def test_is_timedelta64_ns_dtype():
assert not com.is_timedelta64_ns_dtype(np.dtype('m8[ps]'))
assert not com.is_timedelta64_ns_dtype(
np.array([1, 2], dtype=np.timedelta64))
assert com.is_timedelta64_ns_dtype(np.dtype('m8[ns]'))
assert com.is_timedelta64_ns_dtype(np.array([1, 2], dtype='m8[ns]'))
def astype(self, dtype, copy=True):
# We handle
# --> timedelta64[ns]
# --> timedelta64
# DatetimeLikeArrayMixin super call handles other cases
dtype = pandas_dtype(dtype)
if is_timedelta64_dtype(dtype) and not is_timedelta64_ns_dtype(dtype):
# by pandas convention, converting to non-nano timedelta64
# returns an int64-dtyped array with ints representing multiples
# of the desired timedelta unit. This is essentially division
if self._hasnans:
# avoid double-copying
result = self._data.astype(dtype, copy=False)
values = self._maybe_mask_results(result,
fill_value=None,
convert='float64')
return values
result = self._data.astype(dtype, copy=copy)
return result.astype('i8')
elif is_timedelta64_ns_dtype(dtype):
if copy:
return self.copy()
return self
return dtl.DatetimeLikeArrayMixin.astype(self, dtype, copy=copy)
def astype(self, dtype, copy=True):
dtype = pandas_dtype(dtype)
if is_timedelta64_dtype(dtype) and not is_timedelta64_ns_dtype(dtype):
# return an index (essentially this is division)
result = self.values.astype(dtype, copy=copy)
if self.hasnans:
values = self._maybe_mask_results(result, convert='float64')
return Index(values, name=self.name)
return Index(result.astype('i8'), name=self.name)
return super(TimedeltaIndex, self).astype(dtype, copy=copy)
def astype(self, dtype, copy=True):
dtype = pandas_dtype(dtype)
if is_timedelta64_dtype(dtype) and not is_timedelta64_ns_dtype(dtype):
# return an index (essentially this is division)
result = self.values.astype(dtype, copy=copy)
if self.hasnans:
values = self._maybe_mask_results(result, convert='float64')
return Index(values, name=self.name)
return Index(result.astype('i8'), name=self.name)
return super(TimedeltaIndex, self).astype(dtype, copy=copy)
def reduce(self: T,
func: Callable,
ignore_failures: bool = False) -> tuple[T, np.ndarray]:
"""
Apply reduction function column-wise, returning a single-row ArrayManager.
Parameters
----------
func : reduction function
ignore_failures : bool, default False
Whether to drop columns where func raises TypeError.
Returns
-------
ArrayManager
np.ndarray
Indexer of column indices that are retained.
"""
result_arrays: list[np.ndarray] = []
result_indices: list[int] = []
for i, arr in enumerate(self.arrays):
try:
res = func(arr, axis=0)
except TypeError:
if not ignore_failures:
raise
else:
# TODO NaT doesn't preserve dtype, so we need to ensure to create
# a timedelta result array if original was timedelta
# what if datetime results in timedelta? (eg std)
if res is NaT and is_timedelta64_ns_dtype(arr.dtype):
result_arrays.append(
np.array(["NaT"], dtype="timedelta64[ns]"))
else:
# error: Argument 1 to "append" of "list" has incompatible type
# "ExtensionArray"; expected "ndarray"
result_arrays.append(
sanitize_array([res], None) # type: ignore[arg-type]
)
result_indices.append(i)
index = Index._simple_new(np.array([None],
dtype=object)) # placeholder
if ignore_failures:
indexer = np.array(result_indices)
columns = self.items[result_indices]
else:
indexer = np.arange(self.shape[0])
columns = self.items
# error: Argument 1 to "ArrayManager" has incompatible type "List[ndarray]";
# expected "List[Union[ndarray, ExtensionArray]]"
new_mgr = type(self)(result_arrays,
[index, columns]) # type: ignore[arg-type]
return new_mgr, indexer
def astype(self, dtype, copy=True):
dtype = pandas_dtype(dtype)
if is_timedelta64_dtype(dtype) and not is_timedelta64_ns_dtype(dtype):
# Have to repeat the check for 'timedelta64' (not ns) dtype
# so that we can return a numeric index, since pandas will return
# a TimedeltaIndex when dtype='timedelta'
result = self._data.astype(dtype, copy=copy)
if self.hasnans:
return Index(result, name=self.name)
return Index(result.astype('i8'), name=self.name)
return DatetimeIndexOpsMixin.astype(self, dtype, copy=copy)
def astype(self, dtype, copy=True):
dtype = pandas_dtype(dtype)
if is_timedelta64_dtype(dtype) and not is_timedelta64_ns_dtype(dtype):
# Have to repeat the check for 'timedelta64' (not ns) dtype
# so that we can return a numeric index, since pandas will return
# a TimedeltaIndex when dtype='timedelta'
result = self._data.astype(dtype, copy=copy)
if self.hasnans:
return Index(result, name=self.name)
return Index(result.astype("i8"), name=self.name)
return DatetimeIndexOpsMixin.astype(self, dtype, copy=copy)
def maybe_castable(arr) -> bool:
# return False to force a non-fastpath
# check datetime64[ns]/timedelta64[ns] are valid
# otherwise try to coerce
kind = arr.dtype.kind
if kind == "M":
return is_datetime64_ns_dtype(arr.dtype)
elif kind == "m":
return is_timedelta64_ns_dtype(arr.dtype)
return arr.dtype.name not in _POSSIBLY_CAST_DTYPES
def astype(self, dtype, copy: bool = True):
# We handle
# --> timedelta64[ns]
# --> timedelta64
# DatetimeLikeArrayMixin super call handles other cases
dtype = pandas_dtype(dtype)
if is_timedelta64_dtype(dtype) and not is_timedelta64_ns_dtype(dtype):
# by pandas convention, converting to non-nano timedelta64
# returns an int64-dtyped array with ints representing multiples
# of the desired timedelta unit. This is essentially division
if self._hasnans:
# avoid double-copying
result = self._data.astype(dtype, copy=False)
return self._maybe_mask_results(
result, fill_value=None, convert="float64"
)
result = self._data.astype(dtype, copy=copy)
return result.astype("i8")
elif is_timedelta64_ns_dtype(dtype):
if copy:
return self.copy()
return self
return dtl.DatetimeLikeArrayMixin.astype(self, dtype, copy=copy)
def test_is_timedelta(self):
self.assertTrue(is_timedelta64_dtype('timedelta64'))
self.assertTrue(is_timedelta64_dtype('timedelta64[ns]'))
self.assertFalse(is_timedelta64_ns_dtype('timedelta64'))
self.assertTrue(is_timedelta64_ns_dtype('timedelta64[ns]'))
tdi = TimedeltaIndex([1e14, 2e14], dtype='timedelta64')
self.assertTrue(is_timedelta64_dtype(tdi))
self.assertTrue(is_timedelta64_ns_dtype(tdi))
self.assertTrue(is_timedelta64_ns_dtype(tdi.astype('timedelta64[ns]')))
# Conversion to Int64Index:
self.assertFalse(is_timedelta64_ns_dtype(tdi.astype('timedelta64')))
self.assertFalse(is_timedelta64_ns_dtype(tdi.astype('timedelta64[h]')))
def test_is_timedelta(self):
self.assertTrue(is_timedelta64_dtype('timedelta64'))
self.assertTrue(is_timedelta64_dtype('timedelta64[ns]'))
self.assertFalse(is_timedelta64_ns_dtype('timedelta64'))
self.assertTrue(is_timedelta64_ns_dtype('timedelta64[ns]'))
tdi = TimedeltaIndex([1e14, 2e14], dtype='timedelta64')
self.assertTrue(is_timedelta64_dtype(tdi))
self.assertTrue(is_timedelta64_ns_dtype(tdi))
self.assertTrue(is_timedelta64_ns_dtype(tdi.astype('timedelta64[ns]')))
# Conversion to Int64Index:
self.assertFalse(is_timedelta64_ns_dtype(tdi.astype('timedelta64')))
self.assertFalse(is_timedelta64_ns_dtype(tdi.astype('timedelta64[h]')))
def test_is_timedelta(self):
assert is_timedelta64_dtype('timedelta64')
assert is_timedelta64_dtype('timedelta64[ns]')
assert not is_timedelta64_ns_dtype('timedelta64')
assert is_timedelta64_ns_dtype('timedelta64[ns]')
tdi = TimedeltaIndex([1e14, 2e14], dtype='timedelta64')
assert is_timedelta64_dtype(tdi)
assert is_timedelta64_ns_dtype(tdi)
assert is_timedelta64_ns_dtype(tdi.astype('timedelta64[ns]'))
# Conversion to Int64Index:
assert not is_timedelta64_ns_dtype(tdi.astype('timedelta64'))
assert not is_timedelta64_ns_dtype(tdi.astype('timedelta64[h]'))
def test_is_timedelta(self):
assert is_timedelta64_dtype('timedelta64')
assert is_timedelta64_dtype('timedelta64[ns]')
assert not is_timedelta64_ns_dtype('timedelta64')
assert is_timedelta64_ns_dtype('timedelta64[ns]')
tdi = TimedeltaIndex([1e14, 2e14], dtype='timedelta64')
assert is_timedelta64_dtype(tdi)
assert is_timedelta64_ns_dtype(tdi)
assert is_timedelta64_ns_dtype(tdi.astype('timedelta64[ns]'))
# Conversion to Int64Index:
assert not is_timedelta64_ns_dtype(tdi.astype('timedelta64'))
assert not is_timedelta64_ns_dtype(tdi.astype('timedelta64[h]'))
def astype(self, dtype, copy=True):
dtype = np.dtype(dtype)
if is_object_dtype(dtype):
return self.asobject
elif is_timedelta64_ns_dtype(dtype):
if copy is True:
return self.copy()
return self
elif is_timedelta64_dtype(dtype):
# return an index (essentially this is division)
result = self.values.astype(dtype, copy=copy)
if self.hasnans:
return Index(self._maybe_mask_results(result,
convert='float64'),
name=self.name)
return Index(result.astype('i8'), name=self.name)
elif is_integer_dtype(dtype):
return Index(self.values.astype('i8', copy=copy), dtype='i8',
name=self.name)
raise ValueError('Cannot cast TimedeltaIndex to dtype %s' % dtype)
def astype(self, dtype, copy=True):
dtype = np.dtype(dtype)
if is_object_dtype(dtype):
return self.asobject
elif is_timedelta64_ns_dtype(dtype):
if copy is True:
return self.copy()
return self
elif is_timedelta64_dtype(dtype):
# return an index (essentially this is division)
result = self.values.astype(dtype, copy=copy)
if self.hasnans:
return Index(self._maybe_mask_results(result,
convert='float64'),
name=self.name)
return Index(result.astype('i8'), name=self.name)
elif is_integer_dtype(dtype):
return Index(self.values.astype('i8', copy=copy), dtype='i8',
name=self.name)
raise ValueError('Cannot cast TimedeltaIndex to dtype %s' % dtype)
def array(self) -> ExtensionArray:
"""
The ExtensionArray of the data backing this Series or Index.
.. versionadded:: 0.24.0
Returns
-------
ExtensionArray
An ExtensionArray of the values stored within. For extension
types, this is the actual array. For NumPy native types, this
is a thin (no copy) wrapper around :class:`numpy.ndarray`.
``.array`` differs ``.values`` which may require converting the
data to a different form.
See Also
--------
Index.to_numpy : Similar method that always returns a NumPy array.
Series.to_numpy : Similar method that always returns a NumPy array.
Notes
-----
This table lays out the different array types for each extension
dtype within pandas.
================== =============================
dtype array type
================== =============================
category Categorical
period PeriodArray
interval IntervalArray
IntegerNA IntegerArray
datetime64[ns, tz] DatetimeArray
================== =============================
For any 3rd-party extension types, the array type will be an
ExtensionArray.
For all remaining dtypes ``.array`` will be a
:class:`arrays.NumpyExtensionArray` wrapping the actual ndarray
stored within. If you absolutely need a NumPy array (possibly with
copying / coercing data), then use :meth:`Series.to_numpy` instead.
Examples
--------
For regular NumPy types like int, and float, a PandasArray
is returned.
>>> pd.Series([1, 2, 3]).array
<PandasArray>
[1, 2, 3]
Length: 3, dtype: int64
For extension types, like Categorical, the actual ExtensionArray
is returned
>>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
>>> ser.array
[a, b, a]
Categories (2, object): [a, b]
"""
# As a mixin, we depend on the mixing class having _values.
# Special mixin syntax may be developed in the future:
# https://github.com/python/typing/issues/246
result = self._values # type: ignore
if is_datetime64_ns_dtype(result.dtype):
from pandas.arrays import DatetimeArray
result = DatetimeArray(result)
elif is_timedelta64_ns_dtype(result.dtype):
from pandas.arrays import TimedeltaArray
result = TimedeltaArray(result)
elif not is_extension_array_dtype(result.dtype):
from pandas.core.arrays.numpy_ import PandasArray
result = PandasArray(result)
return result
def array(
data: Union[Sequence[object], AnyArrayLike],
dtype: Optional[Dtype] = None,
copy: bool = True,
) -> "ExtensionArray":
"""
Create an array.
.. versionadded:: 0.24.0
Parameters
----------
data : Sequence of objects
The scalars inside `data` should be instances of the
scalar type for `dtype`. It's expected that `data`
represents a 1-dimensional array of data.
When `data` is an Index or Series, the underlying array
will be extracted from `data`.
dtype : str, np.dtype, or ExtensionDtype, optional
The dtype to use for the array. This may be a NumPy
dtype or an extension type registered with pandas using
:meth:`pandas.api.extensions.register_extension_dtype`.
If not specified, there are two possibilities:
1. When `data` is a :class:`Series`, :class:`Index`, or
:class:`ExtensionArray`, the `dtype` will be taken
from the data.
2. Otherwise, pandas will attempt to infer the `dtype`
from the data.
Note that when `data` is a NumPy array, ``data.dtype`` is
*not* used for inferring the array type. This is because
NumPy cannot represent all the types of data that can be
held in extension arrays.
Currently, pandas will infer an extension dtype for sequences of
============================== =====================================
Scalar Type Array Type
============================== =====================================
:class:`pandas.Interval` :class:`pandas.arrays.IntervalArray`
:class:`pandas.Period` :class:`pandas.arrays.PeriodArray`
:class:`datetime.datetime` :class:`pandas.arrays.DatetimeArray`
:class:`datetime.timedelta` :class:`pandas.arrays.TimedeltaArray`
:class:`int` :class:`pandas.arrays.IntegerArray`
:class:`str` :class:`pandas.arrays.StringArray`
:class:`bool` :class:`pandas.arrays.BooleanArray`
============================== =====================================
For all other cases, NumPy's usual inference rules will be used.
.. versionchanged:: 1.0.0
Pandas infers nullable-integer dtype for integer data,
string dtype for string data, and nullable-boolean dtype
for boolean data.
copy : bool, default True
Whether to copy the data, even if not necessary. Depending
on the type of `data`, creating the new array may require
copying data, even if ``copy=False``.
Returns
-------
ExtensionArray
The newly created array.
Raises
------
ValueError
When `data` is not 1-dimensional.
See Also
--------
numpy.array : Construct a NumPy array.
Series : Construct a pandas Series.
Index : Construct a pandas Index.
arrays.PandasArray : ExtensionArray wrapping a NumPy array.
Series.array : Extract the array stored within a Series.
Notes
-----
Omitting the `dtype` argument means pandas will attempt to infer the
best array type from the values in the data. As new array types are
added by pandas and 3rd party libraries, the "best" array type may
change. We recommend specifying `dtype` to ensure that
1. the correct array type for the data is returned
2. the returned array type doesn't change as new extension types
are added by pandas and third-party libraries
Additionally, if the underlying memory representation of the returned
array matters, we recommend specifying the `dtype` as a concrete object
rather than a string alias or allowing it to be inferred. For example,
a future version of pandas or a 3rd-party library may include a
dedicated ExtensionArray for string data. In this event, the following
would no longer return a :class:`arrays.PandasArray` backed by a NumPy
array.
>>> pd.array(['a', 'b'], dtype=str)
<PandasArray>
['a', 'b']
Length: 2, dtype: str32
This would instead return the new ExtensionArray dedicated for string
data. If you really need the new array to be backed by a NumPy array,
specify that in the dtype.
>>> pd.array(['a', 'b'], dtype=np.dtype("<U1"))
<PandasArray>
['a', 'b']
Length: 2, dtype: str32
Finally, Pandas has arrays that mostly overlap with NumPy
* :class:`arrays.DatetimeArray`
* :class:`arrays.TimedeltaArray`
When data with a ``datetime64[ns]`` or ``timedelta64[ns]`` dtype is
passed, pandas will always return a ``DatetimeArray`` or ``TimedeltaArray``
rather than a ``PandasArray``. This is for symmetry with the case of
timezone-aware data, which NumPy does not natively support.
>>> pd.array(['2015', '2016'], dtype='datetime64[ns]')
<DatetimeArray>
['2015-01-01 00:00:00', '2016-01-01 00:00:00']
Length: 2, dtype: datetime64[ns]
>>> pd.array(["1H", "2H"], dtype='timedelta64[ns]')
<TimedeltaArray>
['0 days 01:00:00', '0 days 02:00:00']
Length: 2, dtype: timedelta64[ns]
Examples
--------
If a dtype is not specified, pandas will infer the best dtype from the values.
See the description of `dtype` for the types pandas infers for.
>>> pd.array([1, 2])
<IntegerArray>
[1, 2]
Length: 2, dtype: Int64
>>> pd.array([1, 2, np.nan])
<IntegerArray>
[1, 2, <NA>]
Length: 3, dtype: Int64
>>> pd.array(["a", None, "c"])
<StringArray>
['a', <NA>, 'c']
Length: 3, dtype: string
>>> pd.array([pd.Period('2000', freq="D"), pd.Period("2000", freq="D")])
<PeriodArray>
['2000-01-01', '2000-01-01']
Length: 2, dtype: period[D]
You can use the string alias for `dtype`
>>> pd.array(['a', 'b', 'a'], dtype='category')
[a, b, a]
Categories (2, object): [a, b]
Or specify the actual dtype
>>> pd.array(['a', 'b', 'a'],
... dtype=pd.CategoricalDtype(['a', 'b', 'c'], ordered=True))
[a, b, a]
Categories (3, object): [a < b < c]
If pandas does not infer a dedicated extension type a
:class:`arrays.PandasArray` is returned.
>>> pd.array([1.1, 2.2])
<PandasArray>
[1.1, 2.2]
Length: 2, dtype: float64
As mentioned in the "Notes" section, new extension types may be added
in the future (by pandas or 3rd party libraries), causing the return
value to no longer be a :class:`arrays.PandasArray`. Specify the `dtype`
as a NumPy dtype if you need to ensure there's no future change in
behavior.
>>> pd.array([1, 2], dtype=np.dtype("int32"))
<PandasArray>
[1, 2]
Length: 2, dtype: int32
`data` must be 1-dimensional. A ValueError is raised when the input
has the wrong dimensionality.
>>> pd.array(1)
Traceback (most recent call last):
...
ValueError: Cannot pass scalar '1' to 'pandas.array'.
"""
from pandas.core.arrays import (
period_array,
BooleanArray,
IntegerArray,
IntervalArray,
PandasArray,
DatetimeArray,
TimedeltaArray,
StringArray,
)
if lib.is_scalar(data):
msg = f"Cannot pass scalar '{data}' to 'pandas.array'."
raise ValueError(msg)
if dtype is None and isinstance(
data, (ABCSeries, ABCIndexClass, ABCExtensionArray)):
dtype = data.dtype
data = extract_array(data, extract_numpy=True)
# this returns None for not-found dtypes.
if isinstance(dtype, str):
dtype = registry.find(dtype) or dtype
if is_extension_array_dtype(dtype):
cls = cast(ExtensionDtype, dtype).construct_array_type()
return cls._from_sequence(data, dtype=dtype, copy=copy)
if dtype is None:
inferred_dtype = lib.infer_dtype(data, skipna=True)
if inferred_dtype == "period":
try:
return period_array(data, copy=copy)
except IncompatibleFrequency:
# We may have a mixture of frequencies.
# We choose to return an ndarray, rather than raising.
pass
elif inferred_dtype == "interval":
try:
return IntervalArray(data, copy=copy)
except ValueError:
# We may have a mixture of `closed` here.
# We choose to return an ndarray, rather than raising.
pass
elif inferred_dtype.startswith("datetime"):
# datetime, datetime64
try:
return DatetimeArray._from_sequence(data, copy=copy)
except ValueError:
# Mixture of timezones, fall back to PandasArray
pass
elif inferred_dtype.startswith("timedelta"):
# timedelta, timedelta64
return TimedeltaArray._from_sequence(data, copy=copy)
elif inferred_dtype == "string":
return StringArray._from_sequence(data, copy=copy)
elif inferred_dtype == "integer":
return IntegerArray._from_sequence(data, copy=copy)
elif inferred_dtype == "boolean":
return BooleanArray._from_sequence(data, copy=copy)
# Pandas overrides NumPy for
# 1. datetime64[ns]
# 2. timedelta64[ns]
# so that a DatetimeArray is returned.
if is_datetime64_ns_dtype(dtype):
return DatetimeArray._from_sequence(data, dtype=dtype, copy=copy)
elif is_timedelta64_ns_dtype(dtype):
return TimedeltaArray._from_sequence(data, dtype=dtype, copy=copy)
result = PandasArray._from_sequence(data, dtype=dtype, copy=copy)
return result
def array(self):
# type: () -> ExtensionArray
"""
The ExtensionArray of the data backing this Series or Index.
.. versionadded:: 0.24.0
Returns
-------
ExtensionArray
An ExtensionArray of the values stored within. For extension
types, this is the actual array. For NumPy native types, this
is a thin (no copy) wrapper around :class:`numpy.ndarray`.
``.array`` differs ``.values`` which may require converting the
data to a different form.
See Also
--------
Index.to_numpy : Similar method that always returns a NumPy array.
Series.to_numpy : Similar method that always returns a NumPy array.
Notes
-----
This table lays out the different array types for each extension
dtype within pandas.
================== =============================
dtype array type
================== =============================
category Categorical
period PeriodArray
interval IntervalArray
IntegerNA IntegerArray
datetime64[ns, tz] DatetimeArray
================== =============================
For any 3rd-party extension types, the array type will be an
ExtensionArray.
For all remaining dtypes ``.array`` will be a
:class:`arrays.NumpyExtensionArray` wrapping the actual ndarray
stored within. If you absolutely need a NumPy array (possibly with
copying / coercing data), then use :meth:`Series.to_numpy` instead.
Examples
--------
For regular NumPy types like int, and float, a PandasArray
is returned.
>>> pd.Series([1, 2, 3]).array
<PandasArray>
[1, 2, 3]
Length: 3, dtype: int64
For extension types, like Categorical, the actual ExtensionArray
is returned
>>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
>>> ser.array
[a, b, a]
Categories (2, object): [a, b]
"""
result = self._values
if is_datetime64_ns_dtype(result.dtype):
from pandas.arrays import DatetimeArray
result = DatetimeArray(result)
elif is_timedelta64_ns_dtype(result.dtype):
from pandas.arrays import TimedeltaArray
result = TimedeltaArray(result)
elif not is_extension_array_dtype(result.dtype):
from pandas.core.arrays.numpy_ import PandasArray
result = PandasArray(result)
return result
def array(data, # type: Sequence[object]
dtype=None, # type: Optional[Union[str, np.dtype, ExtensionDtype]]
copy=True, # type: bool
):
# type: (...) -> ExtensionArray
"""
Create an array.
.. versionadded:: 0.24.0
Parameters
----------
data : Sequence of objects
The scalars inside `data` should be instances of the
scalar type for `dtype`. It's expected that `data`
represents a 1-dimensional array of data.
When `data` is an Index or Series, the underlying array
will be extracted from `data`.
dtype : str, np.dtype, or ExtensionDtype, optional
The dtype to use for the array. This may be a NumPy
dtype or an extension type registered with pandas using
:meth:`pandas.api.extensions.register_extension_dtype`.
If not specified, there are two possibilities:
1. When `data` is a :class:`Series`, :class:`Index`, or
:class:`ExtensionArray`, the `dtype` will be taken
from the data.
2. Otherwise, pandas will attempt to infer the `dtype`
from the data.
Note that when `data` is a NumPy array, ``data.dtype`` is
*not* used for inferring the array type. This is because
NumPy cannot represent all the types of data that can be
held in extension arrays.
Currently, pandas will infer an extension dtype for sequences of
============================== =====================================
Scalar Type Array Type
============================== =====================================
:class:`pandas.Interval` :class:`pandas.arrays.IntervalArray`
:class:`pandas.Period` :class:`pandas.arrays.PeriodArray`
:class:`datetime.datetime` :class:`pandas.arrays.DatetimeArray`
:class:`datetime.timedelta` :class:`pandas.arrays.TimedeltaArray`
============================== =====================================
For all other cases, NumPy's usual inference rules will be used.
copy : bool, default True
Whether to copy the data, even if not necessary. Depending
on the type of `data`, creating the new array may require
copying data, even if ``copy=False``.
Returns
-------
ExtensionArray
The newly created array.
Raises
------
ValueError
When `data` is not 1-dimensional.
See Also
--------
numpy.array : Construct a NumPy array.
Series : Construct a pandas Series.
Index : Construct a pandas Index.
arrays.PandasArray : ExtensionArray wrapping a NumPy array.
Series.array : Extract the array stored within a Series.
Notes
-----
Omitting the `dtype` argument means pandas will attempt to infer the
best array type from the values in the data. As new array types are
added by pandas and 3rd party libraries, the "best" array type may
change. We recommend specifying `dtype` to ensure that
1. the correct array type for the data is returned
2. the returned array type doesn't change as new extension types
are added by pandas and third-party libraries
Additionally, if the underlying memory representation of the returned
array matters, we recommend specifying the `dtype` as a concrete object
rather than a string alias or allowing it to be inferred. For example,
a future version of pandas or a 3rd-party library may include a
dedicated ExtensionArray for string data. In this event, the following
would no longer return a :class:`arrays.PandasArray` backed by a NumPy
array.
>>> pd.array(['a', 'b'], dtype=str)
<PandasArray>
['a', 'b']
Length: 2, dtype: str32
This would instead return the new ExtensionArray dedicated for string
data. If you really need the new array to be backed by a NumPy array,
specify that in the dtype.
>>> pd.array(['a', 'b'], dtype=np.dtype("<U1"))
<PandasArray>
['a', 'b']
Length: 2, dtype: str32
Or use the dedicated constructor for the array you're expecting, and
wrap that in a PandasArray
>>> pd.array(np.array(['a', 'b'], dtype='<U1'))
<PandasArray>
['a', 'b']
Length: 2, dtype: str32
Finally, Pandas has arrays that mostly overlap with NumPy
* :class:`arrays.DatetimeArray`
* :class:`arrays.TimedeltaArray`
When data with a ``datetime64[ns]`` or ``timedelta64[ns]`` dtype is
passed, pandas will always return a ``DatetimeArray`` or ``TimedeltaArray``
rather than a ``PandasArray``. This is for symmetry with the case of
timezone-aware data, which NumPy does not natively support.
>>> pd.array(['2015', '2016'], dtype='datetime64[ns]')
<DatetimeArray>
['2015-01-01 00:00:00', '2016-01-01 00:00:00']
Length: 2, dtype: datetime64[ns]
>>> pd.array(["1H", "2H"], dtype='timedelta64[ns]')
<TimedeltaArray>
['01:00:00', '02:00:00']
Length: 2, dtype: timedelta64[ns]
Examples
--------
If a dtype is not specified, `data` is passed through to
:meth:`numpy.array`, and a :class:`arrays.PandasArray` is returned.
>>> pd.array([1, 2])
<PandasArray>
[1, 2]
Length: 2, dtype: int64
Or the NumPy dtype can be specified
>>> pd.array([1, 2], dtype=np.dtype("int32"))
<PandasArray>
[1, 2]
Length: 2, dtype: int32
You can use the string alias for `dtype`
>>> pd.array(['a', 'b', 'a'], dtype='category')
[a, b, a]
Categories (2, object): [a, b]
Or specify the actual dtype
>>> pd.array(['a', 'b', 'a'],
... dtype=pd.CategoricalDtype(['a', 'b', 'c'], ordered=True))
[a, b, a]
Categories (3, object): [a < b < c]
Because omitting the `dtype` passes the data through to NumPy,
a mixture of valid integers and NA will return a floating-point
NumPy array.
>>> pd.array([1, 2, np.nan])
<PandasArray>
[1.0, 2.0, nan]
Length: 3, dtype: float64
To use pandas' nullable :class:`pandas.arrays.IntegerArray`, specify
the dtype:
>>> pd.array([1, 2, np.nan], dtype='Int64')
<IntegerArray>
[1, 2, NaN]
Length: 3, dtype: Int64
Pandas will infer an ExtensionArray for some types of data:
>>> pd.array([pd.Period('2000', freq="D"), pd.Period("2000", freq="D")])
<PeriodArray>
['2000-01-01', '2000-01-01']
Length: 2, dtype: period[D]
`data` must be 1-dimensional. A ValueError is raised when the input
has the wrong dimensionality.
>>> pd.array(1)
Traceback (most recent call last):
...
ValueError: Cannot pass scalar '1' to 'pandas.array'.
"""
from pandas.core.arrays import (
period_array, ExtensionArray, IntervalArray, PandasArray,
DatetimeArray,
TimedeltaArray,
)
from pandas.core.internals.arrays import extract_array
if lib.is_scalar(data):
msg = (
"Cannot pass scalar '{}' to 'pandas.array'."
)
raise ValueError(msg.format(data))
data = extract_array(data, extract_numpy=True)
if dtype is None and isinstance(data, ExtensionArray):
dtype = data.dtype
# this returns None for not-found dtypes.
if isinstance(dtype, compat.string_types):
dtype = registry.find(dtype) or dtype
if is_extension_array_dtype(dtype):
cls = dtype.construct_array_type()
return cls._from_sequence(data, dtype=dtype, copy=copy)
if dtype is None:
inferred_dtype = lib.infer_dtype(data, skipna=False)
if inferred_dtype == 'period':
try:
return period_array(data, copy=copy)
except tslibs.IncompatibleFrequency:
# We may have a mixture of frequencies.
# We choose to return an ndarray, rather than raising.
pass
elif inferred_dtype == 'interval':
try:
return IntervalArray(data, copy=copy)
except ValueError:
# We may have a mixture of `closed` here.
# We choose to return an ndarray, rather than raising.
pass
elif inferred_dtype.startswith('datetime'):
# datetime, datetime64
try:
return DatetimeArray._from_sequence(data, copy=copy)
except ValueError:
# Mixture of timezones, fall back to PandasArray
pass
elif inferred_dtype.startswith('timedelta'):
# timedelta, timedelta64
return TimedeltaArray._from_sequence(data, copy=copy)
# TODO(BooleanArray): handle this type
# Pandas overrides NumPy for
# 1. datetime64[ns]
# 2. timedelta64[ns]
# so that a DatetimeArray is returned.
if is_datetime64_ns_dtype(dtype):
return DatetimeArray._from_sequence(data, dtype=dtype, copy=copy)
elif is_timedelta64_ns_dtype(dtype):
return TimedeltaArray._from_sequence(data, dtype=dtype, copy=copy)
result = PandasArray._from_sequence(data, dtype=dtype, copy=copy)
return result
def test_is_timedelta64_ns_dtype():
assert not com.is_timedelta64_ns_dtype(np.dtype("m8[ps]"))
assert not com.is_timedelta64_ns_dtype(np.array([1, 2], dtype=np.timedelta64))
assert com.is_timedelta64_ns_dtype(np.dtype("m8[ns]"))
assert com.is_timedelta64_ns_dtype(np.array([1, 2], dtype="m8[ns]"))
def array(data: Sequence[object],
dtype: Optional[Union[str, np.dtype, ExtensionDtype]] = None,
copy: bool = True,
) -> ABCExtensionArray:
"""
Create an array.
.. versionadded:: 0.24.0
Parameters
----------
data : Sequence of objects
The scalars inside `data` should be instances of the
scalar type for `dtype`. It's expected that `data`
represents a 1-dimensional array of data.
When `data` is an Index or Series, the underlying array
will be extracted from `data`.
dtype : str, np.dtype, or ExtensionDtype, optional
The dtype to use for the array. This may be a NumPy
dtype or an extension type registered with pandas using
:meth:`pandas.api.extensions.register_extension_dtype`.
If not specified, there are two possibilities:
1. When `data` is a :class:`Series`, :class:`Index`, or
:class:`ExtensionArray`, the `dtype` will be taken
from the data.
2. Otherwise, pandas will attempt to infer the `dtype`
from the data.
Note that when `data` is a NumPy array, ``data.dtype`` is
*not* used for inferring the array type. This is because
NumPy cannot represent all the types of data that can be
held in extension arrays.
Currently, pandas will infer an extension dtype for sequences of
============================== =====================================
Scalar Type Array Type
============================== =====================================
:class:`pandas.Interval` :class:`pandas.arrays.IntervalArray`
:class:`pandas.Period` :class:`pandas.arrays.PeriodArray`
:class:`datetime.datetime` :class:`pandas.arrays.DatetimeArray`
:class:`datetime.timedelta` :class:`pandas.arrays.TimedeltaArray`
============================== =====================================
For all other cases, NumPy's usual inference rules will be used.
copy : bool, default True
Whether to copy the data, even if not necessary. Depending
on the type of `data`, creating the new array may require
copying data, even if ``copy=False``.
Returns
-------
ExtensionArray
The newly created array.
Raises
------
ValueError
When `data` is not 1-dimensional.
See Also
--------
numpy.array : Construct a NumPy array.
Series : Construct a pandas Series.
Index : Construct a pandas Index.
arrays.PandasArray : ExtensionArray wrapping a NumPy array.
Series.array : Extract the array stored within a Series.
Notes
-----
Omitting the `dtype` argument means pandas will attempt to infer the
best array type from the values in the data. As new array types are
added by pandas and 3rd party libraries, the "best" array type may
change. We recommend specifying `dtype` to ensure that
1. the correct array type for the data is returned
2. the returned array type doesn't change as new extension types
are added by pandas and third-party libraries
Additionally, if the underlying memory representation of the returned
array matters, we recommend specifying the `dtype` as a concrete object
rather than a string alias or allowing it to be inferred. For example,
a future version of pandas or a 3rd-party library may include a
dedicated ExtensionArray for string data. In this event, the following
would no longer return a :class:`arrays.PandasArray` backed by a NumPy
array.
>>> pd.array(['a', 'b'], dtype=str)
<PandasArray>
['a', 'b']
Length: 2, dtype: str32
This would instead return the new ExtensionArray dedicated for string
data. If you really need the new array to be backed by a NumPy array,
specify that in the dtype.
>>> pd.array(['a', 'b'], dtype=np.dtype("<U1"))
<PandasArray>
['a', 'b']
Length: 2, dtype: str32
Or use the dedicated constructor for the array you're expecting, and
wrap that in a PandasArray
>>> pd.array(np.array(['a', 'b'], dtype='<U1'))
<PandasArray>
['a', 'b']
Length: 2, dtype: str32
Finally, Pandas has arrays that mostly overlap with NumPy
* :class:`arrays.DatetimeArray`
* :class:`arrays.TimedeltaArray`
When data with a ``datetime64[ns]`` or ``timedelta64[ns]`` dtype is
passed, pandas will always return a ``DatetimeArray`` or ``TimedeltaArray``
rather than a ``PandasArray``. This is for symmetry with the case of
timezone-aware data, which NumPy does not natively support.
>>> pd.array(['2015', '2016'], dtype='datetime64[ns]')
<DatetimeArray>
['2015-01-01 00:00:00', '2016-01-01 00:00:00']
Length: 2, dtype: datetime64[ns]
>>> pd.array(["1H", "2H"], dtype='timedelta64[ns]')
<TimedeltaArray>
['01:00:00', '02:00:00']
Length: 2, dtype: timedelta64[ns]
Examples
--------
If a dtype is not specified, `data` is passed through to
:meth:`numpy.array`, and a :class:`arrays.PandasArray` is returned.
>>> pd.array([1, 2])
<PandasArray>
[1, 2]
Length: 2, dtype: int64
Or the NumPy dtype can be specified
>>> pd.array([1, 2], dtype=np.dtype("int32"))
<PandasArray>
[1, 2]
Length: 2, dtype: int32
You can use the string alias for `dtype`
>>> pd.array(['a', 'b', 'a'], dtype='category')
[a, b, a]
Categories (2, object): [a, b]
Or specify the actual dtype
>>> pd.array(['a', 'b', 'a'],
... dtype=pd.CategoricalDtype(['a', 'b', 'c'], ordered=True))
[a, b, a]
Categories (3, object): [a < b < c]
Because omitting the `dtype` passes the data through to NumPy,
a mixture of valid integers and NA will return a floating-point
NumPy array.
>>> pd.array([1, 2, np.nan])
<PandasArray>
[1.0, 2.0, nan]
Length: 3, dtype: float64
To use pandas' nullable :class:`pandas.arrays.IntegerArray`, specify
the dtype:
>>> pd.array([1, 2, np.nan], dtype='Int64')
<IntegerArray>
[1, 2, NaN]
Length: 3, dtype: Int64
Pandas will infer an ExtensionArray for some types of data:
>>> pd.array([pd.Period('2000', freq="D"), pd.Period("2000", freq="D")])
<PeriodArray>
['2000-01-01', '2000-01-01']
Length: 2, dtype: period[D]
`data` must be 1-dimensional. A ValueError is raised when the input
has the wrong dimensionality.
>>> pd.array(1)
Traceback (most recent call last):
...
ValueError: Cannot pass scalar '1' to 'pandas.array'.
"""
from pandas.core.arrays import (
period_array, ExtensionArray, IntervalArray, PandasArray,
DatetimeArray,
TimedeltaArray,
)
from pandas.core.internals.arrays import extract_array
if lib.is_scalar(data):
msg = (
"Cannot pass scalar '{}' to 'pandas.array'."
)
raise ValueError(msg.format(data))
data = extract_array(data, extract_numpy=True)
if dtype is None and isinstance(data, ExtensionArray):
dtype = data.dtype
# this returns None for not-found dtypes.
if isinstance(dtype, str):
dtype = registry.find(dtype) or dtype
if is_extension_array_dtype(dtype):
cls = dtype.construct_array_type()
return cls._from_sequence(data, dtype=dtype, copy=copy)
if dtype is None:
inferred_dtype = lib.infer_dtype(data, skipna=False)
if inferred_dtype == 'period':
try:
return period_array(data, copy=copy)
except tslibs.IncompatibleFrequency:
# We may have a mixture of frequencies.
# We choose to return an ndarray, rather than raising.
pass
elif inferred_dtype == 'interval':
try:
return IntervalArray(data, copy=copy)
except ValueError:
# We may have a mixture of `closed` here.
# We choose to return an ndarray, rather than raising.
pass
elif inferred_dtype.startswith('datetime'):
# datetime, datetime64
try:
return DatetimeArray._from_sequence(data, copy=copy)
except ValueError:
# Mixture of timezones, fall back to PandasArray
pass
elif inferred_dtype.startswith('timedelta'):
# timedelta, timedelta64
return TimedeltaArray._from_sequence(data, copy=copy)
# TODO(BooleanArray): handle this type
# Pandas overrides NumPy for
# 1. datetime64[ns]
# 2. timedelta64[ns]
# so that a DatetimeArray is returned.
if is_datetime64_ns_dtype(dtype):
return DatetimeArray._from_sequence(data, dtype=dtype, copy=copy)
elif is_timedelta64_ns_dtype(dtype):
return TimedeltaArray._from_sequence(data, dtype=dtype, copy=copy)
result = PandasArray._from_sequence(data, dtype=dtype, copy=copy)
return result
