Grouping data by category in pandas

Working with Categorical Data in Python

Kasey Jones

Research Data Scientist

The basics of .groupby(): splitting data

adult = pd.read_csv("data/adult.csv")
adult1 = adult[adult["Above/Below 50k"] == " <=50K"]
adult2 = adult[adult["Above/Below 50k"] == " >50K"]

is replaced by

groupby_object = adult.groupby(by=["Above/Below 50k"])
Working with Categorical Data in Python

The basics of .groupby(): apply a function

groupby_object = adult.groupby(by=["Above/Below 50k"])

Apply a function:

groupby_object.mean()
                       Age        fnlgwt  Education Num  Capital Gain ...
Above/Below 50k                                                                                
 <=50K           36.783738  190340.86517       9.595065    148.752468 ...   
 >50K            44.249841  188005.00000      11.611657   4006.142456 ...

One liner:

adult.groupby(by=["Above/Below 50k"]).mean()
Working with Categorical Data in Python

Specifying columns

Option 1: only runs .sum() on two columns.

adult.groupby(by=["Above/Below 50k"])['Age', 'Education Num'].sum()
                    Age  Education Num
Above/Below 50k                       
 <=50K           909294         237190
 >50K            346963          91047

Option 2: runs .sum() on all numeric columns and then subsets.

adult.groupby(by=["Above/Below 50k"]).sum()[['Age', 'Education Num']]

Option 1 is preferred - especially when using large datasets

Working with Categorical Data in Python

Groupby multiple columns

adult.groupby(by=["Above/Below 50k", "Marital Status"]).size()
Above/Below 50k  Marital Status        
 <=50K            Divorced                  3980
                  Married-AF-spouse           13
                  Married-civ-spouse        8284
                  Married-spouse-absent      384
                  Never-married            10192
                  Separated                  959
                  Widowed                    908
 >50K             Divorced                   463
                  Married-AF-spouse           10 <--- Only 10 records
                  Married-civ-spouse        6692
    ...
Working with Categorical Data in Python

Practice using .groupby()

Working with Categorical Data in Python

Preparing Video For Download...