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, numpy.dtype, …)

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

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.dayofyear`	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`(self, tz)	Convert tz-aware Timestamp to another time zone.
`Timestamp.ceil`(self, freq[, ambiguous, …])	return a new Timestamp ceiled to this resolution.
`Timestamp.combine`(date, time)	date, time -> 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`(self[, locale])	Return the day name of the Timestamp with specified locale.
`Timestamp.dst`()	Return self.tzinfo.dst(self).
`Timestamp.floor`(self, freq[, ambiguous, …])	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)	timestamp[, tz] -> tz’s local time from POSIX timestamp.
`Timestamp.isocalendar`()	Return a 3-tuple containing ISO year, week number, and weekday.
`Timestamp.isoformat`(self[, sep])
`Timestamp.isoweekday`()	Return the day of the week represented by the date.
`Timestamp.month_name`(self[, locale])	Return the month name of the Timestamp with specified locale.
`Timestamp.normalize`(self)	Normalize Timestamp to midnight, preserving tz information.
`Timestamp.now`([tz])	Return new Timestamp object representing current time local to tz.
`Timestamp.replace`(self[, year, month, day, …])	implements datetime.replace, handles nanoseconds.
`Timestamp.round`(self, freq[, ambiguous, …])	Round the Timestamp to the specified resolution.
`Timestamp.strftime`()	format -> strftime() style 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`(self)	Convert TimeStamp to a Julian Date.
`Timestamp.to_period`(self[, freq])	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`(self, tz)	Convert tz-aware Timestamp to another time zone.
`Timestamp.tz_localize`(self, 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

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`(self, freq)	Return a new Timedelta ceiled to this resolution.
`Timedelta.floor`(self, 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`(self, 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 duration of timedelta in seconds (to ns precision).

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

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.dayofyear`	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[, freq, dtype, copy])

Pandas ExtensionArray for storing Period data.

PeriodDtype

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

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

IntervalDtype

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__(self[, 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.