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.
8.1 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.
8.1.1 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
8.1.2 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.)
8.1.3 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.
8.1.4 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().
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.’
8.2 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
8.2.1 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
8.2.2 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.
Important
Converting data sets between long and wide form is a longer and more complicated task than anything we’ve done up to this point. Please don’t let it make you feel overwhelmed! Especially don’t think “I could never do this on my own.” You won’t have to–this chapter will be here when you need to do this task with your data.
Also, don’t worry too much about why each step does what it does. That will be easier to see when you’ve gone through the whole process.
All that said, this is the very last topic in the book. If you don’t need to convert data between long and wide form right away, you have my permission to stop reading here and come back if and when you need it. (As if you need my permission.)
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 a DataFrame has a columns attribute with exactly that.
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]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)].notna(),'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]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
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.