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 |
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Timedeltas |
(none) |
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Period (time spans) |
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Intervals |
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Nullable Integer |
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(none) |
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Nullable Float |
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(none) |
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Categorical |
(none) |
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Sparse |
(none) |
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Strings |
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Nullable Boolean |
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PyArrow |
Python Scalars or |
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.
The table below shows the equivalent pyarrow-backed (pa), pandas extension, and numpy (np) types that are recognized by pandas.
Pyarrow-backed types below need to be passed into ArrowDtype to be recognized by pandas e.g. pd.ArrowDtype(pa.bool_())
PyArrow type |
pandas extension type |
NumPy type |
|---|---|---|
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(none) |
(none) |
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(none) |
(none) |
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(none) |
(none) |
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(none) |
(none) |
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(none) |
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(none) |
(none) |
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(none) |
(none) |
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(none) |
(none) |
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(none) |
(none) |
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(none) |
Note
Pyarrow-backed string support is provided by both pd.StringDtype("pyarrow") and pd.ArrowDtype(pa.string()).
pd.StringDtype("pyarrow") is described below in the string section
and will be returned if the string alias "string[pyarrow]" is specified. pd.ArrowDtype(pa.string())
generally has better interoperability with ArrowDtype of different types.
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.
For more information, please see the PyArrow user guide
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, year, month, day, ...])
Pandas replacement for python datetime.datetime object.
Properties#
Return numpy datetime64 format in nanoseconds.
Return day of the week.
Return day of the week.
Return the day of the year.
Return the day of the year.
Return the number of days in the month.
Return the number of days in the month.
Return True if year is a leap year.
Check if the date is the last day of the month.
Check if the date is the first day of the month.
Check if date is last day of the quarter.
Check if the date is the first day of the quarter.
Return True if date is last day of the year.
Return True if date is first day of the year.
Return the quarter of the year.
Alias for tzinfo.
The abbreviation associated with self._creso.
Return the week number of the year.
Return the week number of the year.
Methods#
Timestamp.as_unit(unit[, round_ok])
Convert the underlying int64 representaton to the given unit.
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.
Return ctime() style string.
Return date object with same year, month and day.
Timestamp.day_name([locale])
Return the day name of the Timestamp with specified locale.
Return the daylight saving time (DST) adjustment.
Timestamp.floor(freq[, ambiguous, nonexistent])
Return a new Timestamp floored to this resolution.
Timestamp.fromordinal(ordinal[, tz])
Construct a timestamp from a a proleptic Gregorian ordinal.
Transform timestamp[, tz] to tz's local time from POSIX timestamp.
Return a named tuple containing ISO year, week number, and weekday.
Timestamp.isoformat([sep, timespec])
Return the time formatted according to ISO 8601.
Return the day of the week represented by the date.
Timestamp.month_name([locale])
Return the month name of the Timestamp with specified locale.
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.
Return time object with same time but with tzinfo=None.
Return POSIX timestamp as float.
Return time tuple, compatible with time.localtime().
Return time object with same time and tzinfo.
Return a numpy.datetime64 object with same precision.
Timestamp.to_numpy([dtype, copy])
Convert the Timestamp to a NumPy datetime64.
Convert TimeStamp to a Julian Date.
Timestamp.to_period([freq])
Return an period of which this timestamp is an observation.
Timestamp.to_pydatetime([warn])
Convert a Timestamp object to a native Python datetime object.
Timestamp.today([tz])
Return the current time in the local timezone.
Return proleptic Gregorian ordinal.
Convert timezone-aware Timestamp to another time zone.
Timestamp.tz_localize(tz[, ambiguous, ...])
Localize the Timestamp to a timezone.
Return time zone name.
Timestamp.utcfromtimestamp(ts)
Construct a timezone-aware UTC datetime from a POSIX timestamp.
Return a new Timestamp representing UTC day and time.
Return utc offset.
Return UTC time tuple, compatible with time.localtime().
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.
Properties#
Return a numpy timedelta64 array scalar view.
Return a components namedtuple-like.
Returns the days of the timedelta.
Return the number of nanoseconds (n), where 0 <= n < 1 microsecond.
Return the total hours, minutes, and seconds of the timedelta as seconds.
Timedelta.view(dtype)
Array view compatibility.
Methods#
Timedelta.as_unit(unit[, round_ok])
Convert the underlying int64 representation to the given unit.
Timedelta.ceil(freq)
Return a new Timedelta ceiled to this resolution.
Timedelta.floor(freq)
Return a new Timedelta floored to this resolution.
Format the Timedelta as ISO 8601 Duration.
Timedelta.round(freq)
Round the Timedelta to the specified resolution.
Convert a pandas Timedelta object into a python datetime.timedelta object.
Return a numpy.timedelta64 object with 'ns' precision.
Timedelta.to_numpy([dtype, copy])
Convert the Timedelta to a NumPy timedelta64.
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#
Get day of the month that a Period falls on.
Day of the week the period lies in, with Monday=0 and Sunday=6.
Day of the week the period lies in, with Monday=0 and Sunday=6.
Return the day of the year.
Return the day of the year.
Get the total number of days in the month that this period falls on.
Get the total number of days of the month that this period falls on.
Get the Timestamp for the end of the period.
Return a string representation of the frequency.
Get the hour of the day component of the Period.
Return True if the period's year is in a leap year.
Get minute of the hour component of the Period.
Return the month this Period falls on.
Return the quarter this Period falls on.
Fiscal year the Period lies in according to its starting-quarter.
Get the second component of the Period.
Get the Timestamp for the start of the period.
Get the week of the year on the given Period.
Day of the week the period lies in, with Monday=0 and Sunday=6.
Get the week of the year on the given Period.
Return the year this Period falls on.
Methods#
Period.asfreq(freq[, how])
Convert Period to desired frequency, at the start or end of the interval.
Period.now(freq)
Return the period of now's date.
Period.strftime(fmt)
Returns a formatted string representation of the Period.
Period.to_timestamp([freq, how])
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.
Immutable object implementing an Interval, a bounded slice-like interval.
Properties#
String describing the inclusive side the intervals.
Check if the interval is closed on the left side.
Check if the interval is closed on the right side.
Indicates if an interval is empty, meaning it contains no points.
Left bound for the interval.
Return the length of the Interval.
Return the midpoint of the Interval.
Check if the interval is open on the left side.
Check if the interval is open on the right side.
Interval.overlaps(other)
Check whether two Interval objects overlap.
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.
An ExtensionDtype for int8 integer data.
An ExtensionDtype for int16 integer data.
An ExtensionDtype for int32 integer data.
An ExtensionDtype for int64 integer data.
An ExtensionDtype for uint8 integer data.
An ExtensionDtype for uint16 integer data.
An ExtensionDtype for uint32 integer data.
An ExtensionDtype for uint64 integer data.
Nullable float#
arrays.FloatingArray(values, mask[, copy])
Array of floating (optional missing) values.
An ExtensionDtype for float32 data.
An ExtensionDtype for float64 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.
An Index containing the unique categories allowed.
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
The CategoricalDtype for this instance.
The categories of this categorical.
Whether the categories have an ordered relationship.
The category codes of this categorical index.
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, copy])
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.
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(value[, skipna])
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_any_real_numeric_dtype(arr_or_dtype)
Check whether the provided array or dtype is of a real number dtype.
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)
(DEPRECATED) 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)
(DEPRECATED) Check whether an array-like or dtype is of a DatetimeTZDtype dtype.
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)
(DEPRECATED) 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)
(DEPRECATED) 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)
(DEPRECATED) 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)
(DEPRECATED) Check whether an array-like is a 1-D pandas sparse array.
Iterable introspection#
Check if the object is dict-like.
Check if the object is a file-like object.
api.types.is_list_like(obj[, allow_sets])
Check if the object is list-like.
Check if the object is a named tuple.
Check if the object is an iterator.
Scalar introspection#
api.types.is_bool(obj)
Return True if given object is boolean.
api.types.is_complex(obj)
Return True if given object is complex.
api.types.is_float(obj)
Return True if given object is float.
Return True if hash(obj) will succeed, False otherwise.
api.types.is_integer(obj)
Return True if given object is integer.
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(val)
Return True if given object is scalar.