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| Current File : //proc/thread-self/root/usr/local/lib/python3.8/dist-packages/pandas/core/base.py |
"""
Base and utility classes for pandas objects.
"""
import builtins
import textwrap
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
FrozenSet,
Optional,
TypeVar,
Union,
cast,
)
import numpy as np
import pandas._libs.lib as lib
from pandas._typing import DtypeObj, IndexLabel
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.common import (
is_categorical_dtype,
is_dict_like,
is_extension_array_dtype,
is_object_dtype,
is_scalar,
)
from pandas.core.dtypes.generic import ABCDataFrame, ABCIndexClass, ABCSeries
from pandas.core.dtypes.missing import isna, remove_na_arraylike
from pandas.core import algorithms
from pandas.core.accessor import DirNamesMixin
from pandas.core.algorithms import duplicated, unique1d, value_counts
from pandas.core.arraylike import OpsMixin
from pandas.core.arrays import ExtensionArray
from pandas.core.construction import create_series_with_explicit_dtype
import pandas.core.nanops as nanops
if TYPE_CHECKING:
from pandas import Categorical
_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.
"""
_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: Optional[str] = None) -> None:
"""
Reset cached properties. If ``key`` is passed, only clears that key.
"""
if getattr(self, "_cache", None) is None:
return
if key is None:
self._cache.clear()
else:
self._cache.pop(key, None)
def __sizeof__(self):
"""
Generates the total memory usage for an object that returns
either a value or Series of values
"""
if hasattr(self, "memory_usage"):
# pandas\core\base.py:84: error: "PandasObject" has no attribute
# "memory_usage" [attr-defined]
mem = self.memory_usage(deep=True) # type: ignore[attr-defined]
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):
"""
Prevents setting additional attributes.
"""
object.__setattr__(self, "__frozen", True)
# prevent adding any attribute via s.xxx.new_attribute = ...
def __setattr__(self, key: str, value):
# _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 DataError(Exception):
pass
class SpecificationError(Exception):
pass
class SelectionMixin:
"""
mixin implementing the selection & aggregation interface on a group-like
object sub-classes need to define: obj, exclusions
"""
_selection: Optional[IndexLabel] = None
_internal_names = ["_cache", "__setstate__"]
_internal_names_set = set(_internal_names)
_builtin_table = {builtins.sum: np.sum, builtins.max: np.max, builtins.min: np.min}
_cython_table = {
builtins.sum: "sum",
builtins.max: "max",
builtins.min: "min",
np.all: "all",
np.any: "any",
np.sum: "sum",
np.nansum: "sum",
np.mean: "mean",
np.nanmean: "mean",
np.prod: "prod",
np.nanprod: "prod",
np.std: "std",
np.nanstd: "std",
np.var: "var",
np.nanvar: "var",
np.median: "median",
np.nanmedian: "median",
np.max: "max",
np.nanmax: "max",
np.min: "min",
np.nanmin: "min",
np.cumprod: "cumprod",
np.nancumprod: "cumprod",
np.cumsum: "cumsum",
np.nancumsum: "cumsum",
}
@property
def _selection_name(self):
"""
Return a name for myself;
This would ideally be called the 'name' property,
but we cannot conflict with the Series.name property which can be set.
"""
return self._selection
@property
def _selection_list(self):
if not isinstance(
self._selection, (list, tuple, ABCSeries, ABCIndexClass, np.ndarray)
):
return [self._selection]
return self._selection
@cache_readonly
def _selected_obj(self):
# pandas\core\base.py:195: error: "SelectionMixin" has no attribute
# "obj" [attr-defined]
if self._selection is None or isinstance(
self.obj, ABCSeries # type: ignore[attr-defined]
):
# pandas\core\base.py:194: error: "SelectionMixin" has no attribute
# "obj" [attr-defined]
return self.obj # type: ignore[attr-defined]
else:
# pandas\core\base.py:204: error: "SelectionMixin" has no attribute
# "obj" [attr-defined]
return self.obj[self._selection] # type: ignore[attr-defined]
@cache_readonly
def ndim(self) -> int:
return self._selected_obj.ndim
@cache_readonly
def _obj_with_exclusions(self):
# pandas\core\base.py:209: error: "SelectionMixin" has no attribute
# "obj" [attr-defined]
if self._selection is not None and isinstance(
self.obj, ABCDataFrame # type: ignore[attr-defined]
):
# pandas\core\base.py:217: error: "SelectionMixin" has no attribute
# "obj" [attr-defined]
return self.obj.reindex( # type: ignore[attr-defined]
columns=self._selection_list
)
# pandas\core\base.py:207: error: "SelectionMixin" has no attribute
# "exclusions" [attr-defined]
if len(self.exclusions) > 0: # type: ignore[attr-defined]
# pandas\core\base.py:208: error: "SelectionMixin" has no attribute
# "obj" [attr-defined]
# pandas\core\base.py:208: error: "SelectionMixin" has no attribute
# "exclusions" [attr-defined]
return self.obj.drop(self.exclusions, axis=1) # type: ignore[attr-defined]
else:
# pandas\core\base.py:210: error: "SelectionMixin" has no attribute
# "obj" [attr-defined]
return self.obj # type: ignore[attr-defined]
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, ABCIndexClass, np.ndarray)):
# pandas\core\base.py:217: error: "SelectionMixin" has no attribute
# "obj" [attr-defined]
if len(
self.obj.columns.intersection(key) # type: ignore[attr-defined]
) != len(key):
# pandas\core\base.py:218: error: "SelectionMixin" has no
# attribute "obj" [attr-defined]
bad_keys = list(
set(key).difference(self.obj.columns) # type: ignore[attr-defined]
)
raise KeyError(f"Columns not found: {str(bad_keys)[1:-1]}")
return self._gotitem(list(key), ndim=2)
elif not getattr(self, "as_index", False):
# error: "SelectionMixin" has no attribute "obj" [attr-defined]
if key not in self.obj.columns: # type: ignore[attr-defined]
raise KeyError(f"Column not found: {key}")
return self._gotitem(key, ndim=2)
else:
# error: "SelectionMixin" has no attribute "obj" [attr-defined]
if key not in self.obj: # type: ignore[attr-defined]
raise KeyError(f"Column not found: {key}")
return self._gotitem(key, ndim=1)
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
def _try_aggregate_string_function(self, arg: str, *args, **kwargs):
"""
if arg is a string, then try to operate on it:
- try to find a function (or attribute) on ourselves
- try to find a numpy function
- raise
"""
assert isinstance(arg, str)
f = getattr(self, arg, None)
if f is not None:
if callable(f):
return f(*args, **kwargs)
# people may try to aggregate on a non-callable attribute
# but don't let them think they can pass args to it
assert len(args) == 0
assert len([kwarg for kwarg in kwargs if kwarg not in ["axis"]]) == 0
return f
f = getattr(np, arg, None)
if f is not None:
if hasattr(self, "__array__"):
# in particular exclude Window
return f(self, *args, **kwargs)
raise AttributeError(
f"'{arg}' is not a valid function for '{type(self).__name__}' object"
)
def _get_cython_func(self, arg: Callable) -> Optional[str]:
"""
if we define an internal function for this argument, return it
"""
return self._cython_table.get(arg)
def _is_builtin_func(self, arg):
"""
if we define an builtin function for this argument, return it,
otherwise return the arg
"""
return self._builtin_table.get(arg, arg)
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) -> Union[ExtensionArray, np.ndarray]:
# must be defined here as a property for mypy
raise AbstractMethodError(self)
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):
"""
Return a tuple of the shape of the underlying data.
"""
return self._values.shape
def __len__(self) -> int:
# We need this defined here for mypy
raise AbstractMethodError(self)
@property
def ndim(self) -> int:
"""
Number of dimensions of the underlying data, by definition 1.
"""
return 1
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.
.. versionadded:: 0.24.0
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)
def to_numpy(self, dtype=None, copy=False, na_value=lib.no_default, **kwargs):
"""
A NumPy ndarray representing the values in this Series or Index.
.. versionadded:: 0.24.0
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
--------
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', freq='D'),
Timestamp('2000-01-02 00:00:00+0100', tz='CET', freq='D')],
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):
# pandas\core\base.py:837: error: Too many arguments for "to_numpy"
# of "ExtensionArray" [call-arg]
return self.array.to_numpy( # type: ignore[call-arg]
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.
"""
nv.validate_minmax_axis(axis)
nv.validate_argmax_with_skipna(skipna, args, kwargs)
return nanops.nanargmax(self._values, 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:
nv.validate_minmax_axis(axis)
nv.validate_argmax_with_skipna(skipna, args, kwargs)
return nanops.nanargmin(self._values, 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.
"""
if not isinstance(self._values, np.ndarray):
# check for ndarray instead of dtype to catch DTA/TDA
return list(self._values)
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):
"""
Return if I have any nans; enables various perf 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)
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
# pandas\core\base.py:893: error: Incompatible types in
# assignment (expression has type "Categorical", variable has
# type "IndexOpsMixin") [assignment]
self = cast("Categorical", self) # type: ignore[assignment]
# pandas\core\base.py:894: error: Item "ExtensionArray" of
# "Union[ExtensionArray, Any]" has no attribute "map"
# [union-attr]
return self._values.map(mapper) # type: ignore[union-attr]
values = self._values
indexer = mapper.index.get_indexer(values)
new_values = algorithms.take_1d(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:
# pandas\core\base.py:1142: error: "IndexOpsMixin" has no attribute
# "astype" [attr-defined]
values = self.astype(object)._values # type: ignore[attr-defined]
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
2.0 1
4.0 1
1.0 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
2.0 0.2
4.0 0.2
1.0 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
2.0 1
NaN 1
4.0 1
1.0 1
dtype: int64
"""
result = value_counts(
self,
sort=sort,
ascending=ascending,
normalize=normalize,
bins=bins,
dropna=dropna,
)
return result
def unique(self):
values = self._values
if not isinstance(values, np.ndarray):
result = 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
"""
obj = remove_na_arraylike(self) if dropna else self
return len(obj.unique())
@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=False):
"""
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"):
# pandas\core\base.py:1379: error: "ExtensionArray" has no
# attribute "memory_usage" [attr-defined]
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:
v += lib.memory_usage_of_objects(self._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: Optional[int] = -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
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`.
.. versionchanged:: 0.24.0
If `value` is a scalar, an int is now always returned.
Previously, scalar inputs returned an 1-item array for
:class:`Series` and :class:`Categorical`.
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
"""
@doc(_shared_docs["searchsorted"], klass="Index")
def searchsorted(self, value, side="left", sorter=None) -> np.ndarray:
return algorithms.searchsorted(self._values, value, side=side, sorter=sorter)
def drop_duplicates(self, keep="first"):
duplicated = self.duplicated(keep=keep)
# pandas\core\base.py:1507: error: Value of type "IndexOpsMixin" is not
# indexable [index]
result = self[np.logical_not(duplicated)] # type: ignore[index]
return result
def duplicated(self, keep="first"):
return duplicated(self._values, keep=keep)