How to Convert String to Float in Pandas DataFrame

Error Handling

There are two methods to convert string to float data type in Pandas DataFrame – DataFrame.astype() and pandas.to_numeric() function.

DataFrame.astype() function enables you to convert Pandas objects to any data type whereas pandas.to_numeric() function lets you convert into int or float data type only.

Let’s look at both methods to convert string to float in DataFrame with examples:

Convert String to Float in DataFrame Using DataFrame.astype()

DataFrame.astype() method is used to cast a Pandas object to a specified datatype.

Syntax: DataFrame.astype(self: ~ FrameOrSeries, dtype, copy: bool = True, errors: str = ‘raise’)

Returns: casted: type of caller

Example: In this example, we’ll convert each value of the ‘Inflation Rate’ column to float.

Python3

# importing pandas library
import pandas as pd
 
# dictionary
Data = {'Year': ['2016', '2017', 
                 '2018', '2019'],
        'Inflation Rate': ['4.47', '5', 
                           '5.98', '4.1']}
# create a dataframe
df = pd.DataFrame(Data)
 
# converting each value 
# of column to a string
df['Inflation Rate'] = df['Inflation Rate'].astype(float)
 
# show the dataframe
print(df)
 
# show the datatypes
print (df.dtypes)

Output:

Convert String to Float in DataFrame Using pandas.to_numeric()

pandas.to_numeric() function is used to convert the argument to a numeric type (int or float).

Syntax: pandas.to_numeric(arg, errors=’raise’, downcast=None)

Returns: numeric if parsing succeeded. Note that the return type depends on the input. Series if Series, otherwise ndarray.

Example: In this example, we’ll convert each value of the ‘Inflation Rate’ column to float.

Python3

# importing pandas library
import pandas as pd
 
# creating a dictionary
Data = {'Year': ['2016', '2017', 
                 '2018', '2019'],
          'Inflation Rate': ['4.47', '5', 
                             '5.98', '4.1']}
# create a dataframe
df = pd.DataFrame(Data)
 
# converting each value of column to a string
df['Inflation Rate'] = pd.to_numeric(df['Inflation Rate'])
 
# show the dataframe
print(df)
 
# show the data types
print (df.dtypes)

Output: