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

Nullable Float

Float64Dtype, …

(none)

Nullable float

Categorical

CategoricalDtype

(none)

Categoricals

Sparse

SparseDtype

(none)

Sparse

Strings

StringDtype

str

Strings

Nullable Boolean

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. NaT is the missing value for datetime data.

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

Pandas replacement for python datetime.datetime object.

NaT

(N)ot-(A)-(T)ime, the time equivalent of NaN.

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. NaT is the missing value for timedelta data.

Timedelta([value, unit])

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

NaT

(N)ot-(A)-(T)ime, the time equivalent of NaN.

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.

NA

NA ("not available") missing value indicator.

Nullable float#

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

Array of floating (optional missing) values.

Float32Dtype()

An ExtensionDtype for float32 data.

Float64Dtype()

An ExtensionDtype for float64 data.

NA

NA ("not available") missing value indicator.

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

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.

NA

NA ("not available") missing value indicator.

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)

(DEPRECATED) 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.