filter(self: 'NDFrameT', items=None, like: 'str | None' = None, regex: 'str | None' = None, axis=None) -> 'NDFrameT'

Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index.

Notes

The items , like , and regex parameters are enforced to be mutually exclusive.

axis defaults to the info axis that is used when indexing with [] .

Parameters

items : list-like

Keep labels from axis which are in items.

like : str

Keep labels from axis for which "like in label == True".

regex : str (regular expression)

Keep labels from axis for which re.search(regex, label) == True.

axis : {0 or ‘index’, 1 or ‘columns’, None}, default None

The axis to filter on, expressed either as an index (int) or axis name (str). By default this is the info axis, 'index' for Series, 'columns' for DataFrame.

Returns

same type as input object

Subset the dataframe rows or columns according to the specified index labels.

See Also

DataFrame.loc

Access a group of rows and columns by label(s) or a boolean array.

Examples

This example is valid syntax, but we were not able to check execution

>>> df = pd.DataFrame(np.array(([1, 2, 3], [4, 5, 6])),
...                   index=['mouse', 'rabbit'],
...                   columns=['one', 'two', 'three'])
... df
        one  two  three
mouse     1    2      3
rabbit    4    5      6

This example is valid syntax, but we were not able to check execution

>>> # select columns by name
... df.filter(items=['one', 'three'])
         one  three
mouse     1      3
rabbit    4      6

This example is valid syntax, but we were not able to check execution

>>> # select columns by regular expression
... df.filter(regex='e$', axis=1)
         one  three
mouse     1      3
rabbit    4      6

This example is valid syntax, but we were not able to check execution

>>> # select rows containing 'bbi'
... df.filter(like='bbi', axis=0)
         one  two  three
rabbit    4    5      6

See :

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File: /pandas/core/generic.py#5048
type: <class 'function'>
Commit: