# Pandas: How to Calculate a Moving Average by Group

You can use the following basic syntax to calculate a moving average by group in pandas:

```#calculate 3-period moving average of 'values' by 'group'
df.groupby('group')['values'].transform(lambda x: x.rolling(3, 1).mean())
```

The following example shows how to use this syntax in practice.

### Example: Calculate Moving Average by Group in Pandas

Suppose we have the following pandas DataFrame that shows the total sales made by two stores during five sales periods:

```import pandas as pd

#create DataFrame
df = pd.DataFrame({'store': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'],
'period': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5],
'sales': [7, 7, 9, 13, 14, 13, 13, 19, 20, 26]})

#view DataFrame
df

store	period	sales
0	A	1	7
1	A	2	7
2	A	3	9
3	A	4	13
4	A	5	14
5	B	1	13
6	B	2	13
7	B	3	19
8	B	4	20
9	B	5	26
```

We can use the following code to calculate a 3-day moving average of sales for each store:

```#calculate 3-day moving average of sales by store
df['ma'] = df.groupby('store')['sales'].transform(lambda x: x.rolling(3, 1).mean())

#view updated DataFrame
df

store	period	sales	ma
0	A	1	7	7.000000
1	A	2	7	7.000000
2	A	3	9	7.666667
3	A	4	13	9.666667
4	A	5	14	12.000000
5	B	1	13	13.000000
6	B	2	13	13.000000
7	B	3	19	15.000000
8	B	4	20	17.333333
9	B	5	26	21.666667
```

Note: x.rolling(3, 1) means to calculate a 3-period moving average and require 1 as the minimum number of periods.

The ‘ma’ column shows the  3-day moving average of sales for each store.

To calculate a different moving average, simply change the value in the rolling() function.

For example, we could calculate the 2-day moving average of sales for each store instead:

```#calculate 2-day moving average of sales by store
df['ma'] = df.groupby('store')['sales'].transform(lambda x: x.rolling(2, 1).mean())

#view updated DataFrame
df

store	period	sales	ma
0	A	1	7	7.0
1	A	2	7	7.0
2	A	3	9	8.0
3	A	4	13	11.0
4	A	5	14	13.5
5	B	1	13	13.0
6	B	2	13	13.0
7	B	3	19	16.0
8	B	4	20	19.5
9	B	5	26	23.0
```