import pandas as pd
= pd.read_csv('2000_acs_sample.csv') acs
5 First Steps With Your Data
Once you’ve read in a new data set, your first goals are to understand the data set and to clean it up. Logically these are two separate processes, but in practice they are intertwined: you can’t clean your data set until you understand it at some level, but you won’t fully understand it until it’s clean. Thus this section will cover both.
We’ll also introduce a lot of data science concepts in this section. This makes for lengthy discussions of tasks that in practice you can complete very quickly.
In this chapter we’ll primarily use the file 2000_acs_sample.csv
. This file started as the 1% unweighted sample of the 2000 American Community Survey available from IPUMS, but then O took a 1% random sample of the households in that dataset just to make it easier to work with. This data set uses the ‘source’ variables directly from the Census Bureau rather than the ‘harmonized’ variables created by IPUMS, which are much cleaner. For real work you can usually use the harmonized variables, but we’re here to learn how to do the kinds of things IPUMS does to create them. We use the 2000 ACS because it’s the last one to be unweighted, and we don’t want to deal with weights right now.
By using this data set in the examples you’ll gain some basic understanding of the U.S. population (as of 2000 anyway) and some experience with a real and important data set, but you would not want to use this particular data file for research.
5.0.1 Setting Up
Start up Jupyter Lab if you haven’t already and navigate to the folder where you put the example files. Then create a new Python Notebook and call it First_Steps_Practice.ipynb
. Have it import Pandas and then use the Pandas read_csv()
function to read in 2000_acs_sample.csv
:
Some of the exercises in this chapter will continue the process of cleaning up the ACS sample. Include them in your First_Steps_Practice.ipynb
Notebook. (The solutions for those exercises are included in this Notebook so it can create the cleaned up data properly, but be sure not to read them before trying the exercise yourself.) Other exercises will ask you to apply what you’ve learned to other data sets. For those exercises, create a second Notebook called First_Steps_Exercises.ipynb
and have it import Pandas as well. Each exercise will tell you which Notebook to use.
5.1 Read the Documentation
When you download a data set, you’ll be tempted to open it up and go to work right away. Resist! Time spent reading the data set’s documentation (assuming there is some) can save you much more time down the road. Data providers may give you files containing documentation along with the data itself, or it may be on their web site. Feel free to skim what’s not relevant to you—this chapter will give you a better sense of what information is most important.
Unfortunately, not all data sets have good documentation, or any documentation at all, so figuring out the nature of a data set by looking at the data set itself is a vital skill. You also can’t assume that the documentation is completely accurate, so you need to check what it says.
The ACS has lots of good documentation, but for practice we’ll make minimal use of it (just the codebook) and figure out everything we can for ourselves. We’d still do all the same things if we were using the documentation, we’d just understand what we were looking at much more quickly.
5.2 Identify the Variables
To see what variables the data set contains, look at its dtypes
(data types):
acs.dtypes
year int64
datanum int64
serial int64
hhwt int64
gq object
us2000c_serialno int64
pernum int64
perwt int64
us2000c_pnum int64
us2000c_sex int64
us2000c_age int64
us2000c_hispan int64
us2000c_race1 int64
us2000c_marstat int64
us2000c_educ int64
us2000c_inctot object
dtype: object
The primary goal of looking at the dtypes
is to see what variables you have and what they’re called. But it will frequently let you start a “to do list” of issues you need to address before analyzing the data. Here are some issues brought out by running describe on this data set:
The data set seems to have an excess of identifiers:
serial
,us2000c_serialno
,pernum
andus2000c_pnum
Since you’re using a single data set from a single year, you don’t need
year
anddatanum
to tell you where each observation come from.For the same reason, you also don’t need
us2000c_
(‘US 2000 Census’) in your variable names to tell you where those variables come from.You have both household weight (
hhwt
) and person weight (pwt
) variables even though this is supposed to be an unweighted sample.us2000c_inctot
is stored as an object, probably a string, even though it should presumably be numeric. (We’ll have to investigategq
.)
Using your First_Steps_Exercises
Notebook (i.e. not the one you’re using to clean up the ACS sample), read in 2000_acs_harm.csv
and examine its dtypes
. This is a similar sample but with the IPUMS ‘harmonized’ variables. What issues did IPUMS resolve? What issues remain?
5.3 Look at the Data
Unless your data set is very small, you can’t possibly read all of it. But just looking at what Jupyter Notebook’s implicit print will give you may allow you to immediately spot patterns that would be difficult to detect using code:
acs
year | datanum | serial | hhwt | gq | us2000c_serialno | pernum | perwt | us2000c_pnum | us2000c_sex | us2000c_age | us2000c_hispan | us2000c_race1 | us2000c_marstat | us2000c_educ | us2000c_inctot | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2000 | 4 | 37 | 100 | Households under 1970 definition | 365663 | 1 | 100 | 1 | 2 | 20 | 1 | 1 | 5 | 11 | 0010000 |
1 | 2000 | 4 | 37 | 100 | Households under 1970 definition | 365663 | 2 | 100 | 2 | 2 | 19 | 1 | 1 | 5 | 11 | 0005300 |
2 | 2000 | 4 | 37 | 100 | Households under 1970 definition | 365663 | 3 | 100 | 3 | 2 | 19 | 1 | 2 | 5 | 11 | 0004700 |
3 | 2000 | 4 | 241 | 100 | Households under 1970 definition | 2894822 | 1 | 100 | 1 | 2 | 50 | 1 | 1 | 5 | 14 | 0032500 |
4 | 2000 | 4 | 242 | 100 | Households under 1970 definition | 2896802 | 1 | 100 | 1 | 2 | 29 | 1 | 1 | 5 | 13 | 0030000 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
28167 | 2000 | 4 | 1236624 | 100 | Households under 1970 definition | 7055193 | 1 | 100 | 1 | 1 | 29 | 1 | 1 | 1 | 11 | 0050100 |
28168 | 2000 | 4 | 1236624 | 100 | Households under 1970 definition | 7055193 | 2 | 100 | 2 | 2 | 26 | 1 | 1 | 1 | 9 | 0012000 |
28169 | 2000 | 4 | 1236756 | 100 | Households under 1970 definition | 8489120 | 1 | 100 | 1 | 2 | 58 | 1 | 1 | 1 | 14 | 0069800 |
28170 | 2000 | 4 | 1236756 | 100 | Households under 1970 definition | 8489120 | 2 | 100 | 2 | 1 | 61 | 1 | 1 | 1 | 14 | 0040800 |
28171 | 2000 | 4 | 1236779 | 100 | Households under 1970 definition | 8733299 | 1 | 100 | 1 | 1 | 30 | 1 | 3 | 3 | 9 | 0022110 |
28172 rows × 16 columns
Some things to notice:
year
anddatanum
seem to have just one value each, suggesting that we don’t need them.hhwt
andperwt
(household and person weights) seem to always be 100, which makes sense given that this is supposed to be an unweighted sample.pernum
andus2000c_pnum
appear to be identical.pernum
seems to count observations, starting over from 1 every time serial changes.us2000c_sex
,us2000c_hispan
,us2000c_race1
, andus2000c_marstat
are clearly describing categories. We will have to refer to the codebook to find out what the numbers mean. (This also applies tous2000c_educ
, it’s just not as obvious at this point.)
You can’t be sure that these patterns hold the for entire data set until you check using methods that examine the entire data set. The value_counts()
function will give you frequencies for a variable:
'year'].value_counts() acs[
2000 28172
Name: year, dtype: int64
There is indeed just one value of year
, 2000, the year the data were collected. This might be useful if we were combining data sets from different years, but since we’re not you’ll soon learn how to drop it.
Use value_counts()
to get the frequencies of datanum
, hhwt
, and perwt
.
Using your First_Steps_Exercises
Notebook, examine acs_harm
in the same way. What issues do you see? Be sure to take a close look at inc_tot
.
5.4 Identify the Indexes and Investigate the Structure of the Data
Logically, row indexes should uniquely identify a row. (Python does not actually enforce this, but duplicate indexes can cause performance problems. More importantly, they don’t make sense.) Back in the DataFrames chapter, we used person
as the row index in the small extract from this data set that we examined. In this not-yet-cleaned-up data set it’s called pernum
, but it clearly does not uniquely identify rows. That’s an important clue about the structure of the data: there’s more going on here than just people.
Suppose that I gave you some ‘student data’ and you found that student_id
uniquely identified the rows. Then you could reasonably conclude that each row represents a student.
But suppose you found instead that each value of student_id
was associated with multiple rows, but a combination of student_id
and class_id
uniquely identified the rows. That would tell you that each row represents a student-class combination, or a class taken by a particular student. Loosely speaking we might just call each row a class, as long as it’s understood that two students taking the same class will be two rows.
This is critical information, and if you come to the SSCC’s statistical consultants for help you’re likely to be asked what a row represents in your data set. You might be surprised by how often the answer we get turns out to be wrong. (“What does a row represent in your data set?” “A patient.” “Then why do these five rows all have the same patient_id?” “They’re different doctor visits.” “Ah, then a row represents a doctor visit in your data set.”) Ideally the data documentation will tell you what the structure of the data set is, but it’s easy to check and well worth doing.
One way to easily check if pernum
is a unique identifier is to look at its frequencies with value_counts()
:
'pernum'].value_counts() acs[
1 11327
2 7786
3 4374
4 2689
5 1210
6 450
7 177
8 81
9 34
10 21
11 11
12 8
13 1
14 1
15 1
16 1
Name: pernum, dtype: int64
Note that the result is a Series, with the values of pernum
as the index and the corresponding frequencies as values.
There are over 11,000 person 1’s, so clearly pernum
is not a unique identifier. How about serial
?
'serial'].value_counts() acs[
1115231 16
562736 12
962652 12
1122678 12
1110995 12
..
790922 1
227103 1
227189 1
790501 1
1236779 1
Name: serial, Length: 11327, dtype: int64
We can’t see all the values of serial
, but one of them is associated with 16 rows so once again we know it is not a unique identifier.
How about the combination of serial
and pernum
? If we give value_counts()
two columns to act on, it will tell us the frequency of each combination of them (i.e. a crosstab). So select both columns by subsetting with a list:
'serial', 'pernum']].value_counts() acs[[
serial pernum
37 1 1
829137 2 1
830078 2 1
1 1
829441 1 1
..
428384 1 1
428351 8 1
7 1
6 1
1236779 1 1
Length: 28172, dtype: int64
All the combinations we can see have just one row, but that’s a tiny fraction of the data set. How can we be sure?
The max()
function finds the maximum value of a Series, like the Series of frequencies created by value_counts()
. Try it first on the frequencies of serial
:
'serial'].value_counts().max() acs[
16
Since that gave us 16 as expected, now try it with the combination of serial
and pernum
:
'serial', 'pernum']].value_counts().max() acs[[
1
With a maximum frequency of one, we now know that the combination of serial
and pernum
uniquely identifies the rows within this data set. In fact serial
is a household identifier and pernum
identifies a person within that household. We now know that this data set consists of people who are grouped into households.
When a combination of two or more variables uniquely identifies observations this is often known as a compound identifier, but Python calls it a MultiIndex. MultiIndex is actually a class in the Pandas package, though we’ll normally only use it in the context of a DataFrame.
It would make sense to set serial
and pernum
as the row indexes now, but we want to rename them first and we don’t yet know how to do that. So hold that thought.
Using your First_Steps_Exercises
Notebook, read in the data set atus.csv
. This is a selection from the American Time Use Survey, which measures how much time people spend on various activities. Find the identifiers in this data set. What does an observation represent? What was the first activity recorded for person 20170101170012?
5.5 Get Rid of Data You Won’t Use
Understanding data takes time. Even skipping past data you don’t care about to get to what you do care about takes time. So if you won’t use parts of a data set, get rid of those parts sooner rather than later. Doing so will also reduce the amount of memory needed to analyze your data and the amount of disk space needed to store it, and make anything you do with it run that much faster. If you change your mind about what you need, you can always change your code later.
You can drop variables from a dataset with the DataFrame function drop()
. You’ll need to pass in a variable name or list of names, plus axis=1
to tell it you want to drop columns rather than rows. .
We’ve determined that year
, datanum
, hhwt
, and perwt
contain no useful information (to us). Also, us2000c_pnum
is identical to pernum
, and us2000c_serialno
appears to be an alternative version of serial
that we don’t need. Remove them with:
= acs.drop(
acs
['year',
'datanum',
'hhwt',
'perwt',
'us2000c_pnum',
'us2000c_serialno'
],=1
axis
) acs
serial | gq | pernum | us2000c_sex | us2000c_age | us2000c_hispan | us2000c_race1 | us2000c_marstat | us2000c_educ | us2000c_inctot | |
---|---|---|---|---|---|---|---|---|---|---|
0 | 37 | Households under 1970 definition | 1 | 2 | 20 | 1 | 1 | 5 | 11 | 0010000 |
1 | 37 | Households under 1970 definition | 2 | 2 | 19 | 1 | 1 | 5 | 11 | 0005300 |
2 | 37 | Households under 1970 definition | 3 | 2 | 19 | 1 | 2 | 5 | 11 | 0004700 |
3 | 241 | Households under 1970 definition | 1 | 2 | 50 | 1 | 1 | 5 | 14 | 0032500 |
4 | 242 | Households under 1970 definition | 1 | 2 | 29 | 1 | 1 | 5 | 13 | 0030000 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
28167 | 1236624 | Households under 1970 definition | 1 | 1 | 29 | 1 | 1 | 1 | 11 | 0050100 |
28168 | 1236624 | Households under 1970 definition | 2 | 2 | 26 | 1 | 1 | 1 | 9 | 0012000 |
28169 | 1236756 | Households under 1970 definition | 1 | 2 | 58 | 1 | 1 | 1 | 14 | 0069800 |
28170 | 1236756 | Households under 1970 definition | 2 | 1 | 61 | 1 | 1 | 1 | 14 | 0040800 |
28171 | 1236779 | Households under 1970 definition | 1 | 1 | 30 | 1 | 3 | 3 | 9 | 0022110 |
28172 rows × 10 columns
You can use drop()
with axis=0
to drop observations. Again you’ll pass in a value or list of values, but this time they are values of the row index (keep in mind that variable names are just values of the column index, so this isn’t really any different from dropping columns). For example, if you wanted to drop the observation whose index is 0 (i.e. the first observation) you could run:
0, axis=0) acs.drop(
serial | gq | pernum | us2000c_sex | us2000c_age | us2000c_hispan | us2000c_race1 | us2000c_marstat | us2000c_educ | us2000c_inctot | |
---|---|---|---|---|---|---|---|---|---|---|
1 | 37 | Households under 1970 definition | 2 | 2 | 19 | 1 | 1 | 5 | 11 | 0005300 |
2 | 37 | Households under 1970 definition | 3 | 2 | 19 | 1 | 2 | 5 | 11 | 0004700 |
3 | 241 | Households under 1970 definition | 1 | 2 | 50 | 1 | 1 | 5 | 14 | 0032500 |
4 | 242 | Households under 1970 definition | 1 | 2 | 29 | 1 | 1 | 5 | 13 | 0030000 |
5 | 296 | Other group quarters | 1 | 2 | 20 | 1 | 6 | 5 | 9 | 0003000 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
28167 | 1236624 | Households under 1970 definition | 1 | 1 | 29 | 1 | 1 | 1 | 11 | 0050100 |
28168 | 1236624 | Households under 1970 definition | 2 | 2 | 26 | 1 | 1 | 1 | 9 | 0012000 |
28169 | 1236756 | Households under 1970 definition | 1 | 2 | 58 | 1 | 1 | 1 | 14 | 0069800 |
28170 | 1236756 | Households under 1970 definition | 2 | 1 | 61 | 1 | 1 | 1 | 14 | 0040800 |
28171 | 1236779 | Households under 1970 definition | 1 | 1 | 30 | 1 | 3 | 3 | 9 | 0022110 |
28171 rows × 10 columns
But you don’t want to drop it, which is why I didn’t store the result as acs
.
It’s much more common to want to drop observations based on a condition. Consider the gq
variable:
'gq'].value_counts() acs[
Households under 1970 definition 27339
Group quarters--Institutions 406
Other group quarters 356
Additional households under 1990 definition 71
Name: gq, dtype: int64
Group quarters are very different from ‘normal’ households, so if your research is about normal households you probably want to exclude them from your analysis. Use the following to keep just the households and drop the group quarters:
= acs[
acs 'gq']=='Households under 1970 definition') |
(acs['gq']=='Additional households under 1990 definition')
(acs[ ]
We don’t care about the distinction between the 1970 and 1990 definition of household, so you can now drop gq
:
= acs.drop('gq', axis=1) acs
5.6 Change Variable Names
A good variable name tells you clearly what the variable contains. Good variable names make code easier to understand, easier to debug, and easier to write. If you have to choose between making a variable name short and making it clear, go with clear.
Many good variable names contain (or should contain) multiple words. Python will allow you to put spaces in variable names, but doing can cause problems for some tasks and should generally be avoided. There are two competing conventions for making multi-word variable names readable. Camel case capitalizes the first letter of each word after the first: householdIncome
, mothersEducation
, etc. Snake case uses underscores instead of spaces: household_income
, mothers_education
, etc. You’ve probably noticed that we prefer snake case, but which one you use is less important than that you choose one and stick with it: don’t force yourself to remember whether you called your variable householdIncome
or household_income
this time!
When you use abbreviations use the same abbreviation every time, even across projects. This data set abbreviates education as educ
, and there’s nothing wrong with that, but if you use edu
in your other projects (and I do) that’s sufficient reason to change it.
You can change the names of variables with the rename()
function. The columns
argument takes a dictionary, with each element of the dictionary containing the old name and desired new name of a variable as a key : value pair. Given the variables we care about in this data set better names with:
= acs.rename(
acs =
columns
{'serial' : 'household',
'pernum' : 'person',
'us2000c_sex' : 'sex',
'us2000c_age' : 'age',
'us2000c_hispan' : 'hispanic',
'us2000c_race1' : 'race',
'us2000c_marstat' : 'marital_status',
'us2000c_educ' : 'edu',
'us2000c_inctot' : 'income'
}
) acs.dtypes
household int64
person int64
sex int64
age int64
hispanic int64
race int64
marital_status int64
edu int64
income object
dtype: object
5.7 Set Indexes
Now that we’ve given the identifiers the variable names we want, it’s time to set them as indexes. You’ll still do that with set_index()
like before, except that you’ll pass in a list containing the two variable names:
= acs.set_index(['household', 'person'])
acs acs
sex | age | hispanic | race | marital_status | edu | income | ||
---|---|---|---|---|---|---|---|---|
household | person | |||||||
37 | 1 | 2 | 20 | 1 | 1 | 5 | 11 | 0010000 |
2 | 2 | 19 | 1 | 1 | 5 | 11 | 0005300 | |
3 | 2 | 19 | 1 | 2 | 5 | 11 | 0004700 | |
241 | 1 | 2 | 50 | 1 | 1 | 5 | 14 | 0032500 |
242 | 1 | 2 | 29 | 1 | 1 | 5 | 13 | 0030000 |
... | ... | ... | ... | ... | ... | ... | ... | ... |
1236624 | 1 | 1 | 29 | 1 | 1 | 1 | 11 | 0050100 |
2 | 2 | 26 | 1 | 1 | 1 | 9 | 0012000 | |
1236756 | 1 | 2 | 58 | 1 | 1 | 1 | 14 | 0069800 |
2 | 1 | 61 | 1 | 1 | 1 | 14 | 0040800 | |
1236779 | 1 | 1 | 30 | 1 | 3 | 3 | 9 | 0022110 |
27410 rows × 7 columns
The row index for acs
is now a MultiIndex, which means Python now understands the hierarchical structure of the data. But how can you use it?
If you pass in a number to loc
it will select that household:
37] acs.loc[
sex | age | hispanic | race | marital_status | edu | income | |
---|---|---|---|---|---|---|---|
person | |||||||
1 | 2 | 20 | 1 | 1 | 5 | 11 | 0010000 |
2 | 2 | 19 | 1 | 1 | 5 | 11 | 0005300 |
3 | 2 | 19 | 1 | 2 | 5 | 11 | 0004700 |
To really take advantage of the MultiIndex, use xs()
. With xs()
you can pass in a value and then tell it whether it is for a household or a person with the level
argument. To select a household, use:
37, level='household') acs.xs(
sex | age | hispanic | race | marital_status | edu | income | |
---|---|---|---|---|---|---|---|
person | |||||||
1 | 2 | 20 | 1 | 1 | 5 | 11 | 0010000 |
2 | 2 | 19 | 1 | 1 | 5 | 11 | 0005300 |
3 | 2 | 19 | 1 | 2 | 5 | 11 | 0004700 |
To select all the people who are person one in their household (much more useful than you may think at this point) use:
1, level='person') acs.xs(
sex | age | hispanic | race | marital_status | edu | income | |
---|---|---|---|---|---|---|---|
household | |||||||
37 | 2 | 20 | 1 | 1 | 5 | 11 | 0010000 |
241 | 2 | 50 | 1 | 1 | 5 | 14 | 0032500 |
242 | 2 | 29 | 1 | 1 | 5 | 13 | 0030000 |
377 | 2 | 69 | 1 | 1 | 5 | 1 | 0051900 |
418 | 2 | 59 | 1 | 1 | 2 | 8 | 0012200 |
... | ... | ... | ... | ... | ... | ... | ... |
1236119 | 1 | 51 | 1 | 1 | 1 | 11 | 0062200 |
1236287 | 1 | 41 | 1 | 3 | 1 | 10 | 0015000 |
1236624 | 1 | 29 | 1 | 1 | 1 | 11 | 0050100 |
1236756 | 2 | 58 | 1 | 1 | 1 | 14 | 0069800 |
1236779 | 1 | 30 | 1 | 3 | 3 | 9 | 0022110 |
10565 rows × 7 columns
To specify both a household number and a person number, pass in a tuple:
37,1)) acs.xs((
sex 2
age 20
hispanic 1
race 1
marital_status 5
edu 11
income 0010000
Name: (37, 1), dtype: object
You can also pass a tuple (or list) to level
, specifying which order the levels are in. This allows you to change the order:
1,37), level=('person', 'household')) acs.xs((
sex | age | hispanic | race | marital_status | edu | income | ||
---|---|---|---|---|---|---|---|---|
household | person | |||||||
37 | 1 | 2 | 20 | 1 | 1 | 5 | 11 | 0010000 |
Note how specifying level
causes the result to be a DataFrame rather than a Series even though it’s a single row.
5.8 Convert Strings Containing Numbers to Numbers
The income
column in acs
looks like it contains numbers, but examining the dtypes
shows it contains objects, specifically strings.
acs.dtypes
sex int64
age int64
hispanic int64
race int64
marital_status int64
edu int64
income object
dtype: object
Unfortunately this is not unusual. In order to do math with income
we need to convert it to an actual numeric variable. The Pandas function to_numeric()
can do that for us, but we’ll need to grapple with why income
is a string in the first place.
Convert the income
column to numbers with to_numeric()
but store the result as a separate Series rather than as part of the acs
DataFrame for now. Also pass in errors='coerce'
:
= pd.to_numeric(
income_test 'income'],
acs[='coerce'
errors
) income_test
household person
37 1 10000.0
2 5300.0
3 4700.0
241 1 32500.0
242 1 30000.0
...
1236624 1 50100.0
2 12000.0
1236756 1 69800.0
2 40800.0
1236779 1 22110.0
Name: income, Length: 27410, dtype: float64
This looks like it worked, but try removing errors='coerce'
and see what happens. The important part of the error message you’ll get is: ValueError: Unable to parse string “BBBBBBB”. This tells you that some values of income are set to BBBBBBB
, which can’t be converted into a number. With errors='coerce'
these are converted to NaN (non-numeric values are ‘coerced’ into becoming numbers by converting them to NaN).
Before we decide what to do about non-numeric values of income
, we need to know if there are non-numeric values other than BBBBBBB
. The rows where income
is non-numeric have NaN for income_test
, so we can use that to select them:
acs.loc[
income_test.isna(),'income'
].value_counts()
BBBBBBB 6144
Name: income, dtype: int64
Note how it’s no problem to use a condition based on income_test
to subset acs
even though income_test
isn’t part of acs
. That’s because income_test
has the same number of rows as acs
and the same indexes, which allows Python to align the two successfully.
The result tells us that BBBBBBB
is in fact the only non-numeric value of income
, but doesn’t tell us what it means. If you’re guessing that it’s a code for missing you’re right, but to know that for sure you’d need to read the data documentation.
Since BBBBBBB
is in fact a code for missing, setting it to NaN like errors='coerce'
did is entirely appropriate. So go ahead and convert the income
column in acs
to numeric using to_numeric()
and errors='coerce'
:
'income'] = pd.to_numeric(
acs['income'],
acs[='coerce'
errors
) acs.dtypes
sex int64
age int64
hispanic int64
race int64
marital_status int64
edu int64
income float64
dtype: object
The code you used to learn what non-numeric values were in income
has served its purpose and does not need to be part of your data wrangling workflow. Given that writing a program and making it work usually involves running it many times, if this code took a non-trivial amount of time to run you definitely wouldn’t want to have to wait for it every time. You could just remove it, but it’s better to document that you checked for other non-numeric values, what you found, and what you did about it. You could do that by putting a # in front of each line so it becomes a comment, or by turning the code cells into Markdown cells. You can tell Markdown not to try to format the code by putting ``` before it and ``` after it, then add more text explaining it.
Using your First_Steps_Exercises
Notebook, load in the student survey data used as an exercise in the previous chapter, which you can do with:
survey = pd.read_csv(
'qualtrics_survey.csv',
skiprows=[1,2],
usecols=['Q1', 'Q17', 'Q3', 'Q4']
)
Convert Q1
to numeric. You may treat ‘Between 20-25’ as a missing value but make sure that’s the only non-numeric value.
5.9 Identify the Type of Each Variable
The most common variable types are continuous variables, categorical variables, string variables, and identifier variables. Categorical variables can be further divided into unordered categorical variables, ordered categorical variables, and indicator variables. (There are other variable types, such as date/time variables, but we’ll focus on these for now.) Often it’s obvious what type a variable is, but it’s worth taking a moment to consider each variable and make sure you know its type.
Continuous variables can, in principle, take on an infinite number of values. They can also be changed by arbitrary amounts, including very small amounts (i.e. they’re differentiable). In practice, all continuous variables must be rounded, as part of the data collection process or just because computers do not have infinite precision. As long as the underlying quantity is continuous, it doesn’t matter how granular the available measurements of that quantity are. You may have a data set where the income variable is measured in thousands of dollars and all the values are integers, but it’s still a continuous variable.
Continuous variables are sometimes called quantitative variables, emphasizing that the numbers they contain correspond to some quantity in the real world. Thus it makes sense to do math with them.
Categorical variables, also called factor variables, take on a finite set of values, often called levels. The levels are sometimes stored as numbers (1=White, 2=Black, 3=Hispanic, for example), but it’s important to remember that the numbers don’t actually represent quantities. Categorical variables can also be stored as strings, but Python has a class specifically designed for storing them that we’ll discuss shortly
With unordered categorical variables, the numbers assigned are completely arbitrary. Nothing would change if you assigned different numbers to each level (1=Black, 2=Hispanic, 3=White). Thus it makes no sense to do any math with them, like finding the mean.
With ordered categorical variables, the levels have some natural order. Likert scales are examples of ordered categorical variables (e.g. 1=Very Dissatisfied, 2=Dissatisfied, 3=Neither Satisfied nor Dissatisfied, 4=Satisfied, 5=Very Satisfied). The numbers assigned to the levels should reflect their ordering, but beyond that they are still arbitrary: multiply them all by 2, or subtract 10 from the lowest level and add 5 to the highest level, and as long as they stay in the same order nothing real has changed. You will see people report means for ordered categorical variables and do other math with them, but you should be aware that doing so imposes assumptions that may or may not be true. Usually the scale has the same numeric interval between levels so moving one person from Satisfied to Very Satisfied and moving one person from Very Dissatisfied to Dissatisfied have exactly the same effect on the mean, but are you really willing to assume that those are equivalent changes?
Indicator variables, also called binary variables, dummy variables, or boolean variables, are just categorical variables with two levels. In principle they can be ordered or unordered but with only two levels it rarely matters. Often they answer the question “Is some condition true for this observation?” Occasionally indicator variables are referred to as flags, and flagging observations where a condition is true means to create an indicator variable for that condition.
String variables contain text. Sometimes the text is just labels for categories, and they can be treated like categorical variables. Other times they contain actual information.
Identifier variables allow you to find observations rather than containing information about them, though some compound identifiers blur the line between identifier variables and categorical variables. You’ll normally turn them into indexes as soon as you identify them.
An easy way to identify the type of a variable is to look at its value_counts()
. Continuous variables will have many, many values (but they won’t all be printed), categorical variables will have fewer, and indicator variables will have just two. Try it with race
and income
:
'race'].value_counts() acs[
1 20636
2 3202
8 1587
6 939
9 728
3 202
5 64
7 37
4 15
Name: race, dtype: int64
'income'].value_counts() acs[
0.0 2579
30000.0 371
20000.0 318
25000.0 279
40000.0 278
...
20330.0 1
19950.0 1
53140.0 1
130300.0 1
69800.0 1
Name: income, Length: 2613, dtype: int64
With just nine unique values, race
is pretty clearly categorical. But income has 2,613, so it’s pretty clearly continuous.
Writing out a line of code like this for each variable would get tedious, so let’s talk about a better way.
5.9.1 For Loops
A for loop consists of a list and some code that is executed once for each item in the list. In this case the list will be the columns in acs
so the code will be executed once for each column. The code will print the value_counts()
for that column, but let’s also have it print the name of the column at above the list of counts.
A good place to start when writing a loop is to pick one item from the list and make the code work for that item. Let’s go with race
, not that it matters much. Use the print()
function to print the word ‘race’ followed by the value_counts()
for race
, followed by a new line character (‘’), with a new line as the separator. Printing a new line character has the effect of putting in a blank line, which will be important when we repeat this for many columns.
print(
'race',
'race'].value_counts(),
acs['\n',
='\n'
sep )
race
1 20636
2 3202
8 1587
6 939
9 728
3 202
5 64
7 37
4 15
Name: race, dtype: int64
Now that we’ve figured out the code we want for race
, what needs to change to do the same thing with a different column? The word ‘race’ appears twice in the code: once in the first argument of print()
and once in subsetting acs
. So we’ll replace ‘race’ wherever it appears with a variable that will eventually contain the name of the column the loop is currently working on:
= 'race'
column print(
column,
acs[column].value_counts(),'\n',
='\n'
sep )
race
1 20636
2 3202
8 1587
6 939
9 728
3 202
5 64
7 37
4 15
Name: race, dtype: int64
Next consider the list that the loop will loop over. In this case the list we want is all the columns in acs
, which we can get with the columns
attribute:
acs.columns
Index(['sex', 'age', 'hispanic', 'race', 'marital_status', 'edu', 'income'], dtype='object')
Now all we need to do is put the list and the code together as a for loop:
for column in acs.columns: print(
column,
acs[column].value_counts(),'\n',
='\n'
sep )
sex
2 14084
1 13326
Name: sex, dtype: int64
age
12 461
41 461
40 459
36 458
38 451
...
86 64
87 59
88 38
89 27
92 1
Name: age, Length: 92, dtype: int64
hispanic
1 23863
2 2073
24 583
3 319
4 129
5 111
11 73
16 46
19 41
7 40
17 33
8 17
12 16
13 15
23 14
6 9
15 7
9 7
21 5
10 4
20 3
22 2
Name: hispanic, dtype: int64
race
1 20636
2 3202
8 1587
6 939
9 728
3 202
5 64
7 37
4 15
Name: race, dtype: int64
marital_status
5 11750
1 11643
3 2177
2 1405
4 435
Name: marital_status, dtype: int64
edu
9 5763
11 3031
13 2920
2 2500
4 1590
10 1495
1 1290
3 1284
12 1202
0 1123
14 1057
6 1021
5 891
7 875
8 860
15 343
16 165
Name: edu, dtype: int64
income
0.0 2579
30000.0 371
20000.0 318
25000.0 279
40000.0 278
...
20330.0 1
19950.0 1
53140.0 1
130300.0 1
69800.0 1
Name: income, Length: 2613, dtype: int64
Now we have value_counts()
for all the variables in acs
. So what can we learn from them?
sex
is an indicator variable, but with the levels 1 and 2. We’ll have to look up what they mean.age
has 92 unique values and a range that looks like actual years, so we can be confident it’s a continuous variable.- You might think
hispanic
would be an indicator variable, but with 22 unique values it must be a categorical variable. race
andmarital_status
are categorical. Again, we’ll have to look up what the numbers mean.- With 17 unique values and examples that are plausible numbers of years in school,
edu
could be a quantitative variable. We’ll examine this variable more closely. - With 2,614 unique values and values that look like plausible incomes we can be confident
income
is a continuous variable.
In your First_Steps_Excercises
Notebook, load atus.csv
and get value_counts()
for all of its variables. Identify the variable types of famincome
, hispan
, asian
.
5.10 Recode Indicator Variables
It is highly convenient to store indicator variables as actual boolean variables containing True or False, with the variable name telling you what it is that is either true or false. Consider the sex
variable: right now it contains either 1 or 2, but we can only guess which of those numbers means male and which means female. If we had a variable called female
and it contained either True or False, the meaning would be immediately obvious. (Of course this only works in a world where sex is binary. That’s true for the vast majority of data sets right now, but expect data to become more nuanced over time.)
There is a file called 2000_acs_codebook.txt
among the example files that contains the actual codebook for this data set. Open it and find the description of Sex, and you’ll see:
US2000C_1009 Sex
1 Male
2 Female
Now that we know what the codes mean, create a variable called female
containing True or False:
'female'] = (acs['sex']==2)
acs[ acs
sex | age | hispanic | race | marital_status | edu | income | female | ||
---|---|---|---|---|---|---|---|---|---|
household | person | ||||||||
37 | 1 | 2 | 20 | 1 | 1 | 5 | 11 | 10000.0 | True |
2 | 2 | 19 | 1 | 1 | 5 | 11 | 5300.0 | True | |
3 | 2 | 19 | 1 | 2 | 5 | 11 | 4700.0 | True | |
241 | 1 | 2 | 50 | 1 | 1 | 5 | 14 | 32500.0 | True |
242 | 1 | 2 | 29 | 1 | 1 | 5 | 13 | 30000.0 | True |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
1236624 | 1 | 1 | 29 | 1 | 1 | 1 | 11 | 50100.0 | False |
2 | 2 | 26 | 1 | 1 | 1 | 9 | 12000.0 | True | |
1236756 | 1 | 2 | 58 | 1 | 1 | 1 | 14 | 69800.0 | True |
2 | 1 | 61 | 1 | 1 | 1 | 14 | 40800.0 | False | |
1236779 | 1 | 1 | 30 | 1 | 3 | 3 | 9 | 22110.0 | False |
27410 rows × 8 columns
A good way to check your work is to run a crosstab of sex
and female
by passing both of them to value_counts()
. Combinations that don’t make sense (e.g. sex
=1 and female
=True) should not appear in the list:
'sex', 'female']].value_counts() acs[[
sex female
2 True 14084
1 False 13326
dtype: int64
Now that you’re confident female
is right you can drop sex
:
= acs.drop('sex', axis=1) acs
Stay in the First__Steps_Practice
Notebook, and convert hispanic
to an indicator for ‘this person is Hispanic’. Look in the codebook to see which values of the existing hispanic
variable mean ‘this person is Hispanic’ and which do not. So you can check your work, first create it as hisp
, run a crosstab of hispanic
and hisp
, then drop the original hispanic
and rename hisp
to hispanic
.
Since this exercise changes the data in ways that the rest of the code relies on we’ll include the solution, but avoid the temptation to read it until you’ve tried to solve it yourself.
'hisp'] = (acs['hispanic'] > 1)
acs['hispanic', 'hisp']].value_counts()
acs[[= (
acs 'hispanic', axis=1).
acs.drop(={'hisp' : 'hispanic'})
rename(columns
) acs
age | race | marital_status | edu | income | female | hispanic | ||
---|---|---|---|---|---|---|---|---|
household | person | |||||||
37 | 1 | 20 | 1 | 5 | 11 | 10000.0 | True | False |
2 | 19 | 1 | 5 | 11 | 5300.0 | True | False | |
3 | 19 | 2 | 5 | 11 | 4700.0 | True | False | |
241 | 1 | 50 | 1 | 5 | 14 | 32500.0 | True | False |
242 | 1 | 29 | 1 | 5 | 13 | 30000.0 | True | False |
... | ... | ... | ... | ... | ... | ... | ... | ... |
1236624 | 1 | 29 | 1 | 1 | 11 | 50100.0 | False | False |
2 | 26 | 1 | 1 | 9 | 12000.0 | True | False | |
1236756 | 1 | 58 | 1 | 1 | 14 | 69800.0 | True | False |
2 | 61 | 1 | 1 | 14 | 40800.0 | False | False | |
1236779 | 1 | 30 | 3 | 3 | 9 | 22110.0 | False | False |
27410 rows × 7 columns
5.11 Define Categories for Categorical Variables
If you look in the codebook for the race
variable, you’ll find the following:
US2000C_1022 Race Recode 1
1 White alone
2 Black or African American alone
3 American Indian alone
4 Alaska Native alone
5 American Indian and Alaska Native tribes specified, and American Indian or Alaska Native, not specified, and no other races
6 Asian alone
7 Native Hawaiian and Other Pacific Islander alone
8 Some other race alone
9 Two or more major race groups
Each number is associated with a text (string) description that tells us what the number means. The data set uses numbers because storing a number for each row uses much less memory than storing a string for each row. But to do anything useful you have to translate the numbers into their text descriptions.
A Python categorical variable does the translating for you. You work with the text descriptions (i.e. strings) and it quietly keeps track of the numbers behind them so you don’t have to. To turn a numeric categorical variable like we have now into a Python categorical variable, you’ll first convert it into a string variable and then have Python convert it into a categorical variable. (We’ll use shorter descriptions than the codebook.)
For the first step we’ll use the replace()
function. To use it, we pass in a dictionary of dictionaries that describes the replacing to be done. That sounds scarier than it is, so let’s just look at one:
= {
changes 'race' : {
1 : 'White',
2 : 'Black',
3 : 'American Indian',
4 : 'Alaska Native',
5 : 'Indigenous, Unspecified',
6 : 'Asian',
7 : 'Pacific Islander',
8 : 'Other',
9 : 'Two or more races'
},
'edu' : {
0 : 'Not in universe',
1 : 'None',
2 : 'Nursery school-4th grade',
3 : '5th-6th grade',
4 : '7th-8th grade',
5 : '9th grade',
6 : '10th grade',
7 : '11th grade',
8 : '12th grade, no diploma',
9 : 'High School graduate',
10 : 'Some college, <1 year',
11 : 'Some college, >=1 year',
12 : 'Associate degree',
13 : "Bachelor's degree",
14 : "Master's degree",
15 : 'Professional degree',
16 : 'Doctorate degree'
} }
For the ‘outer’ dictionary, the keys are column names, and the values are ‘inner’ dictionaries describing what needs to be done with that column. For the inner dictionaries, the keys are the old levels (i.e. what’s in the column now) and the values are the corresponding new levels (i.e. what they should be replace with).
Now pass that into replace()
:
= acs.replace(changes)
acs acs
age | race | marital_status | edu | income | female | hispanic | ||
---|---|---|---|---|---|---|---|---|
household | person | |||||||
37 | 1 | 20 | White | 5 | Some college, >=1 year | 10000.0 | True | False |
2 | 19 | White | 5 | Some college, >=1 year | 5300.0 | True | False | |
3 | 19 | Black | 5 | Some college, >=1 year | 4700.0 | True | False | |
241 | 1 | 50 | White | 5 | Master's degree | 32500.0 | True | False |
242 | 1 | 29 | White | 5 | Bachelor's degree | 30000.0 | True | False |
... | ... | ... | ... | ... | ... | ... | ... | ... |
1236624 | 1 | 29 | White | 1 | Some college, >=1 year | 50100.0 | False | False |
2 | 26 | White | 1 | High School graduate | 12000.0 | True | False | |
1236756 | 1 | 58 | White | 1 | Master's degree | 69800.0 | True | False |
2 | 61 | White | 1 | Master's degree | 40800.0 | False | False | |
1236779 | 1 | 30 | American Indian | 3 | High School graduate | 22110.0 | False | False |
27410 rows × 7 columns
Do the same with marital_status
, looking up the meanings in the codebook.
Since this exercise changes the data in ways that the rest of the code relies on we’ll include the solution, but avoid the temptation to read it until you’ve tried to solve it yourself.
= {
marital_changes 'marital_status': {
1 : 'Now married',
2 : 'Widowed',
3 : 'Divorced',
4 : 'Separated',
5 : 'Never married'
}
}= acs.replace(marital_changes)
acs acs
age | race | marital_status | edu | income | female | hispanic | ||
---|---|---|---|---|---|---|---|---|
household | person | |||||||
37 | 1 | 20 | White | Never married | Some college, >=1 year | 10000.0 | True | False |
2 | 19 | White | Never married | Some college, >=1 year | 5300.0 | True | False | |
3 | 19 | Black | Never married | Some college, >=1 year | 4700.0 | True | False | |
241 | 1 | 50 | White | Never married | Master's degree | 32500.0 | True | False |
242 | 1 | 29 | White | Never married | Bachelor's degree | 30000.0 | True | False |
... | ... | ... | ... | ... | ... | ... | ... | ... |
1236624 | 1 | 29 | White | Now married | Some college, >=1 year | 50100.0 | False | False |
2 | 26 | White | Now married | High School graduate | 12000.0 | True | False | |
1236756 | 1 | 58 | White | Now married | Master's degree | 69800.0 | True | False |
2 | 61 | White | Now married | Master's degree | 40800.0 | False | False | |
1236779 | 1 | 30 | American Indian | Divorced | High School graduate | 22110.0 | False | False |
27410 rows × 7 columns
At this point, race
, marital_status
, and edu
are all strings. Convert them to categories using the astype()
function:
'race', 'marital_status', 'edu']] = (
acs[['race', 'marital_status', 'edu']].
acs[['category')
astype(
) acs
age | race | marital_status | edu | income | female | hispanic | ||
---|---|---|---|---|---|---|---|---|
household | person | |||||||
37 | 1 | 20 | White | Never married | Some college, >=1 year | 10000.0 | True | False |
2 | 19 | White | Never married | Some college, >=1 year | 5300.0 | True | False | |
3 | 19 | Black | Never married | Some college, >=1 year | 4700.0 | True | False | |
241 | 1 | 50 | White | Never married | Master's degree | 32500.0 | True | False |
242 | 1 | 29 | White | Never married | Bachelor's degree | 30000.0 | True | False |
... | ... | ... | ... | ... | ... | ... | ... | ... |
1236624 | 1 | 29 | White | Now married | Some college, >=1 year | 50100.0 | False | False |
2 | 26 | White | Now married | High School graduate | 12000.0 | True | False | |
1236756 | 1 | 58 | White | Now married | Master's degree | 69800.0 | True | False |
2 | 61 | White | Now married | Master's degree | 40800.0 | False | False | |
1236779 | 1 | 30 | American Indian | Divorced | High School graduate | 22110.0 | False | False |
27410 rows × 7 columns
It may not look like anything has changed, but this DataFrame is now a lot smaller. But you still refer to the string descriptions in writing code. For example, to get a subset with just the people who are High School graduates, use:
'edu']=='High School graduate'] acs[acs[
age | race | marital_status | edu | income | female | hispanic | ||
---|---|---|---|---|---|---|---|---|
household | person | |||||||
484 | 1 | 33 | Asian | Now married | High School graduate | 16800.0 | False | False |
894 | 1 | 72 | White | Now married | High School graduate | 22500.0 | False | False |
930 | 1 | 47 | White | Divorced | High School graduate | 23010.0 | True | False |
2 | 36 | White | Never married | High School graduate | 6800.0 | True | False | |
3 | 46 | White | Now married | High School graduate | 6400.0 | True | False | |
... | ... | ... | ... | ... | ... | ... | ... | ... |
1235861 | 1 | 19 | American Indian | Now married | High School graduate | 12000.0 | False | False |
1236287 | 2 | 42 | White | Now married | High School graduate | 27000.0 | True | False |
3 | 23 | White | Divorced | High School graduate | 11400.0 | True | False | |
1236624 | 2 | 26 | White | Now married | High School graduate | 12000.0 | True | False |
1236779 | 1 | 30 | American Indian | Divorced | High School graduate | 22110.0 | False | False |
5763 rows × 7 columns
How about selecting people with a Master’s degree? Here we have a complication: we normally start and end strings with '
, but the string Master's degree
contains '
. If we’re not careful, Python will think that the string ends after Master
.
The solution is to start and end this string with "
. Python is fine with either '
or "
as a way to mark strings. When a string starts with "
, Python knows the string doesn’t end until it sees "
again.
'edu']=="Master's degree"] acs[acs[
age | race | marital_status | edu | income | female | hispanic | ||
---|---|---|---|---|---|---|---|---|
household | person | |||||||
241 | 1 | 50 | White | Never married | Master's degree | 32500.0 | True | False |
894 | 2 | 69 | White | Now married | Master's degree | 11300.0 | True | False |
1014 | 1 | 52 | White | Now married | Master's degree | 97000.0 | False | False |
2 | 54 | White | Now married | Master's degree | 23750.0 | True | False | |
1509 | 1 | 67 | White | Widowed | Master's degree | 8000.0 | False | False |
... | ... | ... | ... | ... | ... | ... | ... | ... |
1232927 | 2 | 56 | White | Now married | Master's degree | 40000.0 | True | False |
1233756 | 4 | 23 | White | Never married | Master's degree | 0.0 | True | False |
1235506 | 1 | 35 | White | Divorced | Master's degree | 33000.0 | True | False |
1236756 | 1 | 58 | White | Now married | Master's degree | 69800.0 | True | False |
2 | 61 | White | Now married | Master's degree | 40800.0 | False | False |
1057 rows × 7 columns
edu
is an ordered categorical variable, but Python doesn’t know that yet. Categorical variables have an attribute called cat
that stores information about the categories, including a function called reorder_categories()
which can also be used to define the order in the first place. To use it, pass in a list containing the categories in order from lowest to highest and the argumented ordered=True
:
'edu'] = acs['edu'].cat.reorder_categories(
acs[
['Not in universe',
'None',
'Nursery school-4th grade',
'5th-6th grade',
'7th-8th grade',
'9th grade',
'10th grade',
'11th grade',
'12th grade, no diploma',
'High School graduate',
'Some college, <1 year',
'Some college, >=1 year',
'Associate degree',
"Bachelor's degree",
"Master's degree",
'Professional degree',
'Doctorate degree'
],= True
ordered )
The fact that edu
is now ordered lets us do things that depend on that order, like comparisons, sorting, or finding the maximum value:
'edu']<='High School graduate'] acs.loc[acs[
age | race | marital_status | edu | income | female | hispanic | ||
---|---|---|---|---|---|---|---|---|
household | person | |||||||
377 | 1 | 69 | White | Never married | None | 51900.0 | True | False |
418 | 1 | 59 | White | Widowed | 12th grade, no diploma | 12200.0 | True | False |
465 | 2 | 47 | Black | Never married | None | 2600.0 | True | False |
484 | 1 | 33 | Asian | Now married | High School graduate | 16800.0 | False | False |
2 | 26 | Asian | Now married | 11th grade | 18000.0 | True | False | |
... | ... | ... | ... | ... | ... | ... | ... | ... |
1236287 | 2 | 42 | White | Now married | High School graduate | 27000.0 | True | False |
3 | 23 | White | Divorced | High School graduate | 11400.0 | True | False | |
4 | 4 | White | Never married | None | NaN | False | False | |
1236624 | 2 | 26 | White | Now married | High School graduate | 12000.0 | True | False |
1236779 | 1 | 30 | American Indian | Divorced | High School graduate | 22110.0 | False | False |
17197 rows × 7 columns
'edu'].max() acs[
'Doctorate degree'
5.12 Examine Variable Distributions
Understanding the distributions of your variables is important for both data cleaning and analysis.
5.12.1 Continuous Variables
For continuous variables, the describe()
function is a great place to start for understanding their distribution:
'income'].describe() acs[
count 21266.000000
mean 27724.104580
std 39166.397371
min -10000.000000
25% 6000.000000
50% 18000.000000
75% 35800.000000
max 720000.000000
Name: income, dtype: float64
First off, note that the count (21,266) is much smaller than the number of rows in the data set (27,410). This is a good reminder that we have a substantial number of missing values.
The mean (27,724) is substantially higher than the 50% percentile, or median (18,000). This tells us that the distribution is right-skewed rather than normal, so our intuition about normal distributions (like the mean being in the center of the distribution) does not apply. This is not a surprise–incomes are almost always right-skewed. Percentiles are helpful for understanding non-normal distributions, but a picture is even better.
5.12.2 A Digression on Data Visualization
Most Python users use the Matplotlib package for creating data visualizations. Seaborn is another popular alternative. But most of our target audience either has done some work in R or will, so we’ll use plotnine, a Python version of Hadley Wickham’s ggplot2 R package.
import plotnine as p9
ggplot2 was based on The Grammar of Graphics by Leland Wilkinson, which defined a systematic way of describing data visualizations. We’ll keep things very simple (we’re making data visualizations to help us understand the data, not for publication), so we’ll only need two components. An aesthetic defines the relationships between the variables used and properties of the graph, for example the x and y coordinates. A geometry defines how that relationship is to be represented. In plotnine (and ggplot) you define a graph by giving it a data set and aesthetics and then add geometries to it.
plotnine is so similar to ggplot that you can almost copy ggplot code from R to Python. The difference is R treats package functions like built-in functions: you don’t have to specify the package name to use them (this causes problems when multiple pacakges have functions with the same name). So where R just refers to ggplot()
, aes()
, and the various geom()
functions, Python needs p9.ggplot()
, p9.aes()
, and p9.geom()
.
5.12.3 Back to Continuous Variables
A histogram will show you the distribution of a continuous variable. To create a histogram, call p9.ggplot()
, passing in the data and a p9.aes()
object that defines the aesthetics. In this case we want the x coordinate to correspond to income
. Then add to it a p9.geom_histogram()
that defines the width of each bin and plotnine will take it from there:
p9.ggplot(
acs,= 'income')
p9.aes(x + p9.geom_histogram(binwidth = 1000) )
/home/r/rdimond/.local/lib/python3.9/site-packages/plotnine/layer.py:333: PlotnineWarning: stat_bin : Removed 6144 rows containing non-finite values.
The warning about ‘non-finite values’ is just telling us that missing values were ignored, which is what we want.
The distribution certainly is skewed, to the point that it’s hard to see much. Try plotting just the incomes below $100,000 but greater than 0:
p9.ggplot('income']<100000) & (acs['income']>0)],
acs[(acs[= 'income')
p9.aes(x + p9.geom_histogram(binwidth = 1000) )
Staying in the First_Steps_Practice
Notebook, create a histogram for age
with binwidth
to set 5 (years). Then create one where binwidth
is set to 1 (i.e. every age gets its own bin). What do you see now that you couldn’t see before?
The age
variable has been top-coded in this data set: 93 really means ‘93 or older’. Larger bin widths are arguably better for giving you an overall sense of variable’s distribution: they avoid ‘missing the forest for the trees’. But small bin widths will sometimes show you very important details.
5.12.4 Categorical Variables
To examine the distribution of categorical variables, use the familiar value_counts()
. Start with edu
. The table will be more useful if the education categories stay in their inherent order rather than being sorted by frequency, so pass in sort=False
:
'edu'].value_counts(sort=False) acs[
Not in universe 1123
None 1290
Nursery school-4th grade 2500
5th-6th grade 1284
7th-8th grade 1590
9th grade 891
10th grade 1021
11th grade 875
12th grade, no diploma 860
High School graduate 5763
Some college, <1 year 1495
Some college, >=1 year 3031
Associate degree 1202
Bachelor's degree 2920
Master's degree 1057
Professional degree 343
Doctorate degree 165
Name: edu, dtype: int64
Things to note:
- There are no missing values, or at least none coded as such.
- The category “Not in universe” needs some investigation.
- There are more people in lower education categories than you might expect
- There may be more categories here than are useful, so you might consider combining them for analysis
There are a lot of numbers to read in that table, but a bar chart would allow you to see the patterns in it immediately. Creating one is almost identical to creating a histogram: it’s exactly the same aesthetic, but add geom_bar()
instead of geom_histogram()
:
p9.ggplot(
acs,='edu')
p9.aes(x+ p9.geom_bar() )
Well that’s not very useful! There’s not enough space on the x-axis for all the labels, which is common. But this problem has a very simple solution: switch to a horizontal bar graph. All you need to do is add p9.coord_flip()
to it:
p9.ggplot(
acs,='edu')
p9.aes(x+ p9.geom_bar() + p9.coord_flip() )
This makes horizontal the format of choice for bar graphs.
5.13 Investigate Anomalies
We’ve identified several oddities in this data set as we’ve explored it. These could have significant effects on your analysis, so it’s important to figure out what they mean.
edu
has a level called ‘Not in universe.’ The Census Bureau probably isn’t actually collecting data on extra-dimensional beings, so what does this mean? Begin by examining the distribution of age
for people who have ‘Not in universe’ for edu
:
acs.loc['edu']=='Not in universe',
acs['age'
].describe()
count 1123.000000
mean 0.994657
std 0.807699
min 0.000000
25% 0.000000
50% 1.000000
75% 2.000000
max 2.000000
Name: age, dtype: float64
Everyone with ‘Not in universe’ for edu
is under the age of three. Is the converse true?
'age']<3, 'edu'].value_counts() acs.loc[acs[
Not in universe 1123
High School graduate 0
Professional degree 0
Master's degree 0
Bachelor's degree 0
Associate degree 0
Some college, >=1 year 0
Some college, <1 year 0
12th grade, no diploma 0
None 0
11th grade 0
10th grade 0
9th grade 0
7th-8th grade 0
5th-6th grade 0
Nursery school-4th grade 0
Doctorate degree 0
Name: edu, dtype: int64
People with ‘Not in universe’ are all under the age of three, and people under the age of three are always ‘Not in universe.’ The Census Bureau uses ‘Not in universe’ to mean the variable does not apply; in this case it’s because if someone is under the age of three they don’t even ask about their education. Legitimate skips are similar: questions a respondent did not answer (skipped) because they did not apply.
This is why the different coding of hispan
and asian
in the ATUS is somewhat puzzling: hispan
is treated as a categorical variable that applies to everyone, with one valid value being ‘Not Hispanic,’ while asian
is treated as a categorical variable that only applies to Asian people, with non-Asians being ‘Not in universe.’ Either way makes sense, but you’d expect them to be the same.
We noted more people with less than a high school education than you might expect in the United States in 2000, but that includes children who simply aren’t old enough to have graduated from high school. If we limit the sample to adults the distribution is more like what you’d expect:
'age']>=18, 'edu'].value_counts(sort=False) acs.loc[acs[
Not in universe 0
None 291
Nursery school-4th grade 148
5th-6th grade 384
7th-8th grade 647
9th grade 489
10th grade 691
11th grade 696
12th grade, no diploma 830
High School graduate 5736
Some college, <1 year 1490
Some college, >=1 year 3029
Associate degree 1202
Bachelor's degree 2920
Master's degree 1057
Professional degree 343
Doctorate degree 165
Name: edu, dtype: int64
This is a good example of how you should check your work as you go: see if your data matches what you know about the population of interest, and if not, be sure there’s a valid reason why.
We also noted people with negative values for income
. Who are they? Look at age
, edu
, and female
:
acs.loc['income']<0,
acs['age'
].describe()
count 29.000000
mean 49.896552
std 13.454539
min 24.000000
25% 40.000000
50% 54.000000
75% 58.000000
max 77.000000
Name: age, dtype: float64
acs.loc['income']<0,
acs['edu'
=False) ].value_counts(sort
Not in universe 0
None 0
Nursery school-4th grade 0
5th-6th grade 0
7th-8th grade 0
9th grade 1
10th grade 0
11th grade 1
12th grade, no diploma 0
High School graduate 6
Some college, <1 year 3
Some college, >=1 year 6
Associate degree 5
Bachelor's degree 5
Master's degree 1
Professional degree 0
Doctorate degree 1
Name: edu, dtype: int64
acs.loc['income']<0,
acs['female'
].value_counts()
False 16
True 13
Name: female, dtype: int64
First off, note that there are only 29 of them, which lowers the stakes in dealing with them.
They’re all adults, but there are no other obvious patterns. One plausible explanation is that these people had losses on investments. However, in order to lose money on investments you have to have investments. So if that’s the explanation, these people may have substantial wealth even though they have negative incomes in this particular year. This is problematic if you plan to use income
as a proxy for broader socio-economic status. You might consider changing negative values of income
to missing.
Who are the people who marked ‘Other’ for race? (Look at the distributions of other variables for the people who have ‘Other’ for race.)
5.14 Recode Values that Mean Missing to NaN
Python uses NaN to denote missing values. Functions like mean()
expect that and act accordingly, typically by omitting NaN for calculations.
Data sets often use different codes to indicate missing values. We saw that with income: ‘BBBBBBB’ meant missing. This was automatically converted to a missing value when you converted income
from a string variable to a numeric variable, but it won’t always be that simple. It’s very common to use negative numbers to mean missing, especially -9. Functions like mean()
will not recognize that -9 means missing and will include it in calculations, giving incorrect results.
The solution is to identify values that really mean missing, and then change them NaN. For example, ‘Not in universe’ for edu
means missing, so change it with:
import numpy as np
acs.loc['edu']=='Not in universe',
acs['edu'
= np.NaN
] 484] acs.loc[
age | race | marital_status | edu | income | female | hispanic | |
---|---|---|---|---|---|---|---|
person | |||||||
1 | 33 | Asian | Now married | High School graduate | 16800.0 | False | False |
2 | 26 | Asian | Now married | 11th grade | 18000.0 | True | False |
3 | 4 | Asian | Never married | None | NaN | False | False |
4 | 2 | Asian | Never married | NaN | NaN | True | False |
Are the negative values for income
codes for missing? Take a look at them with:
acs.loc['income']<0,
acs['income'
].value_counts()
-10000.0 4
-4200.0 3
-200.0 2
-510.0 2
-100.0 1
-1300.0 1
-2040.0 1
-2500.0 1
-1700.0 1
-220.0 1
-4900.0 1
-8960.0 1
-3200.0 1
-550.0 1
-3800.0 1
-1600.0 1
-480.0 1
-3000.0 1
-4000.0 1
-140.0 1
-2300.0 1
-1000.0 1
Name: income, dtype: int64
The variety of values suggest these are actual quantities, not codes.
The codebook says that for marital_status 5 means “Never married (includes under 15 years).” In other words, the Census Bureau didn’t ask about the marital status of children under the age of fifteen just like they didn’t ask about the education of children under the age of three. But for marital status they coded it as if it were known that the children were never married. Undo this choice by changing marital_status to missing for children under fifteen.
Since this exercise changes the data in ways that the rest of the code relies on we’ll include the solution, but avoid the temptation to read it until you’ve tried to solve it yourself.
acs.loc['age']<15,
acs['marital_status'
= np.NaN ]
5.15 Examine Missing Data
Now that missing data is all coded as NaN, isna()
will be True for all of it. If you do ask Python to do math with True/False, it will convert True to 1 and False to 0. So the sum of isna()
is the number of observations with a missing value, which gives you a very convenient way to see how much missing data you have:
sum() acs.isna().
age 0
race 0
marital_status 6144
edu 1123
income 6144
female 0
hispanic 0
dtype: int64
It’s good to know you don’t have to worry about missing values for age
, race
, female
, or hispanic
, but you definitely have to think carefully about marital_status
, edu
, and income
.
What this cannot tell you is why the data are missing. Often you can answer this question by examining the relationships between missing values and other variables. For example, who are the people with missing values of income?
'income'].isna(), 'age'].describe() acs.loc[acs[
count 6144.000000
mean 7.245117
std 4.315471
min 0.000000
25% 3.000000
50% 7.000000
75% 11.000000
max 14.000000
Name: age, dtype: float64
'age']<15, 'income'].describe() acs.loc[acs[
count 0.0
mean NaN
std NaN
min NaN
25% NaN
50% NaN
75% NaN
max NaN
Name: income, dtype: float64
The Census Bureau did not ask about the income of children under the age of 15 just like they did not ask about their marital status.
You should also consider relationships between missing values. In this data set, people with a missing value of income
also have a missing value for marital_status
because both variables were not collected for children under the age of 15. But in many data sets there are direct relationships between missing values. For example, if a subject could not be located in a wave of a survey then they may have missing values for all the variables for that survey wave.
Data is missing completely at random if the probability of it being missing is unrelated to either the observed data or the unobserved data. Thus the unobserved data has the same distribution as the observed data. Complete cases analysis (analysis of just the observed data) will have less power due to the missing data, but will be unbiased.
Data is missing at random if the probability of it being missing is related only to the observed data (the missingness is random conditional on the observed data.) Thus the unobserved data can be distributed differently from the observed data. Complete cases analysis will be biased, but methods such as weighting or multiple imputation may be able to correct that bias–though they generally depend on being able to make inferences about the unobserved data using the observed data. For example, pollsters know people with lower levels of education are less likely to respond to polls, but they can use the responses of those people with low levels of education who do respond to make inferences about those who don’t (typically by weighting).
Data is missing not at random if the probability of it being missing is related to the unobserved data. You cannot correct for bias due to missing not at random data; worse, you can’t even detect it using the observed data. For example, UW-Madison once did a survey on the use of electronic calendars. The response rate was low and it seemed likely that people who use calendars heavily would be more motivated to respond, meaning estimates of usage would be biased high. But there was no way to know for sure if this was true since the probability of answering the survey would depend on the values of usage that were not observed.
Consider edu
, which is missing for children under the age of three. If we could observe the values of edu
for these children, they would probably be mostly “None” with perhaps a few “Nursery school-4th grade” (presumably all “Nursery School”). This is very different from the observed distribution of edu
. This is an example of “missing at random” since the probability of edu
being missing depends on age
and age
is always observed, but it would be difficult to correct for the bias introduced because we have no actual data (just assumptions) about the distribution of edu
for children under three.
What is the likely distribution of income
for people with a missing value for income
? How is that different from the distribution of income
for those where income
is known? How would the mean of income
change if all its missing values became known? (In other words, how do the missing values of income
bias estimates of the population mean of income
?)
5.16 Save Your Work
At this point the data set is reasonably clean, and what you do with it next will depend on how you plan to use it. For example, if you wanted to use education as a predictor in a regression model it would probably be wise to combine some of the categories, but if you were doing a descriptive study you might leave it as is.
If we saved it as a CSV, we’d need to do a lot of work we’ve done again after loading it. Instead we’ll save the data in a format designed for saving Python DataFrames. There are several, but pickle format is simple and easy to use. You can save a DataFrame as a pickle by calling its to_pickle()
function, and you read it with the Pandas read_pickle()
function. In both cases you’ll pass in the desired file name.
'acs_cleaned.pickle') acs.to_pickle(
Read the data set again and you’ll see that everything is exactly how it was before you saved it:
'acs_cleaned.pickle') pd.read_pickle(
age | race | marital_status | edu | income | female | hispanic | ||
---|---|---|---|---|---|---|---|---|
household | person | |||||||
37 | 1 | 20 | White | Never married | Some college, >=1 year | 10000.0 | True | False |
2 | 19 | White | Never married | Some college, >=1 year | 5300.0 | True | False | |
3 | 19 | Black | Never married | Some college, >=1 year | 4700.0 | True | False | |
241 | 1 | 50 | White | Never married | Master's degree | 32500.0 | True | False |
242 | 1 | 29 | White | Never married | Bachelor's degree | 30000.0 | True | False |
... | ... | ... | ... | ... | ... | ... | ... | ... |
1236624 | 1 | 29 | White | Now married | Some college, >=1 year | 50100.0 | False | False |
2 | 26 | White | Now married | High School graduate | 12000.0 | True | False | |
1236756 | 1 | 58 | White | Now married | Master's degree | 69800.0 | True | False |
2 | 61 | White | Now married | Master's degree | 40800.0 | False | False | |
1236779 | 1 | 30 | American Indian | Divorced | High School graduate | 22110.0 | False | False |
27410 rows × 7 columns
In the example files you’ll find acs_clean.pickle
. It’s the result if you get by running all the steps in this chapter, including the exercises. Hopefully your acs_cleaned.pickle
file is identical to it, but we’ll use acs_clean.pickle
in subsequent chapters just in case.
5.17 Review
As a review, here are the first steps you should take with your data. Many of them are very quick; and the ones that take more time are especially valuable. Do not skip these steps!