You can use the following syntax to subtract one column from another in a pandas DataFrame:

#subtract column 'B' from column 'A' df['A-B'] = df.A- df.B

The following examples show how to use this syntax in practice.

**Example 1: Subtract Two Columns in Pandas**

The following code shows how to subtract one column from another in a pandas DataFrame and assign the result to a new column:

import pandas as pd #create DataFrame df = pd.DataFrame({'A': [25, 12, 15, 14, 19, 23, 25, 29], 'B': [5, 7, 8, 9, 12, 9, 12, 4], 'C': [11, 8, 10, 6, 6, 5, 9, 12]}) #subtract column B from column A df['A-B'] = df.A - df.B #view DataFrame df A B C A-B 0 25 5 11 20 1 12 7 8 5 2 15 8 10 7 3 14 9 6 5 4 19 12 6 7 5 23 9 5 14 6 25 12 9 13 7 29 4 12 25

The new column called ‘**A-B**‘ displays the results of subtracting the values in column B from the values in column A.

**Example 2: Subtract Two Columns with Missing Values**

If we subtract one column from another in a pandas DataFrame and there happen to be missing values in one of the columns, the result of the subtraction will always be a missing value:

import pandas as pd import numpy as np #create DataFrame with some missing values df = pd.DataFrame({'A': [25, 12, 15, 14, 19, 23, 25, 29], 'B': [5, 7, np.nan, 9, 12, np.nan, 12, 4], 'C': [np.nan, 8, 10, 6, 6, 5, 9, 12]}) #subtract column B from column A df['A-B'] = df.A - df.B #view DataFrame df A B C A-B 0 25 5.0 NaN 20.0 1 12 7.0 8.0 5.0 2 15 NaN 10.0 NaN 3 14 9.0 6.0 5.0 4 19 12.0 6.0 7.0 5 23 NaN 5.0 NaN 6 25 12.0 9.0 13.0 7 29 4.0 12.0 25.0

If you’d like, you can replace all of the missing values in the dataFrame with zeros using the **df.fillna(0)** function before subtracting one column from another:

import pandas as pd import numpy as np #create DataFrame with some missing values df = pd.DataFrame({'A': [25, 12, 15, 14, 19, 23, 25, 29], 'B': [5, 7, np.nan, 9, 12, np.nan, 12, 4], 'C': [np.nan, 8, 10, 6, 6, 5, 9, 12]}) #replace all missing values with zeros df = df.fillna(0) #subtract column B from column A df['A-B'] = df.A - df.B #view DataFrame df A B C A-B 0 25 5.0 0.0 20.0 1 12 7.0 8.0 5.0 2 15 0.0 10.0 15.0 3 14 9.0 6.0 5.0 4 19 12.0 6.0 7.0 5 23 0.0 5.0 23.0 6 25 12.0 9.0 13.0 7 29 4.0 12.0 25.0

**Additional Resources**

How to Add Rows to a Pandas DataFrame

How to Add a Numpy Array to a Pandas DataFrame

How to Count Number of Rows in Pandas DataFrame