How to Group Pandas Data, Count Occurrences, and Find Maximum Counts?

Pandas Data Grouping Counts and Max Count Retrieval

Problem:

Given a Pandas DataFrame with multiple columns, how can you efficiently group rows by two specific columns and obtain counts in each group? Additionally, how do you determine the maximum count for each value in one of the grouping columns?

Solution:

To group the DataFrame rows by two columns and count occurrences, use the groupby() function followed by the size() method:

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

This operation creates groups based on the specified columns and returns the count of rows in each group. The output will resemble the following:

col5  col2  count
1     A       1
      D       3
2     B       2
...

To find the maximum count for each value in the col2 column:

<code class="python">df.groupby(['col5', 'col2']).size().groupby(level=1).max()</code>

This operation groups the count data by the col2 column level and returns the maximum count for each col2 value, producing an output like:

col2
A       3
B       2
C       1
D       3

Additional Notes:

To group by multiple columns and obtain counts and additional summary statistics, you can use groupby() in conjunction with other methods like agg(), which allows you to specify multiple aggregation functions:

<code class="python">df.groupby(['col5', 'col2']).agg(['count', 'mean', 'max'])</code>

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