pandas arrays

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	Datetime data
Timedeltas	(none)	Timedelta	Timedelta data
Period (time spans)	PeriodDtype	Period	Timespan data
Intervals	IntervalDtype	Interval	Interval data
Nullable Integer	Int64Dtype, …	(none)	Nullable integer
Categorical	CategoricalDtype	(none)	Categorical data
Sparse	SparseDtype	(none)	Sparse data
Strings	StringDtype	str	Text data
Boolean (with NA)	BooleanDtype	bool	Boolean data with missing values

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.

Datetime data

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 tz-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])	Passed an ordinal, translate and convert to a ts.
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	[sep] -> string in ISO 8601 format, YYYY-MM-DDT[HH[:MM[:SS[.mmm[uuu]]]]][+HH:MM].
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 string representing the given POSIX timestamp controlled by an explicit format string.
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(cls[, tz])	Return the current time in the local timezone.
Timestamp.toordinal	Return proleptic Gregorian ordinal.
Timestamp.tz_convert(tz)	Convert tz-aware Timestamp to another time zone.
Timestamp.tz_localize(tz[, ambiguous, …])	Convert naive Timestamp to local time zone, or remove timezone from tz-aware Timestamp.
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 DatetimeArray is a 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.

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.

Timedelta data

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	Number of days.
Timedelta.delta	Return the timedelta in nanoseconds (ns), for internal compatibility.
Timedelta.freq
Timedelta.is_populated
Timedelta.max
Timedelta.microseconds	Number of microseconds (>= 0 and less than 1 second).
Timedelta.min
Timedelta.nanoseconds	Return the number of nanoseconds (n), where 0 <= n < 1 microsecond.
Timedelta.resolution
Timedelta.seconds	Number of seconds (>= 0 and less than 1 day).
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 Timedelta as ISO 8601 Duration like P[n]Y[n]M[n]DT[n]H[n]M[n]S, where the [n] s are replaced by the values.
Timedelta.round(freq)	Round the Timedelta to the specified resolution.
Timedelta.to_pytimedelta	Convert a pandas Timedelta object into a python 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 timedeltas may be stored in a TimedeltaArray.

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

Pandas ExtensionArray for timedelta data.

Timespan data

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 the Period falls in.
Period.end_time
Period.freq
Period.freqstr
Period.hour	Get the hour of the day component of the Period.
Period.is_leap_year
Period.minute	Get minute of the hour component of the Period.
Period.month
Period.ordinal
Period.quarter
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
Period.year

Methods

Period.asfreq	Convert Period to desired frequency, at the start or end of the interval.
Period.now
Period.strftime	Returns the string representation of the Period, depending on the selected fmt.
Period.to_timestamp	Return the Timestamp representation of the Period.

A collection of timedeltas may be stored in a arrays.PeriodArray. Every period in a 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.

Interval data

Arbitrary intervals can be represented as Interval objects.

Interval

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

Properties

Interval.closed	Whether the interval is closed on the left-side, right-side, both or neither.
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])

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.

Categorical data

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 pandas.api.types.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

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 for more.

Text data

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.

StringDtype()

Extension dtype for string data.

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

Boolean data with missing values

The boolean dtype (with the alias "boolean") provides support for storing boolean data (True, False values) 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.

pandas.DataFrame.sparse.to_dense pandas.array