2003 行
62 KiB
Python
2003 行
62 KiB
Python
"""
|
|
Generic data algorithms. This module is experimental at the moment and not
|
|
intended for public consumption
|
|
"""
|
|
from __future__ import annotations
|
|
|
|
import inspect
|
|
import operator
|
|
from textwrap import dedent
|
|
from typing import (
|
|
TYPE_CHECKING,
|
|
Hashable,
|
|
Literal,
|
|
Sequence,
|
|
cast,
|
|
final,
|
|
overload,
|
|
)
|
|
import warnings
|
|
|
|
import numpy as np
|
|
|
|
from pandas._libs import (
|
|
algos,
|
|
hashtable as htable,
|
|
iNaT,
|
|
lib,
|
|
)
|
|
from pandas._typing import (
|
|
AnyArrayLike,
|
|
ArrayLike,
|
|
DtypeObj,
|
|
IndexLabel,
|
|
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,
|
|
sanitize_to_nanoseconds,
|
|
)
|
|
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_datetime64_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,
|
|
is_timedelta64_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,
|
|
ABCRangeIndex,
|
|
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,
|
|
DataFrame,
|
|
Index,
|
|
MultiIndex,
|
|
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):
|
|
if isinstance(values, np.ndarray):
|
|
values = sanitize_to_nanoseconds(values)
|
|
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:
|
|
if is_datetime64_dtype(dtype):
|
|
dtype = np.dtype("datetime64[ns]")
|
|
elif is_timedelta64_dtype(dtype):
|
|
dtype = np.dtype("timedelta64[ns]")
|
|
|
|
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)
|
|
htable = _hashtables[ndtype]
|
|
return htable, 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 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
|
|
htable, values = _get_hashtable_algo(values)
|
|
|
|
table = htable(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 = values
|
|
values = _ensure_arraylike(list(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(list(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,
|
|
na_sentinel: int | None = -1,
|
|
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
|
|
na_sentinel : int, default -1
|
|
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
|
|
"""
|
|
ignore_na = na_sentinel is not None
|
|
if not ignore_na:
|
|
na_sentinel = -1
|
|
|
|
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=na_sentinel,
|
|
na_value=na_value,
|
|
mask=mask,
|
|
ignore_na=ignore_na,
|
|
)
|
|
|
|
# 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,
|
|
na_sentinel: int | None | lib.NoDefault = lib.no_default,
|
|
use_na_sentinel: bool | lib.NoDefault = lib.no_default,
|
|
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}
|
|
na_sentinel : int or None, default -1
|
|
Value to mark "not found". If None, will not drop the NaN
|
|
from the uniques of the values.
|
|
|
|
.. deprecated:: 1.5.0
|
|
The na_sentinel argument is deprecated and
|
|
will be removed in a future version of pandas. Specify use_na_sentinel as
|
|
either True or False.
|
|
|
|
.. versionchanged:: 1.1.2
|
|
|
|
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.
|
|
|
|
# GH#46910 deprecated na_sentinel in favor of use_na_sentinel:
|
|
# na_sentinel=None corresponds to use_na_sentinel=False
|
|
# na_sentinel=-1 correspond to use_na_sentinel=True
|
|
# Other na_sentinel values will not be supported when the deprecation is enforced.
|
|
na_sentinel = resolve_na_sentinel(na_sentinel, use_na_sentinel)
|
|
if isinstance(values, ABCRangeIndex):
|
|
return values.factorize(sort=sort)
|
|
|
|
values = _ensure_arraylike(values)
|
|
original = values
|
|
if not isinstance(values, ABCMultiIndex):
|
|
values = extract_array(values, extract_numpy=True)
|
|
|
|
# GH35667, if na_sentinel=None, we will not dropna NaNs from the uniques
|
|
# of values, assign na_sentinel=-1 to replace code value for NaN.
|
|
dropna = na_sentinel is not None
|
|
|
|
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 _re_wrap_factorize(original, uniques, codes)
|
|
|
|
elif not isinstance(values.dtype, np.dtype):
|
|
if (
|
|
na_sentinel == -1 or na_sentinel is None
|
|
) and "use_na_sentinel" in inspect.signature(values.factorize).parameters:
|
|
# Avoid using catch_warnings when possible
|
|
# GH#46910 - TimelikeOps has deprecated signature
|
|
codes, uniques = values.factorize( # type: ignore[call-arg]
|
|
use_na_sentinel=na_sentinel is not None
|
|
)
|
|
else:
|
|
na_sentinel_arg = -1 if na_sentinel is None else na_sentinel
|
|
with warnings.catch_warnings():
|
|
# We've already warned above
|
|
warnings.filterwarnings("ignore", ".*use_na_sentinel.*", FutureWarning)
|
|
codes, uniques = values.factorize(na_sentinel=na_sentinel_arg)
|
|
|
|
else:
|
|
values = np.asarray(values) # convert DTA/TDA/MultiIndex
|
|
# TODO: pass na_sentinel=na_sentinel to factorize_array. When sort is True and
|
|
# na_sentinel is None we append NA on the end because safe_sort does not
|
|
# handle null values in uniques.
|
|
if na_sentinel is None and sort:
|
|
na_sentinel_arg = -1
|
|
elif na_sentinel is None:
|
|
na_sentinel_arg = None
|
|
else:
|
|
na_sentinel_arg = na_sentinel
|
|
|
|
if not dropna and not sort 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,
|
|
na_sentinel=na_sentinel_arg,
|
|
size_hint=size_hint,
|
|
)
|
|
|
|
if sort and len(uniques) > 0:
|
|
if na_sentinel is None:
|
|
# TODO: Can remove when na_sentinel=na_sentinel as in TODO above
|
|
na_sentinel = -1
|
|
uniques, codes = safe_sort(
|
|
uniques, codes, na_sentinel=na_sentinel, assume_unique=True, verify=False
|
|
)
|
|
|
|
if not dropna and sort:
|
|
# TODO: Can remove entire block when na_sentinel=na_sentinel as in TODO above
|
|
if na_sentinel is None:
|
|
na_sentinel_arg = -1
|
|
else:
|
|
na_sentinel_arg = na_sentinel
|
|
code_is_na = codes == na_sentinel_arg
|
|
if code_is_na.any():
|
|
# na_value is set based on the dtype of uniques, and compat set to False is
|
|
# because we do not want na_value to be 0 for integers
|
|
na_value = na_value_for_dtype(uniques.dtype, compat=False)
|
|
uniques = np.append(uniques, [na_value])
|
|
codes = np.where(code_is_na, len(uniques) - 1, codes)
|
|
|
|
uniques = _reconstruct_data(uniques, original.dtype, original)
|
|
|
|
return _re_wrap_factorize(original, uniques, codes)
|
|
|
|
|
|
def resolve_na_sentinel(
|
|
na_sentinel: int | None | lib.NoDefault,
|
|
use_na_sentinel: bool | lib.NoDefault,
|
|
) -> int | None:
|
|
"""
|
|
Determine value of na_sentinel for factorize methods.
|
|
|
|
See GH#46910 for details on the deprecation.
|
|
|
|
Parameters
|
|
----------
|
|
na_sentinel : int, None, or lib.no_default
|
|
Value passed to the method.
|
|
use_na_sentinel : bool or lib.no_default
|
|
Value passed to the method.
|
|
|
|
Returns
|
|
-------
|
|
Resolved value of na_sentinel.
|
|
"""
|
|
if na_sentinel is not lib.no_default and use_na_sentinel is not lib.no_default:
|
|
raise ValueError(
|
|
"Cannot specify both `na_sentinel` and `use_na_sentile`; "
|
|
f"got `na_sentinel={na_sentinel}` and `use_na_sentinel={use_na_sentinel}`"
|
|
)
|
|
if na_sentinel is lib.no_default:
|
|
result = -1 if use_na_sentinel is lib.no_default or use_na_sentinel else None
|
|
else:
|
|
if na_sentinel is None:
|
|
msg = (
|
|
"Specifying `na_sentinel=None` is deprecated, specify "
|
|
"`use_na_sentinel=False` instead."
|
|
)
|
|
elif na_sentinel == -1:
|
|
msg = (
|
|
"Specifying `na_sentinel=-1` is deprecated, specify "
|
|
"`use_na_sentinel=True` instead."
|
|
)
|
|
else:
|
|
msg = (
|
|
"Specifying the specific value to use for `na_sentinel` is "
|
|
"deprecated and will be removed in a future version of pandas. "
|
|
"Specify `use_na_sentinel=True` to use the sentinel value -1, and "
|
|
"`use_na_sentinel=False` to encode NaN values."
|
|
)
|
|
warnings.warn(msg, FutureWarning, stacklevel=find_stack_level())
|
|
result = na_sentinel
|
|
return result
|
|
|
|
|
|
def _re_wrap_factorize(original, uniques, codes: np.ndarray):
|
|
"""
|
|
Wrap factorize results in Series or Index depending on original type.
|
|
"""
|
|
if isinstance(original, ABCIndex):
|
|
uniques = ensure_wrapped_if_datetimelike(uniques)
|
|
uniques = original._shallow_copy(uniques, name=None)
|
|
elif isinstance(original, ABCSeries):
|
|
from pandas import Index
|
|
|
|
uniques = Index(uniques)
|
|
|
|
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,
|
|
)
|
|
|
|
name = getattr(values, "name", None)
|
|
|
|
if bins is not None:
|
|
from pandas.core.reshape.tile import cut
|
|
|
|
values = Series(values)
|
|
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 = 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)._values.value_counts(dropna=dropna)
|
|
result.name = name
|
|
counts = result._values
|
|
|
|
else:
|
|
values = _ensure_arraylike(values)
|
|
keys, counts = value_counts_arraylike(values, dropna)
|
|
|
|
# For backwards compatibility, we let Index do its normal type
|
|
# inference, _except_ for if if infers from object to bool.
|
|
idx = Index._with_infer(keys)
|
|
if idx.dtype == bool and keys.dtype == object:
|
|
idx = idx.astype(object)
|
|
|
|
result = Series(counts, index=idx, name=name)
|
|
|
|
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]
|
|
"""
|
|
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: int = 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
|
|
|
|
|
|
# --------------- #
|
|
# select n #
|
|
# --------------- #
|
|
|
|
|
|
class SelectN:
|
|
def __init__(self, obj, n: int, keep: str) -> None:
|
|
self.obj = obj
|
|
self.n = n
|
|
self.keep = keep
|
|
|
|
if self.keep not in ("first", "last", "all"):
|
|
raise ValueError('keep must be either "first", "last" or "all"')
|
|
|
|
def compute(self, method: str) -> DataFrame | Series:
|
|
raise NotImplementedError
|
|
|
|
@final
|
|
def nlargest(self):
|
|
return self.compute("nlargest")
|
|
|
|
@final
|
|
def nsmallest(self):
|
|
return self.compute("nsmallest")
|
|
|
|
@final
|
|
@staticmethod
|
|
def is_valid_dtype_n_method(dtype: DtypeObj) -> bool:
|
|
"""
|
|
Helper function to determine if dtype is valid for
|
|
nsmallest/nlargest methods
|
|
"""
|
|
return (
|
|
is_numeric_dtype(dtype) and not is_complex_dtype(dtype)
|
|
) or needs_i8_conversion(dtype)
|
|
|
|
|
|
class SelectNSeries(SelectN):
|
|
"""
|
|
Implement n largest/smallest for Series
|
|
|
|
Parameters
|
|
----------
|
|
obj : Series
|
|
n : int
|
|
keep : {'first', 'last'}, default 'first'
|
|
|
|
Returns
|
|
-------
|
|
nordered : Series
|
|
"""
|
|
|
|
def compute(self, method: str) -> Series:
|
|
|
|
from pandas.core.reshape.concat import concat
|
|
|
|
n = self.n
|
|
dtype = self.obj.dtype
|
|
if not self.is_valid_dtype_n_method(dtype):
|
|
raise TypeError(f"Cannot use method '{method}' with dtype {dtype}")
|
|
|
|
if n <= 0:
|
|
return self.obj[[]]
|
|
|
|
dropped = self.obj.dropna()
|
|
nan_index = self.obj.drop(dropped.index)
|
|
|
|
# slow method
|
|
if n >= len(self.obj):
|
|
ascending = method == "nsmallest"
|
|
return self.obj.sort_values(ascending=ascending).head(n)
|
|
|
|
# fast method
|
|
new_dtype = dropped.dtype
|
|
arr = _ensure_data(dropped.values)
|
|
if method == "nlargest":
|
|
arr = -arr
|
|
if is_integer_dtype(new_dtype):
|
|
# GH 21426: ensure reverse ordering at boundaries
|
|
arr -= 1
|
|
|
|
elif is_bool_dtype(new_dtype):
|
|
# GH 26154: ensure False is smaller than True
|
|
arr = 1 - (-arr)
|
|
|
|
if self.keep == "last":
|
|
arr = arr[::-1]
|
|
|
|
nbase = n
|
|
narr = len(arr)
|
|
n = min(n, narr)
|
|
|
|
# arr passed into kth_smallest must be contiguous. We copy
|
|
# here because kth_smallest will modify its input
|
|
kth_val = algos.kth_smallest(arr.copy(order="C"), n - 1)
|
|
(ns,) = np.nonzero(arr <= kth_val)
|
|
inds = ns[arr[ns].argsort(kind="mergesort")]
|
|
|
|
if self.keep != "all":
|
|
inds = inds[:n]
|
|
findex = nbase
|
|
else:
|
|
if len(inds) < nbase and len(nan_index) + len(inds) >= nbase:
|
|
findex = len(nan_index) + len(inds)
|
|
else:
|
|
findex = len(inds)
|
|
|
|
if self.keep == "last":
|
|
# reverse indices
|
|
inds = narr - 1 - inds
|
|
|
|
return concat([dropped.iloc[inds], nan_index]).iloc[:findex]
|
|
|
|
|
|
class SelectNFrame(SelectN):
|
|
"""
|
|
Implement n largest/smallest for DataFrame
|
|
|
|
Parameters
|
|
----------
|
|
obj : DataFrame
|
|
n : int
|
|
keep : {'first', 'last'}, default 'first'
|
|
columns : list or str
|
|
|
|
Returns
|
|
-------
|
|
nordered : DataFrame
|
|
"""
|
|
|
|
def __init__(self, obj: DataFrame, n: int, keep: str, columns: IndexLabel) -> None:
|
|
super().__init__(obj, n, keep)
|
|
if not is_list_like(columns) or isinstance(columns, tuple):
|
|
columns = [columns]
|
|
|
|
columns = cast(Sequence[Hashable], columns)
|
|
columns = list(columns)
|
|
self.columns = columns
|
|
|
|
def compute(self, method: str) -> DataFrame:
|
|
|
|
from pandas.core.api import Int64Index
|
|
|
|
n = self.n
|
|
frame = self.obj
|
|
columns = self.columns
|
|
|
|
for column in columns:
|
|
dtype = frame[column].dtype
|
|
if not self.is_valid_dtype_n_method(dtype):
|
|
raise TypeError(
|
|
f"Column {repr(column)} has dtype {dtype}, "
|
|
f"cannot use method {repr(method)} with this dtype"
|
|
)
|
|
|
|
def get_indexer(current_indexer, other_indexer):
|
|
"""
|
|
Helper function to concat `current_indexer` and `other_indexer`
|
|
depending on `method`
|
|
"""
|
|
if method == "nsmallest":
|
|
return current_indexer.append(other_indexer)
|
|
else:
|
|
return other_indexer.append(current_indexer)
|
|
|
|
# Below we save and reset the index in case index contains duplicates
|
|
original_index = frame.index
|
|
cur_frame = frame = frame.reset_index(drop=True)
|
|
cur_n = n
|
|
indexer = Int64Index([])
|
|
|
|
for i, column in enumerate(columns):
|
|
# For each column we apply method to cur_frame[column].
|
|
# If it's the last column or if we have the number of
|
|
# results desired we are done.
|
|
# Otherwise there are duplicates of the largest/smallest
|
|
# value and we need to look at the rest of the columns
|
|
# to determine which of the rows with the largest/smallest
|
|
# value in the column to keep.
|
|
series = cur_frame[column]
|
|
is_last_column = len(columns) - 1 == i
|
|
values = getattr(series, method)(
|
|
cur_n, keep=self.keep if is_last_column else "all"
|
|
)
|
|
|
|
if is_last_column or len(values) <= cur_n:
|
|
indexer = get_indexer(indexer, values.index)
|
|
break
|
|
|
|
# Now find all values which are equal to
|
|
# the (nsmallest: largest)/(nlargest: smallest)
|
|
# from our series.
|
|
border_value = values == values[values.index[-1]]
|
|
|
|
# Some of these values are among the top-n
|
|
# some aren't.
|
|
unsafe_values = values[border_value]
|
|
|
|
# These values are definitely among the top-n
|
|
safe_values = values[~border_value]
|
|
indexer = get_indexer(indexer, safe_values.index)
|
|
|
|
# Go on and separate the unsafe_values on the remaining
|
|
# columns.
|
|
cur_frame = cur_frame.loc[unsafe_values.index]
|
|
cur_n = n - len(indexer)
|
|
|
|
frame = frame.take(indexer)
|
|
|
|
# Restore the index on frame
|
|
frame.index = original_index.take(indexer)
|
|
|
|
# If there is only one column, the frame is already sorted.
|
|
if len(columns) == 1:
|
|
return frame
|
|
|
|
ascending = method == "nsmallest"
|
|
|
|
return frame.sort_values(columns, ascending=ascending, kind="mergesort")
|
|
|
|
|
|
# ---- #
|
|
# take #
|
|
# ---- #
|
|
|
|
|
|
def take(
|
|
arr,
|
|
indices: TakeIndexer,
|
|
axis: int = 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.
|
|
|
|
.. versionadded:: 0.25.0
|
|
|
|
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: int = 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:
|
|
warnings.warn(
|
|
"dtype lost in 'diff()'. In the future this will raise a "
|
|
"TypeError. Convert to a suitable dtype prior to calling 'diff'.",
|
|
FutureWarning,
|
|
stacklevel=find_stack_level(),
|
|
)
|
|
arr = np.asarray(arr)
|
|
dtype = arr.dtype
|
|
|
|
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,
|
|
na_sentinel: int = -1,
|
|
assume_unique: bool = False,
|
|
verify: bool = True,
|
|
) -> np.ndarray | MultiIndex | tuple[np.ndarray | MultiIndex, 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 ``na_sentinel``.
|
|
na_sentinel : int, default -1
|
|
Value in ``codes`` to mark "not found".
|
|
Ignored when ``codes`` is None.
|
|
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 na_sentinel. If ``verify=False``, it is assumed there
|
|
are no out of bound codes. Ignored when ``codes`` is None.
|
|
|
|
.. versionadded:: 0.25.0
|
|
|
|
Returns
|
|
-------
|
|
ordered : ndarray or MultiIndex
|
|
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"
|
|
)
|
|
original_values = values
|
|
is_mi = isinstance(original_values, ABCMultiIndex)
|
|
|
|
if not isinstance(values, (np.ndarray, ABCExtensionArray)):
|
|
# 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: np.ndarray | MultiIndex
|
|
|
|
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()
|
|
if is_mi:
|
|
# Operate on original object instead of casted array (MultiIndex)
|
|
ordered = original_values.take(sorter)
|
|
else:
|
|
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, original_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 na_sentinel == -1:
|
|
# 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 `na_sentinel` next, so we
|
|
# may deal with them here without performance loss using `mode='wrap'`
|
|
new_codes = reverse_indexer.take(codes, mode="wrap")
|
|
|
|
mask = codes == na_sentinel
|
|
if verify:
|
|
mask = mask | (codes < -len(values)) | (codes >= len(values))
|
|
|
|
if mask is not None:
|
|
np.putmask(new_codes, mask, na_sentinel)
|
|
|
|
return ordered, ensure_platform_int(new_codes)
|
|
|
|
|
|
def _sort_mixed(values) -> np.ndarray:
|
|
"""order ints before strings in 1d arrays, safe in py3"""
|
|
str_pos = np.array([isinstance(x, str) for x in values], dtype=bool)
|
|
none_pos = np.array([x is None for x in values], dtype=bool)
|
|
nums = np.sort(values[~str_pos & ~none_pos])
|
|
strs = np.sort(values[str_pos])
|
|
return np.concatenate(
|
|
[nums, np.asarray(strs, dtype=object), np.array(values[none_pos])]
|
|
)
|
|
|
|
|
|
@overload
|
|
def _sort_tuples(values: np.ndarray, original_values: np.ndarray) -> np.ndarray:
|
|
...
|
|
|
|
|
|
@overload
|
|
def _sort_tuples(values: np.ndarray, original_values: MultiIndex) -> MultiIndex:
|
|
...
|
|
|
|
|
|
def _sort_tuples(
|
|
values: np.ndarray, original_values: np.ndarray | MultiIndex
|
|
) -> np.ndarray | MultiIndex:
|
|
"""
|
|
Convert array of tuples (1d) to array or array (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).
|
|
We have to apply the indexer to the original values to keep the dtypes in
|
|
case of MultiIndexes
|
|
"""
|
|
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 original_values[indexer]
|
|
|
|
|
|
def union_with_duplicates(lvals: ArrayLike, rvals: ArrayLike) -> ArrayLike:
|
|
"""
|
|
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.
|
|
"""
|
|
indexer = []
|
|
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)
|
|
unique_array = unique(concat_compat([lvals, rvals]))
|
|
unique_array = ensure_wrapped_if_datetimelike(unique_array)
|
|
|
|
for i, value in enumerate(unique_array):
|
|
indexer += [i] * int(max(l_count.at[value], r_count.at[value]))
|
|
return unique_array.take(indexer)
|