DataFrame.replace()

This method is used to replace null or null values with a specific value.

Syntax: DataFrame.replace(self, to_replace=None, value=None, inplace=False, limit=None, regex=False,                         method=’pad’)

Parameters: This method will take following parameters:

• to_replace(str, regex, list, dict, Series, int, float, None): Specify the values that will be replaced.
• value(scalar, dict, list, str, regex, default value is None): Specify the value to replace any values matching to_replace with.
• inplace(bool, default False): If a value is True, in place. Note: this will modify any other views on this object.
• limit(int, default None): Specify the maximum size gap to forward or backward fill.
• regex(bool or same types as to_replace, default False): If a value is True then to_replace must be a string. Alternatively, this could be a regular expression or a list,  dict, or array of regular expressions in which case to_replace must be None.
• method {‘pad’, ‘ffill’, ‘bfill’, None}: Specify the method to use when for replacement, when to_replace is a scalar, list or tuple and value is None.

Returns: DataFrame. Object after replacement.

Code: Create a Dataframe.

Output:

Code: Replace all the NaN values with Zero’s

Output:

Replacing the NaN or the null values in  a dataframe can be easily performed using a single line DataFrame.fillna() and DataFrame.replace() method. We will discuss these methods along with an example demonstrating how to use it.