In this chapter we’ll learn about two of the most common ways you might need to change the structure of a data set: aggregating observations, and switching between long form and wide form.

7.1 Aggregating Observations

Sometimes you’ll get a hierarchical data set with level one and level two units, but all you care about is the level two units. For example, you may need one observation per household and just household-level variables from the ACS. But those household-level variables are likely to be aggregates of the level one variables, like household_income. More broadly, there are many situations where you may want to combine observations that belong to the same group. Pandas has functions that makes this easy to do.

7.1.1 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 Restructuring_Data_Practice.ipynb. Have it import Pandas and load acs_clean.pickle. Then create a household_income variable so we’ll have a household-level variable to practice with.

import pandas as pdacs = pd.read_pickle('acs_clean.pickle')acs['household_income'] = acs['income'].groupby('household').transform('sum')acs

age

race

marital_status

edu

income

female

hispanic

household_income

household

person

37

1

20

White

Never married

Some college, >=1 year

10000.0

True

False

20000.0

2

19

White

Never married

Some college, >=1 year

5300.0

True

False

20000.0

3

19

Black

Never married

Some college, >=1 year

4700.0

True

False

20000.0

241

1

50

White

Never married

Master's degree

32500.0

True

False

32500.0

242

1

29

White

Never married

Bachelor's degree

30000.0

True

False

30000.0

...

...

...

...

...

...

...

...

...

...

1236624

1

29

White

Now married

Some college, >=1 year

50100.0

False

False

62100.0

2

26

White

Now married

High School graduate

12000.0

True

False

62100.0

1236756

1

58

White

Now married

Master's degree

69800.0

True

False

110600.0

2

61

White

Now married

Master's degree

40800.0

False

False

110600.0

1236779

1

30

American Indian

Divorced

High School graduate

22110.0

False

False

22110.0

27410 rows × 8 columns

7.1.2 Aggregating With A Subset

If the data set you need consists of level two units and level two variables that already exist, all you need to do is create a subset selecting the level two variable(s) and just one row per level two unit. Since level two variables are always the same for all level one units in the same level two unit, it doesn’t matter which row you choose. But it’s easiest to choose the first row in each group since every group will have a first row. You can do that with xs():

Create a household-level variable containing the mean age of the children in the household. Then create a data set that contains one row per household and the mean age of the children. (Hint: to find the mean age of the children, first create a column containing the ages of just the children, with NaN for the adults.)

7.1.3 Aggregating With An Aggregate Function

If the data set you need consists of level two units and either one level two variable or multiple level two variables that can all be created with the same aggregate function, just apply the aggregate function to the variables you need. For example, you can create a data set of household incomes directly with:

The apply() function allows you to aggregate using a lambda function, so you can create your own aggregate functions. Create a data set containing households and their income per person with:

Create a data set of households containing the education level of the most educated person in the household. Recall that edu is an ordered categorical variable, so you can apply max() to it.

7.1.4 Aggregating Using Named Aggregates

If you need to create a data set of level two units and level two variables that must be made using different aggregate functions, you can do so using named aggregates. A named aggregate allows you to specify the column to be aggregated, the aggregate function to use, and what the result should be called. This gives you complete control over the DataFrame to be created.

The Pandas NamedAgg() function takes two arguments: the column to act on and the aggregate function to use. To use them, pass them as key word arguments to the agg() function, with the name being the column to create in the resulting data set.

For example, suppose we need a data set of households with the variables ‘household income’, ‘number of people in the household’, and ‘proportion of the household that is female.’ We already have household_income as a level two variable, so we can just take its first value with the aggregate function first–but since household income will be the only income we care about in the data set of households, let’s just call it income. Number of people can be obtained from the aggregate function size, but remember it needs a single column, any column, to act on. And proportion female is just the mean of female. Thus the code is:

The naming of the new columns is a rare instance where column names do not go in quotes. That’s because they are key word arguments for the function agg().

7.1.4.1 Exercise

Create a data set with one row per household and variables for ‘at least one member of this household is Hispanic’ and ‘all the members of this household are Hispanic.’

7.2 Switching Between Long and Wide Form

Unlike aggregating data sets, switching between long and wide form keeps all of the level one data–no data are lost, they’re just organized differently. Doing so is very easy if you can use a MultiIndex for the columns in wide form. Unfortunately, this does not work if the data set contains level two variables. Also, most statistical packages do not have an equivalent of Python’s MultiIndex for columns. Thus the wide form we saw in the chapter on hierarchical data is much more common, with the level one identifiers (person number in the ACS) as part of the variable names.

Reload the acs_clean data set as acs:

acs = 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

7.2.1 Switching With A MultiIndex

This data set is in the long form, so one way to describe it would be to say that people in the same household are stacked on top of each other. So to convert it to the wide form, all you need to do is unstack them:

acs_wide = acs.unstack()acs_wide

age

...

hispanic

person

1

2

3

4

5

6

7

8

9

10

...

7

8

9

10

11

12

13

14

15

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

The result is the same dataset in wide form (though you’d have to look at more of it to know that for sure). This was very easy to do because the MultiIndex tells Python everything it needs to know about the data structure (i.e. people grouped into households). All it needed to do was convert the row MultiIndex to a column MultiIndex.

To go back to long form, just stack it again:

acs_wide.stack()

age

race

marital_status

edu

income

female

hispanic

household

person

37

1

20.0

White

Never married

Some college, >=1 year

10000.0

True

False

2

19.0

White

Never married

Some college, >=1 year

5300.0

True

False

3

19.0

Black

Never married

Some college, >=1 year

4700.0

True

False

241

1

50.0

White

Never married

Master's degree

32500.0

True

False

242

1

29.0

White

Never married

Bachelor's degree

30000.0

True

False

...

...

...

...

...

...

...

...

...

1236624

1

29.0

White

Now married

Some college, >=1 year

50100.0

False

False

2

26.0

White

Now married

High School graduate

12000.0

True

False

1236756

1

58.0

White

Now married

Master's degree

69800.0

True

False

2

61.0

White

Now married

Master's degree

40800.0

False

False

1236779

1

30.0

American Indian

Divorced

High School graduate

22110.0

False

False

27410 rows × 7 columns

Since we’ve never used a column MultiIndex before, let’s take a moment to see how they work.

To select a household, just use loc:

acs_wide.loc[37]

person
age 1 20.0
2 19.0
3 19.0
4 NaN
5 NaN
...
hispanic 12 NaN
13 NaN
14 NaN
15 NaN
16 NaN
Name: 37, Length: 112, dtype: object

To select a particular column, you can use square brackets with a tuple containing the variable name and person number:

acs_wide[('race', 1)]

household
37 White
241 White
242 White
377 White
418 White
...
1236119 White
1236287 American Indian
1236624 White
1236756 White
1236779 American Indian
Name: (race, 1), Length: 10565, dtype: category
Categories (9, object): ['Alaska Native', 'American Indian', 'Asian', 'Black', ..., 'Other', 'Pacific Islander', 'Two or more races', 'White']

To subset all the variables for a particular person number (in all households), use xs() with level='person' and axis=1:

acs_wide.xs(1, level='person', axis=1)

age

race

marital_status

edu

income

female

hispanic

household

37

20.0

White

Never married

Some college, >=1 year

10000.0

True

False

241

50.0

White

Never married

Master's degree

32500.0

True

False

242

29.0

White

Never married

Bachelor's degree

30000.0

True

False

377

69.0

White

Never married

None

51900.0

True

False

418

59.0

White

Widowed

12th grade, no diploma

12200.0

True

False

...

...

...

...

...

...

...

...

1236119

51.0

White

Now married

Some college, >=1 year

62200.0

False

False

1236287

41.0

American Indian

Now married

Some college, <1 year

15000.0

False

False

1236624

29.0

White

Now married

Some college, >=1 year

50100.0

False

False

1236756

58.0

White

Now married

Master's degree

69800.0

True

False

1236779

30.0

American Indian

Divorced

High School graduate

22110.0

False

False

10565 rows × 7 columns

You can use the same approach to select a variable for all the people in the household. The list of variables doesn’t have a name like person (we could give it one if we really wanted to), but you can refer to it as level 0. To select all the age variables, use:

acs_wide.xs('age', level=0, axis=1)

person

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

household

37

20.0

19.0

19.0

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

242

29.0

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

418

59.0

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

1236287

41.0

42.0

23.0

4.0

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

1236756

58.0

61.0

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

10565 rows × 16 columns

7.2.2 Switching From Long to Wide

So why didn’t we use a column MultiIndex all along? Well, let’s add a level two variable to the mix again, household_income:

Now see what happens to it when you unstack() the DataFrame:

acs_wide=acs.unstack()acs_wide

age

...

household_income

person

1

2

3

4

5

6

7

8

9

10

...

7

8

9

10

11

12

13

14

15

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 × 128 columns

unstack() does not know or care that household_income is a level two variable. It went ahead and created 16 copies of it and copied over the values on each row. Take a look at the values for household 37:

acs_wide.loc[37].filter(like='household_income')

person
household_income 1 20000.0
2 20000.0
3 20000.0
4 NaN
5 NaN
6 NaN
7 NaN
8 NaN
9 NaN
10 NaN
11 NaN
12 NaN
13 NaN
14 NaN
15 NaN
16 NaN
Name: 37, dtype: object

But the problem is not just unstack(): the column MultiIndex assumes every variable is associated with 16 values of person and we’re not aware of any way to change that. That’s why the wide form data set used in the last chapter put the person index into the variable names rather than using a MultiIndex.

The leaves us with the problem of putting the data set in that form. Given that long form is generally easier to use, converting to wide form is a somewhat uncommon task. We’ll do it anyway for two reasons: first, because doing so will provide a road map for the much more common transformation of wide to long. Second, we’ll learn a very useful tool along the way.

The columns attribute of the DataFrame has a function called to_flat_index() that eliminates the MultiIndex:

This replaced the MultiIndex with a single index where each column is labeled by a tuple. But tuples would be hard to work with, so we still want to convert them to a single string.

This is a job for a loop, so as usual we’ll start with just one example. Store the tuple ('age', 1) as name. Recall that you can access the elements of a tuple with square brackets, in this case name[0] and name[1]. So to convert ('age', 1) to age_1, use:

name = ('age', 1)name[0] +'_'+str(name[1])

'age_1'

To do this for all the columns in acs_wide, we need them in a list we can loop over. Fortunately the DataFrame columns have a to_list() function that gives us exactly that:

acs_wide.columns.tolist()

Our goal is to replace the current acs_wide.columns with a new list of string names. We can do that very easily with a backwards sort of for loop called a list comprehension. A list comprehension looks almost the same as a regular for loop, except the whole thing goes in brackets, the code to execute comes first, and the for loop definition goes at the end. The big difference is that the result is a list. Start with a simple example:

my_list = [1, 2, 3][x**2for x in my_list]

[1, 4, 9]

Think of a list comprehension as an easy way to carry out a transformation on each element of a list. Now use a list comprehension to transform all the column names in acs_wide from tuples to strings and store the result as the new column names:

acs_wide.columns = [ name[0] +'_'+str(name[1]) for name in acs_wide.columns.tolist()]acs_wide

age_1

age_2

age_3

age_4

age_5

age_6

age_7

age_8

age_9

age_10

...

household_income_7

household_income_8

household_income_9

household_income_10

household_income_11

household_income_12

household_income_13

household_income_14

household_income_15

household_income_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 × 128 columns

We’re not done yet: we still have 16 copies of household income and we only want one: household_income_1 since it’s the one that’s never missing. Rename it with:

If we now use filter() with the regular expression '^household_income_\d*$, we’ll get all the household income variables except the one we want to keep:

This data set is now in wide form with the indexes stored in the column names, which is a very common format to receive data in. We’ll next convert it to long form, a very common task since long form is usually easier to work with. To do so we’ll just reverse all the steps we took in converting it to wide form.

First, we need to create 16 household income variables. They should contain a copy of the existing household_income variable for the columns describing a person that actually exists, and NaN for the others. We know the age variables are never missing, so we’ll use age not being NaN as our indicator that a person actually exists.

This requires a loop from 1 to 16, but hopefully these are becoming familiar to you and this one’s not complicated so we won’t take the time to build it from its component parts. Check on household 37 (which has three people) to make sure it worked properly:

for i inrange(1,17): acs_wide.loc[~acs_wide['age_'+str(i)].isna(),'household_income_'+str(i) ] = acs_wide['household_income']acs_wide.filter(like='household_income').loc[37]

household_income 20000.0
household_income_1 20000.0
household_income_2 20000.0
household_income_3 20000.0
household_income_4 NaN
household_income_5 NaN
household_income_6 NaN
household_income_7 NaN
household_income_8 NaN
household_income_9 NaN
household_income_10 NaN
household_income_11 NaN
household_income_12 NaN
household_income_13 NaN
household_income_14 NaN
household_income_15 NaN
household_income_16 NaN
Name: 37, dtype: float64

Now that we know all is well, drop household_income:

The next task is to convert the column names into a MultiIndex, but the first step is to convert them back into tuples. The column names are in the form variable_person, and we need to split those two components up. This is a common task in working with text data called parsing. In this case the split() function will make it very easy. It acts on a string, splits it into pieces based on a string you pass in, and returns the results in a list (which you can convert to a tuple with the tuple() function). In our case, it’s the underscore character, ’_’, that separates the variable from the person number:

name ='age_1'tuple(name.split('_'))

('age', '1')

There’s just one complication: household_income contains an underscore.

name ='household_income_1'tuple(name.split('_'))

('household', 'income', '1')

If we tried to make that into a MultiIndex we’d end up with three levels. The solution is a combination of two things. First, we’ll pass in a second argument, 1, that tells the function to only split the string based on the first underscore it sees. Second, we’ll switch from split() to rsplit(). They do the same thing, but rsplit() starts splitting from the right, so when we pass in 1 it will only split based on the last underscore in the string. Thus:

tuple(name.rsplit('_', 1))

('household_income', '1')

Applying this to all the column names is a job for a list comprehension:

acs_wide.columns = [tuple(name.rsplit('_', 1)) for name in acs_wide.columns.tolist()]acs_wide

(age, 1)

(age, 2)

(age, 3)

(age, 4)

(age, 5)

(age, 6)

(age, 7)

(age, 8)

(age, 9)

(age, 10)

...

(household_income, 7)

(household_income, 8)

(household_income, 9)

(household_income, 10)

(household_income, 11)

(household_income, 12)

(household_income, 13)

(household_income, 14)

(household_income, 15)

(household_income, 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 × 128 columns

Now you’re ready to convert the tuples into a MultiIndex. Pandas has a MultiIndex.from_tuples() function that does exactly that:

With the column names now set up as a MultiIndex, the actual conversion to long form is easily done with stack():

acs_wide.stack()

age

edu

female

hispanic

household_income

income

marital_status

race

household

37

1

20.0

Some college, >=1 year

True

False

20000.0

10000.0

Never married

White

2

19.0

Some college, >=1 year

True

False

20000.0

5300.0

Never married

White

3

19.0

Some college, >=1 year

True

False

20000.0

4700.0

Never married

Black

241

1

50.0

Master's degree

True

False

32500.0

32500.0

Never married

White

242

1

29.0

Bachelor's degree

True

False

30000.0

30000.0

Never married

White

...

...

...

...

...

...

...

...

...

...

1236624

1

29.0

Some college, >=1 year

False

False

62100.0

50100.0

Now married

White

2

26.0

High School graduate

True

False

62100.0

12000.0

Now married

White

1236756

1

58.0

Master's degree

True

False

110600.0

69800.0

Now married

White

2

61.0

Master's degree

False

False

110600.0

40800.0

Now married

White

1236779

1

30.0

High School graduate

False

False

22110.0

22110.0

Divorced

American Indian

27410 rows × 8 columns

7.2.3.1 Exercise

Load the example extract from the National Longitudinal Survey of Youth contained in nlsy.pickle. Convert it to wide form with the level one index stored in the column names. Then convert it back to long form.