How to Group and Count Pandas DataFrames by Multiple Columns and Find Maximum Counts?

Grouping Pandas DataFrames by Two Columns to Obtain Counts

Consider a DataFrame named df with columns col1, col2, col3, col4, and col5, as shown in the provided code snippet. To determine the count of rows based on specific values in col5 and col2, follow these steps:

Obtaining Row Counts by Group:

To count the occurrences within each row based on unique combinations of col5 and col2 values, use the size() method as follows:

<code class="python">df.groupby(['col5', 'col2']).size()</code>

This operation groups the DataFrame by both col5 and col2 and calculates the count of rows within each group. The output will be a series with index pairs (col5, col2) and corresponding counts.

Example:

The provided code snippet demonstrates this operation using the df DataFrame, producing the following output:

col5  col2
1     A       1
      D       3
2     B       2
3     A       3
      C       1
4     B       1
5     B       2
6     B       1
dtype: int64

In this output, each row represents a unique combination of col5 and col2, and the corresponding count indicates how many times that combination occurs in the DataFrame.

Finding Largest Counts for Each col2 Value:

To determine the largest count for each unique value of col2, perform the following steps:

1. Group the DataFrame by col2 only, excluding col5.
2. Calculate the row counts for each col2 group using size().
3. Get the maximum count for each col2 group using the max() method on the grouped series.

Example:

<code class="python">df.groupby(['col2']).size().groupby(level=1).max()</code>

This code snippet groups df by col2, calculates the counts, and then finds the maximum count for each col2 value, resulting in the following output:

col2
A       3
B       2
C       1
D       3
dtype: int64

In this output, each col2 value is associated with the maximum count of rows that share that value in col2.

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