Get statistics for each group (such as count, mean, etc) using pandas GroupBy?

In pandas, you can use the groupby() method to group data by one or more columns and then use the agg() method to compute various statistics for each group.

For example, suppose you have a DataFrame called df with columns 'A' and 'B' and you want to group the data by column 'A' and calculate the count, mean, and sum for column 'B' for each group:

Get statistics for each group (such as count, mean, etc) using pandas GroupBy in Python

import pandas as pd

df = pd.DataFrame({'A': ['foo', 'bar', 'baz', 'foo', 'bar', 'baz'],
                   'B': [1, 2, 3, 4, 5, 6]})
grouped = df.groupby('A')
result = grouped['B'].agg(['count', 'mean', 'sum'])
print(result)

This will output a DataFrame that shows the count, mean, and sum of column 'B' for each unique value of column 'A':

count      mean  sum
A                     
bar      2  3.500000    7
baz      2  4.500000    9
foo      2  2.500000    5

You can also use a dictionary to specify the statistics for each column:

Use a dictionary to specify the statistics for each column in Python's pandas

import pandas as pd

df = pd.DataFrame({'A': ['foo', 'bar', 'baz', 'foo', 'bar', 'baz'],
                   'B': [1, 2, 3, 4, 5, 6]})
grouped = df.groupby('A')
result = grouped.agg({'B': ['count', 'mean', 'sum']})
print(result)