CODEWITHSUNDEEP
pythonpandaspivot-tableaggregate-functions
Vincent Labrecque
Vincent Labrecque

Reputation: 324

How to apply an aggregate function to all columns of a pivot table in Pandas

A pivot table is counting the monthly occurrences of a phenomenon. Here's the simplified sample data followed by the pivot:

+--------+------------+------------+
| ad_id  | entreprise | date       |
+--------+------------+------------+
| 172788 | A          | 2020-01-28 |
| 172931 | A          | 2020-01-26 |
| 172793 | B          | 2020-01-26 |
| 172768 | C          | 2020-01-19 |
| 173219 | C          | 2020-01-14 |
| 173213 | D          | 2020-01-13 |
+--------+------------+------------+

My pivot_table code is the following:

my_pivot_table = pd.pivot_table(df[(df['date'] >= some_date) & ['date'] <= some_other_date)], 
                                values=['ad_id'], index=['entreprise'], 
                                columns=['year', 'month'], aggfunc=['count'])

The resulting table looks like this:

+-------------+---------+----------+-----+----------+
|             |  2018   |          |     |          |
+-------------+---------+----------+-----+----------+
| entreprise  | january | february | ... | december |
| A           | 12      | 10       | ... | 8        |
| B           | 24      | 12       | ... | 3        |
| ...         | ...     | ...      | ... | ...      |
| D           | 31      | 18       | ... | 24       |
+-------------+---------+----------+-----+----------+

Now, I would like to add a column that gives me the monthly average, and perform other operations such as comparing last month's count to the monthly average of, say, the last 12 months...

I tried to fiddle with the aggfunc parameter of the pivot_table, as well as trying to add an average column to the original dataframe, but without success.

Thanks in advance!

Upvotes: 1

Views: 691

Answers (1)

jezrael
jezrael

Reputation: 863226

Because you get Multiindex table after pivot_table you can use:

df1 = df.mean(axis=1, level=0)
df1.columns = pd.MultiIndex.from_product([df1.columns, ['mean']])

Or:

df2 = df.mean(axis=1, level=1)
df2.columns = pd.MultiIndex.from_product([['all_years'], df2.columns])

Upvotes: 3

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