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Pandas DataFrame.replace()

Pandas replace() is a very rich function that is used to replace a string, regex, dictionary, list, and series from the DataFrame. The values of the DataFrame can be replaced with other values dynamically. It is capable of working with the Python regex(regular expression).

It differs from updating with .loc or .iloc, which requires you to specify a location where you want to update with some value.



  • to_replace: Defines a pattern that we are trying to replace in dataframe.
  • value: It is a value that is used to fill holes in the DataFrame (e.g., 0), alternately a dict of values that specify which value to use for each column (columns not in the dict will not be filled).
    It also allow such objects of regular expressions, strings, and lists or dicts, etc.
  • inplace: If it is True, then it replaces in place.

Note: It will also modify any other views on this object (e.g., a column from a DataFrame). Returns the caller if this is True.

  • limit: It defines the maximum size gap to forward or backward fill.
  • regex: It checks whether to interpret to_replace and/or value as regular expressions. If it 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: It is a method to use for replacement when to_replace is a list.

Returns: It returns a DataFrame object after the replacement.



           Language known
Parker        Python
Smith         Android
John          C
William       Android
Dean          Python
Christina     C++
Cornelia      C

             Language known
Parker         1
Smith          4
John           3
William        4
Dean           1
Christina      5
Cornelia       3


The below example replaces a value with another in a DataFrame.


     name       age       City
0	Parker      27         US
1	Smith       34         Belgium
2	John        38         London


The below example replaces the values from a dict:


    name        age     City
0	Parker      27       US
1	William     29       Belgium
2	John        31       London


The below example replaces the values from regex:


    name       age      City
0	Parker     27        US
1	Ela        34        Belgium
2	John       31        London

Next TopicDataFrame.iloc[]

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