# How to Calculate Rolling Median in Pandas (With Examples)

A rolling median is the median of a certain number of previous periods in a time series.

To calculate the rolling median for a column in a pandas DataFrame, we can use the following syntax:

```#calculate rolling median of previous 3 periods
df['column_name'].rolling(3).median()
```

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

### Example: Calculate Rolling Median of Column

Suppose we have the following pandas DataFrame:

```import pandas as pd

#create DataFrame
df = pd.DataFrame({'month': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
'leads': [13, 15, 16, 15, 17, 20, 22, 24, 25, 26, 23, 24],
'sales': [22, 24, 23, 27, 26, 26, 27, 30, 33, 32, 27, 25]})

#view DataFrame
df

month	leads	sales
0	1	13	22
1	2	15	24
2	3	16	23
3	4	15	27
4	5	17	26
5	6	20	26
6	7	22	27
7	8	24	30
8	9	25	33
9	10	26	32
10	11	23	27
11	12	24	25
```

We can use the following syntax to create a new column that contains the rolling median of ‘sales’ for the previous 3 periods:

```#calculate 3-month rolling median
df['sales_rolling3'] = df['sales'].rolling(3).median()

#view updated data frame
df

month	leads	sales	sales_rolling3
0	1	13	22	NaN
1	2	15	24	NaN
2	3	16	23	23.0
3	4	15	27	24.0
4	5	17	26	26.0
5	6	20	26	26.0
6	7	22	27	26.0
7	8	24	30	27.0
8	9	25	33	30.0
9	10	26	32	32.0
10	11	23	27	32.0
11	12	24	25	27.0```

We can manually verify that the rolling median sales displayed for month 3 is the median of the previous 3 months:

• Median of 22, 24, 23 = 23.0

Similarly, we can verify the rolling median sales of month 4:

• Median of 24, 23, 27 = 24.0

We can use similar syntax to calculate the rolling 6-month median:

```#calculate 6-month rolling median
df['sales_rolling6'] = df['sales'].rolling(6).median()

#view updated data frame
df

month	leads	sales	sales_rolling3	sales_rolling6
0	1	13	22	NaN	NaN
1	2	15	24	NaN	NaN
2	3	16	23	23.0	NaN
3	4	15	27	24.0	NaN
4	5	17	26	26.0	NaN
5	6	20	26	26.0	25.0
6	7	22	27	26.0	26.0
7	8	24	30	27.0	26.5
8	9	25	33	30.0	27.0
9	10	26	32	32.0	28.5
10	11	23	27	32.0	28.5
11	12	24	25	27.0	28.5
```

### Additional Resources

The following tutorials explain how to perform other common operations in pandas: