cudf.Series.truncate#

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

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.

Pandas Compatibility Note

DataFrame.truncate, Series.truncate

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

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
a  f
b  g
c  h
d  i
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