cudf.Series.truncate#

Series.truncate(before=None, after=None, axis=0, copy=True)[source]#

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.

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:
The truncated Series or DataFrame.

Notes

If the index being truncated contains only datetime values, before and after may be specified as strings instead of Timestamps.

Examples

Series

>>> import cudf
>>> cs1 = cudf.Series([1, 2, 3, 4])
>>> cs1
0    1
1    2
2    3
3    4
dtype: int64

>>> cs1.truncate(before=1, after=2)
1    2
2    3
dtype: int64

>>> import cudf
>>> dates = cudf.date_range(
...     '2021-01-01 23:45:00', '2021-01-01 23:46:00', freq='s'
... )
>>> cs2 = cudf.Series(range(len(dates)), index=dates)
>>> cs2
2021-01-01 23:45:00     0
2021-01-01 23:45:01     1
2021-01-01 23:45:02     2
2021-01-01 23:45:03     3
2021-01-01 23:45:04     4
2021-01-01 23:45:05     5
2021-01-01 23:45:06     6
2021-01-01 23:45:07     7
2021-01-01 23:45:08     8
2021-01-01 23:45:09     9
2021-01-01 23:45:10    10
2021-01-01 23:45:11    11
2021-01-01 23:45:12    12
2021-01-01 23:45:13    13
2021-01-01 23:45:14    14
2021-01-01 23:45:15    15
2021-01-01 23:45:16    16
2021-01-01 23:45:17    17
2021-01-01 23:45:18    18
2021-01-01 23:45:19    19
2021-01-01 23:45:20    20
2021-01-01 23:45:21    21
2021-01-01 23:45:22    22
2021-01-01 23:45:23    23
2021-01-01 23:45:24    24
...
2021-01-01 23:45:56    56
2021-01-01 23:45:57    57
2021-01-01 23:45:58    58
2021-01-01 23:45:59    59
dtype: int64

>>> cs2.truncate(
...     before="2021-01-01 23:45:18", after="2021-01-01 23:45:27"
... )
2021-01-01 23:45:18    18
2021-01-01 23:45:19    19
2021-01-01 23:45:20    20
2021-01-01 23:45:21    21
2021-01-01 23:45:22    22
2021-01-01 23:45:23    23
2021-01-01 23:45:24    24
2021-01-01 23:45:25    25
2021-01-01 23:45:26    26
2021-01-01 23:45:27    27
dtype: int64

>>> cs3 = cudf.Series({'A': 1, 'B': 2, 'C': 3, 'D': 4})
>>> cs3
A    1
B    2
C    3
D    4
dtype: int64

>>> cs3.truncate(before='B', after='C')
B    2
C    3
dtype: int64

DataFrame

>>> df = cudf.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

>>> 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

>>> import cudf
>>> dates = cudf.date_range(
...     '2021-01-01 23:45:00', '2021-01-01 23:46:00', freq='s'
... )
>>> df2 = cudf.DataFrame(data={'A': 1, 'B': 2}, index=dates)
>>> df2.head()
                     A  B
2021-01-01 23:45:00  1  2
2021-01-01 23:45:01  1  2
2021-01-01 23:45:02  1  2
2021-01-01 23:45:03  1  2
2021-01-01 23:45:04  1  2

>>> df2.truncate(
...     before="2021-01-01 23:45:18", after="2021-01-01 23:45:27"
... )
                     A  B
2021-01-01 23:45:18  1  2
2021-01-01 23:45:19  1  2
2021-01-01 23:45:20  1  2
2021-01-01 23:45:21  1  2
2021-01-01 23:45:22  1  2
2021-01-01 23:45:23  1  2
2021-01-01 23:45:24  1  2
2021-01-01 23:45:25  1  2
2021-01-01 23:45:26  1  2
2021-01-01 23:45:27  1  2

Pandas Compatibility Note

pandas.DataFrame.truncate(), pandas.Series.truncate()

The copy parameter is only present for API compatibility, but copy=False is not supported. This method always generates a copy.

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