You can use the shift() function in pandas to create a column that displays the lagged values of another column.

This function uses the following basic syntax:

df['lagged_col1'] = df['col1'].shift(1)

Note that the value in the shift() function indicates the number of values to calculate the lag for.

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

Example: Create a Lag Column in Pandas

Suppose we have the following pandas DataFrame that shows the sales made by some store on 10 consecutive days:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'day': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                   'sales': [18, 10, 14, 13, 19, 24, 25, 29, 15, 18]})
#view DataFrame
print(df)

   day  sales
0    1     18
1    2     10
2    3     14
3    4     13
4    5     19
5    6     24
6    7     25
7    8     29
8    9     15
9   10     18

We can use the shift() function to create a lag column that displays the sales for the previous day for each row:

#add column that represents lag of sales column
df['sales_previous_day'] = df['sales'].shift(1)

#view updated DataFrame
print(df)

   day  sales  sales_previous_day
0    1     18                 NaN
1    2     10                18.0
2    3     14                10.0
3    4     13                14.0
4    5     19                13.0
5    6     24                19.0
6    7     25                24.0
7    8     29                25.0
8    9     15                29.0
9   10     18                15.0

Here’s how to interpret the output:

• The first value in the lag column is NaN since there is no prior value in the sales column.
• The second value in the lag column is 18 since this is the prior value in the sales column.
• The third value in the lag column is 10 since this is the prior value in the sales column.

And so on.

Note that we can also add multiple lag columns to the DataFrame if we’d like:

#add two lag columns
df['sales_previous_day'] = df['sales'].shift(1)
df['sales_previous_day2'] = df['sales'].shift(2) 

#view updated DataFrame
print(df)

   day  sales  sales_previous_day  sales_previous_day2
0    1     18                 NaN                  NaN
1    2     10                18.0                  NaN
2    3     14                10.0                 18.0
3    4     13                14.0                 10.0
4    5     19                13.0                 14.0
5    6     24                19.0                 13.0
6    7     25                24.0                 19.0
7    8     29                25.0                 24.0
8    9     15                29.0                 25.0
9   10     18                15.0                 29.0

You can use the same general approach to add as many lag columns as you’d like.

Note: To create a lead column, simply use negative values in the shift() function.

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