1_page_timing	ukraine2_page_timing	excessive_page_timing	rural1alt1_page_timing	rural1alt2_page_timing	rural2alt1_page_timing	rural2alt2_page_timing	rural3alt1_page_timing	rural3alt2_page_timing	rural4alt1_page_timing	rural4alt2_page_timing	unemp_page_timing	interfere_page_timing	autism1_page_timing	autism2_page_timing	gmo1_page_timing	gmo2_page_timing	warm_page_timing	illegal_page_timing	impeach1_page_timing	impeach2_page_timing	pk2_intro_page_timing	pk_cjus_page_timing	pk_germ_page_timing	pk_sen_page_timing	pk_spend_page_timing	pk_geer_page_timing	cheat_page_timing	pid1d_page_timing	pid1r_page_timing	pidstr_page_timing	pidlean_page_timing	ngun_page_timing	shooting_page_timing	dem_activduty_page_timing	milyears_page_timing	milyr_page_timing	combat_page_timing	harass1a_page_timing	harass1b_page_timing	rr_grid_page_timing	health1a_page_timing	health1b_page_timing	hospital_page_timing	tall_page_timing	nweight_page_timing	disable_grid_page_timing	smoker1_page_timing	smoker2_page_timing	exercise_page_timing	relig1a_page_timing	relig2a_page_timing	att1_page_timing	att2_page_timing	att3_page_timing	relig1b_page_timing	relig2b_page_timing	relig3b_page_timing	attother_page_timing	exp1_grid_page_timing	exp2_grid_page_timing	rexp1_grid_page_timing	rexp2_grid_page_timing	cexp1_grid_page_timing	cexp2_grid_page_timing	socmed_page_timing	facebook1_page_timing	facebook2_page_timing	facebook3_page_timing	twitter1_page_timing	twitter2_page_timing	twitter3_page_timing	instagram1_page_timing	instagram2_page_timing	instagram3_page_timing	reddit1_page_timing	reddit2_page_timing	reddit3_page_timing	youtube1_page_timing	youtube2_page_timing	youtube3_page_timing	snapchat1_page_timing	snapchat2_page_timing	snapchat3_page_timing	tiktok1_page_timing	tiktok2_page_timing	tiktok3_page_timing	raceid_page_timing	racework_page_timing	whitejob_page_timing	voterid1_page_timing	voterid2_page_timing	serious_page_timing	photo1_page_timing	photo2_page_timing	photo3_page_timing	photo4_page_timing	reinterview_page_timing	ord_fttrump	ord_ftobama	ord_ftbiden	ord_ftwarren	ord_ftsanders	ord_ftbuttigieg	ord_ftharris	ord_ftblack	ord_ftwhite	ord_fthisp	ord_ftasian	ord_ftmuslim	ord_ftillegal	ord_ftimmig1	ord_ftimmig2	ord_ftjournal	ord_ftnato	ord_ftun	ord_ftice	ord_ftnra	ord_ftchina	ord_ftnkorea	ord_ftmexico	ord_ftsaudi	ord_ftukraine	ord_ftiran	ord_ftbritain	ord_ftgermany	ord_ftjapan	ord_ftisrael	ord_ftfrance	ord_ftcanada	ord_ftturkey	ord_ftrussia	ord_ftpales	ord_electable_1	ord_electable_2	ord_conspire1	ord_conspire2	ord_conspire3	ord_lcself	ord_lcd	ord_lcr	ord_pathway	ord_preturn	ord_popen	ord_release1	ord_release2	ord_famsep	ord_tchina	ord_trussia	ord_tiran	ord_tmexico	ord_tjapan	ord_gw1	ord_gw2	ord_elite1	ord_elite2	ord_elite3	ord_elite4	ord_disable1	ord_disable2	ord_disable3	ord_disable4	ord_disable5	ord_disable6	ord_exptravel_ever	ord_exphomesch	ord_expfarm	ord_expffood	ord_expconvert	ord_expholiday	ord_explie	ord_expshark	ord_expdivorce	ord_exparrest	ord_expoverdose	ord_expdefault	ord_exppubasst	ord_exphybrid	ord_expmistake	ord_explightning	ord_exptravel_year	ord_expindian	ord_exphunt	ord_expflag	ord_exppublib	ord_explottery	ord_expshoponline	ord_exppubtrans	ord_expfight	ord_expavoid	ord_expknowimmig	ord_expknowtrans	ord_expbuyusa	ord_expretire	ord_expcolldebt	ord_expknowpris	ord_lc_reverse	ord_att2_reverse	starttime	endtime	duration	pop_density_public	flag_state

To generate a dataframe without these duplicted rows, use the ~ operator to negate the logical values. That tells Python to keep the rows that are not duplicated:

anes.loc[~anes.duplicated(),]

	version	caseid	weight	weight_spss	form	follow	reg1a	reg1b	liveurban	youthurban	placeid1a	placeid1a_t	placeid1b	placeidimport	turnout16a	turnout16a1	turnout16b	turnout16c	vote16	turnout18a	turnout18a1	particip_1	particip_2	particip_3	particip_4	particip_5	particip_6	particip_7	particip_8	particip_9	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftimmig1	ftimmig2	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales	vote20dem	vote20cand	vote20cand2	electable	vote20jb	vote20ew	vote20bs	cvote2020	tsplit1	contact1a	contact1b	contact2a	contact2b	contact3	contact4m_1	contact4m_2	contact4m_3	apppres5	frnpres5	immpres5	econpres5	apppres7	frnpres7	immpres7	econpres7	mip	econnow	finworry	confecon	improve1	national1	national2	conspire1	conspire2	conspire3	taxecon	billtax	trade1	trade2	trade3	trade4	richpoor2	guarinc	lcself	lcd	lcr	pop1	pop2	pop3	corrupt	immignum	refugees	dreamstr	dreamer	dreamstr1	dreamstr2	wall5	wall7	pathway	preturn	popen	release1	release2	famsep	tchina	trussia	tiran	tmexico	tjapan	hlthcare1	hlthcare2	abortion1	abortion2	freecol	loans	diversity5	diversity7	language	buyback	gw1	gw2	knowopioid1	knowopioid2	opioiddo	sentence	prek	demo4	experts	science	exphelp	elite1	elite2	elite3	elite4	ukraine1	ukraine2	excessive	rural1alt1	rural1alt2	rural2alt1	rural2alt2	rural3alt1	rural3alt2	rural4alt1	rural4alt2	conf_unemp	unemp	conf_interfere	interfere	conf_autism	autism1	autism2	conf_gmo	gmo1	gmo2	conf_warm	warm	conf_illegal	illegal	impeach1	impeach2	pk_cjus	pk_cjus_correct	pk_germ	pk_germ_correct	pk_sen	pk_spend	pk_geer	cheat	pid7x	pid1d	pid2d	pid1r	pid2r	pidstr	pidlean	ngun	shooting	dem_activduty	milyears	milyr1	milyr2	milyr3	milyr4	milyr5	milyr6	combat	harass1a	harass1b	rr1	rr2	rr3	rr4	health1a	health1b	hospital	feet	inches	nweight	bmi	disable1	disable2	disable3	disable4	disable5	disable6	smoker1	smoker2	exercise	relig1a	relig1a_txt	relig2a	att1	att2	att3	relig1b	relig1b_t	relig2b	relig3b	relig3b_t	attother	exptravel_ever	exphomesch	expfarm	expffood	expconvert	expholiday	explie	expshark	expdivorce	exparrest	expoverdose	expdefault	exppubasst	exphybrid	expmistake	explightning	exptravel_year	expindian	exphunt	expflag	exppublib	explottery	expshoponline	exppubtrans	expfight	expavoid	expknowimmig	expknowtrans	expbuyusa	expretire	expcolldebt	expknowpris	socmed_1	socmed_2	socmed_3	socmed_4	socmed_5	socmed_6	socmed_7	socmed_8	socmed_9	socmed_t	facebook1	facebook2	facebook3	twitter1	twitter2	twitter3	instagram1	instagram2	instagram3	reddit1	reddit2	reddit3	youtube1	youtube2	youtube3	snapchat1	snapchat2	snapchat3	tiktok1	tiktok2	tiktok3	raceid	racework	whitejob	race_sub1	race_sub2	voterid1	voterid2	serious	photo1	photo2	photo3	photo4	reinterview	birthyr	gender	educ	marstat	child18	race	employ	employ_t	faminc_new	votereg	ideo5	pid7	newsint	presvote16post	presvote16post_t	pew_bornagain	pew_religimp	pew_churatd	pew_prayer	religpew	religpew_t	religpew_protestant	religpew_protestant_t	inputstate	zipCode	FIPCounty	region	EnrollmentDate	CompletedSurveys	qualityControl_overall_scale	tsmart_P2012	tsmart_G2012	tsmart_P2016	tsmart_G2016	tsmart_P2018	tsmart_G2018	abortion1_skp	abortion2_skp	apppres5_skp	apppres7_skp	att1_skp	att2_skp	att3_skp	attother_skp	autism1_skp	autism2_skp	billtax_skp	buyback_skp	cexp1_grid_skp	cexp2_grid_skp	cheat_skp	combat_skp	confecon_skp	conspire1_skp	conspire2_skp	conspire3_skp	contact1a_skp	contact1b_skp	contact2a_skp	contact2b_skp	contact3_skp	corrupt_skp	cvote2020_skp	dem_activduty_skp	demo4_skp	disable_grid_skp	diversity5_skp	diversity7_skp	dreamer_skp	econnow_skp	econpres5_skp	econpres7_skp	electable_skp	elite1_skp	elite2_skp	elite3_skp	elite4_skp	excessive_skp	exercise_skp	exp1_grid_skp	exp2_grid_skp	experts_skp	exphelp_skp	facebook1_skp	facebook2_skp	facebook3_skp	finworry_skp	follow_skp	freecol_skp	frnpres5_skp	frnpres7_skp	ftasian_skp	ftbiden_skp	ftblack_skp	ftbritain_skp	ftbuttigieg_skp	ftcanada_skp	ftchina_skp	ftfrance_skp	ftgermany_skp	ftharris_skp	fthisp_skp	ftice_skp	ftillegal_skp	ftimmig1_skp	ftimmig2_skp	ftiran_skp	ftisrael_skp	ftjapan_skp	ftjournal_skp	ftmexico_skp	ftmuslim_skp	ftnato_skp	ftnkorea_skp	ftnra_skp	ftobama_skp	ftpales_skp	ftrussia_skp	ftsanders_skp	ftsaudi_skp	fttrump_skp	ftturkey_skp	ftukraine_skp	ftun_skp	ftwarren_skp	ftwhite_skp	gmo1_skp	gmo2_skp	guarinc_skp	gw_grid_skp	harass1a_skp	harass1b_skp	health1a_skp	health1b_skp	hlthcare1_skp	hlthcare2_skp	hospital_skp	illegal_skp	immignum_skp	immpres5_skp	immpres7_skp	impeach1_skp	impeach2_skp	improve1_skp	instagram1_skp	instagram2_skp	instagram3_skp	interfere_skp	knowopioid1_skp	knowopioid2_skp	language_skp	lc_grid_skp	liveurban_skp	loans_skp	milyears_skp	milyr_skp	mip_skp	national1_skp	national2_skp	ngun_skp	opioiddo_skp	particip_skp	path_grid_skp	pid1d_skp	pid1r_skp	pidlean_skp	pidstr_skp	pk_cjus_skp	pk_geer_skp	pk_germ_skp	pk_sen_skp	pk_spend_skp	placeid1a_skp	placeid1b_skp	placeidimport_skp	pop_grid_skp	prek_skp	raceid_skp	racework_skp	reddit1_skp	reddit2_skp	reddit3_skp	refugees_skp	reg1a_skp	reg1b_skp	reinterivew_skp	relig1a_skp	relig1b_skp	relig2a_skp	relig2b_skp	relig3b_skp	rexp1_grid_skp	rexp2_grid_skp	richpoor2_skp	rr_grid_skp	rural1alt1_skp	rural1alt2_skp	rural2alt1_skp	rural2alt2_skp	rural3alt1_skp	rural4alt1_skp	rural4alt2_skp	science_skp	sentence_skp	serious_skp	shooting_skp	smoker1_skp	smoker2_skp	snapchat1_skp	snapchat2_skp	snapchat3_skp	socmed_skp	tall_skp	taxecon_skp	threat_grid_skp	tiktok1_skp	tiktok2_skp	tiktok3_skp	trade1_skp	trade2_skp	trade3_skp	trade4_skp	tsplit1_skp	turnout16a_skp	turnout16b_skp	turnout16c_skp	turnout18a_skp	twitter1_skp	twitter2_skp	twitter3_skp	ukraine1_skp	ukraine2_skp	unemp_skp	vote16_skp	vote20bs_skp	vote20cand2_skp	vote20cand_skp	vote20dem_skp	vote20ew_skp	vote20jb_skp	voterid1_skp	voterid2_skp	wall7_skp	wall5_skp	warm_skp	nweight_skp	whitejob_skp	youthurban_skp	youtube1_skp	youtube2_skp	youtube3_skp	rural3alt2_skp	follow_page_timing	reg1a_page_timing	reg1b_page_timing	liveurban_page_timing	youthurban_page_timing	placeid1a_page_timing	placeid1b_page_timing	placeidimport_page_timing	turnout16a_page_timing	turnout16b_page_timing	turnout16c_page_timing	vote16_page_timing	turnout18a_page_timing	particip_page_timing	fttrump_page_timing	ftobama_page_timing	ftbiden_page_timing	ftwarren_page_timing	ftsanders_page_timing	ftbuttigieg_page_timing	ftharris_page_timing	ftblack_page_timing	ftwhite_page_timing	fthisp_page_timing	ftasian_page_timing	ftmuslim_page_timing	ftillegal_page_timing	ftimmig1_page_timing	ftimmig2_page_timing	ftjournal_page_timing	ftnato_page_timing	ftun_page_timing	ftice_page_timing	ftnra_page_timing	ftchina_page_timing	ftnkorea_page_timing	ftmexico_page_timing	ftsaudi_page_timing	ftukraine_page_timing	ftiran_page_timing	ftbritain_page_timing	ftgermany_page_timing	ftjapan_page_timing	ftisrael_page_timing	ftfrance_page_timing	ftcanada_page_timing	ftturkey_page_timing	ftrussia_page_timing	ftpales_page_timing	vote20dem_page_timing	vote20cand_page_timing	vote20cand2_page_timing	electable_page_timing	vote20jb_page_timing	vote20ew_page_timing	vote20bs_page_timing	cvote2020_page_timing	tsplit1_page_timing	contact1a_page_timing	contact1b_page_timing	contact2a_page_timing	contact2b_page_timing	contact3_page_timing	apppres5_page_timing	frnpres5_page_timing	immpres5_page_timing	econpres5_page_timing	apppres7_page_timing	frnpres7_page_timing	immpres7_page_timing	econpres7_page_timing	mip_page_timing	econnow_page_timing	finworry_page_timing	confecon_page_timing	improve1_page_timing	national1_page_timing	national2_page_timing	conspire1_page_timing	conspire2_page_timing	conspire3_page_timing	taxecon_page_timing	billtax_page_timing	trade1_page_timing	trade2_page_timing	trade3_page_timing	trade4_page_timing	richpoor2_page_timing	guarinc_page_timing	lc_grid_page_timing	pop_grid_page_timing	corrupt_page_timing	immignum_page_timing	refugees_page_timing	dreamer_page_timing	wall_page_timing	wall7_page_timing	path_grid_page_timing	threat_grid_page_timing	hlthcare1_page_timing	hlthcare2_page_timing	abortion1_page_timing	abortion2_page_timing	freecol_page_timing	loans_page_timing	diversity5_page_timing	diversity7_page_timing	language_page_timing	buyback_page_timing	gw_grid_page_timing	knowopioid1_page_timing	knowopioid2_page_timing	opioiddo_page_timing	sentence_page_timing	prek_page_timing	demo4_page_timing	experts_page_timing	science_page_timing	exphelp_page_timing	elite1_page_timing	elite2_page_timing	elite3_page_timing	elite4_page_timing	ukraine1_page_timing	ukraine2_page_timing	excessive_page_timing	rural1alt1_page_timing	rural1alt2_page_timing	rural2alt1_page_timing	rural2alt2_page_timing	rural3alt1_page_timing	rural3alt2_page_timing	rural4alt1_page_timing	rural4alt2_page_timing	unemp_page_timing	interfere_page_timing	autism1_page_timing	autism2_page_timing	gmo1_page_timing	gmo2_page_timing	warm_page_timing	illegal_page_timing	impeach1_page_timing	impeach2_page_timing	pk2_intro_page_timing	pk_cjus_page_timing	pk_germ_page_timing	pk_sen_page_timing	pk_spend_page_timing	pk_geer_page_timing	cheat_page_timing	pid1d_page_timing	pid1r_page_timing	pidstr_page_timing	pidlean_page_timing	ngun_page_timing	shooting_page_timing	dem_activduty_page_timing	milyears_page_timing	milyr_page_timing	combat_page_timing	harass1a_page_timing	harass1b_page_timing	rr_grid_page_timing	health1a_page_timing	health1b_page_timing	hospital_page_timing	tall_page_timing	nweight_page_timing	disable_grid_page_timing	smoker1_page_timing	smoker2_page_timing	exercise_page_timing	relig1a_page_timing	relig2a_page_timing	att1_page_timing	att2_page_timing	att3_page_timing	relig1b_page_timing	relig2b_page_timing	relig3b_page_timing	attother_page_timing	exp1_grid_page_timing	exp2_grid_page_timing	rexp1_grid_page_timing	rexp2_grid_page_timing	cexp1_grid_page_timing	cexp2_grid_page_timing	socmed_page_timing	facebook1_page_timing	facebook2_page_timing	facebook3_page_timing	twitter1_page_timing	twitter2_page_timing	twitter3_page_timing	instagram1_page_timing	instagram2_page_timing	instagram3_page_timing	reddit1_page_timing	reddit2_page_timing	reddit3_page_timing	youtube1_page_timing	youtube2_page_timing	youtube3_page_timing	snapchat1_page_timing	snapchat2_page_timing	snapchat3_page_timing	tiktok1_page_timing	tiktok2_page_timing	tiktok3_page_timing	raceid_page_timing	racework_page_timing	whitejob_page_timing	voterid1_page_timing	voterid2_page_timing	serious_page_timing	photo1_page_timing	photo2_page_timing	photo3_page_timing	photo4_page_timing	reinterview_page_timing	ord_fttrump	ord_ftobama	ord_ftbiden	ord_ftwarren	ord_ftsanders	ord_ftbuttigieg	ord_ftharris	ord_ftblack	ord_ftwhite	ord_fthisp	ord_ftasian	ord_ftmuslim	ord_ftillegal	ord_ftimmig1	ord_ftimmig2	ord_ftjournal	ord_ftnato	ord_ftun	ord_ftice	ord_ftnra	ord_ftchina	ord_ftnkorea	ord_ftmexico	ord_ftsaudi	ord_ftukraine	ord_ftiran	ord_ftbritain	ord_ftgermany	ord_ftjapan	ord_ftisrael	ord_ftfrance	ord_ftcanada	ord_ftturkey	ord_ftrussia	ord_ftpales	ord_electable_1	ord_electable_2	ord_conspire1	ord_conspire2	ord_conspire3	ord_lcself	ord_lcd	ord_lcr	ord_pathway	ord_preturn	ord_popen	ord_release1	ord_release2	ord_famsep	ord_tchina	ord_trussia	ord_tiran	ord_tmexico	ord_tjapan	ord_gw1	ord_gw2	ord_elite1	ord_elite2	ord_elite3	ord_elite4	ord_disable1	ord_disable2	ord_disable3	ord_disable4	ord_disable5	ord_disable6	ord_exptravel_ever	ord_exphomesch	ord_expfarm	ord_expffood	ord_expconvert	ord_expholiday	ord_explie	ord_expshark	ord_expdivorce	ord_exparrest	ord_expoverdose	ord_expdefault	ord_exppubasst	ord_exphybrid	ord_expmistake	ord_explightning	ord_exptravel_year	ord_expindian	ord_exphunt	ord_expflag	ord_exppublib	ord_explottery	ord_expshoponline	ord_exppubtrans	ord_expfight	ord_expavoid	ord_expknowimmig	ord_expknowtrans	ord_expbuyusa	ord_expretire	ord_expcolldebt	ord_expknowpris	ord_lc_reverse	ord_att2_reverse	starttime	endtime	duration	pop_density_public	flag_state
0	ANES 2019 Pilot Study version 20200204	1	1.34719693063187	1.10160293017768	1	2	2	-1	3	4	1	__NA__	-1	1	1	-1	-1	-1	3	2	-1	2	2	2	2	2	2	2	2	1	47	90	52	52	49	997	50	99	99	99	100	88	79	97	-1	99	82	71	86	88	90	66	89	88	81	77	98	94	89	88	99	99	92	89	86	1	1	-1	1	2	3	4	1	2	2	-1	-1	-1	2	-1	-1	-1	-1	-1	-1	-1	3	5	6	2	Health Care	3	2	2	3	2	4	4	3	2	4	2	1	2	2	2	1	2	2	3	6	2	1	4	3	5	3	1	2	-1	1	-1	6	4	4	4	4	2	5	2	2	4	1	1	-1	-1	3	6	2	3	-1	2	1	-1	1	1	1	5	2	-1	1	2	4	3	4	3	4	1	3	4	1	-1	2	-1	6	-1	4	-1	4	-1	2	2	2	1	2	1	-1	1	2	-1	5	1	2	2	2	2	supreme court	0.5	chancellor of germany	1	6	1	1896	1	2	-1	__NA__	1	__NA__	2	-1	-1	-1	3	-1	-1	-1	-1	-1	-1	-1	-1	2	-1	2	5	3	2	-1	3	-1	5	7.0	167	26.1530407663177	-1	-1	-1	-1	-1	-1	-1	-1	-1	11	-3, restricted access	-1	1	4	-1	-1	-3, restricted access	-1	-1	-3, restricted access	2	1	2	1	2	1	1	1	2	-1	-1	-1	-1	-1	-1	-1	-1	2	2	2	1	1	-1	-1	-1	-1	-1	1	2	1	-1	-1	-1	1	2	2	2	1	1	2	2	2	__NA__	4	4	5	-1	-1	-1	-1	-1	-1	-1	-1	-1	6	4	5	4	5	5	-1	-1	-1	4	4	2	3	3	1	-1	4	-1	-1	-1	-1	1	1969	1	2	6	1	3	1	__NA__	9	1	4	2	3	1	__NA__	1	2	4	2	1	__NA__	3	-3, restricted access	48	-3	-3, restricted acces	3	952	8	6							0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	16.589	9.859	0.000	10.154	4.701	14.499	0.000	5.820	20.567	0.000	0.000	9.181	8.710	21.348	18.756	8.893	10.590	8.145	6.211	5.852	13.662	8.236	4.700	2.560	3.830	8.659	1.679	3.532	0.000	5.137	3.904	3.249	5.800	7.497	10.249	4.214	5.650	3.823	3.182	2.453	4.631	4.221	3.878	3.864	13.908	2.109	2.791	4.533	4.073	7.851	2.061	0.000	1.581	10.187	10.604	10.280	7.675	11.450	9.791	0.000	0.0	0.000	7.406	0.000	0.000	0.000	0.000	11.550	12.644	9.552	4.790	24.282	9.484	7.464	7.862	8.550	8.196	12.529	10.322	26.776	16.807	12.225	13.644	6.283	7.447	9.175	7.214	16.818	13.215	26.492	23.471	23.151	14.269	8.762	25.964	0.000	8.449	81.175	27.595	0.000	0.000	10.938	15.648	23.579	8.986	0.000	10.292	10.475	0.000	18.166	9.738	9.304	7.880	0.000	9.317	11.263	10.350	8.196	8.431	9.988	7.999	7.395	7.857	21.232	7.656	0.000	9.238	0.000	17.678	0.000	9.944	0.000	11.431	0.000	16.626	15.182	13.521	0.000	22.262	0.000	16.507	15.448	13.736	9.093	0.000	148.197	43.565	19.686	13.172	63.358	4.055	0.000	4.712	3.661	0.000	0.000	0.000	6.431	0.0	0.000	0.000	6.640	0.000	5.461	0.000	12.976	0.000	11.782	11.122	0.000	0.000	0.000	0.000	27.980	0.0	10.877	11.099	0.000	0.000	0.000	0.000	11.208	42.442	0.000	20.666	0.000	15.841	0.000	12.346	6.977	7.873	6.404	0.000	0.000	0.000	0.000	0.000	0.000	0.00	0.000	0.000	4.989	5.937	3.575	6.285	8.925	4.660	0.0	0.0	0.0	7.330	8.978	7.691	6.749	0.000	11.122	0.000	0.000	0.000	0.000	8.620	1	2	3	5	7	6	4	8	11	12	10	9		14	0	15	19	18	17	16	27	22	23	25	24	33	32	20	21	30	26	34	31	29	28	1	2	1	3	2	1	3	2	1	2	3	4	5	6	1	2	5	4	3	2	1	3	1	4	2							1	7	4	8	5	6	2	3									1	5	2	4	3						1	3	2				0	1	12/31/2019 18:57:33	12/31/2019 19:39:49	2536	1520	0
1	ANES 2019 Pilot Study version 20200204	2	.780822076219216	.638478211724453	1	1	1	-1	3	3	2	__NA__	-1	2	1	-1	-1	-1	1	1	-1	2	2	2	2	2	2	2	2	1	41	30	41	17	31	30	29	91	96	92	93	93	25	94	-1	67	86	78	91	93	44	19	93	25	82	22	89	91	94	71	66	100	20	25	77	2	-1	2	-1	1	1	1	2	3	2	-1	-1	-1	2	-1	-1	-1	-1	-1	-1	-1	5	3	2	1	Working together	1	2	2	3	2	4	2	5	3	2	5	2	2	2	2	4	6	6	1	7	2	2	4	3	2	3	1	2	-1	1	-1	2	2	2	2	4	4	4	5	4	3	1	1	-1	-1	3	1	5	6	-1	1	1	-1	1	1	2	-1	2	-1	3	2	4	4	4	3	3	1	2	1	1	-1	2	-1	5	-1	4	-1	4	-1	5	2	4	1	5	2	-1	2	1	-1	5	1	4	2	7	7	chief justice supremer court	1.0	germany chanceller	1	6	4	-7	2	6	2	__NA__	-1	__NA__	2	-1	-1	-1	3	-1	-1	-1	-1	-1	-1	-1	-1	3	-1	2	2	4	2	3	-1	-1	5	10.0	235	33.715306122449	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	-3, restricted access	-1	1	1	1	-1	-3, restricted access	-1	-1	-3, restricted access	1	1	2	2	2	2	1	1	2	-1	-1	-1	-1	-1	-1	-1	-1	1	1	1	1	1	-1	-1	-1	-1	-1	1	2	1	-1	-1	-1	1	2	2	2	1	2	2	2	2	__NA__	4	2	5	-1	-1	-1	-1	-1	-1	-1	-1	-1	6	5	5	-1	-1	-1	-1	-1	-1	2	4	2	1	1	1	-1	5	-1	-1	-1	-1	1	1942	1	6	1	2	1	5	__NA__	10	1	4	6	1	2	__NA__	2	1	2	2	2	__NA__	-7	-3, restricted access	1	-3	-3, restricted acces	3	1851	102	8	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	18.260	4.491	0.000	4.347	4.402	7.792	0.000	7.883	9.701	0.000	0.000	4.269	4.675	13.778	12.653	4.982	3.735	3.534	2.999	4.752	3.720	5.888	3.343	3.709	3.461	3.497	2.850	2.800	0.000	4.368	3.392	4.289	5.103	3.714	3.830	3.684	3.034	3.203	4.249	3.204	4.261	2.965	3.921	3.944	3.005	3.957	2.563	2.851	5.058	10.103	0.000	16.162	0.000	5.488	2.545	4.166	3.917	7.401	15.224	0.000	0.0	0.000	9.504	0.000	0.000	0.000	0.000	8.313	9.936	18.458	2.612	17.717	14.047	12.074	8.786	16.296	8.308	14.036	10.536	11.471	41.226	17.611	12.546	15.419	9.987	11.953	9.101	47.424	23.917	23.035	22.959	19.200	14.184	17.988	28.093	0.000	9.900	59.269	31.066	0.000	0.000	9.629	8.167	26.090	5.929	0.000	12.640	8.760	0.000	19.786	12.204	0.000	11.883	0.000	9.104	20.069	13.238	9.484	8.421	7.949	10.471	9.505	12.731	9.809	10.753	0.000	10.894	0.000	17.091	0.000	14.769	0.000	22.217	0.000	16.001	10.516	6.040	0.000	10.127	0.000	7.370	12.535	12.185	13.065	0.000	41.833	19.554	12.117	18.485	21.098	16.150	6.339	0.000	2.188	0.000	0.000	0.000	4.077	0.0	0.000	0.000	8.858	0.000	48.761	4.232	0.000	0.000	8.812	7.203	0.000	0.000	0.000	0.000	2.755	0.0	14.926	12.028	7.928	0.000	0.000	0.000	8.769	27.447	0.000	16.748	0.000	35.828	0.000	7.674	7.823	9.306	6.101	0.000	0.000	0.000	0.000	0.000	0.000	0.00	0.000	0.000	6.035	9.364	8.051	0.000	0.000	0.000	0.0	0.0	0.0	8.698	13.225	14.061	5.518	0.000	14.695	0.000	0.000	0.000	0.000	7.927	1	2	3	6	5	4	7	8	9	10	12	11		14	0	15	17	16	18	19	21	27	24	28	22	32	34	23	20	31	30	26	33	29	25			2	3	1	1	3	2	3	2	1	4	5	6	1	3	2	5	4	1	2	3	1	2	4							8	5	7	4	3	1	6	2									5	2	1	4	3						1	2	3				0	1	12/21/2019 4:19:56	12/21/2019 4:53:19	2003	1800	0
2	ANES 2019 Pilot Study version 20200204	3	.966366930694957	.790198239229266	1	1	1	-1	1	4	1	__NA__	-1	4	1	-1	-1	-1	2	1	-1	1	2	2	2	2	1	2	1	2	0	91	88	15	60	70	68	48	49	49	49	50	39	69	-1	63	66	51	40	2	2	2	1	3	59	1	50	1	1	1	51	87	50	1	3	1	1	-1	1	2	3	2	1	2	2	-1	-1	-1	2	-1	-1	-1	-1	-1	-1	-1	7	7	7	7	health care	4	5	5	7	1	1	5	4	1	7	1	1	3	3	1	1	4	4	4	7	2	2	5	5	4	4	2	2	-1	2	-1	7	3	1	5	5	5	5	5	5	5	5	5	-1	-1	3	7	6	7	-1	1	5	-1	1	1	2	-1	1	-1	1	6	3	3	5	5	1	1	5	5	1	-1	2	-1	4	-1	4	-1	4	-1	2	2	5	1	1	2	-1	3	1	-1	5	1	5	2	1	1	don't know	0.0	don't know	0	4	4	1960	2	1	1	__NA__	-1	__NA__	1	-1	-1	-1	3	-1	-1	-1	-1	-1	-1	-1	-1	3	-1	1	5	4	1	2	-1	-1	5	4.0	160	27.4609375	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	-3, restricted access	-1	1	1	1	-1	-3, restricted access	-1	-1	-3, restricted access	1	2	2	2	2	2	1	1	2	-1	-1	-1	-1	-1	-1	-1	-1	2	2	2	2	1	-1	-1	-1	-1	-1	1	2	1	-1	-1	-1	1	2	2	2	2	2	2	2	2	__NA__	1	1	5	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	3	5	3	1	1	1	-1	5	-1	-1	-1	-1	1	1954	2	2	1	2	1	1	__NA__	8	1	3	1	2	1	__NA__	2	1	2	2	2	__NA__	-7	-3, restricted access	37	-3	-3, restricted acces	3	1576	249	9							0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	10.393	4.406	0.000	5.095	5.183	9.068	0.000	7.562	20.654	0.000	0.000	3.642	8.004	52.884	14.342	8.833	5.336	6.344	9.386	7.735	7.817	4.727	4.530	6.723	4.873	6.401	11.542	7.602	0.000	5.969	6.955	10.404	20.956	9.733	4.223	5.717	5.417	6.487	9.220	3.874	13.394	4.204	8.374	4.453	5.695	4.635	4.992	6.135	5.758	5.716	6.970	0.000	33.173	7.789	12.756	9.381	10.139	5.447	19.189	0.000	0.0	0.000	10.866	0.000	0.000	0.000	0.000	8.407	5.273	4.474	4.124	19.337	17.748	6.833	5.940	30.722	20.596	13.606	12.951	9.781	37.086	11.142	11.334	15.668	17.238	25.370	13.978	10.143	72.549	15.953	34.345	15.078	21.909	18.511	29.479	0.000	8.484	88.518	19.246	0.000	0.000	28.604	9.095	26.145	11.639	0.000	24.933	12.824	0.000	22.079	24.131	0.000	7.846	0.000	15.807	24.690	36.809	17.871	14.538	9.213	21.222	6.331	6.392	7.599	8.802	0.000	26.009	0.000	11.535	0.000	9.976	0.000	12.721	0.000	28.735	8.838	12.885	0.000	15.169	0.000	11.453	17.480	12.401	8.518	0.000	24.951	14.769	21.653	19.182	19.827	6.654	0.000	0.000	0.000	0.000	0.000	0.000	7.439	0.0	0.000	0.000	12.501	0.000	47.529	7.273	0.000	0.000	5.697	5.141	0.000	0.000	0.000	0.000	3.550	0.0	9.917	3.977	6.014	0.000	0.000	0.000	4.070	30.007	0.000	18.690	0.000	20.602	0.000	4.427	4.098	7.478	5.348	0.000	0.000	0.000	0.000	0.000	0.000	0.00	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.0	0.0	0.0	20.608	39.129	16.251	7.152	0.000	10.665	0.000	0.000	0.000	0.000	9.366	1	2	4	6	5	3	7	10	12	8	9	11		14	0	15	17	16	19	18	27	30	23	22	25	32	20	34	29	26	21	24	33	31	28	1	2	2	3	1	1	2	3	2	3	1	4	5	6	4	5	1	3	2	1	2	2	1	4	3							4	5	6	3	2	8	7	1									2	5	1	3	4						3	2	1				0	0	12/22/2019 23:03:28	12/22/2019 23:41:43	2295	70	0
3	ANES 2019 Pilot Study version 20200204	4	1.10348514780374	.902319805359118	2	1	-1	1	4	4	-1	__NA__	1	2	-1	-1	-1	1	2	1	-1	2	2	2	2	2	2	2	2	1	100	50	0	0	0	0	0	0	0	0	0	0	0	-1	100	25	75	75	75	100	25	50	15	50	50	15	85	10	85	50	75	50	75	25	0	3	-1	-1	-1	1	1	1	5	4	-1	2	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	1	1	1	1	The economy.	4	1	2	3	2	3	3	1	2	3	4	1	2	3	2	3	4	4	4	4	3	3	3	3	3	1	1	2	-1	1	-1	1	1	1	2	1	5	3	-1	-1	-1	-1	-1	3	1	-1	-1	3	2	-1	4	3	2	3	4	2	-1	4	1	2	4	3	5	2	2	2	2	3	3	1	5	-1	4	-1	4	-1	4	-1	4	2	1	1	2	1	-1	1	3	-1	1	1	1	1	2	4	4	senator	0.0	congress	0	2	4	2018	2	1	1	__NA__	-1	__NA__	1	-1	0	2	3	-1	-1	-1	-1	-1	-1	-1	-1	-1	3	2	4	5	3	-1	5	1	5	7.0	130	20.3586544887503	2	2	2	2	2	1	1	2	1	-1	-3, restricted access	-1	2	-1	-1	-1	-3, restricted access	1	4	-3, restricted access	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	1	2	2	2	2	1	2	-1	-1	-1	-1	-1	2	1	1	2	2	-1	-1	-1	2	2	2	1	1	1	2	1	1	2	2	2	__NA__	5	4	5	6	4	5	7	5	5	-1	-1	-1	6	5	5	7	5	5	-1	-1	-1	3	3	2	2	2	-1	1	5	3	2	1	1	1	1979	1	3	5	2	2	4	__NA__	1	1	3	1	2	1	__NA__	2	1	6	6	1	__NA__	1	-3, restricted access	34	-3	-3, restricted acces	1	449	316	9							0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	7.355	0.000	3.015	5.634	4.752	0.000	6.117	6.117	0.000	0.000	25.453	3.205	8.434	19.452	6.123	4.024	7.260	4.035	8.174	4.913	2.623	2.955	5.917	3.436	5.724	5.769	7.385	0.000	8.174	17.756	3.771	0.898	15.432	4.706	8.266	5.902	0.105	8.623	0.298	0.344	8.744	8.606	0.306	0.233	0.118	3.571	0.185	0.139	0.137	19.703	0.000	0.000	0.000	9.573	11.093	13.510	14.088	17.716	0.000	10.595	0.0	0.000	0.000	0.000	0.000	0.000	0.000	5.296	2.995	2.697	3.050	9.228	9.225	5.702	4.036	7.313	7.280	11.251	9.825	10.956	11.436	10.477	11.462	8.520	8.410	8.445	7.809	8.006	27.961	20.960	17.817	12.307	9.984	10.863	13.238	0.000	6.591	53.585	0.000	9.050	5.992	0.000	0.000	13.343	12.444	0.000	16.436	5.801	13.328	15.890	9.343	0.000	7.042	20.790	13.362	9.483	5.976	6.921	12.258	3.463	5.331	9.523	7.178	9.529	10.555	6.688	0.000	7.251	0.000	8.888	0.000	7.974	0.000	5.905	13.807	12.798	0.000	13.145	0.000	6.747	8.651	10.521	14.677	12.310	10.861	9.252	19.042	8.109	7.307	22.516	4.401	2.958	0.000	2.324	0.000	4.068	7.710	10.894	0.0	0.000	0.000	0.000	6.439	24.644	0.000	5.466	11.519	7.414	5.397	13.889	2.892	3.793	3.624	0.000	0.0	12.653	0.000	0.000	0.000	5.653	9.921	0.000	0.000	30.724	0.000	15.845	0.000	16.906	8.749	6.571	5.427	2.759	6.433	5.193	3.908	3.845	9.878	3.745	0.00	0.000	0.000	5.210	5.191	3.118	3.128	2.727	1.686	0.0	0.0	0.0	3.603	6.559	5.987	0.000	8.355	11.791	7.147	5.023	5.635	3.662	5.399	1	2	6	7	4	3	5	11	9	8	10	12		0	14	15	18	19	16	17	31	30	22	32	28	26	34	29	25	27	20	33	24	21	23			1	3	2	1	2	3	1	3	2	4	5	6						2	1	2	4	1	3	2	5	1	4	6	3									5	6	7	4	3	2	1	8						1	5	4	3	2				2	3	1	0	0	12/31/2019 19:53:14	12/31/2019 20:23:11	1797	7600	0
4	ANES 2019 Pilot Study version 20200204	5	1.09069730256741	.891863184309371	2	1	-1	1	4	2	-1	__NA__	1	2	-1	-1	-1	1	1	1	-1	2	1	2	1	2	1	1	1	2	94	18	25	1	10	16	7	93	94	94	94	50	9	-1	92	20	68	16	94	84	23	29	50	51	49	5	73	44	72	97	58	96	60	45	45	2	-1	2	-1	1	1	1	2	3	-1	1	-1	3	-1	-1	-1	-1	-1	-1	-1	-1	1	1	1	1	China	1	1	1	3	1	3	3	3	4	1	6	1	2	2	2	5	7	6	1	6	1	1	5	4	4	3	2	2	-1	2	-1	1	4	2	5	3	2	3	-1	-1	-1	-1	-1	7	1	-1	-1	7	7	-1	2	1	7	3	3	2	-1	2	3	6	3	1	2	3	1	1	1	1	2	1	3	-1	6	-1	4	-1	4	-1	6	5	2	3	1	4	-1	2	2	-1	2	2	1	3	1	7	7	supreme court justice	0.5	german government	1	6	3	1945	2	5	3	__NA__	-1	__NA__	-1	1	0	2	3	-1	-1	-1	-1	-1	-1	-1	-1	-1	3	2	2	5	3	2	-1	1	5	4.0	115	19.737548828125	1	2	2	2	2	2	1	3	1	-1	-3, restricted access	-1	1	2	-1	1	-3, restricted access	-1	-1	-3, restricted access	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	2	2	2	2	2	1	2	-1	-1	-1	-1	-1	2	2	2	2	1	-1	-1	-1	1	2	2	1	2	2	2	1	2	2	2	2	__NA__	2	4	4	-1	-1	-1	-1	-1	-1	-1	-1	-1	6	4	5	-1	-1	-1	-1	-1	-1	2	1	2	1	1	-1	1	5	1	1	1	1	1	1940	2	6	4	2	1	2	__NA__	4	1	4	5	1	2	__NA__	1	2	2	2	1	__NA__	8	-3, restricted access	48	-3	-3, restricted acces	3	1458	114	7	1	1					0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	6.650	0.000	5.215	8.832	7.189	0.000	14.528	18.620	0.000	0.000	15.916	9.759	9.539	36.795	23.543	16.482	12.024	7.980	13.030	15.878	5.829	8.313	27.213	9.984	7.684	20.659	10.477	0.000	7.723	6.285	11.818	7.484	9.420	13.706	8.573	9.659	6.841	9.907	8.938	7.456	6.822	9.949	10.211	7.729	11.479	16.550	25.597	10.016	8.424	7.267	0.000	6.899	0.000	7.568	5.653	6.144	7.263	13.493	0.000	18.915	0.0	5.855	0.000	0.000	0.000	0.000	0.000	7.106	7.472	5.876	4.771	12.012	7.335	5.810	4.144	25.072	5.327	12.318	11.809	15.418	12.966	11.500	13.556	10.114	14.232	6.407	9.205	13.196	9.442	23.755	19.737	7.286	27.185	20.099	25.152	0.000	14.288	60.303	0.000	9.405	8.757	0.000	0.000	16.253	12.081	0.000	19.039	15.935	22.264	36.410	8.487	0.000	27.085	18.716	22.277	22.396	9.082	18.881	10.559	11.392	8.088	6.894	11.185	16.819	11.196	9.384	0.000	9.897	0.000	8.123	0.000	7.764	0.000	13.198	9.493	13.591	0.000	9.258	0.000	12.838	18.180	11.142	12.765	18.178	7.661	24.172	14.766	7.403	31.376	10.408	5.901	6.121	0.000	0.000	5.075	8.438	12.465	8.864	0.0	0.000	0.000	0.000	8.179	50.590	4.953	0.000	9.559	10.113	6.883	31.148	4.872	11.640	10.414	0.000	0.0	10.178	7.629	0.000	5.266	0.000	0.000	0.000	0.000	23.919	0.000	27.860	0.000	11.717	9.191	7.747	12.386	7.801	0.000	0.000	0.000	0.000	0.000	0.000	0.00	0.000	0.000	8.335	7.852	6.884	0.000	0.000	0.000	0.0	0.0	0.0	18.744	17.437	10.886	0.000	10.790	10.326	28.427	13.455	10.683	7.614	6.967	1	2	4	5	6	3	7	12	11	9	10	8		0	14	15	16	17	18	19	26	34	29	31	20	27	30	22	33	32	28	24	25	23	21			1	2	3	1	3	2	1	3	2	4	5	6						1	2	1	3	4	2	6	3	2	5	1	4									6	2	1	8	5	4	3	7						3	2	4	1	5				2	1	3	0	1	12/21/2019 4:07:09	12/21/2019 4:48:50	2501	4430	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3160	ANES 2019 Pilot Study version 20200204	3161			2	1	-1	1	2	2	-1	__NA__	3	5	-1	-1	1	-1	1	1	-1	2	2	2	2	1	2	2	1	2	81	20	7	4	6	1	2	94	94	94	95	21	31	-1	85	11	30	16	95	91	26	10	22	20	30	4	70	19	90	60	61	60	10	25	10	3	-1	-1	-1	1	1	1	2	3	-1	2	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	2	5	2	The infiltration of Marxists into the institut...	2	2	2	6	2	2	4	2	3	2	7	2	5	1	4	5	7	6	1	4	3	2	4	4	6	5	2	1	2	-1	-1	2	5	1	5	1	1	2	-1	-1	-1	-1	-1	7	3	-1	-1	7	7	-1	6	1	7	5	5	2	-1	3	4	7	4	2	2	2	4	4	1	2	3	2	3	-1	4	-1	4	-1	7	-1	7	4	2	4	1	2	-1	2	3	-1	1	3	1	3	2	7	7	chief justice of the supreme court of the unit...	1.0	germany's head of state (chancellor?)	1	6	1	1998	2	5	3	__NA__	-1	__NA__	-1	1	20	1	2	-1	2	2	1	1	2	2	1	-1	2	2	4	5	2	-1	2	2	6	0.0	248	33.6312	1	2	2	2	2	2	2	-1	3	-1	-3, restricted access	-1	1	2	-1	1	-3, restricted access	-1	-1	-3, restricted access	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	2	1	2	2	2	1	2	-1	-1	-1	-1	-1	1	1	2	2	2	-1	-1	-1	1	2	2	2	2	2	2	1	2	2	2	2	__NA__	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	3	4	5	-1	-1	-1	-1	-1	-1	2	5	5	1	1	-1	2	5	-1	-1	-1	-1	1	1948	1	4	5	2	1	5	__NA__	11	1	4	5	1	2	__NA__	1	1	2	1	1	__NA__	3	-3, restricted access	41	-3	-3, restricted acces	4	2012	376	7	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	12.658	0.000	5.778	5.783	6.036	0.000	12.642	5.884	0.000	8.795	0.000	4.466	13.156	28.332	14.782	7.416	11.474	4.640	4.339	5.535	7.655	6.589	4.458	5.938	7.065	6.149	7.974	0.000	6.836	5.705	4.740	5.174	7.268	5.181	4.495	8.080	4.128	4.693	4.255	3.599	3.672	8.122	4.087	4.636	4.553	4.947	4.711	4.298	7.147	10.580	0.000	0.000	0.000	8.537	5.442	4.295	6.421	16.741	0.000	27.493	0.0	0.000	0.000	0.000	0.000	0.000	0.000	11.004	8.919	10.577	6.670	181.083	9.471	10.258	5.928	12.634	6.665	15.102	8.540	19.486	19.554	11.058	12.564	12.333	13.571	12.483	23.473	14.276	14.281	24.805	20.130	7.045	11.666	15.988	34.446	0.000	22.897	104.110	0.000	12.347	6.914	0.000	0.000	9.177	11.000	0.000	9.985	9.245	13.400	67.957	14.667	0.000	15.250	15.053	13.159	12.076	10.769	9.022	15.833	5.796	9.754	6.787	16.760	59.903	9.880	6.719	0.000	23.071	0.000	13.098	0.000	14.039	0.000	14.180	18.466	15.172	0.000	16.742	0.000	13.711	12.713	17.519	13.952	15.393	31.516	34.917	32.466	5.650	8.289	12.633	6.331	0.000	0.000	0.000	0.000	12.628	9.736	8.337	0.0	24.381	7.364	0.000	7.137	50.250	0.000	14.375	9.096	8.322	5.567	20.771	3.527	0.000	9.397	0.000	0.0	12.456	5.092	0.000	2.991	0.000	0.000	0.000	0.000	28.867	0.000	46.919	0.000	21.846	12.689	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.00	0.000	0.000	5.233	11.940	4.548	0.000	0.000	0.000	0.0	0.0	0.0	7.990	8.240	10.123	0.000	10.628	13.336	0.000	0.000	0.000	0.000	7.247	1	2	3	6	7	5	4	9	10	11	8	12		0	14	15	18	17	19	16	20	22	34	21	24	27	32	29	26	25	33	31	30	28	23			3	2	1	1	2	3	3	2	1	4	5	6						2	1	2	1	3	4	4	3	2	6	1	5									2	7	4	5	3	6	8	1						1	5	3	2	4				2	3	1	0	0	12/31/2019 19:38:13	12/31/2019 20:24:56	2803
3161	ANES 2019 Pilot Study version 20200204	3162	7.03646496881757	5.75371740500213	2	1	-1	2	4	3	-1	__NA__	2	2	-1	-1	1	-1	3	2	-1	2	2	2	1	1	2	2	1	2	35	41	25	61	92	3	1	45	56	53	69	11	3	-1	50	52	40	55	50	7	20	6	26	1	55	2	60	60	56	50	66	80	50	31	50	1	6	-1	1	3	2	2	5	4	-1	2	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	6	7	2	5	Lack of basic resources being provided and off...	3	5	5	7	4	4	5	3	3	6	1	3	4	2	3	1	1	4	1	7	2	1	5	5	5	5	2	2	-1	2	-1	4	3	2	5	3	5	3	-1	-1	-1	-1	-1	1	5	-1	-1	3	2	-1	5	3	5	1	3	1	2	2	4	3	7	3	3	4	5	5	1	5	5	1	4	-1	4	-1	5	-1	3	-1	4	3	2	3	2	5	-1	2	4	-1	1	5	1	2	2	4	4	no clue	0.0	german president	1	6	1	-7	2	3	3	__NA__	-1	__NA__	-1	2	0	3	3	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	2	3	5	3	-1	3	1	5	10.5	189	26.7325	2	2	1	2	2	1	2	-1	3	-1	-3, restricted access	-1	2	-1	-1	-1	-3, restricted access	2	-1	-3, restricted access	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	1	1	2	1	2	1	2	-1	-1	-1	-1	-1	2	2	2	1	1	-1	-1	-1	2	2	2	2	2	2	1	1	2	2	2	2	__NA__	-1	-1	-1	-1	-1	-1	-1	-1	-1	4	3	4	1	1	2	-1	-1	-1	-1	-1	-1	1	2	3	1	1	-1	3	5	-1	-1	-1	-1	1	1996	1	2	5	2	1	6	__NA__	2	1	6	3	1	7	__NA__	2	4	6	7	9	__NA__	-7	-3, restricted access	45	-3	-3, restricted acces	3	185	43	8							0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	9.206	0.000	7.676	10.548	5.611	0.000	8.851	7.265	0.000	6.931	0.000	2.264	13.780	18.855	18.700	5.598	14.185	8.829	15.345	4.423	4.170	10.773	9.436	8.375	8.106	4.082	9.835	0.000	4.260	3.779	5.621	7.558	4.840	3.928	5.125	7.228	5.117	7.930	3.783	4.570	4.545	7.165	8.701	5.656	5.030	6.814	3.597	4.896	5.006	6.276	7.231	0.000	8.906	8.610	7.301	4.463	9.899	15.497	0.000	11.230	0.0	0.000	0.000	0.000	0.000	0.000	0.000	8.513	6.346	4.092	7.925	136.102	8.319	6.427	3.097	6.488	5.003	7.355	9.124	5.540	7.153	8.038	13.826	9.944	5.596	4.491	23.326	8.556	29.241	17.261	15.479	7.747	7.028	8.460	8.868	0.000	4.358	76.265	0.000	14.162	9.167	0.000	0.000	18.278	6.011	0.000	11.778	13.489	13.199	14.620	4.936	9.199	4.298	24.503	3.680	12.700	10.252	5.263	6.803	2.824	3.600	5.110	4.489	6.302	4.786	3.727	0.000	6.428	0.000	10.008	0.000	10.775	0.000	5.677	13.644	5.738	0.000	7.512	0.000	15.623	10.582	12.633	5.995	13.181	6.796	15.602	80.627	2.802	74.741	2.986	8.375	0.000	0.000	0.000	0.000	3.676	5.726	7.888	0.0	0.000	0.000	0.000	6.361	39.550	0.000	5.036	4.929	7.875	7.979	24.003	2.484	0.000	3.940	0.000	0.0	8.726	0.000	0.000	0.000	3.422	0.000	0.000	0.000	23.377	0.000	12.742	0.000	7.413	15.292	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	4.87	5.851	6.339	2.933	3.589	3.480	0.000	0.000	0.000	0.0	0.0	0.0	5.320	6.632	4.129	0.000	8.689	14.486	0.000	0.000	0.000	0.000	6.529	1	2	4	3	5	6	7	11	8	10	12	9		0	14	15	18	17	16	19	28	24	27	30	33	31	20	25	23	22	32	29	34	26	21	2	1	1	3	2	1	3	2	2	3	1	4	5	6						1	2	2	3	4	1	5	4	2	1	3	6									1	8	3	6	7	4	5	2						2	5	3	4	1				2	3	1	0	1	12/31/2019 20:14:34	12/31/2019 20:53:50	2356	1800	0
3162	ANES 2019 Pilot Study version 20200204	3163	.892833236147303	.73006973719765	2	3	-1	3	4	1	-1	__NA__	4	3	-1	-1	2	-1	-1	2	-1	2	2	2	2	2	2	2	2	1	6	31	50	2	59	0	31	88	100	100	100	60	100	-1	100	51	3	60	94	41	100	60	100	78	40	59	100	72	99	99	99	100	51	41	87	3	-1	-1	-1	4	4	4	4	4	-1	2	-1	-1	-1	-1	-1	-1	5	5	5	5	-1	-1	-1	-1	donald trump	2	5	4	6	1	5	5	1	1	1	7	5	4	2	2	1	7	7	4	4	5	5	5	1	7	7	1	2	-1	1	5	-1	5	5	5	5	1	5	-1	-1	-1	-1	-1	1	5	-1	-1	7	7	1	-1	1	7	5	5	2	-1	1	1	1	4	5	5	5	1	5	1	5	5	1	2	-1	1	-1	1	-1	1	-1	1	1	2	3	1	5	-1	2	5	-1	2	1	2	3	1	7	7	no	0.0	chancellor of europe	1	2	1	1444	2	4	-1	__NA__	-1	__NA__	-1	3	0	3	3	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	1	1	3	3	3	-1	1	7	5.0	175	15.5315	2	2	1	2	1	1	2	-1	2	-1	-3, restricted access	-1	1	2	-1	3	-3, restricted access	-1	-1	-3, restricted access	-1	-1	-1	-1	-1	-1	-1	-1	-1	1	1	2	1	1	2	1	2	-1	-1	-1	-1	-1	2	2	1	2	2	-1	-1	-1	2	1	2	1	1	1	2	2	2	2	2	2	__NA__	1	1	5	7	5	5	7	5	5	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	5	5	3	3	3	-1	2	5	-1	-1	-1	-1	1	1980	1	4	1	2	3	2	__NA__	4	2	2	4	7	7	__NA__	2	1	1	1	1	__NA__	3	-3, restricted access	47	-3	-3, restricted acces	3	179	156	9							0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	6.171	0.000	4.744	5.487	9.117	0.000	11.729	17.977	0.000	4.232	0.000	0.000	4.955	4.118	13.230	7.743	6.538	5.253	6.740	7.512	3.692	7.074	8.041	6.904	8.615	7.851	5.253	0.000	4.181	3.884	4.136	4.484	6.832	5.209	3.727	9.289	5.675	4.046	4.884	5.471	4.010	6.924	4.154	4.677	4.877	4.444	5.091	3.690	4.879	3.157	0.000	0.000	0.000	3.565	2.815	2.423	3.292	3.182	0.000	3.111	0.0	0.000	0.000	4.075	3.729	3.661	2.817	0.000	0.000	0.000	0.000	14.868	4.227	3.807	4.957	4.420	3.858	12.512	3.739	5.764	7.959	7.423	4.858	4.506	4.239	6.822	3.527	3.324	4.559	8.038	6.835	3.517	13.014	6.245	13.085	5.254	0.000	14.771	0.000	3.734	2.392	0.000	0.000	2.039	4.682	7.654	0.000	12.898	7.020	5.208	4.205	0.000	5.766	4.442	8.525	5.126	3.792	3.755	3.969	4.736	5.764	4.824	9.553	6.415	4.100	7.151	0.000	3.328	0.000	3.266	0.000	7.566	0.000	3.913	10.925	10.081	0.000	8.310	0.000	9.105	6.381	7.238	4.501	2.297	1.720	10.167	22.402	5.258	11.603	9.610	3.496	0.000	0.000	0.000	0.000	9.293	5.065	4.192	0.0	0.000	0.000	0.000	10.153	23.828	3.930	0.000	4.101	13.219	6.197	15.415	4.411	0.000	6.875	0.000	0.0	6.199	5.176	0.000	4.704	0.000	0.000	0.000	0.000	25.929	0.000	12.167	0.000	10.375	4.302	3.099	2.531	3.725	3.716	4.002	2.135	3.862	3.258	1.885	0.00	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.0	0.0	0.0	4.102	5.759	11.928	0.000	5.281	15.987	0.000	0.000	0.000	0.000	5.334	1	2	6	4	5	3	7	10	12	8	9	11		0	14	15	16	19	17	18	26	23	28	27	31	34	30	24	25	20	21	32	29	33	22			1	2	3	1	3	2	2	1	3	4	5	6						2	1	2	3	4	1	4	6	3	5	1	2									7	3	4	5	1	8	2	6						3	2	5	4	1				3	1	2	0	1	12/31/2019 20:10:04	12/31/2019 20:29:15	1151	200	0
3163	ANES 2019 Pilot Study version 20200204	3164	1.58161278448241	1.29328477387127	2	1	-1	3	3	4	-1	__NA__	2	4	-1	-1	2	-1	-1	2	-1	2	2	2	2	2	2	2	2	1	1	100	95	62	79	59	51	100	65	50	52	50	51	-1	49	91	84	72	59	0	54	0	62	0	60	0	73	55	50	1	56	89	0	0	1	1	1	-1	2	2	3	2	1	2	-1	2	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	7	7	7	4	Donald Trump	3	4	3	5	1	3	4	5	5	6	2	1	4	3	4	3	2	2	2	4	1	1	4	5	4	3	2	2	-1	2	-1	7	2	3	2	4	2	4	-1	-1	-1	-1	-1	3	5	-1	-1	4	5	-1	4	5	1	1	3	2	-1	1	5	2	1	4	5	4	5	5	2	5	5	1	3	-1	3	-1	5	-1	5	-1	5	2	2	5	1	5	-1	2	5	-1	2	5	1	5	2	1	1	republican party	0.0	not sure	0	4	4	1970	2	2	-1	__NA__	1	__NA__	2	-1	0	3	3	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	1	3	3	2	3	-1	1	5	11.0	190	26.4967	2	2	2	2	2	2	1	1	3	-1	-3, restricted access	-1	2	-1	-1	-1	-3, restricted access	2	-1	-3, restricted access	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	1	2	2	2	2	1	2	-1	-1	-1	-1	-1	1	2	2	2	2	-1	-1	-1	2	2	2	2	2	2	2	1	2	2	2	2	__NA__	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	5	4	5	-1	-1	-1	-1	-1	-1	5	5	1	2	2	-1	2	5	-1	-1	-1	-1	1	1960	1	1	1	2	2	4	__NA__	1	2	2	2	1	7	__NA__	2	2	6	7	11	__NA__	-7	-3, restricted access	6	-3	-3, restricted acces	4	1072	1117	9							0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	22.776	0.000	5.979	7.954	19.452	0.000	12.340	10.267	0.000	23.684	0.000	0.000	15.141	17.413	13.859	10.821	4.535	7.973	4.811	7.034	4.996	4.527	3.683	4.917	9.405	5.766	6.478	0.000	8.614	5.032	5.439	4.327	8.170	4.455	3.603	5.639	3.643	10.550	4.175	8.272	5.764	3.426	4.688	4.580	5.099	4.287	4.478	4.883	4.066	16.823	19.913	0.000	34.058	9.328	9.508	3.291	8.991	44.269	0.000	21.831	0.0	0.000	0.000	0.000	0.000	0.000	0.000	11.710	8.499	7.077	6.332	51.329	14.182	13.553	8.190	17.121	10.773	18.920	11.721	13.302	17.977	17.030	11.952	8.882	14.055	7.388	15.926	11.318	40.049	39.888	29.432	10.023	18.666	12.519	13.439	0.000	8.737	54.015	0.000	18.292	17.948	0.000	0.000	18.384	13.031	0.000	18.828	9.921	13.785	17.596	7.415	0.000	9.176	18.835	13.171	10.482	19.440	9.917	15.687	9.472	7.964	9.106	8.164	13.930	10.141	11.132	0.000	12.841	0.000	26.118	0.000	11.323	0.000	9.259	44.698	10.561	0.000	32.948	0.000	13.943	9.923	10.111	11.093	11.843	7.698	135.410	19.693	6.403	34.352	16.268	10.749	0.000	0.000	0.000	0.000	8.387	7.313	5.852	0.0	0.000	0.000	0.000	20.717	33.046	7.973	0.000	16.569	9.735	6.780	23.313	3.822	4.375	6.345	0.000	0.0	11.195	0.000	0.000	0.000	10.963	0.000	0.000	0.000	34.899	0.000	15.704	0.000	9.907	5.757	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.00	0.000	0.000	5.548	14.599	5.226	0.000	0.000	0.000	0.0	0.0	0.0	4.902	9.533	14.596	0.000	6.922	18.059	0.000	0.000	0.000	0.000	13.644	1	2	6	3	4	7	5	10	12	9	11	8		0	14	15	19	16	18	17	34	20	30	26	32	23	24	31	33	29	22	25	21	28	27	1	2	2	3	1	1	3	2	2	3	1	4	5	6						2	1	1	3	2	4	2	5	6	3	1	4									1	4	2	6	7	3	5	8						4	1	2	3	5				2	1	3	0	0	12/31/2019 22:10:05	12/31/2019 22:52:37	2552	6600	0
3164	ANES 2019 Pilot Study version 20200204	3165	.809576969671362	.661991088100273	1	2	1	-1	2	4	2	__NA__	-1	1	1	-1	-1	-1	2	1	-1	2	2	2	2	2	2	2	2	1	0	100	70	51	100	100	39	100	0	100	100	99	100	100	-1	98	100	98	2	0	99	98	98	99	96	100	100	99	100	97	99	98	98	98	99	1	3	-1	2	2	2	2	1	2	2	-1	-1	-1	2	-1	-1	-1	5	5	5	5	-1	-1	-1	-1	trump	5	5	5	7	5	3	3	3	1	7	7	3	3	3	1	1	7	6	6	2	1	1	1	3	2	2	1	2	-1	1	5	-1	1	1	1	1	1	1	1	1	1	1	1	-1	-1	3	4	7	7	5	-1	1	-1	1	1	2	-1	4	-1	4	4	3	3	3	5	5	5	5	5	2	-1	2	-1	7	-1	7	-1	7	-1	5	1	5	1	5	1	-1	5	1	-1	5	1	5	1	7	7		0.0	/////////////	0	4	4	1950	2	1	-1	__NA__	1	__NA__	1	-1	-1	-1	3	-1	-1	-1	-1	-1	-1	-1	-1	2	-1	5	5	1	5	-1	3	-1	6	0.0	310	42.039	-1	-1	-1	-1	-1	-1	-1	-1	-1	2	-3, restricted access	-1	2	-1	-1	-1	-3, restricted access	-1	-1	-3, restricted access	-1	2	2	2	1	2	2	2	2	-1	-1	-1	-1	-1	-1	-1	-1	2	2	1	2	1	-1	-1	-1	-1	-1	1	1	1	-1	-1	-1	2	2	2	2	2	2	2	2	1	__NA__	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1	5	5	1	5	5	1	-1	5	-1	-1	-1	-1	1	1960	1	2	1	2	5	1	__NA__	5	1	3	1	2	1	__NA__	2	1	6	2	2	__NA__	-7	-3, restricted access	15	-3	-3, restricted acces	4	716	455	9	1	1	1	1			0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	11.733	6.713	0.000	10.621	7.110	18.052	0.000	12.675	21.724	0.000	0.000	2.929	8.263	19.129	12.883	12.828	7.378	12.400	7.377	5.027	6.363	4.359	12.839	8.849	6.745	6.412	3.484	6.198	0.000	6.742	6.963	8.125	7.621	6.460	5.808	4.202	3.431	3.106	13.730	5.581	5.026	5.597	7.950	3.580	3.297	6.614	10.652	3.413	3.623	3.663	14.878	0.000	20.579	7.179	4.245	2.677	3.247	4.174	16.213	0.000	0.0	0.000	21.869	9.980	8.292	3.435	3.672	0.000	0.000	0.000	0.000	15.404	13.541	8.883	15.064	12.369	7.302	19.684	15.286	14.684	30.504	19.788	6.883	23.589	7.179	3.066	25.757	8.636	9.898	26.573	28.218	6.090	44.205	20.003	11.859	23.348	0.000	18.054	21.005	0.000	0.000	9.468	15.818	18.156	7.710	7.826	0.000	10.682	0.000	18.799	11.448	0.000	11.778	0.000	20.925	4.175	23.073	13.272	12.735	7.339	17.971	16.864	20.221	9.399	3.971	0.000	18.039	0.000	8.350	0.000	8.390	0.000	3.772	0.000	24.187	9.203	8.986	0.000	5.161	0.000	4.762	7.808	11.134	5.327	0.000	1.740	9.498	17.707	13.880	21.359	6.560	0.000	0.000	0.000	0.000	0.000	0.000	6.391	0.0	0.000	0.000	14.481	0.000	1348.991	0.000	10.312	0.000	15.887	8.462	0.000	0.000	0.000	0.000	5.547	0.0	10.946	0.000	0.000	0.000	0.000	0.000	0.000	25.084	0.000	18.232	0.000	20.694	0.000	5.296	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.00	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.0	0.0	0.0	9.516	5.433	15.367	6.790	0.000	12.530	0.000	0.000	0.000	0.000	11.353	1	2	3	4	5	6	7	11	8	12	9	10		14	0	15	19	18	17	16	22	34	28	21	26	24	29	32	33	31	30	20	23	27	25	2	1	3	2	1	1	2	3	3	2	1	4	5	6	4	2	1	3	5	2	1	4	3	1	2							8	3	1	4	2	7	5	6									3	4	2	5	1						2	1	3				0	1	12/31/2019 23:27:51	1/1/2020 0:21:59	3248	1	0

3165 rows × 900 columns

To examine the values of a specific column, call the column name either as an attribute of the dataframe (for example, to see people’s “feeling thermometer” ratings of Joe Biden, type anes.ftbiden) or as a string within a list (anes['ftbiden']). Then it is possible to employ methods like .describe() on this single column:

anes['ftbiden'].describe() # or anes.ftbiden.describe()

count    3165.000000
mean       56.269194
std       120.196311
min        -7.000000
25%         7.000000
50%        44.000000
75%        71.000000
max       997.000000
Name: ftbiden, dtype: float64

For categorical columns, to list of the unique categories and their frequencies, use the .value_counts() method:

anes.vote20cand.value_counts()

-1    1769
   419
   281
   267
   133
    71
    71
    58
    57
    38
-7       1
Name: vote20cand, dtype: int64

There’s a nicer-looking and more informative version of this frequency table available in the sidetable package, which adds the .stb.freq() method to a pandas dataframe. Pass a list with the column name to this function:

anes.stb.freq(['vote20cand'])

	vote20cand	Count	Percent	Cumulative Count	Cumulative Percent
0	-1	1769	0.558926	1769	0.558926
1	1	419	0.132385	2188	0.691311
2	7	281	0.088784	2469	0.780095
3	6	267	0.084360	2736	0.864455
4	3	133	0.042022	2869	0.906477
5	9	71	0.022433	2940	0.928910
6	4	71	0.022433	3011	0.951343
7	5	58	0.018325	3069	0.969668
8	8	57	0.018009	3126	0.987678
9	2	38	0.012006	3164	0.999684
10	-7	1	0.000316	3165	1.000000

8.4. Indexing a Data Frame #

Indexing a dataframe is the act of extracting a selection of rows or columns from a dataframe based on either the numeric position of the row or column, or based on the name of the row or column. There are four ways to index a dataframe:

Using the .iloc attribute of the dataframe. This attribute allows us to extract certain rows and columns from a dataframe by directly entering in the row and column numbers of the selection.
Using the .loc attribute of the dataframe. This attribute allows us to extract rows and columns based on the row and column names.
Calling a specific column by name as an attribute of a dataframe.
Using square brackets [] after writing the dataframe’s name to extract one or more columns by referring to those columns’ names.

To extract based on numeric position, use the .iloc attribute of a dataframe as follows:

df.iloc[rownumbers, columnnumbers]

.iloc provides a version of the dataframe that can take row and column coordinates as the first and second elements within an associated list, like an array. To specify more than one row or more than one column, these elements can be lists or can use : to express a range. For example, to extract rows 1 through 4 (remembering that the first row is row 0 and tha the second number in the range is excluded) and columns 12 through 14, we can type:

anes.iloc[1:5, 12:15]

	placeid1b	placeidimport	turnout16a
1	-1	2	1
2	-1	4	1
3	1	2	-1
4	1	2	-1

To extract rows 2, 4, 6, and 8 for columns 20, 25, and 30, we type:

anes.iloc[[2,4,6,8], [20, 25, 30]]

	turnout18a1	particip_5	fttrump
2	-1	2	0
4	-1	2	94
6	-1	1	0
8	-1	1	61

The advantage of the .loc approach to extract rows and columns by name is the ability to use slicing - that is, specifying a range of columns from the leftmost to the rightmost column, and all columns in between. Writing : alone in the rows slot returns all of the rows. For example, the first of the feeling therometer features from left-to-right in the anes data is fttrump and the last column is ftpales. To extract all of these columns, we type:

anes.loc[:,'fttrump':'ftpales']

	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftimmig1	ftimmig2	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales
0	47	90	52	52	49	997	50	99	99	99	100	88	79	97	-1	99	82	71	86	88	90	66	89	88	81	77	98	94	89	88	99	99	92	89	86
1	41	30	41	17	31	30	29	91	96	92	93	93	25	94	-1	67	86	78	91	93	44	19	93	25	82	22	89	91	94	71	66	100	20	25	77
2	0	91	88	15	60	70	68	48	49	49	49	50	39	69	-1	63	66	51	40	2	2	2	1	3	59	1	50	1	1	1	51	87	50	1	3
3	100	50	0	0	0	0	0	0	0	0	0	0	0	-1	100	25	75	75	75	100	25	50	15	50	50	15	85	10	85	50	75	50	75	25	0
4	94	18	25	1	10	16	7	93	94	94	94	50	9	-1	92	20	68	16	94	84	23	29	50	51	49	5	73	44	72	97	58	96	60	45	45
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3160	81	20	7	4	6	1	2	94	94	94	95	21	31	-1	85	11	30	16	95	91	26	10	22	20	30	4	70	19	90	60	61	60	10	25	10
3161	35	41	25	61	92	3	1	45	56	53	69	11	3	-1	50	52	40	55	50	7	20	6	26	1	55	2	60	60	56	50	66	80	50	31	50
3162	6	31	50	2	59	0	31	88	100	100	100	60	100	-1	100	51	3	60	94	41	100	60	100	78	40	59	100	72	99	99	99	100	51	41	87
3163	1	100	95	62	79	59	51	100	65	50	52	50	51	-1	49	91	84	72	59	0	54	0	62	0	60	0	73	55	50	1	56	89	0	0	1
3164	0	100	70	51	100	100	39	100	0	100	100	99	100	100	-1	98	100	98	2	0	99	98	98	99	96	100	100	99	100	97	99	98	98	98	99

3165 rows × 35 columns

We can return one column by calling its name just like any other attribute by typing the dataframe, a period, and a column name. This approach is appropriate when we want to extract just one column for the datadrame. To extract the ftbiden column from anes, we type:

anes.ftbiden

     52
     41
     88
      0
     25
        ..
   7
  25
  50
  95
  70
Name: ftbiden, Length: 3165, dtype: int64

Equivalently, we can call this column within brackets as follows:

anes['ftbiden']

     52
     41
     88
      0
     25
        ..
   7
  25
  50
  95
  70
Name: ftbiden, Length: 3165, dtype: int64

This approach allows us to extract more than one column by including a list of column names in the index:

anes[['ftbiden', 'fttrump']]

	ftbiden	fttrump
0	52	47
1	41	41
2	88	0
3	0	100
4	25	94
...	...	...
3160	7	81
3161	25	35
3162	50	6
3163	95	1
3164	70	0

3165 rows × 2 columns

8.5. Selecting, Renaming, and Rearranging Columns #

The anes data has 900 columns, which is too many for us to feasibly work with in this notebook. Even if the intention is to run a machine learning model that processes a large number of columns, it is probably the case that some columns should not be included in the feature space. The first task in data manipulation is to restrict the data to only those columns we actually need.

For the ANES, let’s keep the following columns:

caseid - the primary key that identifies individual survey respondents
liveurban - Do you currently live in a rural area, small town, suburb, or a city?
vote16 - In the 2016 presidential election, who did you vote for?
particip_3 - In the last 12 months, have you joined in a protest march, rally, or demonstration?
vote20jb - If the election is between Donald Trump and Joe Biden, who will you vote for?
mip - What do you think is the most important problem facing this country? (free response)
confecon - Overall, how worried are you about the national economy?
ideo5 - How would you describe your personal political ideology?
pid7 - Which party to you identify with?
guarinc - Do you favor or oppose a universal basic income?
famsep - Do you favor or oppose the policy of family separation at the border?
freecol - Do you favor or oppose a policy of free tuition at public universities?
loans - Do you favor or oppose a policy of student loan debt forgiveness?
race - Race
birthyr - Birth year
gender - Gender
educ - Educational attainment
inputstate - State of residence
weight - The survey probability weights that need to be applied to rows to make results more representative of the U.S. adult population

The easiest way to reduce the dataframe to include only these columns is to define a list of these column names, then pass the list to the dataframe index as follows:

mycols = ['caseid','liveurban','vote16','particip_3','vote20jb',
          'mip','confecon','ideo5','pid7','guarinc','famsep',
          'freecol','loans','race','birthyr','gender','educ','inputstate', 'weight']
anes[mycols]

	caseid	liveurban	vote16	particip_3	vote20jb	mip	confecon	ideo5	pid7	guarinc	famsep	freecol	loans	race	birthyr	gender	educ	inputstate	weight
0	1	3	3	2	2	Health Care	2	4	2	2	5	2	3	3	1969	1	2	48	1.34719693063187
1	2	3	1	2	1	Working together	2	4	6	6	4	5	6	1	1942	1	6	1	.780822076219216
2	3	1	2	2	2	health care	5	3	1	4	5	6	7	1	1954	2	2	37	.966366930694957
3	4	4	2	2	1	The economy.	2	3	1	4	3	3	2	2	1979	1	3	34	1.10348514780374
4	5	4	1	2	1	China	1	4	5	7	3	7	7	1	1940	2	6	48	1.09069730256741
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3160	3161	2	1	2	1	The infiltration of Marxists into the institut...	2	4	5	7	2	7	7	1	1948	1	4	41
3161	3162	4	3	2	3	Lack of basic resources being provided and off...	5	6	3	1	3	3	2	1	1996	1	2	45	7.03646496881757
3162	3163	4	-1	2	4	donald trump	4	2	4	7	5	7	7	3	1980	1	4	47	.892833236147303
3163	3164	3	-1	2	2	Donald Trump	3	2	2	2	4	4	5	2	1960	1	1	6	1.58161278448241
3164	3165	2	2	2	2	trump	5	3	1	7	1	7	7	5	1960	1	2	15	.809576969671362

3165 rows × 19 columns

Note that while we displayed the reduced dataframe, we have not yet saved this reduced dataframe as a Python variable.

In addition to the features listed above, let’s also include the columns that contain the “feeling thermometers”. The respondents are asked to rate many things from 0 (strongly dislike) to 100 (strongly like) including politicians like Joe Biden and Donald Trump, countries like Turkey and Canada, groups of people like Asians, immigrants, and journalists, and organizations like NATO and the NRA. These columns all begin with “ft”: the ratings for Donald Trump and Joe Biden are contained in fttrump and ftbiden, for example. Other columns contain metadata regarding these responses: ftbiden_skp indicates whether the respondent skipped this question, ord_ftbiden reports where this question appeared in each respondent’s randomized question ordering, and ftbiden_page_timing records how long the respondent took to answer this question. For this example, we only want to keep the feeling thermometers.

In this case, we want to use code that automatically identifies all of the columns that begin with a specific string like “ft” or end with a specific string like “timing”. There are two approaches to identifying and selecting columns that start or end a certain way. First, we can use the .str.startswith(), .str.endswith(), and str.contains() methods of the .columns attribute of a dataframe, and pass the result to the .loc attribute to extract the matching columns. To extract the columns that begin “ft”, we write

anes.loc[:, anes.columns.str.startswith('ft')]

	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftimmig1	ftimmig2	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales	ftasian_skp	ftbiden_skp	ftblack_skp	ftbritain_skp	ftbuttigieg_skp	ftcanada_skp	ftchina_skp	ftfrance_skp	ftgermany_skp	ftharris_skp	fthisp_skp	ftice_skp	ftillegal_skp	ftimmig1_skp	ftimmig2_skp	ftiran_skp	ftisrael_skp	ftjapan_skp	ftjournal_skp	ftmexico_skp	ftmuslim_skp	ftnato_skp	ftnkorea_skp	ftnra_skp	ftobama_skp	ftpales_skp	ftrussia_skp	ftsanders_skp	ftsaudi_skp	fttrump_skp	ftturkey_skp	ftukraine_skp	ftun_skp	ftwarren_skp	ftwhite_skp	fttrump_page_timing	ftobama_page_timing	ftbiden_page_timing	ftwarren_page_timing	ftsanders_page_timing	ftbuttigieg_page_timing	ftharris_page_timing	ftblack_page_timing	ftwhite_page_timing	fthisp_page_timing	ftasian_page_timing	ftmuslim_page_timing	ftillegal_page_timing	ftimmig1_page_timing	ftimmig2_page_timing	ftjournal_page_timing	ftnato_page_timing	ftun_page_timing	ftice_page_timing	ftnra_page_timing	ftchina_page_timing	ftnkorea_page_timing	ftmexico_page_timing	ftsaudi_page_timing	ftukraine_page_timing	ftiran_page_timing	ftbritain_page_timing	ftgermany_page_timing	ftjapan_page_timing	ftisrael_page_timing	ftfrance_page_timing	ftcanada_page_timing	ftturkey_page_timing	ftrussia_page_timing	ftpales_page_timing
0	47	90	52	52	49	997	50	99	99	99	100	88	79	97	-1	99	82	71	86	88	90	66	89	88	81	77	98	94	89	88	99	99	92	89	86	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	18.756	8.893	10.590	8.145	6.211	5.852	13.662	8.236	4.700	2.560	3.830	8.659	1.679	3.532	0.000	5.137	3.904	3.249	5.800	7.497	10.249	4.214	5.650	3.823	3.182	2.453	4.631	4.221	3.878	3.864	13.908	2.109	2.791	4.533	4.073
1	41	30	41	17	31	30	29	91	96	92	93	93	25	94	-1	67	86	78	91	93	44	19	93	25	82	22	89	91	94	71	66	100	20	25	77	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	12.653	4.982	3.735	3.534	2.999	4.752	3.720	5.888	3.343	3.709	3.461	3.497	2.850	2.800	0.000	4.368	3.392	4.289	5.103	3.714	3.830	3.684	3.034	3.203	4.249	3.204	4.261	2.965	3.921	3.944	3.005	3.957	2.563	2.851	5.058
2	0	91	88	15	60	70	68	48	49	49	49	50	39	69	-1	63	66	51	40	2	2	2	1	3	59	1	50	1	1	1	51	87	50	1	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	14.342	8.833	5.336	6.344	9.386	7.735	7.817	4.727	4.530	6.723	4.873	6.401	11.542	7.602	0.000	5.969	6.955	10.404	20.956	9.733	4.223	5.717	5.417	6.487	9.220	3.874	13.394	4.204	8.374	4.453	5.695	4.635	4.992	6.135	5.758
3	100	50	0	0	0	0	0	0	0	0	0	0	0	-1	100	25	75	75	75	100	25	50	15	50	50	15	85	10	85	50	75	50	75	25	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	6.123	4.024	7.260	4.035	8.174	4.913	2.623	2.955	5.917	3.436	5.724	5.769	7.385	0.000	8.174	17.756	3.771	0.898	15.432	4.706	8.266	5.902	0.105	8.623	0.298	0.344	8.744	8.606	0.306	0.233	0.118	3.571	0.185	0.139	0.137
4	94	18	25	1	10	16	7	93	94	94	94	50	9	-1	92	20	68	16	94	84	23	29	50	51	49	5	73	44	72	97	58	96	60	45	45	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	23.543	16.482	12.024	7.980	13.030	15.878	5.829	8.313	27.213	9.984	7.684	20.659	10.477	0.000	7.723	6.285	11.818	7.484	9.420	13.706	8.573	9.659	6.841	9.907	8.938	7.456	6.822	9.949	10.211	7.729	11.479	16.550	25.597	10.016	8.424
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3160	81	20	7	4	6	1	2	94	94	94	95	21	31	-1	85	11	30	16	95	91	26	10	22	20	30	4	70	19	90	60	61	60	10	25	10	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	14.782	7.416	11.474	4.640	4.339	5.535	7.655	6.589	4.458	5.938	7.065	6.149	7.974	0.000	6.836	5.705	4.740	5.174	7.268	5.181	4.495	8.080	4.128	4.693	4.255	3.599	3.672	8.122	4.087	4.636	4.553	4.947	4.711	4.298	7.147
3161	35	41	25	61	92	3	1	45	56	53	69	11	3	-1	50	52	40	55	50	7	20	6	26	1	55	2	60	60	56	50	66	80	50	31	50	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	18.700	5.598	14.185	8.829	15.345	4.423	4.170	10.773	9.436	8.375	8.106	4.082	9.835	0.000	4.260	3.779	5.621	7.558	4.840	3.928	5.125	7.228	5.117	7.930	3.783	4.570	4.545	7.165	8.701	5.656	5.030	6.814	3.597	4.896	5.006
3162	6	31	50	2	59	0	31	88	100	100	100	60	100	-1	100	51	3	60	94	41	100	60	100	78	40	59	100	72	99	99	99	100	51	41	87	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	13.230	7.743	6.538	5.253	6.740	7.512	3.692	7.074	8.041	6.904	8.615	7.851	5.253	0.000	4.181	3.884	4.136	4.484	6.832	5.209	3.727	9.289	5.675	4.046	4.884	5.471	4.010	6.924	4.154	4.677	4.877	4.444	5.091	3.690	4.879
3163	1	100	95	62	79	59	51	100	65	50	52	50	51	-1	49	91	84	72	59	0	54	0	62	0	60	0	73	55	50	1	56	89	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	13.859	10.821	4.535	7.973	4.811	7.034	4.996	4.527	3.683	4.917	9.405	5.766	6.478	0.000	8.614	5.032	5.439	4.327	8.170	4.455	3.603	5.639	3.643	10.550	4.175	8.272	5.764	3.426	4.688	4.580	5.099	4.287	4.478	4.883	4.066
3164	0	100	70	51	100	100	39	100	0	100	100	99	100	100	-1	98	100	98	2	0	99	98	98	99	96	100	100	99	100	97	99	98	98	98	99	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	12.883	12.828	7.378	12.400	7.377	5.027	6.363	4.359	12.839	8.849	6.745	6.412	3.484	6.198	0.000	6.742	6.963	8.125	7.621	6.460	5.808	4.202	3.431	3.106	13.730	5.581	5.026	5.597	7.950	3.580	3.297	6.614	10.652	3.413	3.623

3165 rows × 105 columns

The issue here is that this call included the page timing columns, which we did not want, because these columns also begin with “ft”. In addition, this approach does not enable us to include the features listed above.

A second approach to selecting columns is technically more complicated, but offers a great deal more control over which columns are included in the edited data and which columns are not. This approach involves using a comprehension loop on the .columns attribute to extract the elements of this list that match given patterns. The individual elements of .columns are strings on which .startswith(), .endswith(), and .contains() apply. The comprehension loop allows us to use these methods in logical statements that determine whether each element is included or excluded in the new list.

For example,

[x for x in anes.colummns]

generates a list of all 900 column names in the anes dataframe. But we can use if to place conditions on which names are included in the list. If we type

[x for x in anes.colummns if x.startswith("ft")]

then names in anes.columns are included only if it is true that they start with “ft”. However, that condition also captures the metadata columns we don’t want that begin with “ft” and end with “timing” or “skp”. We can exclude these columns by using not clauses in the logical condition as follows:

ftcols = [x for x in anes.columns if x.startswith("ft") and not x.endswith("timing") and not x.endswith("skp")]
ftcols

['fttrump',
 'ftobama',
 'ftbiden',
 'ftwarren',
 'ftsanders',
 'ftbuttigieg',
 'ftharris',
 'ftblack',
 'ftwhite',
 'fthisp',
 'ftasian',
 'ftmuslim',
 'ftillegal',
 'ftimmig1',
 'ftimmig2',
 'ftjournal',
 'ftnato',
 'ftun',
 'ftice',
 'ftnra',
 'ftchina',
 'ftnkorea',
 'ftmexico',
 'ftsaudi',
 'ftukraine',
 'ftiran',
 'ftbritain',
 'ftgermany',
 'ftjapan',
 'ftisrael',
 'ftfrance',
 'ftcanada',
 'ftturkey',
 'ftrussia',
 'ftpales']

To append two lists together, we simply add them together. Here we can append the mycols list defined above to the ftcols list to create a comprehensive list of all the columns we want to extract from anes, and we can create a new variable named anes_clean that contains only these columns:

anes_clean = anes[mycols + ftcols]
anes_clean

	caseid	liveurban	vote16	particip_3	vote20jb	mip	confecon	ideo5	pid7	guarinc	famsep	freecol	loans	race	birthyr	gender	educ	inputstate	weight	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftimmig1	ftimmig2	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales
0	1	3	3	2	2	Health Care	2	4	2	2	5	2	3	3	1969	1	2	48	1.34719693063187	47	90	52	52	49	997	50	99	99	99	100	88	79	97	-1	99	82	71	86	88	90	66	89	88	81	77	98	94	89	88	99	99	92	89	86
1	2	3	1	2	1	Working together	2	4	6	6	4	5	6	1	1942	1	6	1	.780822076219216	41	30	41	17	31	30	29	91	96	92	93	93	25	94	-1	67	86	78	91	93	44	19	93	25	82	22	89	91	94	71	66	100	20	25	77
2	3	1	2	2	2	health care	5	3	1	4	5	6	7	1	1954	2	2	37	.966366930694957	0	91	88	15	60	70	68	48	49	49	49	50	39	69	-1	63	66	51	40	2	2	2	1	3	59	1	50	1	1	1	51	87	50	1	3
3	4	4	2	2	1	The economy.	2	3	1	4	3	3	2	2	1979	1	3	34	1.10348514780374	100	50	0	0	0	0	0	0	0	0	0	0	0	-1	100	25	75	75	75	100	25	50	15	50	50	15	85	10	85	50	75	50	75	25	0
4	5	4	1	2	1	China	1	4	5	7	3	7	7	1	1940	2	6	48	1.09069730256741	94	18	25	1	10	16	7	93	94	94	94	50	9	-1	92	20	68	16	94	84	23	29	50	51	49	5	73	44	72	97	58	96	60	45	45
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3160	3161	2	1	2	1	The infiltration of Marxists into the institut...	2	4	5	7	2	7	7	1	1948	1	4	41		81	20	7	4	6	1	2	94	94	94	95	21	31	-1	85	11	30	16	95	91	26	10	22	20	30	4	70	19	90	60	61	60	10	25	10
3161	3162	4	3	2	3	Lack of basic resources being provided and off...	5	6	3	1	3	3	2	1	1996	1	2	45	7.03646496881757	35	41	25	61	92	3	1	45	56	53	69	11	3	-1	50	52	40	55	50	7	20	6	26	1	55	2	60	60	56	50	66	80	50	31	50
3162	3163	4	-1	2	4	donald trump	4	2	4	7	5	7	7	3	1980	1	4	47	.892833236147303	6	31	50	2	59	0	31	88	100	100	100	60	100	-1	100	51	3	60	94	41	100	60	100	78	40	59	100	72	99	99	99	100	51	41	87
3163	3164	3	-1	2	2	Donald Trump	3	2	2	2	4	4	5	2	1960	1	1	6	1.58161278448241	1	100	95	62	79	59	51	100	65	50	52	50	51	-1	49	91	84	72	59	0	54	0	62	0	60	0	73	55	50	1	56	89	0	0	1
3164	3165	2	2	2	2	trump	5	3	1	7	1	7	7	5	1960	1	2	15	.809576969671362	0	100	70	51	100	100	39	100	0	100	100	99	100	100	-1	98	100	98	2	0	99	98	98	99	96	100	100	99	100	97	99	98	98	98	99

3165 rows × 54 columns

A related method is choosing which columns in a dataframe to drop, instead of which columns to keep. This technique is useful when we want to keep most of the columns, and it is easier to specify the few columns we want to drop. The method to drop columns is .drop(), applied to a dataframe. There are two ways to use this method. First,

df = df.drop(colstodrop, axis=1)

where colstodrop is a vector of column names for the columns we want to drop. Alternatively, we can write

df.drop(colstodrop, axis=1, inplace=True)

These two versions of the command have the same effect of dropping the columns defined in colstodrop and overwriting the Python variable for the dataframe with the new version. axis=1 specifies that the names refer to columns to be dropped, in contrast to axis=0 which would refer to rows. inplace=True provides an equivalent to df = . Either approach is fine, although I tend to avoid using inplace=True so that the code is more consistent. To delete the inputstate column from anes_clean, we type

anes_clean = anes_clean.drop('inputstate', axis=1)

If we wanted to drop multiple columns, we would write a list of the column names we want to drop in the first argument.

There are many ways to rename the columns in a dataframe, including assigning a list of new column names to the .columns attribute of a dataframe. I do not recommend that approach, however, because it requires an entry for every column and it requires these names to be written in exactly the same order as the existing columns, or else the data will be corrupted. Instead, use the .rename() method. This method does not require us to think about the left-to-right order of the columns, and it allows us to rename only a few columns without worrying about the ones we do not want to rename. To use this method, specify two parameters: first a dictionary that contains elements in the form of 'oldname':'newname', and second axis='columns' or axis=1 to work with columns.

To rename some of the columns in anes_clean, I use the following code:

anes_clean = anes_clean.rename({'particip_3':'protest',
                                'vote20jb':'vote',
                                'mip':'most_important_issue',
                                'ideo5':'ideology',
                                'pid7':'partyID',
                                'guarinc':'universal_income',
                                'famsep':'family_separation',
                                'freecol':'free_college',
                                'loans':'forgive_loans',
                                'gender':'sex',
                                'educ':'education'}, axis=1)
anes_clean

	caseid	liveurban	vote16	protest	vote	most_important_issue	confecon	ideology	partyID	universal_income	family_separation	free_college	forgive_loans	race	birthyr	sex	education	weight	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftimmig1	ftimmig2	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales
0	1	3	3	2	2	Health Care	2	4	2	2	5	2	3	3	1969	1	2	1.34719693063187	47	90	52	52	49	997	50	99	99	99	100	88	79	97	-1	99	82	71	86	88	90	66	89	88	81	77	98	94	89	88	99	99	92	89	86
1	2	3	1	2	1	Working together	2	4	6	6	4	5	6	1	1942	1	6	.780822076219216	41	30	41	17	31	30	29	91	96	92	93	93	25	94	-1	67	86	78	91	93	44	19	93	25	82	22	89	91	94	71	66	100	20	25	77
2	3	1	2	2	2	health care	5	3	1	4	5	6	7	1	1954	2	2	.966366930694957	0	91	88	15	60	70	68	48	49	49	49	50	39	69	-1	63	66	51	40	2	2	2	1	3	59	1	50	1	1	1	51	87	50	1	3
3	4	4	2	2	1	The economy.	2	3	1	4	3	3	2	2	1979	1	3	1.10348514780374	100	50	0	0	0	0	0	0	0	0	0	0	0	-1	100	25	75	75	75	100	25	50	15	50	50	15	85	10	85	50	75	50	75	25	0
4	5	4	1	2	1	China	1	4	5	7	3	7	7	1	1940	2	6	1.09069730256741	94	18	25	1	10	16	7	93	94	94	94	50	9	-1	92	20	68	16	94	84	23	29	50	51	49	5	73	44	72	97	58	96	60	45	45
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3160	3161	2	1	2	1	The infiltration of Marxists into the institut...	2	4	5	7	2	7	7	1	1948	1	4		81	20	7	4	6	1	2	94	94	94	95	21	31	-1	85	11	30	16	95	91	26	10	22	20	30	4	70	19	90	60	61	60	10	25	10
3161	3162	4	3	2	3	Lack of basic resources being provided and off...	5	6	3	1	3	3	2	1	1996	1	2	7.03646496881757	35	41	25	61	92	3	1	45	56	53	69	11	3	-1	50	52	40	55	50	7	20	6	26	1	55	2	60	60	56	50	66	80	50	31	50
3162	3163	4	-1	2	4	donald trump	4	2	4	7	5	7	7	3	1980	1	4	.892833236147303	6	31	50	2	59	0	31	88	100	100	100	60	100	-1	100	51	3	60	94	41	100	60	100	78	40	59	100	72	99	99	99	100	51	41	87
3163	3164	3	-1	2	2	Donald Trump	3	2	2	2	4	4	5	2	1960	1	1	1.58161278448241	1	100	95	62	79	59	51	100	65	50	52	50	51	-1	49	91	84	72	59	0	54	0	62	0	60	0	73	55	50	1	56	89	0	0	1
3164	3165	2	2	2	2	trump	5	3	1	7	1	7	7	5	1960	1	2	.809576969671362	0	100	70	51	100	100	39	100	0	100	100	99	100	100	-1	98	100	98	2	0	99	98	98	99	96	100	100	99	100	97	99	98	98	98	99

3165 rows × 53 columns

8.6. Working with Categorical Features #

8.6.1. Recoding a Single Categorical Column #

The ANES uses numeric codes to represent both ordinal and nominal (unordered) categorical features. That cuts down on the memory size of the data file (storing 2 is smaller than storing Trump), but it also makes it so we can’t really understand what the individual datapoints represent. For example, here’s a frequency table of the values of vote:

anes_clean.vote.value_counts()

  1288
  1273
   321
   283
Name: vote, dtype: int64

According to the codebook, these values mean

1 - Donald Trump
2 - Joe Biden
3 - Someone else
4 - Probably will not vote

It would be much better to replace the numeric datapoints with these labels so that we can better understand the data we see. Carefully replacing the numbers with text will keep us from potentially confusing which number stands for which category. Also, if we want to construct any tables or graphs, it’s much better to display the text labels than the numeric codes. Replacing the numeric codes with text for the categorical labels is a two step process:

Create a dictionary in which the keys are existing values we want to recode, and the values are the new labels we want to replace these categories with.
Use the .map() method on the column of interest to apply the mapping defined by the dictionary to the data.

An alternative method is .replace() as applied to the entire dataframe, but .map() tends to be much faster than .replace(). For example, to put the text labels onto the vote column, we can first define the following dictionary, then we can apply it to the data:

replace_map = {1:'Donald Trump', 
               2:'Joe Biden', 
               3:'Someone else', 
               4:'Probably will not vote'}
anes_clean.vote = anes_clean.vote.map(replace_map)
anes_clean.vote

                  Joe Biden
               Donald Trump
                  Joe Biden
               Donald Trump
               Donald Trump
                 ...          
            Donald Trump
            Someone else
  Probably will not vote
               Joe Biden
               Joe Biden
Name: vote, Length: 3165, dtype: object

One drawback of .map() compared to .replace() is that all of the categories must be replaced. If only some categories need to be recoded and a dictionary only lists the categories to replace, then .map() replaces all of the unlisted categories with missing values, while .replace() leaves these categories as they are.

This method can also be used to collapse categories into a smaller number of categories and can be used to replace numeric codes that represent missing values with codes recognized as missing by Python. To combine categories, we set existing values to the same label. To turn some categories to missing values, we set those categories equal to np.nan.

Take ideology, for example. According to the codebook, the values of this feature mean

-7 - No answer
1 - Very liberal
2 - Liberal
3 - Moderate
4 - Conservative
5 - Very conservative
6 - Not sure Suppose that we want to label these categories in a way that combines categories 1 and 2 to be “Liberal”, sets category 3 as “Moderate”, combines categories 4 and 5 to be “Conservative”, and replaces categories -7 and 6 with missing values. We can do so with the following dictionary:

replace_map = {-7:np.nan, 1:'Liberal', 2:'Liberal', 3:'Moderate', 
               4:'Conservative', 5:'Conservative', 6:np.nan}
anes_clean.ideology = anes_clean.ideology.map(replace_map)
anes_clean.ideology

     Conservative
     Conservative
         Moderate
         Moderate
     Conservative
            ...     
  Conservative
           NaN
       Liberal
       Liberal
      Moderate
Name: ideology, Length: 3165, dtype: object

8.6.2. Recoding Many Categorical Columns At Once #

To label the categories of many features, the simplest code defines a dictionary in which each feature to be recoded is a key and the mapping dictionary for that feature is the value. Then we can pass this entire dictionary to the whole dataframe with .replace(). This approach might take longer than defining a loop that uses .map() on each of these columns (.map() is only defined for series and not for dataframes), but using .replace() here is more intuitive and elegant.

We can recode all the remaining categorical features in the ANES data as follows:

replace_map = {'liveurban':{1:'Rural', 2:'Town', 3:'Suburb', 4:'City'},
              'vote16':{-1:'Did not vote', 1:'Donald Trump', 2:'Hillary Clinton', 3:'Someone else'},
              'protest':{1:True, 2:False},
              'confecon':{1:'Not at all worried', 2:'A little worried', 3:'Moderately worried', 4:'Very worried', 5:'Extremely worried'},
              'partyID':{-7:np.nan, 8: np.nan, 1:'Democrat', 2:'Democrat', 3:'Democrat', 4:'Independent', 5:'Republican', 
                         6:'Republican', 7:'Republican'},
              'universal_income':{1:'Favor a great deal', 2:'Favor a moderate amount', 3:'Favor a little', 4:'Neither favor nor oppose', 
                                  5:'Oppose a little', 6:'Oppose a moderate amount', 7:'Oppose a great deal'},
              'family_separation':{-7:np.nan, 1:'Favor strongly', 2:'Favor somewhat', 3:'Neither favor nor disagree',
                                   4:'Oppose somewhat', 5:'Oppose strongly'},
               'free_college':{-7:np.nan, 1:'Favor a great deal', 2:'Favor a moderate amount', 3:'Favor a little', 
                               4:'Neither favor nor oppose', 5:'Oppose a little', 6:'Oppose a moderate amount', 7:'Oppose a great deal'},
               'forgive_loans':{-7:np.nan, 1:'Favor a great deal', 2:'Favor a moderate amount', 3:'Favor a little', 
                               4:'Neither favor nor oppose', 5:'Oppose a little', 6:'Oppose a moderate amount', 7:'Oppose a great deal'},
               'race':{1:'White', 2:'Black', 3:'Hispanic', 4:'Other', 5:'Other', 6:'Other', 7:'Other', 8:'Other'},
               'sex':{1:'Male',2:'Female'},
               'education':{1:'No HS diploma', 2:'High school graduate', 3:'Some college', 4:'2-year degree', 
                            5:'4-year degree', 6:'Post-graduate'}}
anes_clean = anes_clean.replace(replace_map)

To apply the same recoding dictionary to many columns in the dataframe, first generate a list of the column names that share the dictionary. Then pass that list to the dataframe index to select those columns. Finally, apply the .replace() to this dataframe, inputting the dictionary for the shared recoding instructions. For example, for all of the feeling thermometer features in the data, various types of non-response are coded as -1, -7, and 997. I want to apply the dictionary

{-1:np.nan, -7:np.nan, 997:np.nan}

to every feeling thermometer feature in the data. If all of the values are to be recoded to the same single (missing) value, a shortcut is to pass to .replace() a list of the values to be recoded and np.nan. So we can equivalently write

.replace([-1, -7, 997], np.nan)

First we can generate a list of these columns, then we pass it to the dataframe. Then we apply .replace() to this selection:

ftcols = [x for x in anes_clean.columns if x.startswith("ft")]
anes_clean[ftcols] = anes_clean[ftcols].replace([-1, -7, 997], np.nan)

All of the features in the data have now been recoded:

anes_clean

	caseid	liveurban	vote16	protest	vote	most_important_issue	confecon	ideology	partyID	universal_income	family_separation	free_college	forgive_loans	race	birthyr	sex	education	weight	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftimmig1	ftimmig2	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales
0	1	Suburb	Someone else	False	Joe Biden	Health Care	A little worried	Conservative	Democrat	Favor a moderate amount	Oppose strongly	Favor a moderate amount	Favor a little	Hispanic	1969	Male	High school graduate	1.34719693063187	47.0	90.0	52.0	52.0	49.0	NaN	50.0	99.0	99.0	99.0	100.0	88.0	79.0	97.0	NaN	99	82.0	71.0	86.0	88.0	90	66.0	89.0	88.0	81.0	77.0	98	94	89	88.0	99.0	99	92.0	89.0	86.0
1	2	Suburb	Donald Trump	False	Donald Trump	Working together	A little worried	Conservative	Republican	Oppose a moderate amount	Oppose somewhat	Oppose a little	Oppose a moderate amount	White	1942	Male	Post-graduate	.780822076219216	41.0	30.0	41.0	17.0	31.0	30.0	29.0	91.0	96.0	92.0	93.0	93.0	25.0	94.0	NaN	67	86.0	78.0	91.0	93.0	44	19.0	93.0	25.0	82.0	22.0	89	91	94	71.0	66.0	100	20.0	25.0	77.0
2	3	Rural	Hillary Clinton	False	Joe Biden	health care	Extremely worried	Moderate	Democrat	Neither favor nor oppose	Oppose strongly	Oppose a moderate amount	Oppose a great deal	White	1954	Female	High school graduate	.966366930694957	0.0	91.0	88.0	15.0	60.0	70.0	68.0	48.0	49.0	49.0	49.0	50.0	39.0	69.0	NaN	63	66.0	51.0	40.0	2.0	2	2.0	1.0	3.0	59.0	1.0	50	1	1	1.0	51.0	87	50.0	1.0	3.0
3	4	City	Hillary Clinton	False	Donald Trump	The economy.	A little worried	Moderate	Democrat	Neither favor nor oppose	Neither favor nor disagree	Favor a little	Favor a moderate amount	Black	1979	Male	Some college	1.10348514780374	100.0	50.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	NaN	100.0	25	75.0	75.0	75.0	100.0	25	50.0	15.0	50.0	50.0	15.0	85	10	85	50.0	75.0	50	75.0	25.0	0.0
4	5	City	Donald Trump	False	Donald Trump	China	Not at all worried	Conservative	Republican	Oppose a great deal	Neither favor nor disagree	Oppose a great deal	Oppose a great deal	White	1940	Female	Post-graduate	1.09069730256741	94.0	18.0	25.0	1.0	10.0	16.0	7.0	93.0	94.0	94.0	94.0	50.0	9.0	NaN	92.0	20	68.0	16.0	94.0	84.0	23	29.0	50.0	51.0	49.0	5.0	73	44	72	97.0	58.0	96	60.0	45.0	45.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3160	3161	Town	Donald Trump	False	Donald Trump	The infiltration of Marxists into the institut...	A little worried	Conservative	Republican	Oppose a great deal	Favor somewhat	Oppose a great deal	Oppose a great deal	White	1948	Male	2-year degree		81.0	20.0	7.0	4.0	6.0	1.0	2.0	94.0	94.0	94.0	95.0	21.0	31.0	NaN	85.0	11	30.0	16.0	95.0	91.0	26	10.0	22.0	20.0	30.0	4.0	70	19	90	60.0	61.0	60	10.0	25.0	10.0
3161	3162	City	Someone else	False	Someone else	Lack of basic resources being provided and off...	Extremely worried	NaN	Democrat	Favor a great deal	Neither favor nor disagree	Favor a little	Favor a moderate amount	White	1996	Male	High school graduate	7.03646496881757	35.0	41.0	25.0	61.0	92.0	3.0	1.0	45.0	56.0	53.0	69.0	11.0	3.0	NaN	50.0	52	40.0	55.0	50.0	7.0	20	6.0	26.0	1.0	55.0	2.0	60	60	56	50.0	66.0	80	50.0	31.0	50.0
3162	3163	City	Did not vote	False	Probably will not vote	donald trump	Very worried	Liberal	Independent	Oppose a great deal	Oppose strongly	Oppose a great deal	Oppose a great deal	Hispanic	1980	Male	2-year degree	.892833236147303	6.0	31.0	50.0	2.0	59.0	0.0	31.0	88.0	100.0	100.0	100.0	60.0	100.0	NaN	100.0	51	3.0	60.0	94.0	41.0	100	60.0	100.0	78.0	40.0	59.0	100	72	99	99.0	99.0	100	51.0	41.0	87.0
3163	3164	Suburb	Did not vote	False	Joe Biden	Donald Trump	Moderately worried	Liberal	Democrat	Favor a moderate amount	Oppose somewhat	Neither favor nor oppose	Oppose a little	Black	1960	Male	No HS diploma	1.58161278448241	1.0	100.0	95.0	62.0	79.0	59.0	51.0	100.0	65.0	50.0	52.0	50.0	51.0	NaN	49.0	91	84.0	72.0	59.0	0.0	54	0.0	62.0	0.0	60.0	0.0	73	55	50	1.0	56.0	89	0.0	0.0	1.0
3164	3165	Town	Hillary Clinton	False	Joe Biden	trump	Extremely worried	Moderate	Democrat	Oppose a great deal	Favor strongly	Oppose a great deal	Oppose a great deal	Other	1960	Male	High school graduate	.809576969671362	0.0	100.0	70.0	51.0	100.0	100.0	39.0	100.0	0.0	100.0	100.0	99.0	100.0	100.0	NaN	98	100.0	98.0	2.0	0.0	99	98.0	98.0	99.0	96.0	100.0	100	99	100	97.0	99.0	98	98.0	98.0	99.0

3165 rows × 53 columns

8.6.3. Dealing With Missing Data #

After recoding, we are left with quite a few missing values in the data. To generate a logical matrix that indicates for every cell whether the datapoint is missing or not, we can use anes_clean.isnull(). To delete every row that has even one missing value, we can use anes_clean.dropna(). That’s a heavy-handed treatment of missing data, however, that can severely reduce sample size and can lead to biases in analytical results of datapoints are missing not at random. There are much better treatments for missing data, called imputation methods. Many methods for missing data imputation are implemented in the scikit-learn package.

One application of the isnull() method is to replace missing values with known values if those values are available elsewhere. For example, the ANES asked two versions of the feeling thermometer for immigrants. A respondent is only asked one of the two questions, so one of ftimmig1 and ftimmig2 is missing:

anes_clean[['ftimmig1','ftimmig2']]

	ftimmig1	ftimmig2
0	97.0	NaN
1	94.0	NaN
2	69.0	NaN
3	NaN	100.0
4	NaN	92.0
...	...	...
3160	NaN	85.0
3161	NaN	50.0
3162	NaN	100.0
3163	NaN	49.0
3164	100.0	NaN

3165 rows × 2 columns

We can create a new column called ftimmig and set it equal to ftimmig1. Then we can subset this column to just the rows for which ftimmig1 is missing by typing

anes_clean.ftimmig[anes_clean.ftimmig1.isnull()]

We can then set these values equal to ftimmig2 for the same subset where ftimmig1 is missing:

anes_clean['ftimmig'] = anes_clean.ftimmig1
anes_clean.ftimmig[anes_clean.ftimmig1.isnull()] = anes_clean.ftimmig2[anes_clean.ftimmig1.isnull()]
anes_clean[['ftimmig','ftimmig1','ftimmig2']]

	ftimmig	ftimmig1	ftimmig2
0	97.0	97.0	NaN
1	94.0	94.0	NaN
2	69.0	69.0	NaN
3	100.0	NaN	100.0
4	92.0	NaN	92.0
...	...	...	...
3160	85.0	NaN	85.0
3161	50.0	NaN	50.0
3162	100.0	NaN	100.0
3163	49.0	NaN	49.0
3164	100.0	100.0	NaN

3165 rows × 3 columns

Now that we have created ftimmig, we can drop ftimmig1 and ftimmig2:

anes_clean = anes_clean.drop(['ftimmig1','ftimmig2'], axis=1)

8.6.4. Why Recoding Categorical Data is Dangerous #

Before we move on to another topic, there are three reasons why recoding categorical data is one of the most dangerous tasks in data manipulation.

First, it is very easy here to mistakenly place the wrong labels on categories. If we make an error here, Python will not inform us of the error. It is important to read the codebook carefully and make certain that the categories are labeled correctly. For example, suppose I want to study the effect of attending a political protest on voters’ preferences and beliefs. The ANES data contains a feature that reports whether or not each individual has attended a political protest in the last year. The value 1 means that the person attended a protest, and the value 2 means that the person did not attend a protest. If we mix up these labels, then any analysis that uses the protest feature will not only be wrong - it will generate results that are exactly the opposite of the truth. Suppose that people who attend a protest are 20% more likely to vote in the upcoming election than people who have not attended a protest. If we mislabel the protest categories, the result would suggest that people who attend a protest are 20% less likely to vote, and this result will have the same level of uncertainty as the truth. Imagine if these data came from a drug trial: if we mislabel the treatment and control groups, we would conclude that a drug that benefits patients harms those patients, or vice versa.

Second, we have an opportunity to change the data in significant ways while recoding categories. These changes might be the correct ones to make, but it is important to be extremely transparent with all of the changes as they can impact the results of any subsequent analysis. For example, the party identification feature in the ANES data is coded as

1 - Strong Democrat
2 - Not very strong Democrat
3 - Independent, closer to Democrats
4 - Independent
5 - Independent, closer to Republicans
6 - Not very strong Republican
7 - Strong Republican

For my purposes I choose to recode this feature as having three categories, Democrat, Republican, and independent. But to do so, I have to choose how exactly to collapse categories. I choose to combine “Strong Democrat”, “Not very strong Democrat”, and “Independent, closer to Democrats” into a category named “Democrat”, and “Strong Republican”, “Not very strong Republican”, and “Independent, closer to Republicans” into a category named “Republican”. I leave “Independent” as it is. There is not an unambiguously correct way to perform this recoding: maybe the two leaning-independent categories should be grouped within “Independent” instead. I recode the feature in this way because I theorize that many people in the survey will declare themselves to be independent, but really behave more like Democrats or Republicans. Even though I have a reason for choosing my coding scheme, it is a decision that can change any analysis that uses party identification. Maybe the results would be very different if we expanded the independent category.

Third, if the categories for a feature can be aligned in a meaningful order, we have a choice about whether to treat this feature as categorical or as numeric. If we treat the feature as categorical, then we label each number. That’s useful especially for generating visualizations in which this feature comprises an axis. If we treat the feature as numeric, we leave the numbers as they are (while ensuring that the categories are in the right order). That’s useful if we want to report statistics like the mean and variance, and we believe that these statistics have meaning for the ordered scale. For features like universal_income in the ANES data, the categories represent degrees of support for a policy. If we label the categories, we see clearly how many people adopt each nuanced position on the spectrum between support and non-support. If we leave the categories as numbers, we can report the average level of support on the 7-point scale. Whether to label the categories of an ordered categorical feature or leave them as numeric should therefore depend on the problems the feature will be applied to.

8.7. Sorting Rows #

Sorting is a largely cosmetic thing to do because most analyses don’t use the ordering of the rows to generate results. That said, especially in the early data cleaning stages of an analysis, it can be very useful to sort the rows of a dataframe according to the values in one or more columns, especially if the data contain an ID with a clear alphabetic or numeric order, like country names or time.

To sort, use the .sort_values() method. Within the method, use the by argument to specify the column you want to sort by. By default, sorting is done in ascending order for numeric features and in alphabetical order for string features, To save the sorting, either write inplace=True or re-assign the sorted dataframe to the same variable. .sort_values() will always put rows that have missing values for the sorting column at the bottom of the dataframe.

For example, to sort by lowest to highest ratings of Donald Trump, we can type:

anes_clean.sort_values(by = 'fttrump')

	caseid	liveurban	vote16	protest	vote	most_important_issue	confecon	ideology	partyID	universal_income	family_separation	free_college	forgive_loans	race	birthyr	sex	education	weight	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales	ftimmig
3164	3165	Town	Hillary Clinton	False	Joe Biden	trump	Extremely worried	Moderate	Democrat	Oppose a great deal	Favor strongly	Oppose a great deal	Oppose a great deal	Other	1960	Male	High school graduate	.809576969671362	0.0	100.0	70.0	51.0	100.0	100.0	39.0	100.0	0.0	100.0	100.0	99.0	100.0	98	100.0	98.0	2.0	0.0	99	98.0	98.0	99.0	96.0	100.0	100	99	100	97.0	99.0	98	98.0	98.0	99.0	100.0
1761	1762	City	Hillary Clinton	False	Joe Biden	The current incumbent in the White House. He h...	Moderately worried	Liberal	Democrat	Neither favor nor oppose	Oppose strongly	Neither favor nor oppose	Oppose a little	White	1974	Male	4-year degree	1.03413132599488	0.0	74.0	67.0	63.0	80.0	41.0	52.0	90.0	90.0	90.0	90.0	90.0	80.0	55	60.0	50.0	1.0	0.0	5	0.0	84.0	0.0	62.0	22.0	70	70	65	30.0	58.0	99	38.0	6.0	56.0	89.0
419	420	City	Hillary Clinton	False	Joe Biden	Climate	Moderately worried	Moderate	Democrat	Favor a little	Oppose strongly	Favor a moderate amount	Favor a little	White	1960	Female	4-year degree	1.0029772835284	0.0	75.0	59.0	65.0	60.0	74.0	66.0	75.0	51.0	64.0	73.0	51.0	48.0	65	75.0	75.0	39.0	37.0	50	44.0	69.0	50.0	69.0	50.0	84	91	79	87.0	88.0	81	43.0	41.0	66.0	60.0
1763	1764	Suburb	Hillary Clinton	False	Joe Biden	Environment	Extremely worried	Liberal	Democrat	Favor a moderate amount	Oppose strongly	Favor a great deal	Favor a great deal	White	1980	Female	4-year degree	1.03404200778801	0.0	95.0	77.0	81.0	78.0	74.0	91.0	95.0	72.0	93.0	89.0	92.0	91.0	100	95.0	93.0	3.0	3.0	39	10.0	81.0	33.0	82.0	59.0	81	93	87	61.0	87.0	86	56.0	6.0	62.0	91.0
1767	1768	Town	Did not vote	True	Joe Biden	Narcissism, sexism, homophobia, & global warming.	Moderately worried	NaN	NaN	Neither favor nor oppose	Oppose strongly	Neither favor nor oppose	Neither favor nor oppose	Hispanic	1991	Female	4-year degree	.818480694706408	0.0	10.0	71.0	22.0	72.0	54.0	67.0	26.0	97.0	59.0	99.0	57.0	72.0	82	52.0	52.0	1.0	48.0	52	50.0	4.0	49.0	50.0	3.0	7	51	49	48.0	53.0	98	51.0	49.0	50.0	80.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2764	2765	City	Did not vote	False	Someone else	make sure poeple dont do bad things	Moderately worried	NaN	Independent	Neither favor nor oppose	Neither favor nor disagree	Neither favor nor oppose	Neither favor nor oppose	White	1997	Female	High school graduate	1.08673596743961	NaN	100.0	NaN	NaN	NaN	NaN	NaN	12.0	100.0	6.0	16.0	39.0	19.0	31	28.0	36.0	72.0	53.0	22	45.0	21.0	29.0	49.0	44.0	10	100	41	39.0	47.0	31	10.0	46.0	26.0	27.0
2823	2824	Town	Did not vote	False	Donald Trump	Poverty	Very worried	NaN	Republican	Neither favor nor oppose	Neither favor nor disagree	Neither favor nor oppose	Neither favor nor oppose	White	1983	Female	2-year degree	.772007610334614	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3.0	9.0	6.0	7.0	4.0	6.0	6	NaN	NaN	NaN	NaN	6	5.0	4.0	5.0	6.0	6.0	6	8	7	6.0	3.0	9	6.0	3.0	6.0	2.0
2910	2911	City	Hillary Clinton	True	Joe Biden		Very worried	Conservative	Democrat	Favor a moderate amount	Neither favor nor disagree	Neither favor nor oppose	Neither favor nor oppose	Black	1972	Female	High school graduate	.894064952261024	NaN	56.0	30.0	38.0	23.0	29.0	42.0	28.0	50.0	71.0	34.0	39.0	23.0	32	17.0	50.0	38.0	41.0	27	25.0	35.0	40.0	43.0	34.0	31	72	27	28.0	45.0	60	32.0	41.0	32.0	27.0
3019	3020	Town	Did not vote	False	Probably will not vote	Idk	Extremely worried	NaN	NaN	Neither favor nor oppose	Neither favor nor disagree	Neither favor nor oppose	Neither favor nor oppose	Black	1994	Female	2-year degree	1.38707451644063	NaN	NaN	NaN	NaN	NaN	NaN	NaN	27.0	36.0	36.0	22.0	27.0	37.0	21	NaN	NaN	29.0	NaN	29	38.0	29.0	19.0	28.0	18.0	20	33	40	22.0	23.0	38	31.0	41.0	40.0	28.0
3091	3092	Suburb	Hillary Clinton	True	Someone else	Donald Trump and the Rep party’s lack of moral...	Extremely worried	Liberal	Democrat	Oppose a great deal	Oppose strongly	Favor a great deal	Favor a great deal	Hispanic	1970	Male	4-year degree	.556427919485323	NaN	68.0	43.0	91.0	97.0	51.0	58.0	96.0	11.0	96.0	96.0	81.0	92.0	58	92.0	93.0	0.0	4.0	16	6.0	97.0	9.0	44.0	53.0	69	82	62	57.0	76.0	100	4.0	4.0	66.0	94.0

3165 rows × 52 columns

To sort in descending order from the highest to lowest values of fttrump, we can use the ascending=False parameter:

anes_clean.sort_values(by = 'fttrump', ascending=False)

	caseid	liveurban	vote16	protest	vote	most_important_issue	confecon	ideology	partyID	universal_income	family_separation	free_college	forgive_loans	race	birthyr	sex	education	weight	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales	ftimmig
2452	2453	Rural	Donald Trump	False	Donald Trump	None	A little worried	Conservative	Republican	Favor a little	Favor strongly	Neither favor nor oppose	Favor a little	White	1995	Female	High school graduate	.753518532246602	100.0	9.0	1.0	5.0	9.0	3.0	3.0	5.0	3.0	7.0	1.0	0.0	2.0	9	16.0	7.0	4.0	2.0	8	13.0	15.0	5.0	6.0	15.0	9	18	8	9.0	9.0	0	2.0	13.0	8.0	4.0
1735	1736	Suburb	Donald Trump	False	Donald Trump	The illegal immigrant entering the country ill...	A little worried	Conservative	Republican	Oppose a great deal	Favor somewhat	Oppose a great deal	Oppose a great deal	White	1981	Female	Some college	.819619380522467	100.0	3.0	0.0	0.0	1.0	0.0	4.0	54.0	53.0	50.0	48.0	3.0	0.0	2	1.0	1.0	99.0	97.0	0	1.0	0.0	0.0	0.0	3.0	53	9	25	88.0	52.0	2	52.0	2.0	2.0	100.0
3076	3077	Suburb	Donald Trump	False	Donald Trump	Fake News Media	A little worried	Conservative	Republican	Oppose a great deal	Neither favor nor disagree	Oppose a great deal	Oppose a great deal	White	1966	Male	Post-graduate	1.25802472009798	100.0	6.0	0.0	2.0	2.0	5.0	5.0	50.0	50.0	50.0	50.0	50.0	3.0	2	15.0	4.0	100.0	100.0	3	3.0	50.0	27.0	4.0	3.0	51	3	62	100.0	30.0	50	4.0	5.0	3.0	70.0
2174	2175	Rural	Donald Trump	False	Donald Trump	lifetime politicians	Not at all worried	Conservative	Republican	Oppose a great deal	Favor somewhat	Oppose a great deal	Oppose a great deal	White	1947	Male	High school graduate		100.0	1.0	0.0	1.0	1.0	1.0	3.0	79.0	79.0	82.0	69.0	2.0	1.0	20	5.0	6.0	69.0	95.0	15	5.0	12.0	20.0	59.0	0.0	60	25	41	89.0	11.0	69	29.0	74.0	4.0	80.0
3077	3078	Suburb	Donald Trump	False	Donald Trump	Illegal immigration	Not at all worried	Conservative	Republican	Oppose a great deal	Neither favor nor disagree	Oppose a great deal	Oppose a great deal	White	1960	Male	Post-graduate	1.0152659648901	100.0	31.0	13.0	33.0	15.0	16.0	25.0	93.0	88.0	94.0	87.0	72.0	40.0	8	54.0	23.0	100.0	82.0	11	3.0	72.0	47.0	39.0	4.0	88	44	96	100.0	19.0	70	23.0	42.0	12.0	90.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2764	2765	City	Did not vote	False	Someone else	make sure poeple dont do bad things	Moderately worried	NaN	Independent	Neither favor nor oppose	Neither favor nor disagree	Neither favor nor oppose	Neither favor nor oppose	White	1997	Female	High school graduate	1.08673596743961	NaN	100.0	NaN	NaN	NaN	NaN	NaN	12.0	100.0	6.0	16.0	39.0	19.0	31	28.0	36.0	72.0	53.0	22	45.0	21.0	29.0	49.0	44.0	10	100	41	39.0	47.0	31	10.0	46.0	26.0	27.0
2823	2824	Town	Did not vote	False	Donald Trump	Poverty	Very worried	NaN	Republican	Neither favor nor oppose	Neither favor nor disagree	Neither favor nor oppose	Neither favor nor oppose	White	1983	Female	2-year degree	.772007610334614	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3.0	9.0	6.0	7.0	4.0	6.0	6	NaN	NaN	NaN	NaN	6	5.0	4.0	5.0	6.0	6.0	6	8	7	6.0	3.0	9	6.0	3.0	6.0	2.0
2910	2911	City	Hillary Clinton	True	Joe Biden		Very worried	Conservative	Democrat	Favor a moderate amount	Neither favor nor disagree	Neither favor nor oppose	Neither favor nor oppose	Black	1972	Female	High school graduate	.894064952261024	NaN	56.0	30.0	38.0	23.0	29.0	42.0	28.0	50.0	71.0	34.0	39.0	23.0	32	17.0	50.0	38.0	41.0	27	25.0	35.0	40.0	43.0	34.0	31	72	27	28.0	45.0	60	32.0	41.0	32.0	27.0
3019	3020	Town	Did not vote	False	Probably will not vote	Idk	Extremely worried	NaN	NaN	Neither favor nor oppose	Neither favor nor disagree	Neither favor nor oppose	Neither favor nor oppose	Black	1994	Female	2-year degree	1.38707451644063	NaN	NaN	NaN	NaN	NaN	NaN	NaN	27.0	36.0	36.0	22.0	27.0	37.0	21	NaN	NaN	29.0	NaN	29	38.0	29.0	19.0	28.0	18.0	20	33	40	22.0	23.0	38	31.0	41.0	40.0	28.0
3091	3092	Suburb	Hillary Clinton	True	Someone else	Donald Trump and the Rep party’s lack of moral...	Extremely worried	Liberal	Democrat	Oppose a great deal	Oppose strongly	Favor a great deal	Favor a great deal	Hispanic	1970	Male	4-year degree	.556427919485323	NaN	68.0	43.0	91.0	97.0	51.0	58.0	96.0	11.0	96.0	96.0	81.0	92.0	58	92.0	93.0	0.0	4.0	16	6.0	97.0	9.0	44.0	53.0	69	82	62	57.0	76.0	100	4.0	4.0	66.0	94.0

3165 rows × 52 columns

We can also sort the rows by the values of more than one column. When we do that, the second (and third, and so on) column is only used to break the ties of the first (and second, etc.) column. All we have to do is write more than one column name in a list, and pass that list to the by argument of .sort_values(). The ascending argument also takes a list of True or False values to denote whether each of the columns should be sorted in ascending or descending order. To sort the rows in ascending order by values of the Donald Trump thermometer, while breaking ties by descending order of the Joe Biden thermometer, we can type:

anes_clean.sort_values(by=['fttrump', 'ftbiden'], ascending = [True, False])

	caseid	liveurban	vote16	protest	vote	most_important_issue	confecon	ideology	partyID	universal_income	family_separation	free_college	forgive_loans	race	birthyr	sex	education	weight	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales	ftimmig
94	95	Suburb	Hillary Clinton	False	Joe Biden	The president.	Not at all worried	Liberal	Democrat	Neither favor nor oppose	Oppose strongly	Favor a moderate amount	Favor a great deal	White	1947	Female	Some college	1.02094882538262	0.0	100.0	100.0	71.0	70.0	99.0	60.0	50.0	50.0	50.0	51.0	50.0	50.0	85	99.0	94.0	25.0	0.0	0	0.0	96.0	56.0	95.0	0.0	100	94	95	41.0	95.0	100	30.0	0.0	69.0	50.0
262	263	City	Hillary Clinton	False	Joe Biden	Trumps presidency	Moderately worried	Liberal	Democrat	Favor a little	Oppose strongly	Favor a moderate amount	Oppose a little	White	1959	Male	High school graduate	.878855634357939	0.0	99.0	100.0	91.0	100.0	91.0	41.0	50.0	50.0	91.0	50.0	40.0	50.0	90	99.0	87.0	2.0	1.0	4	2.0	80.0	0.0	51.0	1.0	96	91	91	40.0	99.0	99	1.0	1.0	91.0	81.0
355	356	Suburb	Hillary Clinton	False	Joe Biden	That Donald Trump is an immoral racist man who...	A little worried	Moderate	Democrat	Oppose a great deal	Oppose strongly	Favor a great deal	Favor a great deal	White	1957	Female	2-year degree	1.03170391458116	0.0	80.0	100.0	27.0	28.0	98.0	31.0	50.0	52.0	51.0	50.0	51.0	71.0	74	66.0	57.0	6.0	1.0	5	1.0	70.0	2.0	51.0	2.0	99	100	86	38.0	78.0	99	3.0	2.0	NaN	74.0
461	462	Town	Hillary Clinton	False	Joe Biden	The division made by Donald Trump as it concer...	Moderately worried	Liberal	Democrat	Oppose a moderate amount	Oppose strongly	Favor a little	Favor a moderate amount	White	1955	Female	Some college	1.03170391458116	0.0	91.0	100.0	70.0	80.0	91.0	51.0	89.0	80.0	91.0	90.0	88.0	71.0	96	100.0	90.0	4.0	5.0	37	4.0	79.0	28.0	84.0	33.0	100	85	90	80.0	85.0	98	40.0	5.0	79.0	90.0
499	500	City	Hillary Clinton	False	Joe Biden	The economy	Extremely worried	NaN	Democrat	Favor a great deal	Oppose strongly	Favor a moderate amount	Favor a great deal	Hispanic	1967	Male	No HS diploma	2.26361888055549	0.0	98.0	100.0	98.0	97.0	NaN	NaN	66.0	99.0	100.0	100.0	97.0	99.0	68	NaN	99.0	2.0	39.0	100	2.0	55.0	16.0	99.0	3.0	99	100	58	97.0	100.0	99	63.0	13.0	99.0	92.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2499	2500	City	Did not vote	False	Probably will not vote	don’t know	A little worried	NaN	NaN	Neither favor nor oppose	Neither favor nor disagree	Neither favor nor oppose	Neither favor nor oppose	Hispanic	1998	Female	Some college	.893556010098802	NaN	NaN	NaN	NaN	NaN	NaN	NaN	50.0	50.0	50.0	51.0	52.0	49.0	51	NaN	NaN	NaN	NaN	50	50.0	51.0	51.0	47.0	49.0	51	48	50	50.0	50.0	50	53.0	52.0	48.0	48.0
2533	2534	Suburb	Did not vote	False	Probably will not vote	Everything	A little worried	NaN	NaN	Neither favor nor oppose	Neither favor nor disagree	Neither favor nor oppose	Neither favor nor oppose	Black	1987	Female	High school graduate	.738770525410191	NaN	NaN	NaN	NaN	NaN	NaN	NaN	99.0	75.0	53.0	13.0	88.0	47.0	88	NaN	13.0	NaN	NaN	6	7.0	4.0	2.0	11.0	6.0	7	3	4	1.0	5.0	3	9.0	6.0	6.0	48.0
2764	2765	City	Did not vote	False	Someone else	make sure poeple dont do bad things	Moderately worried	NaN	Independent	Neither favor nor oppose	Neither favor nor disagree	Neither favor nor oppose	Neither favor nor oppose	White	1997	Female	High school graduate	1.08673596743961	NaN	100.0	NaN	NaN	NaN	NaN	NaN	12.0	100.0	6.0	16.0	39.0	19.0	31	28.0	36.0	72.0	53.0	22	45.0	21.0	29.0	49.0	44.0	10	100	41	39.0	47.0	31	10.0	46.0	26.0	27.0
2823	2824	Town	Did not vote	False	Donald Trump	Poverty	Very worried	NaN	Republican	Neither favor nor oppose	Neither favor nor disagree	Neither favor nor oppose	Neither favor nor oppose	White	1983	Female	2-year degree	.772007610334614	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3.0	9.0	6.0	7.0	4.0	6.0	6	NaN	NaN	NaN	NaN	6	5.0	4.0	5.0	6.0	6.0	6	8	7	6.0	3.0	9	6.0	3.0	6.0	2.0
3019	3020	Town	Did not vote	False	Probably will not vote	Idk	Extremely worried	NaN	NaN	Neither favor nor oppose	Neither favor nor disagree	Neither favor nor oppose	Neither favor nor oppose	Black	1994	Female	2-year degree	1.38707451644063	NaN	NaN	NaN	NaN	NaN	NaN	NaN	27.0	36.0	36.0	22.0	27.0	37.0	21	NaN	NaN	29.0	NaN	29	38.0	29.0	19.0	28.0	18.0	20	33	40	22.0	23.0	38	31.0	41.0	40.0	28.0

3165 rows × 52 columns

8.8. Filtering Rows #

Filtering is the task of keeping or deleting rows based on a logical condition. Prior to discussing the code for filtering, let’s review logical operators. Every logical statement returns a value of True or False in Python, and these statements are useful for keeping rows for which a condition is True and deleting rows for which a condition is False.

Python has the following logical operators:

== “is equal to”
!= “is not equal to”
> “is greater than”
>= “is greater than or equal to”
< “is less than”
<= “is less than or equal to”
in “is in the list”
not in “is not in the list”

It also has the following symbols to connect logical statements to build more complicated statements:

& “and” (both conditions must be true for the whole statement to be true)
| “or” (at least one of the conditiona must be true for the whole statement to be true)
not turns True results to False, and vice versa
( and ) (parentheses work with logic the way they do with algebra – consider this part of the statement first)

There are many ways to filter the rows in a dataframe based on a logical condition, but the best method is .query() because of its notational ease and because it allows chaining: that is, specifying many logical conditions with repeated calls to .query(). Chaining is another way to express “and” in a combined logical statement. To use .query(), type it as a method applied to the dataframe in question, and put the logical condition in double quotes - that allows us to use single quotes within the query. For example, to see just the Joe Biden voters, type:

anes_clean.query("vote=='Joe Biden'")

	caseid	liveurban	vote16	protest	vote	most_important_issue	confecon	ideology	partyID	universal_income	family_separation	free_college	forgive_loans	race	birthyr	sex	education	weight	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales	ftimmig
0	1	Suburb	Someone else	False	Joe Biden	Health Care	A little worried	Conservative	Democrat	Favor a moderate amount	Oppose strongly	Favor a moderate amount	Favor a little	Hispanic	1969	Male	High school graduate	1.34719693063187	47.0	90.0	52.0	52.0	49.0	NaN	50.0	99.0	99.0	99.0	100.0	88.0	79.0	99	82.0	71.0	86.0	88.0	90	66.0	89.0	88.0	81.0	77.0	98	94	89	88.0	99.0	99	92.0	89.0	86.0	97.0
2	3	Rural	Hillary Clinton	False	Joe Biden	health care	Extremely worried	Moderate	Democrat	Neither favor nor oppose	Oppose strongly	Oppose a moderate amount	Oppose a great deal	White	1954	Female	High school graduate	.966366930694957	0.0	91.0	88.0	15.0	60.0	70.0	68.0	48.0	49.0	49.0	49.0	50.0	39.0	63	66.0	51.0	40.0	2.0	2	2.0	1.0	3.0	59.0	1.0	50	1	1	1.0	51.0	87	50.0	1.0	3.0	69.0
5	6	Suburb	Hillary Clinton	False	Joe Biden	The influence of big money on our political sy...	Not at all worried	Moderate	Democrat	Favor a moderate amount	Oppose strongly	Favor a moderate amount	Favor a great deal	Black	1942	Male	4-year degree	1.02140871415171	46.0	83.0	80.0	69.0	80.0	59.0	64.0	87.0	69.0	80.0	61.0	70.0	63.0	56	68.0	70.0	58.0	43.0	61	47.0	76.0	51.0	81.0	49.0	81	65	74	95.0	72.0	71	51.0	44.0	81.0	81.0
6	7	City	Did not vote	False	Joe Biden		Moderately worried	Liberal	Democrat	Neither favor nor oppose	Oppose strongly	Neither favor nor oppose	Neither favor nor oppose	White	2000	Female	Some college	.964514474045239	0.0	100.0	75.0	91.0	52.0	71.0	81.0	98.0	85.0	97.0	98.0	98.0	73.0	70	97.0	97.0	47.0	3.0	53	51.0	75.0	50.0	70.0	50.0	59	74	73	50.0	90.0	95	63.0	18.0	70.0	99.0
7	8	City	Hillary Clinton	True	Joe Biden	climate change	Not at all worried	Liberal	Democrat	Favor a little	Oppose strongly	Favor a little	Oppose a moderate amount	White	1994	Female	Post-graduate	.83469258858232	1.0	83.0	45.0	86.0	71.0	71.0	79.0	73.0	75.0	76.0	76.0	79.0	75.0	75	50.0	86.0	40.0	1.0	23	0.0	70.0	38.0	50.0	35.0	60	66	78	56.0	58.0	82	50.0	33.0	46.0	76.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3153	3154	City	Donald Trump	False	Joe Biden	The manner in which Donald Trump conducts hims...	Moderately worried	Moderate	Democrat	Favor a great deal	Oppose strongly	Oppose a great deal	Oppose a great deal	White	1939	Male	Some college	.83972997938151	6.0	69.0	12.0	20.0	50.0	99.0	13.0	69.0	69.0	69.0	69.0	69.0	31.0	59	51.0	51.0	79.0	1.0	15	2.0	48.0	3.0	54.0	4.0	83	72	70	50.0	75.0	84	13.0	9.0	50.0	69.0
3154	3155	Town	Did not vote	False	Joe Biden	trump	Moderately worried	Liberal	Democrat	Favor a great deal	Oppose strongly	Favor a great deal	Favor a great deal	White	1978	Male	4-year degree	.919903784560563	1.0	81.0	61.0	65.0	81.0	70.0	65.0	50.0	60.0	46.0	56.0	45.0	50.0	60	70.0	60.0	30.0	6.0	35	21.0	41.0	11.0	50.0	11.0	69	45	75	46.0	56.0	85	27.0	15.0	36.0	55.0
3156	3157	Suburb	Hillary Clinton	False	Joe Biden	health care	A little worried	Moderate	Democrat	Favor a moderate amount	Oppose strongly	Oppose a moderate amount	Oppose a great deal	White	1980	Male	Post-graduate	.783602487218187	1.0	95.0	86.0	41.0	41.0	81.0	70.0	99.0	99.0	100.0	100.0	45.0	60.0	100	94.0	95.0	46.0	16.0	79	30.0	99.0	48.0	65.0	28.0	85	99	100	66.0	99.0	99	40.0	70.0	56.0	100.0
3163	3164	Suburb	Did not vote	False	Joe Biden	Donald Trump	Moderately worried	Liberal	Democrat	Favor a moderate amount	Oppose somewhat	Neither favor nor oppose	Oppose a little	Black	1960	Male	No HS diploma	1.58161278448241	1.0	100.0	95.0	62.0	79.0	59.0	51.0	100.0	65.0	50.0	52.0	50.0	51.0	91	84.0	72.0	59.0	0.0	54	0.0	62.0	0.0	60.0	0.0	73	55	50	1.0	56.0	89	0.0	0.0	1.0	49.0
3164	3165	Town	Hillary Clinton	False	Joe Biden	trump	Extremely worried	Moderate	Democrat	Oppose a great deal	Favor strongly	Oppose a great deal	Oppose a great deal	Other	1960	Male	High school graduate	.809576969671362	0.0	100.0	70.0	51.0	100.0	100.0	39.0	100.0	0.0	100.0	100.0	99.0	100.0	98	100.0	98.0	2.0	0.0	99	98.0	98.0	99.0	96.0	100.0	100	99	100	97.0	99.0	98	98.0	98.0	99.0	100.0

1288 rows × 52 columns

To keep the rows for people that will vote for Joe Biden, but rate him less than 40:

anes_clean.query("vote=='Joe Biden' & ftbiden < 40")

	caseid	liveurban	vote16	protest	vote	most_important_issue	confecon	ideology	partyID	universal_income	family_separation	free_college	forgive_loans	race	birthyr	sex	education	weight	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales	ftimmig
12	13	City	Donald Trump	False	Joe Biden	Assumenda mollit bla	Extremely worried	Moderate	Independent	Favor a moderate amount	Favor somewhat	Oppose a little	Neither favor nor oppose	White	1994	Female	2-year degree	1.08428147456979	25.0	66.0	31.0	86.0	NaN	53.0	78.0	69.0	21.0	51.0	47.0	70.0	54.0	62	49.0	NaN	NaN	NaN	65	42.0	26.0	33.0	51.0	53.0	78	61	46	76.0	38.0	36	10.0	67.0	50.0	34.0
63	64	Suburb	Hillary Clinton	False	Joe Biden	Climate change and partly because we refuse to...	A little worried	Liberal	Democrat	Oppose a little	Neither favor nor disagree	Oppose a little	Favor a little	White	1930	Female	Post-graduate	1.02996454702489	5.0	76.0	37.0	73.0	64.0	55.0	39.0	67.0	64.0	71.0	67.0	59.0	64.0	66	63.0	50.0	22.0	6.0	21	5.0	50.0	50.0	22.0	26.0	68	59	60	31.0	50.0	87	44.0	22.0	71.0	74.0
65	66	Rural	Hillary Clinton	False	Joe Biden	Trump	Moderately worried	Liberal	Democrat	Neither favor nor oppose	Oppose strongly	Oppose a little	Oppose a moderate amount	White	1952	Female	High school graduate	1.02515128827396	0.0	100.0	33.0	34.0	96.0	96.0	42.0	90.0	49.0	21.0	50.0	50.0	0.0	71	50.0	51.0	3.0	0.0	53	0.0	22.0	0.0	61.0	1.0	80	50	30	1.0	53.0	94	12.0	0.0	10.0	76.0
105	106	Town	Did not vote	False	Joe Biden	guns	Moderately worried	NaN	NaN	Neither favor nor oppose	Oppose strongly	Favor a little	Favor a great deal	Other	1996	Female	4-year degree	.77435239235677	0.0	51.0	8.0	59.0	73.0	76.0	2.0	53.0	0.0	44.0	87.0	3.0	50.0	52	8.0	33.0	5.0	0.0	4	50.0	2.0	1.0	38.0	37.0	5	51	66	1.0	6.0	30	100.0	2.0	47.0	50.0
129	130	Town	Hillary Clinton	False	Joe Biden	Lack of access to healthcare, racism, white na...	Very worried	Liberal	Democrat	Favor a great deal	Oppose strongly	Favor a great deal	Favor a great deal	White	1977	Female	Post-graduate	1.15605850208199	0.0	61.0	9.0	90.0	100.0	15.0	42.0	100.0	64.0	95.0	100.0	100.0	79.0	89	71.0	90.0	0.0	0.0	59	0.0	69.0	6.0	50.0	59.0	88	100	86	27.0	100.0	91	15.0	22.0	83.0	100.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3063	3064	Rural	Hillary Clinton	False	Joe Biden	Gun violence	A little worried	Liberal	Democrat	Oppose a moderate amount	Oppose strongly	Favor a great deal	Favor a moderate amount	White	1957	Male	High school graduate	.897550307642941	5.0	65.0	21.0	71.0	84.0	34.0	59.0	69.0	59.0	50.0	46.0	39.0	36.0	59	50.0	50.0	50.0	5.0	53	0.0	61.0	29.0	18.0	18.0	97	69	67	59.0	52.0	89	39.0	6.0	17.0	50.0
3069	3070	Suburb	Hillary Clinton	False	Joe Biden	ECONOMY	Very worried	Liberal	Democrat	Favor a moderate amount	Oppose somewhat	Favor a great deal	Favor a moderate amount	White	1983	Male	2-year degree	.938881851047225	11.0	71.0	31.0	39.0	99.0	30.0	19.0	30.0	51.0	71.0	94.0	47.0	28.0	51	50.0	51.0	59.0	49.0	61	59.0	69.0	50.0	50.0	50.0	50	69	70	50.0	59.0	60	49.0	51.0	51.0	69.0
3136	3137	Rural	Hillary Clinton	False	Joe Biden	Donald J. Trump	Moderately worried	Liberal	Democrat	Favor a little	Oppose strongly	Favor a little	Favor a little	White	1950	Male	Some college	.944411233915414	3.0	86.0	22.0	94.0	9.0	94.0	25.0	53.0	58.0	59.0	57.0	59.0	60.0	94	84.0	85.0	11.0	3.0	5	6.0	94.0	5.0	88.0	6.0	92	84	84	76.0	88.0	95	5.0	4.0	53.0	67.0
3146	3147	Suburb	Hillary Clinton	False	Joe Biden	The ever-growing populism from both sides of t...	Extremely worried	Liberal	Democrat	Favor a great deal	Oppose strongly	Favor a moderate amount	Favor a great deal	White	1995	Male	4-year degree	1.08737874806145	0.0	76.0	34.0	87.0	22.0	66.0	62.0	60.0	61.0	61.0	72.0	46.0	85.0	88	82.0	41.0	0.0	1.0	1	0.0	86.0	0.0	84.0	29.0	18	81	77	0.0	35.0	68	4.0	0.0	49.0	86.0
3153	3154	City	Donald Trump	False	Joe Biden	The manner in which Donald Trump conducts hims...	Moderately worried	Moderate	Democrat	Favor a great deal	Oppose strongly	Oppose a great deal	Oppose a great deal	White	1939	Male	Some college	.83972997938151	6.0	69.0	12.0	20.0	50.0	99.0	13.0	69.0	69.0	69.0	69.0	69.0	31.0	59	51.0	51.0	79.0	1.0	15	2.0	48.0	3.0	54.0	4.0	83	72	70	50.0	75.0	84	13.0	9.0	50.0	69.0

121 rows × 52 columns

Or alternatively,

anes_clean.query("vote=='Joe Biden'").query('ftbiden < 40')

	caseid	liveurban	vote16	protest	vote	most_important_issue	confecon	ideology	partyID	universal_income	family_separation	free_college	forgive_loans	race	birthyr	sex	education	weight	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales	ftimmig
12	13	City	Donald Trump	False	Joe Biden	Assumenda mollit bla	Extremely worried	Moderate	Independent	Favor a moderate amount	Favor somewhat	Oppose a little	Neither favor nor oppose	White	1994	Female	2-year degree	1.08428147456979	25.0	66.0	31.0	86.0	NaN	53.0	78.0	69.0	21.0	51.0	47.0	70.0	54.0	62	49.0	NaN	NaN	NaN	65	42.0	26.0	33.0	51.0	53.0	78	61	46	76.0	38.0	36	10.0	67.0	50.0	34.0
63	64	Suburb	Hillary Clinton	False	Joe Biden	Climate change and partly because we refuse to...	A little worried	Liberal	Democrat	Oppose a little	Neither favor nor disagree	Oppose a little	Favor a little	White	1930	Female	Post-graduate	1.02996454702489	5.0	76.0	37.0	73.0	64.0	55.0	39.0	67.0	64.0	71.0	67.0	59.0	64.0	66	63.0	50.0	22.0	6.0	21	5.0	50.0	50.0	22.0	26.0	68	59	60	31.0	50.0	87	44.0	22.0	71.0	74.0
65	66	Rural	Hillary Clinton	False	Joe Biden	Trump	Moderately worried	Liberal	Democrat	Neither favor nor oppose	Oppose strongly	Oppose a little	Oppose a moderate amount	White	1952	Female	High school graduate	1.02515128827396	0.0	100.0	33.0	34.0	96.0	96.0	42.0	90.0	49.0	21.0	50.0	50.0	0.0	71	50.0	51.0	3.0	0.0	53	0.0	22.0	0.0	61.0	1.0	80	50	30	1.0	53.0	94	12.0	0.0	10.0	76.0
105	106	Town	Did not vote	False	Joe Biden	guns	Moderately worried	NaN	NaN	Neither favor nor oppose	Oppose strongly	Favor a little	Favor a great deal	Other	1996	Female	4-year degree	.77435239235677	0.0	51.0	8.0	59.0	73.0	76.0	2.0	53.0	0.0	44.0	87.0	3.0	50.0	52	8.0	33.0	5.0	0.0	4	50.0	2.0	1.0	38.0	37.0	5	51	66	1.0	6.0	30	100.0	2.0	47.0	50.0
129	130	Town	Hillary Clinton	False	Joe Biden	Lack of access to healthcare, racism, white na...	Very worried	Liberal	Democrat	Favor a great deal	Oppose strongly	Favor a great deal	Favor a great deal	White	1977	Female	Post-graduate	1.15605850208199	0.0	61.0	9.0	90.0	100.0	15.0	42.0	100.0	64.0	95.0	100.0	100.0	79.0	89	71.0	90.0	0.0	0.0	59	0.0	69.0	6.0	50.0	59.0	88	100	86	27.0	100.0	91	15.0	22.0	83.0	100.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3063	3064	Rural	Hillary Clinton	False	Joe Biden	Gun violence	A little worried	Liberal	Democrat	Oppose a moderate amount	Oppose strongly	Favor a great deal	Favor a moderate amount	White	1957	Male	High school graduate	.897550307642941	5.0	65.0	21.0	71.0	84.0	34.0	59.0	69.0	59.0	50.0	46.0	39.0	36.0	59	50.0	50.0	50.0	5.0	53	0.0	61.0	29.0	18.0	18.0	97	69	67	59.0	52.0	89	39.0	6.0	17.0	50.0
3069	3070	Suburb	Hillary Clinton	False	Joe Biden	ECONOMY	Very worried	Liberal	Democrat	Favor a moderate amount	Oppose somewhat	Favor a great deal	Favor a moderate amount	White	1983	Male	2-year degree	.938881851047225	11.0	71.0	31.0	39.0	99.0	30.0	19.0	30.0	51.0	71.0	94.0	47.0	28.0	51	50.0	51.0	59.0	49.0	61	59.0	69.0	50.0	50.0	50.0	50	69	70	50.0	59.0	60	49.0	51.0	51.0	69.0
3136	3137	Rural	Hillary Clinton	False	Joe Biden	Donald J. Trump	Moderately worried	Liberal	Democrat	Favor a little	Oppose strongly	Favor a little	Favor a little	White	1950	Male	Some college	.944411233915414	3.0	86.0	22.0	94.0	9.0	94.0	25.0	53.0	58.0	59.0	57.0	59.0	60.0	94	84.0	85.0	11.0	3.0	5	6.0	94.0	5.0	88.0	6.0	92	84	84	76.0	88.0	95	5.0	4.0	53.0	67.0
3146	3147	Suburb	Hillary Clinton	False	Joe Biden	The ever-growing populism from both sides of t...	Extremely worried	Liberal	Democrat	Favor a great deal	Oppose strongly	Favor a moderate amount	Favor a great deal	White	1995	Male	4-year degree	1.08737874806145	0.0	76.0	34.0	87.0	22.0	66.0	62.0	60.0	61.0	61.0	72.0	46.0	85.0	88	82.0	41.0	0.0	1.0	1	0.0	86.0	0.0	84.0	29.0	18	81	77	0.0	35.0	68	4.0	0.0	49.0	86.0
3153	3154	City	Donald Trump	False	Joe Biden	The manner in which Donald Trump conducts hims...	Moderately worried	Moderate	Democrat	Favor a great deal	Oppose strongly	Oppose a great deal	Oppose a great deal	White	1939	Male	Some college	.83972997938151	6.0	69.0	12.0	20.0	50.0	99.0	13.0	69.0	69.0	69.0	69.0	69.0	31.0	59	51.0	51.0	79.0	1.0	15	2.0	48.0	3.0	54.0	4.0	83	72	70	50.0	75.0	84	13.0	9.0	50.0	69.0

121 rows × 52 columns

Who in their right mind would rate Joe Biden 100 and Trump 100? These 6 people!

anes_clean.query("ftbiden == 100 & fttrump == 100")

	caseid	liveurban	vote16	protest	vote	most_important_issue	confecon	ideology	partyID	universal_income	family_separation	free_college	forgive_loans	race	birthyr	sex	education	weight	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales	ftimmig
267	268	City	Donald Trump	False	Donald Trump	Education.	A little worried	Conservative	Republican	Favor a little	Oppose somewhat	Favor a little	Favor a great deal	White	1959	Female	Post-graduate	.746904935636401	100.0	0.0	100.0	1.0	4.0	NaN	NaN	70.0	90.0	61.0	63.0	15.0	6.0	21	48.0	36.0	49.0	43.0	50	50.0	43.0	39.0	60.0	43.0	58	40	63	90.0	69.0	53	53.0	56.0	54.0	16.0
1526	1527	City	Donald Trump	False	Donald Trump	EVERYTHING	Not at all worried	Conservative	Republican	Favor a great deal	Neither favor nor disagree	Neither favor nor oppose	Neither favor nor oppose	White	1956	Male	High school graduate	.847649185791497	100.0	99.0	100.0	71.0	81.0	80.0	90.0	99.0	100.0	95.0	91.0	82.0	50.0	98	94.0	89.0	96.0	99.0	71	70.0	78.0	68.0	69.0	47.0	85	77	83	57.0	100.0	100	91.0	88.0	76.0	99.0
1971	1972	Rural	Did not vote	False	Joe Biden	Proverty	Moderately worried	NaN	Republican	Neither favor nor oppose	Oppose strongly	Favor a great deal	Favor a great deal	White	1993	Female	2-year degree	.872100489544827	100.0	99.0	100.0	66.0	100.0	60.0	57.0	100.0	100.0	100.0	100.0	100.0	100.0	53	100.0	67.0	100.0	100.0	100	100.0	100.0	99.0	100.0	100.0	100	100	98	100.0	100.0	100	100.0	98.0	100.0	100.0
2271	2272	Town	Hillary Clinton	False	Someone else	jobs and homeless and Police just shoting norm...	Extremely worried	Conservative	Democrat	Favor a great deal	Favor strongly	Favor a great deal	Favor a great deal	White	1980	Male	Some college	1.05546596271457	100.0	78.0	100.0	58.0	29.0	5.0	100.0	90.0	94.0	89.0	9.0	88.0	89.0	86	86.0	79.0	79.0	69.0	68	27.0	57.0	7.0	9.0	8.0	68	10	77	5.0	8.0	62	95.0	9.0	78.0	88.0
2408	2409	City	Did not vote	False	Joe Biden	Not being the best number one country.	Extremely worried	Moderate	Independent	Favor a great deal	Favor strongly	Oppose a great deal	Favor a great deal	Black	1995	Male	Some college	3.47027368867645	100.0	100.0	100.0	99.0	100.0	99.0	100.0	100.0	100.0	100.0	99.0	75.0	2.0	100	100.0	100.0	99.0	99.0	100	100.0	100.0	100.0	100.0	100.0	100	100	100	100.0	100.0	100	100.0	100.0	100.0	100.0
2721	2722	Rural	Hillary Clinton	True	Joe Biden	Racism	Extremely worried	Liberal	Democrat	Favor a great deal	Favor strongly	Favor a great deal	Favor a great deal	Black	1990	Male	High school graduate	2.13773400773957	100.0	98.0	100.0	98.0	99.0	79.0	99.0	100.0	98.0	85.0	82.0	84.0	94.0	90	91.0	97.0	97.0	92.0	88	92.0	97.0	94.0	94.0	88.0	92	93	94	86.0	96.0	88	93.0	94.0	89.0	93.0

I want to see all of the voters that are extremely worried about the economy and either live in rural areas or were born prior to 1950:

anes_clean.query("confecon == 'Extremely worried' & (liveurban == 'Rural' | birthyr < 1950)")

	caseid	liveurban	vote16	protest	vote	most_important_issue	confecon	ideology	partyID	universal_income	family_separation	free_college	forgive_loans	race	birthyr	sex	education	weight	fttrump	ftobama	ftbiden	ftwarren	ftsanders	ftbuttigieg	ftharris	ftblack	ftwhite	fthisp	ftasian	ftmuslim	ftillegal	ftjournal	ftnato	ftun	ftice	ftnra	ftchina	ftnkorea	ftmexico	ftsaudi	ftukraine	ftiran	ftbritain	ftgermany	ftjapan	ftisrael	ftfrance	ftcanada	ftturkey	ftrussia	ftpales	ftimmig
2	3	Rural	Hillary Clinton	False	Joe Biden	health care	Extremely worried	Moderate	Democrat	Neither favor nor oppose	Oppose strongly	Oppose a moderate amount	Oppose a great deal	White	1954	Female	High school graduate	.966366930694957	0.0	91.0	88.0	15.0	60.0	70.0	68.0	48.0	49.0	49.0	49.0	50.0	39.0	63	66.0	51.0	40.0	2.0	2	2.0	1.0	3.0	59.0	1.0	50	1	1	1.0	51.0	87	50.0	1.0	3.0	69.0
27	28	Town	Hillary Clinton	False	Joe Biden	Climate change	Extremely worried	Liberal	Democrat	Favor a great deal	Oppose strongly	Favor a moderate amount	Favor a great deal	Other	1944	Female	4-year degree	1.01184109114119	2.0	91.0	49.0	96.0	100.0	87.0	76.0	51.0	52.0	50.0	46.0	45.0	62.0	99	99.0	49.0	0.0	0.0	31	6.0	73.0	7.0	64.0	56.0	92	85	78	25.0	87.0	99	38.0	3.0	52.0	96.0
72	73	Rural	Hillary Clinton	False	Joe Biden	Climate change	Extremely worried	Liberal	Democrat	Favor a little	Oppose strongly	Favor a great deal	Favor a great deal	White	1950	Female	Post-graduate	1.12291843396482	0.0	99.0	97.0	91.0	81.0	100.0	99.0	89.0	62.0	94.0	83.0	89.0	99.0	99	98.0	100.0	11.0	6.0	37	3.0	100.0	2.0	67.0	6.0	100	96	100	98.0	100.0	99	2.0	3.0	91.0	97.0
117	118	Rural	Did not vote	False	Joe Biden	Holding on to our Democracy is our most pressi...	Extremely worried	Moderate	Democrat	Favor a great deal	Oppose strongly	Favor a moderate amount	Favor a great deal	Black	1949	Male	2-year degree	1.04281941711299	50.0	98.0	96.0	91.0	90.0	88.0	86.0	91.0	90.0	90.0	90.0	87.0	89.0	95	100.0	100.0	60.0	50.0	77	50.0	91.0	50.0	91.0	72.0	93	85	83	81.0	94.0	91	71.0	40.0	83.0	91.0
149	150	Suburb	Hillary Clinton	False	Joe Biden	Infrastructure	Extremely worried	Moderate	Democrat	Oppose a great deal	Oppose strongly	Oppose a great deal	Oppose a great deal	White	1947	Male	Post-graduate	.826789085004023	0.0	98.0	91.0	1.0	3.0	95.0	1.0	60.0	72.0	68.0	81.0	41.0	0.0	90	90.0	69.0	6.0	0.0	0	0.0	59.0	0.0	50.0	0.0	100	99	50	31.0	60.0	100	0.0	0.0	10.0	100.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3023	3024	Rural	Hillary Clinton	False	Joe Biden	Imagation	Extremely worried	Conservative	Democrat	Neither favor nor oppose	Favor strongly	Oppose a great deal	Oppose a great deal	White	1962	Male	High school graduate	.878855634357939	49.0	85.0	68.0	66.0	97.0	49.0	54.0	3.0	100.0	5.0	2.0	2.0	2.0	1	99.0	100.0	99.0	99.0	98	0.0	95.0	6.0	9.0	98.0	70	94	96	3.0	6.0	97	1.0	97.0	0.0	1.0
3025	3026	Rural	Hillary Clinton	False	Joe Biden	The presidency of Donald J. Trump.	Extremely worried	Moderate	Democrat	Favor a little	Oppose strongly	Oppose a great deal	Oppose a great deal	Black	1960	Male	High school graduate	.989348149771041	0.0	100.0	90.0	75.0	70.0	55.0	75.0	60.0	49.0	50.0	50.0	60.0	49.0	50	70.0	75.0	50.0	0.0	50	0.0	49.0	20.0	54.0	50.0	49	50	50	40.0	65.0	70	40.0	0.0	85.0	50.0
3029	3030	Rural	Hillary Clinton	False	Joe Biden	racism hate crimes	Extremely worried	Liberal	Democrat	Neither favor nor oppose	Oppose strongly	Favor a great deal	Favor a moderate amount	White	1951	Male	Some college	.98338714821771	4.0	99.0	81.0	99.0	92.0	80.0	86.0	83.0	89.0	75.0	71.0	75.0	64.0	72	85.0	76.0	6.0	4.0	56	26.0	59.0	31.0	9.0	16.0	85	94	72	69.0	83.0	94	26.0	5.0	31.0	74.0
3103	3104	Town	Donald Trump	False	Joe Biden	climate change	Extremely worried	Liberal	Republican	Favor a little	Oppose strongly	Favor a little	Favor a little	White	1944	Male	4-year degree		3.0	30.0	96.0	97.0	100.0	96.0	51.0	97.0	97.0	97.0	95.0	96.0	99.0	91	98.0	97.0	50.0	2.0	80	50.0	98.0	72.0	97.0	50.0	97	98	97	40.0	97.0	99	40.0	61.0	98.0	100.0
3115	3116	Suburb	Hillary Clinton	False	Joe Biden	Health care crisis	Extremely worried	Moderate	Republican	Oppose a great deal	Oppose strongly	Favor a moderate amount	Oppose a little	White	1947	Male	4-year degree		4.0	78.0	57.0	55.0	78.0	51.0	51.0	70.0	72.0	72.0	69.0	49.0	15.0	85	55.0	64.0	58.0	41.0	54	0.0	70.0	19.0	18.0	2.0	88	76	78	88.0	79.0	96	41.0	0.0	52.0	88.0

77 rows × 52 columns

8.9. Creating New Columns and Replacing Existing Columns #

8.9.1. Using Arithmetic Operations #

There are many situations in which it makes sense to create new columns based on calculations with existing columns. There are two methods for creating a new column or replace an existing one. The first approach is to define a new column name in a dataframe’s index, and assign it to new values constructed from existing values in the dataframe.

For example, we can create a column that represents the difference between the Biden thermometer and the Trump thermometer, so that negative values (up to -100) represent partisanship for Trump, and positive values (up to 100) represent partisanship for Biden. We can either create a new column that contains these differences, or we can replace an existing column with this new column. To create a new column called partisanship, we can type:

anes_clean['partisanship'] = anes_clean.ftbiden - anes_clean.fttrump
anes_clean[['ftbiden', 'fttrump', 'partisanship']]

	ftbiden	fttrump	partisanship
0	52.0	47.0	5.0
1	41.0	41.0	0.0
2	88.0	0.0	88.0
3	0.0	100.0	-100.0
4	25.0	94.0	-69.0
...	...	...	...
3160	7.0	81.0	-74.0
3161	25.0	35.0	-10.0
3162	50.0	6.0	44.0
3163	95.0	1.0	94.0
3164	70.0	0.0	70.0

3165 rows × 3 columns

The second approach is to use the dataframe’s .assign() method. Within assign(), we write the name of the column we are creating, and we set it equal to an arithmetic or logical expression involving existing columns. The equivalent code that creates a partisanship column using the .assign() method is:

anes_clean = anes_clean.assign(partisanship = anes_clean.ftbiden - anes_clean.fttrump)
anes_clean[['ftbiden', 'fttrump', 'partisanship']]

	ftbiden	fttrump	partisanship
0	52.0	47.0	5.0
1	41.0	41.0	0.0
2	88.0	0.0	88.0
3	0.0	100.0	-100.0
4	25.0	94.0	-69.0
...	...	...	...
3160	7.0	81.0	-74.0
3161	25.0	35.0	-10.0
3162	50.0	6.0	44.0
3163	95.0	1.0	94.0
3164	70.0	0.0	70.0

3165 rows × 3 columns

To replace an existing column with a new version of that column, we use the same syntax, but we write an existing column name to set equal to an expression. For example, to replace the Barack Obama feeling thermometer with a Z-score standardized version of this column (subtracting the mean and dividing by the standard deviation) so that the values of ftobama change to represent the number of standard deviations away from the mean of ftobama. We can type

obama_mean = anes_clean.ftobama.mean()
obama_sd = anes_clean.ftobama.std()
anes_clean['ftobama'] = (anes_clean.ftobama - obama_mean)/obama_sd
anes_clean[['ftbiden', 'fttrump', 'ftobama']]

	ftbiden	fttrump	ftobama
0	52.0	47.0	0.971498
1	41.0	41.0	-0.626842
2	88.0	0.0	0.998137
3	0.0	100.0	-0.094062
4	25.0	94.0	-0.946510
...	...	...	...
3160	7.0	81.0	-0.893232
3161	25.0	35.0	-0.333813
3162	50.0	6.0	-0.600203
3163	95.0	1.0	1.237888
3164	70.0	0.0	1.237888

3165 rows × 3 columns

Then we can reconstruct the original ftobama feature:

anes_clean['ftobama'] = anes_clean.ftobama*obama_sd + obama_mean
anes_clean[['ftbiden', 'fttrump', 'ftobama']]

	ftbiden	fttrump	ftobama
0	52.0	47.0	90.0
1	41.0	41.0	30.0
2	88.0	0.0	91.0
3	0.0	100.0	50.0
4	25.0	94.0	18.0
...	...	...	...
3160	7.0	81.0	20.0
3161	25.0	35.0	41.0
3162	50.0	6.0	31.0
3163	95.0	1.0	100.0
3164	70.0	0.0	100.0

3165 rows × 3 columns

We can also perform both tasks using .assign(). We can replace ftobama with a standardized version of the feature:

anes_clean = anes_clean.assign(ftobama = (anes_clean.ftobama - obama_mean)/obama_sd)
anes_clean[['ftbiden', 'fttrump', 'ftobama']]

	ftbiden	fttrump	ftobama
0	52.0	47.0	0.971498
1	41.0	41.0	-0.626842
2	88.0	0.0	0.998137
3	0.0	100.0	-0.094062
4	25.0	94.0	-0.946510
...	...	...	...
3160	7.0	81.0	-0.893232
3161	25.0	35.0	-0.333813
3162	50.0	6.0	-0.600203
3163	95.0	1.0	1.237888
3164	70.0	0.0	1.237888

3165 rows × 3 columns

Then we can reconstruct the original values of ftobama:

anes_clean = anes_clean.assign(ftobama = anes_clean.ftobama*obama_sd + obama_mean)
anes_clean[['ftbiden', 'fttrump', 'ftobama']]

	ftbiden	fttrump	ftobama
0	52.0	47.0	90.0
1	41.0	41.0	30.0
2	88.0	0.0	91.0
3	0.0	100.0	50.0
4	25.0	94.0	18.0
...	...	...	...
3160	7.0	81.0	20.0
3161	25.0	35.0	41.0
3162	50.0	6.0	31.0
3163	95.0	1.0	100.0
3164	70.0	0.0	100.0

3165 rows × 3 columns

8.9.2. Breaking Continuous Features into Categories #

A useful function in pandas is pd.cut() which creates categories from break points in a continuous-valued column. There are three arguments for pd.cut(): first, the continuous column whose values we want to categorize, second bins takes either an integer to express the number of uniformly spaced categories, or a list of the breakpoints (inclusive for the upper bound but not the lower bound), and labels is a tuple of the labels to assign to each category. To cut the ftbiden column into three categories, we can write

anes_clean['ftbiden_level'] = pd.cut(anes_clean.ftbiden, bins=[-0.1,40,70,100], labels=("dislike", "neutral", "like"))
anes_clean[['ftbiden', 'ftbiden_level']]

	ftbiden	ftbiden_level
0	52.0	neutral
1	41.0	neutral
2	88.0	like
3	0.0	dislike
4	25.0	dislike
...	...	...
3160	7.0	dislike
3161	25.0	dislike
3162	50.0	neutral
3163	95.0	like
3164	70.0	neutral

3165 rows × 2 columns

Or equivalently,

anes_clean = anes_clean.assign(ftbiden_level = 
                               pd.cut(anes_clean.ftbiden, 
                                      bins=[-0.1,40,70,100], 
                                      labels=("dislike", "neutral", "like")))
anes_clean[['ftbiden', 'ftbiden_level']]

	ftbiden	ftbiden_level
0	52.0	neutral
1	41.0	neutral
2	88.0	like
3	0.0	dislike
4	25.0	dislike
...	...	...
3160	7.0	dislike
3161	25.0	dislike
3162	50.0	neutral
3163	95.0	like
3164	70.0	neutral

3165 rows × 2 columns

8.9.3. Indexing Versus `.assign()`#

The advantage of using .assign() as opposed to writing new columns into the dataframe’s index is that we can create or replace multiple columns within one call to .assign() by separating each expression with commas. We can calculate age from birthyr and we can also generate a column containing the square of age (which is useful to place a curvilinear effect into a linear regression model):

anes_clean = anes_clean.assign(age = 2020 - anes_clean.birthyr,
                              age2 = (2020 - anes_clean.birthyr)**2)
anes_clean[['vote', 'ftbiden', 'fttrump', 'age', 'age2']]

	vote	ftbiden	fttrump	age	age2
0	Joe Biden	52.0	47.0	51	2601
1	Donald Trump	41.0	41.0	78	6084
2	Joe Biden	88.0	0.0	66	4356
3	Donald Trump	0.0	100.0	41	1681
4	Donald Trump	25.0	94.0	80	6400
...	...	...	...	...	...
3160	Donald Trump	7.0	81.0	72	5184
3161	Someone else	25.0	35.0	24	576
3162	Probably will not vote	50.0	6.0	40	1600
3163	Joe Biden	95.0	1.0	60	3600
3164	Joe Biden	70.0	0.0	60	3600

3165 rows × 5 columns

A warning: if you want to perform a calculation that uses a column you create, you must create the columns you will use first, then use them in a separate call to .assign(). Suppose that I had called age directly to create age2 as follows:

anes_clean.assign(age = 2020 - anes_clean.birthyr,
                  age2 = anes_clean.age**2)

This code will not work because age is not yet defined until the end of the entire call to .assign(), so it cannot be used to create age2.

8.9.4. Changing a Column’s Data Type #

One important way we might need to create a new column or revise an existing one is by casting the column to a different data type. Every column in the dataframe has a data type, which we can see listed with the .dtypes attribute. If we want to change the data type, we can use the .astype() method applied to the column whose type we want to change. To convert the column’s values to strings type .astype('str'), to convert the column’s values to integers type .astype('int'), and to convert the column’s values to floats type .astype('float'). pandas also recognizes a categorical data type, and to convert a column to categorical type .astype('category'). As an example, let’s convert the Joe Biden thermometer scores to each data type:

anes_clean = anes_clean.assign(ftbiden_float = anes_clean.ftbiden.astype('float'),
                              ftbiden_cat = anes_clean.ftbiden.astype('category'),
                              ftbiden_str = anes_clean.ftbiden.astype('str'))
anes_clean[['ftbiden', 'ftbiden_float', 'ftbiden_cat', 'ftbiden_str']].dtypes

ftbiden           float64
ftbiden_float     float64
ftbiden_cat      category
ftbiden_str        object
dtype: object

By default, missing values np.nan are stored as floats, so it is not possible to convert a column to integer if there are missing values.

We can convert all of the categorical columns in anes_clean to categorical data types as follows:

catcolumns = ['liveurban', 'vote16', 'protest', 'vote','confecon', 'ideology', 'partyID',
       'universal_income', 'family_separation', 'free_college','forgive_loans', 'race', 'sex', 'education']
anes_clean[catcolumns] = anes_clean[catcolumns].astype('category')
anes_clean.dtypes

caseid                     int64
liveurban               category
vote16                  category
protest                 category
vote                    category
most_important_issue      object
confecon                category
ideology                category
partyID                 category
universal_income        category
family_separation       category
free_college            category
forgive_loans           category
race                    category
birthyr                    int64
sex                     category
education               category
weight                    object
fttrump                  float64
ftobama                  float64
ftbiden                  float64
ftwarren                 float64
ftsanders                float64
ftbuttigieg              float64
ftharris                 float64
ftblack                  float64
ftwhite                  float64
fthisp                   float64
ftasian                  float64
ftmuslim                 float64
ftillegal                float64
ftjournal                  int64
ftnato                   float64
ftun                     float64
ftice                    float64
ftnra                    float64
ftchina                    int64
ftnkorea                 float64
ftmexico                 float64
ftsaudi                  float64
ftukraine                float64
ftiran                   float64
ftbritain                  int64
ftgermany                  int64
ftjapan                    int64
ftisrael                 float64
ftfrance                 float64
ftcanada                   int64
ftturkey                 float64
ftrussia                 float64
ftpales                  float64
ftimmig                  float64
partisanship             float64
ftbiden_level           category
age                        int64
age2                       int64
ftbiden_float            float64
ftbiden_cat             category
ftbiden_str               object
dtype: object

8.9.5. Using Logical Operators #

Logical expressions can be used to create new columns. Columns that are set equal to a logical condition are populated by values that are either True when the logical condition is true, or False when the logical condition is false. To create a binary column indicating the participants who like Biden more than Trump, we can type

anes_clean['prefersbiden'] = anes_clean.ftbiden > anes_clean.fttrump
anes_clean[['vote','ftbiden', 'fttrump', 'prefersbiden']]

	vote	ftbiden	fttrump	prefersbiden
0	Joe Biden	52.0	47.0	True
1	Donald Trump	41.0	41.0	False
2	Joe Biden	88.0	0.0	True
3	Donald Trump	0.0	100.0	False
4	Donald Trump	25.0	94.0	False
...	...	...	...	...
3160	Donald Trump	7.0	81.0	False
3161	Someone else	25.0	35.0	False
3162	Probably will not vote	50.0	6.0	True
3163	Joe Biden	95.0	1.0	True
3164	Joe Biden	70.0	0.0	True

3165 rows × 4 columns

Or using .assign():

anes_clean = anes_clean.assign(prefersbiden = anes_clean.ftbiden > anes_clean.fttrump)
anes_clean[['vote','ftbiden', 'fttrump', 'prefersbiden']]

	vote	ftbiden	fttrump	prefersbiden
0	Joe Biden	52.0	47.0	True
1	Donald Trump	41.0	41.0	False
2	Joe Biden	88.0	0.0	True
3	Donald Trump	0.0	100.0	False
4	Donald Trump	25.0	94.0	False
...	...	...	...	...
3160	Donald Trump	7.0	81.0	False
3161	Someone else	25.0	35.0	False
3162	Probably will not vote	50.0	6.0	True
3163	Joe Biden	95.0	1.0	True
3164	Joe Biden	70.0	0.0	True

3165 rows × 4 columns

Logical expressions can work in two ways: elementwise or objectwise. When a logical expression is evaluated elementwise, it matches the corresponding elements in multiple columns and returns a column of equal length that is populated by True and False values depending on the values on the same row. When a logical expression is evaluated objectwise, it considers whether a statement is true regarding a column as a whole. For example, the == sign asks whether the first object is equal to the second object. If I define two lists, [1,2,3] and [3,2,1] and evaluate them using ==, I get:

[1,2,3] == [3,2,1]

False

The output is false because [1,2,3] == [3,2,1] is evaluated objectwise. The question Python answered was: is the list [1,2,3] the same as the list [3,2,1]? These two lists are not the same, so the answer is False. If we convert these two lists to numpy arrays instead, then the same comparison is elementwise:

np.array([1,2,3]) == np.array([3,2,1])

array([False,  True, False])

In this case, we compare corresponding elements and find that the first elements are not the same, the second elements are the same, and the third elements are not the same, so the answer is a list: [False, True, False]. Because pandas dataframes are built using numpy under the hood, individual columns are numpy arrays, and some logical operators - namely <, <=, >, >=, ~, &, and | - work elementwise. That’s why the commands listed above to generate Boolean features in anes_clean work.

However, some operators do not work elementwise, even within numpy arrays. Python includes two versions of “and”, for example: in addition to &, there is also and. While & works elementwise, and only works objectwise. For example the following expression

(np.array([1,2,3,4]) >= np.array([4,3,2,1])) & (np.array([1,2,3,4]) == np.array([5,4,3,2]))

array([False, False,  True, False])

yields an array of logical values, but switching the “and” operator:

(np.array([1,2,3,4]) >= np.array([4,3,2,1])) and (np.array([1,2,3,4]) == np.array([5,4,3,2]))

yields an error: “The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()”. The only really important logical operator we will want to use that does not work elementwise in pandas is in. To create a new column that uses an objectwise operator like in, we have to loop across the rows of the dataframe using the .apply() method and a lambda function. lambda defines the operation that will be evaluated on each row, and constructs the sequence of logical values one row at a time. Using a loop is computationally inefficient, but it gets the job done.

For example, if we want to create the worried_econ column using in, we can use the following code:

anes_clean['worried_econ'] = anes_clean.confecon.apply(
        lambda x: x in ["Moderately worried","Extremely worried"]
    )
anes_clean[['confecon', 'worried_econ']]    

	confecon	worried_econ
0	A little worried	False
1	A little worried	False
2	Extremely worried	True
3	A little worried	False
4	Not at all worried	False
...	...	...
3160	A little worried	False
3161	Extremely worried	True
3162	Very worried	False
3163	Moderately worried	True
3164	Extremely worried	True

3165 rows × 2 columns

The first line creates the worried_econ column and sets it equal to the output of the loop that is applied to the rows of confecon. Inside the loop, lambda x defines x as an index that represents each individual row. The function x in ["Moderately worried","Extremely worried"] returns True if the value of confecon on a particular row is “Moderately worried” or “Extremely worried” and False otherwise.

As another example, let’s create a column that identifies the voters that favor a universal basic income, at least a little, and also favor free college tuition. In this case, we have to specify that axis=1 so that the loop works down columns and applies the function on every row in the dataframe. The following code works:

anes_clean['favor_both'] = anes_clean.apply(
    lambda x: x['universal_income'] in ["Favor a little","Favor a moderate amount","Favor a great deal"] and
    x['free_college'] in ["Favor a little","Favor a moderate amount","Favor a great deal"],
    axis=1
)
anes_clean[['universal_income', 'free_college', 'favor_both']]

	universal_income	free_college	favor_both
0	Favor a moderate amount	Favor a moderate amount	True
1	Oppose a moderate amount	Oppose a little	False
2	Neither favor nor oppose	Oppose a moderate amount	False
3	Neither favor nor oppose	Favor a little	False
4	Oppose a great deal	Oppose a great deal	False
...	...	...	...
3160	Oppose a great deal	Oppose a great deal	False
3161	Favor a great deal	Favor a little	True
3162	Oppose a great deal	Oppose a great deal	False
3163	Favor a moderate amount	Neither favor nor oppose	False
3164	Oppose a great deal	Oppose a great deal	False

3165 rows × 3 columns

8.10. Data Aggregation #

Data aggregation is the process of collapsing a dataframe to have one row for every group of a given categorical column, where other columns are summarized with statistics like the sum, mean, and median of values within each group. In pandas, data aggregation follows a split-apply-combine approach. First, the data are split into subsets for every group in the grouping column. Then a function, such as the mean, is applied to the same columns in each subset and each subset is compressed to have just one row. Then the subsets are combined into the final resulting dataframe.

The general syntax for aggregating a dataframe is

df.groupby('groupcolumn').aggfunctions.sort_values()

Commands to aggregate dataframes can get confusing very quickly. It helps to break down the syntax for data aggregation into parts for each method listed in the syntax. First, df is the name of the dataframe we want to compress. Second, .groupby('groupcolumn') specifies the column whose categories will define the rows of the aggregated dataframe. If more than one column defines these groups (examples below), then the syntax becomes .groupby(['groupcolumn1', 'groupcolumn2']). Third, .aggfunctions defines how other columns in the data can be included and summarized in the aggregated data. Finally, .sort_values() is optional, but often helps to generate a better presentation for the aggregated data table.

As an example, let’s collapse the ANES data by the candidates that people report they will vote for. We start with anes_clean and set the grouping variable with .groupby('vote'). We apply the .size() function, which reports the number of rows in each group, and we use .sort_values(ascending=False) to list these counts from biggest to smallest (no by argument is needed because there is only one column in the aggregated data). In all, this command provides a tally of people’s voting intentions as of December 2019:

anes_clean.groupby('vote').size().sort_values(ascending=False)

vote
Joe Biden                 1288
Donald Trump              1273
Someone else               321
Probably will not vote     283
dtype: int64

So according to the ANES data, the election is close, but Joe Biden has the most votes in the sample.

To use aggregation functions other than .size(), the best option is to use the .agg() method instead. .agg() takes a dictionary as its argument, and this dictionary should contain the names of the columns we want to summarize, as keys, and the functions we want to use to collapse these columns, as values. For example, we can construct a table in which the rows are the voting groups, and the columns contain the mean feeling thermometers for Trump, Biden, immigrants, and journalists:

anes_clean.groupby('vote').agg({'fttrump':'mean',
                               'ftbiden':'mean',
                               'ftimmig':'mean',
                               'ftjournal':'mean'})

	fttrump	ftbiden	ftimmig	ftjournal
vote
Donald Trump	87.835303	15.743286	68.801887	26.502749
Joe Biden	8.450549	70.724649	75.315217	72.365683
Probably will not vote	28.750000	39.595238	61.734043	49.706714
Someone else	23.126582	34.022293	67.503125	52.386293

There are aggregation functions other than 'mean' available: count gives the number of nonmissing values within the group, sum takes the sum of the values in each group, min and max report the minimum and maximum values, median reports the 50th percentile, std and var calculate the standard deviation and variance, sem takes the standard error of the mean, and first and last report the first and last value that appear in the subset defined by each group. For example, we can report all of these statistics for ftbiden across voting groups. If we want more than one statistic for a column, we can specify all of these functions in a list within the dictionary as follows:

anes_clean.groupby('vote').agg({'ftbiden':['count','mean','sum','min','max','median','std','var','sem','first','last']})

	ftbiden
	count	mean	sum	min	max	median	std	var	sem	first	last
vote
Donald Trump	1266	15.743286	19931.0	0.0	100.0	6.0	20.644470	426.194125	0.580212	41.0	7.0
Joe Biden	1282	70.724649	90669.0	0.0	100.0	73.0	22.384124	501.049025	0.625167	52.0	70.0
Probably will not vote	252	39.595238	9978.0	0.0	100.0	48.0	25.337303	641.978941	1.596100	29.0	50.0
Someone else	314	34.022293	10683.0	0.0	100.0	32.0	24.396672	595.197585	1.376784	20.0	25.0

More than one column may define the groups. For example, we might want to count the number of voters for each candidate in cities, in rural areas, in suburbs, and in towns. In that case, we can specify groupby(['vote','liveurban']) and .size() to count the number of voters of each type:

anes_clean.groupby(['vote','liveurban']).size()

vote                    liveurban
Donald Trump            City         235
                        Rural        314
                        Suburb       465
                        Town         259
Joe Biden               City         401
                        Rural        204
                        Suburb       462
                        Town         221
Probably will not vote  City          88
                        Rural         61
                        Suburb        83
                        Town          51
Someone else            City          92
                        Rural         64
                        Suburb       107
                        Town          58
dtype: int64

This table looks nicer as a pandas dataframe:

pd.DataFrame(anes_clean.groupby(['vote','liveurban']).size())

		0
vote	liveurban
Donald Trump	City	235
	Rural	314
	Suburb	465
	Town	259
Joe Biden	City	401
	Rural	204
	Suburb	462
	Town	221
Probably will not vote	City	88
	Rural	61
	Suburb	83
	Town	51
Someone else	City	92
	Rural	64
	Suburb	107
	Town	58

The table looks even nicer if we also apply the .unstack() method to move the data into different columns:

pd.DataFrame(anes_clean.groupby(['vote','liveurban']).size().unstack())

liveurban	City	Rural	Suburb	Town
vote
Donald Trump	235	314	465	259
Joe Biden	401	204	462	221
Probably will not vote	88	61	83	51
Someone else	92	64	107	58

Biden has more voters than Trump in cities, and Trump has more in rural areas. Trump has a slight lead among people in towns, and Trump and Biden are within three votes among people in suburbs. It looks like the election will depend on voters from the suburbs.

The standard functions in .agg() will usually suffice, but there are situations in which we might want to use a custom aggregation function. For that, we can use .apply() and a lambda function. The following code searches the most_important_issue column, which is a free text entry field in which respondents wrote their answer for the most important issue in the country today, and counts the number of times the word “trump” is mentioned in these responses:

anes_clean.groupby('vote').most_important_issue.apply(
    lambda x: x.str.contains("trump", case=False).sum()
).sort_values(ascending=False)

vote
Joe Biden                 325
Donald Trump               59
Someone else               25
Probably will not vote     10
Name: most_important_issue, dtype: int64

Biden voters mentioned “trump” 325 times, and Trump voters only mentioned “trump” 59 times. This code groups by voting group, then selects the most_important_issue column and uses the .apply() method on this column which creates a loop across groups. Within .apply(), the lambda function denotes a token x that represents the most_important_issue column within each group. On this column, the function uses the .str.contains("trump", case=False) function, which outputs True if the string “trump” is found within each value of most_important_issue without case sensitivity, and False otherwise. Finally,the .sum() function counts the number of times these searches were True.

Finally, we can save the cleaned ANES data as a separate CSV:

anes_clean.to_csv('anes_pilot2019_clean.csv', index=False)

Data Wrangling with pandas

Contents