DataFrame({"a":["x", "y"], "b":[1,2]}) In [126]: def identity(df): .....: print df .....: return df .....: In [127]: d.groupby("a").apply(identity) a b 0 x 1 a b 0 x 1 a b 1 y 2 Out[127]: a b object containing counts of unique values. view([cls]) this is defined as a copy with the same identity where(cond[, other]) pandas.MultiIndex.all MultiIndex.all(other=None) pandas.MultiIndex.any MultiIndex.copy MultiIndex.copy(names=None, dtype=None, levels=None, labels=None, deep=False, _set_identity=False, **kwargs) Make a copy of this object. Names, dtype, levels and labels can be passed and

DataFrame({"a":["x", "y"], "b":[1,2]}) In [126]: def identity(df): .....: print df .....: return df .....: In [127]: d.groupby("a").apply(identity) a b 0 x 1 a b 0 x 1 a b 1 y 2 Out[127]: a b Table 35.105 – continued from previous page view([cls]) this is defined as a copy with the same identity where(cond[, other]) pandas.MultiIndex.all MultiIndex.all(other=None) pandas.MultiIndex.any MultiIndex.copy MultiIndex.copy(names=None, dtype=None, levels=None, labels=None, deep=False, _set_identity=False, **kwargs) Make a copy of this object. Names, dtype, levels and labels can be passed and

DataFrame({"a":["x", "y"], "b":[1,2]}) In [145]: def identity(df): .....: print(df) .....: return df .....: In [146]: d.groupby("a").apply(identity) a b 0 x 1 a b 0 x 1 a b 1 y 2 Out[146]: a object containing counts of unique values. view([cls]) this is defined as a copy with the same identity where(cond[, other]) 34.10.1.31 pandas.MultiIndex.all MultiIndex.all(other=None) 34.10.1.32 MultiIndex.copy MultiIndex.copy(names=None, dtype=None, levels=None, labels=None, deep=False, _set_identity=False, **kwargs) Make a copy of this object. Names, dtype, levels and labels can be passed and

twice for the first group. In [144]: d = pd.DataFrame({"a":["x", "y"], "b":[1,2]}) In [145]: def identity(df): .....: print df .....: return df .....: File "", line 2 object containing counts of unique values. view([cls]) this is defined as a copy with the same identity where(cond[, other]) 34.9.1.31 pandas.MultiIndex.all MultiIndex.all(other=None) 34.9.1.32 pandas MultiIndex.copy MultiIndex.copy(names=None, dtype=None, levels=None, labels=None, deep=False, _set_identity=False, **kwargs) Make a copy of this object. Names, dtype, levels and labels can be passed and

DataFrame({"a":["x", "y"], "b":[1,2]}) In [111]: def identity(df): .....: print df .....: return df .....: In [112]: d.groupby("a").apply(identity) a b 0 x 1 a b 0 x 1 a b 1 y 2 Out[112]: a b

allow_fill, ...]) Return a new Index of the values selected by the in- dices. to_flat_index(self) Identity method. to_frame(self[, index, name]) Create a DataFrame with a column containing the In- dex. indices. See also: numpy.ndarray.take pandas.Index.to_flat_index Index.to_flat_index(self) Identity method. New in version 0.24.0. This is implemented for compatibility with subclass implementations sortorder=None, names=None, dtype=None, copy=False, name=None, verify_integrity: bool = True, _set_identity: bool = True) A multi-level, or hierarchical, index object for pandas objects. Parameters levels

allow_fill, fill_value]) Return a new Index of the values selected by the in- dices. to_flat_index() Identity method. to_frame([index, name]) Create a DataFrame with a column containing the In- dex. to_list() given indices. See also: numpy.ndarray.take pandas.Index.to_flat_index Index.to_flat_index() Identity method. New in version 0.24.0. This is implemented for compatibility with subclass implementations codes=None, sortorder=None, names=None, dtype=None, copy=False, name=None, verify_integrity=True, _set_identity=True) A multi-level, or hierarchical, index object for pandas objects. Parameters levels [sequence

allow_fill, fill_value]) Return a new Index of the values selected by the in- dices. to_flat_index() Identity method. to_frame([index, name]) Create a DataFrame with a column containing the In- dex. to_list() given indices. See also: numpy.ndarray.take pandas.Index.to_flat_index Index.to_flat_index() Identity method. New in version 0.24.0. This is implemented for compatibility with subclass implementations codes=None, sortorder=None, names=None, dtype=None, copy=False, name=None, verify_integrity=True, _set_identity=True) A multi-level, or hierarchical, index object for pandas objects. Parameters levels [sequence

DataFrame({"a": ["x", "y"], "b": [1, 2]}) In [153]: def identity(df): .....: print(df) .....: return df .....: In [154]: d.groupby("a").apply(identity) a b 0 x 1 a b 0 x 1 a b 1 y 2 Out[154]: a allow_fill, fill_value]) Return a new Index of the values selected by the in- dices. to_flat_index() Identity method. to_frame([index, name]) Create a DataFrame with a column containing the In- dex. to_list() raise ValueError See also: numpy.ndarray.take pandas.Index.to_flat_index Index.to_flat_index() Identity method. New in version 0.24.0. This is implemented for compatability with subclass implementations

DataFrame({"a":["x", "y"], "b":[1,2]}) In [118]: def identity(df): .....: print df .....: return df .....: In [119]: d.groupby("a").apply(identity) a b 0 x 1 a b 0 x 1 a b 1 y 2 Out[119]: a b