pyspark.pandas.DataFrame.truncate¶
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DataFrame.truncate(before: Optional[Any] = None, after: Optional[Any] = None, axis: Union[int, str, None] = None, copy: bool = True) → Union[DataFrame, Series]¶ Truncate a Series or DataFrame before and after some index value.
This is a useful shorthand for boolean indexing based on index values above or below certain thresholds.
Note
This API is dependent on
Index.is_monotonic_increasing()which can be expensive.- Parameters
- beforedate, str, int
Truncate all rows before this index value.
- afterdate, str, int
Truncate all rows after this index value.
- axis{0 or ‘index’, 1 or ‘columns’}, optional
Axis to truncate. Truncates the index (rows) by default.
- copybool, default is True,
Return a copy of the truncated section.
- Returns
- type of caller
The truncated Series or DataFrame.
See also
DataFrame.locSelect a subset of a DataFrame by label.
DataFrame.ilocSelect a subset of a DataFrame by position.
Examples
>>> df = ps.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'], ... 'B': ['f', 'g', 'h', 'i', 'j'], ... 'C': ['k', 'l', 'm', 'n', 'o']}, ... index=[1, 2, 3, 4, 5]) >>> df A B C 1 a f k 2 b g l 3 c h m 4 d i n 5 e j o
>>> df.truncate(before=2, after=4) A B C 2 b g l 3 c h m 4 d i n
The columns of a DataFrame can be truncated.
>>> df.truncate(before="A", after="B", axis="columns") A B 1 a f 2 b g 3 c h 4 d i 5 e j
For Series, only rows can be truncated.
>>> df['A'].truncate(before=2, after=4) 2 b 3 c 4 d Name: A, dtype: object
A Series has index that sorted integers.
>>> s = ps.Series([10, 20, 30, 40, 50, 60, 70], ... index=[1, 2, 3, 4, 5, 6, 7]) >>> s 1 10 2 20 3 30 4 40 5 50 6 60 7 70 dtype: int64
>>> s.truncate(2, 5) 2 20 3 30 4 40 5 50 dtype: int64
A Series has index that sorted strings.
>>> s = ps.Series([10, 20, 30, 40, 50, 60, 70], ... index=['a', 'b', 'c', 'd', 'e', 'f', 'g']) >>> s a 10 b 20 c 30 d 40 e 50 f 60 g 70 dtype: int64
>>> s.truncate('b', 'e') b 20 c 30 d 40 e 50 dtype: int64