Many data sets involve some sort of hierarchical structure. The American Community Survey is an example of one of the most common hierarchical data structures: individuals grouped into households. Another common hierarchical data structure is panel or longitudinal data and repeated measures, where things are observed multiple times. These structures may seem very different, but the same concepts apply to both and often even the same code.

6.1 Hierarchical Data Concepts

In this section we’ll introduce some of the core concepts and vocabulary for thinking carefully about hierarchical data.

6.1.1 Levels

Hierarchical data can be described in terms of levels (note that these are not the same as the levels of a categorical variable). A level one unit is the smallest unit in the data set. A level two unit is then a group of level one units. Some examples:

EXAMPLE DATA STRUCTURE

LEVEL ONE UNIT

LEVEL TWO UNIT

Individuals living in households

Individuals

Households

States grouped into regions

States

Regions

Students in classes

Students

Classes

Tests taken by students

Tests

Students

Monthly observations of individuals

Monthly observations

Individuals

Visits to the doctor by patients

Doctor visits

Patients

Social media posts by individuals

Posts

Individuals

If the hierarchy has more than two levels, simply keep counting up: if you have students grouped into classes grouped into schools grouped into districts, students are level one, classes are level two, schools are level three, and districts are level four.

Each variable is associated with a specific level. A variable is a level two variable if all the level one units within a given level two unit have the same value for the variable. For data structures where a level two unit is observed over time, level two variables are variables that do not change over time.

EXAMPLE DATA STRUCTURE

LEVEL ONE VARIABLES

LEVEL TWO VARIABLES

Individuals living in households

Age, Sex

Household income, Address

States grouped into regions

State population

Regional income per capita

Students in classes

Student’s race

Teacher’s race, Class size

Tests taken by students

Test Score

Free lunch eligibility

Monthly observations of individuals

Employment Status

Highest degree earned

Visits to the doctor by patients

BMI, Diagnosis

Race

Social media posts by individuals

Length, Topic

Sex

Of course all of these depend on the details of the actual data set. In a study that observes individuals for a few months, it’s unlikely that their highest degree earned will change. But it might! And if it does, highest degree earned becomes a level one variable. It does not meet the definition of a level two variable because different level one units (monthly observations) have different values for it.

6.1.2 Data Forms

With a hierarchical data set, an observation (row) can represent either a level one unit or a level two unit. Consider observing two people for three months:

person_id

month

years_edu

employed

1

1

16

True

1

2

16

True

1

3

16

True

2

1

12

False

2

2

12

True

2

3

12

True

Exercise

Identify the level one units, level two units, level one variable(s), and level two variable(s) in the above data set.

In this form, each observation represents a month (or more precisely, a person-month combination). Now consider the exact same data in a different form:

person_id

years_edu

employed_1

employed_2

employed_3

1

16

True

True

True

2

12

False

True

True

In this form, each observation represents a person. Because years_edu is a level two variable, there’s just one value per person and thus one variable (column). However, employed is a level one variable with three (potentially) different values per person. Thus it needs three variables (columns).

We call the first form the long form (or occasionally the tall form) and the second the wide form. The long form is longer because it has more rows; the wide form is wider because it has more columns. In most cases the long form is easier to work with, so we’ll do most of our examples in this form.

Now consider the indexes in this data set. In the long form, person_id and month are a compound identifier and could be easily turned into a MultiIndex, while the variable names are a simple column identifier. In the wide form, person_id is a simple row identifier, but now the variable names for the level one variables are a compound identifier with two parts: the variable name (employed) and the month in which the variable was observed. To identify a specific value in the data set we still need to know the person_id, month, and a variable name, but month has been converted from a row identifier to part of a compound column identifier. We’ll learn how to actually carry out that transformation in the next chapter.

6.1.3 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 Hierarchical_Data_Practice.ipynb. Have it import Pandas and load acs_clean.pickle:

import pandas as pdacs = pd.read_pickle('acs_clean.pickle')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

Exercise

Load the example extract from the National Longitudinal Survey of Youth contained in nlsy.pickle. Identify the level one and level two units in the data and the level one and level two variables. (We’re not so worried about having a streamlined data cleaning workflow in this chapter, so just use your Hierarchical_Data_Practice.ipynb Notebook.)

6.2 Long Form Data

For some purposes, if a hierarchical data set is in long form you don’t have to worry about the hierarchical structure. On the other hand, a very common data wrangling task is creating level two variables based on the level one variables in the data set, and then the hierarchical structure is critical. In long form the critical tool for doing so is groupby(), so that the calculations are carried out separately for each level two unit.

6.2.1 Using groupby()

The groupby() function creates a new object which contains the DataFrame split up into groups. Functions act on that object, carry out calculations on the groups, and can return results in various forms. For example, if you apply basic summary statistic functions, you’ll get one row per group:

acs.groupby('household').mean()

/tmp/ipykernel_1582033/3152870129.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
acs.groupby('household').mean()

age

income

female

hispanic

household

37

19.333333

6666.666667

1.0

0.0

241

50.000000

32500.000000

1.0

0.0

242

29.000000

30000.000000

1.0

0.0

377

69.000000

51900.000000

1.0

0.0

418

59.000000

12200.000000

1.0

0.0

...

...

...

...

...

1236119

51.000000

46600.000000

0.5

0.0

1236287

27.500000

17800.000000

0.5

0.0

1236624

27.500000

31050.000000

0.5

0.0

1236756

59.500000

55300.000000

0.5

0.0

1236779

30.000000

22110.000000

0.0

0.0

10565 rows × 4 columns

Note how the boolean (True/False) variables female and hispanic were converted, or coerced, into numeric variables before the mean was calculated. This is very useful and we’ll talk more about it shortly. The categorical variables race, edu, and marital_status were ignored, as means would be meaningless for them.

mean() is an example of an aggregate function: it takes multiple values and returns a single value. Useful aggregate functions include:

mean()

sum() (total)

max()

min()

count() (number of rows with non-missing values)

size() (number of rows)

first() (value for the first observation in the group)

last() (value for the last observation in the group)

nth() (value for the nth observation in the group)

The first five functions listed, mean() through count(), can be applied to a DataFrame, a Series, or the result of a groupby(). However, the last four, size() through nth(), only work with groupby().

With the transform() function, you pass in the name of an aggregate function which is then applied to the groups, but the result is copied or broadcast to all the observations in the group. This is the primary tool you’ll use to add level two variables to a DataFrame. The result is a Series with the same number of rows as the original DataFrame, but the value for level one units in the same level two unit is always the same.

We’ll start with examples of calculating continuous (or quantitative) level two variables based on continuous level one variables.

For many purposes, the total income of a household is more relevant than individual incomes. Create a column called household_income containing the total income of everyone in the household with:

Now calculate the household size, or the number of people in the household. Since there is one row per person, we can use the size() function. You still need to have it act on a single column, but it makes no difference which column you choose:

Create a variable mean_age containg the mean age of the household.

Now calculate the income per person, spot-checking household 484. Since income per person is the same as the mean income for the household, you might think you can use:

This does not give the right answer because of the way mean() handled missing values: it took the mean of the two observed values of income and ignored the two people with NaN. However, we know that NaN for income means the person is under 15 years old, and almost certainly their income is zero or close to it. What we want is the total income of the household divided by the number of people in the household, whether they have observed values of income or not.

Since you already created household_income and household_size, you can just use them:

Note that we did not need to use groupby()! groupby() is relevant when you’re using an aggregation function and need to control which rows are aggregated (e.g. the people in the same household). This code does not use an aggregation function: income_per_person for each row depends solely on household_income and household_size for the same row.

What if you hadn’t already created household_income and household_size? Is there a way to calculate income_per_person directly? Absolutely. We just need a function to pass to transform() that first calculates the sum and then divides by the size. No such function exists–but we can create one.

6.2.3 Lambda Functions

Python allows you to create and name your own functions, but you most likely won’t need to for data wrangling. You’ll probably be able to do everything you need to do with temporary, unnamed functions, known as lambda functions. You define a lambda function with:

lamba x: (f(x))

where f(x) should be replaced by the function you need. x is a parameter that is being passed in to the function–you can actually give it any name you want.

What seems like the obvious f(x) here is:

lambda x: (x.sum()/x.size())

but that doesn’t work. The reason is that the passed in x is no longer a groupby() object so you can’t use the size() function. However, Series and DataFrames have a size attribute which gives the total number of elements they contain. For a DataFrame this is the number of rows times the number of columns and rarely useful. However, for a Series which is one column from a DataFrame size is the number of rows, and that is exactly what you need:

Suppose you’re interested in the income children contribute to their household (household 8787 is a good example), so you want to create a child_income containing the household’s total income earned by children. You might think this would be a straightforward process of adding up the income of everyone that’s younger than 18:

The trouble is that in excluding the adults from the sum(), you also excluded them from getting the result. Thus child_income is not a proper household-level variable, as it is not the same for all the observations in the same household–and you’d really be in trouble if your research question involved the effect of children’s contributions on the adults in the household. The solution is to create a column that only contains the quantities you want to add up, but can be added up for everyone:

For children income_if_child contains their income, while for adults it contains NaN. If we now pass income_if_child to sum(), it will add up just the children’s incomes without us having to exclude the adults. Thus the result will be stored in the rows for the household’s adults too:

Since all the aggregate functions ignore missing values, you can use this approach any time you need to use an aggregate function on a subset of the data:

Create a new column containing the value of interest for the observations to be included and NaN for the values to be excluded

Aggregate that column with no subsetting

Exercise

Find the mean age of the adults in each household.

Again, just to keep things neat drop the variables created in this section:

As we’ve seen, if you do math with an indicator variable, it will be coerced into a number with True converted to 1 and False converted to 0. Aggregate functions take on new and very useful meanings when applied to indicator variables.

Start by creating an indicator variable for ‘this person is a child’:

acs['child'] = acs['age']<18acs.loc[[37, 8787]]

age

race

marital_status

edu

income

female

hispanic

child

household

person

37

1

20

White

Never married

Some college, >=1 year

10000.0

True

False

False

2

19

White

Never married

Some college, >=1 year

5300.0

True

False

False

3

19

Black

Never married

Some college, >=1 year

4700.0

True

False

False

8787

1

45

White

Divorced

Some college, >=1 year

38000.0

True

False

False

2

16

White

Never married

9th grade

3600.0

False

False

True

3

15

White

Never married

7th-8th grade

200.0

True

False

True

Just to see the conversion in action, use the astype() function and pass in int32 (32-bit integer).

This merits some explanation. If a household has no children, everyone in the household has a 0 (False) for child and the maximum value is 0. If a household has children, then at least one person in the household has 1 (True) for child, and the maximum value is 1. Python is clever enough to realize that the max() of a boolean variable is also a boolean variable, so it stores True/False in has_children rather than 1/0.

The minimum value of child tells you if the household consists entirely of children:

If everyone in the household is a child, everyone has a 1 (True) for child and the minimum value is 1 (True). If anyone in the household is not a child, then at least one person has a 0 (False) for child and the minimum is 0 (False).

Exercise

Create a variable containing the number of people in the household with income greater than zero and an indicator variable for ‘this household has someone with income>0’.

6.3 Wide Form Data

Up to this point we’ve worked with the American Community Survey in long form, with one observation per person. Some tasks are easier to do in long form than in wide form, but others are just as easy either way–once you know the tricks for working in wide form.

The file acs_wide.pickle contains the same ACS data we’ve been working with, but in wide form. Read it in as our new acs:

acs = pd.read_pickle('acs_wide.pickle')acs

age_1

age_2

age_3

age_4

age_5

age_6

age_7

age_8

age_9

age_10

...

female_7

female_8

female_9

female_10

female_11

female_12

female_13

female_14

female_15

female_16

household

37

20.0

19.0

19.0

NaN

NaN

NaN

NaN

NaN

NaN

NaN

...

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

241

50.0

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

...

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

242

29.0

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

...

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

377

69.0

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

...

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

418

59.0

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

...

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

1236119

51.0

51.0

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

...

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

1236287

41.0

42.0

23.0

4.0

NaN

NaN

NaN

NaN

NaN

NaN

...

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

1236624

29.0

26.0

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

...

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

1236756

58.0

61.0

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

...

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

1236779

30.0

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

...

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

10565 rows × 112 columns

Before proceeding, let’s review what we’re seeing here. In wide form, there is one row per household rather than one row per person as in long form. Each person is described by a set of columns. For example, person 1 is described by age_1, race_1, etc. There are 16 people in one household, so there are 16 of all the columns (i.e. they go up to age_16, race_16, etc.). Smaller households will have NaN in the columns for the people they don’t have. For example, household 37 has three people, so age_1, age_2, and age_3 have values while all the others have NaN.

In long form, household and person made up a MultiIndex row index, while the variable names (age, race, etc.) made up the column index. In wide form household is the row index. We could use the variable names plus person as a MultiIndex column index, and you’ll see what that looks like in the next chapter. However, we have not found a good way to have a MultiIndex describe data that contain both level 1 and level 2 variables. Thus rather than using an official MultiIndex, the column names contain both the variable name and the person number. This seems to be the most common approach.

6.3.1 Subsetting in Wide Form

If we were using a MultiIndex for the columns we could use loc() for subsetting just like we did in long form. However, since the information we need is embedded in the column names, we’ll use filter() instead.

The filter() function allows you to subset based on patterns in the column names. A very simple pattern is like=. This will select all the columns that contain the string you pass in. Select all the income columns by passing in like='income':

We’ll learn shortly how to use this to create level two variables like household_income. But that raises a problem: once you create a household_income variable, like='income' will select it too.

Now consider trying to select all the variables that describe person 1. You might think like='_1' would do the job, but try it:

Instead we got the variables for persons 10 through 16 as well, because they also contain ’_1’.

What we need is the ability to be much more specific about the columns to be selected. The solution is regular expressions. Regular expressions are a syntax for describing a pattern in text so that a computer can detect text that matches it. They’ve been around for more than 50 years and most programming languages and statistical packages implement them. They can be used for a variety of purposes; we won’t discuss working with text data (strings) but we’re laying the foundation by introducing you to regular expressions. Regular expressions can be extremely complex. We’ll introduce you to just a few of the most important features but you can find many, many tutorials online.

You use a regular expression with filter() by passing in regex=. The simplest regular expression is just some text, and a string matches it if it contains that text. In other words, it does the same thing as like=:

But a regular expression can do much more. Here are the tools we’ll use:

If a regular expression starts with ^, then the string only matches if it starts with the pattern.

If a regular expression ends with $, then the string only matches if it ends with the pattern.

\d matches any digit (number).

A * after something means the string can contain any number of that thing at that point. So \d* means the string can contain any number of digits at that point.

Putting this all together, the regular expression ^income_\d*$ will match strings that start with ‘income_’ followed by any number of digits and then end. This will allow us to match the columns containing the income for each person (income_1, income_2, etc.) without also matching columns like household_income:

The regular expression _1$ will match strings that end with ’_1’. This excludes variables that end with ’_10’, ’_11’, etc. and thus allows us to select just the columns describing person 1:

Use a regular expression to select all the race variables. Then use one to select all the variables for person 5.

6.3.2 Creating Level Two Variables

When we used the sum() function in the last section to add up household incomes, it was adding all the values in a column. However, with the axis=1 argument it will add up values across a row. You can use this along with filter() to calculate household income in wide form:

If you want to be able to see both the individual incomes and household_income so you can spot-check the results, go ahead and use like='income' (and scroll right):

acs.filter(like='income')

income_1

income_2

income_3

income_4

income_5

income_6

income_7

income_8

income_9

income_10

income_11

income_12

income_13

income_14

income_15

income_16

household_income

household

37

10000.0

5300.0

4700.0

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

20000.0

241

32500.0

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

32500.0

242

30000.0

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

30000.0

377

51900.0

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

51900.0

418

12200.0

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

12200.0

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

1236119

62200.0

31000.0

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

93200.0

1236287

15000.0

27000.0

11400.0

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

53400.0

1236624

50100.0

12000.0

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

62100.0

1236756

69800.0

40800.0

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

110600.0

1236779

22110.0

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

22110.0

10565 rows × 17 columns

This works for all of the aggregate functions from the last section, except those that only work with groupby(). Thus the code to carry out the tasks from the last section is almost the same: just use filter() instead of loc to select the right subsample, don’t bother with transform(), and pass axis=1 to the aggregate function itself.

How can we find the size of each household (number of people)? Recall that count() gives the number of non-missing values. So as long as there’s at least one variable with no missing values in the data set, you can use count() to find the number of people. This data set has multiple candidates, but use age:

What about income per person? That’s actually easier than in long form. There’s no need to broadcast the result to all the rows in the same household, so you don’t need transform() or a lambda function. Just add up the household income and divide by the number of people in the household:

income_1 16800.0
income_2 18000.0
income_3 NaN
income_4 NaN
income_5 NaN
income_6 NaN
income_7 NaN
income_8 NaN
income_9 NaN
income_10 NaN
income_11 NaN
income_12 NaN
income_13 NaN
income_14 NaN
income_15 NaN
income_16 NaN
household_income 34800.0
income_per_person 8700.0
Name: 484, dtype: float64

Recall that household 484 has two children who have missing values for income because they are under 15.

Exercise

Find the mean age for each household. Then create an indicator variable for ‘everyone in this household is female’.

6.3.3 Creating Level One Variables

Creating level one variables in long form is straightforward. For example, to create an indicator for ‘this person is black’ you’d just run:

acs['black'] = (acs['race']=='Black')

But in wide form there are 16 race variables, and there will need to be 16 indicators for Black as well. If you need to do a lot of work with level one variables, this is a pretty good reason to convert your data to long form. We’ll learn how in the next chapter. But it’s not too hard to make all 16 variables as long as you use a for loop.

Once again, we’ll start by considering what it would take to create just one of the 16 new indicator variables:

acs['black_1'] = (acs['race_1']=='Black')

That would take care of person 1. What would need to change in order to take care of person 2? The number at the end of the variable names. That tells us that this time we need to loop over a range of numbers. You can do that with the range() function. In its simplest form it takes two arguments, a starting number and an ending number. In true Python style the ending number is not included. To see how this works in a loop, try:

for i inrange(1, 17): print(i)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

So far so good, but how do we get those numbers into the variable names? If you add two strings together, they’ll be concatenated:

'Hello '+'world!'

'Hello world!'

The only wrinkle is that i is a number, But you can convert it to a string with the str() function and then add it:

'black_'+str(i)

'black_16'

This allows us to build the variable names we need, so now you’re ready to write the loop. One last complication: remember that any comparison involving NaN returns False. If a race variable is NaN the corresponding black variable should also be NaN, so select only the rows where race is not NaN:

for i inrange(1,17): acs.loc[ acs['race_'+str(i)].notna(),'black_'+str(i)] = (acs['race_'+str(i)]=='Black')acs.filter(like='black')

/tmp/ipykernel_1582033/611018561.py:1: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
for i in range(1,17): acs.loc[

black_1

black_2

black_3

black_4

black_5

black_6

black_7

black_8

black_9

black_10

black_11

black_12

black_13

black_14

black_15

black_16

household

37

False

False

True

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

241

False

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

242

False

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

377

False

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

418

False

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

1236119

False

False

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

1236287

False

False

False

False

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

1236624

False

False

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

1236756

False

False

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

1236779

False

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

NaN

10565 rows × 16 columns

Exercise

Create a set of indicator variables for ‘this person has experienced the end of a marriage’. This would include anyone who is divorced, widowed, or separated (we’ll call that an ‘end’, though you could argue otherwise), and not anyone who is currently married or has never been married.

6.4 Panel Data

Panel data or longitudinal data is just another form of hierarchical data, with subjects as level two units and times they were observed as level one units. With panel data, the timing of the observations or at least their order is important. If it’s not, then we refer to it as repeated measures data. Since panel data is just hierarchical data, a lot of the code we’ve already learned applies directly to panel data, but there are some panel-specific tricks too.

You should already have the NLSY extract in memory from the second exercise. If not, load it with:

nlsy = pd.read_pickle('nlsy.pickle')nlsy

birth_year

edu

income

age

id

year

1.0

1979

58.0

12TH GRADE

4620.0

21.0

1980

58.0

NaN

NaN

22.0

1981

58.0

12TH GRADE

5000.0

23.0

1982

58.0

NaN

NaN

24.0

1983

58.0

NaN

NaN

25.0

...

...

...

...

...

...

12686.0

1993

60.0

12TH GRADE

31000.0

33.0

1994

60.0

NaN

NaN

34.0

1996

60.0

NaN

NaN

36.0

1998

60.0

NaN

NaN

38.0

2000

60.0

NaN

NaN

40.0

241034 rows × 4 columns

6.4.1 Creating Level Two Variables

The National Longitudinal Survey of Youth tracks young people for many years, making it classic panel data. In the NLSY a level two unit is a year and a level one unit is a person-year combination. You can create level two variables using the same tools we used with the ACS. For example, create max_edu containing the person’s highest level of education with:

Note that this relies on edu being an ordered categorical variable.

The first(), last() and nth() functions are particularly useful with panel data. But their meaning depends on the order the data are sorted in. This data set appears to be in chronological order, but you shouldn’t trust that if you’re going to write code that will give you the wrong answer if it is not. So before using the first() function to find the person’s age at the start of the study, sort by id and year:

The nth() function requires an argument specifying ‘n’, the desired observation (starting with zero). If you were using it on its own, that would just go in parentheses as usual:

nlsy['edu'].groupby('id').nth(2)

id
1.0 12TH GRADE
2.0 9TH GRADE
3.0 NaN
4.0 9TH GRADE
5.0 3RD YEAR COLLEGE
...
12682.0 12TH GRADE
12683.0 12TH GRADE
12684.0 12TH GRADE
12685.0 1ST YEAR COLLEGE
12686.0 12TH GRADE
Name: edu, Length: 12686, dtype: category
Categories (21, object): ['NONE' < '1ST GRADE' < '2ND GRADE' < '3RD GRADE' ... '5TH YEAR COLLEGE' < '6TH YEAR COLLEGE' < '7TH YEAR COLLEGE' < '8TH YEAR COLLEGE OR MORE']

With transform(), the arguments that would normally be passed in to the function are instead passed in to transform() as additional arguments. So the transform() equivalent is:

The NLSY frequently has missing data as subjects were frequently not located in a given year. Somewhat unusually for panel data, they often were able to find people in later years so there are gaps as well as attrition. Consider what we know about the education of person 5:

nlsy.loc[11]

birth_year

edu

income

age

year

1979

59.0

1ST YEAR COLLEGE

2900.0

20.0

1980

59.0

NaN

NaN

21.0

1981

59.0

3RD YEAR COLLEGE

2000.0

22.0

1982

59.0

4TH YEAR COLLEGE

4002.0

23.0

1983

59.0

4TH YEAR COLLEGE

8000.0

24.0

1984

59.0

4TH YEAR COLLEGE

17000.0

25.0

1985

59.0

4TH YEAR COLLEGE

20000.0

26.0

1986

59.0

NaN

NaN

27.0

1987

59.0

NaN

NaN

28.0

1988

59.0

NaN

NaN

29.0

1989

59.0

4TH YEAR COLLEGE

46000.0

30.0

1990

59.0

4TH YEAR COLLEGE

74283.0

31.0

1991

59.0

4TH YEAR COLLEGE

81481.0

32.0

1992

59.0

4TH YEAR COLLEGE

55000.0

33.0

1993

59.0

4TH YEAR COLLEGE

60000.0

34.0

1994

59.0

4TH YEAR COLLEGE

101653.0

35.0

1996

59.0

4TH YEAR COLLEGE

60000.0

37.0

1998

59.0

4TH YEAR COLLEGE

25000.0

39.0

2000

59.0

4TH YEAR COLLEGE

55000.0

41.0

They were not interviewed in 1986, 1987, and 1988, but given that their education level both before and after that gap is ‘4TH YEAR COLLEGE’ we know it was ‘4TH YEAR COLLEGE’ in those years as well. Meanwhile, while we might think that in 1980 their education level was ‘2ND YEAR COLLEGE’ that is less certain–and if that were a two-year gap we’d have no idea. So let’s fill in gaps only where the edu is the same before and after the gap.

To actually do that, we’ll fill in all the gaps in two ways: first by carrying past values forward, and then by carrying future values back. If the two methods agree, then that is a gap we can fill.

The fillna() function allows you to fill in missing values. If you pass in method='ffill' it will do a forward fill, and if you pass in method='bfill' it will do a backwards fill. While fillna() is not an aggregate function, it’s still important to use groupby('id') because that ensures each person is considered separately and no values are carried from one person to another.

Now we’ll replace edu if edu is missing and edu_forward and edu_backward are the same. Since they are the same it doesn’t matter which of them we set edu to:

Note that we will not attempt to fill in other gaps where we are not perfectly certain what the missing value should be. There are more or less valid methods for filling in missing values like multiple imputation, but they are complicated and a lot of work. Methods that are less complicated and less work, like filling in the mean or linear interpolation, can create serious problems with any statistical analysis. It’s unusual to be able to fill in gaps like we were able to here but worth doing when you can.

6.4.3 Variables Based On An Observations Neighbors

The shift() function can shift observations forward or backwards: forward if you pass in a positive number, backwards if you pass in a negative number. For easy viewing, let’s build a DataFrame by passing a dictionary to the Pandas DataFrame function. The dictionary must contain the column names as keys and Series (which we’ll create based on the edu Series in nsly) as values. Our DataFrame will have one column for the original edu, one for shifting forward, and one for shifting backward:

Note that when shifting forward you’ll have NaN at the beginning of the series, and when shifting backwards at the end. In this case we did not need groupby() because we extracted just one person, but in general you’ll need it to prevent values from being shifted between people.

Now consider trying to identify the year in which a person graduates from high school. In that year their edu will be ‘12TH GRADE’ or higher and the year before their edu will be less than ‘12TH GRADE’. Note that for whatever reason the NLSY frequently records people as having gained multiple years of education in one year, and we won’t worry about that. For person 2, the year in which they graduated is 1985:

nlsy.loc[2]

birth_year

edu

income

age

edu_forward

edu_backward

year

1979

59.0

9TH GRADE

4000.0

20.0

9TH GRADE

9TH GRADE

1980

59.0

9TH GRADE

5000.0

21.0

9TH GRADE

9TH GRADE

1981

59.0

9TH GRADE

6000.0

22.0

9TH GRADE

9TH GRADE

1982

59.0

9TH GRADE

10000.0

23.0

9TH GRADE

9TH GRADE

1983

59.0

9TH GRADE

11000.0

24.0

9TH GRADE

9TH GRADE

1984

59.0

9TH GRADE

11500.0

25.0

9TH GRADE

9TH GRADE

1985

59.0

12TH GRADE

11000.0

26.0

12TH GRADE

12TH GRADE

1986

59.0

12TH GRADE

14000.0

27.0

12TH GRADE

12TH GRADE

1987

59.0

12TH GRADE

16000.0

28.0

12TH GRADE

12TH GRADE

1988

59.0

12TH GRADE

20000.0

29.0

12TH GRADE

12TH GRADE

1989

59.0

12TH GRADE

19000.0

30.0

12TH GRADE

12TH GRADE

1990

59.0

12TH GRADE

20000.0

31.0

12TH GRADE

12TH GRADE

1991

59.0

12TH GRADE

20000.0

32.0

12TH GRADE

12TH GRADE

1992

59.0

12TH GRADE

22000.0

33.0

12TH GRADE

12TH GRADE

1993

59.0

12TH GRADE

25000.0

34.0

12TH GRADE

12TH GRADE

1994

59.0

12TH GRADE

0.0

35.0

12TH GRADE

12TH GRADE

1996

59.0

12TH GRADE

0.0

37.0

12TH GRADE

12TH GRADE

1998

59.0

12TH GRADE

0.0

39.0

12TH GRADE

12TH GRADE

2000

59.0

12TH GRADE

0.0

41.0

12TH GRADE

12TH GRADE

Now create an indicator variable for ‘graduated this year’ with:

Note that if either edu or shifted edu is NaN, the result will be false. Thus this indicator variable is really for ‘we know the person graduated this year.’ We could set it to NaN for years with missing values if we wanted to, using the techniques we’ve used before.

Logically each person should only graduate from high school once, but it’s not hard to image data entry errors that would make it look like they graduated multiple times. Recall that the sum of an indicator variable is the number of times it is true, so check with:

A little over half the subjects have True for grad zero times: either they never graduated from high school, they graduated outside the study period, or we could not identify their graduation year due to missing data. But no one graduated more than once.

Now let’s find their age at graduation. This is just a matter of subsetting with an aggregate function, except that the desired subset has just one observation per group. So start by creating age_if_grad containing the person’s age for the year they graduated and NaN for all others.

and that would absolutely work. But let’s learn an alternative method. The numpy where() function takes three arguments: a condition, the result to return if the condition is true, and the result to return if the condition is false. Build age_if_grad by importing numpy as np and then passing to where() the three arguments grad, age, and NaN:

import numpy as npnlsy['age_if_grad'] = np.where( nlsy['grad'], nlsy['age'], np.NaN)nlsy.loc[2]

birth_year

edu

income

age

edu_forward

edu_backward

grad

age_if_grad

year

1979

59.0

9TH GRADE

4000.0

20.0

9TH GRADE

9TH GRADE

False

NaN

1980

59.0

9TH GRADE

5000.0

21.0

9TH GRADE

9TH GRADE

False

NaN

1981

59.0

9TH GRADE

6000.0

22.0

9TH GRADE

9TH GRADE

False

NaN

1982

59.0

9TH GRADE

10000.0

23.0

9TH GRADE

9TH GRADE

False

NaN

1983

59.0

9TH GRADE

11000.0

24.0

9TH GRADE

9TH GRADE

False

NaN

1984

59.0

9TH GRADE

11500.0

25.0

9TH GRADE

9TH GRADE

False

NaN

1985

59.0

12TH GRADE

11000.0

26.0

12TH GRADE

12TH GRADE

True

26.0

1986

59.0

12TH GRADE

14000.0

27.0

12TH GRADE

12TH GRADE

False

NaN

1987

59.0

12TH GRADE

16000.0

28.0

12TH GRADE

12TH GRADE

False

NaN

1988

59.0

12TH GRADE

20000.0

29.0

12TH GRADE

12TH GRADE

False

NaN

1989

59.0

12TH GRADE

19000.0

30.0

12TH GRADE

12TH GRADE

False

NaN

1990

59.0

12TH GRADE

20000.0

31.0

12TH GRADE

12TH GRADE

False

NaN

1991

59.0

12TH GRADE

20000.0

32.0

12TH GRADE

12TH GRADE

False

NaN

1992

59.0

12TH GRADE

22000.0

33.0

12TH GRADE

12TH GRADE

False

NaN

1993

59.0

12TH GRADE

25000.0

34.0

12TH GRADE

12TH GRADE

False

NaN

1994

59.0

12TH GRADE

0.0

35.0

12TH GRADE

12TH GRADE

False

NaN

1996

59.0

12TH GRADE

0.0

37.0

12TH GRADE

12TH GRADE

False

NaN

1998

59.0

12TH GRADE

0.0

39.0

12TH GRADE

12TH GRADE

False

NaN

2000

59.0

12TH GRADE

0.0

41.0

12TH GRADE

12TH GRADE

False

NaN

You may find that syntax clearer. where() would definitely be easier to use than the first syntax if you needed to set the rows where grad was false to something other than NaN.

age_if_grad for each person is a single number plus a bunch of NaNs. Many aggregate functions have the property that if you give them a single number they’ll return that number, so we could use any of them. We’ll go with mean:

Assume for the moment that if someone’s income is zero they are unemployed and if their income is positive they are employed. Create an indicator variable for ‘person lost their job’ which is true if someone is unemployed (income is zero) in the current year and was employed (income is positive) in the previous year.