@@ -3924,41 +3924,40 @@ def filter(self, items=None, like=None, regex=None, axis=None):
39243924 Parameters
39253925 ----------
39263926 items : list-like
3927- List of info axis to restrict to (must not all be present)
3927+ List of axis to restrict to (must not all be present).
39283928 like : string
3929- Keep info axis where "arg in col == True"
3929+ Keep axis where "arg in col == True".
39303930 regex : string (regular expression)
3931- Keep info axis with re.search(regex, col) == True
3931+ Keep axis with re.search(regex, col) == True.
39323932 axis : int or string axis name
39333933 The axis to filter on. By default this is the info axis,
3934- 'index' for Series, 'columns' for DataFrame
3934+ 'index' for Series, 'columns' for DataFrame.
39353935
39363936 Returns
39373937 -------
39383938 same type as input object
39393939
39403940 Examples
39413941 --------
3942- >>> df
3943- one two three
3944- mouse 1 2 3
3945- rabbit 4 5 6
3942+ >>> df = pd.DataFrame(np.array(([1,2,3], [4,5,6])),
3943+ ... index=['mouse', 'rabbit'],
3944+ ... columns=['one', 'two', 'three'])
39463945
39473946 >>> # select columns by name
39483947 >>> df.filter(items=['one', 'three'])
3949- one three
3948+ one three
39503949 mouse 1 3
39513950 rabbit 4 6
39523951
39533952 >>> # select columns by regular expression
39543953 >>> df.filter(regex='e$', axis=1)
3955- one three
3954+ one three
39563955 mouse 1 3
39573956 rabbit 4 6
39583957
39593958 >>> # select rows containing 'bbi'
39603959 >>> df.filter(like='bbi', axis=0)
3961- one two three
3960+ one two three
39623961 rabbit 4 5 6
39633962
39643963 See Also
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