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"""
Generic data algorithms. This module is experimental at the moment and not
intended for public consumption
"""
from textwrap import dedent
from typing import Dict
from warnings import catch_warnings, simplefilter, warn
import numpy as np
from pandas._libs import algos, hashtable as htable, lib
from pandas._libs.tslib import iNaT
from pandas.util._decorators import Appender, Substitution, deprecate_kwarg
from pandas.core.dtypes.cast import (
construct_1d_object_array_from_listlike,
maybe_promote,
)
from pandas.core.dtypes.common import (
ensure_float64,
ensure_int64,
ensure_object,
ensure_platform_int,
ensure_uint64,
is_array_like,
is_bool_dtype,
is_categorical_dtype,
is_complex_dtype,
is_datetime64_any_dtype,
is_datetime64_ns_dtype,
is_datetime64tz_dtype,
is_datetimelike,
is_extension_array_dtype,
is_float_dtype,
is_integer,
is_integer_dtype,
is_interval_dtype,
is_list_like,
is_numeric_dtype,
is_object_dtype,
is_period_dtype,
is_scalar,
is_signed_integer_dtype,
is_sparse,
is_timedelta64_dtype,
is_unsigned_integer_dtype,
needs_i8_conversion,
)
from pandas.core.dtypes.generic import ABCIndex, ABCIndexClass, ABCSeries
from pandas.core.dtypes.missing import isna, na_value_for_dtype
from pandas.core import common as com
from pandas.core.indexers import validate_indices
_shared_docs = {} # type: Dict[str, str]
# --------------- #
# dtype access #
# --------------- #
def _ensure_data(values, dtype=None):
"""
routine to ensure that our data is of the correct
input dtype for lower-level routines
This will coerce:
- ints -> int64
- uint -> uint64
- bool -> uint64 (TODO this should be uint8)
- datetimelike -> i8
- datetime64tz -> i8 (in local tz)
- categorical -> codes
Parameters
----------
values : array-like
dtype : pandas_dtype, optional
coerce to this dtype
Returns
-------
(ndarray, pandas_dtype, algo dtype as a string)
"""
# we check some simple dtypes first
try:
if is_object_dtype(dtype):
return ensure_object(np.asarray(values)), "object", "object"
if is_bool_dtype(values) or is_bool_dtype(dtype):
# we are actually coercing to uint64
# until our algos support uint8 directly (see TODO)
return np.asarray(values).astype("uint64"), "bool", "uint64"
elif is_signed_integer_dtype(values) or is_signed_integer_dtype(dtype):
return ensure_int64(values), "int64", "int64"
elif is_unsigned_integer_dtype(values) or is_unsigned_integer_dtype(dtype):
return ensure_uint64(values), "uint64", "uint64"
elif is_float_dtype(values) or is_float_dtype(dtype):
return ensure_float64(values), "float64", "float64"
elif is_object_dtype(values) and dtype is None:
return ensure_object(np.asarray(values)), "object", "object"
elif is_complex_dtype(values) or is_complex_dtype(dtype):
# ignore the fact that we are casting to float
# which discards complex parts
with catch_warnings():
simplefilter("ignore", np.ComplexWarning)
values = ensure_float64(values)
return values, "float64", "float64"
except (TypeError, ValueError, OverflowError):
# if we are trying to coerce to a dtype
# and it is incompat this will fall thru to here
return ensure_object(values), "object", "object"
# datetimelike
if (
needs_i8_conversion(values)
or is_period_dtype(dtype)
or is_datetime64_any_dtype(dtype)
or is_timedelta64_dtype(dtype)
):
if is_period_dtype(values) or is_period_dtype(dtype):
from pandas import PeriodIndex
values = PeriodIndex(values)
dtype = values.dtype
elif is_timedelta64_dtype(values) or is_timedelta64_dtype(dtype):
from pandas import TimedeltaIndex
values = TimedeltaIndex(values)
dtype = values.dtype
else:
# Datetime
if values.ndim > 1 and is_datetime64_ns_dtype(values):
# Avoid calling the DatetimeIndex constructor as it is 1D only
# Note: this is reached by DataFrame.rank calls GH#27027
asi8 = values.view("i8")
dtype = values.dtype
return asi8, dtype, "int64"
from pandas import DatetimeIndex
values = DatetimeIndex(values)
dtype = values.dtype
return values.asi8, dtype, "int64"
elif is_categorical_dtype(values) and (
is_categorical_dtype(dtype) or dtype is None
):
values = getattr(values, "values", values)
values = values.codes
dtype = "category"
# we are actually coercing to int64
# until our algos support int* directly (not all do)
values = ensure_int64(values)
return values, dtype, "int64"
# we have failed, return object
values = np.asarray(values, dtype=np.object)
return ensure_object(values), "object", "object"
def _reconstruct_data(values, dtype, original):
"""
reverse of _ensure_data
Parameters
----------
values : ndarray
dtype : pandas_dtype
original : ndarray-like
Returns
-------
Index for extension types, otherwise ndarray casted to dtype
"""
from pandas import Index
if is_extension_array_dtype(dtype):
values = dtype.construct_array_type()._from_sequence(values)
elif is_datetime64tz_dtype(dtype) or is_period_dtype(dtype):
values = Index(original)._shallow_copy(values, name=None)
elif is_bool_dtype(dtype):
values = values.astype(dtype)
# we only support object dtypes bool Index
if isinstance(original, Index):
values = values.astype(object)
elif dtype is not None:
values = values.astype(dtype)
return values
def _ensure_arraylike(values):
"""
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", "unicode"]:
if isinstance(values, tuple):
values = list(values)
values = construct_1d_object_array_from_listlike(values)
else:
values = np.asarray(values)
return values
_hashtables = {
"float64": (htable.Float64HashTable, htable.Float64Vector),
"uint64": (htable.UInt64HashTable, htable.UInt64Vector),
"int64": (htable.Int64HashTable, htable.Int64Vector),
"string": (htable.StringHashTable, htable.ObjectVector),
"object": (htable.PyObjectHashTable, htable.ObjectVector),
}
def _get_hashtable_algo(values):
"""
Parameters
----------
values : arraylike
Returns
-------
tuples(hashtable class,
vector class,
values,
dtype,
ndtype)
"""
values, dtype, ndtype = _ensure_data(values)
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"
else:
ndtype = "object"
htable, table = _hashtables[ndtype]
return (htable, table, values, dtype, ndtype)
def _get_data_algo(values, func_map):
if is_categorical_dtype(values):
values = values._values_for_rank()
values, dtype, ndtype = _ensure_data(values)
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"
f = func_map.get(ndtype, func_map["object"])
return f, values
# --------------- #
# top-level algos #
# --------------- #
def match(to_match, values, na_sentinel=-1):
"""
Compute locations of to_match into values
Parameters
----------
to_match : array-like
values to find positions of
values : array-like
Unique set of values
na_sentinel : int, default -1
Value to mark "not found"
Examples
--------
Returns
-------
match : ndarray of integers
"""
values = com.asarray_tuplesafe(values)
htable, _, values, dtype, ndtype = _get_hashtable_algo(values)
to_match, _, _ = _ensure_data(to_match, dtype)
table = htable(min(len(to_match), 1000000))
table.map_locations(values)
result = table.lookup(to_match)
if na_sentinel != -1:
# replace but return a numpy array
# use a Series because it handles dtype conversions properly
from pandas import Series
result = Series(result.ravel()).replace(-1, na_sentinel)
result = result.values.reshape(result.shape)
return result
def unique(values):
"""
Hash table-based unique. Uniques are returned in order
of appearance. This does NOT sort.
Significantly faster than numpy.unique. 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
Series.unique
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')]))
array([Timestamp('2016-01-01 00:00:00-0500', tz='US/Eastern')],
dtype=object)
>>> 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): [b, a, c]
>>> pd.unique(pd.Series(pd.Categorical(list('baabc'),
... categories=list('abc'))))
[b, a, c]
Categories (3, object): [b, a, 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)
"""
values = _ensure_arraylike(values)
if is_extension_array_dtype(values):
# Dispatch to extension dtype's unique.
return values.unique()
original = values
htable, _, values, dtype, ndtype = _get_hashtable_algo(values)
table = htable(len(values))
uniques = table.unique(values)
uniques = _reconstruct_data(uniques, dtype, original)
return uniques
unique1d = unique
def isin(comps, values):
"""
Compute the isin boolean array
Parameters
----------
comps : array-like
values : array-like
Returns
-------
boolean array same length as comps
"""
if not is_list_like(comps):
raise TypeError(
"only list-like objects are allowed to be passed"
" to isin(), you passed a [{comps_type}]".format(
comps_type=type(comps).__name__
)
)
if not is_list_like(values):
raise TypeError(
"only list-like objects are allowed to be passed"
" to isin(), you passed a [{values_type}]".format(
values_type=type(values).__name__
)
)
if not isinstance(values, (ABCIndex, ABCSeries, np.ndarray)):
values = construct_1d_object_array_from_listlike(list(values))
if is_categorical_dtype(comps):
# TODO(extension)
# handle categoricals
return comps._values.isin(values)
comps = com.values_from_object(comps)
comps, dtype, _ = _ensure_data(comps)
values, _, _ = _ensure_data(values, dtype=dtype)
# faster for larger cases to use np.in1d
f = lambda x, y: htable.ismember_object(x, values)
# GH16012
# Ensure np.in1d doesn't get object types or it *may* throw an exception
if len(comps) > 1000000 and not is_object_dtype(comps):
f = lambda x, y: np.in1d(x, y)
elif is_integer_dtype(comps):
try:
values = values.astype("int64", copy=False)
comps = comps.astype("int64", copy=False)
f = lambda x, y: htable.ismember_int64(x, y)
except (TypeError, ValueError, OverflowError):
values = values.astype(object)
comps = comps.astype(object)
elif is_float_dtype(comps):
try:
values = values.astype("float64", copy=False)
comps = comps.astype("float64", copy=False)
f = lambda x, y: htable.ismember_float64(x, y)
except (TypeError, ValueError):
values = values.astype(object)
comps = comps.astype(object)
return f(comps, values)
def _factorize_array(values, na_sentinel=-1, size_hint=None, na_value=None):
"""Factorize an array-like to labels 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
Passsed 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.).
Returns
-------
labels, uniques : ndarray
"""
(hash_klass, _), values = _get_data_algo(values, _hashtables)
table = hash_klass(size_hint or len(values))
uniques, labels = table.factorize(
values, na_sentinel=na_sentinel, na_value=na_value
)
labels = ensure_platform_int(labels)
return labels, uniques
_shared_docs[
"factorize"
] = """
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)s%(sort)s%(order)s
na_sentinel : int, default -1
Value to mark "not found".
%(size_hint)s\
Returns
-------
labels : ndarray
An integer ndarray that's an indexer into `uniques`.
``uniques.take(labels)`` 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.
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`.
>>> labels, uniques = pd.factorize(['b', 'b', 'a', 'c', 'b'])
>>> labels
array([0, 0, 1, 2, 0])
>>> uniques
array(['b', 'a', 'c'], dtype=object)
With ``sort=True``, the `uniques` will be sorted, and `labels` will be
shuffled so that the relationship is the maintained.
>>> labels, uniques = pd.factorize(['b', 'b', 'a', 'c', 'b'], sort=True)
>>> labels
array([1, 1, 0, 2, 1])
>>> uniques
array(['a', 'b', 'c'], dtype=object)
Missing values are indicated in `labels` with `na_sentinel`
(``-1`` by default). Note that missing values are never
included in `uniques`.
>>> labels, uniques = pd.factorize(['b', None, 'a', 'c', 'b'])
>>> labels
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'])
>>> labels, uniques = pd.factorize(cat)
>>> labels
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'])
>>> labels, uniques = pd.factorize(cat)
>>> labels
array([0, 0, 1])
>>> uniques
Index(['a', 'c'], dtype='object')
"""
@Substitution(
values=dedent(
"""\
values : sequence
A 1-D sequence. Sequences that aren't pandas objects are
coerced to ndarrays before factorization.
"""
),
order=dedent(
"""\
order : None
.. deprecated:: 0.23.0
This parameter has no effect and is deprecated.
"""
),
sort=dedent(
"""\
sort : bool, default False
Sort `uniques` and shuffle `labels` to maintain the
relationship.
"""
),
size_hint=dedent(
"""\
size_hint : int, optional
Hint to the hashtable sizer.
"""
),
)
@Appender(_shared_docs["factorize"])
@deprecate_kwarg(old_arg_name="order", new_arg_name=None)
def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None):
# Implementation notes: This method is responsible for 3 things
# 1.) coercing data to array-like (ndarray, Index, extension array)
# 2.) factorizing labels and uniques
# 3.) Maybe boxing the output 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.
values = _ensure_arraylike(values)
original = values
if is_extension_array_dtype(values):
values = getattr(values, "_values", values)
labels, uniques = values.factorize(na_sentinel=na_sentinel)
dtype = original.dtype
else:
values, dtype, _ = _ensure_data(values)
if (
is_datetime64_any_dtype(original)
or is_timedelta64_dtype(original)
or is_period_dtype(original)
):
na_value = na_value_for_dtype(original.dtype)
else:
na_value = None
labels, uniques = _factorize_array(
values, na_sentinel=na_sentinel, size_hint=size_hint, na_value=na_value
)
if sort and len(uniques) > 0:
from pandas.core.sorting import safe_sort
uniques, labels = safe_sort(
uniques, labels, na_sentinel=na_sentinel, assume_unique=True, verify=False
)
uniques = _reconstruct_data(uniques, dtype, original)
# return original tenor
if isinstance(original, ABCIndexClass):
uniques = original._shallow_copy(uniques, name=None)
elif isinstance(original, ABCSeries):
from pandas import Index
uniques = Index(uniques)
return labels, uniques
def value_counts(
values, sort=True, ascending=False, normalize=False, bins=None, dropna=True
):
"""
Compute a histogram of the counts of non-null values.
Parameters
----------
values : ndarray (1-d)
sort : boolean, default True
Sort by values
ascending : boolean, default False
Sort in ascending order
normalize: boolean, 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 : boolean, default True
Don't include counts of NaN
Returns
-------
value_counts : Series
"""
from pandas.core.series import Series, Index
name = getattr(values, "name", None)
if bins is not None:
try:
from pandas.core.reshape.tile import cut
values = Series(values)
ii = cut(values, bins, include_lowest=True)
except TypeError:
raise TypeError("bins argument only works with numeric data.")
# 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) or is_sparse(values):
# handle Categorical and sparse,
result = Series(values)._values.value_counts(dropna=dropna)
result.name = name
counts = result.values
else:
keys, counts = _value_counts_arraylike(values, dropna)
if not isinstance(keys, Index):
keys = Index(keys)
result = Series(counts, index=keys, name=name)
if sort:
result = result.sort_values(ascending=ascending)
if normalize:
result = result / float(counts.sum())
return result
def _value_counts_arraylike(values, dropna):
"""
Parameters
----------
values : arraylike
dropna : boolean
Returns
-------
(uniques, counts)
"""
values = _ensure_arraylike(values)
original = values
values, dtype, ndtype = _ensure_data(values)
if needs_i8_conversion(dtype):
# i8
keys, counts = htable.value_count_int64(values, dropna)
if dropna:
msk = keys != iNaT
keys, counts = keys[msk], counts[msk]
else:
# ndarray like
# TODO: handle uint8
f = getattr(htable, "value_count_{dtype}".format(dtype=ndtype))
keys, counts = f(values, dropna)
mask = isna(values)
if not dropna and mask.any():
if not isna(keys).any():
keys = np.insert(keys, 0, np.NaN)
counts = np.insert(counts, 0, mask.sum())
keys = _reconstruct_data(keys, original.dtype, original)
return keys, counts
def duplicated(values, keep="first"):
"""
Return boolean ndarray denoting duplicate values.
.. versionadded:: 0.19.0
Parameters
----------
values : ndarray-like
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
"""
values, dtype, ndtype = _ensure_data(values)
f = getattr(htable, "duplicated_{dtype}".format(dtype=ndtype))
return f(values, keep=keep)
def mode(values, dropna=True):
"""
Returns the mode(s) of an array.
Parameters
----------
values : array-like
Array over which to check for duplicate values.
dropna : boolean, default True
Don't consider counts of NaN/NaT.
.. versionadded:: 0.24.0
Returns
-------
mode : Series
"""
from pandas import Series
values = _ensure_arraylike(values)
original = values
# categorical is a fast-path
if is_categorical_dtype(values):
if isinstance(values, Series):
return Series(values.values.mode(dropna=dropna), name=values.name)
return values.mode(dropna=dropna)
if dropna and is_datetimelike(values):
mask = values.isnull()
values = values[~mask]
values, dtype, ndtype = _ensure_data(values)
f = getattr(htable, "mode_{dtype}".format(dtype=ndtype))
result = f(values, dropna=dropna)
try:
result = np.sort(result)
except TypeError as e:
warn("Unable to sort modes: {error}".format(error=e))
result = _reconstruct_data(result, original.dtype, original)
return Series(result)
def rank(values, axis=0, method="average", na_option="keep", ascending=True, pct=False):
"""
Rank the values along a given axis.
Parameters
----------
values : array-like
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 : boolean, default True
Whether or not the elements should be ranked in ascending order.
pct : boolean, 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).
"""
if values.ndim == 1:
f, values = _get_data_algo(values, _rank1d_functions)
ranks = f(
values,
ties_method=method,
ascending=ascending,
na_option=na_option,
pct=pct,
)
elif values.ndim == 2:
f, values = _get_data_algo(values, _rank2d_functions)
ranks = f(
values,
axis=axis,
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, b, arr_mask=None, b_mask=None):
"""
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 : array addend.
b : array or scalar addend.
arr_mask : boolean array or None
array indicating which elements to exclude from checking
b_mask : boolean array or boolean or 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:
not_nan = np.logical_not(b2_mask)
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.
mask1 = b2 > 0
mask2 = b2 < 0
if not mask1.any():
to_raise = ((np.iinfo(np.int64).min - b2 > arr) & not_nan).any()
elif not mask2.any():
to_raise = ((np.iinfo(np.int64).max - b2 < arr) & not_nan).any()
else:
to_raise = (
((np.iinfo(np.int64).max - b2[mask1] < arr[mask1]) & not_nan[mask1]).any()
or (
(np.iinfo(np.int64).min - b2[mask2] > arr[mask2]) & not_nan[mask2]
).any()
)
if to_raise:
raise OverflowError("Overflow in int64 addition")
return arr + b
_rank1d_functions = {
"float64": algos.rank_1d_float64,
"int64": algos.rank_1d_int64,
"uint64": algos.rank_1d_uint64,
"object": algos.rank_1d_object,
}
_rank2d_functions = {
"float64": algos.rank_2d_float64,
"int64": algos.rank_2d_int64,
"uint64": algos.rank_2d_uint64,
"object": algos.rank_2d_object,
}
def quantile(x, q, interpolation_method="fraction"):
"""
Compute sample quantile or quantiles of the input array. For example, q=0.5
computes the median.
The `interpolation_method` parameter supports three values, namely
`fraction` (default), `lower` and `higher`. Interpolation is done only,
if the desired quantile lies between two data points `i` and `j`. For
`fraction`, the result is an interpolated value between `i` and `j`;
for `lower`, the result is `i`, for `higher` the result is `j`.
Parameters
----------
x : ndarray
Values from which to extract score.
q : scalar or array
Percentile at which to extract score.
interpolation_method : {'fraction', 'lower', 'higher'}, optional
This optional parameter specifies the interpolation method to use,
when the desired quantile lies between two data points `i` and `j`:
- fraction: `i + (j - i)*fraction`, where `fraction` is the
fractional part of the index surrounded by `i` and `j`.
-lower: `i`.
- higher: `j`.
Returns
-------
score : float
Score at percentile.
Examples
--------
>>> from scipy import stats
>>> a = np.arange(100)
>>> stats.scoreatpercentile(a, 50)
49.5
"""
x = np.asarray(x)
mask = isna(x)
x = x[~mask]
values = np.sort(x)
def _interpolate(a, b, fraction):
"""Returns the point at the given fraction between a and b, where
'fraction' must be between 0 and 1.
"""
return a + (b - a) * fraction
def _get_score(at):
if len(values) == 0:
return np.nan
idx = at * (len(values) - 1)
if idx % 1 == 0:
score = values[int(idx)]
else:
if interpolation_method == "fraction":
score = _interpolate(values[int(idx)], values[int(idx) + 1], idx % 1)
elif interpolation_method == "lower":
score = values[np.floor(idx)]
elif interpolation_method == "higher":
score = values[np.ceil(idx)]
else:
raise ValueError(
"interpolation_method can only be 'fraction' "
", 'lower' or 'higher'"
)
return score
if is_scalar(q):
return _get_score(q)
else:
q = np.asarray(q, np.float64)
return algos.arrmap_float64(q, _get_score)
# --------------- #
# select n #
# --------------- #
class SelectN:
def __init__(self, obj, n, keep):
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 nlargest(self):
return self.compute("nlargest")
def nsmallest(self):
return self.compute("nsmallest")
@staticmethod
def is_valid_dtype_n_method(dtype):
"""
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):
n = self.n
dtype = self.obj.dtype
if not self.is_valid_dtype_n_method(dtype):
raise TypeError(
"Cannot use method '{method}' with "
"dtype {dtype}".format(method=method, dtype=dtype)
)
if n <= 0:
return self.obj[[]]
dropped = self.obj.dropna()
# slow method
if n >= len(self.obj):
reverse_it = self.keep == "last" or method == "nlargest"
ascending = method == "nsmallest"
slc = np.s_[::-1] if reverse_it else np.s_[:]
return dropped[slc].sort_values(ascending=ascending).head(n)
# fast method
arr, pandas_dtype, _ = _ensure_data(dropped.values)
if method == "nlargest":
arr = -arr
if is_integer_dtype(pandas_dtype):
# GH 21426: ensure reverse ordering at boundaries
arr -= 1
elif is_bool_dtype(pandas_dtype):
# GH 26154: ensure False is smaller than True
arr = 1 - (-arr)
if self.keep == "last":
arr = arr[::-1]
narr = len(arr)
n = min(n, narr)
kth_val = algos.kth_smallest(arr.copy(), n - 1)
ns, = np.nonzero(arr <= kth_val)
inds = ns[arr[ns].argsort(kind="mergesort")]
if self.keep != "all":
inds = inds[:n]
if self.keep == "last":
# reverse indices
inds = narr - 1 - inds
return dropped.iloc[inds]
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, n, keep, columns):
super().__init__(obj, n, keep)
if not is_list_like(columns) or isinstance(columns, tuple):
columns = [columns]
columns = list(columns)
self.columns = columns
def compute(self, method):
from pandas 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(
(
"Column {column!r} has dtype {dtype}, cannot use method "
"{method!r} with this dtype"
).format(column=column, dtype=dtype, method=method)
)
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)/(nlarrgest: 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 _view_wrapper(f, arr_dtype=None, out_dtype=None, fill_wrap=None):
def wrapper(arr, indexer, out, fill_value=np.nan):
if arr_dtype is not None:
arr = arr.view(arr_dtype)
if out_dtype is not None:
out = out.view(out_dtype)
if fill_wrap is not None:
fill_value = fill_wrap(fill_value)
f(arr, indexer, out, fill_value=fill_value)
return wrapper
def _convert_wrapper(f, conv_dtype):
def wrapper(arr, indexer, out, fill_value=np.nan):
arr = arr.astype(conv_dtype)
f(arr, indexer, out, fill_value=fill_value)
return wrapper
def _take_2d_multi_object(arr, indexer, out, fill_value, mask_info):
# this is not ideal, performance-wise, but it's better than raising
# an exception (best to optimize in Cython to avoid getting here)
row_idx, col_idx = indexer
if mask_info is not None:
(row_mask, col_mask), (row_needs, col_needs) = mask_info
else:
row_mask = row_idx == -1
col_mask = col_idx == -1
row_needs = row_mask.any()
col_needs = col_mask.any()
if fill_value is not None:
if row_needs:
out[row_mask, :] = fill_value
if col_needs:
out[:, col_mask] = fill_value
for i in range(len(row_idx)):
u_ = row_idx[i]
for j in range(len(col_idx)):
v = col_idx[j]
out[i, j] = arr[u_, v]
def _take_nd_object(arr, indexer, out, axis, fill_value, mask_info):
if mask_info is not None:
mask, needs_masking = mask_info
else:
mask = indexer == -1
needs_masking = mask.any()
if arr.dtype != out.dtype:
arr = arr.astype(out.dtype)
if arr.shape[axis] > 0:
arr.take(ensure_platform_int(indexer), axis=axis, out=out)
if needs_masking:
outindexer = [slice(None)] * arr.ndim
outindexer[axis] = mask
out[tuple(outindexer)] = fill_value
_take_1d_dict = {
("int8", "int8"): algos.take_1d_int8_int8,
("int8", "int32"): algos.take_1d_int8_int32,
("int8", "int64"): algos.take_1d_int8_int64,
("int8", "float64"): algos.take_1d_int8_float64,
("int16", "int16"): algos.take_1d_int16_int16,
("int16", "int32"): algos.take_1d_int16_int32,
("int16", "int64"): algos.take_1d_int16_int64,
("int16", "float64"): algos.take_1d_int16_float64,
("int32", "int32"): algos.take_1d_int32_int32,
("int32", "int64"): algos.take_1d_int32_int64,
("int32", "float64"): algos.take_1d_int32_float64,
("int64", "int64"): algos.take_1d_int64_int64,
("int64", "float64"): algos.take_1d_int64_float64,
("float32", "float32"): algos.take_1d_float32_float32,
("float32", "float64"): algos.take_1d_float32_float64,
("float64", "float64"): algos.take_1d_float64_float64,
("object", "object"): algos.take_1d_object_object,
("bool", "bool"): _view_wrapper(algos.take_1d_bool_bool, np.uint8, np.uint8),
("bool", "object"): _view_wrapper(algos.take_1d_bool_object, np.uint8, None),
("datetime64[ns]", "datetime64[ns]"): _view_wrapper(
algos.take_1d_int64_int64, np.int64, np.int64, np.int64
),
}
_take_2d_axis0_dict = {
("int8", "int8"): algos.take_2d_axis0_int8_int8,
("int8", "int32"): algos.take_2d_axis0_int8_int32,
("int8", "int64"): algos.take_2d_axis0_int8_int64,
("int8", "float64"): algos.take_2d_axis0_int8_float64,
("int16", "int16"): algos.take_2d_axis0_int16_int16,
("int16", "int32"): algos.take_2d_axis0_int16_int32,
("int16", "int64"): algos.take_2d_axis0_int16_int64,
("int16", "float64"): algos.take_2d_axis0_int16_float64,
("int32", "int32"): algos.take_2d_axis0_int32_int32,
("int32", "int64"): algos.take_2d_axis0_int32_int64,
("int32", "float64"): algos.take_2d_axis0_int32_float64,
("int64", "int64"): algos.take_2d_axis0_int64_int64,
("int64", "float64"): algos.take_2d_axis0_int64_float64,
("float32", "float32"): algos.take_2d_axis0_float32_float32,
("float32", "float64"): algos.take_2d_axis0_float32_float64,
("float64", "float64"): algos.take_2d_axis0_float64_float64,
("object", "object"): algos.take_2d_axis0_object_object,
("bool", "bool"): _view_wrapper(algos.take_2d_axis0_bool_bool, np.uint8, np.uint8),
("bool", "object"): _view_wrapper(algos.take_2d_axis0_bool_object, np.uint8, None),
("datetime64[ns]", "datetime64[ns]"): _view_wrapper(
algos.take_2d_axis0_int64_int64, np.int64, np.int64, fill_wrap=np.int64
),
}
_take_2d_axis1_dict = {
("int8", "int8"): algos.take_2d_axis1_int8_int8,
("int8", "int32"): algos.take_2d_axis1_int8_int32,
("int8", "int64"): algos.take_2d_axis1_int8_int64,
("int8", "float64"): algos.take_2d_axis1_int8_float64,
("int16", "int16"): algos.take_2d_axis1_int16_int16,
("int16", "int32"): algos.take_2d_axis1_int16_int32,
("int16", "int64"): algos.take_2d_axis1_int16_int64,
("int16", "float64"): algos.take_2d_axis1_int16_float64,
("int32", "int32"): algos.take_2d_axis1_int32_int32,
("int32", "int64"): algos.take_2d_axis1_int32_int64,
("int32", "float64"): algos.take_2d_axis1_int32_float64,
("int64", "int64"): algos.take_2d_axis1_int64_int64,
("int64", "float64"): algos.take_2d_axis1_int64_float64,
("float32", "float32"): algos.take_2d_axis1_float32_float32,
("float32", "float64"): algos.take_2d_axis1_float32_float64,
("float64", "float64"): algos.take_2d_axis1_float64_float64,
("object", "object"): algos.take_2d_axis1_object_object,
("bool", "bool"): _view_wrapper(algos.take_2d_axis1_bool_bool, np.uint8, np.uint8),
("bool", "object"): _view_wrapper(algos.take_2d_axis1_bool_object, np.uint8, None),
("datetime64[ns]", "datetime64[ns]"): _view_wrapper(
algos.take_2d_axis1_int64_int64, np.int64, np.int64, fill_wrap=np.int64
),
}
_take_2d_multi_dict = {
("int8", "int8"): algos.take_2d_multi_int8_int8,
("int8", "int32"): algos.take_2d_multi_int8_int32,
("int8", "int64"): algos.take_2d_multi_int8_int64,
("int8", "float64"): algos.take_2d_multi_int8_float64,
("int16", "int16"): algos.take_2d_multi_int16_int16,
("int16", "int32"): algos.take_2d_multi_int16_int32,
("int16", "int64"): algos.take_2d_multi_int16_int64,
("int16", "float64"): algos.take_2d_multi_int16_float64,
("int32", "int32"): algos.take_2d_multi_int32_int32,
("int32", "int64"): algos.take_2d_multi_int32_int64,
("int32", "float64"): algos.take_2d_multi_int32_float64,
("int64", "int64"): algos.take_2d_multi_int64_int64,
("int64", "float64"): algos.take_2d_multi_int64_float64,
("float32", "float32"): algos.take_2d_multi_float32_float32,
("float32", "float64"): algos.take_2d_multi_float32_float64,
("float64", "float64"): algos.take_2d_multi_float64_float64,
("object", "object"): algos.take_2d_multi_object_object,
("bool", "bool"): _view_wrapper(algos.take_2d_multi_bool_bool, np.uint8, np.uint8),
("bool", "object"): _view_wrapper(algos.take_2d_multi_bool_object, np.uint8, None),
("datetime64[ns]", "datetime64[ns]"): _view_wrapper(
algos.take_2d_multi_int64_int64, np.int64, np.int64, fill_wrap=np.int64
),
}
def _get_take_nd_function(ndim, arr_dtype, out_dtype, axis=0, mask_info=None):
if ndim <= 2:
tup = (arr_dtype.name, out_dtype.name)
if ndim == 1:
func = _take_1d_dict.get(tup, None)
elif ndim == 2:
if axis == 0:
func = _take_2d_axis0_dict.get(tup, None)
else:
func = _take_2d_axis1_dict.get(tup, None)
if func is not None:
return func
tup = (out_dtype.name, out_dtype.name)
if ndim == 1:
func = _take_1d_dict.get(tup, None)
elif ndim == 2:
if axis == 0:
func = _take_2d_axis0_dict.get(tup, None)
else:
func = _take_2d_axis1_dict.get(tup, None)
if func is not None:
func = _convert_wrapper(func, out_dtype)
return func
def func(arr, indexer, out, fill_value=np.nan):
indexer = ensure_int64(indexer)
_take_nd_object(
arr, indexer, out, axis=axis, fill_value=fill_value, mask_info=mask_info
)
return func
def take(arr, indices, axis=0, allow_fill=False, fill_value=None):
"""
Take elements from an array.
.. versionadded:: 0.23.0
Parameters
----------
arr : sequence
Non array-likes (sequences without a dtype) are coerced
to an ndarray.
indices : sequence of integers
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
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
Examples
--------
>>> from pandas.api.extensions import take
With the default ``allow_fill=False``, negative numbers indicate
positional indices from the right.
>>> take(np.array([10, 20, 30]), [0, 0, -1])
array([10, 10, 30])
Setting ``allow_fill=True`` will place `fill_value` in those positions.
>>> take(np.array([10, 20, 30]), [0, 0, -1], allow_fill=True)
array([10., 10., nan])
>>> 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_1d(
arr, indices, axis=axis, allow_fill=True, fill_value=fill_value
)
else:
# NumPy style
result = arr.take(indices, axis=axis)
return result
def take_nd(
arr, indexer, axis=0, out=None, fill_value=np.nan, mask_info=None, allow_fill=True
):
"""
Specialized Cython take which sets NaN values in one pass
This dispatches to ``take`` defined on ExtensionArrays. It does not
currently dispatch to ``SparseArray.take`` for sparse ``arr``.
Parameters
----------
arr : array-like
Input array.
indexer : ndarray
1-D array of indices to take, subarrays corresponding to -1 value
indices are filed with fill_value
axis : int, default 0
Axis to take from
out : ndarray or None, default None
Optional output array, must be appropriate type to hold input and
fill_value together, if indexer has any -1 value entries; call
_maybe_promote to determine this type for any fill_value
fill_value : any, default np.nan
Fill value to replace -1 values with
mask_info : tuple of (ndarray, boolean)
If provided, value should correspond to:
(indexer != -1, (indexer != -1).any())
If not provided, it will be computed internally if necessary
allow_fill : boolean, default True
If False, indexer is assumed to contain no -1 values so no filling
will be done. This short-circuits computation of a mask. Result is
undefined if allow_fill == False and -1 is present in indexer.
Returns
-------
subarray : array-like
May be the same type as the input, or cast to an ndarray.
"""
# TODO(EA): Remove these if / elifs as datetimeTZ, interval, become EAs
# dispatch to internal type takes
if is_extension_array_dtype(arr):
return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill)
elif is_datetime64tz_dtype(arr):
return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill)
elif is_interval_dtype(arr):
return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill)
if is_sparse(arr):
arr = arr.to_dense()
elif isinstance(arr, (ABCIndexClass, ABCSeries)):
arr = arr.values
arr = np.asarray(arr)
if indexer is None:
indexer = np.arange(arr.shape[axis], dtype=np.int64)
dtype, fill_value = arr.dtype, arr.dtype.type()
else:
indexer = ensure_int64(indexer, copy=False)
if not allow_fill:
dtype, fill_value = arr.dtype, arr.dtype.type()
mask_info = None, False
else:
# check for promotion based on types only (do this first because
# it's faster than computing a mask)
dtype, fill_value = maybe_promote(arr.dtype, fill_value)
if dtype != arr.dtype and (out is None or out.dtype != dtype):
# check if promotion is actually required based on indexer
if mask_info is not None:
mask, needs_masking = mask_info
else:
mask = indexer == -1
needs_masking = mask.any()
mask_info = mask, needs_masking
if needs_masking:
if out is not None and out.dtype != dtype:
raise TypeError("Incompatible type for fill_value")
else:
# if not, then depromote, set fill_value to dummy
# (it won't be used but we don't want the cython code
# to crash when trying to cast it to dtype)
dtype, fill_value = arr.dtype, arr.dtype.type()
flip_order = False
if arr.ndim == 2:
if arr.flags.f_contiguous:
flip_order = True
if flip_order:
arr = arr.T
axis = arr.ndim - axis - 1
if out is not None:
out = out.T
# at this point, it's guaranteed that dtype can hold both the arr values
# and the fill_value
if out is None:
out_shape = list(arr.shape)
out_shape[axis] = len(indexer)
out_shape = tuple(out_shape)
if arr.flags.f_contiguous and axis == arr.ndim - 1:
# minor tweak that can make an order-of-magnitude difference
# for dataframes initialized directly from 2-d ndarrays
# (s.t. df.values is c-contiguous and df._data.blocks[0] is its
# f-contiguous transpose)
out = np.empty(out_shape, dtype=dtype, order="F")
else:
out = np.empty(out_shape, dtype=dtype)
func = _get_take_nd_function(
arr.ndim, arr.dtype, out.dtype, axis=axis, mask_info=mask_info
)
func(arr, indexer, out, fill_value)
if flip_order:
out = out.T
return out
take_1d = take_nd
def take_2d_multi(
arr, indexer, out=None, fill_value=np.nan, mask_info=None, allow_fill=True
):
"""
Specialized Cython take which sets NaN values in one pass
"""
if indexer is None or (indexer[0] is None and indexer[1] is None):
row_idx = np.arange(arr.shape[0], dtype=np.int64)
col_idx = np.arange(arr.shape[1], dtype=np.int64)
indexer = row_idx, col_idx
dtype, fill_value = arr.dtype, arr.dtype.type()
else:
row_idx, col_idx = indexer
if row_idx is None:
row_idx = np.arange(arr.shape[0], dtype=np.int64)
else:
row_idx = ensure_int64(row_idx)
if col_idx is None:
col_idx = np.arange(arr.shape[1], dtype=np.int64)
else:
col_idx = ensure_int64(col_idx)
indexer = row_idx, col_idx
if not allow_fill:
dtype, fill_value = arr.dtype, arr.dtype.type()
mask_info = None, False
else:
# check for promotion based on types only (do this first because
# it's faster than computing a mask)
dtype, fill_value = maybe_promote(arr.dtype, fill_value)
if dtype != arr.dtype and (out is None or out.dtype != dtype):
# check if promotion is actually required based on indexer
if mask_info is not None:
(row_mask, col_mask), (row_needs, col_needs) = mask_info
else:
row_mask = row_idx == -1
col_mask = col_idx == -1
row_needs = row_mask.any()
col_needs = col_mask.any()
mask_info = (row_mask, col_mask), (row_needs, col_needs)
if row_needs or col_needs:
if out is not None and out.dtype != dtype:
raise TypeError("Incompatible type for fill_value")
else:
# if not, then depromote, set fill_value to dummy
# (it won't be used but we don't want the cython code
# to crash when trying to cast it to dtype)
dtype, fill_value = arr.dtype, arr.dtype.type()
# at this point, it's guaranteed that dtype can hold both the arr values
# and the fill_value
if out is None:
out_shape = len(row_idx), len(col_idx)
out = np.empty(out_shape, dtype=dtype)
func = _take_2d_multi_dict.get((arr.dtype.name, out.dtype.name), None)
if func is None and arr.dtype != out.dtype:
func = _take_2d_multi_dict.get((out.dtype.name, out.dtype.name), None)
if func is not None:
func = _convert_wrapper(func, out.dtype)
if func is None:
def func(arr, indexer, out, fill_value=np.nan):
_take_2d_multi_object(
arr, indexer, out, fill_value=fill_value, mask_info=mask_info
)
func(arr, indexer, out=out, fill_value=fill_value)
return out
# ------------ #
# searchsorted #
# ------------ #
def searchsorted(arr, value, side="left", sorter=None):
"""
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: array-like
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
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
Array of insertion points with the same shape as `value`.
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)
and (is_integer(value) or is_integer_dtype(value))
):
from .arrays.array_ import array
# 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):
value = dtype.type(value)
else:
value = array(value, dtype=dtype)
elif not (
is_object_dtype(arr) or is_numeric_dtype(arr) or is_categorical_dtype(arr)
):
from pandas.core.series import Series
# E.g. if `arr` is an array with dtype='datetime64[ns]'
# and `value` is a pd.Timestamp, we may need to convert value
value_ser = Series(value)._values
value = value_ser[0] if is_scalar(value) else value_ser
result = arr.searchsorted(value, side=side, sorter=sorter)
return result
# ---- #
# diff #
# ---- #
_diff_special = {
"float64": algos.diff_2d_float64,
"float32": algos.diff_2d_float32,
"int64": algos.diff_2d_int64,
"int32": algos.diff_2d_int32,
"int16": algos.diff_2d_int16,
"int8": algos.diff_2d_int8,
}
def diff(arr, n, axis=0):
"""
difference of n between self,
analogous to s-s.shift(n)
Parameters
----------
arr : ndarray
n : int
number of periods
axis : int
axis to shift on
Returns
-------
shifted
"""
n = int(n)
na = np.nan
dtype = arr.dtype
is_timedelta = False
if needs_i8_conversion(arr):
dtype = np.float64
arr = arr.view("i8")
na = iNaT
is_timedelta = True
elif is_bool_dtype(dtype):
dtype = np.object_
elif is_integer_dtype(dtype):
dtype = np.float64
dtype = np.dtype(dtype)
out_arr = np.empty(arr.shape, dtype=dtype)
na_indexer = [slice(None)] * arr.ndim
na_indexer[axis] = slice(None, n) if n >= 0 else slice(n, None)
out_arr[tuple(na_indexer)] = na
if arr.ndim == 2 and arr.dtype.name in _diff_special:
f = _diff_special[arr.dtype.name]
f(arr, out_arr, n, axis)
else:
res_indexer = [slice(None)] * arr.ndim
res_indexer[axis] = slice(n, None) if n >= 0 else slice(None, n)
res_indexer = tuple(res_indexer)
lag_indexer = [slice(None)] * arr.ndim
lag_indexer[axis] = slice(None, -n) if n > 0 else slice(-n, None)
lag_indexer = tuple(lag_indexer)
# need to make sure that we account for na for datelike/timedelta
# we don't actually want to subtract these i8 numbers
if is_timedelta:
res = arr[res_indexer]
lag = arr[lag_indexer]
mask = (arr[res_indexer] == na) | (arr[lag_indexer] == na)
if mask.any():
res = res.copy()
res[mask] = 0
lag = lag.copy()
lag[mask] = 0
result = res - lag
result[mask] = na
out_arr[res_indexer] = result
else:
out_arr[res_indexer] = arr[res_indexer] - arr[lag_indexer]
if is_timedelta:
from pandas import TimedeltaIndex
out_arr = (
TimedeltaIndex(out_arr.ravel().astype("int64"))
.asi8.reshape(out_arr.shape)
.astype("timedelta64[ns]")
)
return out_arr