pandas.DataFrame.filter#

DataFrame.filter(items=None, like=None, regex=None, axis=None)[source]#

Subset the DataFrame or Series according to the specified index labels.

For DataFrame, filter rows or columns depending on axis argument. Note that this routine does not filter based on content. The filter is applied to the labels of the index.

Parameters:

itemslist-like: Keep labels from axis which are in items.
likestr: Keep labels from axis for which “like in label == True”.
regexstr (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, ‘columns’ for DataFrame. For Series this parameter is unused and defaults to None.

Returns:

Same type as caller: The filtered subset of the DataFrame or Series.

See also

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

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 [].

Examples

>>> 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

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

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

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