pandas.DataFrame.replace

DataFrame.replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method=None, axis=None)

Replace values given in ‘to_replace’ with ‘value’.

Parameters :

to_replace : str, regex, list, dict, Series, numeric, or None

• str or regex:

  • 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 and regex rules apply as above.

• dict:

  • Nested dictionaries, e.g., {‘a’: {‘b’: nan}}, are read as follows: look in column ‘a’ for the value ‘b’ and replace it with nan. You can nest regular expressions as well. Note that column names (the top-level dictionary keys in a nested dictionary) cannot be regular expressions.
  • Keys map to column names and values map to substitution values. You can treat this as a special case of passing two lists except that you are specifying the column to search in.

• 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.

value : scalar, dict, list, str, regex, default None

Value to use to fill holes (e.g. 0), alternately a dict of values specifying 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.

inplace : boolean, default False

If True, fill the DataFrame in place. Note: this will modify any other views on this DataFrame, like if you took a no-copy slice of an existing DataFrame, for example a column in a DataFrame. Returns a reference to the filled object, which is self if inplace=True

limit : int, default None

Maximum size gap to forward or backward fill

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. 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.

Returns :

filled : DataFrame

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.

ValueError :

• If to_replace and value are list s or ndarray s, but they are not the same length.

See also

reindex, asfreq, fillna

Notes

• Regex substitution is performed under the hood with re.sub. The rules for substitution for re.sub are the same.
• Regular expressions will only substitute on strings, meaning you cannot provide, for example, a regular expression matching floating point numbers and expect the columns in your frame that have a numeric dtype to be matched. However, if those floating point numbers are strings, then you can do this.
• This method has a lot of options. You are encouraged to experiment and play with this method to gain intuition about how it works.