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Inzynierka/Lib/site-packages/pandas/core/base.py

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2023-06-02 12:51:02 +02:00
"""
Base and utility classes for pandas objects.
"""
from __future__ import annotations
import textwrap
from typing import (
TYPE_CHECKING,
Any,
Generic,
Hashable,
Iterator,
Literal,
TypeVar,
cast,
final,
overload,
)
import numpy as np
from pandas._config import using_copy_on_write
from pandas._libs import lib
from pandas._typing import (
Axis,
AxisInt,
DtypeObj,
IndexLabel,
NDFrameT,
Shape,
npt,
)
from pandas.compat import PYPY
from pandas.compat.numpy import function as nv
from pandas.errors import AbstractMethodError
from pandas.util._decorators import (
cache_readonly,
doc,
)
from pandas.core.dtypes.cast import can_hold_element
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_dict_like,
is_extension_array_dtype,
is_object_dtype,
is_scalar,
)
from pandas.core.dtypes.generic import (
ABCDataFrame,
ABCIndex,
ABCSeries,
)
from pandas.core.dtypes.missing import (
isna,
remove_na_arraylike,
)
from pandas.core import (
algorithms,
nanops,
ops,
)
from pandas.core.accessor import DirNamesMixin
from pandas.core.arraylike import OpsMixin
from pandas.core.arrays import ExtensionArray
from pandas.core.construction import (
ensure_wrapped_if_datetimelike,
extract_array,
)
if TYPE_CHECKING:
from pandas._typing import (
DropKeep,
NumpySorter,
NumpyValueArrayLike,
ScalarLike_co,
)
from pandas import (
Categorical,
Index,
Series,
)
_shared_docs: dict[str, str] = {}
_indexops_doc_kwargs = {
"klass": "IndexOpsMixin",
"inplace": "",
"unique": "IndexOpsMixin",
"duplicated": "IndexOpsMixin",
}
_T = TypeVar("_T", bound="IndexOpsMixin")
class PandasObject(DirNamesMixin):
"""
Baseclass for various pandas objects.
"""
# results from calls to methods decorated with cache_readonly get added to _cache
_cache: dict[str, Any]
@property
def _constructor(self):
"""
Class constructor (for this class it's just `__class__`.
"""
return type(self)
def __repr__(self) -> str:
"""
Return a string representation for a particular object.
"""
# Should be overwritten by base classes
return object.__repr__(self)
def _reset_cache(self, key: str | None = None) -> None:
"""
Reset cached properties. If ``key`` is passed, only clears that key.
"""
if not hasattr(self, "_cache"):
return
if key is None:
self._cache.clear()
else:
self._cache.pop(key, None)
def __sizeof__(self) -> int:
"""
Generates the total memory usage for an object that returns
either a value or Series of values
"""
memory_usage = getattr(self, "memory_usage", None)
if memory_usage:
mem = memory_usage(deep=True) # pylint: disable=not-callable
return int(mem if is_scalar(mem) else mem.sum())
# no memory_usage attribute, so fall back to object's 'sizeof'
return super().__sizeof__()
class NoNewAttributesMixin:
"""
Mixin which prevents adding new attributes.
Prevents additional attributes via xxx.attribute = "something" after a
call to `self.__freeze()`. Mainly used to prevent the user from using
wrong attributes on an accessor (`Series.cat/.str/.dt`).
If you really want to add a new attribute at a later time, you need to use
`object.__setattr__(self, key, value)`.
"""
def _freeze(self) -> None:
"""
Prevents setting additional attributes.
"""
object.__setattr__(self, "__frozen", True)
# prevent adding any attribute via s.xxx.new_attribute = ...
def __setattr__(self, key: str, value) -> None:
# _cache is used by a decorator
# We need to check both 1.) cls.__dict__ and 2.) getattr(self, key)
# because
# 1.) getattr is false for attributes that raise errors
# 2.) cls.__dict__ doesn't traverse into base classes
if getattr(self, "__frozen", False) and not (
key == "_cache"
or key in type(self).__dict__
or getattr(self, key, None) is not None
):
raise AttributeError(f"You cannot add any new attribute '{key}'")
object.__setattr__(self, key, value)
class SelectionMixin(Generic[NDFrameT]):
"""
mixin implementing the selection & aggregation interface on a group-like
object sub-classes need to define: obj, exclusions
"""
obj: NDFrameT
_selection: IndexLabel | None = None
exclusions: frozenset[Hashable]
_internal_names = ["_cache", "__setstate__"]
_internal_names_set = set(_internal_names)
@final
@property
def _selection_list(self):
if not isinstance(
self._selection, (list, tuple, ABCSeries, ABCIndex, np.ndarray)
):
return [self._selection]
return self._selection
@cache_readonly
def _selected_obj(self):
if self._selection is None or isinstance(self.obj, ABCSeries):
return self.obj
else:
return self.obj[self._selection]
@final
@cache_readonly
def ndim(self) -> int:
return self._selected_obj.ndim
@final
@cache_readonly
def _obj_with_exclusions(self):
if isinstance(self.obj, ABCSeries):
return self.obj
if self._selection is not None:
return self.obj._getitem_nocopy(self._selection_list)
if len(self.exclusions) > 0:
# equivalent to `self.obj.drop(self.exclusions, axis=1)
# but this avoids consolidating and making a copy
# TODO: following GH#45287 can we now use .drop directly without
# making a copy?
return self.obj._drop_axis(self.exclusions, axis=1, only_slice=True)
else:
return self.obj
def __getitem__(self, key):
if self._selection is not None:
raise IndexError(f"Column(s) {self._selection} already selected")
if isinstance(key, (list, tuple, ABCSeries, ABCIndex, np.ndarray)):
if len(self.obj.columns.intersection(key)) != len(set(key)):
bad_keys = list(set(key).difference(self.obj.columns))
raise KeyError(f"Columns not found: {str(bad_keys)[1:-1]}")
return self._gotitem(list(key), ndim=2)
else:
if key not in self.obj:
raise KeyError(f"Column not found: {key}")
ndim = self.obj[key].ndim
return self._gotitem(key, ndim=ndim)
def _gotitem(self, key, ndim: int, subset=None):
"""
sub-classes to define
return a sliced object
Parameters
----------
key : str / list of selections
ndim : {1, 2}
requested ndim of result
subset : object, default None
subset to act on
"""
raise AbstractMethodError(self)
def aggregate(self, func, *args, **kwargs):
raise AbstractMethodError(self)
agg = aggregate
class IndexOpsMixin(OpsMixin):
"""
Common ops mixin to support a unified interface / docs for Series / Index
"""
# ndarray compatibility
__array_priority__ = 1000
_hidden_attrs: frozenset[str] = frozenset(
["tolist"] # tolist is not deprecated, just suppressed in the __dir__
)
@property
def dtype(self) -> DtypeObj:
# must be defined here as a property for mypy
raise AbstractMethodError(self)
@property
def _values(self) -> ExtensionArray | np.ndarray:
# must be defined here as a property for mypy
raise AbstractMethodError(self)
@final
def transpose(self: _T, *args, **kwargs) -> _T:
"""
Return the transpose, which is by definition self.
Returns
-------
%(klass)s
"""
nv.validate_transpose(args, kwargs)
return self
T = property(
transpose,
doc="""
Return the transpose, which is by definition self.
""",
)
@property
def shape(self) -> Shape:
"""
Return a tuple of the shape of the underlying data.
Examples
--------
>>> s = pd.Series([1, 2, 3])
>>> s.shape
(3,)
"""
return self._values.shape
def __len__(self) -> int:
# We need this defined here for mypy
raise AbstractMethodError(self)
@property
def ndim(self) -> Literal[1]:
"""
Number of dimensions of the underlying data, by definition 1.
"""
return 1
@final
def item(self):
"""
Return the first element of the underlying data as a Python scalar.
Returns
-------
scalar
The first element of %(klass)s.
Raises
------
ValueError
If the data is not length-1.
"""
if len(self) == 1:
return next(iter(self))
raise ValueError("can only convert an array of size 1 to a Python scalar")
@property
def nbytes(self) -> int:
"""
Return the number of bytes in the underlying data.
"""
return self._values.nbytes
@property
def size(self) -> int:
"""
Return the number of elements in the underlying data.
"""
return len(self._values)
@property
def array(self) -> ExtensionArray:
"""
The ExtensionArray of the data backing this Series or Index.
Returns
-------
ExtensionArray
An ExtensionArray of the values stored within. For extension
types, this is the actual array. For NumPy native types, this
is a thin (no copy) wrapper around :class:`numpy.ndarray`.
``.array`` differs ``.values`` which may require converting the
data to a different form.
See Also
--------
Index.to_numpy : Similar method that always returns a NumPy array.
Series.to_numpy : Similar method that always returns a NumPy array.
Notes
-----
This table lays out the different array types for each extension
dtype within pandas.
================== =============================
dtype array type
================== =============================
category Categorical
period PeriodArray
interval IntervalArray
IntegerNA IntegerArray
string StringArray
boolean BooleanArray
datetime64[ns, tz] DatetimeArray
================== =============================
For any 3rd-party extension types, the array type will be an
ExtensionArray.
For all remaining dtypes ``.array`` will be a
:class:`arrays.NumpyExtensionArray` wrapping the actual ndarray
stored within. If you absolutely need a NumPy array (possibly with
copying / coercing data), then use :meth:`Series.to_numpy` instead.
Examples
--------
For regular NumPy types like int, and float, a PandasArray
is returned.
>>> pd.Series([1, 2, 3]).array
<PandasArray>
[1, 2, 3]
Length: 3, dtype: int64
For extension types, like Categorical, the actual ExtensionArray
is returned
>>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
>>> ser.array
['a', 'b', 'a']
Categories (2, object): ['a', 'b']
"""
raise AbstractMethodError(self)
@final
def to_numpy(
self,
dtype: npt.DTypeLike | None = None,
copy: bool = False,
na_value: object = lib.no_default,
**kwargs,
) -> np.ndarray:
"""
A NumPy ndarray representing the values in this Series or Index.
Parameters
----------
dtype : str or numpy.dtype, optional
The dtype to pass to :meth:`numpy.asarray`.
copy : bool, default False
Whether to ensure that the returned value is not a view on
another array. Note that ``copy=False`` does not *ensure* that
``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
a copy is made, even if not strictly necessary.
na_value : Any, optional
The value to use for missing values. The default value depends
on `dtype` and the type of the array.
**kwargs
Additional keywords passed through to the ``to_numpy`` method
of the underlying array (for extension arrays).
Returns
-------
numpy.ndarray
See Also
--------
Series.array : Get the actual data stored within.
Index.array : Get the actual data stored within.
DataFrame.to_numpy : Similar method for DataFrame.
Notes
-----
The returned array will be the same up to equality (values equal
in `self` will be equal in the returned array; likewise for values
that are not equal). When `self` contains an ExtensionArray, the
dtype may be different. For example, for a category-dtype Series,
``to_numpy()`` will return a NumPy array and the categorical dtype
will be lost.
For NumPy dtypes, this will be a reference to the actual data stored
in this Series or Index (assuming ``copy=False``). Modifying the result
in place will modify the data stored in the Series or Index (not that
we recommend doing that).
For extension types, ``to_numpy()`` *may* require copying data and
coercing the result to a NumPy type (possibly object), which may be
expensive. When you need a no-copy reference to the underlying data,
:attr:`Series.array` should be used instead.
This table lays out the different dtypes and default return types of
``to_numpy()`` for various dtypes within pandas.
================== ================================
dtype array type
================== ================================
category[T] ndarray[T] (same dtype as input)
period ndarray[object] (Periods)
interval ndarray[object] (Intervals)
IntegerNA ndarray[object]
datetime64[ns] datetime64[ns]
datetime64[ns, tz] ndarray[object] (Timestamps)
================== ================================
Examples
--------
>>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
>>> ser.to_numpy()
array(['a', 'b', 'a'], dtype=object)
Specify the `dtype` to control how datetime-aware data is represented.
Use ``dtype=object`` to return an ndarray of pandas :class:`Timestamp`
objects, each with the correct ``tz``.
>>> ser = pd.Series(pd.date_range('2000', periods=2, tz="CET"))
>>> ser.to_numpy(dtype=object)
array([Timestamp('2000-01-01 00:00:00+0100', tz='CET'),
Timestamp('2000-01-02 00:00:00+0100', tz='CET')],
dtype=object)
Or ``dtype='datetime64[ns]'`` to return an ndarray of native
datetime64 values. The values are converted to UTC and the timezone
info is dropped.
>>> ser.to_numpy(dtype="datetime64[ns]")
... # doctest: +ELLIPSIS
array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00...'],
dtype='datetime64[ns]')
"""
if is_extension_array_dtype(self.dtype):
return self.array.to_numpy(dtype, copy=copy, na_value=na_value, **kwargs)
elif kwargs:
bad_keys = list(kwargs.keys())[0]
raise TypeError(
f"to_numpy() got an unexpected keyword argument '{bad_keys}'"
)
if na_value is not lib.no_default:
values = self._values
if not can_hold_element(values, na_value):
# if we can't hold the na_value asarray either makes a copy or we
# error before modifying values. The asarray later on thus won't make
# another copy
values = np.asarray(values, dtype=dtype)
else:
values = values.copy()
values[np.asanyarray(self.isna())] = na_value
else:
values = self._values
result = np.asarray(values, dtype=dtype)
if (copy and na_value is lib.no_default) or (
not copy and using_copy_on_write()
):
if np.shares_memory(self._values[:2], result[:2]):
# Take slices to improve performance of check
if using_copy_on_write() and not copy:
result = result.view()
result.flags.writeable = False
else:
result = result.copy()
return result
@final
@property
def empty(self) -> bool:
return not self.size
def max(self, axis: AxisInt | None = None, skipna: bool = True, *args, **kwargs):
"""
Return the maximum value of the Index.
Parameters
----------
axis : int, optional
For compatibility with NumPy. Only 0 or None are allowed.
skipna : bool, default True
Exclude NA/null values when showing the result.
*args, **kwargs
Additional arguments and keywords for compatibility with NumPy.
Returns
-------
scalar
Maximum value.
See Also
--------
Index.min : Return the minimum value in an Index.
Series.max : Return the maximum value in a Series.
DataFrame.max : Return the maximum values in a DataFrame.
Examples
--------
>>> idx = pd.Index([3, 2, 1])
>>> idx.max()
3
>>> idx = pd.Index(['c', 'b', 'a'])
>>> idx.max()
'c'
For a MultiIndex, the maximum is determined lexicographically.
>>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])
>>> idx.max()
('b', 2)
"""
nv.validate_minmax_axis(axis)
nv.validate_max(args, kwargs)
return nanops.nanmax(self._values, skipna=skipna)
@doc(op="max", oppose="min", value="largest")
def argmax(
self, axis: AxisInt | None = None, skipna: bool = True, *args, **kwargs
) -> int:
"""
Return int position of the {value} value in the Series.
If the {op}imum is achieved in multiple locations,
the first row position is returned.
Parameters
----------
axis : {{None}}
Unused. Parameter needed for compatibility with DataFrame.
skipna : bool, default True
Exclude NA/null values when showing the result.
*args, **kwargs
Additional arguments and keywords for compatibility with NumPy.
Returns
-------
int
Row position of the {op}imum value.
See Also
--------
Series.arg{op} : Return position of the {op}imum value.
Series.arg{oppose} : Return position of the {oppose}imum value.
numpy.ndarray.arg{op} : Equivalent method for numpy arrays.
Series.idxmax : Return index label of the maximum values.
Series.idxmin : Return index label of the minimum values.
Examples
--------
Consider dataset containing cereal calories
>>> s = pd.Series({{'Corn Flakes': 100.0, 'Almond Delight': 110.0,
... 'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}})
>>> s
Corn Flakes 100.0
Almond Delight 110.0
Cinnamon Toast Crunch 120.0
Cocoa Puff 110.0
dtype: float64
>>> s.argmax()
2
>>> s.argmin()
0
The maximum cereal calories is the third element and
the minimum cereal calories is the first element,
since series is zero-indexed.
"""
delegate = self._values
nv.validate_minmax_axis(axis)
skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs)
if isinstance(delegate, ExtensionArray):
if not skipna and delegate.isna().any():
return -1
else:
return delegate.argmax()
else:
# error: Incompatible return value type (got "Union[int, ndarray]", expected
# "int")
return nanops.nanargmax( # type: ignore[return-value]
delegate, skipna=skipna
)
def min(self, axis: AxisInt | None = None, skipna: bool = True, *args, **kwargs):
"""
Return the minimum value of the Index.
Parameters
----------
axis : {None}
Dummy argument for consistency with Series.
skipna : bool, default True
Exclude NA/null values when showing the result.
*args, **kwargs
Additional arguments and keywords for compatibility with NumPy.
Returns
-------
scalar
Minimum value.
See Also
--------
Index.max : Return the maximum value of the object.
Series.min : Return the minimum value in a Series.
DataFrame.min : Return the minimum values in a DataFrame.
Examples
--------
>>> idx = pd.Index([3, 2, 1])
>>> idx.min()
1
>>> idx = pd.Index(['c', 'b', 'a'])
>>> idx.min()
'a'
For a MultiIndex, the minimum is determined lexicographically.
>>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])
>>> idx.min()
('a', 1)
"""
nv.validate_minmax_axis(axis)
nv.validate_min(args, kwargs)
return nanops.nanmin(self._values, skipna=skipna)
@doc(argmax, op="min", oppose="max", value="smallest")
def argmin(
self, axis: AxisInt | None = None, skipna: bool = True, *args, **kwargs
) -> int:
delegate = self._values
nv.validate_minmax_axis(axis)
skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs)
if isinstance(delegate, ExtensionArray):
if not skipna and delegate.isna().any():
return -1
else:
return delegate.argmin()
else:
# error: Incompatible return value type (got "Union[int, ndarray]", expected
# "int")
return nanops.nanargmin( # type: ignore[return-value]
delegate, skipna=skipna
)
def tolist(self):
"""
Return a list of the values.
These are each a scalar type, which is a Python scalar
(for str, int, float) or a pandas scalar
(for Timestamp/Timedelta/Interval/Period)
Returns
-------
list
See Also
--------
numpy.ndarray.tolist : Return the array as an a.ndim-levels deep
nested list of Python scalars.
"""
return self._values.tolist()
to_list = tolist
def __iter__(self) -> Iterator:
"""
Return an iterator of the values.
These are each a scalar type, which is a Python scalar
(for str, int, float) or a pandas scalar
(for Timestamp/Timedelta/Interval/Period)
Returns
-------
iterator
"""
# We are explicitly making element iterators.
if not isinstance(self._values, np.ndarray):
# Check type instead of dtype to catch DTA/TDA
return iter(self._values)
else:
return map(self._values.item, range(self._values.size))
@cache_readonly
def hasnans(self) -> bool:
"""
Return True if there are any NaNs.
Enables various performance speedups.
Returns
-------
bool
"""
# error: Item "bool" of "Union[bool, ndarray[Any, dtype[bool_]], NDFrame]"
# has no attribute "any"
return bool(isna(self).any()) # type: ignore[union-attr]
def isna(self) -> npt.NDArray[np.bool_]:
return isna(self._values)
def _reduce(
self,
op,
name: str,
*,
axis: Axis = 0,
skipna: bool = True,
numeric_only=None,
filter_type=None,
**kwds,
):
"""
Perform the reduction type operation if we can.
"""
func = getattr(self, name, None)
if func is None:
raise TypeError(
f"{type(self).__name__} cannot perform the operation {name}"
)
return func(skipna=skipna, **kwds)
@final
def _map_values(self, mapper, na_action=None):
"""
An internal function that maps values using the input
correspondence (which can be a dict, Series, or function).
Parameters
----------
mapper : function, dict, or Series
The input correspondence object
na_action : {None, 'ignore'}
If 'ignore', propagate NA values, without passing them to the
mapping function
Returns
-------
Union[Index, MultiIndex], inferred
The output of the mapping function applied to the index.
If the function returns a tuple with more than one element
a MultiIndex will be returned.
"""
# we can fastpath dict/Series to an efficient map
# as we know that we are not going to have to yield
# python types
if is_dict_like(mapper):
if isinstance(mapper, dict) and hasattr(mapper, "__missing__"):
# If a dictionary subclass defines a default value method,
# convert mapper to a lookup function (GH #15999).
dict_with_default = mapper
mapper = lambda x: dict_with_default[
np.nan if isinstance(x, float) and np.isnan(x) else x
]
else:
# Dictionary does not have a default. Thus it's safe to
# convert to an Series for efficiency.
# we specify the keys here to handle the
# possibility that they are tuples
# The return value of mapping with an empty mapper is
# expected to be pd.Series(np.nan, ...). As np.nan is
# of dtype float64 the return value of this method should
# be float64 as well
from pandas import Series
if len(mapper) == 0:
mapper = Series(mapper, dtype=np.float64)
else:
mapper = Series(mapper)
if isinstance(mapper, ABCSeries):
if na_action not in (None, "ignore"):
msg = (
"na_action must either be 'ignore' or None, "
f"{na_action} was passed"
)
raise ValueError(msg)
if na_action == "ignore":
mapper = mapper[mapper.index.notna()]
# Since values were input this means we came from either
# a dict or a series and mapper should be an index
if is_categorical_dtype(self.dtype):
# use the built in categorical series mapper which saves
# time by mapping the categories instead of all values
cat = cast("Categorical", self._values)
return cat.map(mapper)
values = self._values
indexer = mapper.index.get_indexer(values)
new_values = algorithms.take_nd(mapper._values, indexer)
return new_values
# we must convert to python types
if is_extension_array_dtype(self.dtype) and hasattr(self._values, "map"):
# GH#23179 some EAs do not have `map`
values = self._values
if na_action is not None:
raise NotImplementedError
map_f = lambda values, f: values.map(f)
else:
values = self._values.astype(object)
if na_action == "ignore":
map_f = lambda values, f: lib.map_infer_mask(
values, f, isna(values).view(np.uint8)
)
elif na_action is None:
map_f = lib.map_infer
else:
msg = (
"na_action must either be 'ignore' or None, "
f"{na_action} was passed"
)
raise ValueError(msg)
# mapper is a function
new_values = map_f(values, mapper)
return new_values
@final
def value_counts(
self,
normalize: bool = False,
sort: bool = True,
ascending: bool = False,
bins=None,
dropna: bool = True,
) -> Series:
"""
Return a Series containing counts of unique values.
The resulting object will be in descending order so that the
first element is the most frequently-occurring element.
Excludes NA values by default.
Parameters
----------
normalize : bool, default False
If True then the object returned will contain the relative
frequencies of the unique values.
sort : bool, default True
Sort by frequencies.
ascending : bool, default False
Sort in ascending order.
bins : int, optional
Rather than count values, group them into half-open bins,
a convenience for ``pd.cut``, only works with numeric data.
dropna : bool, default True
Don't include counts of NaN.
Returns
-------
Series
See Also
--------
Series.count: Number of non-NA elements in a Series.
DataFrame.count: Number of non-NA elements in a DataFrame.
DataFrame.value_counts: Equivalent method on DataFrames.
Examples
--------
>>> index = pd.Index([3, 1, 2, 3, 4, np.nan])
>>> index.value_counts()
3.0 2
1.0 1
2.0 1
4.0 1
Name: count, dtype: int64
With `normalize` set to `True`, returns the relative frequency by
dividing all values by the sum of values.
>>> s = pd.Series([3, 1, 2, 3, 4, np.nan])
>>> s.value_counts(normalize=True)
3.0 0.4
1.0 0.2
2.0 0.2
4.0 0.2
Name: proportion, dtype: float64
**bins**
Bins can be useful for going from a continuous variable to a
categorical variable; instead of counting unique
apparitions of values, divide the index in the specified
number of half-open bins.
>>> s.value_counts(bins=3)
(0.996, 2.0] 2
(2.0, 3.0] 2
(3.0, 4.0] 1
Name: count, dtype: int64
**dropna**
With `dropna` set to `False` we can also see NaN index values.
>>> s.value_counts(dropna=False)
3.0 2
1.0 1
2.0 1
4.0 1
NaN 1
Name: count, dtype: int64
"""
return algorithms.value_counts(
self,
sort=sort,
ascending=ascending,
normalize=normalize,
bins=bins,
dropna=dropna,
)
def unique(self):
values = self._values
if not isinstance(values, np.ndarray):
# i.e. ExtensionArray
result = values.unique()
else:
result = algorithms.unique1d(values)
return result
@final
def nunique(self, dropna: bool = True) -> int:
"""
Return number of unique elements in the object.
Excludes NA values by default.
Parameters
----------
dropna : bool, default True
Don't include NaN in the count.
Returns
-------
int
See Also
--------
DataFrame.nunique: Method nunique for DataFrame.
Series.count: Count non-NA/null observations in the Series.
Examples
--------
>>> s = pd.Series([1, 3, 5, 7, 7])
>>> s
0 1
1 3
2 5
3 7
4 7
dtype: int64
>>> s.nunique()
4
"""
uniqs = self.unique()
if dropna:
uniqs = remove_na_arraylike(uniqs)
return len(uniqs)
@property
def is_unique(self) -> bool:
"""
Return boolean if values in the object are unique.
Returns
-------
bool
"""
return self.nunique(dropna=False) == len(self)
@property
def is_monotonic_increasing(self) -> bool:
"""
Return boolean if values in the object are monotonically increasing.
Returns
-------
bool
"""
from pandas import Index
return Index(self).is_monotonic_increasing
@property
def is_monotonic_decreasing(self) -> bool:
"""
Return boolean if values in the object are monotonically decreasing.
Returns
-------
bool
"""
from pandas import Index
return Index(self).is_monotonic_decreasing
@final
def _memory_usage(self, deep: bool = False) -> int:
"""
Memory usage of the values.
Parameters
----------
deep : bool, default False
Introspect the data deeply, interrogate
`object` dtypes for system-level memory consumption.
Returns
-------
bytes used
See Also
--------
numpy.ndarray.nbytes : Total bytes consumed by the elements of the
array.
Notes
-----
Memory usage does not include memory consumed by elements that
are not components of the array if deep=False or if used on PyPy
"""
if hasattr(self.array, "memory_usage"):
return self.array.memory_usage( # pyright: ignore[reportGeneralTypeIssues]
deep=deep,
)
v = self.array.nbytes
if deep and is_object_dtype(self) and not PYPY:
values = cast(np.ndarray, self._values)
v += lib.memory_usage_of_objects(values)
return v
@doc(
algorithms.factorize,
values="",
order="",
size_hint="",
sort=textwrap.dedent(
"""\
sort : bool, default False
Sort `uniques` and shuffle `codes` to maintain the
relationship.
"""
),
)
def factorize(
self,
sort: bool = False,
use_na_sentinel: bool = True,
) -> tuple[npt.NDArray[np.intp], Index]:
codes, uniques = algorithms.factorize(
self._values, sort=sort, use_na_sentinel=use_na_sentinel
)
if uniques.dtype == np.float16:
uniques = uniques.astype(np.float32)
if isinstance(self, ABCIndex):
# preserve e.g. MultiIndex
uniques = self._constructor(uniques)
else:
from pandas import Index
uniques = Index(uniques)
return codes, uniques
_shared_docs[
"searchsorted"
] = """
Find indices where elements should be inserted to maintain order.
Find the indices into a sorted {klass} `self` such that, if the
corresponding elements in `value` were inserted before the indices,
the order of `self` would be preserved.
.. note::
The {klass} *must* be monotonically sorted, otherwise
wrong locations will likely be returned. Pandas does *not*
check this for you.
Parameters
----------
value : array-like or scalar
Values to insert into `self`.
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 `self` into ascending
order. They are typically the result of ``np.argsort``.
Returns
-------
int or array of int
A scalar or array of insertion points with the
same shape as `value`.
See Also
--------
sort_values : Sort by the values along either axis.
numpy.searchsorted : Similar method from NumPy.
Notes
-----
Binary search is used to find the required insertion points.
Examples
--------
>>> ser = pd.Series([1, 2, 3])
>>> ser
0 1
1 2
2 3
dtype: int64
>>> ser.searchsorted(4)
3
>>> ser.searchsorted([0, 4])
array([0, 3])
>>> ser.searchsorted([1, 3], side='left')
array([0, 2])
>>> ser.searchsorted([1, 3], side='right')
array([1, 3])
>>> ser = pd.Series(pd.to_datetime(['3/11/2000', '3/12/2000', '3/13/2000']))
>>> ser
0 2000-03-11
1 2000-03-12
2 2000-03-13
dtype: datetime64[ns]
>>> ser.searchsorted('3/14/2000')
3
>>> ser = pd.Categorical(
... ['apple', 'bread', 'bread', 'cheese', 'milk'], ordered=True
... )
>>> ser
['apple', 'bread', 'bread', 'cheese', 'milk']
Categories (4, object): ['apple' < 'bread' < 'cheese' < 'milk']
>>> ser.searchsorted('bread')
1
>>> ser.searchsorted(['bread'], side='right')
array([3])
If the values are not monotonically sorted, wrong locations
may be returned:
>>> ser = pd.Series([2, 1, 3])
>>> ser
0 2
1 1
2 3
dtype: int64
>>> ser.searchsorted(1) # doctest: +SKIP
0 # wrong result, correct would be 1
"""
# This overload is needed so that the call to searchsorted in
# pandas.core.resample.TimeGrouper._get_period_bins picks the correct result
@overload
# The following ignore is also present in numpy/__init__.pyi
# Possibly a mypy bug??
# error: Overloaded function signatures 1 and 2 overlap with incompatible
# return types [misc]
def searchsorted( # type: ignore[misc]
self,
value: ScalarLike_co,
side: Literal["left", "right"] = ...,
sorter: NumpySorter = ...,
) -> np.intp:
...
@overload
def searchsorted(
self,
value: npt.ArrayLike | ExtensionArray,
side: Literal["left", "right"] = ...,
sorter: NumpySorter = ...,
) -> npt.NDArray[np.intp]:
...
@doc(_shared_docs["searchsorted"], klass="Index")
def searchsorted(
self,
value: NumpyValueArrayLike | ExtensionArray,
side: Literal["left", "right"] = "left",
sorter: NumpySorter = None,
) -> npt.NDArray[np.intp] | np.intp:
if isinstance(value, ABCDataFrame):
msg = (
"Value must be 1-D array-like or scalar, "
f"{type(value).__name__} is not supported"
)
raise ValueError(msg)
values = self._values
if not isinstance(values, np.ndarray):
# Going through EA.searchsorted directly improves performance GH#38083
return values.searchsorted(value, side=side, sorter=sorter)
return algorithms.searchsorted(
values,
value,
side=side,
sorter=sorter,
)
def drop_duplicates(self, *, keep: DropKeep = "first"):
duplicated = self._duplicated(keep=keep)
# error: Value of type "IndexOpsMixin" is not indexable
return self[~duplicated] # type: ignore[index]
@final
def _duplicated(self, keep: DropKeep = "first") -> npt.NDArray[np.bool_]:
return algorithms.duplicated(self._values, keep=keep)
def _arith_method(self, other, op):
res_name = ops.get_op_result_name(self, other)
lvalues = self._values
rvalues = extract_array(other, extract_numpy=True, extract_range=True)
rvalues = ops.maybe_prepare_scalar_for_op(rvalues, lvalues.shape)
rvalues = ensure_wrapped_if_datetimelike(rvalues)
with np.errstate(all="ignore"):
result = ops.arithmetic_op(lvalues, rvalues, op)
return self._construct_result(result, name=res_name)
def _construct_result(self, result, name):
"""
Construct an appropriately-wrapped result from the ArrayLike result
of an arithmetic-like operation.
"""
raise AbstractMethodError(self)
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