How Can I Group and Sum Data in Pandas to Calculate Total Purchases by Customer and Fruit Type?

Grouping and Summing Data in Pandas

In data analysis, it is often necessary to aggregate data by specific criteria to derive meaningful insights. Pandas, a powerful Python library for data manipulation, provides the groupby() method to group data based on one or more columns. This method can be combined with aggregation functions, such as sum(), to compute aggregate values for each group.

Calculating the Sum of Values by Group

Suppose we have a DataFrame containing information about fruit consumption by individuals. Each row represents a fruit purchase, including the fruit type, purchase date, customer name, and number of fruits purchased.

To calculate the total number of fruits purchased by each individual, grouped by both fruit type and customer name, we can use the following steps:

Step 1: Group the Data

First, we group the DataFrame by both the 'Fruit' and 'Name' columns using the groupby() method:

df_grouped = df.groupby(['Fruit', 'Name'])

This creates a SeriesGroupBy object, which represents the grouped data.

Step 2: Apply the Sum Function

To calculate the total number of fruits purchased by each group, we apply the sum() function to the grouped Series:

df_grouped_sum = df_grouped['Number'].sum()

The resulting Series, df_grouped_sum, contains the sum of fruit purchases for each unique combination of fruit type and customer name.

Example

Consider the following DataFrame:

   Fruit   Date      Name  Number
Apples  10/6/2016 Bob    7
Apples  10/6/2016 Bob    8
Apples  10/6/2016 Mike   9
Apples  10/7/2016 Steve 10
Apples  10/7/2016 Bob    1
Oranges 10/7/2016 Bob    2
Oranges 10/6/2016 Tom   15
Oranges 10/6/2016 Mike  57
Oranges 10/6/2016 Bob   65
Oranges 10/7/2016 Tony   1
Grapes  10/7/2016 Bob    1
Grapes  10/7/2016 Tom   87
Grapes  10/7/2016 Bob   22
Grapes  10/7/2016 Bob   12
Grapes  10/7/2016 Tony  15

Applying the groupby() and sum() operations to this DataFrame, we get the following result:

                 Number
Fruit   Name         
Apples  Bob        16
        Mike        9
        Steve      10
Grapes  Bob        35
        Tom        87
        Tony       15
Oranges Bob        67
        Mike       57
        Tom        15
        Tony        1

This output shows the total number of fruits purchased by each individual, broken down by fruit type.

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