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    Pandas Convert Column to Float in DataFrame

    Pandas Convert Column to Float in DataFrame

    By using pandas DataFrame.astype() and pandas.to_numeric() methods you can convert a column from string/int type to float. In this article, I will explain how to convert one or multiple string columns to float type using examples.

    Key Points -

    <a id="h-quick-examples-of-converting-string-to-float-in-dataframe"></a>

    Quick Examples of Converting String to Float in DataFrame

    If you are in a hurry, below are some quick examples of how to convert string to float. You can apply these to convert from any type in Pandas.

    
# Quick examples of converting string to float

# Example 1: Convert "Fee" from string to float
df['Fee'] = df['Fee'].astype(float)
print(df.dtypes)

# Example 2: Convert multiple columns
df = df.astype({'Fee':'float','Discount':'float'})

# Example 3: Convert all columns to floats
df = df.astype(float)
print(df.dtypes)

# Example 4: Convert numeric function string to float
df['Discount'] = pd.to_numeric(df['Discount'])
print(df.dtypes)

# Example 5: Convert DataFrame column from string to float
df["Discount"] = pd.to_numeric(df["Discount"], downcast="float")
print(df.dtypes)

# Example 6: Convert each value of the column to a string
df['Discount'] = pd.to_numeric(df['Discount'], errors='coerce')
print(df.dtypes)

# Example 7: Using df.replace() to replace nan values 0 before convertion
df['Discount'] = pd.to_numeric(df['Discount'], errors='coerce')
df = df.replace(np.nan, 0, regex=True)
print(df.dtypes)

# Example 8: Replace empty string ('') with np.nan before convertion
df['Discount']=df.Discount.replace('',np.nan).astype(float)
print(df.dtypes)

    Now, let's create a DataFrame with a few rows and columns, execute the above examples, and validate the results. Our DataFrame contains column names Fee and Discount.

    
# Create DataFrame
import pandas as pd
import numpy as np
technologies= ({
         'Fee' :['22000.30','25000.40','23000.20','24000.50','26000.10'],
         'Discount':['1000.10',np.nan,'1000.5',np.nan,'2500.20']
             })
df = pd.DataFrame(technologies)
print("Create DataFrame:\n", df)
print("-----------------------------------")
print("Type of the columns:\n", df.dtypes)

    You can identify the data type of each column by using dtypes. For Instance, print(df.dtypes) the outputs are as below. Here object means a String type.

    <a id="h-pandas-convert-string-to-float"></a>

    Pandas Convert String to Float

    You can use the Pandas DataFrame.astype() function to convert a column from string/int to float, you can apply this on a specific column or on an entire DataFrame. To cast the data type to a 54-bit signed float, you can use numpy.float64, numpy.float_, float, float64 as param. To cast to 32-bit signed float use numpy.float32 or float32.

    The Below example converts Fee column from string dtype to float64.

    
# Convert "Fee" from string to float
df = df.astype({'Fee':'float'})
print("Convert specific column to float type:\n", df)
print("-----------------------------------")
print("Type of the columns:\n", df.dtypes)

    Yields below output.

    You can also use the Series.astype() to convert a specific column. since each column on DataFrame is a pandas Series, I will get the column from DataFrame as a Series and use astype() function. In the below. example df.Fee or df['Fee'] returns Series object.

    
# Convert "Fee" from string to float
df['Fee'] = df['Fee'].astype(float)
print(df.dtypes)

    Yields the same output as above.

    <a id="h-convert-multiple-columns-to-float"></a>

    Convert Multiple Columns to Float

    You can also convert multiple columns to float by sending a dict of column name: data type to astype() the method. The below example converts the column Fee and Discount to float dtype.

    
# Convert multiple columns 
df = df.astype({'Fee':'float','Discount':'float'})
print("Convert multiple columns to float type:")
print("Type of the columns:\n", df.dtypes)

# Output:
# Convert multiple columns to float type:
# Type of the columns:
#  Fee         float64
# Discount    float64
# dtype: object

    <a id="h-convert-all-columns-to-float-type"></a>

    Convert All Columns to Float Type

    By default astype() function converts all columns to the same type. The below example converts all DataFrame columns to float type. If you have any column with alpha-numeric values, you will get an error.

    
# Convert entire DataFrame string to float
df = df.astype(float)
print("Convert all columns to float type:)
print("Type of the columns:\n", df.dtypes)

    Yields below output.

    
# Output:
# Convert all columns to float type:
# Type of the columns:
Fee         float64
Discount    float64
dtype: object

    <a id="h-using-pandas-to-numeric"></a>

    Using pandas.to_numeric()

    Alternatively, you can convert all string columns to float type using pandas.to_numeric(). For example use df['Discount'] = pd.to_numeric(df['Discount']) function to convert ‘Discount’ column to float.

    
# Convert numeric function string to float
df['Discount'] = pd.to_numeric(df['Discount'])
print("Type of the columns:\n", df.dtypes)

# Convert DataFrame column from string to float
df["Discount"] = pd.to_numeric(df["Discount"], downcast="float")
print("Type of the columns:\n", df.dtypes)

    Yields below output.

    
# Output:
# Type of the columns:
Fee          object
Discount    float64
dtype: object

    <a id="h-handling-non-numeric-values"></a>

    Handling Non-numeric Values

    When you have some cells with character values on a column you want to convert to float, it returns an error. To ignore the error and convert the char values to NaN use errors='coerce' attribute.

    
# Convert each value of the column to a string
df['Discount'] = pd.to_numeric(df['Discount'], errors='coerce')
print("Type of the columns:\n", df.dtypes)

    This yields the same output as above.

    <a id="h-replace-the-nan-values-with-zeros"></a>

    Replace the ‘NaN’ Values with Zeros

    Use df=df.replace(np.nan,0,regex=True) function to replace the ‘NaN’ values with ‘0’ values.

    
# Using df.replace() to replace nan values 0
df['Discount'] = pd.to_numeric(df['Discount'], errors='coerce')
df = df.replace(np.nan, 0, regex=True)
print("After replacing NaN with zeros:\n", df)
print("Type of the columns:\n", df.dtypes)

    Yields below output.

    
# Output:
# After replacing NaN with zeros:
        Fee  Discount
0  22000.30    1000.1
1  25000.40       0.0
2  23000.20    1000.5
3  24000.50       0.0
4  26000.10    2500.2

# Type of the columns:
Fee          object
Discount    float64
dtype: object

    <a id="h-replace-empty-string-before-convert"></a>

    Replace Empty String before Convert

    If you have empty values in a string, convert the empty string ('') with np.nan before converting it to float.

    
import pandas as pd
import numpy as np
technologies= ({
         'Fee' :['22000.30','25000.40','23000.20','24000.50','26000.10','21000'],
         'Discount':['1000.10',np.nan,"",np.nan,'2500.20',""]
             })
df = pd.DataFrame(technologies)
# Replace empty string ('') with np.nan
df['Discount']=df.Discount.replace('',np.nan).astype(float)
print("After replacing an empty string with NaN:\n", df)
print("Type of the columns:\n", df.dtypes)

    Yields below output.

    
# Output:
# After replacing an empty string with NaN:
        Fee  Discount
0  22000.30    1000.1
1  25000.40       NaN
2  23000.20       NaN
3  24000.50       NaN
4  26000.10    2500.2
5     21000       NaN

# Type of the columns:
Fee          object
Discount    float64
dtype: object

    <a id="h-frequently-asked-questions-on-convert-pandas-column-to-float"></a>

    Frequently Asked Questions on Convert Pandas Column to Float

    You can use the astype() method to convert a specific string column to a string. For example, df['specified_column'] = df['specified_col'].astype(float)

    You can convert multiple columns simultaneously using the Pandas astype() method. For example, df[['Column1', 'Column2']] = df[['Column1', 'Column2']].astype(float)

    If the string column contains non-numeric values, the conversion will raise a ValueError. You may need to handle or clean the non-numeric values before converting.

    If you have NaN values in your column, you can use the pd.to_numeric function with the errors='coerce' parameter to handle missing values. For example, df['ColumnName'] = pd.to_numeric(df['ColumnName'], errors='coerce').astype(float)

    If the string column contains non-numeric characters like commas, you may need to remove them before conversion. For example, df['ColumnName'] = df['ColumnName'].str.replace(',', '').astype(float)

    You can convert all string columns in a DataFrame to float using the astype() method along with the select_dtypes method. For example, df = df.astype(float, errors='ignore')

    <a id="h-conclusion"></a>

    Conclusion

    In this article, you have learned how to convert single, multiple, and all columns from string type to float in Pandas DataFrame using DataFrame.astype(float) and pandas.to_numeric() function with multiple examples.

    Happy Learning !!

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    References