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.. currentmodule:: pandas
For most data types, pandas uses NumPy arrays as the concrete objects contained with a :class:`Index`, :class:`Series`, or :class:`DataFrame`.
For some data types, pandas extends NumPy's type system.
| Kind of Data | Pandas Data Type | Scalar | Array |
|---|---|---|---|
| TZ-aware datetime | :class:`DatetimeTZDtype` | :class:`Timestamp` | :ref:`api.arrays.datetime` |
| Timedeltas | (none) | :class:`Timedelta` | :ref:`api.arrays.timedelta` |
| Period (time spans) | :class:`PeriodDtype` | :class:`Period` | :ref:`api.arrays.period` |
| Intervals | :class:`IntervalDtype` | :class:`Interval` | :ref:`api.arrays.interval` |
| Nullable Integer | :class:`Int64Dtype`, ... | (none) | :ref:`api.arrays.integer_na` |
| Categorical | :class:`CategoricalDtype` | (none) | :ref:`api.arrays.categorical` |
| Sparse | :class:`SparseDtype` | (none) | :ref:`api.arrays.sparse` |
Pandas and third-party libraries can extend NumPy's type system (see :ref:`extending.extension-types`). The top-level :meth:`array` method can be used to create a new array, which may be stored in a :class:`Series`, :class:`Index`, or as a column in a :class:`DataFrame`.
.. autosummary:: :toctree: generated/ array
NumPy cannot natively represent timezone-aware datetimes. Pandas supports this with the :class:`arrays.DatetimeArray` extension array, which can hold timezone-naive or timezone-aware values.
:class:`Timestamp`, a subclass of :class:`datetime.datetime`, is pandas' scalar type for timezone-naive or timezone-aware datetime data.
.. autosummary:: :toctree: generated/ Timestamp
.. autosummary:: :toctree: generated/ Timestamp.asm8 Timestamp.day Timestamp.dayofweek Timestamp.dayofyear Timestamp.days_in_month Timestamp.daysinmonth Timestamp.fold Timestamp.hour Timestamp.is_leap_year Timestamp.is_month_end Timestamp.is_month_start Timestamp.is_quarter_end Timestamp.is_quarter_start Timestamp.is_year_end Timestamp.is_year_start Timestamp.max Timestamp.microsecond Timestamp.min Timestamp.minute Timestamp.month Timestamp.nanosecond Timestamp.quarter Timestamp.resolution Timestamp.second Timestamp.tz Timestamp.tzinfo Timestamp.value Timestamp.week Timestamp.weekofyear Timestamp.year
.. autosummary:: :toctree: generated/ Timestamp.astimezone Timestamp.ceil Timestamp.combine Timestamp.ctime Timestamp.date Timestamp.day_name Timestamp.dst Timestamp.floor Timestamp.freq Timestamp.freqstr Timestamp.fromordinal Timestamp.fromtimestamp Timestamp.isocalendar Timestamp.isoformat Timestamp.isoweekday Timestamp.month_name Timestamp.normalize Timestamp.now Timestamp.replace Timestamp.round Timestamp.strftime Timestamp.strptime Timestamp.time Timestamp.timestamp Timestamp.timetuple Timestamp.timetz Timestamp.to_datetime64 Timestamp.to_julian_date Timestamp.to_period Timestamp.to_pydatetime Timestamp.today Timestamp.toordinal Timestamp.tz_convert Timestamp.tz_localize Timestamp.tzname Timestamp.utcfromtimestamp Timestamp.utcnow Timestamp.utcoffset Timestamp.utctimetuple Timestamp.weekday
A collection of timestamps may be stored in a :class:`arrays.DatetimeArray`.
For timezone-aware data, the .dtype of a DatetimeArray is a
:class:`DatetimeTZDtype`. For timezone-naive data, np.dtype("datetime64[ns]")
is used.
If the data are tz-aware, then every value in the array must have the same timezone.
.. autosummary:: :toctree: generated/ arrays.DatetimeArray DatetimeTZDtype
NumPy can natively represent timedeltas. Pandas provides :class:`Timedelta` for symmetry with :class:`Timestamp`.
.. autosummary:: :toctree: generated/ Timedelta
.. autosummary:: :toctree: generated/ Timedelta.asm8 Timedelta.components Timedelta.days Timedelta.delta Timedelta.freq Timedelta.is_populated Timedelta.max Timedelta.microseconds Timedelta.min Timedelta.nanoseconds Timedelta.resolution Timedelta.seconds Timedelta.value Timedelta.view
.. autosummary:: :toctree: generated/ Timedelta.ceil Timedelta.floor Timedelta.isoformat Timedelta.round Timedelta.to_pytimedelta Timedelta.to_timedelta64 Timedelta.total_seconds
A collection of timedeltas may be stored in a :class:`TimedeltaArray`.
.. autosummary:: :toctree: generated/ arrays.TimedeltaArray
Pandas represents spans of times as :class:`Period` objects.
.. autosummary:: :toctree: generated/ Period
.. autosummary:: :toctree: generated/ Period.day Period.dayofweek Period.dayofyear Period.days_in_month Period.daysinmonth Period.end_time Period.freq Period.freqstr Period.hour Period.is_leap_year Period.minute Period.month Period.ordinal Period.quarter Period.qyear Period.second Period.start_time Period.week Period.weekday Period.weekofyear Period.year
.. autosummary:: :toctree: generated/ Period.asfreq Period.now Period.strftime Period.to_timestamp
A collection of timedeltas may be stored in a :class:`arrays.PeriodArray`.
Every period in a PeriodArray must have the same freq.
.. autosummary:: :toctree: generated/ arrays.DatetimeArray PeriodDtype
Arbitrary intervals can be represented as :class:`Interval` objects.
.. autosummary::
:toctree: generated/
Interval
.. autosummary:: :toctree: generated/ Interval.closed Interval.closed_left Interval.closed_right Interval.left Interval.length Interval.mid Interval.open_left Interval.open_right Interval.overlaps Interval.right
A collection of intervals may be stored in an :class:`arrays.IntervalArray`.
.. autosummary:: :toctree: generated/ arrays.IntervalArray IntervalDtype
:class:`numpy.ndarray` cannot natively represent integer-data with missing values. Pandas provides this through :class:`arrays.IntegerArray`.
.. autosummary:: :toctree: generated/ arrays.IntegerArray Int8Dtype Int16Dtype Int32Dtype Int64Dtype UInt8Dtype UInt16Dtype UInt32Dtype UInt64Dtype
Pandas defines a custom data type for representing data that can take only a
limited, fixed set of values. The dtype of a Categorical can be described by
a :class:`pandas.api.types.CategoricalDtype`.
.. autosummary:: :toctree: generated/ :template: autosummary/class_without_autosummary.rst CategoricalDtype
.. autosummary:: :toctree: generated/ CategoricalDtype.categories CategoricalDtype.ordered
Categorical data can be stored in a :class:`pandas.Categorical`
.. autosummary:: :toctree: generated/ :template: autosummary/class_without_autosummary.rst Categorical
The alternative :meth:`Categorical.from_codes` constructor can be used when you have the categories and integer codes already:
.. autosummary:: :toctree: generated/ Categorical.from_codes
The dtype information is available on the Categorical
.. autosummary:: :toctree: generated/ Categorical.dtype Categorical.categories Categorical.ordered Categorical.codes
np.asarray(categorical) works by implementing the array interface. Be aware, that this converts
the Categorical back to a NumPy array, so categories and order information is not preserved!
.. autosummary:: :toctree: generated/ Categorical.__array__
A Categorical can be stored in a Series or DataFrame.
To create a Series of dtype category, use cat = s.astype(dtype) or
Series(..., dtype=dtype) where dtype is either
- the string
'category' - an instance of :class:`~pandas.api.types.CategoricalDtype`.
If the Series is of dtype CategoricalDtype, Series.cat can be used to change the categorical
data. See :ref:`api.series.cat` for more.
Data where a single value is repeated many times (e.g. 0 or NaN) may
be stored efficiently as a :class:`SparseArray`.
.. autosummary:: :toctree: generated/ SparseArray SparseDtype
The Series.sparse accessor may be used to access sparse-specific attributes
and methods if the :class:`Series` contains sparse values. See
:ref:`api.series.sparse` for more.