pyspark.pandas.Series.align¶
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Series.align(other: Union[pyspark.pandas.frame.DataFrame, Series], join: str = 'outer', axis: Union[int, str, None] = None, copy: bool = True) → Tuple[pyspark.pandas.series.Series, Union[pyspark.pandas.frame.DataFrame, pyspark.pandas.series.Series]][source]¶ Align two objects on their axes with the specified join method.
Join method is specified for each axis Index.
- Parameters
- otherDataFrame or Series
- join{{‘outer’, ‘inner’, ‘left’, ‘right’}}, default ‘outer’
- axisallowed axis of the other object, default None
Align on index (0), columns (1), or both (None).
- copybool, default True
Always returns new objects. If copy=False and no reindexing is required then original objects are returned.
- Returns
- (left, right)(Series, type of other)
Aligned objects.
Examples
>>> ps.set_option("compute.ops_on_diff_frames", True) >>> s1 = ps.Series([7, 8, 9], index=[10, 11, 12]) >>> s2 = ps.Series(["g", "h", "i"], index=[10, 20, 30])
>>> aligned_l, aligned_r = s1.align(s2) >>> aligned_l.sort_index() 10 7.0 11 8.0 12 9.0 20 NaN 30 NaN dtype: float64 >>> aligned_r.sort_index() 10 g 11 None 12 None 20 h 30 i dtype: object
Align with the join type “inner”:
>>> aligned_l, aligned_r = s1.align(s2, join="inner") >>> aligned_l.sort_index() 10 7 dtype: int64 >>> aligned_r.sort_index() 10 g dtype: object
Align with a DataFrame:
>>> df = ps.DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]}, index=[10, 20, 30]) >>> aligned_l, aligned_r = s1.align(df) >>> aligned_l.sort_index() 10 7.0 11 8.0 12 9.0 20 NaN 30 NaN dtype: float64 >>> aligned_r.sort_index() a b 10 1.0 a 11 NaN None 12 NaN None 20 2.0 b 30 3.0 c
>>> ps.reset_option("compute.ops_on_diff_frames")