This tutorial will show you how to operate pandas groupby() and sum() without even a mild headache being able to distract your mind.

Let’s enter the fray!

Example For Dataframes

We’ll begin as usual by importing the Pandas library and making a straightforward DataFrame that we’ll utilize for the duration of this illustration.

# Create a pandas DataFrame.
import pandas as pd
englishteaching   = ({
    'Teachers':["Simmon","Pinmark","Hanson","Putin","Plex","Hanson","Simmon","Putin"],
    'Fee' :[32000,45000,33000,44000,36000,45000,35000,42000],
    'Prolongation':['50days','30days','40days','35days','35days','60days','50days','55days'],
    'Discount':[2000,1300,2000,1100,2500,2300,1200,1500]
                })

df = pd.DataFrame(englishteaching, columns=['Teachers','Fee','Prolongation','Discount'])
print(df)

In this manner, we produce the following result:

Teachers    Fee Prolongation Discount
0    Simmon  32000   50days      2000
1  Pinmark  45000   30days      1300
2   Hanson  33000   40days      2000
3   Putin  44000   35days      1100
4   Plex  36000   35days      2500
5   Hanson  45000   60days      2300
6    Simmon  35000   50days      1200
7   Putin  42000   55days      1500

Pandas Groupby() & Sum() According To Column Name

To combine similar data into a group so that you may apply aggregate functions, use the groupby() method in Pandas. This method provides a DataFrameGroupBy object that has aggregate methods like sum, mean, and other similar ones.

For instance, the function df.groupby(['Teachers']).sum() groups data based on the Teachers column and totals all the numeric columns in the DataFrame.

Keep in mind that the group key you are using turns into an Index of the resulting DataFrame. Following that, add an Index use as_index =False argument to make this advertisement disappear. We will show you how to do this in one of the instances below.

Running the code:

# Use GroupBy() to compute the sum
df2 = df.groupby('Teachers').sum()
print(df2)

Output:

Teachers    Fee Discount
Simmon  32000   2000
Pinmark  45000   1300
Hanson  33000   2000
Putin  44000   1100
Plex  36000   2500
Hanson  45000   2300
Simmon  35000   1200
Putin  42000   1500

Additionally, you may specifically state which column you wish to do a sum() action on. The sum in the Fee column is applied in the example below.

Running the code:

# Use GroupBy() & compute sum on specific column
df2 = df.groupby('Teachers')['Fee'].sum()
print(df2)

Output:

Teachers    
Simmon  32000
Pinmark  45000
Hanson  33000
Putin  44000
Plex  36000
Hanson  45000
Simmon  35000
Putin  42000

Name: Fee, dtype: int64

Multiple Columns Pandas groupby() & sum() 

In order to apply a group by to multiple columns and calculate a total over each combination group, you can also pass a list of the columns you wish to group to the groupby() function.

As an illustration, the function df.groupby(['Teachers','Prolongation'])['Fee']. sum() groups by the Teachers and Prolongation columns before calculating the sum.

Running the code:

# Using GroupBy multiple column
df2 = df.groupby(['Teachers','Prolongation'])['Fee'].sum()
print(df2)

Output:

Teachers Prolongation  Fee   
Simmon   50days  32000
Pinmark   30days  45000
Hanson   40days  33000
Putin   35days  44000
Plex   35days  36000
Hanson   60days  45000
Simmon   50days  35000
Putin   55days  42000
Name: Fee, dtype: int64

The Bottom Line

These two methods might work best for your pandas groupby() and sum() operation, depending on your coding style.Now, hesitate any longer but give it a shot and don’t forget to let us know how it goes in the comments. Also, check out our article on how to apply a function to a column in pandas dataframe if you urge to learn further.