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You can use the following methods to convert a string to a float in pandas:

**Method 1: Convert a Single Column to Float**

#convert "assists" column from string to float df['assists'] = df['assists'].astype(float)

**Method 2: Convert Multiple Columns to Float**

#convert both "assists" and "rebounds" from strings to floats df[['assists', 'rebounds']] = df[['assists', 'rebounds']].astype(float)

**Method 3: Convert All Columns to Float**

#convert all columns to float df = df.astype(float)

The following examples show how to use each method in practice with the following pandas 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**Â 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

**Bonus: Convert String to Float and Fill in NaN Values**

The following syntax shows how to convert the **assists** column from string to float 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

**Additional Resources**

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

Pandas: How to Convert object to int

Pandas: How to Convert Floats to Integers

Pandas: How to Convert Specific Columns to NumPy Array