# 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:

``````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:

``````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)``````