bigframes.pandas.DataFrame.replace

DataFrame.replace(to_replace: Any, value: Any = None, *, regex: bool = False)

Replace values given in to_replace with value.

Values of the Series/DataFrame are replaced with other values dynamically. This differs from updating with .loc or .iloc, which require you to specify a location to update with some value.

Examples:

>>> import bigframes.pandas as bpd
>>> df = bpd.DataFrame({
...     'int_col': [1, 1, 2, 3],
...     'string_col': ["a", "b", "c", "b"],
...     })

Using scalar to_replace and value:

>>> df.replace("b", "e")
   int_col string_col
0        1          a
1        1          e
2        2          c
3        3          e

[4 rows x 2 columns]

Using dictionary:

>>> df.replace({"a": "e", 2: 5})
   int_col string_col
0        1          e
1        1          b
2        5          c
3        3          b

[4 rows x 2 columns]

Using regex:

>>> df.replace("[ab]", "e", regex=True)
   int_col string_col
0        1          e
1        1          e
2        2          c
3        3          e

[4 rows x 2 columns]

Parameters:

• to_replace (str, regex, list, int, float or None) – How to find the values that will be replaced. numeric: numeric values equal to to_replace will be replaced with value str: string exactly matching to_replace will be replaced with value regex: regexs matching to_replace will be replaced with`value` list of str, regex, or numeric: First, if to_replace and value are both lists, they must be the same length. Second, if regex=True then all of the strings in both lists will be interpreted as regexs otherwise they will match directly. This doesn’t matter much for value since there are only a few possible substitution regexes you can use. str, regex and numeric rules apply as above.

• value (scalar, default None) – Value to replace any values matching to_replace with. For a DataFrame a dict of values can be used to specify 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.

• regex (bool, default False) – Whether to interpret to_replace and/or value as regular expressions. If this is True then to_replace must be a string.

Returns:

Object after replacement.

Return type:

bigframes.pandas.DataFrame or bigframes.pandas.Series