You can use the following basic syntax to extract numbers from a string in pandas:

df['my_column'].str.extract('(\d+)')

This particular syntax will extract the numbers from each string in a column called **my_column** in a pandas DataFrame.

**Note**: When using a regular expression, **\d** represents “any digit” and **+** stands for “one or more.”

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

**Example: Extract Number from String in Pandas**

Suppose we have the following pandas DataFrame that contains information about the sales of various products:

import pandas as pd #create DataFrame df = pd.DataFrame({'product': ['A33', 'B34', 'A22', 'A50', 'C200', 'D7', 'A9', 'A13'], 'sales': [18, 22, 19, 14, 14, 11, 20, 28]}) #view DataFrame print(df) product sales 0 A33 18 1 B34 22 2 A22 19 3 A50 14 4 C200 14 5 D7 11 6 A9 20 7 A13 28

Suppose we would like to extract the number from each string in the **product** column.

We can use the following syntax to do so:

#extract numbers from strings in 'product' column df['product'].str.extract('(\d+)') 0 0 33 1 34 2 22 3 50 4 200 5 7 6 9 7 13

The result is a DataFrame that contains only the numbers from each row in the **product** column.

For example:

- The formula extracts
**33**from the string**A33**in the first row. - The formula extracts
**34**from the string**B34**in the first row. - The formula extracts
**22**from the string**A22**in the first row.

And so on.

If you’d like, you can also store these numerical values in a new column in the DataFrame:

#extract numbers from strings in 'product' column and store them in new column df['product_numbers'] = df['product'].str.extract('(\d+)') #view updated DataFrame print(df) product sales product_numbers 0 A33 18 33 1 B34 22 34 2 A22 19 22 3 A50 14 50 4 C200 14 200 5 D7 11 7 6 A9 20 9 7 A13 28 13

The new column called **product_numbers** contains only the numbers from each string in the **product** column.

**Additional Resources**

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

Pandas: How to Sort DataFrame Based on String Column

Pandas: How to Remove Specific Characters from Strings

Pandas: Search for String in All Columns of DataFrame