Skip to main content
`Ctrl`+`K`

# pandas.DataFrame.truncate

DataFrame.truncate(before=None, after=None, axis=None, copy=<no_default>)

  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:
    before date, str, int

      Truncate all rows before this index value.

    after date, 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. For Series this parameter is unused and defaults to 0.

    copy bool, default False

      This keyword is now ignored; changing its value will have no impact on the method.

      Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.

  Returns:
    type of caller

      The truncated Series or DataFrame.

  See also

  `DataFrame.loc`

    Select a subset of a DataFrame by label.

  `DataFrame.iloc`

    Select a subset of a DataFrame by position.

  Notes

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

  Examples

      >>> df = pd.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: str

  The index values in `truncate` can be datetimes or string dates.

      >>> dates = pd.date_range("2016-01-01", "2016-02-01", freq="s")
      >>> df = pd.DataFrame(index=dates, data={"A": 1})
      >>> df.tail()
                           A
      2016-01-31 23:59:56  1
      2016-01-31 23:59:57  1
      2016-01-31 23:59:58  1
      2016-01-31 23:59:59  1
      2016-02-01 00:00:00  1

      >>> df.truncate(
      ...     before=pd.Timestamp("2016-01-05"), after=pd.Timestamp("2016-01-10")
      ... ).tail()
                           A
      2016-01-09 23:59:56  1
      2016-01-09 23:59:57  1
      2016-01-09 23:59:58  1
      2016-01-09 23:59:59  1
      2016-01-10 00:00:00  1

  Because the index is a DatetimeIndex containing only dates, we can specify before and after as strings. They will be coerced to Timestamps before truncation.

      >>> df.truncate("2016-01-05", "2016-01-10").tail()
                           A
      2016-01-09 23:59:56  1
      2016-01-09 23:59:57  1
      2016-01-09 23:59:58  1
      2016-01-09 23:59:59  1
      2016-01-10 00:00:00  1

  Note that `truncate` assumes a 0 value for any unspecified time component (midnight). This differs from partial string slicing, which returns any partially matching dates.

      >>> df.loc["2016-01-05":"2016-01-10", :].tail()
                           A
      2016-01-10 23:59:55  1
      2016-01-10 23:59:56  1
      2016-01-10 23:59:57  1
      2016-01-10 23:59:58  1
      2016-01-10 23:59:59  1