pandas.Series.reset_index — pandas 0.24.0rc1 documentation (original) (raw)
Series. reset_index(level=None, drop=False, name=None, inplace=False)[source]¶
Generate a new DataFrame or Series with the index reset.
This is useful when the index needs to be treated as a column, or when the index is meaningless and needs to be reset to the default before another operation.
| Parameters: | level : int, str, tuple, or list, default optional For a Series with a MultiIndex, only remove the specified levels from the index. Removes all levels by default. drop : bool, default False Just reset the index, without inserting it as a column in the new DataFrame. name : object, optional The name to use for the column containing the original Series values. Uses self.name by default. This argument is ignored when drop is True. inplace : bool, default False Modify the Series in place (do not create a new object). |
|---|---|
| Returns: | Series or DataFrame When drop is False (the default), a DataFrame is returned. The newly created columns will come first in the DataFrame, followed by the original Series values. When drop is True, a Series is returned. In either case, if inplace=True, no value is returned. |
Examples
s = pd.Series([1, 2, 3, 4], name='foo', ... index=pd.Index(['a', 'b', 'c', 'd'], name='idx'))
Generate a DataFrame with default index.
s.reset_index() idx foo 0 a 1 1 b 2 2 c 3 3 d 4
To specify the name of the new column use name.
s.reset_index(name='values') idx values 0 a 1 1 b 2 2 c 3 3 d 4
To generate a new Series with the default set drop to True.
s.reset_index(drop=True) 0 1 1 2 2 3 3 4 Name: foo, dtype: int64
To update the Series in place, without generating a new one set inplace to True. Note that it also requires drop=True.
s.reset_index(inplace=True, drop=True) s 0 1 1 2 2 3 3 4 Name: foo, dtype: int64
The level parameter is interesting for Series with a multi-level index.
arrays = [np.array(['bar', 'bar', 'baz', 'baz']), ... np.array(['one', 'two', 'one', 'two'])] s2 = pd.Series( ... range(4), name='foo', ... index=pd.MultiIndex.from_arrays(arrays, ... names=['a', 'b']))
To remove a specific level from the Index, use level.
s2.reset_index(level='a') a foo b one bar 0 two bar 1 one baz 2 two baz 3
If level is not set, all levels are removed from the Index.
s2.reset_index() a b foo 0 bar one 0 1 bar two 1 2 baz one 2 3 baz two 3