The **groupby()**function is one of the most useful functions when dealing with large dataframes in Pandas. A groupby operation typically involves a combination of splitting the object, applying a function, and combining the results.

If you are new to the **groupby()** function, however, things can be a little intimidating at first. So the aim of this article is to provide a gentle introduction to this simple, and yet extremely powerful function.

As usual, the best way to learn something is through examples, many of them. So let’s get started!

Loading the Sample DataFrame

Let’s load a dataframe with the following code snippet:

import pandas as pd

scores = {'Zone': ['North','South','South',
                   'East','East','West','West','West'], 
          'School': ['Rushmore','Bayside','Rydell',
                     'Shermer','Shermer','Ridgemont','Hogwarts','Hogwarts'],             
          'Name': ['Jonny','Joe','Jakob', 
                   'Jimmy','Erik','Lam','Yip','Chen'], 
          'Math': [78,76,56,67,89,100,55,76],
          'Science': [70,68,90,45,66,89,32,98]}
df = pd.DataFrame(scores, columns = 
                  ['Zone', 'School', 'Name', 
                   'Science', 'Math'])
df

The dataframe (**df**) looks like this:

Calling groupby() with a string (name of column)

Let’s group the dataframe by **Zone**:

gp = df.groupby('Zone')      # pass in a string to groupby()
for zone, group in gp:
    print(zone)
    print(group)             # group is a dataframe

It should print out the following output:

East
   Zone   School   Name  Science  Math
3  East  Shermer  Jimmy       45    67
4  East  Shermer   Erik       66    89
North
    Zone    School   Name  Science  Math
0  North  Rushmore  Jonny       70    78
South
    Zone   School   Name  Science  Math
1  South  Bayside    Joe       68    76
2  South   Rydell  Jakob       90    56
West
   Zone     School  Name  Science  Math
5  West  Ridgemont   Lam       89   100
6  West   Hogwarts   Yip       32    55
7  West   Hogwarts  Chen       98    76

As you can see from the output, you are grouping the dataframe by **Zone**. The **groupby()** function in this example returns a **DataFrameGroupBy** object. To print out each zone and its associated grouping, you can use the for-in loop to iterate through the **DataFrameGroupBy** object. The **zone** variable in this case will take on the values of each zone, and the **group** variable will contain the rows associated with each zone (contained within a dataframe). To print out the **Name** of each student and his/her **Science** and **Math** scores, you can select the respective columns in the group dataframe:

gp = df.groupby('Zone')
for zone, group in gp:
    print(zone)
    print(group[['Name','Science','Math']])

The above modified statement prints the following output:

East
    Name  Science  Math
3  Jimmy       45    67
4   Erik       66    89
North
    Name  Science  Math
0  Jonny       70    78
South
    Name  Science  Math
1    Joe       68    76
2  Jakob       90    56
West
   Name  Science  Math
5   Lam       89   100
6   Yip       32    55
7  Chen       98    76

Calling groupby() with a list of strings (column names)

Suppose you now want to group by **Zone** and **School**, so now you call the **groupby()** function with a list of strings (containing the columns names):

gp = df.groupby(['Zone','School'])        # pass a list to groupby()
for zone_school, group in gp:
    print(zone_school)
    print(group[['Name','Science','Math']])

#pandas #python #dataframes #series #group-by #function