How to Group and Sort Data within Specific Columns in a DataFrame?

Pandas Groupby and Sorting within Groups

Grouping a DataFrame by multiple columns is a common task in data manipulation. It allows us to aggregate data by these columns and perform further operations on the aggregated results. However, it is often necessary to sort the aggregated results within each group to obtain the top or bottom rows.

Consider the DataFrame df provided in the question:

   count     job source
0      2   sales      A
1      4   sales      B
2      6   sales      C
3      3   sales      D
4      7   sales      E
5      5  market      A
6      3  market      B
7      2  market      C
8      4  market      D
9      1  market      E

The goal is to group df by job and source columns and then sort the 'count' column in descending order within each of the groups. To achieve this, we can use the groupby() and sort_values() functions as follows:

<code class="python">df.groupby(['job', 'source'])['count'].sum().sort_values(ascending=False)</code>

This will sort the 'count' column in descending order within each group, providing the following output:

job    source       
sales  E           7
       C           6
       B           4
       D           3
       A           2
market A           5
       D           4
       B           3
       C           2
       E           1

However, if we want to obtain only the top three rows within each group, we can use the head() function:

<code class="python">df.groupby(['job', 'source'])['count'].sum().sort_values(ascending=False).groupby('job').head(3)</code>

This will give us the following result:

   count     job source
4      7   sales      E
2      6   sales      C
1      4   sales      B
5      5  market      A
8      4  market      D
6      3  market      B

By combining the groupby(), sort_values(), and head() functions, we can effectively group, sort, and select the top or bottom rows within each group in pandas.

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