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1304 lines
38 KiB
1304 lines
38 KiB
"""
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|
Base and utility classes for pandas objects.
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|
"""
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|
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|
from __future__ import annotations
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|
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|
import textwrap
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from typing import (
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TYPE_CHECKING,
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Any,
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Generic,
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Hashable,
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|
Literal,
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TypeVar,
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cast,
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final,
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overload,
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)
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import numpy as np
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import pandas._libs.lib as lib
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from pandas._typing import (
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ArrayLike,
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DtypeObj,
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IndexLabel,
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NDFrameT,
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Shape,
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npt,
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)
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from pandas.compat import PYPY
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from pandas.compat.numpy import function as nv
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from pandas.errors import AbstractMethodError
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from pandas.util._decorators import (
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cache_readonly,
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doc,
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)
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from pandas.core.dtypes.common import (
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is_categorical_dtype,
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is_dict_like,
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is_extension_array_dtype,
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is_object_dtype,
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is_scalar,
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)
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from pandas.core.dtypes.generic import (
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ABCDataFrame,
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ABCIndex,
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ABCSeries,
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)
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from pandas.core.dtypes.missing import (
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isna,
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remove_na_arraylike,
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)
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|
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from pandas.core import (
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algorithms,
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nanops,
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ops,
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)
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from pandas.core.accessor import DirNamesMixin
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from pandas.core.algorithms import (
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duplicated,
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unique1d,
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value_counts,
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)
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from pandas.core.arraylike import OpsMixin
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from pandas.core.arrays import ExtensionArray
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from pandas.core.construction import (
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create_series_with_explicit_dtype,
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ensure_wrapped_if_datetimelike,
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extract_array,
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)
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if TYPE_CHECKING:
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from pandas._typing import (
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NumpySorter,
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NumpyValueArrayLike,
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)
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from pandas import Categorical
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|
|
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_shared_docs: dict[str, str] = {}
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_indexops_doc_kwargs = {
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"klass": "IndexOpsMixin",
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"inplace": "",
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"unique": "IndexOpsMixin",
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"duplicated": "IndexOpsMixin",
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}
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_T = TypeVar("_T", bound="IndexOpsMixin")
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|
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class PandasObject(DirNamesMixin):
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"""
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Baseclass for various pandas objects.
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"""
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# results from calls to methods decorated with cache_readonly get added to _cache
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_cache: dict[str, Any]
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@property
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def _constructor(self):
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"""
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Class constructor (for this class it's just `__class__`.
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"""
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return type(self)
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def __repr__(self) -> str:
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"""
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|
Return a string representation for a particular object.
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|
"""
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|
# Should be overwritten by base classes
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return object.__repr__(self)
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def _reset_cache(self, key: str | None = None) -> None:
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"""
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|
Reset cached properties. If ``key`` is passed, only clears that key.
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|
"""
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if not hasattr(self, "_cache"):
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return
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if key is None:
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self._cache.clear()
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else:
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self._cache.pop(key, None)
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|
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|
def __sizeof__(self) -> int:
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|
"""
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|
Generates the total memory usage for an object that returns
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|
either a value or Series of values
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|
"""
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|
memory_usage = getattr(self, "memory_usage", None)
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|
if memory_usage:
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mem = memory_usage(deep=True)
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return int(mem if is_scalar(mem) else mem.sum())
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|
|
# no memory_usage attribute, so fall back to object's 'sizeof'
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return super().__sizeof__()
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|
|
|
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|
class NoNewAttributesMixin:
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|
"""
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|
Mixin which prevents adding new attributes.
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|
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|
Prevents additional attributes via xxx.attribute = "something" after a
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call to `self.__freeze()`. Mainly used to prevent the user from using
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wrong attributes on an accessor (`Series.cat/.str/.dt`).
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|
If you really want to add a new attribute at a later time, you need to use
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`object.__setattr__(self, key, value)`.
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"""
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|
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def _freeze(self):
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"""
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Prevents setting additional attributes.
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|
"""
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|
object.__setattr__(self, "__frozen", True)
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# prevent adding any attribute via s.xxx.new_attribute = ...
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def __setattr__(self, key: str, value):
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# _cache is used by a decorator
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# We need to check both 1.) cls.__dict__ and 2.) getattr(self, key)
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# because
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|
# 1.) getattr is false for attributes that raise errors
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|
# 2.) cls.__dict__ doesn't traverse into base classes
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|
if getattr(self, "__frozen", False) and not (
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key == "_cache"
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or key in type(self).__dict__
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|
or getattr(self, key, None) is not None
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):
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raise AttributeError(f"You cannot add any new attribute '{key}'")
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object.__setattr__(self, key, value)
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|
|
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class DataError(Exception):
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pass
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|
|
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class SpecificationError(Exception):
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pass
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|
|
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class SelectionMixin(Generic[NDFrameT]):
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|
"""
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mixin implementing the selection & aggregation interface on a group-like
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object sub-classes need to define: obj, exclusions
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"""
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obj: NDFrameT
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_selection: IndexLabel | None = None
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exclusions: frozenset[Hashable]
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_internal_names = ["_cache", "__setstate__"]
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_internal_names_set = set(_internal_names)
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@final
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@property
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def _selection_list(self):
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if not isinstance(
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self._selection, (list, tuple, ABCSeries, ABCIndex, np.ndarray)
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):
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return [self._selection]
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return self._selection
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@cache_readonly
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def _selected_obj(self):
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if self._selection is None or isinstance(self.obj, ABCSeries):
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return self.obj
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else:
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return self.obj[self._selection]
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@final
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@cache_readonly
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def ndim(self) -> int:
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return self._selected_obj.ndim
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@final
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@cache_readonly
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def _obj_with_exclusions(self):
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if self._selection is not None and isinstance(self.obj, ABCDataFrame):
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return self.obj[self._selection_list]
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if len(self.exclusions) > 0:
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# equivalent to `self.obj.drop(self.exclusions, axis=1)
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# but this avoids consolidating and making a copy
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return self.obj._drop_axis(
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self.exclusions, axis=1, consolidate=False, only_slice=True
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)
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else:
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return self.obj
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def __getitem__(self, key):
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if self._selection is not None:
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raise IndexError(f"Column(s) {self._selection} already selected")
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if isinstance(key, (list, tuple, ABCSeries, ABCIndex, np.ndarray)):
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if len(self.obj.columns.intersection(key)) != len(set(key)):
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bad_keys = list(set(key).difference(self.obj.columns))
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raise KeyError(f"Columns not found: {str(bad_keys)[1:-1]}")
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return self._gotitem(list(key), ndim=2)
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elif not getattr(self, "as_index", False):
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if key not in self.obj.columns:
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raise KeyError(f"Column not found: {key}")
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return self._gotitem(key, ndim=2)
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else:
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if key not in self.obj:
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raise KeyError(f"Column not found: {key}")
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subset = self.obj[key]
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ndim = subset.ndim
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return self._gotitem(key, ndim=ndim, subset=subset)
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def _gotitem(self, key, ndim: int, subset=None):
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"""
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sub-classes to define
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return a sliced object
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Parameters
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----------
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key : str / list of selections
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ndim : {1, 2}
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requested ndim of result
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subset : object, default None
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subset to act on
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"""
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raise AbstractMethodError(self)
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def aggregate(self, func, *args, **kwargs):
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raise AbstractMethodError(self)
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agg = aggregate
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class IndexOpsMixin(OpsMixin):
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"""
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Common ops mixin to support a unified interface / docs for Series / Index
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"""
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# ndarray compatibility
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__array_priority__ = 1000
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_hidden_attrs: frozenset[str] = frozenset(
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["tolist"] # tolist is not deprecated, just suppressed in the __dir__
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)
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@property
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def dtype(self) -> DtypeObj:
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# must be defined here as a property for mypy
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raise AbstractMethodError(self)
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@property
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def _values(self) -> ExtensionArray | np.ndarray:
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# must be defined here as a property for mypy
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raise AbstractMethodError(self)
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def transpose(self: _T, *args, **kwargs) -> _T:
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|
"""
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|
Return the transpose, which is by definition self.
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|
Returns
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-------
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|
%(klass)s
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|
"""
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nv.validate_transpose(args, kwargs)
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return self
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T = property(
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transpose,
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doc="""
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Return the transpose, which is by definition self.
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""",
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)
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@property
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def shape(self) -> Shape:
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|
"""
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|
Return a tuple of the shape of the underlying data.
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|
"""
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|
return self._values.shape
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|
def __len__(self) -> int:
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# We need this defined here for mypy
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raise AbstractMethodError(self)
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|
@property
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def ndim(self) -> int:
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|
"""
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|
Number of dimensions of the underlying data, by definition 1.
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"""
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return 1
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|
def item(self):
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|
"""
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|
Return the first element of the underlying data as a Python scalar.
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|
Returns
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|
-------
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scalar
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The first element of %(klass)s.
|
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|
Raises
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|
------
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ValueError
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|
If the data is not length-1.
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"""
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if len(self) == 1:
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return next(iter(self))
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raise ValueError("can only convert an array of size 1 to a Python scalar")
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|
|
@property
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def nbytes(self) -> int:
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"""
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|
Return the number of bytes in the underlying data.
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|
"""
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return self._values.nbytes
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|
|
@property
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|
def size(self) -> int:
|
|
"""
|
|
Return the number of elements in the underlying data.
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|
"""
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|
return len(self._values)
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|
|
@property
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def array(self) -> ExtensionArray:
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|
"""
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|
The ExtensionArray of the data backing this Series or Index.
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|
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|
Returns
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|
-------
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ExtensionArray
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|
An ExtensionArray of the values stored within. For extension
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|
types, this is the actual array. For NumPy native types, this
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|
is a thin (no copy) wrapper around :class:`numpy.ndarray`.
|
|
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|
``.array`` differs ``.values`` which may require converting the
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|
data to a different form.
|
|
|
|
See Also
|
|
--------
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|
Index.to_numpy : Similar method that always returns a NumPy array.
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Series.to_numpy : Similar method that always returns a NumPy array.
|
|
|
|
Notes
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|
-----
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This table lays out the different array types for each extension
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dtype within pandas.
|
|
|
|
================== =============================
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dtype array type
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================== =============================
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category Categorical
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period PeriodArray
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interval IntervalArray
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IntegerNA IntegerArray
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string StringArray
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|
boolean BooleanArray
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datetime64[ns, tz] DatetimeArray
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|
================== =============================
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|
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|
For any 3rd-party extension types, the array type will be an
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ExtensionArray.
|
|
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|
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
|
|
--------
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For regular NumPy types like int, and float, a PandasArray
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is returned.
|
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|
>>> pd.Series([1, 2, 3]).array
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|
<PandasArray>
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[1, 2, 3]
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Length: 3, dtype: int64
|
|
|
|
For extension types, like Categorical, the actual ExtensionArray
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|
is returned
|
|
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|
>>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
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|
>>> ser.array
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['a', 'b', 'a']
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Categories (2, object): ['a', 'b']
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"""
|
|
raise AbstractMethodError(self)
|
|
|
|
def to_numpy(
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|
self,
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|
dtype: npt.DTypeLike | None = None,
|
|
copy: bool = False,
|
|
na_value=lib.no_default,
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|
**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.
|
|
|
|
.. versionadded:: 1.0.0
|
|
|
|
**kwargs
|
|
Additional keywords passed through to the ``to_numpy`` method
|
|
of the underlying array (for extension arrays).
|
|
|
|
.. versionadded:: 1.0.0
|
|
|
|
Returns
|
|
-------
|
|
numpy.ndarray
|
|
|
|
See Also
|
|
--------
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|
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}'"
|
|
)
|
|
|
|
result = np.asarray(self._values, dtype=dtype)
|
|
# TODO(GH-24345): Avoid potential double copy
|
|
if copy or na_value is not lib.no_default:
|
|
result = result.copy()
|
|
if na_value is not lib.no_default:
|
|
result[self.isna()] = na_value
|
|
return result
|
|
|
|
@property
|
|
def empty(self) -> bool:
|
|
return not self.size
|
|
|
|
def max(self, axis=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=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}}
|
|
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
|
|
-------
|
|
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=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=None, skipna=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):
|
|
"""
|
|
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.
|
|
"""
|
|
return bool(isna(self).any())
|
|
|
|
def isna(self):
|
|
return isna(self._values)
|
|
|
|
def _reduce(
|
|
self,
|
|
op,
|
|
name: str,
|
|
*,
|
|
axis=0,
|
|
skipna=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[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
|
|
mapper = create_series_with_explicit_dtype(
|
|
mapper, dtype_if_empty=np.float64
|
|
)
|
|
|
|
if isinstance(mapper, ABCSeries):
|
|
# 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
|
|
|
|
def value_counts(
|
|
self,
|
|
normalize: bool = False,
|
|
sort: bool = True,
|
|
ascending: bool = False,
|
|
bins=None,
|
|
dropna: bool = True,
|
|
):
|
|
"""
|
|
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
|
|
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
|
|
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
|
|
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
|
|
dtype: int64
|
|
"""
|
|
return 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):
|
|
result: ArrayLike = values.unique()
|
|
if self.dtype.kind in ["m", "M"] and isinstance(self, ABCSeries):
|
|
# GH#31182 Series._values returns EA, unpack for backward-compat
|
|
if getattr(self.dtype, "tz", None) is None:
|
|
result = np.asarray(result)
|
|
else:
|
|
result = unique1d(values)
|
|
|
|
return result
|
|
|
|
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(self) -> bool:
|
|
"""
|
|
Return boolean if values in the object are
|
|
monotonic_increasing.
|
|
|
|
Returns
|
|
-------
|
|
bool
|
|
"""
|
|
from pandas import Index
|
|
|
|
return Index(self).is_monotonic
|
|
|
|
@property
|
|
def is_monotonic_increasing(self) -> bool:
|
|
"""
|
|
Alias for is_monotonic.
|
|
"""
|
|
# mypy complains if we alias directly
|
|
return self.is_monotonic
|
|
|
|
@property
|
|
def is_monotonic_decreasing(self) -> bool:
|
|
"""
|
|
Return boolean if values in the object are
|
|
monotonic_decreasing.
|
|
|
|
Returns
|
|
-------
|
|
bool
|
|
"""
|
|
from pandas import Index
|
|
|
|
return Index(self).is_monotonic_decreasing
|
|
|
|
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"):
|
|
# https://github.com/python/mypy/issues/1424
|
|
# error: "ExtensionArray" has no attribute "memory_usage"
|
|
return self.array.memory_usage(deep=deep) # type: ignore[attr-defined]
|
|
|
|
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, na_sentinel: int | None = -1):
|
|
return algorithms.factorize(self, sort=sort, na_sentinel=na_sentinel)
|
|
|
|
_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
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|
-----
|
|
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)
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|
3
|
|
|
|
>>> ser.searchsorted([0, 4])
|
|
array([0, 3])
|
|
|
|
>>> ser.searchsorted([1, 3], side='left')
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|
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: npt._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:
|
|
|
|
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="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: Literal["first", "last", False] = "first"
|
|
) -> npt.NDArray[np.bool_]:
|
|
return 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)
|
|
|