Often you may be interested in converting one or more string columns in a pandas DataFrame to float columns. Fortunately this is easy to do using the astype() function.
This tutorial shows several examples of how to use this function in practice using the following DataFrame:
import numpy as np import pandas as pd #create DataFrame df = pd.DataFrame({'points': [np.nan, 12, 15, 14, 19], 'assists': ['5', np.nan, '7', '9', '12'], 'rebounds': ['11', '8', '10', '6', '6']}) #view DataFrame df points assists rebounds 0 NaN 5.0 11 1 12.0 NaN 8 2 15.0 7.0 10 3 14.0 9.0 6 4 19.0 12.0 6 #view column data types df.dtypes points float64 assists object rebounds object dtype: object
Example 1: Convert A Single Column to Float
The following syntax shows how to convert the “assists” column from a string to a float:
#convert "assists" from string to float df['assists'] = df['assists'].astype(float) #view column data types df.dtypes points float64 assists float64 rebounds object dtype: object
Example 2: Convert Multiple Columns to Float
The following syntax shows how to convert both the “assists” and “rebounds” columns from strings to floats:
#convert both "assists" and "rebounds" from strings to floats df[['assists', 'rebounds']] = df[['assists', 'rebounds']].astype(float) #view column data types df.dtypes points float64 assists float64 rebounds float64 dtype: object
Example 3: Convert All Columns to Float
The following syntax shows how to convert all of the columns in the DataFrame to floats:
#convert all columns to float df = df.astype(float) #view column data types df.dtypes points float64 assists float64 rebounds float64 dtype: object
Example 4: Convert String to Float and Fill in NaN Values
The following syntax shows how to convert the “assists” column from strings to floats and simultaneously fill in the NaN values with zeros:
#convert "assists" from string to float and fill in NaN values with zeros df['assists'] = df['assists'].astype(float).fillna(0) #view DataFrame df points assists rebounds 0 NaN 5.0 11 1 12.0 0.0 8 2 15.0 7.0 10 3 14.0 9.0 6 4 19.0 12.0 6
