pandas.DataFrame.truncate — pandas 3.0.0.dev0+2099.g3832e85779 documentation (original) (raw)

DataFrame.truncate(before=None, after=None, axis=None, copy=<no_default>)[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. For Series this parameter is unused and defaults to 0.

copybool, default is False,

Return a copy of the truncated section.

Note

The copy keyword will change behavior in pandas 3.0.Copy-on-Writewill be enabled by default, which means that all methods with acopy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Deprecated since version 3.0.0.

Returns:

type of caller

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

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