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"""
Constructor functions intended to be shared by pd.array, Series.__init__,
and Index.__new__.
These should not depend on core.internals.
"""
from __future__ import annotations
from collections.abc import Sequence
from typing import (
TYPE_CHECKING,
Optional,
Union,
cast,
overload,
)
import warnings
import numpy as np
from numpy import ma
from pandas._config import using_string_dtype
from pandas._libs import lib
from pandas._libs.tslibs import (
Period,
get_supported_dtype,
is_supported_dtype,
)
from pandas._typing import (
AnyArrayLike,
ArrayLike,
Dtype,
DtypeObj,
T,
)
from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.base import ExtensionDtype
from pandas.core.dtypes.cast import (
construct_1d_arraylike_from_scalar,
construct_1d_object_array_from_listlike,
maybe_cast_to_datetime,
maybe_cast_to_integer_array,
maybe_convert_platform,
maybe_infer_to_datetimelike,
maybe_promote,
)
from pandas.core.dtypes.common import (
is_list_like,
is_object_dtype,
is_string_dtype,
pandas_dtype,
)
from pandas.core.dtypes.dtypes import NumpyEADtype
from pandas.core.dtypes.generic import (
ABCDataFrame,
ABCExtensionArray,
ABCIndex,
ABCSeries,
)
from pandas.core.dtypes.missing import isna
import pandas.core.common as com
if TYPE_CHECKING:
from pandas import (
Index,
Series,
)
from pandas.core.arrays.base import ExtensionArray
def array(
data: Sequence[object] | AnyArrayLike,
dtype: Dtype | None = None,
copy: bool = True,
) -> ExtensionArray:
"""
Create an array.
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:`float` :class:`pandas.arrays.FloatingArray`
:class:`str` :class:`pandas.arrays.StringArray` or
:class:`pandas.arrays.ArrowStringArray`
:class:`bool` :class:`pandas.arrays.BooleanArray`
============================== =======================================
The ExtensionArray created when the scalar type is :class:`str` is determined by
``pd.options.mode.string_storage`` if the dtype is not explicitly given.
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.NumpyExtensionArray : 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.NumpyExtensionArray` backed by a
NumPy array.
>>> pd.array(['a', 'b'], dtype=str)
<NumpyExtensionArray>
['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"))
<NumpyExtensionArray>
['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 ``NumpyExtensionArray``. 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([1.1, 2.2])
<FloatingArray>
[1.1, 2.2]
Length: 2, dtype: Float64
>>> pd.array(["a", None, "c"])
<StringArray>
['a', <NA>, 'c']
Length: 3, dtype: string
>>> with pd.option_context("string_storage", "pyarrow"):
... arr = pd.array(["a", None, "c"])
...
>>> arr
<ArrowStringArray>
['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.NumpyExtensionArray` is returned.
>>> pd.array([1 + 1j, 3 + 2j])
<NumpyExtensionArray>
[(1+1j), (3+2j)]
Length: 2, dtype: complex128
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.NumpyExtensionArray`. 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"))
<NumpyExtensionArray>
[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 (
BooleanArray,
DatetimeArray,
ExtensionArray,
FloatingArray,
IntegerArray,
IntervalArray,
NumpyExtensionArray,
PeriodArray,
TimedeltaArray,
)
from pandas.core.arrays.string_ import StringDtype
if lib.is_scalar(data):
msg = f"Cannot pass scalar '{data}' to 'pandas.array'."
raise ValueError(msg)
elif isinstance(data, ABCDataFrame):
raise TypeError("Cannot pass DataFrame to 'pandas.array'")
if dtype is None and isinstance(data, (ABCSeries, ABCIndex, ExtensionArray)):
# Note: we exclude np.ndarray here, will do type inference on it
dtype = data.dtype
data = extract_array(data, extract_numpy=True)
# this returns None for not-found dtypes.
if dtype is not None:
dtype = pandas_dtype(dtype)
if isinstance(data, ExtensionArray) and (dtype is None or data.dtype == dtype):
# e.g. TimedeltaArray[s], avoid casting to NumpyExtensionArray
if copy:
return data.copy()
return data
if isinstance(dtype, ExtensionDtype):
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=True)
if inferred_dtype == "period":
period_data = cast(Union[Sequence[Optional[Period]], AnyArrayLike], data)
return PeriodArray._from_sequence(period_data, copy=copy)
elif inferred_dtype == "interval":
return IntervalArray(data, copy=copy)
elif inferred_dtype.startswith("datetime"):
# datetime, datetime64
try:
return DatetimeArray._from_sequence(data, copy=copy)
except ValueError:
# Mixture of timezones, fall back to NumpyExtensionArray
pass
elif inferred_dtype.startswith("timedelta"):
# timedelta, timedelta64
return TimedeltaArray._from_sequence(data, copy=copy)
elif inferred_dtype == "string":
# StringArray/ArrowStringArray depending on pd.options.mode.string_storage
dtype = StringDtype()
cls = dtype.construct_array_type()
return cls._from_sequence(data, dtype=dtype, copy=copy)
elif inferred_dtype == "integer":
return IntegerArray._from_sequence(data, copy=copy)
elif inferred_dtype == "empty" and not hasattr(data, "dtype") and not len(data):
return FloatingArray._from_sequence(data, copy=copy)
elif (
inferred_dtype in ("floating", "mixed-integer-float")
and getattr(data, "dtype", None) != np.float16
):
# GH#44715 Exclude np.float16 bc FloatingArray does not support it;
# we will fall back to NumpyExtensionArray.
return FloatingArray._from_sequence(data, copy=copy)
elif inferred_dtype == "boolean":
return BooleanArray._from_sequence(data, dtype="boolean", copy=copy)
# Pandas overrides NumPy for
# 1. datetime64[ns,us,ms,s]
# 2. timedelta64[ns,us,ms,s]
# so that a DatetimeArray is returned.
if lib.is_np_dtype(dtype, "M") and is_supported_dtype(dtype):
return DatetimeArray._from_sequence(data, dtype=dtype, copy=copy)
if lib.is_np_dtype(dtype, "m") and is_supported_dtype(dtype):
return TimedeltaArray._from_sequence(data, dtype=dtype, copy=copy)
elif lib.is_np_dtype(dtype, "mM"):
warnings.warn(
r"datetime64 and timedelta64 dtype resolutions other than "
r"'s', 'ms', 'us', and 'ns' are deprecated. "
r"In future releases passing unsupported resolutions will "
r"raise an exception.",
FutureWarning,
stacklevel=find_stack_level(),
)
return NumpyExtensionArray._from_sequence(data, dtype=dtype, copy=copy)
_typs = frozenset(
{
"index",
"rangeindex",
"multiindex",
"datetimeindex",
"timedeltaindex",
"periodindex",
"categoricalindex",
"intervalindex",
"series",
}
)
@overload
def extract_array(
obj: Series | Index, extract_numpy: bool = ..., extract_range: bool = ...
) -> ArrayLike:
...
@overload
def extract_array(
obj: T, extract_numpy: bool = ..., extract_range: bool = ...
) -> T | ArrayLike:
...
def extract_array(
obj: T, extract_numpy: bool = False, extract_range: bool = False
) -> T | ArrayLike:
"""
Extract the ndarray or ExtensionArray from a Series or Index.
For all other types, `obj` is just returned as is.
Parameters
----------
obj : object
For Series / Index, the underlying ExtensionArray is unboxed.
extract_numpy : bool, default False
Whether to extract the ndarray from a NumpyExtensionArray.
extract_range : bool, default False
If we have a RangeIndex, return range._values if True
(which is a materialized integer ndarray), otherwise return unchanged.
Returns
-------
arr : object
Examples
--------
>>> extract_array(pd.Series(['a', 'b', 'c'], dtype='category'))
['a', 'b', 'c']
Categories (3, object): ['a', 'b', 'c']
Other objects like lists, arrays, and DataFrames are just passed through.
>>> extract_array([1, 2, 3])
[1, 2, 3]
For an ndarray-backed Series / Index the ndarray is returned.
>>> extract_array(pd.Series([1, 2, 3]))
array([1, 2, 3])
To extract all the way down to the ndarray, pass ``extract_numpy=True``.
>>> extract_array(pd.Series([1, 2, 3]), extract_numpy=True)
array([1, 2, 3])
"""
typ = getattr(obj, "_typ", None)
if typ in _typs:
# i.e. isinstance(obj, (ABCIndex, ABCSeries))
if typ == "rangeindex":
if extract_range:
# error: "T" has no attribute "_values"
return obj._values # type: ignore[attr-defined]
return obj
# error: "T" has no attribute "_values"
return obj._values # type: ignore[attr-defined]
elif extract_numpy and typ == "npy_extension":
# i.e. isinstance(obj, ABCNumpyExtensionArray)
# error: "T" has no attribute "to_numpy"
return obj.to_numpy() # type: ignore[attr-defined]
return obj
def ensure_wrapped_if_datetimelike(arr):
"""
Wrap datetime64 and timedelta64 ndarrays in DatetimeArray/TimedeltaArray.
"""
if isinstance(arr, np.ndarray):
if arr.dtype.kind == "M":
from pandas.core.arrays import DatetimeArray
dtype = get_supported_dtype(arr.dtype)
return DatetimeArray._from_sequence(arr, dtype=dtype)
elif arr.dtype.kind == "m":
from pandas.core.arrays import TimedeltaArray
dtype = get_supported_dtype(arr.dtype)
return TimedeltaArray._from_sequence(arr, dtype=dtype)
return arr
def sanitize_masked_array(data: ma.MaskedArray) -> np.ndarray:
"""
Convert numpy MaskedArray to ensure mask is softened.
"""
mask = ma.getmaskarray(data)
if mask.any():
dtype, fill_value = maybe_promote(data.dtype, np.nan)
dtype = cast(np.dtype, dtype)
data = ma.asarray(data.astype(dtype, copy=True))
data.soften_mask() # set hardmask False if it was True
data[mask] = fill_value
else:
data = data.copy()
return data
def sanitize_array(
data,
index: Index | None,
dtype: DtypeObj | None = None,
copy: bool = False,
*,
allow_2d: bool = False,
) -> ArrayLike:
"""
Sanitize input data to an ndarray or ExtensionArray, copy if specified,
coerce to the dtype if specified.
Parameters
----------
data : Any
index : Index or None, default None
dtype : np.dtype, ExtensionDtype, or None, default None
copy : bool, default False
allow_2d : bool, default False
If False, raise if we have a 2D Arraylike.
Returns
-------
np.ndarray or ExtensionArray
"""
original_dtype = dtype
if isinstance(data, ma.MaskedArray):
data = sanitize_masked_array(data)
if isinstance(dtype, NumpyEADtype):
# Avoid ending up with a NumpyExtensionArray
dtype = dtype.numpy_dtype
object_index = False
if isinstance(data, ABCIndex) and data.dtype == object and dtype is None:
object_index = True
# extract ndarray or ExtensionArray, ensure we have no NumpyExtensionArray
data = extract_array(data, extract_numpy=True, extract_range=True)
if isinstance(data, np.ndarray) and data.ndim == 0:
if dtype is None:
dtype = data.dtype
data = lib.item_from_zerodim(data)
elif isinstance(data, range):
# GH#16804
data = range_to_ndarray(data)
copy = False
if not is_list_like(data):
if index is None:
raise ValueError("index must be specified when data is not list-like")
if isinstance(data, str) and using_string_dtype() and original_dtype is None:
from pandas.core.arrays.string_ import StringDtype
dtype = StringDtype(na_value=np.nan)
data = construct_1d_arraylike_from_scalar(data, len(index), dtype)
return data
elif isinstance(data, ABCExtensionArray):
# it is already ensured above this is not a NumpyExtensionArray
# Until GH#49309 is fixed this check needs to come before the
# ExtensionDtype check
if dtype is not None:
subarr = data.astype(dtype, copy=copy)
elif copy:
subarr = data.copy()
else:
subarr = data
elif isinstance(dtype, ExtensionDtype):
# create an extension array from its dtype
_sanitize_non_ordered(data)
cls = dtype.construct_array_type()
if not hasattr(data, "__array__"):
data = list(data)
subarr = cls._from_sequence(data, dtype=dtype, copy=copy)
# GH#846
elif isinstance(data, np.ndarray):
if isinstance(data, np.matrix):
data = data.A
if dtype is None:
subarr = data
if data.dtype == object:
subarr = maybe_infer_to_datetimelike(data)
if object_index and using_string_dtype() and is_string_dtype(subarr):
# Avoid inference when string option is set
subarr = data
elif data.dtype.kind == "U" and using_string_dtype():
from pandas.core.arrays.string_ import StringDtype
dtype = StringDtype(na_value=np.nan)
subarr = dtype.construct_array_type()._from_sequence(data, dtype=dtype)
if (
subarr is data
or (subarr.dtype == "str" and subarr.dtype.storage == "python") # type: ignore[union-attr]
) and copy:
subarr = subarr.copy()
else:
# we will try to copy by-definition here
subarr = _try_cast(data, dtype, copy)
elif hasattr(data, "__array__"):
# e.g. dask array GH#38645
if not copy:
data = np.asarray(data)
else:
data = np.array(data, copy=copy)
return sanitize_array(
data,
index=index,
dtype=dtype,
copy=False,
allow_2d=allow_2d,
)
else:
_sanitize_non_ordered(data)
# materialize e.g. generators, convert e.g. tuples, abc.ValueView
data = list(data)
if len(data) == 0 and dtype is None:
# We default to float64, matching numpy
subarr = np.array([], dtype=np.float64)
elif dtype is not None:
subarr = _try_cast(data, dtype, copy)
else:
subarr = maybe_convert_platform(data)
if subarr.dtype == object:
subarr = cast(np.ndarray, subarr)
subarr = maybe_infer_to_datetimelike(subarr)
subarr = _sanitize_ndim(subarr, data, dtype, index, allow_2d=allow_2d)
if isinstance(subarr, np.ndarray):
# at this point we should have dtype be None or subarr.dtype == dtype
dtype = cast(np.dtype, dtype)
subarr = _sanitize_str_dtypes(subarr, data, dtype, copy)
return subarr
def range_to_ndarray(rng: range) -> np.ndarray:
"""
Cast a range object to ndarray.
"""
# GH#30171 perf avoid realizing range as a list in np.array
try:
arr = np.arange(rng.start, rng.stop, rng.step, dtype="int64")
except OverflowError:
# GH#30173 handling for ranges that overflow int64
if (rng.start >= 0 and rng.step > 0) or (rng.step < 0 <= rng.stop):
try:
arr = np.arange(rng.start, rng.stop, rng.step, dtype="uint64")
except OverflowError:
arr = construct_1d_object_array_from_listlike(list(rng))
else:
arr = construct_1d_object_array_from_listlike(list(rng))
return arr
def _sanitize_non_ordered(data) -> None:
"""
Raise only for unordered sets, e.g., not for dict_keys
"""
if isinstance(data, (set, frozenset)):
raise TypeError(f"'{type(data).__name__}' type is unordered")
def _sanitize_ndim(
result: ArrayLike,
data,
dtype: DtypeObj | None,
index: Index | None,
*,
allow_2d: bool = False,
) -> ArrayLike:
"""
Ensure we have a 1-dimensional result array.
"""
if getattr(result, "ndim", 0) == 0:
raise ValueError("result should be arraylike with ndim > 0")
if result.ndim == 1:
# the result that we want
result = _maybe_repeat(result, index)
elif result.ndim > 1:
if isinstance(data, np.ndarray):
if allow_2d:
return result
raise ValueError(
f"Data must be 1-dimensional, got ndarray of shape {data.shape} instead"
)
if is_object_dtype(dtype) and isinstance(dtype, ExtensionDtype):
# i.e. NumpyEADtype("O")
result = com.asarray_tuplesafe(data, dtype=np.dtype("object"))
cls = dtype.construct_array_type()
result = cls._from_sequence(result, dtype=dtype)
else:
# error: Argument "dtype" to "asarray_tuplesafe" has incompatible type
# "Union[dtype[Any], ExtensionDtype, None]"; expected "Union[str,
# dtype[Any], None]"
result = com.asarray_tuplesafe(data, dtype=dtype) # type: ignore[arg-type]
return result
def _sanitize_str_dtypes(
result: np.ndarray, data, dtype: np.dtype | None, copy: bool
) -> np.ndarray:
"""
Ensure we have a dtype that is supported by pandas.
"""
# This is to prevent mixed-type Series getting all casted to
# NumPy string type, e.g. NaN --> '-1#IND'.
if issubclass(result.dtype.type, str):
# GH#16605
# If not empty convert the data to dtype
# GH#19853: If data is a scalar, result has already the result
if not lib.is_scalar(data):
if not np.all(isna(data)):
data = np.asarray(data, dtype=dtype)
if not copy:
result = np.asarray(data, dtype=object)
else:
result = np.array(data, dtype=object, copy=copy)
return result
def _maybe_repeat(arr: ArrayLike, index: Index | None) -> ArrayLike:
"""
If we have a length-1 array and an index describing how long we expect
the result to be, repeat the array.
"""
if index is not None:
if 1 == len(arr) != len(index):
arr = arr.repeat(len(index))
return arr
def _try_cast(
arr: list | np.ndarray,
dtype: np.dtype,
copy: bool,
) -> ArrayLike:
"""
Convert input to numpy ndarray and optionally cast to a given dtype.
Parameters
----------
arr : ndarray or list
Excludes: ExtensionArray, Series, Index.
dtype : np.dtype
copy : bool
If False, don't copy the data if not needed.
Returns
-------
np.ndarray or ExtensionArray
"""
is_ndarray = isinstance(arr, np.ndarray)
if dtype == object:
if not is_ndarray:
subarr = construct_1d_object_array_from_listlike(arr)
return subarr
return ensure_wrapped_if_datetimelike(arr).astype(dtype, copy=copy)
elif dtype.kind == "U":
# TODO: test cases with arr.dtype.kind in "mM"
if is_ndarray:
arr = cast(np.ndarray, arr)
shape = arr.shape
if arr.ndim > 1:
arr = arr.ravel()
else:
shape = (len(arr),)
return lib.ensure_string_array(arr, convert_na_value=False, copy=copy).reshape(
shape
)
elif dtype.kind in "mM":
return maybe_cast_to_datetime(arr, dtype)
# GH#15832: Check if we are requesting a numeric dtype and
# that we can convert the data to the requested dtype.
elif dtype.kind in "iu":
# this will raise if we have e.g. floats
subarr = maybe_cast_to_integer_array(arr, dtype)
elif not copy:
subarr = np.asarray(arr, dtype=dtype)
else:
subarr = np.array(arr, dtype=dtype, copy=copy)
return subarr