pyspark.pandas.DataFrame.replace¶

DataFrame.replace(to_replace: Union[Any, List, Tuple, Dict, None] = None, value: Optional[Any] = None, inplace: bool = False, limit: Optional[int] = None, regex: bool = False, method: str = 'pad') → Optional[pyspark.pandas.frame.DataFrame]¶

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)

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

pyspark.pandas.DataFrame.fillna

pyspark.pandas.DataFrame.bfill