How Can Pandas GroupBy Be Used to Calculate Group-Wise Statistics in Python?

Calculate Group-Wise Statistics with Pandas GroupBy

Introduction

When working with data, it's often desirable to analyze and compare statistics across different groups. Pandas, a prominent Python library for data manipulation, offers GroupBy functionality to effortlessly perform these operations.

Getting Group-Wise Row Counts

The simplest way to obtain row counts for each group is through the .size() method. This method returns a Series containing group-wise counts:

df.groupby(['col1','col2']).size()

To retrieve the counts in tabular format (i.e., as a DataFrame with a "counts" column):

df.groupby(['col1', 'col2']).size().reset_index(name='counts')

Calculating Multiple Group-Wise Statistics

To compute multiple statistics, use the .agg() method with a dictionary. The keys specify the columns to be calculated, while the values are lists of the desired aggregations (e.g., 'mean', 'median', and 'count'):

df.groupby(['col1', 'col2']).agg({
    'col3': ['mean', 'count'],
    'col4': ['median', 'min', 'count']
})

Customizing Data Output

For more control over the output, individual aggregations can be joined:

counts = df.groupby(['col1', 'col2']).size().to_frame(name='counts')
counts.join(gb.agg({'col3': 'mean'}).rename(columns={'col3': 'col3_mean'})) \
    .join(gb.agg({'col4': 'median'}).rename(columns={'col4': 'col4_median'})) \
    .join(gb.agg({'col4': 'min'}).rename(columns={'col4': 'col4_min'})) \
    .reset_index()

This produces a more structured DataFrame with un-nested column labels.

Footnotes

In the example provided, null values can lead to discrepancies in the row count used for different calculations. This emphasizes the importance of considering null values when interpreting group-wise statistics.

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