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Classe « Series »

Méthode pandas.Series.to_timestamp

Signature de la méthode to_timestamp

def to_timestamp(self, freq: 'Frequency | None' = None, how: "Literal['s', 'e', 'start', 'end']" = 'start', copy: 'bool | None' = None) -> 'Series' 

Description

help(Series.to_timestamp)

Cast to DatetimeIndex of Timestamps, at *beginning* of period.

Parameters
----------
freq : str, default frequency of PeriodIndex
    Desired frequency.
how : {'s', 'e', 'start', 'end'}
    Convention for converting period to timestamp; start of period
    vs. end.
copy : bool, default True
    Whether or not to return a copy.

    .. note::
        The `copy` keyword will change behavior in pandas 3.0.
        `Copy-on-Write
        <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
        will be enabled by default, which means that all methods with a
        `copy` 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``

Returns
-------
Series with DatetimeIndex

Examples
--------
>>> idx = pd.PeriodIndex(['2023', '2024', '2025'], freq='Y')
>>> s1 = pd.Series([1, 2, 3], index=idx)
>>> s1
2023    1
2024    2
2025    3
Freq: Y-DEC, dtype: int64

The resulting frequency of the Timestamps is `YearBegin`

>>> s1 = s1.to_timestamp()
>>> s1
2023-01-01    1
2024-01-01    2
2025-01-01    3
Freq: YS-JAN, dtype: int64

Using `freq` which is the offset that the Timestamps will have

>>> s2 = pd.Series([1, 2, 3], index=idx)
>>> s2 = s2.to_timestamp(freq='M')
>>> s2
2023-01-31    1
2024-01-31    2
2025-01-31    3
Freq: YE-JAN, dtype: int64

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