Skip to main content
`Ctrl`+`K`

# pandas.arrays.PeriodArray

class pandas.arrays.PeriodArray(values, dtype=None, freq=None, copy=False)

  Pandas ExtensionArray for storing Period data.

  Users should use `array()` to create new instances.

  Parameters:
    valuesUnion[PeriodArray, Series[period], ndarray[int], PeriodIndex]

      The data to store. These should be arrays that can be directly converted to ordinals without inference or copy (PeriodArray, ndarray[int64]), or a box around such an array (Series[period], PeriodIndex).

    dtypePeriodDtype, optional

      A PeriodDtype instance from which to extract a freq. If both freq and dtype are specified, then the frequencies must match.

    freqstr or DateOffset

      The freq to use for the array. Mostly applicable when values is an ndarray of integers, when freq is required. When values is a PeriodArray (or box around), it’s checked that `values.freq` matches freq.

    copybool, default False

      Whether to copy the ordinals before storing.

  Attributes

  None |

  Methods

  None |

  See also

  `Period`

    Represents a period of time.

  `PeriodIndex`

    Immutable Index for period data.

  `period_range`

    Create a fixed-frequency PeriodArray.

  `array`

    Construct a pandas array.

  Notes

  There are two components to a PeriodArray

  - ordinals : integer ndarray

  - freq : pd.tseries.offsets.Offset

  The values are physically stored as a 1-D ndarray of integers. These are called “ordinals” and represent some kind of offset from a base.

  The freq indicates the span covered by each element of the array. All elements in the PeriodArray have the same freq.

  Examples

      >>> pd.arrays.PeriodArray(pd.PeriodIndex(['2023-01-01',
      ...                                       '2023-01-02'], freq='D'))
      <PeriodArray>
      ['2023-01-01', '2023-01-02']
      Length: 2, dtype: period[D]