You can use the **shift()** function to shift the values of a column up or down in a pandas DataFrame:

#shift values down by 1 df['column1'] = df['column1'].shift(1) #shift values up by 1 df['column1'] = df['column1'].shift(-1)

The following examples show how to use this function in practice with the following pandas DataFrame:

import pandas as pd #create DataFrame df = pd.DataFrame({'product': ['A', 'B', 'C', 'D', 'E', 'F'], 'sales': [4, 7, 8, 12, 15, 19]}) #view DataFrame df product sales 0 A 4 1 B 7 2 C 8 3 D 12 4 E 15 5 F 19

**Example 1: Shift One Column Up or Down**

The following code shows how to shift all of the values of the ‘product’ column down by 1:

#shift all 'product' values down by 1 df['product'] = df['product'].shift(1) #view updated DataFrame df product sales 0 NaN 4 1 A 7 2 B 8 3 C 12 4 D 15 5 E 19

Notice that each value in the ‘product’ column has been shifted down by 1 and the first value in the column has been replaced with NaN.

Also notice that the last value in the product column (‘F’) has been removed from the DataFrame entirely.

To keep the ‘F’ value in the DataFrame, we need to first add an empty row to the bottom of the DataFrame and then perform the shift:

import numpy as np #add empty row to bottom of DataFrame df.loc[len(df.index)] = [np.nan, np.nan] #shift all 'product' values down by 1 df['product'] = df['product'].shift(1) #view updated DataFrame df product sales 0 NaN 4.0 1 A 7.0 2 B 8.0 3 C 12.0 4 D 15.0 5 E 19.0 6 F NaN

Notice that the ‘F’ value is retained as the last value in the ‘product’ column.

**Example 2: Shift Multiple Columns Up or Down**

The following code shows how to shift all of the values of the ‘product’ and ‘sales’ columns up by 2:

#shift all 'product' and 'sales' values up by 2 df[['product', 'sales']] = df[['product', 'sales']].shift(-2) #view updated DataFrame df product sales 0 C 8.0 1 D 12.0 2 E 15.0 3 F 19.0 4 NaN NaN 5 NaN NaN

Notice that each value in the ‘product’ and ‘sales’ column has been shifted up by 2 and the bottom two values in each column have been replaced with NaN.

**Note**: You can find the complete documentation for the **shift()** function here.

**Additional Resources**

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

How to Add Rows to a Pandas DataFrame

How to Add a Column to a Pandas DataFrame

How to Add Header Row to a Pandas DataFrame