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pythonkurs/teil20b/lib/python3.11/site-packages/pandas/_libs/lib.pyx

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virtuelle Umgebung teil20b
1 year ago
from collections import abc
from decimal import Decimal
from enum import Enum
from sys import getsizeof
from typing import (
Literal,
_GenericAlias,
)
cimport cython
from cpython.datetime cimport (
PyDate_Check,
PyDateTime_Check,
PyDelta_Check,
PyTime_Check,
date,
datetime,
import_datetime,
time,
timedelta,
)
from cpython.iterator cimport PyIter_Check
from cpython.number cimport PyNumber_Check
from cpython.object cimport (
Py_EQ,
PyObject,
PyObject_RichCompareBool,
PyTypeObject,
)
from cpython.ref cimport Py_INCREF
from cpython.sequence cimport PySequence_Check
from cpython.tuple cimport (
PyTuple_New,
PyTuple_SET_ITEM,
)
from cython cimport (
Py_ssize_t,
floating,
)
from pandas._config import using_pyarrow_string_dtype
from pandas._libs.missing import check_na_tuples_nonequal
import_datetime()
import numpy as np
cimport numpy as cnp
from numpy cimport (
NPY_OBJECT,
PyArray_Check,
PyArray_GETITEM,
PyArray_ITER_DATA,
PyArray_ITER_NEXT,
PyArray_IterNew,
complex128_t,
flatiter,
float64_t,
int32_t,
int64_t,
intp_t,
ndarray,
uint8_t,
uint64_t,
)
cnp.import_array()
cdef extern from "Python.h":
# Note: importing extern-style allows us to declare these as nogil
# functions, whereas `from cpython cimport` does not.
bint PyObject_TypeCheck(object obj, PyTypeObject* type) nogil
cdef extern from "numpy/arrayobject.h":
# cython's numpy.dtype specification is incorrect, which leads to
# errors in issubclass(self.dtype.type, np.bool_), so we directly
# include the correct version
# https://github.com/cython/cython/issues/2022
ctypedef class numpy.dtype [object PyArray_Descr]:
# Use PyDataType_* macros when possible, however there are no macros
# for accessing some of the fields, so some are defined. Please
# ask on cython-dev if you need more.
cdef:
int type_num
int itemsize "elsize"
char byteorder
object fields
tuple names
PyTypeObject PySignedIntegerArrType_Type
PyTypeObject PyUnsignedIntegerArrType_Type
cdef extern from "numpy/ndarrayobject.h":
bint PyArray_CheckScalar(obj) nogil
cdef extern from "pandas/parser/pd_parser.h":
int floatify(object, float64_t *result, int *maybe_int) except -1
void PandasParser_IMPORT()
PandasParser_IMPORT
from pandas._libs cimport util
from pandas._libs.util cimport (
INT64_MAX,
INT64_MIN,
UINT64_MAX,
is_nan,
)
from pandas._libs.tslibs import (
OutOfBoundsDatetime,
OutOfBoundsTimedelta,
)
from pandas._libs.tslibs.period import Period
from pandas._libs.missing cimport (
C_NA,
checknull,
is_matching_na,
is_null_datetime64,
is_null_timedelta64,
)
from pandas._libs.tslibs.conversion cimport (
_TSObject,
convert_to_tsobject,
)
from pandas._libs.tslibs.nattype cimport (
NPY_NAT,
c_NaT as NaT,
checknull_with_nat,
)
from pandas._libs.tslibs.np_datetime cimport NPY_FR_ns
from pandas._libs.tslibs.offsets cimport is_offset_object
from pandas._libs.tslibs.period cimport is_period_object
from pandas._libs.tslibs.timedeltas cimport convert_to_timedelta64
from pandas._libs.tslibs.timezones cimport tz_compare
# constants that will be compared to potentially arbitrarily large
# python int
cdef:
object oINT64_MAX = <int64_t>INT64_MAX
object oINT64_MIN = <int64_t>INT64_MIN
object oUINT64_MAX = <uint64_t>UINT64_MAX
float64_t NaN = <float64_t>np.nan
# python-visible
i8max = <int64_t>INT64_MAX
u8max = <uint64_t>UINT64_MAX
cdef bint PYARROW_INSTALLED = False
try:
import pyarrow as pa
PYARROW_INSTALLED = True
except ImportError:
pa = None
@cython.wraparound(False)
@cython.boundscheck(False)
def memory_usage_of_objects(arr: object[:]) -> int64_t:
"""
Return the memory usage of an object array in bytes.
Does not include the actual bytes of the pointers
"""
cdef:
Py_ssize_t i
Py_ssize_t n
int64_t size = 0
n = len(arr)
for i in range(n):
size += getsizeof(arr[i])
return size
# ----------------------------------------------------------------------
def is_scalar(val: object) -> bool:
"""
Return True if given object is scalar.
Parameters
----------
val : object
This includes:
- numpy array scalar (e.g. np.int64)
- Python builtin numerics
- Python builtin byte arrays and strings
- None
- datetime.datetime
- datetime.timedelta
- Period
- decimal.Decimal
- Interval
- DateOffset
- Fraction
- Number.
Returns
-------
bool
Return True if given object is scalar.
Examples
--------
>>> import datetime
>>> dt = datetime.datetime(2018, 10, 3)
>>> pd.api.types.is_scalar(dt)
True
>>> pd.api.types.is_scalar([2, 3])
False
>>> pd.api.types.is_scalar({0: 1, 2: 3})
False
>>> pd.api.types.is_scalar((0, 2))
False
pandas supports PEP 3141 numbers:
>>> from fractions import Fraction
>>> pd.api.types.is_scalar(Fraction(3, 5))
True
"""
# Start with C-optimized checks
if (cnp.PyArray_IsAnyScalar(val)
# PyArray_IsAnyScalar is always False for bytearrays on Py3
or PyDate_Check(val)
or PyDelta_Check(val)
or PyTime_Check(val)
# We differ from numpy, which claims that None is not scalar;
# see np.isscalar
or val is C_NA
or val is None):
return True
# Next use C-optimized checks to exclude common non-scalars before falling
# back to non-optimized checks.
if PySequence_Check(val):
# e.g. list, tuple
# includes np.ndarray, Series which PyNumber_Check can return True for
return False
# Note: PyNumber_Check check includes Decimal, Fraction, numbers.Number
return (PyNumber_Check(val)
or is_period_object(val)
or is_interval(val)
or is_offset_object(val))
cdef int64_t get_itemsize(object val):
"""
Get the itemsize of a NumPy scalar, -1 if not a NumPy scalar.
Parameters
----------
val : object
Returns
-------
is_ndarray : bool
"""
if PyArray_CheckScalar(val):
return cnp.PyArray_DescrFromScalar(val).itemsize
else:
return -1
def is_iterator(obj: object) -> bool:
"""
Check if the object is an iterator.
This is intended for generators, not list-like objects.
Parameters
----------
obj : The object to check
Returns
-------
is_iter : bool
Whether `obj` is an iterator.
Examples
--------
>>> import datetime
>>> from pandas.api.types import is_iterator
>>> is_iterator((x for x in []))
True
>>> is_iterator([1, 2, 3])
False
>>> is_iterator(datetime.datetime(2017, 1, 1))
False
>>> is_iterator("foo")
False
>>> is_iterator(1)
False
"""
return PyIter_Check(obj)
def item_from_zerodim(val: object) -> object:
"""
If the value is a zerodim array, return the item it contains.
Parameters
----------
val : object
Returns
-------
object
Examples
--------
>>> item_from_zerodim(1)
1
>>> item_from_zerodim('foobar')
'foobar'
>>> item_from_zerodim(np.array(1))
1
>>> item_from_zerodim(np.array([1]))
array([1])
"""
if cnp.PyArray_IsZeroDim(val):
return cnp.PyArray_ToScalar(cnp.PyArray_DATA(val), val)
return val
@cython.wraparound(False)
@cython.boundscheck(False)
def fast_unique_multiple_list(lists: list, sort: bool | None = True) -> list:
cdef:
list buf
Py_ssize_t k = len(lists)
Py_ssize_t i, j, n
list uniques = []
dict table = {}
object val, stub = 0
for i in range(k):
buf = lists[i]
n = len(buf)
for j in range(n):
val = buf[j]
if val not in table:
table[val] = stub
uniques.append(val)
if sort:
try:
uniques.sort()
except TypeError:
pass
return uniques
@cython.wraparound(False)
@cython.boundscheck(False)
def fast_unique_multiple_list_gen(object gen, bint sort=True) -> list:
"""
Generate a list of unique values from a generator of lists.
Parameters
----------
gen : generator object
Generator of lists from which the unique list is created.
sort : bool
Whether or not to sort the resulting unique list.
Returns
-------
list of unique values
"""
cdef:
list buf
Py_ssize_t j, n
list uniques = []
dict table = {}
object val, stub = 0
for buf in gen:
n = len(buf)
for j in range(n):
val = buf[j]
if val not in table:
table[val] = stub
uniques.append(val)
if sort:
try:
uniques.sort()
except TypeError:
pass
return uniques
@cython.wraparound(False)
@cython.boundscheck(False)
def dicts_to_array(dicts: list, columns: list):
cdef:
Py_ssize_t i, j, k, n
ndarray[object, ndim=2] result
dict row
object col, onan = np.nan
k = len(columns)
n = len(dicts)
result = np.empty((n, k), dtype="O")
for i in range(n):
row = dicts[i]
for j in range(k):
col = columns[j]
if col in row:
result[i, j] = row[col]
else:
result[i, j] = onan
return result
def fast_zip(list ndarrays) -> ndarray[object]:
"""
For zipping multiple ndarrays into an ndarray of tuples.
"""
cdef:
Py_ssize_t i, j, k, n
ndarray[object, ndim=1] result
flatiter it
object val, tup
k = len(ndarrays)
n = len(ndarrays[0])
result = np.empty(n, dtype=object)
# initialize tuples on first pass
arr = ndarrays[0]
it = <flatiter>PyArray_IterNew(arr)
for i in range(n):
val = PyArray_GETITEM(arr, PyArray_ITER_DATA(it))
tup = PyTuple_New(k)
PyTuple_SET_ITEM(tup, 0, val)
Py_INCREF(val)
result[i] = tup
PyArray_ITER_NEXT(it)
for j in range(1, k):
arr = ndarrays[j]
it = <flatiter>PyArray_IterNew(arr)
if len(arr) != n:
raise ValueError("all arrays must be same length")
for i in range(n):
val = PyArray_GETITEM(arr, PyArray_ITER_DATA(it))
PyTuple_SET_ITEM(result[i], j, val)
Py_INCREF(val)
PyArray_ITER_NEXT(it)
return result
def get_reverse_indexer(const intp_t[:] indexer, Py_ssize_t length) -> ndarray:
"""
Reverse indexing operation.
Given `indexer`, make `indexer_inv` of it, such that::
indexer_inv[indexer[x]] = x
Parameters
----------
indexer : np.ndarray[np.intp]
length : int
Returns
-------
np.ndarray[np.intp]
Notes
-----
If indexer is not unique, only first occurrence is accounted.
"""
cdef:
Py_ssize_t i, n = len(indexer)
ndarray[intp_t, ndim=1] rev_indexer
intp_t idx
rev_indexer = np.empty(length, dtype=np.intp)
rev_indexer[:] = -1
for i in range(n):
idx = indexer[i]
if idx != -1:
rev_indexer[idx] = i
return rev_indexer
@cython.wraparound(False)
@cython.boundscheck(False)
# TODO(cython3): Can add const once cython#1772 is resolved
def has_infs(floating[:] arr) -> bool:
cdef:
Py_ssize_t i, n = len(arr)
floating inf, neginf, val
bint ret = False
inf = np.inf
neginf = -inf
with nogil:
for i in range(n):
val = arr[i]
if val == inf or val == neginf:
ret = True
break
return ret
@cython.boundscheck(False)
@cython.wraparound(False)
def has_only_ints_or_nan(floating[:] arr) -> bool:
cdef:
floating val
intp_t i
for i in range(len(arr)):
val = arr[i]
if (val != val) or (val == <int64_t>val):
continue
else:
return False
return True
def maybe_indices_to_slice(ndarray[intp_t, ndim=1] indices, int max_len):
cdef:
Py_ssize_t i, n = len(indices)
intp_t k, vstart, vlast, v
if n == 0:
return slice(0, 0)
vstart = indices[0]
if vstart < 0 or max_len <= vstart:
return indices
if n == 1:
return slice(vstart, <intp_t>(vstart + 1))
vlast = indices[n - 1]
if vlast < 0 or max_len <= vlast:
return indices
k = indices[1] - indices[0]
if k == 0:
return indices
else:
for i in range(2, n):
v = indices[i]
if v - indices[i - 1] != k:
return indices
if k > 0:
return slice(vstart, <intp_t>(vlast + 1), k)
else:
if vlast == 0:
return slice(vstart, None, k)
else:
return slice(vstart, <intp_t>(vlast - 1), k)
@cython.wraparound(False)
@cython.boundscheck(False)
def maybe_booleans_to_slice(ndarray[uint8_t, ndim=1] mask):
cdef:
Py_ssize_t i, n = len(mask)
Py_ssize_t start = 0, end = 0
bint started = False, finished = False
for i in range(n):
if mask[i]:
if finished:
return mask.view(np.bool_)
if not started:
started = True
start = i
else:
if finished:
continue
if started:
end = i
finished = True
if not started:
return slice(0, 0)
if not finished:
return slice(start, None)
else:
return slice(start, end)
@cython.wraparound(False)
@cython.boundscheck(False)
def array_equivalent_object(ndarray left, ndarray right) -> bool:
"""
Perform an element by element comparison on N-d object arrays
taking into account nan positions.
"""
# left and right both have object dtype, but we cannot annotate that
# without limiting ndim.
cdef:
Py_ssize_t i, n = left.size
object x, y
cnp.broadcast mi = cnp.PyArray_MultiIterNew2(left, right)
# Caller is responsible for checking left.shape == right.shape
for i in range(n):
# Analogous to: x = left[i]
x = <object>(<PyObject**>cnp.PyArray_MultiIter_DATA(mi, 0))[0]
y = <object>(<PyObject**>cnp.PyArray_MultiIter_DATA(mi, 1))[0]
# we are either not equal or both nan
# I think None == None will be true here
try:
if PyArray_Check(x) and PyArray_Check(y):
if x.shape != y.shape:
return False
if x.dtype == y.dtype == object:
if not array_equivalent_object(x, y):
return False
else:
# Circular import isn't great, but so it goes.
# TODO: could use np.array_equal?
from pandas.core.dtypes.missing import array_equivalent
if not array_equivalent(x, y):
return False
elif (x is C_NA) ^ (y is C_NA):
return False
elif not (
PyObject_RichCompareBool(x, y, Py_EQ)
or is_matching_na(x, y, nan_matches_none=True)
):
return False
except (ValueError, TypeError):
# Avoid raising ValueError when comparing Numpy arrays to other types
if cnp.PyArray_IsAnyScalar(x) != cnp.PyArray_IsAnyScalar(y):
# Only compare scalars to scalars and non-scalars to non-scalars
return False
elif (not (cnp.PyArray_IsPythonScalar(x) or cnp.PyArray_IsPythonScalar(y))
and not (isinstance(x, type(y)) or isinstance(y, type(x)))):
# Check if non-scalars have the same type
return False
elif check_na_tuples_nonequal(x, y):
# We have tuples where one Side has a NA and the other side does not
# Only condition we may end up with a TypeError
return False
raise
cnp.PyArray_MultiIter_NEXT(mi)
return True
ctypedef fused int6432_t:
int64_t
int32_t
@cython.wraparound(False)
@cython.boundscheck(False)
def is_range_indexer(ndarray[int6432_t, ndim=1] left, Py_ssize_t n) -> bool:
"""
Perform an element by element comparison on 1-d integer arrays, meant for indexer
comparisons
"""
cdef:
Py_ssize_t i
if left.size != n:
return False
for i in range(n):
if left[i] != i:
return False
return True
ctypedef fused ndarr_object:
ndarray[object, ndim=1]
ndarray[object, ndim=2]
# TODO: get rid of this in StringArray and modify
# and go through ensure_string_array instead
@cython.wraparound(False)
@cython.boundscheck(False)
def convert_nans_to_NA(ndarr_object arr) -> ndarray:
"""
Helper for StringArray that converts null values that
are not pd.NA(e.g. np.nan, None) to pd.NA. Assumes elements
have already been validated as null.
"""
cdef:
Py_ssize_t i, m, n
object val
ndarr_object result
result = np.asarray(arr, dtype="object")
if arr.ndim == 2:
m, n = arr.shape[0], arr.shape[1]
for i in range(m):
for j in range(n):
val = arr[i, j]
if not isinstance(val, str):
result[i, j] = <object>C_NA
else:
n = len(arr)
for i in range(n):
val = arr[i]
if not isinstance(val, str):
result[i] = <object>C_NA
return result
@cython.wraparound(False)
@cython.boundscheck(False)
cpdef ndarray[object] ensure_string_array(
arr,
object na_value=np.nan,
bint convert_na_value=True,
bint copy=True,
bint skipna=True,
):
"""
Returns a new numpy array with object dtype and only strings and na values.
Parameters
----------
arr : array-like
The values to be converted to str, if needed.
na_value : Any, default np.nan
The value to use for na. For example, np.nan or pd.NA.
convert_na_value : bool, default True
If False, existing na values will be used unchanged in the new array.
copy : bool, default True
Whether to ensure that a new array is returned.
skipna : bool, default True
Whether or not to coerce nulls to their stringified form
(e.g. if False, NaN becomes 'nan').
Returns
-------
np.ndarray[object]
An array with the input array's elements casted to str or nan-like.
"""
cdef:
Py_ssize_t i = 0, n = len(arr)
bint already_copied = True
if hasattr(arr, "to_numpy"):
if hasattr(arr, "dtype") and arr.dtype.kind in "mM":
# dtype check to exclude DataFrame
# GH#41409 TODO: not a great place for this
out = arr.astype(str).astype(object)
out[arr.isna()] = na_value
return out
arr = arr.to_numpy()
elif not util.is_array(arr):
arr = np.array(arr, dtype="object")
result = np.asarray(arr, dtype="object")
if copy and result is arr:
result = result.copy()
elif not copy and result is arr:
already_copied = False
if issubclass(arr.dtype.type, np.str_):
# short-circuit, all elements are str
return result
for i in range(n):
val = arr[i]
if isinstance(val, str):
continue
elif not already_copied:
result = result.copy()
already_copied = True
if not checknull(val):
if isinstance(val, bytes):
# GH#49658 discussion of desired behavior here
result[i] = val.decode()
elif not util.is_float_object(val):
# f"{val}" is faster than str(val)
result[i] = f"{val}"
else:
# f"{val}" is not always equivalent to str(val) for floats
result[i] = str(val)
else:
if convert_na_value:
val = na_value
if skipna:
result[i] = val
else:
result[i] = f"{val}"
return result
def is_all_arraylike(obj: list) -> bool:
"""
Should we treat these as levels of a MultiIndex, as opposed to Index items?
"""
cdef:
Py_ssize_t i, n = len(obj)
object val
bint all_arrays = True
for i in range(n):
val = obj[i]
if not (isinstance(val, list) or
util.is_array(val) or hasattr(val, "_data")):
# TODO: EA?
# exclude tuples, frozensets as they may be contained in an Index
all_arrays = False
break
return all_arrays
# ------------------------------------------------------------------------------
# Groupby-related functions
# TODO: could do even better if we know something about the data. eg, index has
# 1-min data, binner has 5-min data, then bins are just strides in index. This
# is a general, O(max(len(values), len(binner))) method.
@cython.boundscheck(False)
@cython.wraparound(False)
def generate_bins_dt64(ndarray[int64_t, ndim=1] values, const int64_t[:] binner,
object closed="left", bint hasnans=False):
"""
Int64 (datetime64) version of generic python version in ``groupby.py``.
"""
cdef:
Py_ssize_t lenidx, lenbin, i, j, bc
ndarray[int64_t, ndim=1] bins
int64_t r_bin, nat_count
bint right_closed = closed == "right"
nat_count = 0
if hasnans:
mask = values == NPY_NAT
nat_count = np.sum(mask)
values = values[~mask]
lenidx = len(values)
lenbin = len(binner)
if lenidx <= 0 or lenbin <= 0:
raise ValueError("Invalid length for values or for binner")
# check binner fits data
if values[0] < binner[0]:
raise ValueError("Values falls before first bin")
if values[lenidx - 1] > binner[lenbin - 1]:
raise ValueError("Values falls after last bin")
bins = np.empty(lenbin - 1, dtype=np.int64)
j = 0 # index into values
bc = 0 # bin count
# linear scan
if right_closed:
for i in range(0, lenbin - 1):
r_bin = binner[i + 1]
# count values in current bin, advance to next bin
while j < lenidx and values[j] <= r_bin:
j += 1
bins[bc] = j
bc += 1
else:
for i in range(0, lenbin - 1):
r_bin = binner[i + 1]
# count values in current bin, advance to next bin
while j < lenidx and values[j] < r_bin:
j += 1
bins[bc] = j
bc += 1
if nat_count > 0:
# shift bins by the number of NaT
bins = bins + nat_count
bins = np.insert(bins, 0, nat_count)
return bins
@cython.boundscheck(False)
@cython.wraparound(False)
def get_level_sorter(
ndarray[int64_t, ndim=1] codes, const intp_t[:] starts
) -> ndarray:
"""
Argsort for a single level of a multi-index, keeping the order of higher
levels unchanged. `starts` points to starts of same-key indices w.r.t
to leading levels; equivalent to:
np.hstack([codes[starts[i]:starts[i+1]].argsort(kind='mergesort')
+ starts[i] for i in range(len(starts) - 1)])
Parameters
----------
codes : np.ndarray[int64_t, ndim=1]
starts : np.ndarray[intp, ndim=1]
Returns
-------
np.ndarray[np.int, ndim=1]
"""
cdef:
Py_ssize_t i, l, r
ndarray[intp_t, ndim=1] out = cnp.PyArray_EMPTY(1, codes.shape, cnp.NPY_INTP, 0)
for i in range(len(starts) - 1):
l, r = starts[i], starts[i + 1]
out[l:r] = l + codes[l:r].argsort(kind="mergesort")
return out
@cython.boundscheck(False)
@cython.wraparound(False)
def count_level_2d(ndarray[uint8_t, ndim=2, cast=True] mask,
const intp_t[:] labels,
Py_ssize_t max_bin,
):
cdef:
Py_ssize_t i, j, k, n
ndarray[int64_t, ndim=2] counts
n, k = (<object>mask).shape
counts = np.zeros((n, max_bin), dtype="i8")
with nogil:
for i in range(n):
for j in range(k):
if mask[i, j]:
counts[i, labels[j]] += 1
return counts
@cython.wraparound(False)
@cython.boundscheck(False)
def generate_slices(const intp_t[:] labels, Py_ssize_t ngroups):
cdef:
Py_ssize_t i, group_size, n, start
intp_t lab
int64_t[::1] starts, ends
n = len(labels)
starts = np.zeros(ngroups, dtype=np.int64)
ends = np.zeros(ngroups, dtype=np.int64)
start = 0
group_size = 0
with nogil:
for i in range(n):
lab = labels[i]
if lab < 0:
start += 1
else:
group_size += 1
if i == n - 1 or lab != labels[i + 1]:
starts[lab] = start
ends[lab] = start + group_size
start += group_size
group_size = 0
return np.asarray(starts), np.asarray(ends)
def indices_fast(ndarray[intp_t, ndim=1] index, const int64_t[:] labels, list keys,
list sorted_labels) -> dict:
"""
Parameters
----------
index : ndarray[intp]
labels : ndarray[int64]
keys : list
sorted_labels : list[ndarray[int64]]
"""
cdef:
Py_ssize_t i, j, k, lab, cur, start, n = len(labels)
dict result = {}
object tup
k = len(keys)
# Start at the first non-null entry
j = 0
for j in range(0, n):
if labels[j] != -1:
break
else:
return result
cur = labels[j]
start = j
for i in range(j+1, n):
lab = labels[i]
if lab != cur:
if lab != -1:
if k == 1:
# When k = 1 we do not want to return a tuple as key
tup = keys[0][sorted_labels[0][i - 1]]
else:
tup = PyTuple_New(k)
for j in range(k):
val = keys[j][sorted_labels[j][i - 1]]
PyTuple_SET_ITEM(tup, j, val)
Py_INCREF(val)
result[tup] = index[start:i]
start = i
cur = lab
if k == 1:
# When k = 1 we do not want to return a tuple as key
tup = keys[0][sorted_labels[0][n - 1]]
else:
tup = PyTuple_New(k)
for j in range(k):
val = keys[j][sorted_labels[j][n - 1]]
PyTuple_SET_ITEM(tup, j, val)
Py_INCREF(val)
result[tup] = index[start:]
return result
# core.common import for fast inference checks
def is_float(obj: object) -> bool:
"""
Return True if given object is float.
Returns
-------
bool
Examples
--------
>>> pd.api.types.is_float(1.0)
True
>>> pd.api.types.is_float(1)
False
"""
return util.is_float_object(obj)
def is_integer(obj: object) -> bool:
"""
Return True if given object is integer.
Returns
-------
bool
Examples
--------
>>> pd.api.types.is_integer(1)
True
>>> pd.api.types.is_integer(1.0)
False
"""
return util.is_integer_object(obj)
def is_int_or_none(obj) -> bool:
"""
Return True if given object is integer or None.
Returns
-------
bool
"""
return obj is None or util.is_integer_object(obj)
def is_bool(obj: object) -> bool:
"""
Return True if given object is boolean.
Returns
-------
bool
Examples
--------
>>> pd.api.types.is_bool(True)
True
>>> pd.api.types.is_bool(1)
False
"""
return util.is_bool_object(obj)
def is_complex(obj: object) -> bool:
"""
Return True if given object is complex.
Returns
-------
bool
Examples
--------
>>> pd.api.types.is_complex(1 + 1j)
True
>>> pd.api.types.is_complex(1)
False
"""
return util.is_complex_object(obj)
cpdef bint is_decimal(object obj):
return isinstance(obj, Decimal)
cpdef bint is_interval(object obj):
return getattr(obj, "_typ", "_typ") == "interval"
def is_period(val: object) -> bool:
"""
Return True if given object is Period.
Returns
-------
bool
"""
return is_period_object(val)
def is_list_like(obj: object, allow_sets: bool = True) -> bool:
"""
Check if the object is list-like.
Objects that are considered list-like are for example Python
lists, tuples, sets, NumPy arrays, and Pandas Series.
Strings and datetime objects, however, are not considered list-like.
Parameters
----------
obj : object
Object to check.
allow_sets : bool, default True
If this parameter is False, sets will not be considered list-like.
Returns
-------
bool
Whether `obj` has list-like properties.
Examples
--------
>>> import datetime
>>> from pandas.api.types import is_list_like
>>> is_list_like([1, 2, 3])
True
>>> is_list_like({1, 2, 3})
True
>>> is_list_like(datetime.datetime(2017, 1, 1))
False
>>> is_list_like("foo")
False
>>> is_list_like(1)
False
>>> is_list_like(np.array([2]))
True
>>> is_list_like(np.array(2))
False
"""
return c_is_list_like(obj, allow_sets)
cdef bint c_is_list_like(object obj, bint allow_sets) except -1:
# first, performance short-cuts for the most common cases
if util.is_array(obj):
# exclude zero-dimensional numpy arrays, effectively scalars
return not cnp.PyArray_IsZeroDim(obj)
elif isinstance(obj, list):
return True
# then the generic implementation
return (
# equiv: `isinstance(obj, abc.Iterable)`
getattr(obj, "__iter__", None) is not None and not isinstance(obj, type)
# we do not count strings/unicode/bytes as list-like
# exclude Generic types that have __iter__
and not isinstance(obj, (str, bytes, _GenericAlias))
# exclude zero-dimensional duck-arrays, effectively scalars
and not (hasattr(obj, "ndim") and obj.ndim == 0)
# exclude sets if allow_sets is False
and not (allow_sets is False and isinstance(obj, abc.Set))
)
def is_pyarrow_array(obj):
"""
Return True if given object is a pyarrow Array or ChunkedArray.
Returns
-------
bool
"""
if PYARROW_INSTALLED:
return isinstance(obj, (pa.Array, pa.ChunkedArray))
return False
_TYPE_MAP = {
"categorical": "categorical",
"category": "categorical",
"int8": "integer",
"int16": "integer",
"int32": "integer",
"int64": "integer",
"i": "integer",
"uint8": "integer",
"uint16": "integer",
"uint32": "integer",
"uint64": "integer",
"u": "integer",
"float32": "floating",
"float64": "floating",
"float128": "floating",
"float256": "floating",
"f": "floating",
"complex64": "complex",
"complex128": "complex",
"complex256": "complex",
"c": "complex",
"string": "string",
str: "string",
"S": "bytes",
"U": "string",
"bool": "boolean",
"b": "boolean",
"datetime64[ns]": "datetime64",
"M": "datetime64",
"timedelta64[ns]": "timedelta64",
"m": "timedelta64",
"interval": "interval",
Period: "period",
datetime: "datetime64",
date: "date",
time: "time",
timedelta: "timedelta64",
Decimal: "decimal",
bytes: "bytes",
}
@cython.internal
cdef class Seen:
"""
Class for keeping track of the types of elements
encountered when trying to perform type conversions.
"""
cdef:
bint int_ # seen_int
bint nat_ # seen nat
bint bool_ # seen_bool
bint null_ # seen_null
bint nan_ # seen_np.nan
bint uint_ # seen_uint (unsigned integer)
bint sint_ # seen_sint (signed integer)
bint float_ # seen_float
bint object_ # seen_object
bint complex_ # seen_complex
bint datetime_ # seen_datetime
bint coerce_numeric # coerce data to numeric
bint timedelta_ # seen_timedelta
bint datetimetz_ # seen_datetimetz
bint period_ # seen_period
bint interval_ # seen_interval
bint str_ # seen_str
def __cinit__(self, bint coerce_numeric=False):
"""
Initialize a Seen instance.
Parameters
----------
coerce_numeric : bool, default False
Whether or not to force conversion to a numeric data type if
initial methods to convert to numeric fail.
"""
self.int_ = False
self.nat_ = False
self.bool_ = False
self.null_ = False
self.nan_ = False
self.uint_ = False
self.sint_ = False
self.float_ = False
self.object_ = False
self.complex_ = False
self.datetime_ = False
self.timedelta_ = False
self.datetimetz_ = False
self.period_ = False
self.interval_ = False
self.str_ = False
self.coerce_numeric = coerce_numeric
cdef bint check_uint64_conflict(self) except -1:
"""
Check whether we can safely convert a uint64 array to a numeric dtype.
There are two cases when conversion to numeric dtype with a uint64
array is not safe (and will therefore not be performed)
1) A NaN element is encountered.
uint64 cannot be safely cast to float64 due to truncation issues
at the extreme ends of the range.
2) A negative number is encountered.
There is no numerical dtype that can hold both negative numbers
and numbers greater than INT64_MAX. Hence, at least one number
will be improperly cast if we convert to a numeric dtype.
Returns
-------
bool
Whether or not we should return the original input array to avoid
data truncation.
Raises
------
ValueError
uint64 elements were detected, and at least one of the
two conflict cases was also detected. However, we are
trying to force conversion to a numeric dtype.
"""
return (self.uint_ and (self.null_ or self.sint_)
and not self.coerce_numeric)
cdef saw_null(self):
"""
Set flags indicating that a null value was encountered.
"""
self.null_ = True
self.float_ = True
cdef saw_int(self, object val):
"""
Set flags indicating that an integer value was encountered.
In addition to setting a flag that an integer was seen, we
also set two flags depending on the type of integer seen:
1) sint_ : a signed numpy integer type or a negative (signed) number in the
range of [-2**63, 0) was encountered
2) uint_ : an unsigned numpy integer type or a positive number in the range of
[2**63, 2**64) was encountered
Parameters
----------
val : Python int
Value with which to set the flags.
"""
self.int_ = True
self.sint_ = (
self.sint_
or (oINT64_MIN <= val < 0)
# Cython equivalent of `isinstance(val, np.signedinteger)`
or PyObject_TypeCheck(val, &PySignedIntegerArrType_Type)
)
self.uint_ = (
self.uint_
or (oINT64_MAX < val <= oUINT64_MAX)
# Cython equivalent of `isinstance(val, np.unsignedinteger)`
or PyObject_TypeCheck(val, &PyUnsignedIntegerArrType_Type)
)
@property
def numeric_(self):
return self.complex_ or self.float_ or self.int_
@property
def is_bool(self):
# i.e. not (anything but bool)
return self.is_bool_or_na and not (self.nan_ or self.null_)
@property
def is_bool_or_na(self):
# i.e. not (anything but bool or missing values)
return self.bool_ and not (
self.datetime_ or self.datetimetz_ or self.nat_ or self.timedelta_
or self.period_ or self.interval_ or self.numeric_ or self.object_
)
cdef object _try_infer_map(object dtype):
"""
If its in our map, just return the dtype.
"""
cdef:
object val
str attr
for attr in ["type", "kind", "name", "base"]:
# Checking type before kind matters for ArrowDtype cases
val = getattr(dtype, attr, None)
if val in _TYPE_MAP:
return _TYPE_MAP[val]
return None
def infer_dtype(value: object, skipna: bool = True) -> str:
"""
Return a string label of the type of a scalar or list-like of values.
Parameters
----------
value : scalar, list, ndarray, or pandas type
skipna : bool, default True
Ignore NaN values when inferring the type.
Returns
-------
str
Describing the common type of the input data.
Results can include:
- string
- bytes
- floating
- integer
- mixed-integer
- mixed-integer-float
- decimal
- complex
- categorical
- boolean
- datetime64
- datetime
- date
- timedelta64
- timedelta
- time
- period
- mixed
- unknown-array
Raises
------
TypeError
If ndarray-like but cannot infer the dtype
Notes
-----
- 'mixed' is the catchall for anything that is not otherwise
specialized
- 'mixed-integer-float' are floats and integers
- 'mixed-integer' are integers mixed with non-integers
- 'unknown-array' is the catchall for something that *is* an array (has
a dtype attribute), but has a dtype unknown to pandas (e.g. external
extension array)
Examples
--------
>>> from pandas.api.types import infer_dtype
>>> infer_dtype(['foo', 'bar'])
'string'
>>> infer_dtype(['a', np.nan, 'b'], skipna=True)
'string'
>>> infer_dtype(['a', np.nan, 'b'], skipna=False)
'mixed'
>>> infer_dtype([b'foo', b'bar'])
'bytes'
>>> infer_dtype([1, 2, 3])
'integer'
>>> infer_dtype([1, 2, 3.5])
'mixed-integer-float'
>>> infer_dtype([1.0, 2.0, 3.5])
'floating'
>>> infer_dtype(['a', 1])
'mixed-integer'
>>> from decimal import Decimal
>>> infer_dtype([Decimal(1), Decimal(2.0)])
'decimal'
>>> infer_dtype([True, False])
'boolean'
>>> infer_dtype([True, False, np.nan])
'boolean'
>>> infer_dtype([pd.Timestamp('20130101')])
'datetime'
>>> import datetime
>>> infer_dtype([datetime.date(2013, 1, 1)])
'date'
>>> infer_dtype([np.datetime64('2013-01-01')])
'datetime64'
>>> infer_dtype([datetime.timedelta(0, 1, 1)])
'timedelta'
>>> infer_dtype(pd.Series(list('aabc')).astype('category'))
'categorical'
"""
cdef:
Py_ssize_t i, n
object val
ndarray values
bint seen_pdnat = False
bint seen_val = False
flatiter it
if util.is_array(value):
values = value
elif hasattr(type(value), "inferred_type") and skipna is False:
# Index, use the cached attribute if possible, populate the cache otherwise
return value.inferred_type
elif hasattr(value, "dtype"):
inferred = _try_infer_map(value.dtype)
if inferred is not None:
return inferred
elif not cnp.PyArray_DescrCheck(value.dtype):
return "unknown-array"
# Unwrap Series/Index
values = np.asarray(value)
else:
if not isinstance(value, list):
value = list(value)
if not value:
return "empty"
from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
values = construct_1d_object_array_from_listlike(value)
inferred = _try_infer_map(values.dtype)
if inferred is not None:
# Anything other than object-dtype should return here.
return inferred
if values.descr.type_num != NPY_OBJECT:
# i.e. values.dtype != np.object_
# This should not be reached
values = values.astype(object)
n = cnp.PyArray_SIZE(values)
if n == 0:
return "empty"
# Iterate until we find our first valid value. We will use this
# value to decide which of the is_foo_array functions to call.
it = PyArray_IterNew(values)
for i in range(n):
# The PyArray_GETITEM and PyArray_ITER_NEXT are faster
# equivalents to `val = values[i]`
val = PyArray_GETITEM(values, PyArray_ITER_DATA(it))
PyArray_ITER_NEXT(it)
# do not use checknull to keep
# np.datetime64('nat') and np.timedelta64('nat')
if val is None or util.is_nan(val) or val is C_NA:
pass
elif val is NaT:
seen_pdnat = True
else:
seen_val = True
break
# if all values are nan/NaT
if seen_val is False and seen_pdnat is True:
return "datetime"
# float/object nan is handled in latter logic
if seen_val is False and skipna:
return "empty"
if util.is_datetime64_object(val):
if is_datetime64_array(values, skipna=skipna):
return "datetime64"
elif is_timedelta(val):
if is_timedelta_or_timedelta64_array(values, skipna=skipna):
return "timedelta"
elif util.is_integer_object(val):
# ordering matters here; this check must come after the is_timedelta
# check otherwise numpy timedelta64 objects would come through here
if is_integer_array(values, skipna=skipna):
return "integer"
elif is_integer_float_array(values, skipna=skipna):
if is_integer_na_array(values, skipna=skipna):
return "integer-na"
else:
return "mixed-integer-float"
return "mixed-integer"
elif PyDateTime_Check(val):
if is_datetime_array(values, skipna=skipna):
return "datetime"
elif is_date_array(values, skipna=skipna):
return "date"
elif PyDate_Check(val):
if is_date_array(values, skipna=skipna):
return "date"
elif PyTime_Check(val):
if is_time_array(values, skipna=skipna):
return "time"
elif is_decimal(val):
if is_decimal_array(values, skipna=skipna):
return "decimal"
elif util.is_complex_object(val):
if is_complex_array(values):
return "complex"
elif util.is_float_object(val):
if is_float_array(values):
return "floating"
elif is_integer_float_array(values, skipna=skipna):
if is_integer_na_array(values, skipna=skipna):
return "integer-na"
else:
return "mixed-integer-float"
elif util.is_bool_object(val):
if is_bool_array(values, skipna=skipna):
return "boolean"
elif isinstance(val, str):
if is_string_array(values, skipna=skipna):
return "string"
elif isinstance(val, bytes):
if is_bytes_array(values, skipna=skipna):
return "bytes"
elif is_period_object(val):
if is_period_array(values, skipna=skipna):
return "period"
elif is_interval(val):
if is_interval_array(values):
return "interval"
cnp.PyArray_ITER_RESET(it)
for i in range(n):
val = PyArray_GETITEM(values, PyArray_ITER_DATA(it))
PyArray_ITER_NEXT(it)
if util.is_integer_object(val):
return "mixed-integer"
return "mixed"
cdef bint is_timedelta(object o):
return PyDelta_Check(o) or util.is_timedelta64_object(o)
@cython.internal
cdef class Validator:
cdef:
Py_ssize_t n
dtype dtype
bint skipna
def __cinit__(self, Py_ssize_t n, dtype dtype=np.dtype(np.object_),
bint skipna=False):
self.n = n
self.dtype = dtype
self.skipna = skipna
cdef bint validate(self, ndarray values) except -1:
if not self.n:
return False
if self.is_array_typed():
# i.e. this ndarray is already of the desired dtype
return True
elif self.dtype.type_num == NPY_OBJECT:
if self.skipna:
return self._validate_skipna(values)
else:
return self._validate(values)
else:
return False
@cython.wraparound(False)
@cython.boundscheck(False)
cdef bint _validate(self, ndarray values) except -1:
cdef:
Py_ssize_t i
Py_ssize_t n = values.size
flatiter it = PyArray_IterNew(values)
for i in range(n):
# The PyArray_GETITEM and PyArray_ITER_NEXT are faster
# equivalents to `val = values[i]`
val = PyArray_GETITEM(values, PyArray_ITER_DATA(it))
PyArray_ITER_NEXT(it)
if not self.is_valid(val):
return False
return True
@cython.wraparound(False)
@cython.boundscheck(False)
cdef bint _validate_skipna(self, ndarray values) except -1:
cdef:
Py_ssize_t i
Py_ssize_t n = values.size
flatiter it = PyArray_IterNew(values)
for i in range(n):
# The PyArray_GETITEM and PyArray_ITER_NEXT are faster
# equivalents to `val = values[i]`
val = PyArray_GETITEM(values, PyArray_ITER_DATA(it))
PyArray_ITER_NEXT(it)
if not self.is_valid_skipna(val):
return False
return True
cdef bint is_valid(self, object value) except -1:
return self.is_value_typed(value)
cdef bint is_valid_skipna(self, object value) except -1:
return self.is_valid(value) or self.is_valid_null(value)
cdef bint is_value_typed(self, object value) except -1:
raise NotImplementedError(f"{type(self).__name__} child class "
"must define is_value_typed")
cdef bint is_valid_null(self, object value) except -1:
return value is None or value is C_NA or util.is_nan(value)
# TODO: include decimal NA?
cdef bint is_array_typed(self) except -1:
return False
@cython.internal
cdef class BoolValidator(Validator):
cdef bint is_value_typed(self, object value) except -1:
return util.is_bool_object(value)
cdef bint is_array_typed(self) except -1:
return issubclass(self.dtype.type, np.bool_)
cpdef bint is_bool_array(ndarray values, bint skipna=False):
cdef:
BoolValidator validator = BoolValidator(len(values),
values.dtype,
skipna=skipna)
return validator.validate(values)
@cython.internal
cdef class IntegerValidator(Validator):
cdef bint is_value_typed(self, object value) except -1:
return util.is_integer_object(value)
cdef bint is_array_typed(self) except -1:
return issubclass(self.dtype.type, np.integer)
# Note: only python-exposed for tests
cpdef bint is_integer_array(ndarray values, bint skipna=True):
cdef:
IntegerValidator validator = IntegerValidator(len(values),
values.dtype,
skipna=skipna)
return validator.validate(values)
@cython.internal
cdef class IntegerNaValidator(Validator):
cdef bint is_value_typed(self, object value) except -1:
return (util.is_integer_object(value)
or (util.is_nan(value) and util.is_float_object(value)))
cdef bint is_integer_na_array(ndarray values, bint skipna=True):
cdef:
IntegerNaValidator validator = IntegerNaValidator(len(values),
values.dtype, skipna=skipna)
return validator.validate(values)
@cython.internal
cdef class IntegerFloatValidator(Validator):
cdef bint is_value_typed(self, object value) except -1:
return util.is_integer_object(value) or util.is_float_object(value)
cdef bint is_array_typed(self) except -1:
return issubclass(self.dtype.type, np.integer)
cdef bint is_integer_float_array(ndarray values, bint skipna=True):
cdef:
IntegerFloatValidator validator = IntegerFloatValidator(len(values),
values.dtype,
skipna=skipna)
return validator.validate(values)
@cython.internal
cdef class FloatValidator(Validator):
cdef bint is_value_typed(self, object value) except -1:
return util.is_float_object(value)
cdef bint is_array_typed(self) except -1:
return issubclass(self.dtype.type, np.floating)
# Note: only python-exposed for tests
cpdef bint is_float_array(ndarray values):
cdef:
FloatValidator validator = FloatValidator(len(values), values.dtype)
return validator.validate(values)
@cython.internal
cdef class ComplexValidator(Validator):
cdef bint is_value_typed(self, object value) except -1:
return (
util.is_complex_object(value)
or (util.is_float_object(value) and is_nan(value))
)
cdef bint is_array_typed(self) except -1:
return issubclass(self.dtype.type, np.complexfloating)
cdef bint is_complex_array(ndarray values):
cdef:
ComplexValidator validator = ComplexValidator(len(values), values.dtype)
return validator.validate(values)
@cython.internal
cdef class DecimalValidator(Validator):
cdef bint is_value_typed(self, object value) except -1:
return is_decimal(value)
cdef bint is_decimal_array(ndarray values, bint skipna=False):
cdef:
DecimalValidator validator = DecimalValidator(
len(values), values.dtype, skipna=skipna
)
return validator.validate(values)
@cython.internal
cdef class StringValidator(Validator):
cdef bint is_value_typed(self, object value) except -1:
return isinstance(value, str)
cdef bint is_array_typed(self) except -1:
return issubclass(self.dtype.type, np.str_)
cpdef bint is_string_array(ndarray values, bint skipna=False):
cdef:
StringValidator validator = StringValidator(len(values),
values.dtype,
skipna=skipna)
return validator.validate(values)
@cython.internal
cdef class BytesValidator(Validator):
cdef bint is_value_typed(self, object value) except -1:
return isinstance(value, bytes)
cdef bint is_array_typed(self) except -1:
return issubclass(self.dtype.type, np.bytes_)
cdef bint is_bytes_array(ndarray values, bint skipna=False):
cdef:
BytesValidator validator = BytesValidator(len(values), values.dtype,
skipna=skipna)
return validator.validate(values)
@cython.internal
cdef class TemporalValidator(Validator):
cdef:
bint all_generic_na
def __cinit__(self, Py_ssize_t n, dtype dtype=np.dtype(np.object_),
bint skipna=False):
self.n = n
self.dtype = dtype
self.skipna = skipna
self.all_generic_na = True
cdef bint is_valid(self, object value) except -1:
return self.is_value_typed(value) or self.is_valid_null(value)
cdef bint is_valid_null(self, object value) except -1:
raise NotImplementedError(f"{type(self).__name__} child class "
"must define is_valid_null")
cdef bint is_valid_skipna(self, object value) except -1:
cdef:
bint is_typed_null = self.is_valid_null(value)
bint is_generic_null = value is None or util.is_nan(value)
if not is_generic_null:
self.all_generic_na = False
return self.is_value_typed(value) or is_typed_null or is_generic_null
cdef bint _validate_skipna(self, ndarray values) except -1:
"""
If we _only_ saw non-dtype-specific NA values, even if they are valid
for this dtype, we do not infer this dtype.
"""
return Validator._validate_skipna(self, values) and not self.all_generic_na
@cython.internal
cdef class DatetimeValidator(TemporalValidator):
cdef bint is_value_typed(self, object value) except -1:
return PyDateTime_Check(value)
cdef bint is_valid_null(self, object value) except -1:
return is_null_datetime64(value)
cpdef bint is_datetime_array(ndarray values, bint skipna=True):
cdef:
DatetimeValidator validator = DatetimeValidator(len(values),
skipna=skipna)
return validator.validate(values)
@cython.internal
cdef class Datetime64Validator(DatetimeValidator):
cdef bint is_value_typed(self, object value) except -1:
return util.is_datetime64_object(value)
# Note: only python-exposed for tests
cpdef bint is_datetime64_array(ndarray values, bint skipna=True):
cdef:
Datetime64Validator validator = Datetime64Validator(len(values),
skipna=skipna)
return validator.validate(values)
@cython.internal
cdef class AnyDatetimeValidator(DatetimeValidator):
cdef bint is_value_typed(self, object value) except -1:
return util.is_datetime64_object(value) or (
PyDateTime_Check(value) and value.tzinfo is None
)
cdef bint is_datetime_or_datetime64_array(ndarray values, bint skipna=True):
cdef:
AnyDatetimeValidator validator = AnyDatetimeValidator(len(values),
skipna=skipna)
return validator.validate(values)
# Note: only python-exposed for tests
def is_datetime_with_singletz_array(values: ndarray) -> bool:
"""
Check values have the same tzinfo attribute.
Doesn't check values are datetime-like types.
"""
cdef:
Py_ssize_t i = 0, j, n = len(values)
object base_val, base_tz, val, tz
if n == 0:
return False
# Get a reference timezone to compare with the rest of the tzs in the array
for i in range(n):
base_val = values[i]
if base_val is not NaT and base_val is not None and not util.is_nan(base_val):
base_tz = getattr(base_val, "tzinfo", None)
break
for j in range(i, n):
# Compare val's timezone with the reference timezone
# NaT can coexist with tz-aware datetimes, so skip if encountered
val = values[j]
if val is not NaT and val is not None and not util.is_nan(val):
tz = getattr(val, "tzinfo", None)
if not tz_compare(base_tz, tz):
return False
# Note: we should only be called if a tzaware datetime has been seen,
# so base_tz should always be set at this point.
return True
@cython.internal
cdef class TimedeltaValidator(TemporalValidator):
cdef bint is_value_typed(self, object value) except -1:
return PyDelta_Check(value)
cdef bint is_valid_null(self, object value) except -1:
return is_null_timedelta64(value)
@cython.internal
cdef class AnyTimedeltaValidator(TimedeltaValidator):
cdef bint is_value_typed(self, object value) except -1:
return is_timedelta(value)
# Note: only python-exposed for tests
cpdef bint is_timedelta_or_timedelta64_array(ndarray values, bint skipna=True):
"""
Infer with timedeltas and/or nat/none.
"""
cdef:
AnyTimedeltaValidator validator = AnyTimedeltaValidator(len(values),
skipna=skipna)
return validator.validate(values)
@cython.internal
cdef class DateValidator(Validator):
cdef bint is_value_typed(self, object value) except -1:
return PyDate_Check(value)
# Note: only python-exposed for tests
cpdef bint is_date_array(ndarray values, bint skipna=False):
cdef:
DateValidator validator = DateValidator(len(values), skipna=skipna)
return validator.validate(values)
@cython.internal
cdef class TimeValidator(Validator):
cdef bint is_value_typed(self, object value) except -1:
return PyTime_Check(value)
# Note: only python-exposed for tests
cpdef bint is_time_array(ndarray values, bint skipna=False):
cdef:
TimeValidator validator = TimeValidator(len(values), skipna=skipna)
return validator.validate(values)
# FIXME: actually use skipna
cdef bint is_period_array(ndarray values, bint skipna=True):
"""
Is this an ndarray of Period objects (or NaT) with a single `freq`?
"""
# values should be object-dtype, but ndarray[object] assumes 1D, while
# this _may_ be 2D.
cdef:
Py_ssize_t i, N = values.size
int dtype_code = -10000 # i.e. c_FreqGroup.FR_UND
object val
flatiter it
if N == 0:
return False
it = PyArray_IterNew(values)
for i in range(N):
# The PyArray_GETITEM and PyArray_ITER_NEXT are faster
# equivalents to `val = values[i]`
val = PyArray_GETITEM(values, PyArray_ITER_DATA(it))
PyArray_ITER_NEXT(it)
if is_period_object(val):
if dtype_code == -10000:
dtype_code = val._dtype._dtype_code
elif dtype_code != val._dtype._dtype_code:
# mismatched freqs
return False
elif checknull_with_nat(val):
pass
else:
# Not a Period or NaT-like
return False
if dtype_code == -10000:
# we saw all-NaTs, no actual Periods
return False
return True
# Note: only python-exposed for tests
cpdef bint is_interval_array(ndarray values):
"""
Is this an ndarray of Interval (or np.nan) with a single dtype?
"""
cdef:
Py_ssize_t i, n = len(values)
str closed = None
bint numeric = False
bint dt64 = False
bint td64 = False
object val
if len(values) == 0:
return False
for i in range(n):
val = values[i]
if is_interval(val):
if closed is None:
closed = val.closed
numeric = (
util.is_float_object(val.left)
or util.is_integer_object(val.left)
)
td64 = is_timedelta(val.left)
dt64 = PyDateTime_Check(val.left)
elif val.closed != closed:
# mismatched closedness
return False
elif numeric:
if not (
util.is_float_object(val.left)
or util.is_integer_object(val.left)
):
# i.e. datetime64 or timedelta64
return False
elif td64:
if not is_timedelta(val.left):
return False
elif dt64:
if not PyDateTime_Check(val.left):
return False
else:
raise ValueError(val)
elif util.is_nan(val) or val is None:
pass
else:
return False
if closed is None:
# we saw all-NAs, no actual Intervals
return False
return True
@cython.boundscheck(False)
@cython.wraparound(False)
def maybe_convert_numeric(
ndarray[object, ndim=1] values,
set na_values,
bint convert_empty=True,
bint coerce_numeric=False,
bint convert_to_masked_nullable=False,
) -> tuple[np.ndarray, np.ndarray | None]:
"""
Convert object array to a numeric array if possible.
Parameters
----------
values : ndarray[object]
Array of object elements to convert.
na_values : set
Set of values that should be interpreted as NaN.
convert_empty : bool, default True
If an empty array-like object is encountered, whether to interpret
that element as NaN or not. If set to False, a ValueError will be
raised if such an element is encountered and 'coerce_numeric' is False.
coerce_numeric : bool, default False
If initial attempts to convert to numeric have failed, whether to
force conversion to numeric via alternative methods or by setting the
element to NaN. Otherwise, an Exception will be raised when such an
element is encountered.
This boolean also has an impact on how conversion behaves when a
numeric array has no suitable numerical dtype to return (i.e. uint64,
int32, uint8). If set to False, the original object array will be
returned. Otherwise, a ValueError will be raised.
convert_to_masked_nullable : bool, default False
Whether to return a mask for the converted values. This also disables
upcasting for ints with nulls to float64.
Returns
-------
np.ndarray
Array of converted object values to numerical ones.
Optional[np.ndarray]
If convert_to_masked_nullable is True,
returns a boolean mask for the converted values, otherwise returns None.
"""
if len(values) == 0:
return (np.array([], dtype="i8"), None)
# fastpath for ints - try to convert all based on first value
cdef:
object val = values[0]
if util.is_integer_object(val):
try:
maybe_ints = values.astype("i8")
if (maybe_ints == values).all():
return (maybe_ints, None)
except (ValueError, OverflowError, TypeError):
pass
# Otherwise, iterate and do full inference.
cdef:
int maybe_int
Py_ssize_t i, n = values.size
Seen seen = Seen(coerce_numeric)
ndarray[float64_t, ndim=1] floats = cnp.PyArray_EMPTY(
1, values.shape, cnp.NPY_FLOAT64, 0
)
ndarray[complex128_t, ndim=1] complexes = cnp.PyArray_EMPTY(
1, values.shape, cnp.NPY_COMPLEX128, 0
)
ndarray[int64_t, ndim=1] ints = cnp.PyArray_EMPTY(
1, values.shape, cnp.NPY_INT64, 0
)
ndarray[uint64_t, ndim=1] uints = cnp.PyArray_EMPTY(
1, values.shape, cnp.NPY_UINT64, 0
)
ndarray[uint8_t, ndim=1] bools = cnp.PyArray_EMPTY(
1, values.shape, cnp.NPY_UINT8, 0
)
ndarray[uint8_t, ndim=1] mask = np.zeros(n, dtype="u1")
float64_t fval
bint allow_null_in_int = convert_to_masked_nullable
for i in range(n):
val = values[i]
# We only want to disable NaNs showing as float if
# a) convert_to_masked_nullable = True
# b) no floats have been seen ( assuming an int shows up later )
# However, if no ints present (all null array), we need to return floats
allow_null_in_int = convert_to_masked_nullable and not seen.float_
if val.__hash__ is not None and val in na_values:
if allow_null_in_int:
seen.null_ = True
mask[i] = 1
else:
if convert_to_masked_nullable:
mask[i] = 1
seen.saw_null()
floats[i] = complexes[i] = NaN
elif util.is_float_object(val):
fval = val
if fval != fval:
seen.null_ = True
if allow_null_in_int:
mask[i] = 1
else:
if convert_to_masked_nullable:
mask[i] = 1
seen.float_ = True
else:
seen.float_ = True
floats[i] = complexes[i] = fval
elif util.is_integer_object(val):
floats[i] = complexes[i] = val
val = int(val)
seen.saw_int(val)
if val >= 0:
if val <= oUINT64_MAX:
uints[i] = val
else:
seen.float_ = True
if oINT64_MIN <= val <= oINT64_MAX:
ints[i] = val
if val < oINT64_MIN or (seen.sint_ and seen.uint_):
seen.float_ = True
elif util.is_bool_object(val):
floats[i] = uints[i] = ints[i] = bools[i] = val
seen.bool_ = True
elif val is None or val is C_NA:
if allow_null_in_int:
seen.null_ = True
mask[i] = 1
else:
if convert_to_masked_nullable:
mask[i] = 1
seen.saw_null()
floats[i] = complexes[i] = NaN
elif hasattr(val, "__len__") and len(val) == 0:
if convert_empty or seen.coerce_numeric:
seen.saw_null()
floats[i] = complexes[i] = NaN
mask[i] = 1
else:
raise ValueError("Empty string encountered")
elif util.is_complex_object(val):
complexes[i] = val
seen.complex_ = True
elif is_decimal(val):
floats[i] = complexes[i] = val
seen.float_ = True
else:
try:
floatify(val, &fval, &maybe_int)
if fval in na_values:
seen.saw_null()
floats[i] = complexes[i] = NaN
mask[i] = 1
else:
if fval != fval:
seen.null_ = True
mask[i] = 1
floats[i] = fval
if maybe_int:
as_int = int(val)
if as_int in na_values:
mask[i] = 1
seen.null_ = True
if not allow_null_in_int:
seen.float_ = True
else:
seen.saw_int(as_int)
if as_int not in na_values:
if as_int < oINT64_MIN or as_int > oUINT64_MAX:
if seen.coerce_numeric:
seen.float_ = True
else:
raise ValueError("Integer out of range.")
else:
if as_int >= 0:
uints[i] = as_int
if as_int <= oINT64_MAX:
ints[i] = as_int
seen.float_ = seen.float_ or (seen.uint_ and seen.sint_)
else:
seen.float_ = True
except (TypeError, ValueError) as err:
if not seen.coerce_numeric:
raise type(err)(f"{err} at position {i}")
mask[i] = 1
if allow_null_in_int:
seen.null_ = True
else:
seen.saw_null()
floats[i] = NaN
if seen.check_uint64_conflict():
return (values, None)
# This occurs since we disabled float nulls showing as null in anticipation
# of seeing ints that were never seen. So then, we return float
if allow_null_in_int and seen.null_ and not seen.int_ and not seen.bool_:
seen.float_ = True
if seen.complex_:
return (complexes, None)
elif seen.float_:
if seen.null_ and convert_to_masked_nullable:
return (floats, mask.view(np.bool_))
return (floats, None)
elif seen.int_:
if seen.null_ and convert_to_masked_nullable:
if seen.uint_:
return (uints, mask.view(np.bool_))
else:
return (ints, mask.view(np.bool_))
if seen.uint_:
return (uints, None)
else:
return (ints, None)
elif seen.bool_:
if allow_null_in_int:
return (bools.view(np.bool_), mask.view(np.bool_))
return (bools.view(np.bool_), None)
elif seen.uint_:
return (uints, None)
return (ints, None)
@cython.boundscheck(False)
@cython.wraparound(False)
def maybe_convert_objects(ndarray[object] objects,
*,
bint try_float=False,
bint safe=False,
bint convert_numeric=True, # NB: different default!
bint convert_to_nullable_dtype=False,
bint convert_non_numeric=False,
object dtype_if_all_nat=None) -> "ArrayLike":
"""
Type inference function-- convert object array to proper dtype
Parameters
----------
objects : ndarray[object]
Array of object elements to convert.
try_float : bool, default False
If an array-like object contains only float or NaN values is
encountered, whether to convert and return an array of float dtype.
safe : bool, default False
Whether to upcast numeric type (e.g. int cast to float). If set to
True, no upcasting will be performed.
convert_numeric : bool, default True
Whether to convert numeric entries.
convert_to_nullable_dtype : bool, default False
If an array-like object contains only integer or boolean values (and NaN) is
encountered, whether to convert and return an Boolean/IntegerArray.
convert_non_numeric : bool, default False
Whether to convert datetime, timedelta, period, interval types.
dtype_if_all_nat : np.dtype, ExtensionDtype, or None, default None
Dtype to cast to if we have all-NaT.
Returns
-------
np.ndarray or ExtensionArray
Array of converted object values to more specific dtypes if applicable.
"""
cdef:
Py_ssize_t i, n, itemsize_max = 0
ndarray[float64_t] floats
ndarray[complex128_t] complexes
ndarray[int64_t] ints
ndarray[uint64_t] uints
ndarray[uint8_t] bools
Seen seen = Seen()
object val
_TSObject tsobj
float64_t fnan = NaN
if dtype_if_all_nat is not None:
# in practice we don't expect to ever pass dtype_if_all_nat
# without both convert_non_numeric, so disallow
# it to avoid needing to handle it below.
if not convert_non_numeric:
raise ValueError(
"Cannot specify 'dtype_if_all_nat' without convert_non_numeric=True"
)
n = len(objects)
floats = cnp.PyArray_EMPTY(1, objects.shape, cnp.NPY_FLOAT64, 0)
complexes = cnp.PyArray_EMPTY(1, objects.shape, cnp.NPY_COMPLEX128, 0)
ints = cnp.PyArray_EMPTY(1, objects.shape, cnp.NPY_INT64, 0)
uints = cnp.PyArray_EMPTY(1, objects.shape, cnp.NPY_UINT64, 0)
bools = cnp.PyArray_EMPTY(1, objects.shape, cnp.NPY_UINT8, 0)
mask = np.full(n, False)
for i in range(n):
val = objects[i]
if itemsize_max != -1:
itemsize = get_itemsize(val)
if itemsize > itemsize_max or itemsize == -1:
itemsize_max = itemsize
if val is None:
seen.null_ = True
floats[i] = complexes[i] = fnan
mask[i] = True
elif val is NaT:
seen.nat_ = True
if not convert_non_numeric:
seen.object_ = True
break
elif util.is_nan(val):
seen.nan_ = True
mask[i] = True
if util.is_complex_object(val):
floats[i] = fnan
complexes[i] = val
seen.complex_ = True
if not convert_numeric:
break
else:
floats[i] = complexes[i] = val
elif util.is_bool_object(val):
seen.bool_ = True
bools[i] = val
if not convert_numeric:
break
elif util.is_float_object(val):
floats[i] = complexes[i] = val
seen.float_ = True
if not convert_numeric:
break
elif is_timedelta(val):
if convert_non_numeric:
seen.timedelta_ = True
try:
convert_to_timedelta64(val, "ns")
except OutOfBoundsTimedelta:
seen.object_ = True
break
break
else:
seen.object_ = True
break
elif util.is_integer_object(val):
seen.int_ = True
floats[i] = <float64_t>val
complexes[i] = <double complex>val
if not seen.null_ or convert_to_nullable_dtype:
seen.saw_int(val)
if ((seen.uint_ and seen.sint_) or
val > oUINT64_MAX or val < oINT64_MIN):
seen.object_ = True
break
if seen.uint_:
uints[i] = val
elif seen.sint_:
ints[i] = val
else:
uints[i] = val
ints[i] = val
if not convert_numeric:
break
elif util.is_complex_object(val):
complexes[i] = val
seen.complex_ = True
if not convert_numeric:
break
elif PyDateTime_Check(val) or util.is_datetime64_object(val):
# if we have an tz's attached then return the objects
if convert_non_numeric:
if getattr(val, "tzinfo", None) is not None:
seen.datetimetz_ = True
break
else:
seen.datetime_ = True
try:
tsobj = convert_to_tsobject(val, None, None, 0, 0)
tsobj.ensure_reso(NPY_FR_ns)
except OutOfBoundsDatetime:
seen.object_ = True
break
else:
seen.object_ = True
break
elif is_period_object(val):
if convert_non_numeric:
seen.period_ = True
break
else:
seen.object_ = True
break
elif try_float and not isinstance(val, str):
# this will convert Decimal objects
try:
floats[i] = float(val)
complexes[i] = complex(val)
seen.float_ = True
except (ValueError, TypeError):
seen.object_ = True
break
elif is_interval(val):
if convert_non_numeric:
seen.interval_ = True
break
else:
seen.object_ = True
break
elif isinstance(val, str):
if convert_non_numeric:
seen.str_ = True
break
else:
seen.object_ = True
break
else:
seen.object_ = True
break
# we try to coerce datetime w/tz but must all have the same tz
if seen.datetimetz_:
if is_datetime_with_singletz_array(objects):
from pandas import DatetimeIndex
try:
dti = DatetimeIndex(objects)
except OutOfBoundsDatetime:
# e.g. test_to_datetime_cache_coerce_50_lines_outofbounds
pass
else:
# unbox to DatetimeArray
return dti._data
seen.object_ = True
elif seen.datetime_:
if is_datetime_or_datetime64_array(objects):
from pandas import DatetimeIndex
try:
dti = DatetimeIndex(objects)
except OutOfBoundsDatetime:
pass
else:
# unbox to ndarray[datetime64[ns]]
return dti._data._ndarray
seen.object_ = True
elif seen.timedelta_:
if is_timedelta_or_timedelta64_array(objects):
from pandas import TimedeltaIndex
try:
tdi = TimedeltaIndex(objects)
except OutOfBoundsTimedelta:
pass
else:
# unbox to ndarray[timedelta64[ns]]
return tdi._data._ndarray
seen.object_ = True
elif seen.period_:
if is_period_array(objects):
from pandas import PeriodIndex
pi = PeriodIndex(objects)
# unbox to PeriodArray
return pi._data
seen.object_ = True
elif seen.str_:
if using_pyarrow_string_dtype() and is_string_array(objects, skipna=True):
from pandas.core.arrays.string_ import StringDtype
dtype = StringDtype(storage="pyarrow_numpy")
return dtype.construct_array_type()._from_sequence(objects, dtype=dtype)
seen.object_ = True
elif seen.interval_:
if is_interval_array(objects):
from pandas import IntervalIndex
ii = IntervalIndex(objects)
# unbox to IntervalArray
return ii._data
seen.object_ = True
elif seen.nat_:
if not seen.object_ and not seen.numeric_ and not seen.bool_:
# all NaT, None, or nan (at least one NaT)
# see GH#49340 for discussion of desired behavior
dtype = dtype_if_all_nat
if cnp.PyArray_DescrCheck(dtype):
# i.e. isinstance(dtype, np.dtype)
if dtype.kind not in "mM":
raise ValueError(dtype)
else:
res = np.empty((<object>objects).shape, dtype=dtype)
res[:] = NPY_NAT
return res
elif dtype is not None:
# EA, we don't expect to get here, but _could_ implement
raise NotImplementedError(dtype)
else:
# we don't guess
seen.object_ = True
else:
seen.object_ = True
if not convert_numeric:
# Note: we count "bool" as numeric here. This is because
# np.array(list_of_items) will convert bools just like it will numeric
# entries.
return objects
if seen.bool_:
if seen.is_bool:
# is_bool property rules out everything else
return bools.view(np.bool_)
elif convert_to_nullable_dtype and seen.is_bool_or_na:
from pandas.core.arrays import BooleanArray
return BooleanArray(bools.view(np.bool_), mask)
seen.object_ = True
if not seen.object_:
result = None
if not safe:
if seen.null_ or seen.nan_:
if seen.complex_:
result = complexes
elif seen.float_:
result = floats
elif seen.int_ or seen.uint_:
if convert_to_nullable_dtype:
from pandas.core.arrays import IntegerArray
if seen.uint_:
result = IntegerArray(uints, mask)
else:
result = IntegerArray(ints, mask)
else:
result = floats
elif seen.nan_:
result = floats
else:
if seen.complex_:
result = complexes
elif seen.float_:
result = floats
elif seen.int_:
if seen.uint_:
result = uints
else:
result = ints
else:
# don't cast int to float, etc.
if seen.null_:
if seen.complex_:
if not seen.int_:
result = complexes
elif seen.float_ or seen.nan_:
if not seen.int_:
result = floats
else:
if seen.complex_:
if not seen.int_:
result = complexes
elif seen.float_ or seen.nan_:
if not seen.int_:
result = floats
elif seen.int_:
if seen.uint_:
result = uints
else:
result = ints
if result is uints or result is ints or result is floats or result is complexes:
# cast to the largest itemsize when all values are NumPy scalars
if itemsize_max > 0 and itemsize_max != result.dtype.itemsize:
result = result.astype(result.dtype.kind + str(itemsize_max))
return result
elif result is not None:
return result
return objects
class _NoDefault(Enum):
# We make this an Enum
# 1) because it round-trips through pickle correctly (see GH#40397)
# 2) because mypy does not understand singletons
no_default = "NO_DEFAULT"
def __repr__(self) -> str:
return "<no_default>"
# Note: no_default is exported to the public API in pandas.api.extensions
no_default = _NoDefault.no_default # Sentinel indicating the default value.
NoDefault = Literal[_NoDefault.no_default]
@cython.boundscheck(False)
@cython.wraparound(False)
def map_infer_mask(ndarray arr, object f, const uint8_t[:] mask, bint convert=True,
object na_value=no_default, cnp.dtype dtype=np.dtype(object)
) -> np.ndarray:
"""
Substitute for np.vectorize with pandas-friendly dtype inference.
Parameters
----------
arr : ndarray
f : function
mask : ndarray
uint8 dtype ndarray indicating values not to apply `f` to.
convert : bool, default True
Whether to call `maybe_convert_objects` on the resulting ndarray
na_value : Any, optional
The result value to use for masked values. By default, the
input value is used
dtype : numpy.dtype
The numpy dtype to use for the result ndarray.
Returns
-------
np.ndarray
"""
cdef:
Py_ssize_t i, n
ndarray result
object val
n = len(arr)
result = np.empty(n, dtype=dtype)
for i in range(n):
if mask[i]:
if na_value is no_default:
val = arr[i]
else:
val = na_value
else:
val = f(arr[i])
if cnp.PyArray_IsZeroDim(val):
# unbox 0-dim arrays, GH#690
val = val.item()
result[i] = val
if convert:
return maybe_convert_objects(result)
else:
return result
@cython.boundscheck(False)
@cython.wraparound(False)
def map_infer(
ndarray arr, object f, bint convert=True, bint ignore_na=False
) -> np.ndarray:
"""
Substitute for np.vectorize with pandas-friendly dtype inference.
Parameters
----------
arr : ndarray
f : function
convert : bint
ignore_na : bint
If True, NA values will not have f applied
Returns
-------
np.ndarray
"""
cdef:
Py_ssize_t i, n
ndarray[object] result
object val
n = len(arr)
result = cnp.PyArray_EMPTY(1, arr.shape, cnp.NPY_OBJECT, 0)
for i in range(n):
if ignore_na and checknull(arr[i]):
result[i] = arr[i]
continue
val = f(arr[i])
if cnp.PyArray_IsZeroDim(val):
# unbox 0-dim arrays, GH#690
val = val.item()
result[i] = val
if convert:
return maybe_convert_objects(result)
else:
return result
def to_object_array(rows: object, min_width: int = 0) -> ndarray:
"""
Convert a list of lists into an object array.
Parameters
----------
rows : 2-d array (N, K)
List of lists to be converted into an array.
min_width : int
Minimum width of the object array. If a list
in `rows` contains fewer than `width` elements,
the remaining elements in the corresponding row
will all be `NaN`.
Returns
-------
np.ndarray[object, ndim=2]
"""
cdef:
Py_ssize_t i, j, n, k, tmp
ndarray[object, ndim=2] result
list row
rows = list(rows)
n = len(rows)
k = min_width
for i in range(n):
tmp = len(rows[i])
if tmp > k:
k = tmp
result = np.empty((n, k), dtype=object)
for i in range(n):
row = list(rows[i])
for j in range(len(row)):
result[i, j] = row[j]
return result
def tuples_to_object_array(ndarray[object] tuples):
cdef:
Py_ssize_t i, j, n, k
ndarray[object, ndim=2] result
tuple tup
n = len(tuples)
k = len(tuples[0])
result = np.empty((n, k), dtype=object)
for i in range(n):
tup = tuples[i]
for j in range(k):
result[i, j] = tup[j]
return result
def to_object_array_tuples(rows: object) -> np.ndarray:
"""
Convert a list of tuples into an object array. Any subclass of
tuple in `rows` will be casted to tuple.
Parameters
----------
rows : 2-d array (N, K)
List of tuples to be converted into an array.
Returns
-------
np.ndarray[object, ndim=2]
"""
cdef:
Py_ssize_t i, j, n, k, tmp
ndarray[object, ndim=2] result
tuple row
rows = list(rows)
n = len(rows)
k = 0
for i in range(n):
tmp = 1 if checknull(rows[i]) else len(rows[i])
if tmp > k:
k = tmp
result = np.empty((n, k), dtype=object)
try:
for i in range(n):
row = rows[i]
for j in range(len(row)):
result[i, j] = row[j]
except TypeError:
# e.g. "Expected tuple, got list"
# upcast any subclasses to tuple
for i in range(n):
row = (rows[i],) if checknull(rows[i]) else tuple(rows[i])
for j in range(len(row)):
result[i, j] = row[j]
return result
@cython.wraparound(False)
@cython.boundscheck(False)
def fast_multiget(dict mapping, ndarray keys, default=np.nan) -> np.ndarray:
cdef:
Py_ssize_t i, n = len(keys)
object val
ndarray[object] output = np.empty(n, dtype="O")
if n == 0:
# kludge, for Series
return np.empty(0, dtype="f8")
for i in range(n):
val = keys[i]
if val in mapping:
output[i] = mapping[val]
else:
output[i] = default
return maybe_convert_objects(output)
def is_bool_list(obj: list) -> bool:
"""
Check if this list contains only bool or np.bool_ objects.
This is appreciably faster than checking `np.array(obj).dtype == bool`
obj1 = [True, False] * 100
obj2 = obj1 * 100
obj3 = obj2 * 100
obj4 = [True, None] + obj1
for obj in [obj1, obj2, obj3, obj4]:
%timeit is_bool_list(obj)
%timeit np.array(obj).dtype.kind == "b"
340 ns ± 8.22 ns
8.78 µs ± 253 ns
28.8 µs ± 704 ns
813 µs ± 17.8 µs
3.4 ms ± 168 µs
78.4 ms ± 1.05 ms
48.1 ns ± 1.26 ns
8.1 µs ± 198 ns
"""
cdef:
object item
for item in obj:
if not util.is_bool_object(item):
return False
# Note: we return True for empty list
return True
cpdef ndarray eq_NA_compat(ndarray[object] arr, object key):
"""
Check for `arr == key`, treating all values as not-equal to pd.NA.
key is assumed to have `not isna(key)`
"""
cdef:
ndarray[uint8_t, cast=True] result = cnp.PyArray_EMPTY(
arr.ndim, arr.shape, cnp.NPY_BOOL, 0
)
Py_ssize_t i
object item
for i in range(len(arr)):
item = arr[i]
if item is C_NA:
result[i] = False
else:
result[i] = item == key
return result
def dtypes_all_equal(list types not None) -> bool:
"""
Faster version for:
first = types[0]
all(is_dtype_equal(first, t) for t in types[1:])
And assuming all elements in the list are np.dtype/ExtensionDtype objects
See timings at https://github.com/pandas-dev/pandas/pull/44594
"""
first = types[0]
for t in types[1:]:
if t is first:
# Fastpath can provide a nice boost for EADtypes
continue
try:
if not t == first:
return False
except (TypeError, AttributeError):
return False
else:
return True
def is_np_dtype(object dtype, str kinds=None) -> bool:
"""
Optimized check for `isinstance(dtype, np.dtype)` with
optional `and dtype.kind in kinds`.
dtype = np.dtype("m8[ns]")
In [7]: %timeit isinstance(dtype, np.dtype)
117 ns ± 1.91 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)
In [8]: %timeit is_np_dtype(dtype)
64 ns ± 1.51 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)
In [9]: %timeit is_timedelta64_dtype(dtype)
209 ns ± 6.96 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
In [10]: %timeit is_np_dtype(dtype, "m")
93.4 ns ± 1.11 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)
"""
if not cnp.PyArray_DescrCheck(dtype):
# i.e. not isinstance(dtype, np.dtype)
return False
if kinds is None:
return True
return dtype.kind in kinds