How Can Pandas GroupBy Calculate Total Fruits per Name?

Pandas Group-By: Determining Fruit Totals by Name

In data manipulation scenarios, the need often arises to aggregate data based on specified criteria. Pandas' groupby method offers a powerful solution for grouping data and performing various aggregations. This article will demonstrate how to use groupby to calculate the sum of fruits for each name in a given dataset.

Problem Statement:

Given a dataframe with columns representing Fruit, Date, Name, and Number:

import pandas as pd

df = pd.DataFrame({
    'Fruit': ['Apples', 'Apples', 'Apples', 'Apples', 'Apples', 'Oranges', 'Oranges', 'Oranges', 'Oranges', 'Oranges', 'Grapes', 'Grapes', 'Grapes', 'Grapes', 'Grapes'],
    'Date': ['10/6/2016', '10/6/2016', '10/6/2016', '10/7/2016', '10/7/2016', '10/7/2016', '10/6/2016', '10/6/2016', '10/6/2016', '10/7/2016', '10/7/2016', '10/7/2016', '10/7/2016', '10/7/2016', '10/7/2016'],
    'Name': ['Bob', 'Bob', 'Mike', 'Steve', 'Bob', 'Bob', 'Tom', 'Mike', 'Bob', 'Tony', 'Bob', 'Tom', 'Bob', 'Bob', 'Tony'],
    'Number': [7, 8, 9, 10, 1, 2, 15, 57, 65, 1, 1, 87, 22, 12, 15]
})

Solution:

To calculate the sum of fruits for each name, follow these steps:

result_df = df.groupby(['Fruit', 'Name'])['Number'].sum()

This operation will group the data by both 'Fruit' and 'Name' columns and aggregate the 'Number' column by taking the sum. The output will be a dataframe containing the total number of fruits for each combination of 'Fruit' and 'Name'.

Result:

The resulted dataframe will resemble the following:

               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

If a specific column needs to be specified for aggregation, the following syntax can be used:

result_df = df.groupby(['Name', 'Fruit'])['Number'].sum()

By using Pandas' groupby and sum functions, we can effectively group and aggregate data to obtain meaningful insights from large datasets, making it a powerful tool for data exploration and analysis.

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