pandas.DataFrame.replace

Replace values given in to_replace with value.

Values of the 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.

param to_replace

str, regex, list, dict, Series, int, float, or None

How to find the values that will be replaced.

• numeric, str or regex:

• 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.

• dict:

• Dicts can be used to specify different replacement values

  for different existing values. For example, {'a': 'b', 'y': 'z'} replaces the value ‘a’ with ‘b’ and ‘y’ with ‘z’. To use a dict in this way the value parameter should be None.

• For a DataFrame a dict can specify that different values

  should be replaced in different columns. For example, {'a': 1, 'b': 'z'} looks for the value 1 in column ‘a’ and the value ‘z’ in column ‘b’ and replaces these values with whatever is specified in value. The value parameter should not be None in this case. You can treat this as a special case of passing two lists except that you are specifying the column to search in.

• For a DataFrame nested dictionaries, e.g.,

  {'a': {'b': np.nan}}, are read as follows: look in column ‘a’ for the value ‘b’ and replace it with NaN. The value parameter should be None to use a nested dict in this way. You can nest regular expressions as well. Note that column names (the top-level dictionary keys in a nested dictionary) cannot be regular expressions.

• None:

• This means that the regex argument must be a string,

  compiled regular expression, or list, dict, ndarray or Series of such elements. If value is also None then this must be a nested dictionary or Series.

See the examples section for examples of each of these.

param value

scalar, dict, list, str, regex, 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.

param inplace

bool, default False

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.

param limit

int, default None

Maximum size gap to forward or backward fill.

param regex

bool or same types as to_replace, 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 or a list, dict, or array of regular expressions in which case to_replace must be None.

param method

{‘pad’, ‘ffill’, ‘bfill’, None}

The method to use when for replacement, when to_replace is a scalar, list or tuple and value is None.

Changed in version 0.23.0.

return

DataFrame Object after replacement.

raises

AssertionError

• If regex is not a bool and to_replace is not

  None. TypeError

• If to_replace is a dict and value is not a list,

  dict, ndarray, or Series

• If to_replace is None and regex is not compilable

  into a regular expression or is a list, dict, ndarray, or Series.

• When replacing multiple bool or datetime64 objects and

  the arguments to to_replace does not match the type of the value being replaced ValueError

• If a list or an ndarray is passed to to_replace and

  value but they are not the same length.

Warning

This feature is currently unsupported by Intel Scalable Dataframe Compiler