pyspark.pandas.DataFrame.replace#

DataFrame.replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad')[source]#

Returns a new DataFrame replacing a value with another value.

Parameters

to_replaceint, float, string, list, tuple or dict: Value to be replaced.
valueint, float, string, list or tuple: Value to use to replace holes. The replacement value must be an int, float, or string. If value is a list or tuple, value should be of the same length with to_replace.
inplaceboolean, default False: Fill in place (do not create a new object)
limitint, default None: Maximum size gap to forward or backward fill.

Deprecated since version 4.0.0.
regexbool or str, default False: Whether to interpret to_replace and/or value as regular expressions. If this is True then to_replace must be a string. Alternatively, this could be a regular expression in which case to_replace must be None.
method‘pad’, default None: The method to use when for replacement, when to_replace is a scalar, list or tuple and value is None.

Deprecated since version 4.0.0.

Returns

DataFrame: Object after replacement.

Examples

>>> df = ps.DataFrame({"name": ['Ironman', 'Captain America', 'Thor', 'Hulk'],
...                    "weapon": ['Mark-45', 'Shield', 'Mjolnir', 'Smash']},
...                   columns=['name', 'weapon'])
>>> df
              name   weapon
0          Ironman  Mark-45
1  Captain America   Shield
2             Thor  Mjolnir
3             Hulk    Smash

Scalar to_replace and value

>>> df.replace('Ironman', 'War-Machine')
              name   weapon
0      War-Machine  Mark-45
1  Captain America   Shield
2             Thor  Mjolnir
3             Hulk    Smash

List like to_replace and value

>>> df.replace(['Ironman', 'Captain America'], ['Rescue', 'Hawkeye'], inplace=True)
>>> df
      name   weapon
0   Rescue  Mark-45
1  Hawkeye   Shield
2     Thor  Mjolnir
3     Hulk    Smash

Dicts can be used to specify different replacement values for different existing values To use a dict in this way the value parameter should be None

>>> df.replace({'Mjolnir': 'Stormbuster'})
      name       weapon
0   Rescue      Mark-45
1  Hawkeye       Shield
2     Thor  Stormbuster
3     Hulk        Smash

Dict can specify that different values should be replaced in different columns The value parameter should not be None in this case

>>> df.replace({'weapon': 'Mjolnir'}, 'Stormbuster')
      name       weapon
0   Rescue      Mark-45
1  Hawkeye       Shield
2     Thor  Stormbuster
3     Hulk        Smash

Nested dictionaries The value parameter should be None to use a nested dict in this way

>>> df.replace({'weapon': {'Mjolnir': 'Stormbuster'}})
      name       weapon
0   Rescue      Mark-45
1  Hawkeye       Shield
2     Thor  Stormbuster
3     Hulk        Smash