The pandas function can be used to encode strings as numeric values.

You can use the following methods to apply the factorize() function to columns in a pandas DataFrame:

Method 1: Factorize One Column

df['col1'] = pd.factorize(df['col'])[0]

Method 2: Factorize Specific Columns

df[['col1', 'col3']] = df[['col1', 'col3']].apply(lambda x: pd.factorize(x)[0])

Method 3: Factorize All Columns

df = df.apply(lambda x: pd.factorize(x)[0])

The following example shows how to use each method with the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'conf': ['West', 'West', 'East', 'East'],
                   'team': ['A', 'B', 'C', 'D'],
                   'position': ['Guard', 'Forward', 'Guard', 'Center'] })

#view DataFrame
df

   conf team position
0  West    A    Guard
1  West    B  Forward
2  East    C    Guard
3  East    D   Center

Example 1: Factorize One Column

The following code shows how to factorize one column in the DataFrame:

#factorize the conf column only
df['conf'] = pd.factorize(df['conf'])[0]

#view updated DataFrame
df

	conf	team	position
0	0	A	Guard
1	0	B	Forward
2	1	C	Guard
3	1	D	Center

Notice that only the ‘conf’ column has been factorized.

Every value that used to be ‘West’ is now 0 and every value that used to be ‘East’ is now 1.

Example 2: Factorize Specific Columns

The following code shows how to factorize specific columns in the DataFrame:

#factorize conf and team columns only
df[['conf', 'team']] = df[['conf', 'team']].apply(lambda x: pd.factorize(x)[0])

#view updated DataFrame
df

        conf	team	position
0	0	0	Guard
1	0	1	Forward
2	1	2	Guard
3	1	3	Center

Notice that the ‘conf’ and ‘team’ columns have both been factorized.

Example 3: Factorize All Columns

The following code shows how to factorize all columns in the DataFrame:

#factorize all columns
df = df.apply(lambda x: pd.factorize(x)[0])

#view updated DataFrame
df

     conf	team	position
0	0	0	0
1	0	1	1
2	1	2	0
3	1	3	2

Notice that all of the columns have been factorized.

Additional Resources

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