pandas.DataFrame.replace

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

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.

Parameters:

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.

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.

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.

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. Alternatively, this could be a regular expression or a list, dict, or array of regular expressions in which case to_replace must be None.

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: Added to DataFrame.

Returns:

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.

See also

DataFrame.fillna

Fill NA values.

DataFrame.where

Replace values based on boolean condition.

Series.str.replace

Simple string replacement.

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.
• When dict is used as the to_replace value, it is like key(s) in the dict are the to_replace part and value(s) in the dict are the value parameter.

Examples

Scalar `to_replace` and `value`

>>> s = pd.Series([0, 1, 2, 3, 4])
>>> s.replace(0, 5)
0    5
1    1
2    2
3    3
4    4
dtype: int64

>>> df = pd.DataFrame({'A': [0, 1, 2, 3, 4],
...                    'B': [5, 6, 7, 8, 9],
...                    'C': ['a', 'b', 'c', 'd', 'e']})
>>> df.replace(0, 5)
   A  B  C
0  5  5  a
1  1  6  b
2  2  7  c
3  3  8  d
4  4  9  e

List-like `to_replace`

>>> df.replace([0, 1, 2, 3], 4)
   A  B  C
0  4  5  a
1  4  6  b
2  4  7  c
3  4  8  d
4  4  9  e

>>> df.replace([0, 1, 2, 3], [4, 3, 2, 1])
   A  B  C
0  4  5  a
1  3  6  b
2  2  7  c
3  1  8  d
4  4  9  e

>>> s.replace([1, 2], method='bfill')
0    0
1    3
2    3
3    3
4    4
dtype: int64

dict-like `to_replace`

>>> df.replace({0: 10, 1: 100})
     A  B  C
0   10  5  a
1  100  6  b
2    2  7  c
3    3  8  d
4    4  9  e

>>> df.replace({'A': 0, 'B': 5}, 100)
     A    B  C
0  100  100  a
1    1    6  b
2    2    7  c
3    3    8  d
4    4    9  e

>>> df.replace({'A': {0: 100, 4: 400}})
     A  B  C
0  100  5  a
1    1  6  b
2    2  7  c
3    3  8  d
4  400  9  e

Regular expression `to_replace`

>>> df = pd.DataFrame({'A': ['bat', 'foo', 'bait'],
...                    'B': ['abc', 'bar', 'xyz']})
>>> df.replace(to_replace=r'^ba.$', value='new', regex=True)
      A    B
0   new  abc
1   foo  new
2  bait  xyz

>>> df.replace({'A': r'^ba.$'}, {'A': 'new'}, regex=True)
      A    B
0   new  abc
1   foo  bar
2  bait  xyz

>>> df.replace(regex=r'^ba.$', value='new')
      A    B
0   new  abc
1   foo  new
2  bait  xyz

>>> df.replace(regex={r'^ba.$': 'new', 'foo': 'xyz'})
      A    B
0   new  abc
1   xyz  new
2  bait  xyz

>>> df.replace(regex=[r'^ba.$', 'foo'], value='new')
      A    B
0   new  abc
1   new  new
2  bait  xyz

Note that when replacing multiple bool or datetime64 objects, the data types in the to_replace parameter must match the data type of the value being replaced:

>>> df = pd.DataFrame({'A': [True, False, True],
...                    'B': [False, True, False]})
>>> df.replace({'a string': 'new value', True: False})  # raises
Traceback (most recent call last):
    ...
TypeError: Cannot compare types 'ndarray(dtype=bool)' and 'str'

This raises a TypeError because one of the dict keys is not of the correct type for replacement.

Compare the behavior of s.replace({'a': None}) and s.replace('a', None) to understand the peculiarities of the to_replace parameter:

>>> s = pd.Series([10, 'a', 'a', 'b', 'a'])

When one uses a dict as the to_replace value, it is like the value(s) in the dict are equal to the value parameter. s.replace({'a': None}) is equivalent to s.replace(to_replace={'a': None}, value=None, method=None):

>>> s.replace({'a': None})
0      10
1    None
2    None
3       b
4    None
dtype: object

When value=None and to_replace is a scalar, list or tuple, replace uses the method parameter (default ‘pad’) to do the replacement. So this is why the ‘a’ values are being replaced by 10 in rows 1 and 2 and ‘b’ in row 4 in this case. The command s.replace('a', None) is actually equivalent to s.replace(to_replace='a', value=None, method='pad'):

>>> s.replace('a', None)
0    10
1    10
2    10
3     b
4     b
dtype: object