def truncate(self, before=None, after=None, axis=None, copy=True):
if axis is None:<EOL><INDENT>axis = self._stat_axis_number<EOL><DEDENT>axis = self._get_axis_number(axis)<EOL>ax = self._get_axis(axis)<EOL>if not ax.is_monotonic_increasing and not ax.is_monotonic_decreasing:<EOL><INDENT>raise ValueError("<STR_LIT>")<EOL><DEDENT>if ax.is_all_dates:<EOL><INDENT>from pandas.core.tools.datetimes import to_datetime<EOL>before = to_datetime(before)<EOL>after = to_datetime(after)<EOL><DEDENT>if before is not None and after is not None:<EOL><INDENT>if before > after:<EOL><INDENT>raise ValueError('<STR_LIT>' %<EOL>(after, before))<EOL><DEDENT><DEDENT>slicer = [slice(None, None)] * self._AXIS_LEN<EOL>slicer[axis] = slice(before, after)<EOL>result = self.loc[tuple(slicer)]<EOL>if isinstance(ax, MultiIndex):<EOL><INDENT>setattr(result, self._get_axis_name(axis),<EOL>ax.truncate(before, after))<EOL><DEDENT>if copy:<EOL><INDENT>result = result.copy()<EOL><DEDENT>return result<EOL>
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, string, int
    Truncate all rows before this index value.
after : date, string, 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.
copy : boolean, default is True,
    Return a copy of the truncated section.

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: object

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