pandas arrays, scalars, and data types

Objects

For most data types, pandas uses NumPy arrays as the concrete objects contained with a Index, Series, or DataFrame.

For some data types, pandas extends NumPy’s type system. String aliases for these types can be found at dtypes.

Kind of Data	pandas Data Type	Scalar	Array
TZ-aware datetime	DatetimeTZDtype	Timestamp	Datetimes
Timedeltas	(none)	Timedelta	Timedeltas
Period (time spans)	PeriodDtype	Period	Periods
Intervals	IntervalDtype	Interval	Intervals
Nullable Integer	Int64Dtype, …	(none)	Nullable integer
Categorical	CategoricalDtype	(none)	Categoricals
Sparse	SparseDtype	(none)	Sparse
Strings	StringDtype	str	Strings
Boolean (with NA)	BooleanDtype	bool	Nullable Boolean
PyArrow	ArrowDtype	Python Scalars or NA	PyArrow

pandas and third-party libraries can extend NumPy’s type system (see Extension types). The top-level array() method can be used to create a new array, which may be stored in a Series, Index, or as a column in a DataFrame.

array(data[, dtype, copy])

Create an array.

PyArrow

Warning

This feature is experimental, and the API can change in a future release without warning.

The arrays.ArrowExtensionArray is backed by a pyarrow.ChunkedArray with a pyarrow.DataType instead of a NumPy array and data type. The .dtype of a arrays.ArrowExtensionArray is an ArrowDtype.

Pyarrow provides similar array and data type support as NumPy including first-class nullability support for all data types, immutability and more.

Note

For string types (pyarrow.string(), string[pyarrow]), PyArrow support is still facilitated by arrays.ArrowStringArray and StringDtype("pyarrow"). See the string section below.

While individual values in an arrays.ArrowExtensionArray are stored as a PyArrow objects, scalars are returned as Python scalars corresponding to the data type, e.g. a PyArrow int64 will be returned as Python int, or NA for missing values.

arrays.ArrowExtensionArray(values)

Pandas ExtensionArray backed by a PyArrow ChunkedArray.

ArrowDtype(pyarrow_dtype)

An ExtensionDtype for PyArrow data types.

Datetimes

NumPy cannot natively represent timezone-aware datetimes. pandas supports this with the arrays.DatetimeArray extension array, which can hold timezone-naive or timezone-aware values.

Timestamp, a subclass of datetime.datetime, is pandas’ scalar type for timezone-naive or timezone-aware datetime data.

Timestamp([ts_input, freq, tz, unit, year, ...])

Pandas replacement for python datetime.datetime object.

Properties

Timestamp.asm8	Return numpy datetime64 format in nanoseconds.
Timestamp.day
Timestamp.dayofweek	Return day of the week.
Timestamp.day_of_week	Return day of the week.
Timestamp.dayofyear	Return the day of the year.
Timestamp.day_of_year	Return the day of the year.
Timestamp.days_in_month	Return the number of days in the month.
Timestamp.daysinmonth	Return the number of days in the month.
Timestamp.fold
Timestamp.hour
Timestamp.is_leap_year	Return True if year is a leap year.
Timestamp.is_month_end	Return True if date is last day of month.
Timestamp.is_month_start	Return True if date is first day of month.
Timestamp.is_quarter_end	Return True if date is last day of the quarter.
Timestamp.is_quarter_start	Return True if date is first day of the quarter.
Timestamp.is_year_end	Return True if date is last day of the year.
Timestamp.is_year_start	Return True if date is first day of the year.
Timestamp.max
Timestamp.microsecond
Timestamp.min
Timestamp.minute
Timestamp.month
Timestamp.nanosecond
Timestamp.quarter	Return the quarter of the year.
Timestamp.resolution
Timestamp.second
Timestamp.tz	Alias for tzinfo.
Timestamp.tzinfo
Timestamp.value
Timestamp.week	Return the week number of the year.
Timestamp.weekofyear	Return the week number of the year.
Timestamp.year

Methods

Timestamp.astimezone(tz)	Convert timezone-aware Timestamp to another time zone.
Timestamp.ceil(freq[, ambiguous, nonexistent])	Return a new Timestamp ceiled to this resolution.
Timestamp.combine(date, time)	Combine date, time into datetime with same date and time fields.
Timestamp.ctime	Return ctime() style string.
Timestamp.date	Return date object with same year, month and day.
Timestamp.day_name	Return the day name of the Timestamp with specified locale.
Timestamp.dst	Return self.tzinfo.dst(self).
Timestamp.floor(freq[, ambiguous, nonexistent])	Return a new Timestamp floored to this resolution.
Timestamp.freq
Timestamp.freqstr	Return the total number of days in the month.
Timestamp.fromordinal(ordinal[, freq, tz])	Construct a timestamp from a a proleptic Gregorian ordinal.
Timestamp.fromtimestamp(ts)	Transform timestamp[, tz] to tz's local time from POSIX timestamp.
Timestamp.isocalendar	Return a 3-tuple containing ISO year, week number, and weekday.
Timestamp.isoformat	Return the time formatted according to ISO 8610.
Timestamp.isoweekday()	Return the day of the week represented by the date.
Timestamp.month_name	Return the month name of the Timestamp with specified locale.
Timestamp.normalize	Normalize Timestamp to midnight, preserving tz information.
Timestamp.now([tz])	Return new Timestamp object representing current time local to tz.
Timestamp.replace([year, month, day, hour, ...])	Implements datetime.replace, handles nanoseconds.
Timestamp.round(freq[, ambiguous, nonexistent])	Round the Timestamp to the specified resolution.
Timestamp.strftime(format)	Return a formatted string of the Timestamp.
Timestamp.strptime(string, format)	Function is not implemented.
Timestamp.time	Return time object with same time but with tzinfo=None.
Timestamp.timestamp	Return POSIX timestamp as float.
Timestamp.timetuple	Return time tuple, compatible with time.localtime().
Timestamp.timetz	Return time object with same time and tzinfo.
Timestamp.to_datetime64	Return a numpy.datetime64 object with 'ns' precision.
Timestamp.to_numpy	Convert the Timestamp to a NumPy datetime64.
Timestamp.to_julian_date()	Convert TimeStamp to a Julian Date.
Timestamp.to_period	Return an period of which this timestamp is an observation.
Timestamp.to_pydatetime	Convert a Timestamp object to a native Python datetime object.
Timestamp.today([tz])	Return the current time in the local timezone.
Timestamp.toordinal	Return proleptic Gregorian ordinal.
Timestamp.tz_convert(tz)	Convert timezone-aware Timestamp to another time zone.
Timestamp.tz_localize(tz[, ambiguous, ...])	Localize the Timestamp to a timezone.
Timestamp.tzname	Return self.tzinfo.tzname(self).
Timestamp.utcfromtimestamp(ts)	Construct a naive UTC datetime from a POSIX timestamp.
Timestamp.utcnow()	Return a new Timestamp representing UTC day and time.
Timestamp.utcoffset	Return self.tzinfo.utcoffset(self).
Timestamp.utctimetuple	Return UTC time tuple, compatible with time.localtime().
Timestamp.weekday()	Return the day of the week represented by the date.

A collection of timestamps may be stored in a arrays.DatetimeArray. For timezone-aware data, the .dtype of a arrays.DatetimeArray is a DatetimeTZDtype. For timezone-naive data, np.dtype("datetime64[ns]") is used.

If the data are timezone-aware, then every value in the array must have the same timezone.

arrays.DatetimeArray(values[, dtype, freq, copy])

Pandas ExtensionArray for tz-naive or tz-aware datetime data.

DatetimeTZDtype([unit, tz])

An ExtensionDtype for timezone-aware datetime data.

Timedeltas

NumPy can natively represent timedeltas. pandas provides Timedelta for symmetry with Timestamp.

Timedelta([value, unit])

Represents a duration, the difference between two dates or times.

Properties

Timedelta.asm8	Return a numpy timedelta64 array scalar view.
Timedelta.components	Return a components namedtuple-like.
Timedelta.days
Timedelta.delta	(DEPRECATED) Return the timedelta in nanoseconds (ns), for internal compatibility.
Timedelta.freq	(DEPRECATED) Freq property.
Timedelta.is_populated	(DEPRECATED) Is_populated property.
Timedelta.max
Timedelta.microseconds
Timedelta.min
Timedelta.nanoseconds	Return the number of nanoseconds (n), where 0 <= n < 1 microsecond.
Timedelta.resolution
Timedelta.seconds
Timedelta.value
Timedelta.view	Array view compatibility.

Methods

Timedelta.ceil(freq)	Return a new Timedelta ceiled to this resolution.
Timedelta.floor(freq)	Return a new Timedelta floored to this resolution.
Timedelta.isoformat	Format the Timedelta as ISO 8601 Duration.
Timedelta.round(freq)	Round the Timedelta to the specified resolution.
Timedelta.to_pytimedelta	Convert a pandas Timedelta object into a python datetime.timedelta object.
Timedelta.to_timedelta64	Return a numpy.timedelta64 object with 'ns' precision.
Timedelta.to_numpy	Convert the Timedelta to a NumPy timedelta64.
Timedelta.total_seconds	Total seconds in the duration.

A collection of Timedelta may be stored in a TimedeltaArray.

arrays.TimedeltaArray(values[, dtype, freq, ...])

Pandas ExtensionArray for timedelta data.

Periods

pandas represents spans of times as Period objects.

Period

Period([value, freq, ordinal, year, month, ...])

Represents a period of time.

Properties

Period.day	Get day of the month that a Period falls on.
Period.dayofweek	Day of the week the period lies in, with Monday=0 and Sunday=6.
Period.day_of_week	Day of the week the period lies in, with Monday=0 and Sunday=6.
Period.dayofyear	Return the day of the year.
Period.day_of_year	Return the day of the year.
Period.days_in_month	Get the total number of days in the month that this period falls on.
Period.daysinmonth	Get the total number of days of the month that this period falls on.
Period.end_time	Get the Timestamp for the end of the period.
Period.freq
Period.freqstr	Return a string representation of the frequency.
Period.hour	Get the hour of the day component of the Period.
Period.is_leap_year	Return True if the period's year is in a leap year.
Period.minute	Get minute of the hour component of the Period.
Period.month	Return the month this Period falls on.
Period.ordinal
Period.quarter	Return the quarter this Period falls on.
Period.qyear	Fiscal year the Period lies in according to its starting-quarter.
Period.second	Get the second component of the Period.
Period.start_time	Get the Timestamp for the start of the period.
Period.week	Get the week of the year on the given Period.
Period.weekday	Day of the week the period lies in, with Monday=0 and Sunday=6.
Period.weekofyear	Get the week of the year on the given Period.
Period.year	Return the year this Period falls on.

Methods

Period.asfreq	Convert Period to desired frequency, at the start or end of the interval.
Period.now	Return the period of now's date.
Period.strftime	Returns a formatted string representation of the Period.
Period.to_timestamp	Return the Timestamp representation of the Period.

A collection of Period may be stored in a arrays.PeriodArray. Every period in a arrays.PeriodArray must have the same freq.

arrays.PeriodArray(values[, dtype, freq, copy])

Pandas ExtensionArray for storing Period data.

PeriodDtype([freq])

An ExtensionDtype for Period data.

Intervals

Arbitrary intervals can be represented as Interval objects.

Interval

Immutable object implementing an Interval, a bounded slice-like interval.

Properties

Interval.closed	String describing the inclusive side the intervals.
Interval.closed_left	Check if the interval is closed on the left side.
Interval.closed_right	Check if the interval is closed on the right side.
Interval.is_empty	Indicates if an interval is empty, meaning it contains no points.
Interval.left	Left bound for the interval.
Interval.length	Return the length of the Interval.
Interval.mid	Return the midpoint of the Interval.
Interval.open_left	Check if the interval is open on the left side.
Interval.open_right	Check if the interval is open on the right side.
Interval.overlaps	Check whether two Interval objects overlap.
Interval.right	Right bound for the interval.

A collection of intervals may be stored in an arrays.IntervalArray.

arrays.IntervalArray(data[, closed, dtype, ...])

Pandas array for interval data that are closed on the same side.

IntervalDtype([subtype, closed])

An ExtensionDtype for Interval data.

Nullable integer

numpy.ndarray cannot natively represent integer-data with missing values. pandas provides this through arrays.IntegerArray.

arrays.IntegerArray(values, mask[, copy])

Array of integer (optional missing) values.

Int8Dtype()	An ExtensionDtype for int8 integer data.
Int16Dtype()	An ExtensionDtype for int16 integer data.
Int32Dtype()	An ExtensionDtype for int32 integer data.
Int64Dtype()	An ExtensionDtype for int64 integer data.
UInt8Dtype()	An ExtensionDtype for uint8 integer data.
UInt16Dtype()	An ExtensionDtype for uint16 integer data.
UInt32Dtype()	An ExtensionDtype for uint32 integer data.
UInt64Dtype()	An ExtensionDtype for uint64 integer data.

Categoricals

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 CategoricalDtype.

CategoricalDtype([categories, ordered])

Type for categorical data with the categories and orderedness.

CategoricalDtype.categories	An Index containing the unique categories allowed.
CategoricalDtype.ordered	Whether the categories have an ordered relationship.

Categorical data can be stored in a pandas.Categorical

Categorical(values[, categories, ordered, ...])

Represent a categorical variable in classic R / S-plus fashion.

The alternative Categorical.from_codes() constructor can be used when you have the categories and integer codes already:

Categorical.from_codes(codes[, categories, ...])

Make a Categorical type from codes and categories or dtype.

The dtype information is available on the Categorical

Categorical.dtype	The CategoricalDtype for this instance.
Categorical.categories	The categories of this categorical.
Categorical.ordered	Whether the categories have an ordered relationship.
Categorical.codes	The category codes of this categorical.

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!

Categorical.__array__([dtype])

The numpy array interface.

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 CategoricalDtype.

If the Series is of dtype CategoricalDtype, Series.cat can be used to change the categorical data. See Categorical accessor for more.

Sparse

Data where a single value is repeated many times (e.g. 0 or NaN) may be stored efficiently as a arrays.SparseArray.

arrays.SparseArray(data[, sparse_index, ...])

An ExtensionArray for storing sparse data.

SparseDtype([dtype, fill_value])

Dtype for data stored in SparseArray.

The Series.sparse accessor may be used to access sparse-specific attributes and methods if the Series contains sparse values. See Sparse accessor and the user guide for more.

Strings

When working with text data, where each valid element is a string or missing, we recommend using StringDtype (with the alias "string").

arrays.StringArray(values[, copy])	Extension array for string data.
arrays.ArrowStringArray(values)	Extension array for string data in a pyarrow.ChunkedArray.

StringDtype([storage])

Extension dtype for string data.

The Series.str accessor is available for Series backed by a arrays.StringArray. See String handling for more.

Nullable Boolean

The boolean dtype (with the alias "boolean") provides support for storing boolean data (True, False) with missing values, which is not possible with a bool numpy.ndarray.

arrays.BooleanArray(values, mask[, copy])

Array of boolean (True/False) data with missing values.

BooleanDtype()

Extension dtype for boolean data.

Utilities

Constructors

api.types.union_categoricals(to_union[, ...])	Combine list-like of Categorical-like, unioning categories.
api.types.infer_dtype	Return a string label of the type of a scalar or list-like of values.
api.types.pandas_dtype(dtype)	Convert input into a pandas only dtype object or a numpy dtype object.

Data type introspection

api.types.is_bool_dtype(arr_or_dtype)	Check whether the provided array or dtype is of a boolean dtype.
api.types.is_categorical_dtype(arr_or_dtype)	Check whether an array-like or dtype is of the Categorical dtype.
api.types.is_complex_dtype(arr_or_dtype)	Check whether the provided array or dtype is of a complex dtype.
api.types.is_datetime64_any_dtype(arr_or_dtype)	Check whether the provided array or dtype is of the datetime64 dtype.
api.types.is_datetime64_dtype(arr_or_dtype)	Check whether an array-like or dtype is of the datetime64 dtype.
api.types.is_datetime64_ns_dtype(arr_or_dtype)	Check whether the provided array or dtype is of the datetime64[ns] dtype.
api.types.is_datetime64tz_dtype(arr_or_dtype)	Check whether an array-like or dtype is of a DatetimeTZDtype dtype.
api.types.is_extension_type(arr)	(DEPRECATED) Check whether an array-like is of a pandas extension class instance.
api.types.is_extension_array_dtype(arr_or_dtype)	Check if an object is a pandas extension array type.
api.types.is_float_dtype(arr_or_dtype)	Check whether the provided array or dtype is of a float dtype.
api.types.is_int64_dtype(arr_or_dtype)	Check whether the provided array or dtype is of the int64 dtype.
api.types.is_integer_dtype(arr_or_dtype)	Check whether the provided array or dtype is of an integer dtype.
api.types.is_interval_dtype(arr_or_dtype)	Check whether an array-like or dtype is of the Interval dtype.
api.types.is_numeric_dtype(arr_or_dtype)	Check whether the provided array or dtype is of a numeric dtype.
api.types.is_object_dtype(arr_or_dtype)	Check whether an array-like or dtype is of the object dtype.
api.types.is_period_dtype(arr_or_dtype)	Check whether an array-like or dtype is of the Period dtype.
api.types.is_signed_integer_dtype(arr_or_dtype)	Check whether the provided array or dtype is of a signed integer dtype.
api.types.is_string_dtype(arr_or_dtype)	Check whether the provided array or dtype is of the string dtype.
api.types.is_timedelta64_dtype(arr_or_dtype)	Check whether an array-like or dtype is of the timedelta64 dtype.
api.types.is_timedelta64_ns_dtype(arr_or_dtype)	Check whether the provided array or dtype is of the timedelta64[ns] dtype.
api.types.is_unsigned_integer_dtype(arr_or_dtype)	Check whether the provided array or dtype is of an unsigned integer dtype.
api.types.is_sparse(arr)	Check whether an array-like is a 1-D pandas sparse array.

Iterable introspection

api.types.is_dict_like(obj)	Check if the object is dict-like.
api.types.is_file_like(obj)	Check if the object is a file-like object.
api.types.is_list_like	Check if the object is list-like.
api.types.is_named_tuple(obj)	Check if the object is a named tuple.
api.types.is_iterator	Check if the object is an iterator.

Scalar introspection

api.types.is_bool	Return True if given object is boolean.
api.types.is_categorical(arr)	Check whether an array-like is a Categorical instance.
api.types.is_complex	Return True if given object is complex.
api.types.is_float	Return True if given object is float.
api.types.is_hashable(obj)	Return True if hash(obj) will succeed, False otherwise.
api.types.is_integer	Return True if given object is integer.
api.types.is_interval
api.types.is_number(obj)	Check if the object is a number.
api.types.is_re(obj)	Check if the object is a regex pattern instance.
api.types.is_re_compilable(obj)	Check if the object can be compiled into a regex pattern instance.
api.types.is_scalar	Return True if given object is scalar.

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