"""
|
Generic data algorithms. This module is experimental at the moment and not
|
intended for public consumption
|
"""
|
from __future__ import annotations
|
|
import operator
|
from textwrap import dedent
|
from typing import (
|
TYPE_CHECKING,
|
Literal,
|
cast,
|
)
|
import warnings
|
|
import numpy as np
|
|
from pandas._libs import (
|
algos,
|
hashtable as htable,
|
iNaT,
|
lib,
|
)
|
from pandas._typing import (
|
AnyArrayLike,
|
ArrayLike,
|
AxisInt,
|
DtypeObj,
|
TakeIndexer,
|
npt,
|
)
|
from pandas.util._decorators import doc
|
from pandas.util._exceptions import find_stack_level
|
|
from pandas.core.dtypes.cast import (
|
construct_1d_object_array_from_listlike,
|
infer_dtype_from_array,
|
)
|
from pandas.core.dtypes.common import (
|
ensure_float64,
|
ensure_object,
|
ensure_platform_int,
|
is_array_like,
|
is_bool_dtype,
|
is_categorical_dtype,
|
is_complex_dtype,
|
is_extension_array_dtype,
|
is_float_dtype,
|
is_integer,
|
is_integer_dtype,
|
is_list_like,
|
is_numeric_dtype,
|
is_object_dtype,
|
is_scalar,
|
is_signed_integer_dtype,
|
needs_i8_conversion,
|
)
|
from pandas.core.dtypes.concat import concat_compat
|
from pandas.core.dtypes.dtypes import (
|
BaseMaskedDtype,
|
ExtensionDtype,
|
PandasDtype,
|
)
|
from pandas.core.dtypes.generic import (
|
ABCDatetimeArray,
|
ABCExtensionArray,
|
ABCIndex,
|
ABCMultiIndex,
|
ABCSeries,
|
ABCTimedeltaArray,
|
)
|
from pandas.core.dtypes.missing import (
|
isna,
|
na_value_for_dtype,
|
)
|
|
from pandas.core.array_algos.take import take_nd
|
from pandas.core.construction import (
|
array as pd_array,
|
ensure_wrapped_if_datetimelike,
|
extract_array,
|
)
|
from pandas.core.indexers import validate_indices
|
|
if TYPE_CHECKING:
|
from pandas._typing import (
|
NumpySorter,
|
NumpyValueArrayLike,
|
)
|
|
from pandas import (
|
Categorical,
|
Index,
|
Series,
|
)
|
from pandas.core.arrays import (
|
BaseMaskedArray,
|
ExtensionArray,
|
)
|
|
|
# --------------- #
|
# dtype access #
|
# --------------- #
|
def _ensure_data(values: ArrayLike) -> np.ndarray:
|
"""
|
routine to ensure that our data is of the correct
|
input dtype for lower-level routines
|
|
This will coerce:
|
- ints -> int64
|
- uint -> uint64
|
- bool -> uint8
|
- datetimelike -> i8
|
- datetime64tz -> i8 (in local tz)
|
- categorical -> codes
|
|
Parameters
|
----------
|
values : np.ndarray or ExtensionArray
|
|
Returns
|
-------
|
np.ndarray
|
"""
|
|
if not isinstance(values, ABCMultiIndex):
|
# extract_array would raise
|
values = extract_array(values, extract_numpy=True)
|
|
if is_object_dtype(values.dtype):
|
return ensure_object(np.asarray(values))
|
|
elif isinstance(values.dtype, BaseMaskedDtype):
|
# i.e. BooleanArray, FloatingArray, IntegerArray
|
values = cast("BaseMaskedArray", values)
|
if not values._hasna:
|
# No pd.NAs -> We can avoid an object-dtype cast (and copy) GH#41816
|
# recurse to avoid re-implementing logic for eg bool->uint8
|
return _ensure_data(values._data)
|
return np.asarray(values)
|
|
elif is_categorical_dtype(values.dtype):
|
# NB: cases that go through here should NOT be using _reconstruct_data
|
# on the back-end.
|
values = cast("Categorical", values)
|
return values.codes
|
|
elif is_bool_dtype(values.dtype):
|
if isinstance(values, np.ndarray):
|
# i.e. actually dtype == np.dtype("bool")
|
return np.asarray(values).view("uint8")
|
else:
|
# e.g. Sparse[bool, False] # TODO: no test cases get here
|
return np.asarray(values).astype("uint8", copy=False)
|
|
elif is_integer_dtype(values.dtype):
|
return np.asarray(values)
|
|
elif is_float_dtype(values.dtype):
|
# Note: checking `values.dtype == "float128"` raises on Windows and 32bit
|
# error: Item "ExtensionDtype" of "Union[Any, ExtensionDtype, dtype[Any]]"
|
# has no attribute "itemsize"
|
if values.dtype.itemsize in [2, 12, 16]: # type: ignore[union-attr]
|
# we dont (yet) have float128 hashtable support
|
return ensure_float64(values)
|
return np.asarray(values)
|
|
elif is_complex_dtype(values.dtype):
|
return cast(np.ndarray, values)
|
|
# datetimelike
|
elif needs_i8_conversion(values.dtype):
|
npvalues = values.view("i8")
|
npvalues = cast(np.ndarray, npvalues)
|
return npvalues
|
|
# we have failed, return object
|
values = np.asarray(values, dtype=object)
|
return ensure_object(values)
|
|
|
def _reconstruct_data(
|
values: ArrayLike, dtype: DtypeObj, original: AnyArrayLike
|
) -> ArrayLike:
|
"""
|
reverse of _ensure_data
|
|
Parameters
|
----------
|
values : np.ndarray or ExtensionArray
|
dtype : np.dtype or ExtensionDtype
|
original : AnyArrayLike
|
|
Returns
|
-------
|
ExtensionArray or np.ndarray
|
"""
|
if isinstance(values, ABCExtensionArray) and values.dtype == dtype:
|
# Catch DatetimeArray/TimedeltaArray
|
return values
|
|
if not isinstance(dtype, np.dtype):
|
# i.e. ExtensionDtype; note we have ruled out above the possibility
|
# that values.dtype == dtype
|
cls = dtype.construct_array_type()
|
|
values = cls._from_sequence(values, dtype=dtype)
|
|
else:
|
values = values.astype(dtype, copy=False)
|
|
return values
|
|
|
def _ensure_arraylike(values) -> ArrayLike:
|
"""
|
ensure that we are arraylike if not already
|
"""
|
if not is_array_like(values):
|
inferred = lib.infer_dtype(values, skipna=False)
|
if inferred in ["mixed", "string", "mixed-integer"]:
|
# "mixed-integer" to ensure we do not cast ["ss", 42] to str GH#22160
|
if isinstance(values, tuple):
|
values = list(values)
|
values = construct_1d_object_array_from_listlike(values)
|
else:
|
values = np.asarray(values)
|
return values
|
|
|
_hashtables = {
|
"complex128": htable.Complex128HashTable,
|
"complex64": htable.Complex64HashTable,
|
"float64": htable.Float64HashTable,
|
"float32": htable.Float32HashTable,
|
"uint64": htable.UInt64HashTable,
|
"uint32": htable.UInt32HashTable,
|
"uint16": htable.UInt16HashTable,
|
"uint8": htable.UInt8HashTable,
|
"int64": htable.Int64HashTable,
|
"int32": htable.Int32HashTable,
|
"int16": htable.Int16HashTable,
|
"int8": htable.Int8HashTable,
|
"string": htable.StringHashTable,
|
"object": htable.PyObjectHashTable,
|
}
|
|
|
def _get_hashtable_algo(values: np.ndarray):
|
"""
|
Parameters
|
----------
|
values : np.ndarray
|
|
Returns
|
-------
|
htable : HashTable subclass
|
values : ndarray
|
"""
|
values = _ensure_data(values)
|
|
ndtype = _check_object_for_strings(values)
|
hashtable = _hashtables[ndtype]
|
return hashtable, values
|
|
|
def _check_object_for_strings(values: np.ndarray) -> str:
|
"""
|
Check if we can use string hashtable instead of object hashtable.
|
|
Parameters
|
----------
|
values : ndarray
|
|
Returns
|
-------
|
str
|
"""
|
ndtype = values.dtype.name
|
if ndtype == "object":
|
# it's cheaper to use a String Hash Table than Object; we infer
|
# including nulls because that is the only difference between
|
# StringHashTable and ObjectHashtable
|
if lib.infer_dtype(values, skipna=False) in ["string"]:
|
ndtype = "string"
|
return ndtype
|
|
|
# --------------- #
|
# top-level algos #
|
# --------------- #
|
|
|
def unique(values):
|
"""
|
Return unique values based on a hash table.
|
|
Uniques are returned in order of appearance. This does NOT sort.
|
|
Significantly faster than numpy.unique for long enough sequences.
|
Includes NA values.
|
|
Parameters
|
----------
|
values : 1d array-like
|
|
Returns
|
-------
|
numpy.ndarray or ExtensionArray
|
|
The return can be:
|
|
* Index : when the input is an Index
|
* Categorical : when the input is a Categorical dtype
|
* ndarray : when the input is a Series/ndarray
|
|
Return numpy.ndarray or ExtensionArray.
|
|
See Also
|
--------
|
Index.unique : Return unique values from an Index.
|
Series.unique : Return unique values of Series object.
|
|
Examples
|
--------
|
>>> pd.unique(pd.Series([2, 1, 3, 3]))
|
array([2, 1, 3])
|
|
>>> pd.unique(pd.Series([2] + [1] * 5))
|
array([2, 1])
|
|
>>> pd.unique(pd.Series([pd.Timestamp("20160101"), pd.Timestamp("20160101")]))
|
array(['2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]')
|
|
>>> pd.unique(
|
... pd.Series(
|
... [
|
... pd.Timestamp("20160101", tz="US/Eastern"),
|
... pd.Timestamp("20160101", tz="US/Eastern"),
|
... ]
|
... )
|
... )
|
<DatetimeArray>
|
['2016-01-01 00:00:00-05:00']
|
Length: 1, dtype: datetime64[ns, US/Eastern]
|
|
>>> pd.unique(
|
... pd.Index(
|
... [
|
... pd.Timestamp("20160101", tz="US/Eastern"),
|
... pd.Timestamp("20160101", tz="US/Eastern"),
|
... ]
|
... )
|
... )
|
DatetimeIndex(['2016-01-01 00:00:00-05:00'],
|
dtype='datetime64[ns, US/Eastern]',
|
freq=None)
|
|
>>> pd.unique(list("baabc"))
|
array(['b', 'a', 'c'], dtype=object)
|
|
An unordered Categorical will return categories in the
|
order of appearance.
|
|
>>> pd.unique(pd.Series(pd.Categorical(list("baabc"))))
|
['b', 'a', 'c']
|
Categories (3, object): ['a', 'b', 'c']
|
|
>>> pd.unique(pd.Series(pd.Categorical(list("baabc"), categories=list("abc"))))
|
['b', 'a', 'c']
|
Categories (3, object): ['a', 'b', 'c']
|
|
An ordered Categorical preserves the category ordering.
|
|
>>> pd.unique(
|
... pd.Series(
|
... pd.Categorical(list("baabc"), categories=list("abc"), ordered=True)
|
... )
|
... )
|
['b', 'a', 'c']
|
Categories (3, object): ['a' < 'b' < 'c']
|
|
An array of tuples
|
|
>>> pd.unique([("a", "b"), ("b", "a"), ("a", "c"), ("b", "a")])
|
array([('a', 'b'), ('b', 'a'), ('a', 'c')], dtype=object)
|
"""
|
return unique_with_mask(values)
|
|
|
def nunique_ints(values: ArrayLike) -> int:
|
"""
|
Return the number of unique values for integer array-likes.
|
|
Significantly faster than pandas.unique for long enough sequences.
|
No checks are done to ensure input is integral.
|
|
Parameters
|
----------
|
values : 1d array-like
|
|
Returns
|
-------
|
int : The number of unique values in ``values``
|
"""
|
if len(values) == 0:
|
return 0
|
values = _ensure_data(values)
|
# bincount requires intp
|
result = (np.bincount(values.ravel().astype("intp")) != 0).sum()
|
return result
|
|
|
def unique_with_mask(values, mask: npt.NDArray[np.bool_] | None = None):
|
"""See algorithms.unique for docs. Takes a mask for masked arrays."""
|
values = _ensure_arraylike(values)
|
|
if is_extension_array_dtype(values.dtype):
|
# Dispatch to extension dtype's unique.
|
return values.unique()
|
|
original = values
|
hashtable, values = _get_hashtable_algo(values)
|
|
table = hashtable(len(values))
|
if mask is None:
|
uniques = table.unique(values)
|
uniques = _reconstruct_data(uniques, original.dtype, original)
|
return uniques
|
|
else:
|
uniques, mask = table.unique(values, mask=mask)
|
uniques = _reconstruct_data(uniques, original.dtype, original)
|
assert mask is not None # for mypy
|
return uniques, mask.astype("bool")
|
|
|
unique1d = unique
|
|
|
def isin(comps: AnyArrayLike, values: AnyArrayLike) -> npt.NDArray[np.bool_]:
|
"""
|
Compute the isin boolean array.
|
|
Parameters
|
----------
|
comps : array-like
|
values : array-like
|
|
Returns
|
-------
|
ndarray[bool]
|
Same length as `comps`.
|
"""
|
if not is_list_like(comps):
|
raise TypeError(
|
"only list-like objects are allowed to be passed "
|
f"to isin(), you passed a [{type(comps).__name__}]"
|
)
|
if not is_list_like(values):
|
raise TypeError(
|
"only list-like objects are allowed to be passed "
|
f"to isin(), you passed a [{type(values).__name__}]"
|
)
|
|
if not isinstance(values, (ABCIndex, ABCSeries, ABCExtensionArray, np.ndarray)):
|
orig_values = list(values)
|
values = _ensure_arraylike(orig_values)
|
|
if (
|
len(values) > 0
|
and is_numeric_dtype(values)
|
and not is_signed_integer_dtype(comps)
|
):
|
# GH#46485 Use object to avoid upcast to float64 later
|
# TODO: Share with _find_common_type_compat
|
values = construct_1d_object_array_from_listlike(orig_values)
|
|
elif isinstance(values, ABCMultiIndex):
|
# Avoid raising in extract_array
|
values = np.array(values)
|
else:
|
values = extract_array(values, extract_numpy=True, extract_range=True)
|
|
comps_array = _ensure_arraylike(comps)
|
comps_array = extract_array(comps_array, extract_numpy=True)
|
if not isinstance(comps_array, np.ndarray):
|
# i.e. Extension Array
|
return comps_array.isin(values)
|
|
elif needs_i8_conversion(comps_array.dtype):
|
# Dispatch to DatetimeLikeArrayMixin.isin
|
return pd_array(comps_array).isin(values)
|
elif needs_i8_conversion(values.dtype) and not is_object_dtype(comps_array.dtype):
|
# e.g. comps_array are integers and values are datetime64s
|
return np.zeros(comps_array.shape, dtype=bool)
|
# TODO: not quite right ... Sparse/Categorical
|
elif needs_i8_conversion(values.dtype):
|
return isin(comps_array, values.astype(object))
|
|
elif isinstance(values.dtype, ExtensionDtype):
|
return isin(np.asarray(comps_array), np.asarray(values))
|
|
# GH16012
|
# Ensure np.in1d doesn't get object types or it *may* throw an exception
|
# Albeit hashmap has O(1) look-up (vs. O(logn) in sorted array),
|
# in1d is faster for small sizes
|
if (
|
len(comps_array) > 1_000_000
|
and len(values) <= 26
|
and not is_object_dtype(comps_array)
|
):
|
# If the values include nan we need to check for nan explicitly
|
# since np.nan it not equal to np.nan
|
if isna(values).any():
|
|
def f(c, v):
|
return np.logical_or(np.in1d(c, v), np.isnan(c))
|
|
else:
|
f = np.in1d
|
|
else:
|
common = np.find_common_type([values.dtype, comps_array.dtype], [])
|
values = values.astype(common, copy=False)
|
comps_array = comps_array.astype(common, copy=False)
|
f = htable.ismember
|
|
return f(comps_array, values)
|
|
|
def factorize_array(
|
values: np.ndarray,
|
use_na_sentinel: bool = True,
|
size_hint: int | None = None,
|
na_value: object = None,
|
mask: npt.NDArray[np.bool_] | None = None,
|
) -> tuple[npt.NDArray[np.intp], np.ndarray]:
|
"""
|
Factorize a numpy array to codes and uniques.
|
|
This doesn't do any coercion of types or unboxing before factorization.
|
|
Parameters
|
----------
|
values : ndarray
|
use_na_sentinel : bool, default True
|
If True, the sentinel -1 will be used for NaN values. If False,
|
NaN values will be encoded as non-negative integers and will not drop the
|
NaN from the uniques of the values.
|
size_hint : int, optional
|
Passed through to the hashtable's 'get_labels' method
|
na_value : object, optional
|
A value in `values` to consider missing. Note: only use this
|
parameter when you know that you don't have any values pandas would
|
consider missing in the array (NaN for float data, iNaT for
|
datetimes, etc.).
|
mask : ndarray[bool], optional
|
If not None, the mask is used as indicator for missing values
|
(True = missing, False = valid) instead of `na_value` or
|
condition "val != val".
|
|
Returns
|
-------
|
codes : ndarray[np.intp]
|
uniques : ndarray
|
"""
|
original = values
|
if values.dtype.kind in ["m", "M"]:
|
# _get_hashtable_algo will cast dt64/td64 to i8 via _ensure_data, so we
|
# need to do the same to na_value. We are assuming here that the passed
|
# na_value is an appropriately-typed NaT.
|
# e.g. test_where_datetimelike_categorical
|
na_value = iNaT
|
|
hash_klass, values = _get_hashtable_algo(values)
|
|
table = hash_klass(size_hint or len(values))
|
uniques, codes = table.factorize(
|
values,
|
na_sentinel=-1,
|
na_value=na_value,
|
mask=mask,
|
ignore_na=use_na_sentinel,
|
)
|
|
# re-cast e.g. i8->dt64/td64, uint8->bool
|
uniques = _reconstruct_data(uniques, original.dtype, original)
|
|
codes = ensure_platform_int(codes)
|
return codes, uniques
|
|
|
@doc(
|
values=dedent(
|
"""\
|
values : sequence
|
A 1-D sequence. Sequences that aren't pandas objects are
|
coerced to ndarrays before factorization.
|
"""
|
),
|
sort=dedent(
|
"""\
|
sort : bool, default False
|
Sort `uniques` and shuffle `codes` to maintain the
|
relationship.
|
"""
|
),
|
size_hint=dedent(
|
"""\
|
size_hint : int, optional
|
Hint to the hashtable sizer.
|
"""
|
),
|
)
|
def factorize(
|
values,
|
sort: bool = False,
|
use_na_sentinel: bool = True,
|
size_hint: int | None = None,
|
) -> tuple[np.ndarray, np.ndarray | Index]:
|
"""
|
Encode the object as an enumerated type or categorical variable.
|
|
This method is useful for obtaining a numeric representation of an
|
array when all that matters is identifying distinct values. `factorize`
|
is available as both a top-level function :func:`pandas.factorize`,
|
and as a method :meth:`Series.factorize` and :meth:`Index.factorize`.
|
|
Parameters
|
----------
|
{values}{sort}
|
use_na_sentinel : bool, default True
|
If True, the sentinel -1 will be used for NaN values. If False,
|
NaN values will be encoded as non-negative integers and will not drop the
|
NaN from the uniques of the values.
|
|
.. versionadded:: 1.5.0
|
{size_hint}\
|
|
Returns
|
-------
|
codes : ndarray
|
An integer ndarray that's an indexer into `uniques`.
|
``uniques.take(codes)`` will have the same values as `values`.
|
uniques : ndarray, Index, or Categorical
|
The unique valid values. When `values` is Categorical, `uniques`
|
is a Categorical. When `values` is some other pandas object, an
|
`Index` is returned. Otherwise, a 1-D ndarray is returned.
|
|
.. note::
|
|
Even if there's a missing value in `values`, `uniques` will
|
*not* contain an entry for it.
|
|
See Also
|
--------
|
cut : Discretize continuous-valued array.
|
unique : Find the unique value in an array.
|
|
Notes
|
-----
|
Reference :ref:`the user guide <reshaping.factorize>` for more examples.
|
|
Examples
|
--------
|
These examples all show factorize as a top-level method like
|
``pd.factorize(values)``. The results are identical for methods like
|
:meth:`Series.factorize`.
|
|
>>> codes, uniques = pd.factorize(['b', 'b', 'a', 'c', 'b'])
|
>>> codes
|
array([0, 0, 1, 2, 0])
|
>>> uniques
|
array(['b', 'a', 'c'], dtype=object)
|
|
With ``sort=True``, the `uniques` will be sorted, and `codes` will be
|
shuffled so that the relationship is the maintained.
|
|
>>> codes, uniques = pd.factorize(['b', 'b', 'a', 'c', 'b'], sort=True)
|
>>> codes
|
array([1, 1, 0, 2, 1])
|
>>> uniques
|
array(['a', 'b', 'c'], dtype=object)
|
|
When ``use_na_sentinel=True`` (the default), missing values are indicated in
|
the `codes` with the sentinel value ``-1`` and missing values are not
|
included in `uniques`.
|
|
>>> codes, uniques = pd.factorize(['b', None, 'a', 'c', 'b'])
|
>>> codes
|
array([ 0, -1, 1, 2, 0])
|
>>> uniques
|
array(['b', 'a', 'c'], dtype=object)
|
|
Thus far, we've only factorized lists (which are internally coerced to
|
NumPy arrays). When factorizing pandas objects, the type of `uniques`
|
will differ. For Categoricals, a `Categorical` is returned.
|
|
>>> cat = pd.Categorical(['a', 'a', 'c'], categories=['a', 'b', 'c'])
|
>>> codes, uniques = pd.factorize(cat)
|
>>> codes
|
array([0, 0, 1])
|
>>> uniques
|
['a', 'c']
|
Categories (3, object): ['a', 'b', 'c']
|
|
Notice that ``'b'`` is in ``uniques.categories``, despite not being
|
present in ``cat.values``.
|
|
For all other pandas objects, an Index of the appropriate type is
|
returned.
|
|
>>> cat = pd.Series(['a', 'a', 'c'])
|
>>> codes, uniques = pd.factorize(cat)
|
>>> codes
|
array([0, 0, 1])
|
>>> uniques
|
Index(['a', 'c'], dtype='object')
|
|
If NaN is in the values, and we want to include NaN in the uniques of the
|
values, it can be achieved by setting ``use_na_sentinel=False``.
|
|
>>> values = np.array([1, 2, 1, np.nan])
|
>>> codes, uniques = pd.factorize(values) # default: use_na_sentinel=True
|
>>> codes
|
array([ 0, 1, 0, -1])
|
>>> uniques
|
array([1., 2.])
|
|
>>> codes, uniques = pd.factorize(values, use_na_sentinel=False)
|
>>> codes
|
array([0, 1, 0, 2])
|
>>> uniques
|
array([ 1., 2., nan])
|
"""
|
# Implementation notes: This method is responsible for 3 things
|
# 1.) coercing data to array-like (ndarray, Index, extension array)
|
# 2.) factorizing codes and uniques
|
# 3.) Maybe boxing the uniques in an Index
|
#
|
# Step 2 is dispatched to extension types (like Categorical). They are
|
# responsible only for factorization. All data coercion, sorting and boxing
|
# should happen here.
|
if isinstance(values, (ABCIndex, ABCSeries)):
|
return values.factorize(sort=sort, use_na_sentinel=use_na_sentinel)
|
|
values = _ensure_arraylike(values)
|
original = values
|
|
if (
|
isinstance(values, (ABCDatetimeArray, ABCTimedeltaArray))
|
and values.freq is not None
|
):
|
# The presence of 'freq' means we can fast-path sorting and know there
|
# aren't NAs
|
codes, uniques = values.factorize(sort=sort)
|
return codes, uniques
|
|
elif not isinstance(values, np.ndarray):
|
# i.e. ExtensionArray
|
codes, uniques = values.factorize(use_na_sentinel=use_na_sentinel)
|
|
else:
|
values = np.asarray(values) # convert DTA/TDA/MultiIndex
|
|
if not use_na_sentinel and is_object_dtype(values):
|
# factorize can now handle differentiating various types of null values.
|
# These can only occur when the array has object dtype.
|
# However, for backwards compatibility we only use the null for the
|
# provided dtype. This may be revisited in the future, see GH#48476.
|
null_mask = isna(values)
|
if null_mask.any():
|
na_value = na_value_for_dtype(values.dtype, compat=False)
|
# Don't modify (potentially user-provided) array
|
values = np.where(null_mask, na_value, values)
|
|
codes, uniques = factorize_array(
|
values,
|
use_na_sentinel=use_na_sentinel,
|
size_hint=size_hint,
|
)
|
|
if sort and len(uniques) > 0:
|
uniques, codes = safe_sort(
|
uniques,
|
codes,
|
use_na_sentinel=use_na_sentinel,
|
assume_unique=True,
|
verify=False,
|
)
|
|
uniques = _reconstruct_data(uniques, original.dtype, original)
|
|
return codes, uniques
|
|
|
def value_counts(
|
values,
|
sort: bool = True,
|
ascending: bool = False,
|
normalize: bool = False,
|
bins=None,
|
dropna: bool = True,
|
) -> Series:
|
"""
|
Compute a histogram of the counts of non-null values.
|
|
Parameters
|
----------
|
values : ndarray (1-d)
|
sort : bool, default True
|
Sort by values
|
ascending : bool, default False
|
Sort in ascending order
|
normalize: bool, default False
|
If True then compute a relative histogram
|
bins : integer, optional
|
Rather than count values, group them into half-open bins,
|
convenience for pd.cut, only works with numeric data
|
dropna : bool, default True
|
Don't include counts of NaN
|
|
Returns
|
-------
|
Series
|
"""
|
from pandas import (
|
Index,
|
Series,
|
)
|
|
index_name = getattr(values, "name", None)
|
name = "proportion" if normalize else "count"
|
|
if bins is not None:
|
from pandas.core.reshape.tile import cut
|
|
values = Series(values, copy=False)
|
try:
|
ii = cut(values, bins, include_lowest=True)
|
except TypeError as err:
|
raise TypeError("bins argument only works with numeric data.") from err
|
|
# count, remove nulls (from the index), and but the bins
|
result = ii.value_counts(dropna=dropna)
|
result.name = name
|
result = result[result.index.notna()]
|
result.index = result.index.astype("interval")
|
result = result.sort_index()
|
|
# if we are dropna and we have NO values
|
if dropna and (result._values == 0).all():
|
result = result.iloc[0:0]
|
|
# normalizing is by len of all (regardless of dropna)
|
counts = np.array([len(ii)])
|
|
else:
|
if is_extension_array_dtype(values):
|
# handle Categorical and sparse,
|
result = Series(values, copy=False)._values.value_counts(dropna=dropna)
|
result.name = name
|
result.index.name = index_name
|
counts = result._values
|
if not isinstance(counts, np.ndarray):
|
# e.g. ArrowExtensionArray
|
counts = np.asarray(counts)
|
|
elif isinstance(values, ABCMultiIndex):
|
# GH49558
|
levels = list(range(values.nlevels))
|
result = (
|
Series(index=values, name=name)
|
.groupby(level=levels, dropna=dropna)
|
.size()
|
)
|
result.index.names = values.names
|
counts = result._values
|
|
else:
|
values = _ensure_arraylike(values)
|
keys, counts = value_counts_arraylike(values, dropna)
|
if keys.dtype == np.float16:
|
keys = keys.astype(np.float32)
|
|
# For backwards compatibility, we let Index do its normal type
|
# inference, _except_ for if if infers from object to bool.
|
idx = Index(keys)
|
if idx.dtype == bool and keys.dtype == object:
|
idx = idx.astype(object)
|
idx.name = index_name
|
|
result = Series(counts, index=idx, name=name, copy=False)
|
|
if sort:
|
result = result.sort_values(ascending=ascending)
|
|
if normalize:
|
result = result / counts.sum()
|
|
return result
|
|
|
# Called once from SparseArray, otherwise could be private
|
def value_counts_arraylike(
|
values: np.ndarray, dropna: bool, mask: npt.NDArray[np.bool_] | None = None
|
) -> tuple[ArrayLike, npt.NDArray[np.int64]]:
|
"""
|
Parameters
|
----------
|
values : np.ndarray
|
dropna : bool
|
mask : np.ndarray[bool] or None, default None
|
|
Returns
|
-------
|
uniques : np.ndarray
|
counts : np.ndarray[np.int64]
|
"""
|
original = values
|
values = _ensure_data(values)
|
|
keys, counts = htable.value_count(values, dropna, mask=mask)
|
|
if needs_i8_conversion(original.dtype):
|
# datetime, timedelta, or period
|
|
if dropna:
|
mask = keys != iNaT
|
keys, counts = keys[mask], counts[mask]
|
|
res_keys = _reconstruct_data(keys, original.dtype, original)
|
return res_keys, counts
|
|
|
def duplicated(
|
values: ArrayLike, keep: Literal["first", "last", False] = "first"
|
) -> npt.NDArray[np.bool_]:
|
"""
|
Return boolean ndarray denoting duplicate values.
|
|
Parameters
|
----------
|
values : nd.array, ExtensionArray or Series
|
Array over which to check for duplicate values.
|
keep : {'first', 'last', False}, default 'first'
|
- ``first`` : Mark duplicates as ``True`` except for the first
|
occurrence.
|
- ``last`` : Mark duplicates as ``True`` except for the last
|
occurrence.
|
- False : Mark all duplicates as ``True``.
|
|
Returns
|
-------
|
duplicated : ndarray[bool]
|
"""
|
if hasattr(values, "dtype") and isinstance(values.dtype, BaseMaskedDtype):
|
values = cast("BaseMaskedArray", values)
|
return htable.duplicated(values._data, keep=keep, mask=values._mask)
|
|
values = _ensure_data(values)
|
return htable.duplicated(values, keep=keep)
|
|
|
def mode(
|
values: ArrayLike, dropna: bool = True, mask: npt.NDArray[np.bool_] | None = None
|
) -> ArrayLike:
|
"""
|
Returns the mode(s) of an array.
|
|
Parameters
|
----------
|
values : array-like
|
Array over which to check for duplicate values.
|
dropna : bool, default True
|
Don't consider counts of NaN/NaT.
|
|
Returns
|
-------
|
np.ndarray or ExtensionArray
|
"""
|
values = _ensure_arraylike(values)
|
original = values
|
|
if needs_i8_conversion(values.dtype):
|
# Got here with ndarray; dispatch to DatetimeArray/TimedeltaArray.
|
values = ensure_wrapped_if_datetimelike(values)
|
values = cast("ExtensionArray", values)
|
return values._mode(dropna=dropna)
|
|
values = _ensure_data(values)
|
|
npresult = htable.mode(values, dropna=dropna, mask=mask)
|
try:
|
npresult = np.sort(npresult)
|
except TypeError as err:
|
warnings.warn(
|
f"Unable to sort modes: {err}",
|
stacklevel=find_stack_level(),
|
)
|
|
result = _reconstruct_data(npresult, original.dtype, original)
|
return result
|
|
|
def rank(
|
values: ArrayLike,
|
axis: AxisInt = 0,
|
method: str = "average",
|
na_option: str = "keep",
|
ascending: bool = True,
|
pct: bool = False,
|
) -> npt.NDArray[np.float64]:
|
"""
|
Rank the values along a given axis.
|
|
Parameters
|
----------
|
values : np.ndarray or ExtensionArray
|
Array whose values will be ranked. The number of dimensions in this
|
array must not exceed 2.
|
axis : int, default 0
|
Axis over which to perform rankings.
|
method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
|
The method by which tiebreaks are broken during the ranking.
|
na_option : {'keep', 'top'}, default 'keep'
|
The method by which NaNs are placed in the ranking.
|
- ``keep``: rank each NaN value with a NaN ranking
|
- ``top``: replace each NaN with either +/- inf so that they
|
there are ranked at the top
|
ascending : bool, default True
|
Whether or not the elements should be ranked in ascending order.
|
pct : bool, default False
|
Whether or not to the display the returned rankings in integer form
|
(e.g. 1, 2, 3) or in percentile form (e.g. 0.333..., 0.666..., 1).
|
"""
|
is_datetimelike = needs_i8_conversion(values.dtype)
|
values = _ensure_data(values)
|
|
if values.ndim == 1:
|
ranks = algos.rank_1d(
|
values,
|
is_datetimelike=is_datetimelike,
|
ties_method=method,
|
ascending=ascending,
|
na_option=na_option,
|
pct=pct,
|
)
|
elif values.ndim == 2:
|
ranks = algos.rank_2d(
|
values,
|
axis=axis,
|
is_datetimelike=is_datetimelike,
|
ties_method=method,
|
ascending=ascending,
|
na_option=na_option,
|
pct=pct,
|
)
|
else:
|
raise TypeError("Array with ndim > 2 are not supported.")
|
|
return ranks
|
|
|
def checked_add_with_arr(
|
arr: npt.NDArray[np.int64],
|
b: int | npt.NDArray[np.int64],
|
arr_mask: npt.NDArray[np.bool_] | None = None,
|
b_mask: npt.NDArray[np.bool_] | None = None,
|
) -> npt.NDArray[np.int64]:
|
"""
|
Perform array addition that checks for underflow and overflow.
|
|
Performs the addition of an int64 array and an int64 integer (or array)
|
but checks that they do not result in overflow first. For elements that
|
are indicated to be NaN, whether or not there is overflow for that element
|
is automatically ignored.
|
|
Parameters
|
----------
|
arr : np.ndarray[int64] addend.
|
b : array or scalar addend.
|
arr_mask : np.ndarray[bool] or None, default None
|
array indicating which elements to exclude from checking
|
b_mask : np.ndarray[bool] or None, default None
|
array or scalar indicating which element(s) to exclude from checking
|
|
Returns
|
-------
|
sum : An array for elements x + b for each element x in arr if b is
|
a scalar or an array for elements x + y for each element pair
|
(x, y) in (arr, b).
|
|
Raises
|
------
|
OverflowError if any x + y exceeds the maximum or minimum int64 value.
|
"""
|
# For performance reasons, we broadcast 'b' to the new array 'b2'
|
# so that it has the same size as 'arr'.
|
b2 = np.broadcast_to(b, arr.shape)
|
if b_mask is not None:
|
# We do the same broadcasting for b_mask as well.
|
b2_mask = np.broadcast_to(b_mask, arr.shape)
|
else:
|
b2_mask = None
|
|
# For elements that are NaN, regardless of their value, we should
|
# ignore whether they overflow or not when doing the checked add.
|
if arr_mask is not None and b2_mask is not None:
|
not_nan = np.logical_not(arr_mask | b2_mask)
|
elif arr_mask is not None:
|
not_nan = np.logical_not(arr_mask)
|
elif b_mask is not None:
|
# error: Argument 1 to "__call__" of "_UFunc_Nin1_Nout1" has
|
# incompatible type "Optional[ndarray[Any, dtype[bool_]]]";
|
# expected "Union[_SupportsArray[dtype[Any]], _NestedSequence
|
# [_SupportsArray[dtype[Any]]], bool, int, float, complex, str
|
# , bytes, _NestedSequence[Union[bool, int, float, complex, str
|
# , bytes]]]"
|
not_nan = np.logical_not(b2_mask) # type: ignore[arg-type]
|
else:
|
not_nan = np.empty(arr.shape, dtype=bool)
|
not_nan.fill(True)
|
|
# gh-14324: For each element in 'arr' and its corresponding element
|
# in 'b2', we check the sign of the element in 'b2'. If it is positive,
|
# we then check whether its sum with the element in 'arr' exceeds
|
# np.iinfo(np.int64).max. If so, we have an overflow error. If it
|
# it is negative, we then check whether its sum with the element in
|
# 'arr' exceeds np.iinfo(np.int64).min. If so, we have an overflow
|
# error as well.
|
i8max = lib.i8max
|
i8min = iNaT
|
|
mask1 = b2 > 0
|
mask2 = b2 < 0
|
|
if not mask1.any():
|
to_raise = ((i8min - b2 > arr) & not_nan).any()
|
elif not mask2.any():
|
to_raise = ((i8max - b2 < arr) & not_nan).any()
|
else:
|
to_raise = ((i8max - b2[mask1] < arr[mask1]) & not_nan[mask1]).any() or (
|
(i8min - b2[mask2] > arr[mask2]) & not_nan[mask2]
|
).any()
|
|
if to_raise:
|
raise OverflowError("Overflow in int64 addition")
|
|
result = arr + b
|
if arr_mask is not None or b2_mask is not None:
|
np.putmask(result, ~not_nan, iNaT)
|
|
return result
|
|
|
# ---- #
|
# take #
|
# ---- #
|
|
|
def take(
|
arr,
|
indices: TakeIndexer,
|
axis: AxisInt = 0,
|
allow_fill: bool = False,
|
fill_value=None,
|
):
|
"""
|
Take elements from an array.
|
|
Parameters
|
----------
|
arr : array-like or scalar value
|
Non array-likes (sequences/scalars without a dtype) are coerced
|
to an ndarray.
|
indices : sequence of int or one-dimensional np.ndarray of int
|
Indices to be taken.
|
axis : int, default 0
|
The axis over which to select values.
|
allow_fill : bool, default False
|
How to handle negative values in `indices`.
|
|
* False: negative values in `indices` indicate positional indices
|
from the right (the default). This is similar to :func:`numpy.take`.
|
|
* True: negative values in `indices` indicate
|
missing values. These values are set to `fill_value`. Any other
|
negative values raise a ``ValueError``.
|
|
fill_value : any, optional
|
Fill value to use for NA-indices when `allow_fill` is True.
|
This may be ``None``, in which case the default NA value for
|
the type (``self.dtype.na_value``) is used.
|
|
For multi-dimensional `arr`, each *element* is filled with
|
`fill_value`.
|
|
Returns
|
-------
|
ndarray or ExtensionArray
|
Same type as the input.
|
|
Raises
|
------
|
IndexError
|
When `indices` is out of bounds for the array.
|
ValueError
|
When the indexer contains negative values other than ``-1``
|
and `allow_fill` is True.
|
|
Notes
|
-----
|
When `allow_fill` is False, `indices` may be whatever dimensionality
|
is accepted by NumPy for `arr`.
|
|
When `allow_fill` is True, `indices` should be 1-D.
|
|
See Also
|
--------
|
numpy.take : Take elements from an array along an axis.
|
|
Examples
|
--------
|
>>> import pandas as pd
|
|
With the default ``allow_fill=False``, negative numbers indicate
|
positional indices from the right.
|
|
>>> pd.api.extensions.take(np.array([10, 20, 30]), [0, 0, -1])
|
array([10, 10, 30])
|
|
Setting ``allow_fill=True`` will place `fill_value` in those positions.
|
|
>>> pd.api.extensions.take(np.array([10, 20, 30]), [0, 0, -1], allow_fill=True)
|
array([10., 10., nan])
|
|
>>> pd.api.extensions.take(np.array([10, 20, 30]), [0, 0, -1], allow_fill=True,
|
... fill_value=-10)
|
array([ 10, 10, -10])
|
"""
|
if not is_array_like(arr):
|
arr = np.asarray(arr)
|
|
indices = np.asarray(indices, dtype=np.intp)
|
|
if allow_fill:
|
# Pandas style, -1 means NA
|
validate_indices(indices, arr.shape[axis])
|
result = take_nd(
|
arr, indices, axis=axis, allow_fill=True, fill_value=fill_value
|
)
|
else:
|
# NumPy style
|
result = arr.take(indices, axis=axis)
|
return result
|
|
|
# ------------ #
|
# searchsorted #
|
# ------------ #
|
|
|
def searchsorted(
|
arr: ArrayLike,
|
value: NumpyValueArrayLike | ExtensionArray,
|
side: Literal["left", "right"] = "left",
|
sorter: NumpySorter = None,
|
) -> npt.NDArray[np.intp] | np.intp:
|
"""
|
Find indices where elements should be inserted to maintain order.
|
|
Find the indices into a sorted array `arr` (a) such that, if the
|
corresponding elements in `value` were inserted before the indices,
|
the order of `arr` would be preserved.
|
|
Assuming that `arr` is sorted:
|
|
====== ================================
|
`side` returned index `i` satisfies
|
====== ================================
|
left ``arr[i-1] < value <= self[i]``
|
right ``arr[i-1] <= value < self[i]``
|
====== ================================
|
|
Parameters
|
----------
|
arr: np.ndarray, ExtensionArray, Series
|
Input array. If `sorter` is None, then it must be sorted in
|
ascending order, otherwise `sorter` must be an array of indices
|
that sort it.
|
value : array-like or scalar
|
Values to insert into `arr`.
|
side : {'left', 'right'}, optional
|
If 'left', the index of the first suitable location found is given.
|
If 'right', return the last such index. If there is no suitable
|
index, return either 0 or N (where N is the length of `self`).
|
sorter : 1-D array-like, optional
|
Optional array of integer indices that sort array a into ascending
|
order. They are typically the result of argsort.
|
|
Returns
|
-------
|
array of ints or int
|
If value is array-like, array of insertion points.
|
If value is scalar, a single integer.
|
|
See Also
|
--------
|
numpy.searchsorted : Similar method from NumPy.
|
"""
|
if sorter is not None:
|
sorter = ensure_platform_int(sorter)
|
|
if (
|
isinstance(arr, np.ndarray)
|
and is_integer_dtype(arr.dtype)
|
and (is_integer(value) or is_integer_dtype(value))
|
):
|
# if `arr` and `value` have different dtypes, `arr` would be
|
# recast by numpy, causing a slow search.
|
# Before searching below, we therefore try to give `value` the
|
# same dtype as `arr`, while guarding against integer overflows.
|
iinfo = np.iinfo(arr.dtype.type)
|
value_arr = np.array([value]) if is_scalar(value) else np.array(value)
|
if (value_arr >= iinfo.min).all() and (value_arr <= iinfo.max).all():
|
# value within bounds, so no overflow, so can convert value dtype
|
# to dtype of arr
|
dtype = arr.dtype
|
else:
|
dtype = value_arr.dtype
|
|
if is_scalar(value):
|
# We know that value is int
|
value = cast(int, dtype.type(value))
|
else:
|
value = pd_array(cast(ArrayLike, value), dtype=dtype)
|
else:
|
# E.g. if `arr` is an array with dtype='datetime64[ns]'
|
# and `value` is a pd.Timestamp, we may need to convert value
|
arr = ensure_wrapped_if_datetimelike(arr)
|
|
# Argument 1 to "searchsorted" of "ndarray" has incompatible type
|
# "Union[NumpyValueArrayLike, ExtensionArray]"; expected "NumpyValueArrayLike"
|
return arr.searchsorted(value, side=side, sorter=sorter) # type: ignore[arg-type]
|
|
|
# ---- #
|
# diff #
|
# ---- #
|
|
_diff_special = {"float64", "float32", "int64", "int32", "int16", "int8"}
|
|
|
def diff(arr, n: int, axis: AxisInt = 0):
|
"""
|
difference of n between self,
|
analogous to s-s.shift(n)
|
|
Parameters
|
----------
|
arr : ndarray or ExtensionArray
|
n : int
|
number of periods
|
axis : {0, 1}
|
axis to shift on
|
stacklevel : int, default 3
|
The stacklevel for the lost dtype warning.
|
|
Returns
|
-------
|
shifted
|
"""
|
|
n = int(n)
|
na = np.nan
|
dtype = arr.dtype
|
|
is_bool = is_bool_dtype(dtype)
|
if is_bool:
|
op = operator.xor
|
else:
|
op = operator.sub
|
|
if isinstance(dtype, PandasDtype):
|
# PandasArray cannot necessarily hold shifted versions of itself.
|
arr = arr.to_numpy()
|
dtype = arr.dtype
|
|
if not isinstance(dtype, np.dtype):
|
# i.e ExtensionDtype
|
if hasattr(arr, f"__{op.__name__}__"):
|
if axis != 0:
|
raise ValueError(f"cannot diff {type(arr).__name__} on axis={axis}")
|
return op(arr, arr.shift(n))
|
else:
|
raise TypeError(
|
f"{type(arr).__name__} has no 'diff' method. "
|
"Convert to a suitable dtype prior to calling 'diff'."
|
)
|
|
is_timedelta = False
|
if needs_i8_conversion(arr.dtype):
|
dtype = np.int64
|
arr = arr.view("i8")
|
na = iNaT
|
is_timedelta = True
|
|
elif is_bool:
|
# We have to cast in order to be able to hold np.nan
|
dtype = np.object_
|
|
elif is_integer_dtype(dtype):
|
# We have to cast in order to be able to hold np.nan
|
|
# int8, int16 are incompatible with float64,
|
# see https://github.com/cython/cython/issues/2646
|
if arr.dtype.name in ["int8", "int16"]:
|
dtype = np.float32
|
else:
|
dtype = np.float64
|
|
orig_ndim = arr.ndim
|
if orig_ndim == 1:
|
# reshape so we can always use algos.diff_2d
|
arr = arr.reshape(-1, 1)
|
# TODO: require axis == 0
|
|
dtype = np.dtype(dtype)
|
out_arr = np.empty(arr.shape, dtype=dtype)
|
|
na_indexer = [slice(None)] * 2
|
na_indexer[axis] = slice(None, n) if n >= 0 else slice(n, None)
|
out_arr[tuple(na_indexer)] = na
|
|
if arr.dtype.name in _diff_special:
|
# TODO: can diff_2d dtype specialization troubles be fixed by defining
|
# out_arr inside diff_2d?
|
algos.diff_2d(arr, out_arr, n, axis, datetimelike=is_timedelta)
|
else:
|
# To keep mypy happy, _res_indexer is a list while res_indexer is
|
# a tuple, ditto for lag_indexer.
|
_res_indexer = [slice(None)] * 2
|
_res_indexer[axis] = slice(n, None) if n >= 0 else slice(None, n)
|
res_indexer = tuple(_res_indexer)
|
|
_lag_indexer = [slice(None)] * 2
|
_lag_indexer[axis] = slice(None, -n) if n > 0 else slice(-n, None)
|
lag_indexer = tuple(_lag_indexer)
|
|
out_arr[res_indexer] = op(arr[res_indexer], arr[lag_indexer])
|
|
if is_timedelta:
|
out_arr = out_arr.view("timedelta64[ns]")
|
|
if orig_ndim == 1:
|
out_arr = out_arr[:, 0]
|
return out_arr
|
|
|
# --------------------------------------------------------------------
|
# Helper functions
|
|
|
# Note: safe_sort is in algorithms.py instead of sorting.py because it is
|
# low-dependency, is used in this module, and used private methods from
|
# this module.
|
def safe_sort(
|
values,
|
codes=None,
|
use_na_sentinel: bool = True,
|
assume_unique: bool = False,
|
verify: bool = True,
|
) -> AnyArrayLike | tuple[AnyArrayLike, np.ndarray]:
|
"""
|
Sort ``values`` and reorder corresponding ``codes``.
|
|
``values`` should be unique if ``codes`` is not None.
|
Safe for use with mixed types (int, str), orders ints before strs.
|
|
Parameters
|
----------
|
values : list-like
|
Sequence; must be unique if ``codes`` is not None.
|
codes : list_like, optional
|
Indices to ``values``. All out of bound indices are treated as
|
"not found" and will be masked with ``-1``.
|
use_na_sentinel : bool, default True
|
If True, the sentinel -1 will be used for NaN values. If False,
|
NaN values will be encoded as non-negative integers and will not drop the
|
NaN from the uniques of the values.
|
assume_unique : bool, default False
|
When True, ``values`` are assumed to be unique, which can speed up
|
the calculation. Ignored when ``codes`` is None.
|
verify : bool, default True
|
Check if codes are out of bound for the values and put out of bound
|
codes equal to ``-1``. If ``verify=False``, it is assumed there
|
are no out of bound codes. Ignored when ``codes`` is None.
|
|
Returns
|
-------
|
ordered : AnyArrayLike
|
Sorted ``values``
|
new_codes : ndarray
|
Reordered ``codes``; returned when ``codes`` is not None.
|
|
Raises
|
------
|
TypeError
|
* If ``values`` is not list-like or if ``codes`` is neither None
|
nor list-like
|
* If ``values`` cannot be sorted
|
ValueError
|
* If ``codes`` is not None and ``values`` contain duplicates.
|
"""
|
if not is_list_like(values):
|
raise TypeError(
|
"Only list-like objects are allowed to be passed to safe_sort as values"
|
)
|
|
if not is_array_like(values):
|
# don't convert to string types
|
dtype, _ = infer_dtype_from_array(values)
|
# error: Argument "dtype" to "asarray" has incompatible type "Union[dtype[Any],
|
# ExtensionDtype]"; expected "Union[dtype[Any], None, type, _SupportsDType, str,
|
# Union[Tuple[Any, int], Tuple[Any, Union[int, Sequence[int]]], List[Any],
|
# _DTypeDict, Tuple[Any, Any]]]"
|
values = np.asarray(values, dtype=dtype) # type: ignore[arg-type]
|
|
sorter = None
|
ordered: AnyArrayLike
|
|
if (
|
not is_extension_array_dtype(values)
|
and lib.infer_dtype(values, skipna=False) == "mixed-integer"
|
):
|
ordered = _sort_mixed(values)
|
else:
|
try:
|
sorter = values.argsort()
|
ordered = values.take(sorter)
|
except TypeError:
|
# Previous sorters failed or were not applicable, try `_sort_mixed`
|
# which would work, but which fails for special case of 1d arrays
|
# with tuples.
|
if values.size and isinstance(values[0], tuple):
|
ordered = _sort_tuples(values)
|
else:
|
ordered = _sort_mixed(values)
|
|
# codes:
|
|
if codes is None:
|
return ordered
|
|
if not is_list_like(codes):
|
raise TypeError(
|
"Only list-like objects or None are allowed to "
|
"be passed to safe_sort as codes"
|
)
|
codes = ensure_platform_int(np.asarray(codes))
|
|
if not assume_unique and not len(unique(values)) == len(values):
|
raise ValueError("values should be unique if codes is not None")
|
|
if sorter is None:
|
# mixed types
|
hash_klass, values = _get_hashtable_algo(values)
|
t = hash_klass(len(values))
|
t.map_locations(values)
|
sorter = ensure_platform_int(t.lookup(ordered))
|
|
if use_na_sentinel:
|
# take_nd is faster, but only works for na_sentinels of -1
|
order2 = sorter.argsort()
|
new_codes = take_nd(order2, codes, fill_value=-1)
|
if verify:
|
mask = (codes < -len(values)) | (codes >= len(values))
|
else:
|
mask = None
|
else:
|
reverse_indexer = np.empty(len(sorter), dtype=np.int_)
|
reverse_indexer.put(sorter, np.arange(len(sorter)))
|
# Out of bound indices will be masked with `-1` next, so we
|
# may deal with them here without performance loss using `mode='wrap'`
|
new_codes = reverse_indexer.take(codes, mode="wrap")
|
|
if use_na_sentinel:
|
mask = codes == -1
|
if verify:
|
mask = mask | (codes < -len(values)) | (codes >= len(values))
|
|
if use_na_sentinel and mask is not None:
|
np.putmask(new_codes, mask, -1)
|
|
return ordered, ensure_platform_int(new_codes)
|
|
|
def _sort_mixed(values) -> AnyArrayLike:
|
"""order ints before strings before nulls in 1d arrays"""
|
str_pos = np.array([isinstance(x, str) for x in values], dtype=bool)
|
null_pos = np.array([isna(x) for x in values], dtype=bool)
|
num_pos = ~str_pos & ~null_pos
|
str_argsort = np.argsort(values[str_pos])
|
num_argsort = np.argsort(values[num_pos])
|
# convert boolean arrays to positional indices, then order by underlying values
|
str_locs = str_pos.nonzero()[0].take(str_argsort)
|
num_locs = num_pos.nonzero()[0].take(num_argsort)
|
null_locs = null_pos.nonzero()[0]
|
locs = np.concatenate([num_locs, str_locs, null_locs])
|
return values.take(locs)
|
|
|
def _sort_tuples(values: np.ndarray) -> np.ndarray:
|
"""
|
Convert array of tuples (1d) to array of arrays (2d).
|
We need to keep the columns separately as they contain different types and
|
nans (can't use `np.sort` as it may fail when str and nan are mixed in a
|
column as types cannot be compared).
|
"""
|
from pandas.core.internals.construction import to_arrays
|
from pandas.core.sorting import lexsort_indexer
|
|
arrays, _ = to_arrays(values, None)
|
indexer = lexsort_indexer(arrays, orders=True)
|
return values[indexer]
|
|
|
def union_with_duplicates(
|
lvals: ArrayLike | Index, rvals: ArrayLike | Index
|
) -> ArrayLike | Index:
|
"""
|
Extracts the union from lvals and rvals with respect to duplicates and nans in
|
both arrays.
|
|
Parameters
|
----------
|
lvals: np.ndarray or ExtensionArray
|
left values which is ordered in front.
|
rvals: np.ndarray or ExtensionArray
|
right values ordered after lvals.
|
|
Returns
|
-------
|
np.ndarray or ExtensionArray
|
Containing the unsorted union of both arrays.
|
|
Notes
|
-----
|
Caller is responsible for ensuring lvals.dtype == rvals.dtype.
|
"""
|
from pandas import Series
|
|
l_count = value_counts(lvals, dropna=False)
|
r_count = value_counts(rvals, dropna=False)
|
l_count, r_count = l_count.align(r_count, fill_value=0)
|
final_count = np.maximum(l_count.values, r_count.values)
|
final_count = Series(final_count, index=l_count.index, dtype="int", copy=False)
|
if isinstance(lvals, ABCMultiIndex) and isinstance(rvals, ABCMultiIndex):
|
unique_vals = lvals.append(rvals).unique()
|
else:
|
if isinstance(lvals, ABCIndex):
|
lvals = lvals._values
|
if isinstance(rvals, ABCIndex):
|
rvals = rvals._values
|
unique_vals = unique(concat_compat([lvals, rvals]))
|
unique_vals = ensure_wrapped_if_datetimelike(unique_vals)
|
repeats = final_count.reindex(unique_vals).values
|
return np.repeat(unique_vals, repeats)
|
