You can use the following basic syntax to convert a categorical variable to a numeric variable in a pandas DataFrame:

df['column_name'] = pd.factorize(df['column_name'])[0]

You can also use the following syntax to convert every categorical variable in a DataFrame to a numeric variable:

#identify all categorical variables cat_columns = df.select_dtypes(['object']).columns #convert all categorical variables to numeric df[cat_columns] = df[cat_columns].apply(lambda x: pd.factorize(x)[0])

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

**Example 1: Convert One Categorical Variable to Numeric**

Suppose we have the following pandas DataFrame:

import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'], 'position': ['G', 'G', 'F', 'G', 'F', 'C', 'G', 'F', 'C'], 'points': [5, 7, 7, 9, 12, 9, 9, 4, 13], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12, 10]}) #view DataFrame df team position points rebounds 0 A G 5 11 1 A G 7 8 2 A F 7 10 3 B G 9 6 4 B F 12 6 5 B C 9 5 6 C G 9 9 7 C F 4 12 8 C C 13 10

We can use the following syntax to convert the ‘team’ column to numeric:

#convert 'team' column to numeric df['team'] = pd.factorize(df['team'])[0] #view updated DataFrame df team position points rebounds 0 0 G 5 11 1 0 G 7 8 2 0 F 7 10 3 1 G 9 6 4 1 F 12 6 5 1 C 9 5 6 2 G 9 9 7 2 F 4 12 8 2 C 13 10

Here is how the conversion worked:

- Each team that had a value of ‘
**A**‘ was converted to**0**. - Each team that had a value of ‘
**B**‘ was converted to**1**. - Each team that had a value of ‘
**C**‘ was converted to**2**.

**Example 2: Convert Multiple Categorical Variables to Numeric**

Once again suppose we have the following pandas DataFrame:

import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'], 'position': ['G', 'G', 'F', 'G', 'F', 'C', 'G', 'F', 'C'], 'points': [5, 7, 7, 9, 12, 9, 9, 4, 13], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12, 10]}) #view DataFrame df team position points rebounds 0 A G 5 11 1 A G 7 8 2 A F 7 10 3 B G 9 6 4 B F 12 6 5 B C 9 5 6 C G 9 9 7 C F 4 12 8 C C 13 10

We can use the following syntax to convert every categorical variable in the DataFrame to a numeric variable:

**#get all categorical columns
cat_columns = df.select_dtypes(['object']).columns
#convert all categorical columns to numeric
df[cat_columns] = df[cat_columns].apply(lambda x: pd.factorize(x)[0])
#view updated DataFrame
df
team position points rebounds
0 0 0 5 11
1 0 0 7 8
2 0 1 7 10
3 1 0 9 6
4 1 1 12 6
5 1 2 9 5
6 2 0 9 9
7 2 1 4 12
8 2 2 13 10
**

Notice that the two categorical columns (team and position) both got converted to numeric while the points and rebounds columns remained the same.

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

**Additional Resources**

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

How to Convert Pandas DataFrame Columns to Strings

How to Convert Pandas DataFrame Columns to Integer

How to Convert Strings to Float in Pandas DataFrame