dtoolkit.transformer.ReplaceTF#

class dtoolkit.transformer.ReplaceTF(*args, **kwargs)[source]#

A transformer could replace values given input parameters.

See also

pandas.DataFrame.replace: This transformer’s prototype method.

Notes

replace()’s inplace parameter is not work for this transformer. Actually this break pipeline stream. If a transformer’s inplace is True, the next tf input would get None.

Examples

Scalar to_replace and value

>>> import pandas as pd
>>> from dtoolkit.transformer import ReplaceTF
>>> df = pd.DataFrame(
...     {
...         'A': [0, 1, 2, 3, 4],
...         'B': [5, 6, 7, 8, 9],
...         'C': ['a', 'b', 'c', 'd', 'e'],
...     },
... )
>>> tf = ReplaceTF(0, 5)
>>> tf.transform(df)
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

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

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

dict-like to_replace

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

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

>>> tf = ReplaceTF({'A': {0: 100, 4: 400}})
>>> tf.transform(df)
    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'],
...     },
... )
>>> tf = ReplaceTF(to_replace=r'^ba.$', value='new', regex=True)
>>> tf.transform(df)
    A    B
0   new  abc
1   foo  new
2  bait  xyz

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

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

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

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

Attributes:

inverse_transform_method

Methods

`fit`(*_)	Fit transformer.
`fit_transform`(X[, y])	Fit to data, then transform it.
`inverse_transform`(X)	Undo transform to `X`.
`set_output`(*[, transform])	Set output container.
`transform`(X)	Transform `X`.
`transform_method`(self[, to_replace, value, ...])	Replace values given in to_replace with value.
`update_invargs`(args, *kwargs)	Inverse transform method argument entry.