pyspark.pandas.Series.reset_index¶
-
Series.reset_index(level: Union[int, Any, Tuple[Any, …], Sequence[Union[int, Any, Tuple[Any, …]]], None] = None, drop: bool = False, name: Union[Any, Tuple[Any, …], None] = None, inplace: bool = False) → Union[pyspark.pandas.series.Series, pyspark.pandas.frame.DataFrame, None]¶ 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
- levelint, 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.
- dropbool, default False
Just reset the index, without inserting it as a column in the new DataFrame.
- nameobject, 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.
- inplacebool, 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 = ps.Series([1, 2, 3, 4], index=pd.Index(['a', 'b', 'c', 'd'], name='idx'))
Generate a DataFrame with default index.
>>> s.reset_index() idx 0 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 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 dtype: int64