Pandas dataframe.replace() Method Syntax

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

Parameters:

• to_replace : [str, regex, list, dict, Series, numeric, or None] pattern that we are trying to replace in dataframe. 
• value : Value to use to fill holes (e.g. 0), alternately a dict of values specifying which value to use for each column (columns not in the dict will not be filled). Regular expressions, strings and lists or dicts of such objects are also allowed. 
• inplace : If True, in place. Note: this will modify any other views on this object (e.g. a column from a DataFrame). Returns the caller if this is True. 
• limit : Maximum size gap to forward or backward fill 
• regex : Whether to interpret to_replace and/or value as regular expressions. If this is True then to_replace must be a string. Otherwise, to_replace must be None because this parameter will be interpreted as a regular expression or a list, dict, or array of regular expressions.
• method : Method to use when for replacement, when to_replace is a list. 

Returns: filled : NDFrame

Simple Example of Pandas dataframe.replace()

Here, we are replacing 49.50 with 60.

Output:

   Array_1  Array_2
0     60.0     65.1
1     70.0     60.0

Pandas dataframe.replace() function is used to replace a string, regex, list, dictionary, series, number, etc. from a Pandas Dataframe in Python. Every instance of the provided value is replaced after a thorough search of the full DataFrame.