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common.py
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common.py
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""" common type operations """
import numpy as np
from pandas.compat import (string_types, text_type, binary_type,
PY3, PY36)
from pandas._libs import algos, lib
from pandas._libs.tslibs import conversion
from .dtypes import (CategoricalDtype, CategoricalDtypeType,
DatetimeTZDtype, DatetimeTZDtypeType,
PeriodDtype, PeriodDtypeType,
IntervalDtype, IntervalDtypeType,
ExtensionDtype, PandasExtensionDtype)
from .generic import (ABCCategorical, ABCPeriodIndex,
ABCDatetimeIndex, ABCSeries,
ABCSparseArray, ABCSparseSeries, ABCCategoricalIndex,
ABCIndexClass, ABCDateOffset)
from .inference import is_string_like, is_list_like
from .inference import * # noqa
_POSSIBLY_CAST_DTYPES = set([np.dtype(t).name
for t in ['O', 'int8', 'uint8', 'int16', 'uint16',
'int32', 'uint32', 'int64', 'uint64']])
_NS_DTYPE = conversion.NS_DTYPE
_TD_DTYPE = conversion.TD_DTYPE
_INT64_DTYPE = np.dtype(np.int64)
# oh the troubles to reduce import time
_is_scipy_sparse = None
_ensure_float64 = algos.ensure_float64
_ensure_float32 = algos.ensure_float32
_ensure_datetime64ns = conversion.ensure_datetime64ns
_ensure_timedelta64ns = conversion.ensure_timedelta64ns
def _ensure_float(arr):
"""
Ensure that an array object has a float dtype if possible.
Parameters
----------
arr : array-like
The array whose data type we want to enforce as float.
Returns
-------
float_arr : The original array cast to the float dtype if
possible. Otherwise, the original array is returned.
"""
if issubclass(arr.dtype.type, (np.integer, np.bool_)):
arr = arr.astype(float)
return arr
_ensure_uint64 = algos.ensure_uint64
_ensure_int64 = algos.ensure_int64
_ensure_int32 = algos.ensure_int32
_ensure_int16 = algos.ensure_int16
_ensure_int8 = algos.ensure_int8
_ensure_platform_int = algos.ensure_platform_int
_ensure_object = algos.ensure_object
def _ensure_categorical(arr):
"""
Ensure that an array-like object is a Categorical (if not already).
Parameters
----------
arr : array-like
The array that we want to convert into a Categorical.
Returns
-------
cat_arr : The original array cast as a Categorical. If it already
is a Categorical, we return as is.
"""
if not is_categorical(arr):
from pandas import Categorical
arr = Categorical(arr)
return arr
def is_object_dtype(arr_or_dtype):
"""
Check whether an array-like or dtype is of the object dtype.
Parameters
----------
arr_or_dtype : array-like
The array-like or dtype to check.
Returns
-------
boolean : Whether or not the array-like or dtype is of the object dtype.
Examples
--------
>>> is_object_dtype(object)
True
>>> is_object_dtype(int)
False
>>> is_object_dtype(np.array([], dtype=object))
True
>>> is_object_dtype(np.array([], dtype=int))
False
>>> is_object_dtype([1, 2, 3])
False
"""
if arr_or_dtype is None:
return False
tipo = _get_dtype_type(arr_or_dtype)
return issubclass(tipo, np.object_)
def is_sparse(arr):
"""
Check whether an array-like is a pandas sparse array.
Parameters
----------
arr : array-like
The array-like to check.
Returns
-------
boolean : Whether or not the array-like is a pandas sparse array.
Examples
--------
>>> is_sparse(np.array([1, 2, 3]))
False
>>> is_sparse(pd.SparseArray([1, 2, 3]))
True
>>> is_sparse(pd.SparseSeries([1, 2, 3]))
True
This function checks only for pandas sparse array instances, so
sparse arrays from other libraries will return False.
>>> from scipy.sparse import bsr_matrix
>>> is_sparse(bsr_matrix([1, 2, 3]))
False
"""
return isinstance(arr, (ABCSparseArray, ABCSparseSeries))
def is_scipy_sparse(arr):
"""
Check whether an array-like is a scipy.sparse.spmatrix instance.
Parameters
----------
arr : array-like
The array-like to check.
Returns
-------
boolean : Whether or not the array-like is a
scipy.sparse.spmatrix instance.
Notes
-----
If scipy is not installed, this function will always return False.
Examples
--------
>>> from scipy.sparse import bsr_matrix
>>> is_scipy_sparse(bsr_matrix([1, 2, 3]))
True
>>> is_scipy_sparse(pd.SparseArray([1, 2, 3]))
False
>>> is_scipy_sparse(pd.SparseSeries([1, 2, 3]))
False
"""
global _is_scipy_sparse
if _is_scipy_sparse is None:
try:
from scipy.sparse import issparse as _is_scipy_sparse
except ImportError:
_is_scipy_sparse = lambda _: False
return _is_scipy_sparse(arr)
def is_categorical(arr):
"""
Check whether an array-like is a Categorical instance.
Parameters
----------
arr : array-like
The array-like to check.
Returns
-------
boolean : Whether or not the array-like is of a Categorical instance.
Examples
--------
>>> is_categorical([1, 2, 3])
False
Categoricals, Series Categoricals, and CategoricalIndex will return True.
>>> cat = pd.Categorical([1, 2, 3])
>>> is_categorical(cat)
True
>>> is_categorical(pd.Series(cat))
True
>>> is_categorical(pd.CategoricalIndex([1, 2, 3]))
True
"""
return isinstance(arr, ABCCategorical) or is_categorical_dtype(arr)
def is_datetimetz(arr):
"""
Check whether an array-like is a datetime array-like with a timezone
component in its dtype.
Parameters
----------
arr : array-like
The array-like to check.
Returns
-------
boolean : Whether or not the array-like is a datetime array-like with
a timezone component in its dtype.
Examples
--------
>>> is_datetimetz([1, 2, 3])
False
Although the following examples are both DatetimeIndex objects,
the first one returns False because it has no timezone component
unlike the second one, which returns True.
>>> is_datetimetz(pd.DatetimeIndex([1, 2, 3]))
False
>>> is_datetimetz(pd.DatetimeIndex([1, 2, 3], tz="US/Eastern"))
True
The object need not be a DatetimeIndex object. It just needs to have
a dtype which has a timezone component.
>>> dtype = DatetimeTZDtype("ns", tz="US/Eastern")
>>> s = pd.Series([], dtype=dtype)
>>> is_datetimetz(s)
True
"""
# TODO: do we need this function?
# It seems like a repeat of is_datetime64tz_dtype.
return ((isinstance(arr, ABCDatetimeIndex) and
getattr(arr, 'tz', None) is not None) or
is_datetime64tz_dtype(arr))
def is_offsetlike(arr_or_obj):
"""
Check if obj or all elements of list-like is DateOffset
Parameters
----------
arr_or_obj : object
Returns
-------
boolean : Whether the object is a DateOffset or listlike of DatetOffsets
Examples
--------
>>> is_offsetlike(pd.DateOffset(days=1))
True
>>> is_offsetlike('offset')
False
>>> is_offsetlike([pd.offsets.Minute(4), pd.offsets.MonthEnd()])
True
>>> is_offsetlike(np.array([pd.DateOffset(months=3), pd.Timestamp.now()]))
False
"""
if isinstance(arr_or_obj, ABCDateOffset):
return True
elif (is_list_like(arr_or_obj) and len(arr_or_obj) and
is_object_dtype(arr_or_obj)):
return all(isinstance(x, ABCDateOffset) for x in arr_or_obj)
return False
def is_period(arr):
"""
Check whether an array-like is a periodical index.
Parameters
----------
arr : array-like
The array-like to check.
Returns
-------
boolean : Whether or not the array-like is a periodical index.
Examples
--------
>>> is_period([1, 2, 3])
False
>>> is_period(pd.Index([1, 2, 3]))
False
>>> is_period(pd.PeriodIndex(["2017-01-01"], freq="D"))
True
"""
# TODO: do we need this function?
# It seems like a repeat of is_period_arraylike.
return isinstance(arr, ABCPeriodIndex) or is_period_arraylike(arr)
def is_datetime64_dtype(arr_or_dtype):
"""
Check whether an array-like or dtype is of the datetime64 dtype.
Parameters
----------
arr_or_dtype : array-like
The array-like or dtype to check.
Returns
-------
boolean : Whether or not the array-like or dtype is of
the datetime64 dtype.
Examples
--------
>>> is_datetime64_dtype(object)
False
>>> is_datetime64_dtype(np.datetime64)
True
>>> is_datetime64_dtype(np.array([], dtype=int))
False
>>> is_datetime64_dtype(np.array([], dtype=np.datetime64))
True
>>> is_datetime64_dtype([1, 2, 3])
False
"""
if arr_or_dtype is None:
return False
try:
tipo = _get_dtype_type(arr_or_dtype)
except TypeError:
return False
return issubclass(tipo, np.datetime64)
def is_datetime64tz_dtype(arr_or_dtype):
"""
Check whether an array-like or dtype is of a DatetimeTZDtype dtype.
Parameters
----------
arr_or_dtype : array-like
The array-like or dtype to check.
Returns
-------
boolean : Whether or not the array-like or dtype is of
a DatetimeTZDtype dtype.
Examples
--------
>>> is_datetime64tz_dtype(object)
False
>>> is_datetime64tz_dtype([1, 2, 3])
False
>>> is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3])) # tz-naive
False
>>> is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3], tz="US/Eastern"))
True
>>> dtype = DatetimeTZDtype("ns", tz="US/Eastern")
>>> s = pd.Series([], dtype=dtype)
>>> is_datetime64tz_dtype(dtype)
True
>>> is_datetime64tz_dtype(s)
True
"""
if arr_or_dtype is None:
return False
return DatetimeTZDtype.is_dtype(arr_or_dtype)
def is_timedelta64_dtype(arr_or_dtype):
"""
Check whether an array-like or dtype is of the timedelta64 dtype.
Parameters
----------
arr_or_dtype : array-like
The array-like or dtype to check.
Returns
-------
boolean : Whether or not the array-like or dtype is
of the timedelta64 dtype.
Examples
--------
>>> is_timedelta64_dtype(object)
False
>>> is_timedelta64_dtype(np.timedelta64)
True
>>> is_timedelta64_dtype([1, 2, 3])
False
>>> is_timedelta64_dtype(pd.Series([], dtype="timedelta64[ns]"))
True
>>> is_timedelta64_dtype('0 days')
False
"""
if arr_or_dtype is None:
return False
try:
tipo = _get_dtype_type(arr_or_dtype)
except:
return False
return issubclass(tipo, np.timedelta64)
def is_period_dtype(arr_or_dtype):
"""
Check whether an array-like or dtype is of the Period dtype.
Parameters
----------
arr_or_dtype : array-like
The array-like or dtype to check.
Returns
-------
boolean : Whether or not the array-like or dtype is of the Period dtype.
Examples
--------
>>> is_period_dtype(object)
False
>>> is_period_dtype(PeriodDtype(freq="D"))
True
>>> is_period_dtype([1, 2, 3])
False
>>> is_period_dtype(pd.Period("2017-01-01"))
False
>>> is_period_dtype(pd.PeriodIndex([], freq="A"))
True
"""
# TODO: Consider making Period an instance of PeriodDtype
if arr_or_dtype is None:
return False
return PeriodDtype.is_dtype(arr_or_dtype)
def is_interval_dtype(arr_or_dtype):
"""
Check whether an array-like or dtype is of the Interval dtype.
Parameters
----------
arr_or_dtype : array-like
The array-like or dtype to check.
Returns
-------
boolean : Whether or not the array-like or dtype is
of the Interval dtype.
Examples
--------
>>> is_interval_dtype(object)
False
>>> is_interval_dtype(IntervalDtype())
True
>>> is_interval_dtype([1, 2, 3])
False
>>>
>>> interval = pd.Interval(1, 2, closed="right")
>>> is_interval_dtype(interval)
False
>>> is_interval_dtype(pd.IntervalIndex([interval]))
True
"""
# TODO: Consider making Interval an instance of IntervalDtype
if arr_or_dtype is None:
return False
return IntervalDtype.is_dtype(arr_or_dtype)
def is_categorical_dtype(arr_or_dtype):
"""
Check whether an array-like or dtype is of the Categorical dtype.
Parameters
----------
arr_or_dtype : array-like
The array-like or dtype to check.
Returns
-------
boolean : Whether or not the array-like or dtype is
of the Categorical dtype.
Examples
--------
>>> is_categorical_dtype(object)
False
>>> is_categorical_dtype(CategoricalDtype())
True
>>> is_categorical_dtype([1, 2, 3])
False
>>> is_categorical_dtype(pd.Categorical([1, 2, 3]))
True
>>> is_categorical_dtype(pd.CategoricalIndex([1, 2, 3]))
True
"""
if arr_or_dtype is None:
return False
return CategoricalDtype.is_dtype(arr_or_dtype)
def is_string_dtype(arr_or_dtype):
"""
Check whether the provided array or dtype is of the string dtype.
Parameters
----------
arr_or_dtype : array-like
The array or dtype to check.
Returns
-------
boolean : Whether or not the array or dtype is of the string dtype.
Examples
--------
>>> is_string_dtype(str)
True
>>> is_string_dtype(object)
True
>>> is_string_dtype(int)
False
>>>
>>> is_string_dtype(np.array(['a', 'b']))
True
>>> is_string_dtype(pd.Series([1, 2]))
False
"""
# TODO: gh-15585: consider making the checks stricter.
if arr_or_dtype is None:
return False
try:
dtype = _get_dtype(arr_or_dtype)
return dtype.kind in ('O', 'S', 'U') and not is_period_dtype(dtype)
except TypeError:
return False
def is_period_arraylike(arr):
"""
Check whether an array-like is a periodical array-like or PeriodIndex.
Parameters
----------
arr : array-like
The array-like to check.
Returns
-------
boolean : Whether or not the array-like is a periodical
array-like or PeriodIndex instance.
Examples
--------
>>> is_period_arraylike([1, 2, 3])
False
>>> is_period_arraylike(pd.Index([1, 2, 3]))
False
>>> is_period_arraylike(pd.PeriodIndex(["2017-01-01"], freq="D"))
True
"""
if isinstance(arr, ABCPeriodIndex):
return True
elif isinstance(arr, (np.ndarray, ABCSeries)):
return arr.dtype == object and lib.infer_dtype(arr) == 'period'
return getattr(arr, 'inferred_type', None) == 'period'
def is_datetime_arraylike(arr):
"""
Check whether an array-like is a datetime array-like or DatetimeIndex.
Parameters
----------
arr : array-like
The array-like to check.
Returns
-------
boolean : Whether or not the array-like is a datetime
array-like or DatetimeIndex.
Examples
--------
>>> is_datetime_arraylike([1, 2, 3])
False
>>> is_datetime_arraylike(pd.Index([1, 2, 3]))
False
>>> is_datetime_arraylike(pd.DatetimeIndex([1, 2, 3]))
True
"""
if isinstance(arr, ABCDatetimeIndex):
return True
elif isinstance(arr, (np.ndarray, ABCSeries)):
return arr.dtype == object and lib.infer_dtype(arr) == 'datetime'
return getattr(arr, 'inferred_type', None) == 'datetime'
def is_datetimelike(arr):
"""
Check whether an array-like is a datetime-like array-like.
Acceptable datetime-like objects are (but not limited to) datetime
indices, periodic indices, and timedelta indices.
Parameters
----------
arr : array-like
The array-like to check.
Returns
-------
boolean : Whether or not the array-like is a datetime-like array-like.
Examples
--------
>>> is_datetimelike([1, 2, 3])
False
>>> is_datetimelike(pd.Index([1, 2, 3]))
False
>>> is_datetimelike(pd.DatetimeIndex([1, 2, 3]))
True
>>> is_datetimelike(pd.DatetimeIndex([1, 2, 3], tz="US/Eastern"))
True
>>> is_datetimelike(pd.PeriodIndex([], freq="A"))
True
>>> is_datetimelike(np.array([], dtype=np.datetime64))
True
>>> is_datetimelike(pd.Series([], dtype="timedelta64[ns]"))
True
>>>
>>> dtype = DatetimeTZDtype("ns", tz="US/Eastern")
>>> s = pd.Series([], dtype=dtype)
>>> is_datetimelike(s)
True
"""
return (is_datetime64_dtype(arr) or is_datetime64tz_dtype(arr) or
is_timedelta64_dtype(arr) or
isinstance(arr, ABCPeriodIndex) or
is_datetimetz(arr))
def is_dtype_equal(source, target):
"""
Check if two dtypes are equal.
Parameters
----------
source : The first dtype to compare
target : The second dtype to compare
Returns
----------
boolean : Whether or not the two dtypes are equal.
Examples
--------
>>> is_dtype_equal(int, float)
False
>>> is_dtype_equal("int", int)
True
>>> is_dtype_equal(object, "category")
False
>>> is_dtype_equal(CategoricalDtype(), "category")
True
>>> is_dtype_equal(DatetimeTZDtype(), "datetime64")
False
"""
try:
source = _get_dtype(source)
target = _get_dtype(target)
return source == target
except (TypeError, AttributeError):
# invalid comparison
# object == category will hit this
return False
def is_dtype_union_equal(source, target):
"""
Check whether two arrays have compatible dtypes to do a union.
numpy types are checked with ``is_dtype_equal``. Extension types are
checked separately.
Parameters
----------
source : The first dtype to compare
target : The second dtype to compare
Returns
----------
boolean : Whether or not the two dtypes are equal.
>>> is_dtype_equal("int", int)
True
>>> is_dtype_equal(CategoricalDtype(['a', 'b'],
... CategoricalDtype(['b', 'c']))
True
>>> is_dtype_equal(CategoricalDtype(['a', 'b'],
... CategoricalDtype(['b', 'c'], ordered=True))
False
"""
source = _get_dtype(source)
target = _get_dtype(target)
if is_categorical_dtype(source) and is_categorical_dtype(target):
# ordered False for both
return source.ordered is target.ordered
return is_dtype_equal(source, target)
def is_any_int_dtype(arr_or_dtype):
"""Check whether the provided array or dtype is of an integer dtype.
.. deprecated:: 0.20.0
In this function, timedelta64 instances are also considered "any-integer"
type objects and will return True.
Parameters
----------
arr_or_dtype : array-like
The array or dtype to check.
Returns
-------
boolean : Whether or not the array or dtype is of an integer dtype.
Examples
--------
>>> is_any_int_dtype(str)
False
>>> is_any_int_dtype(int)
True
>>> is_any_int_dtype(float)
False
>>> is_any_int_dtype(np.uint64)
True
>>> is_any_int_dtype(np.datetime64)
False
>>> is_any_int_dtype(np.timedelta64)
True
>>> is_any_int_dtype(np.array(['a', 'b']))
False
>>> is_any_int_dtype(pd.Series([1, 2]))
True
>>> is_any_int_dtype(np.array([], dtype=np.timedelta64))
True
>>> is_any_int_dtype(pd.Index([1, 2.])) # float
False
"""
if arr_or_dtype is None:
return False
tipo = _get_dtype_type(arr_or_dtype)
return issubclass(tipo, np.integer)
def is_integer_dtype(arr_or_dtype):
"""
Check whether the provided array or dtype is of an integer dtype.
Unlike in `in_any_int_dtype`, timedelta64 instances will return False.
Parameters
----------
arr_or_dtype : array-like
The array or dtype to check.
Returns
-------
boolean : Whether or not the array or dtype is of an integer dtype
and not an instance of timedelta64.
Examples
--------
>>> is_integer_dtype(str)
False
>>> is_integer_dtype(int)
True
>>> is_integer_dtype(float)
False
>>> is_integer_dtype(np.uint64)
True
>>> is_integer_dtype(np.datetime64)
False
>>> is_integer_dtype(np.timedelta64)
False
>>> is_integer_dtype(np.array(['a', 'b']))
False
>>> is_integer_dtype(pd.Series([1, 2]))
True
>>> is_integer_dtype(np.array([], dtype=np.timedelta64))
False
>>> is_integer_dtype(pd.Index([1, 2.])) # float
False
"""
if arr_or_dtype is None:
return False
tipo = _get_dtype_type(arr_or_dtype)
return (issubclass(tipo, np.integer) and
not issubclass(tipo, (np.datetime64, np.timedelta64)))
def is_signed_integer_dtype(arr_or_dtype):
"""
Check whether the provided array or dtype is of a signed integer dtype.
Unlike in `in_any_int_dtype`, timedelta64 instances will return False.
Parameters
----------
arr_or_dtype : array-like
The array or dtype to check.
Returns
-------
boolean : Whether or not the array or dtype is of a signed integer dtype
and not an instance of timedelta64.
Examples
--------
>>> is_signed_integer_dtype(str)
False
>>> is_signed_integer_dtype(int)
True
>>> is_signed_integer_dtype(float)
False
>>> is_signed_integer_dtype(np.uint64) # unsigned
False
>>> is_signed_integer_dtype(np.datetime64)
False
>>> is_signed_integer_dtype(np.timedelta64)
False
>>> is_signed_integer_dtype(np.array(['a', 'b']))
False
>>> is_signed_integer_dtype(pd.Series([1, 2]))
True
>>> is_signed_integer_dtype(np.array([], dtype=np.timedelta64))
False
>>> is_signed_integer_dtype(pd.Index([1, 2.])) # float
False
>>> is_signed_integer_dtype(np.array([1, 2], dtype=np.uint32)) # unsigned
False
"""
if arr_or_dtype is None:
return False
tipo = _get_dtype_type(arr_or_dtype)
return (issubclass(tipo, np.signedinteger) and
not issubclass(tipo, (np.datetime64, np.timedelta64)))
def is_unsigned_integer_dtype(arr_or_dtype):
"""
Check whether the provided array or dtype is of an unsigned integer dtype.
Parameters
----------
arr_or_dtype : array-like
The array or dtype to check.
Returns
-------
boolean : Whether or not the array or dtype is of an
unsigned integer dtype.
Examples
--------
>>> is_unsigned_integer_dtype(str)
False
>>> is_unsigned_integer_dtype(int) # signed
False
>>> is_unsigned_integer_dtype(float)
False
>>> is_unsigned_integer_dtype(np.uint64)
True
>>> is_unsigned_integer_dtype(np.array(['a', 'b']))
False
>>> is_unsigned_integer_dtype(pd.Series([1, 2])) # signed
False
>>> is_unsigned_integer_dtype(pd.Index([1, 2.])) # float
False
>>> is_unsigned_integer_dtype(np.array([1, 2], dtype=np.uint32))
True
"""
if arr_or_dtype is None:
return False
tipo = _get_dtype_type(arr_or_dtype)
return (issubclass(tipo, np.unsignedinteger) and
not issubclass(tipo, (np.datetime64, np.timedelta64)))
def is_int64_dtype(arr_or_dtype):
"""
Check whether the provided array or dtype is of the int64 dtype.
Parameters
----------
arr_or_dtype : array-like
The array or dtype to check.
Returns
-------
boolean : Whether or not the array or dtype is of the int64 dtype.
Notes
-----
Depending on system architecture, the return value of `is_int64_dtype(
int)` will be True if the OS uses 64-bit integers and False if the OS
uses 32-bit integers.
Examples
--------
>>> is_int64_dtype(str)
False
>>> is_int64_dtype(np.int32)
False
>>> is_int64_dtype(np.int64)
True
>>> is_int64_dtype(float)
False
>>> is_int64_dtype(np.uint64) # unsigned
False
>>> is_int64_dtype(np.array(['a', 'b']))
False
>>> is_int64_dtype(np.array([1, 2], dtype=np.int64))
True
>>> is_int64_dtype(pd.Index([1, 2.])) # float
False
>>> is_int64_dtype(np.array([1, 2], dtype=np.uint32)) # unsigned
False
"""
if arr_or_dtype is None:
return False
tipo = _get_dtype_type(arr_or_dtype)
return issubclass(tipo, np.int64)
def is_int_or_datetime_dtype(arr_or_dtype):
"""
Check whether the provided array or dtype is of an
integer, timedelta64, or datetime64 dtype.
Parameters