""" Data structure for 1-dimensional cross-sectional and time series data """ from __future__ import annotations from textwrap import dedent from typing import ( IO, TYPE_CHECKING, Any, Callable, Hashable, Iterable, Literal, Mapping, Sequence, Union, cast, overload, ) import warnings import weakref import numpy as np from pandas._config import get_option from pandas._libs import ( lib, properties, reshape, tslibs, ) from pandas._libs.lib import no_default from pandas._typing import ( AggFuncType, AnyArrayLike, ArrayLike, Axis, Dtype, DtypeObj, FilePath, FillnaOptions, Frequency, IgnoreRaise, IndexKeyFunc, IndexLabel, Level, NaPosition, QuantileInterpolation, Renamer, SingleManager, SortKind, StorageOptions, TimedeltaConvertibleTypes, TimestampConvertibleTypes, ValueKeyFunc, WriteBuffer, npt, ) from pandas.compat.numpy import function as nv from pandas.errors import InvalidIndexError from pandas.util._decorators import ( Appender, Substitution, deprecate_kwarg, deprecate_nonkeyword_arguments, doc, ) from pandas.util._exceptions import find_stack_level from pandas.util._validators import ( validate_ascending, validate_bool_kwarg, validate_percentile, ) from pandas.core.dtypes.cast import ( LossySetitemError, convert_dtypes, maybe_box_native, maybe_cast_pointwise_result, ) from pandas.core.dtypes.common import ( ensure_platform_int, is_dict_like, is_integer, is_iterator, is_list_like, is_numeric_dtype, is_object_dtype, is_scalar, pandas_dtype, validate_all_hashable, ) from pandas.core.dtypes.generic import ABCDataFrame from pandas.core.dtypes.inference import is_hashable from pandas.core.dtypes.missing import ( isna, na_value_for_dtype, notna, remove_na_arraylike, ) from pandas.core import ( algorithms, base, common as com, missing, nanops, ops, ) from pandas.core.accessor import CachedAccessor from pandas.core.apply import SeriesApply from pandas.core.arrays import ExtensionArray from pandas.core.arrays.categorical import CategoricalAccessor from pandas.core.arrays.sparse import SparseAccessor from pandas.core.construction import ( create_series_with_explicit_dtype, extract_array, is_empty_data, sanitize_array, ) from pandas.core.generic import NDFrame from pandas.core.indexers import ( deprecate_ndim_indexing, unpack_1tuple, ) from pandas.core.indexes.accessors import CombinedDatetimelikeProperties from pandas.core.indexes.api import ( CategoricalIndex, DatetimeIndex, Float64Index, Index, MultiIndex, PeriodIndex, TimedeltaIndex, default_index, ensure_index, ) import pandas.core.indexes.base as ibase from pandas.core.indexing import ( check_bool_indexer, check_deprecated_indexers, ) from pandas.core.internals import ( SingleArrayManager, SingleBlockManager, ) from pandas.core.shared_docs import _shared_docs from pandas.core.sorting import ( ensure_key_mapped, nargsort, ) from pandas.core.strings import StringMethods from pandas.core.tools.datetimes import to_datetime import pandas.io.formats.format as fmt from pandas.io.formats.info import ( INFO_DOCSTRING, SeriesInfo, series_sub_kwargs, ) import pandas.plotting if TYPE_CHECKING: from pandas._typing import ( NumpySorter, NumpyValueArrayLike, Suffixes, ) from pandas.core.frame import DataFrame from pandas.core.groupby.generic import SeriesGroupBy from pandas.core.resample import Resampler __all__ = ["Series"] _shared_doc_kwargs = { "axes": "index", "klass": "Series", "axes_single_arg": "{0 or 'index'}", "axis": """axis : {0 or 'index'} Unused. Parameter needed for compatibility with DataFrame.""", "inplace": """inplace : bool, default False If True, performs operation inplace and returns None.""", "unique": "np.ndarray", "duplicated": "Series", "optional_by": "", "optional_mapper": "", "optional_labels": "", "optional_axis": "", "replace_iloc": """ This differs from updating with ``.loc`` or ``.iloc``, which require you to specify a location to update with some value.""", } def _coerce_method(converter): """ Install the scalar coercion methods. """ def wrapper(self): if len(self) == 1: return converter(self.iloc[0]) raise TypeError(f"cannot convert the series to {converter}") wrapper.__name__ = f"__{converter.__name__}__" return wrapper # ---------------------------------------------------------------------- # Series class class Series(base.IndexOpsMixin, NDFrame): """ One-dimensional ndarray with axis labels (including time series). Labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. Statistical methods from ndarray have been overridden to automatically exclude missing data (currently represented as NaN). Operations between Series (+, -, /, \\*, \\*\\*) align values based on their associated index values-- they need not be the same length. The result index will be the sorted union of the two indexes. Parameters ---------- data : array-like, Iterable, dict, or scalar value Contains data stored in Series. If data is a dict, argument order is maintained. index : array-like or Index (1d) Values must be hashable and have the same length as `data`. Non-unique index values are allowed. Will default to RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like and index is None, then the keys in the data are used as the index. If the index is not None, the resulting Series is reindexed with the index values. dtype : str, numpy.dtype, or ExtensionDtype, optional Data type for the output Series. If not specified, this will be inferred from `data`. See the :ref:`user guide ` for more usages. name : str, optional The name to give to the Series. copy : bool, default False Copy input data. Only affects Series or 1d ndarray input. See examples. Notes ----- Please reference the :ref:`User Guide ` for more information. Examples -------- Constructing Series from a dictionary with an Index specified >>> d = {'a': 1, 'b': 2, 'c': 3} >>> ser = pd.Series(data=d, index=['a', 'b', 'c']) >>> ser a 1 b 2 c 3 dtype: int64 The keys of the dictionary match with the Index values, hence the Index values have no effect. >>> d = {'a': 1, 'b': 2, 'c': 3} >>> ser = pd.Series(data=d, index=['x', 'y', 'z']) >>> ser x NaN y NaN z NaN dtype: float64 Note that the Index is first build with the keys from the dictionary. After this the Series is reindexed with the given Index values, hence we get all NaN as a result. Constructing Series from a list with `copy=False`. >>> r = [1, 2] >>> ser = pd.Series(r, copy=False) >>> ser.iloc[0] = 999 >>> r [1, 2] >>> ser 0 999 1 2 dtype: int64 Due to input data type the Series has a `copy` of the original data even though `copy=False`, so the data is unchanged. Constructing Series from a 1d ndarray with `copy=False`. >>> r = np.array([1, 2]) >>> ser = pd.Series(r, copy=False) >>> ser.iloc[0] = 999 >>> r array([999, 2]) >>> ser 0 999 1 2 dtype: int64 Due to input data type the Series has a `view` on the original data, so the data is changed as well. """ _typ = "series" _HANDLED_TYPES = (Index, ExtensionArray, np.ndarray) _name: Hashable _metadata: list[str] = ["name"] _internal_names_set = {"index"} | NDFrame._internal_names_set _accessors = {"dt", "cat", "str", "sparse"} _hidden_attrs = ( base.IndexOpsMixin._hidden_attrs | NDFrame._hidden_attrs | frozenset(["compress", "ptp"]) ) # Override cache_readonly bc Series is mutable # error: Incompatible types in assignment (expression has type "property", # base class "IndexOpsMixin" defined the type as "Callable[[IndexOpsMixin], bool]") hasnans = property( # type: ignore[assignment] # error: "Callable[[IndexOpsMixin], bool]" has no attribute "fget" base.IndexOpsMixin.hasnans.fget, # type: ignore[attr-defined] doc=base.IndexOpsMixin.hasnans.__doc__, ) _mgr: SingleManager div: Callable[[Series, Any], Series] rdiv: Callable[[Series, Any], Series] # ---------------------------------------------------------------------- # Constructors def __init__( self, data=None, index=None, dtype: Dtype | None = None, name=None, copy: bool = False, fastpath: bool = False, ) -> None: if ( isinstance(data, (SingleBlockManager, SingleArrayManager)) and index is None and dtype is None and copy is False ): # GH#33357 called with just the SingleBlockManager NDFrame.__init__(self, data) if fastpath: # e.g. from _box_col_values, skip validation of name object.__setattr__(self, "_name", name) else: self.name = name return # we are called internally, so short-circuit if fastpath: # data is an ndarray, index is defined if not isinstance(data, (SingleBlockManager, SingleArrayManager)): manager = get_option("mode.data_manager") if manager == "block": data = SingleBlockManager.from_array(data, index) elif manager == "array": data = SingleArrayManager.from_array(data, index) if copy: data = data.copy() if index is None: index = data.index else: name = ibase.maybe_extract_name(name, data, type(self)) if is_empty_data(data) and dtype is None: # gh-17261 warnings.warn( "The default dtype for empty Series will be 'object' instead " "of 'float64' in a future version. Specify a dtype explicitly " "to silence this warning.", FutureWarning, stacklevel=find_stack_level(), ) # uncomment the line below when removing the FutureWarning # dtype = np.dtype(object) if index is not None: index = ensure_index(index) if data is None: data = {} if dtype is not None: dtype = self._validate_dtype(dtype) if isinstance(data, MultiIndex): raise NotImplementedError( "initializing a Series from a MultiIndex is not supported" ) elif isinstance(data, Index): if dtype is not None: # astype copies data = data.astype(dtype) else: # GH#24096 we need to ensure the index remains immutable data = data._values.copy() copy = False elif isinstance(data, np.ndarray): if len(data.dtype): # GH#13296 we are dealing with a compound dtype, which # should be treated as 2D raise ValueError( "Cannot construct a Series from an ndarray with " "compound dtype. Use DataFrame instead." ) elif isinstance(data, Series): if index is None: index = data.index else: data = data.reindex(index, copy=copy) copy = False data = data._mgr elif is_dict_like(data): data, index = self._init_dict(data, index, dtype) dtype = None copy = False elif isinstance(data, (SingleBlockManager, SingleArrayManager)): if index is None: index = data.index elif not data.index.equals(index) or copy: # GH#19275 SingleBlockManager input should only be called # internally raise AssertionError( "Cannot pass both SingleBlockManager " "`data` argument and a different " "`index` argument. `copy` must be False." ) elif isinstance(data, ExtensionArray): pass else: data = com.maybe_iterable_to_list(data) if index is None: if not is_list_like(data): data = [data] index = default_index(len(data)) elif is_list_like(data): com.require_length_match(data, index) # create/copy the manager if isinstance(data, (SingleBlockManager, SingleArrayManager)): if dtype is not None: data = data.astype(dtype=dtype, errors="ignore", copy=copy) elif copy: data = data.copy() else: data = sanitize_array(data, index, dtype, copy) manager = get_option("mode.data_manager") if manager == "block": data = SingleBlockManager.from_array(data, index) elif manager == "array": data = SingleArrayManager.from_array(data, index) NDFrame.__init__(self, data) if fastpath: # skips validation of the name object.__setattr__(self, "_name", name) else: self.name = name self._set_axis(0, index) def _init_dict( self, data, index: Index | None = None, dtype: DtypeObj | None = None ): """ Derive the "_mgr" and "index" attributes of a new Series from a dictionary input. Parameters ---------- data : dict or dict-like Data used to populate the new Series. index : Index or None, default None Index for the new Series: if None, use dict keys. dtype : np.dtype, ExtensionDtype, or None, default None The dtype for the new Series: if None, infer from data. Returns ------- _data : BlockManager for the new Series index : index for the new Series """ keys: Index | tuple # Looking for NaN in dict doesn't work ({np.nan : 1}[float('nan')] # raises KeyError), so we iterate the entire dict, and align if data: # GH:34717, issue was using zip to extract key and values from data. # using generators in effects the performance. # Below is the new way of extracting the keys and values keys = tuple(data.keys()) values = list(data.values()) # Generating list of values- faster way elif index is not None: # fastpath for Series(data=None). Just use broadcasting a scalar # instead of reindexing. values = na_value_for_dtype(pandas_dtype(dtype), compat=False) keys = index else: keys, values = (), [] # Input is now list-like, so rely on "standard" construction: # TODO: passing np.float64 to not break anything yet. See GH-17261 s = create_series_with_explicit_dtype( # error: Argument "index" to "create_series_with_explicit_dtype" has # incompatible type "Tuple[Any, ...]"; expected "Union[ExtensionArray, # ndarray, Index, None]" values, index=keys, # type: ignore[arg-type] dtype=dtype, dtype_if_empty=np.float64, ) # Now we just make sure the order is respected, if any if data and index is not None: s = s.reindex(index, copy=False) return s._mgr, s.index # ---------------------------------------------------------------------- @property def _constructor(self) -> Callable[..., Series]: return Series @property def _constructor_expanddim(self) -> Callable[..., DataFrame]: """ Used when a manipulation result has one higher dimension as the original, such as Series.to_frame() """ from pandas.core.frame import DataFrame return DataFrame # types @property def _can_hold_na(self) -> bool: return self._mgr._can_hold_na def _set_axis(self, axis: int, labels: AnyArrayLike | list) -> None: """ Override generic, we want to set the _typ here. This is called from the cython code when we set the `index` attribute directly, e.g. `series.index = [1, 2, 3]`. """ labels = ensure_index(labels) if labels._is_all_dates and not ( type(labels) is Index and not isinstance(labels.dtype, np.dtype) ): # exclude e.g. timestamp[ns][pyarrow] dtype from this casting deep_labels = labels if isinstance(labels, CategoricalIndex): deep_labels = labels.categories if not isinstance( deep_labels, (DatetimeIndex, PeriodIndex, TimedeltaIndex) ): try: labels = DatetimeIndex(labels) except (tslibs.OutOfBoundsDatetime, ValueError): # labels may exceeds datetime bounds, # or not be a DatetimeIndex pass # The ensure_index call above ensures we have an Index object self._mgr.set_axis(axis, labels) # ndarray compatibility @property def dtype(self) -> DtypeObj: """ Return the dtype object of the underlying data. """ return self._mgr.dtype @property def dtypes(self) -> DtypeObj: """ Return the dtype object of the underlying data. """ # DataFrame compatibility return self.dtype @property def name(self) -> Hashable: """ Return the name of the Series. The name of a Series becomes its index or column name if it is used to form a DataFrame. It is also used whenever displaying the Series using the interpreter. Returns ------- label (hashable object) The name of the Series, also the column name if part of a DataFrame. See Also -------- Series.rename : Sets the Series name when given a scalar input. Index.name : Corresponding Index property. Examples -------- The Series name can be set initially when calling the constructor. >>> s = pd.Series([1, 2, 3], dtype=np.int64, name='Numbers') >>> s 0 1 1 2 2 3 Name: Numbers, dtype: int64 >>> s.name = "Integers" >>> s 0 1 1 2 2 3 Name: Integers, dtype: int64 The name of a Series within a DataFrame is its column name. >>> df = pd.DataFrame([[1, 2], [3, 4], [5, 6]], ... columns=["Odd Numbers", "Even Numbers"]) >>> df Odd Numbers Even Numbers 0 1 2 1 3 4 2 5 6 >>> df["Even Numbers"].name 'Even Numbers' """ return self._name @name.setter def name(self, value: Hashable) -> None: validate_all_hashable(value, error_name=f"{type(self).__name__}.name") object.__setattr__(self, "_name", value) @property def values(self): """ Return Series as ndarray or ndarray-like depending on the dtype. .. warning:: We recommend using :attr:`Series.array` or :meth:`Series.to_numpy`, depending on whether you need a reference to the underlying data or a NumPy array. Returns ------- numpy.ndarray or ndarray-like See Also -------- Series.array : Reference to the underlying data. Series.to_numpy : A NumPy array representing the underlying data. Examples -------- >>> pd.Series([1, 2, 3]).values array([1, 2, 3]) >>> pd.Series(list('aabc')).values array(['a', 'a', 'b', 'c'], dtype=object) >>> pd.Series(list('aabc')).astype('category').values ['a', 'a', 'b', 'c'] Categories (3, object): ['a', 'b', 'c'] Timezone aware datetime data is converted to UTC: >>> pd.Series(pd.date_range('20130101', periods=3, ... tz='US/Eastern')).values array(['2013-01-01T05:00:00.000000000', '2013-01-02T05:00:00.000000000', '2013-01-03T05:00:00.000000000'], dtype='datetime64[ns]') """ return self._mgr.external_values() @property def _values(self): """ Return the internal repr of this data (defined by Block.interval_values). This are the values as stored in the Block (ndarray or ExtensionArray depending on the Block class), with datetime64[ns] and timedelta64[ns] wrapped in ExtensionArrays to match Index._values behavior. Differs from the public ``.values`` for certain data types, because of historical backwards compatibility of the public attribute (e.g. period returns object ndarray and datetimetz a datetime64[ns] ndarray for ``.values`` while it returns an ExtensionArray for ``._values`` in those cases). Differs from ``.array`` in that this still returns the numpy array if the Block is backed by a numpy array (except for datetime64 and timedelta64 dtypes), while ``.array`` ensures to always return an ExtensionArray. Overview: dtype | values | _values | array | ----------- | ------------- | ------------- | ------------- | Numeric | ndarray | ndarray | PandasArray | Category | Categorical | Categorical | Categorical | dt64[ns] | ndarray[M8ns] | DatetimeArray | DatetimeArray | dt64[ns tz] | ndarray[M8ns] | DatetimeArray | DatetimeArray | td64[ns] | ndarray[m8ns] | TimedeltaArray| ndarray[m8ns] | Period | ndarray[obj] | PeriodArray | PeriodArray | Nullable | EA | EA | EA | """ return self._mgr.internal_values() # error: Decorated property not supported @Appender(base.IndexOpsMixin.array.__doc__) # type: ignore[misc] @property def array(self) -> ExtensionArray: return self._mgr.array_values() # ops def ravel(self, order: str = "C") -> np.ndarray: """ Return the flattened underlying data as an ndarray. Returns ------- numpy.ndarray or ndarray-like Flattened data of the Series. See Also -------- numpy.ndarray.ravel : Return a flattened array. """ return self._values.ravel(order=order) def __len__(self) -> int: """ Return the length of the Series. """ return len(self._mgr) def view(self, dtype: Dtype | None = None) -> Series: """ Create a new view of the Series. This function will return a new Series with a view of the same underlying values in memory, optionally reinterpreted with a new data type. The new data type must preserve the same size in bytes as to not cause index misalignment. Parameters ---------- dtype : data type Data type object or one of their string representations. Returns ------- Series A new Series object as a view of the same data in memory. See Also -------- numpy.ndarray.view : Equivalent numpy function to create a new view of the same data in memory. Notes ----- Series are instantiated with ``dtype=float64`` by default. While ``numpy.ndarray.view()`` will return a view with the same data type as the original array, ``Series.view()`` (without specified dtype) will try using ``float64`` and may fail if the original data type size in bytes is not the same. Examples -------- >>> s = pd.Series([-2, -1, 0, 1, 2], dtype='int8') >>> s 0 -2 1 -1 2 0 3 1 4 2 dtype: int8 The 8 bit signed integer representation of `-1` is `0b11111111`, but the same bytes represent 255 if read as an 8 bit unsigned integer: >>> us = s.view('uint8') >>> us 0 254 1 255 2 0 3 1 4 2 dtype: uint8 The views share the same underlying values: >>> us[0] = 128 >>> s 0 -128 1 -1 2 0 3 1 4 2 dtype: int8 """ # self.array instead of self._values so we piggyback on PandasArray # implementation res_values = self.array.view(dtype) res_ser = self._constructor(res_values, index=self.index) return res_ser.__finalize__(self, method="view") # ---------------------------------------------------------------------- # NDArray Compat _HANDLED_TYPES = (Index, ExtensionArray, np.ndarray) def __array__(self, dtype: npt.DTypeLike | None = None) -> np.ndarray: """ Return the values as a NumPy array. Users should not call this directly. Rather, it is invoked by :func:`numpy.array` and :func:`numpy.asarray`. Parameters ---------- dtype : str or numpy.dtype, optional The dtype to use for the resulting NumPy array. By default, the dtype is inferred from the data. Returns ------- numpy.ndarray The values in the series converted to a :class:`numpy.ndarray` with the specified `dtype`. See Also -------- array : Create a new array from data. Series.array : Zero-copy view to the array backing the Series. Series.to_numpy : Series method for similar behavior. Examples -------- >>> ser = pd.Series([1, 2, 3]) >>> np.asarray(ser) array([1, 2, 3]) For timezone-aware data, the timezones may be retained with ``dtype='object'`` >>> tzser = pd.Series(pd.date_range('2000', periods=2, tz="CET")) >>> np.asarray(tzser, 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 the values may be localized to UTC and the tzinfo discarded with ``dtype='datetime64[ns]'`` >>> np.asarray(tzser, dtype="datetime64[ns]") # doctest: +ELLIPSIS array(['1999-12-31T23:00:00.000000000', ...], dtype='datetime64[ns]') """ return np.asarray(self._values, dtype) # ---------------------------------------------------------------------- # Unary Methods # coercion __float__ = _coerce_method(float) __long__ = _coerce_method(int) __int__ = _coerce_method(int) # ---------------------------------------------------------------------- # indexers @property def axes(self) -> list[Index]: """ Return a list of the row axis labels. """ return [self.index] # ---------------------------------------------------------------------- # Indexing Methods @Appender(NDFrame.take.__doc__) def take( self, indices, axis: Axis = 0, is_copy: bool | None = None, **kwargs ) -> Series: if is_copy is not None: warnings.warn( "is_copy is deprecated and will be removed in a future version. " "'take' always returns a copy, so there is no need to specify this.", FutureWarning, stacklevel=find_stack_level(), ) nv.validate_take((), kwargs) indices = ensure_platform_int(indices) new_index = self.index.take(indices) new_values = self._values.take(indices) result = self._constructor(new_values, index=new_index, fastpath=True) return result.__finalize__(self, method="take") def _take_with_is_copy(self, indices, axis=0) -> Series: """ Internal version of the `take` method that sets the `_is_copy` attribute to keep track of the parent dataframe (using in indexing for the SettingWithCopyWarning). For Series this does the same as the public take (it never sets `_is_copy`). See the docstring of `take` for full explanation of the parameters. """ return self.take(indices=indices, axis=axis) def _ixs(self, i: int, axis: int = 0) -> Any: """ Return the i-th value or values in the Series by location. Parameters ---------- i : int Returns ------- scalar (int) or Series (slice, sequence) """ return self._values[i] def _slice(self, slobj: slice, axis: int = 0) -> Series: # axis kwarg is retained for compat with NDFrame method # _slice is *always* positional return self._get_values(slobj) def __getitem__(self, key): check_deprecated_indexers(key) key = com.apply_if_callable(key, self) if key is Ellipsis: return self key_is_scalar = is_scalar(key) if isinstance(key, (list, tuple)): key = unpack_1tuple(key) if is_integer(key) and self.index._should_fallback_to_positional: return self._values[key] elif key_is_scalar: return self._get_value(key) if is_hashable(key): # Otherwise index.get_value will raise InvalidIndexError try: # For labels that don't resolve as scalars like tuples and frozensets result = self._get_value(key) return result except (KeyError, TypeError, InvalidIndexError): # InvalidIndexError for e.g. generator # see test_series_getitem_corner_generator if isinstance(key, tuple) and isinstance(self.index, MultiIndex): # We still have the corner case where a tuple is a key # in the first level of our MultiIndex return self._get_values_tuple(key) if is_iterator(key): key = list(key) if com.is_bool_indexer(key): key = check_bool_indexer(self.index, key) key = np.asarray(key, dtype=bool) return self._get_values(key) return self._get_with(key) def _get_with(self, key): # other: fancy integer or otherwise if isinstance(key, slice): # _convert_slice_indexer to determine if this slice is positional # or label based, and if the latter, convert to positional slobj = self.index._convert_slice_indexer(key, kind="getitem") return self._slice(slobj) elif isinstance(key, ABCDataFrame): raise TypeError( "Indexing a Series with DataFrame is not " "supported, use the appropriate DataFrame column" ) elif isinstance(key, tuple): return self._get_values_tuple(key) elif not is_list_like(key): # e.g. scalars that aren't recognized by lib.is_scalar, GH#32684 return self.loc[key] if not isinstance(key, (list, np.ndarray, ExtensionArray, Series, Index)): key = list(key) if isinstance(key, Index): key_type = key.inferred_type else: key_type = lib.infer_dtype(key, skipna=False) # Note: The key_type == "boolean" case should be caught by the # com.is_bool_indexer check in __getitem__ if key_type == "integer": # We need to decide whether to treat this as a positional indexer # (i.e. self.iloc) or label-based (i.e. self.loc) if not self.index._should_fallback_to_positional: return self.loc[key] else: return self.iloc[key] # handle the dup indexing case GH#4246 return self.loc[key] def _get_values_tuple(self, key: tuple): # mpl hackaround if com.any_none(*key): # mpl compat if we look up e.g. ser[:, np.newaxis]; # see tests.series.timeseries.test_mpl_compat_hack # the asarray is needed to avoid returning a 2D DatetimeArray result = np.asarray(self._values[key]) deprecate_ndim_indexing(result, stacklevel=find_stack_level()) return result if not isinstance(self.index, MultiIndex): raise KeyError("key of type tuple not found and not a MultiIndex") # If key is contained, would have returned by now indexer, new_index = self.index.get_loc_level(key) return self._constructor(self._values[indexer], index=new_index).__finalize__( self ) def _get_values(self, indexer: slice | npt.NDArray[np.bool_]) -> Series: new_mgr = self._mgr.getitem_mgr(indexer) return self._constructor(new_mgr).__finalize__(self) def _get_value(self, label, takeable: bool = False): """ Quickly retrieve single value at passed index label. Parameters ---------- label : object takeable : interpret the index as indexers, default False Returns ------- scalar value """ if takeable: return self._values[label] # Similar to Index.get_value, but we do not fall back to positional loc = self.index.get_loc(label) return self.index._get_values_for_loc(self, loc, label) def __setitem__(self, key, value) -> None: check_deprecated_indexers(key) key = com.apply_if_callable(key, self) cacher_needs_updating = self._check_is_chained_assignment_possible() if key is Ellipsis: key = slice(None) if isinstance(key, slice): indexer = self.index._convert_slice_indexer(key, kind="getitem") return self._set_values(indexer, value) try: self._set_with_engine(key, value) except KeyError: # We have a scalar (or for MultiIndex or object-dtype, scalar-like) # key that is not present in self.index. if is_integer(key) and self.index.inferred_type != "integer": # positional setter if not self.index._should_fallback_to_positional: # GH#33469 warnings.warn( "Treating integers as positional in Series.__setitem__ " "with a Float64Index is deprecated. In a future version, " "`series[an_int] = val` will insert a new key into the " "Series. Use `series.iloc[an_int] = val` to treat the " "key as positional.", FutureWarning, stacklevel=find_stack_level(), ) # can't use _mgr.setitem_inplace yet bc could have *both* # KeyError and then ValueError, xref GH#45070 self._set_values(key, value) else: # GH#12862 adding a new key to the Series self.loc[key] = value except (TypeError, ValueError, LossySetitemError): # The key was OK, but we cannot set the value losslessly indexer = self.index.get_loc(key) self._set_values(indexer, value) except InvalidIndexError as err: if isinstance(key, tuple) and not isinstance(self.index, MultiIndex): # cases with MultiIndex don't get here bc they raise KeyError # e.g. test_basic_getitem_setitem_corner raise KeyError( "key of type tuple not found and not a MultiIndex" ) from err if com.is_bool_indexer(key): key = check_bool_indexer(self.index, key) key = np.asarray(key, dtype=bool) if ( is_list_like(value) and len(value) != len(self) and not isinstance(value, Series) and not is_object_dtype(self.dtype) ): # Series will be reindexed to have matching length inside # _where call below # GH#44265 indexer = key.nonzero()[0] self._set_values(indexer, value) return # otherwise with listlike other we interpret series[mask] = other # as series[mask] = other[mask] try: self._where(~key, value, inplace=True) except InvalidIndexError: # test_where_dups self.iloc[key] = value return else: self._set_with(key, value) if cacher_needs_updating: self._maybe_update_cacher(inplace=True) def _set_with_engine(self, key, value) -> None: loc = self.index.get_loc(key) # this is equivalent to self._values[key] = value self._mgr.setitem_inplace(loc, value) def _set_with(self, key, value): # We got here via exception-handling off of InvalidIndexError, so # key should always be listlike at this point. assert not isinstance(key, tuple) if is_iterator(key): # Without this, the call to infer_dtype will consume the generator key = list(key) if not self.index._should_fallback_to_positional: # Regardless of the key type, we're treating it as labels self._set_labels(key, value) else: # Note: key_type == "boolean" should not occur because that # should be caught by the is_bool_indexer check in __setitem__ key_type = lib.infer_dtype(key, skipna=False) if key_type == "integer": self._set_values(key, value) else: self._set_labels(key, value) def _set_labels(self, key, value) -> None: key = com.asarray_tuplesafe(key) indexer: np.ndarray = self.index.get_indexer(key) mask = indexer == -1 if mask.any(): raise KeyError(f"{key[mask]} not in index") self._set_values(indexer, value) def _set_values(self, key, value) -> None: if isinstance(key, (Index, Series)): key = key._values self._mgr = self._mgr.setitem(indexer=key, value=value) self._maybe_update_cacher() def _set_value(self, label, value, takeable: bool = False): """ Quickly set single value at passed label. If label is not contained, a new object is created with the label placed at the end of the result index. Parameters ---------- label : object Partial indexing with MultiIndex not allowed. value : object Scalar value. takeable : interpret the index as indexers, default False """ if not takeable: try: loc = self.index.get_loc(label) except KeyError: # set using a non-recursive method self.loc[label] = value return else: loc = label self._set_values(loc, value) # ---------------------------------------------------------------------- # Lookup Caching @property def _is_cached(self) -> bool: """Return boolean indicating if self is cached or not.""" return getattr(self, "_cacher", None) is not None def _get_cacher(self): """return my cacher or None""" cacher = getattr(self, "_cacher", None) if cacher is not None: cacher = cacher[1]() return cacher def _reset_cacher(self) -> None: """ Reset the cacher. """ if hasattr(self, "_cacher"): del self._cacher def _set_as_cached(self, item, cacher) -> None: """ Set the _cacher attribute on the calling object with a weakref to cacher. """ self._cacher = (item, weakref.ref(cacher)) def _clear_item_cache(self) -> None: # no-op for Series pass def _check_is_chained_assignment_possible(self) -> bool: """ See NDFrame._check_is_chained_assignment_possible.__doc__ """ if self._is_view and self._is_cached: ref = self._get_cacher() if ref is not None and ref._is_mixed_type: self._check_setitem_copy(t="referent", force=True) return True return super()._check_is_chained_assignment_possible() def _maybe_update_cacher( self, clear: bool = False, verify_is_copy: bool = True, inplace: bool = False ) -> None: """ See NDFrame._maybe_update_cacher.__doc__ """ cacher = getattr(self, "_cacher", None) if cacher is not None: assert self.ndim == 1 ref: DataFrame = cacher[1]() # we are trying to reference a dead referent, hence # a copy if ref is None: del self._cacher # for CoW, we never want to update the parent DataFrame cache # if the Series changed, and always pop the cached item elif ( not ( get_option("mode.copy_on_write") and get_option("mode.data_manager") == "block" ) and len(self) == len(ref) and self.name in ref.columns ): # GH#42530 self.name must be in ref.columns # to ensure column still in dataframe # otherwise, either self or ref has swapped in new arrays ref._maybe_cache_changed(cacher[0], self, inplace=inplace) else: # GH#33675 we have swapped in a new array, so parent # reference to self is now invalid ref._item_cache.pop(cacher[0], None) super()._maybe_update_cacher( clear=clear, verify_is_copy=verify_is_copy, inplace=inplace ) # ---------------------------------------------------------------------- # Unsorted @property def _is_mixed_type(self): return False def repeat(self, repeats: int | Sequence[int], axis: None = None) -> Series: """ Repeat elements of a Series. Returns a new Series where each element of the current Series is repeated consecutively a given number of times. Parameters ---------- repeats : int or array of ints The number of repetitions for each element. This should be a non-negative integer. Repeating 0 times will return an empty Series. axis : None Unused. Parameter needed for compatibility with DataFrame. Returns ------- Series Newly created Series with repeated elements. See Also -------- Index.repeat : Equivalent function for Index. numpy.repeat : Similar method for :class:`numpy.ndarray`. Examples -------- >>> s = pd.Series(['a', 'b', 'c']) >>> s 0 a 1 b 2 c dtype: object >>> s.repeat(2) 0 a 0 a 1 b 1 b 2 c 2 c dtype: object >>> s.repeat([1, 2, 3]) 0 a 1 b 1 b 2 c 2 c 2 c dtype: object """ nv.validate_repeat((), {"axis": axis}) new_index = self.index.repeat(repeats) new_values = self._values.repeat(repeats) return self._constructor(new_values, index=new_index).__finalize__( self, method="repeat" ) @overload def reset_index( self, level: IndexLabel = ..., *, drop: Literal[False] = ..., name: Level = ..., inplace: Literal[False] = ..., allow_duplicates: bool = ..., ) -> DataFrame: ... @overload def reset_index( self, level: IndexLabel = ..., *, drop: Literal[True], name: Level = ..., inplace: Literal[False] = ..., allow_duplicates: bool = ..., ) -> Series: ... @overload def reset_index( self, level: IndexLabel = ..., *, drop: bool = ..., name: Level = ..., inplace: Literal[True], allow_duplicates: bool = ..., ) -> None: ... @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "level"]) def reset_index( self, level: IndexLabel = None, drop: bool = False, name: Level = lib.no_default, inplace: bool = False, allow_duplicates: bool = False, ) -> DataFrame | Series | None: """ Generate a new DataFrame or Series with the index reset. This is useful when the index needs to be treated as a column, or when the index is meaningless and needs to be reset to the default before another operation. Parameters ---------- level : int, str, tuple, or list, default optional For a Series with a MultiIndex, only remove the specified levels from the index. Removes all levels by default. drop : bool, default False Just reset the index, without inserting it as a column in the new DataFrame. name : object, optional The name to use for the column containing the original Series values. Uses ``self.name`` by default. This argument is ignored when `drop` is True. inplace : bool, default False Modify the Series in place (do not create a new object). allow_duplicates : bool, default False Allow duplicate column labels to be created. .. versionadded:: 1.5.0 Returns ------- Series or DataFrame or None When `drop` is False (the default), a DataFrame is returned. The newly created columns will come first in the DataFrame, followed by the original Series values. When `drop` is True, a `Series` is returned. In either case, if ``inplace=True``, no value is returned. See Also -------- DataFrame.reset_index: Analogous function for DataFrame. Examples -------- >>> s = pd.Series([1, 2, 3, 4], name='foo', ... index=pd.Index(['a', 'b', 'c', 'd'], name='idx')) Generate a DataFrame with default index. >>> s.reset_index() idx foo 0 a 1 1 b 2 2 c 3 3 d 4 To specify the name of the new column use `name`. >>> s.reset_index(name='values') idx values 0 a 1 1 b 2 2 c 3 3 d 4 To generate a new Series with the default set `drop` to True. >>> s.reset_index(drop=True) 0 1 1 2 2 3 3 4 Name: foo, dtype: int64 To update the Series in place, without generating a new one set `inplace` to True. Note that it also requires ``drop=True``. >>> s.reset_index(inplace=True, drop=True) >>> s 0 1 1 2 2 3 3 4 Name: foo, dtype: int64 The `level` parameter is interesting for Series with a multi-level index. >>> arrays = [np.array(['bar', 'bar', 'baz', 'baz']), ... np.array(['one', 'two', 'one', 'two'])] >>> s2 = pd.Series( ... range(4), name='foo', ... index=pd.MultiIndex.from_arrays(arrays, ... names=['a', 'b'])) To remove a specific level from the Index, use `level`. >>> s2.reset_index(level='a') a foo b one bar 0 two bar 1 one baz 2 two baz 3 If `level` is not set, all levels are removed from the Index. >>> s2.reset_index() a b foo 0 bar one 0 1 bar two 1 2 baz one 2 3 baz two 3 """ inplace = validate_bool_kwarg(inplace, "inplace") if drop: new_index = default_index(len(self)) if level is not None: level_list: Sequence[Hashable] if not isinstance(level, (tuple, list)): level_list = [level] else: level_list = level level_list = [self.index._get_level_number(lev) for lev in level_list] if len(level_list) < self.index.nlevels: new_index = self.index.droplevel(level_list) if inplace: self.index = new_index else: return self._constructor( self._values.copy(), index=new_index ).__finalize__(self, method="reset_index") elif inplace: raise TypeError( "Cannot reset_index inplace on a Series to create a DataFrame" ) else: if name is lib.no_default: # For backwards compatibility, keep columns as [0] instead of # [None] when self.name is None if self.name is None: name = 0 else: name = self.name df = self.to_frame(name) return df.reset_index( level=level, drop=drop, allow_duplicates=allow_duplicates ) return None # ---------------------------------------------------------------------- # Rendering Methods def __repr__(self) -> str: """ Return a string representation for a particular Series. """ repr_params = fmt.get_series_repr_params() return self.to_string(**repr_params) @overload def to_string( self, buf: None = ..., na_rep: str = ..., float_format: str | None = ..., header: bool = ..., index: bool = ..., length=..., dtype=..., name=..., max_rows: int | None = ..., min_rows: int | None = ..., ) -> str: ... @overload def to_string( self, buf: FilePath | WriteBuffer[str], na_rep: str = ..., float_format: str | None = ..., header: bool = ..., index: bool = ..., length=..., dtype=..., name=..., max_rows: int | None = ..., min_rows: int | None = ..., ) -> None: ... def to_string( self, buf: FilePath | WriteBuffer[str] | None = None, na_rep: str = "NaN", float_format: str | None = None, header: bool = True, index: bool = True, length=False, dtype=False, name=False, max_rows: int | None = None, min_rows: int | None = None, ) -> str | None: """ Render a string representation of the Series. Parameters ---------- buf : StringIO-like, optional Buffer to write to. na_rep : str, optional String representation of NaN to use, default 'NaN'. float_format : one-parameter function, optional Formatter function to apply to columns' elements if they are floats, default None. header : bool, default True Add the Series header (index name). index : bool, optional Add index (row) labels, default True. length : bool, default False Add the Series length. dtype : bool, default False Add the Series dtype. name : bool, default False Add the Series name if not None. max_rows : int, optional Maximum number of rows to show before truncating. If None, show all. min_rows : int, optional The number of rows to display in a truncated repr (when number of rows is above `max_rows`). Returns ------- str or None String representation of Series if ``buf=None``, otherwise None. """ formatter = fmt.SeriesFormatter( self, name=name, length=length, header=header, index=index, dtype=dtype, na_rep=na_rep, float_format=float_format, min_rows=min_rows, max_rows=max_rows, ) result = formatter.to_string() # catch contract violations if not isinstance(result, str): raise AssertionError( "result must be of type str, type " f"of result is {repr(type(result).__name__)}" ) if buf is None: return result else: if hasattr(buf, "write"): # error: Item "str" of "Union[str, PathLike[str], WriteBuffer # [str]]" has no attribute "write" buf.write(result) # type: ignore[union-attr] else: # error: Argument 1 to "open" has incompatible type "Union[str, # PathLike[str], WriteBuffer[str]]"; expected "Union[Union[str, # bytes, PathLike[str], PathLike[bytes]], int]" with open(buf, "w") as f: # type: ignore[arg-type] f.write(result) return None @doc( klass=_shared_doc_kwargs["klass"], storage_options=_shared_docs["storage_options"], examples=dedent( """Examples -------- >>> s = pd.Series(["elk", "pig", "dog", "quetzal"], name="animal") >>> print(s.to_markdown()) | | animal | |---:|:---------| | 0 | elk | | 1 | pig | | 2 | dog | | 3 | quetzal | Output markdown with a tabulate option. >>> print(s.to_markdown(tablefmt="grid")) +----+----------+ | | animal | +====+==========+ | 0 | elk | +----+----------+ | 1 | pig | +----+----------+ | 2 | dog | +----+----------+ | 3 | quetzal | +----+----------+""" ), ) def to_markdown( self, buf: IO[str] | None = None, mode: str = "wt", index: bool = True, storage_options: StorageOptions = None, **kwargs, ) -> str | None: """ Print {klass} in Markdown-friendly format. .. versionadded:: 1.0.0 Parameters ---------- buf : str, Path or StringIO-like, optional, default None Buffer to write to. If None, the output is returned as a string. mode : str, optional Mode in which file is opened, "wt" by default. index : bool, optional, default True Add index (row) labels. .. versionadded:: 1.1.0 {storage_options} .. versionadded:: 1.2.0 **kwargs These parameters will be passed to `tabulate \ `_. Returns ------- str {klass} in Markdown-friendly format. Notes ----- Requires the `tabulate `_ package. {examples} """ return self.to_frame().to_markdown( buf, mode, index, storage_options=storage_options, **kwargs ) # ---------------------------------------------------------------------- def items(self) -> Iterable[tuple[Hashable, Any]]: """ Lazily iterate over (index, value) tuples. This method returns an iterable tuple (index, value). This is convenient if you want to create a lazy iterator. Returns ------- iterable Iterable of tuples containing the (index, value) pairs from a Series. See Also -------- DataFrame.items : Iterate over (column name, Series) pairs. DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs. Examples -------- >>> s = pd.Series(['A', 'B', 'C']) >>> for index, value in s.items(): ... print(f"Index : {index}, Value : {value}") Index : 0, Value : A Index : 1, Value : B Index : 2, Value : C """ return zip(iter(self.index), iter(self)) def iteritems(self) -> Iterable[tuple[Hashable, Any]]: """ Lazily iterate over (index, value) tuples. .. deprecated:: 1.5.0 iteritems is deprecated and will be removed in a future version. Use .items instead. This method returns an iterable tuple (index, value). This is convenient if you want to create a lazy iterator. Returns ------- iterable Iterable of tuples containing the (index, value) pairs from a Series. See Also -------- Series.items : Recommended alternative. DataFrame.items : Iterate over (column name, Series) pairs. DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs. """ warnings.warn( "iteritems is deprecated and will be removed in a future version. " "Use .items instead.", FutureWarning, stacklevel=find_stack_level(), ) return self.items() # ---------------------------------------------------------------------- # Misc public methods def keys(self) -> Index: """ Return alias for index. Returns ------- Index Index of the Series. """ return self.index def to_dict(self, into: type[dict] = dict) -> dict: """ Convert Series to {label -> value} dict or dict-like object. Parameters ---------- into : class, default dict The collections.abc.Mapping subclass to use as the return object. Can be the actual class or an empty instance of the mapping type you want. If you want a collections.defaultdict, you must pass it initialized. Returns ------- collections.abc.Mapping Key-value representation of Series. Examples -------- >>> s = pd.Series([1, 2, 3, 4]) >>> s.to_dict() {0: 1, 1: 2, 2: 3, 3: 4} >>> from collections import OrderedDict, defaultdict >>> s.to_dict(OrderedDict) OrderedDict([(0, 1), (1, 2), (2, 3), (3, 4)]) >>> dd = defaultdict(list) >>> s.to_dict(dd) defaultdict(, {0: 1, 1: 2, 2: 3, 3: 4}) """ # GH16122 into_c = com.standardize_mapping(into) return into_c((k, maybe_box_native(v)) for k, v in self.items()) def to_frame(self, name: Hashable = lib.no_default) -> DataFrame: """ Convert Series to DataFrame. Parameters ---------- name : object, optional The passed name should substitute for the series name (if it has one). Returns ------- DataFrame DataFrame representation of Series. Examples -------- >>> s = pd.Series(["a", "b", "c"], ... name="vals") >>> s.to_frame() vals 0 a 1 b 2 c """ if name is None: warnings.warn( "Explicitly passing `name=None` currently preserves the Series' name " "or uses a default name of 0. This behaviour is deprecated, and in " "the future `None` will be used as the name of the resulting " "DataFrame column.", FutureWarning, stacklevel=find_stack_level(), ) name = lib.no_default columns: Index if name is lib.no_default: name = self.name if name is None: # default to [0], same as we would get with DataFrame(self) columns = default_index(1) else: columns = Index([name]) else: columns = Index([name]) mgr = self._mgr.to_2d_mgr(columns) df = self._constructor_expanddim(mgr) return df.__finalize__(self, method="to_frame") def _set_name(self, name, inplace=False) -> Series: """ Set the Series name. Parameters ---------- name : str inplace : bool Whether to modify `self` directly or return a copy. """ inplace = validate_bool_kwarg(inplace, "inplace") ser = self if inplace else self.copy() ser.name = name return ser @Appender( """ Examples -------- >>> ser = pd.Series([390., 350., 30., 20.], ... index=['Falcon', 'Falcon', 'Parrot', 'Parrot'], name="Max Speed") >>> ser Falcon 390.0 Falcon 350.0 Parrot 30.0 Parrot 20.0 Name: Max Speed, dtype: float64 >>> ser.groupby(["a", "b", "a", "b"]).mean() a 210.0 b 185.0 Name: Max Speed, dtype: float64 >>> ser.groupby(level=0).mean() Falcon 370.0 Parrot 25.0 Name: Max Speed, dtype: float64 >>> ser.groupby(ser > 100).mean() Max Speed False 25.0 True 370.0 Name: Max Speed, dtype: float64 **Grouping by Indexes** We can groupby different levels of a hierarchical index using the `level` parameter: >>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'], ... ['Captive', 'Wild', 'Captive', 'Wild']] >>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type')) >>> ser = pd.Series([390., 350., 30., 20.], index=index, name="Max Speed") >>> ser Animal Type Falcon Captive 390.0 Wild 350.0 Parrot Captive 30.0 Wild 20.0 Name: Max Speed, dtype: float64 >>> ser.groupby(level=0).mean() Animal Falcon 370.0 Parrot 25.0 Name: Max Speed, dtype: float64 >>> ser.groupby(level="Type").mean() Type Captive 210.0 Wild 185.0 Name: Max Speed, dtype: float64 We can also choose to include `NA` in group keys or not by defining `dropna` parameter, the default setting is `True`. >>> ser = pd.Series([1, 2, 3, 3], index=["a", 'a', 'b', np.nan]) >>> ser.groupby(level=0).sum() a 3 b 3 dtype: int64 >>> ser.groupby(level=0, dropna=False).sum() a 3 b 3 NaN 3 dtype: int64 >>> arrays = ['Falcon', 'Falcon', 'Parrot', 'Parrot'] >>> ser = pd.Series([390., 350., 30., 20.], index=arrays, name="Max Speed") >>> ser.groupby(["a", "b", "a", np.nan]).mean() a 210.0 b 350.0 Name: Max Speed, dtype: float64 >>> ser.groupby(["a", "b", "a", np.nan], dropna=False).mean() a 210.0 b 350.0 NaN 20.0 Name: Max Speed, dtype: float64 """ ) @Appender(_shared_docs["groupby"] % _shared_doc_kwargs) def groupby( self, by=None, axis: Axis = 0, level: Level = None, as_index: bool = True, sort: bool = True, group_keys: bool | lib.NoDefault = no_default, squeeze: bool | lib.NoDefault = no_default, observed: bool = False, dropna: bool = True, ) -> SeriesGroupBy: from pandas.core.groupby.generic import SeriesGroupBy if squeeze is not no_default: warnings.warn( ( "The `squeeze` parameter is deprecated and " "will be removed in a future version." ), FutureWarning, stacklevel=find_stack_level(), ) else: squeeze = False if level is None and by is None: raise TypeError("You have to supply one of 'by' and 'level'") axis = self._get_axis_number(axis) return SeriesGroupBy( obj=self, keys=by, axis=axis, level=level, as_index=as_index, sort=sort, group_keys=group_keys, squeeze=squeeze, observed=observed, dropna=dropna, ) # ---------------------------------------------------------------------- # Statistics, overridden ndarray methods # TODO: integrate bottleneck def count(self, level: Level = None): """ Return number of non-NA/null observations in the Series. Parameters ---------- level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a smaller Series. Returns ------- int or Series (if level specified) Number of non-null values in the Series. See Also -------- DataFrame.count : Count non-NA cells for each column or row. Examples -------- >>> s = pd.Series([0.0, 1.0, np.nan]) >>> s.count() 2 """ if level is None: return notna(self._values).sum().astype("int64") else: warnings.warn( "Using the level keyword in DataFrame and Series aggregations is " "deprecated and will be removed in a future version. Use groupby " "instead. ser.count(level=1) should use ser.groupby(level=1).count().", FutureWarning, stacklevel=find_stack_level(), ) if not isinstance(self.index, MultiIndex): raise ValueError("Series.count level is only valid with a MultiIndex") index = self.index assert isinstance(index, MultiIndex) # for mypy if isinstance(level, str): level = index._get_level_number(level) lev = index.levels[level] level_codes = np.array(index.codes[level], subok=False, copy=True) mask = level_codes == -1 if mask.any(): level_codes[mask] = cnt = len(lev) lev = lev.insert(cnt, lev._na_value) obs = level_codes[notna(self._values)] # error: Argument "minlength" to "bincount" has incompatible type # "Optional[int]"; expected "SupportsIndex" out = np.bincount(obs, minlength=len(lev) or None) # type: ignore[arg-type] return self._constructor(out, index=lev, dtype="int64").__finalize__( self, method="count" ) def mode(self, dropna: bool = True) -> Series: """ Return the mode(s) of the Series. The mode is the value that appears most often. There can be multiple modes. Always returns Series even if only one value is returned. Parameters ---------- dropna : bool, default True Don't consider counts of NaN/NaT. Returns ------- Series Modes of the Series in sorted order. """ # TODO: Add option for bins like value_counts() values = self._values if isinstance(values, np.ndarray): res_values = algorithms.mode(values, dropna=dropna) else: res_values = values._mode(dropna=dropna) # Ensure index is type stable (should always use int index) return self._constructor( res_values, index=range(len(res_values)), name=self.name ) def unique(self) -> ArrayLike: """ Return unique values of Series object. Uniques are returned in order of appearance. Hash table-based unique, therefore does NOT sort. Returns ------- ndarray or ExtensionArray The unique values returned as a NumPy array. See Notes. See Also -------- Series.drop_duplicates : Return Series with duplicate values removed. unique : Top-level unique method for any 1-d array-like object. Index.unique : Return Index with unique values from an Index object. Notes ----- Returns the unique values as a NumPy array. In case of an extension-array backed Series, a new :class:`~api.extensions.ExtensionArray` of that type with just the unique values is returned. This includes * Categorical * Period * Datetime with Timezone * Interval * Sparse * IntegerNA See Examples section. Examples -------- >>> pd.Series([2, 1, 3, 3], name='A').unique() array([2, 1, 3]) >>> pd.Series([pd.Timestamp('2016-01-01') for _ in range(3)]).unique() array(['2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]') >>> pd.Series([pd.Timestamp('2016-01-01', tz='US/Eastern') ... for _ in range(3)]).unique() ['2016-01-01 00:00:00-05:00'] Length: 1, dtype: datetime64[ns, US/Eastern] An Categorical will return categories in the order of appearance and with the same dtype. >>> pd.Series(pd.Categorical(list('baabc'))).unique() ['b', 'a', 'c'] Categories (3, object): ['a', 'b', 'c'] >>> pd.Series(pd.Categorical(list('baabc'), categories=list('abc'), ... ordered=True)).unique() ['b', 'a', 'c'] Categories (3, object): ['a' < 'b' < 'c'] """ return super().unique() @overload def drop_duplicates( self, keep: Literal["first", "last", False] = ..., *, inplace: Literal[False] = ..., ) -> Series: ... @overload def drop_duplicates( self, keep: Literal["first", "last", False] = ..., *, inplace: Literal[True] ) -> None: ... @overload def drop_duplicates( self, keep: Literal["first", "last", False] = ..., *, inplace: bool = ... ) -> Series | None: ... @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) def drop_duplicates( self, keep: Literal["first", "last", False] = "first", inplace=False ) -> Series | None: """ Return Series with duplicate values removed. Parameters ---------- keep : {'first', 'last', ``False``}, default 'first' Method to handle dropping duplicates: - 'first' : Drop duplicates except for the first occurrence. - 'last' : Drop duplicates except for the last occurrence. - ``False`` : Drop all duplicates. inplace : bool, default ``False`` If ``True``, performs operation inplace and returns None. Returns ------- Series or None Series with duplicates dropped or None if ``inplace=True``. See Also -------- Index.drop_duplicates : Equivalent method on Index. DataFrame.drop_duplicates : Equivalent method on DataFrame. Series.duplicated : Related method on Series, indicating duplicate Series values. Series.unique : Return unique values as an array. Examples -------- Generate a Series with duplicated entries. >>> s = pd.Series(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'], ... name='animal') >>> s 0 lama 1 cow 2 lama 3 beetle 4 lama 5 hippo Name: animal, dtype: object With the 'keep' parameter, the selection behaviour of duplicated values can be changed. The value 'first' keeps the first occurrence for each set of duplicated entries. The default value of keep is 'first'. >>> s.drop_duplicates() 0 lama 1 cow 3 beetle 5 hippo Name: animal, dtype: object The value 'last' for parameter 'keep' keeps the last occurrence for each set of duplicated entries. >>> s.drop_duplicates(keep='last') 1 cow 3 beetle 4 lama 5 hippo Name: animal, dtype: object The value ``False`` for parameter 'keep' discards all sets of duplicated entries. Setting the value of 'inplace' to ``True`` performs the operation inplace and returns ``None``. >>> s.drop_duplicates(keep=False, inplace=True) >>> s 1 cow 3 beetle 5 hippo Name: animal, dtype: object """ inplace = validate_bool_kwarg(inplace, "inplace") result = super().drop_duplicates(keep=keep) if inplace: self._update_inplace(result) return None else: return result def duplicated(self, keep: Literal["first", "last", False] = "first") -> Series: """ Indicate duplicate Series values. Duplicated values are indicated as ``True`` values in the resulting Series. Either all duplicates, all except the first or all except the last occurrence of duplicates can be indicated. Parameters ---------- keep : {'first', 'last', False}, default 'first' Method to handle dropping duplicates: - 'first' : Mark duplicates as ``True`` except for the first occurrence. - 'last' : Mark duplicates as ``True`` except for the last occurrence. - ``False`` : Mark all duplicates as ``True``. Returns ------- Series[bool] Series indicating whether each value has occurred in the preceding values. See Also -------- Index.duplicated : Equivalent method on pandas.Index. DataFrame.duplicated : Equivalent method on pandas.DataFrame. Series.drop_duplicates : Remove duplicate values from Series. Examples -------- By default, for each set of duplicated values, the first occurrence is set on False and all others on True: >>> animals = pd.Series(['lama', 'cow', 'lama', 'beetle', 'lama']) >>> animals.duplicated() 0 False 1 False 2 True 3 False 4 True dtype: bool which is equivalent to >>> animals.duplicated(keep='first') 0 False 1 False 2 True 3 False 4 True dtype: bool By using 'last', the last occurrence of each set of duplicated values is set on False and all others on True: >>> animals.duplicated(keep='last') 0 True 1 False 2 True 3 False 4 False dtype: bool By setting keep on ``False``, all duplicates are True: >>> animals.duplicated(keep=False) 0 True 1 False 2 True 3 False 4 True dtype: bool """ res = self._duplicated(keep=keep) result = self._constructor(res, index=self.index) return result.__finalize__(self, method="duplicated") def idxmin(self, axis: Axis = 0, skipna: bool = True, *args, **kwargs) -> Hashable: """ Return the row label of the minimum value. If multiple values equal the minimum, the first row label with that value is returned. Parameters ---------- axis : {0 or 'index'} Unused. Parameter needed for compatibility with DataFrame. skipna : bool, default True Exclude NA/null values. If the entire Series is NA, the result will be NA. *args, **kwargs Additional arguments and keywords have no effect but might be accepted for compatibility with NumPy. Returns ------- Index Label of the minimum value. Raises ------ ValueError If the Series is empty. See Also -------- numpy.argmin : Return indices of the minimum values along the given axis. DataFrame.idxmin : Return index of first occurrence of minimum over requested axis. Series.idxmax : Return index *label* of the first occurrence of maximum of values. Notes ----- This method is the Series version of ``ndarray.argmin``. This method returns the label of the minimum, while ``ndarray.argmin`` returns the position. To get the position, use ``series.values.argmin()``. Examples -------- >>> s = pd.Series(data=[1, None, 4, 1], ... index=['A', 'B', 'C', 'D']) >>> s A 1.0 B NaN C 4.0 D 1.0 dtype: float64 >>> s.idxmin() 'A' If `skipna` is False and there is an NA value in the data, the function returns ``nan``. >>> s.idxmin(skipna=False) nan """ i = self.argmin(axis, skipna, *args, **kwargs) if i == -1: return np.nan return self.index[i] def idxmax(self, axis: Axis = 0, skipna: bool = True, *args, **kwargs) -> Hashable: """ Return the row label of the maximum value. If multiple values equal the maximum, the first row label with that value is returned. Parameters ---------- axis : {0 or 'index'} Unused. Parameter needed for compatibility with DataFrame. skipna : bool, default True Exclude NA/null values. If the entire Series is NA, the result will be NA. *args, **kwargs Additional arguments and keywords have no effect but might be accepted for compatibility with NumPy. Returns ------- Index Label of the maximum value. Raises ------ ValueError If the Series is empty. See Also -------- numpy.argmax : Return indices of the maximum values along the given axis. DataFrame.idxmax : Return index of first occurrence of maximum over requested axis. Series.idxmin : Return index *label* of the first occurrence of minimum of values. Notes ----- This method is the Series version of ``ndarray.argmax``. This method returns the label of the maximum, while ``ndarray.argmax`` returns the position. To get the position, use ``series.values.argmax()``. Examples -------- >>> s = pd.Series(data=[1, None, 4, 3, 4], ... index=['A', 'B', 'C', 'D', 'E']) >>> s A 1.0 B NaN C 4.0 D 3.0 E 4.0 dtype: float64 >>> s.idxmax() 'C' If `skipna` is False and there is an NA value in the data, the function returns ``nan``. >>> s.idxmax(skipna=False) nan """ i = self.argmax(axis, skipna, *args, **kwargs) if i == -1: return np.nan return self.index[i] def round(self, decimals: int = 0, *args, **kwargs) -> Series: """ Round each value in a Series to the given number of decimals. Parameters ---------- decimals : int, default 0 Number of decimal places to round to. If decimals is negative, it specifies the number of positions to the left of the decimal point. *args, **kwargs Additional arguments and keywords have no effect but might be accepted for compatibility with NumPy. Returns ------- Series Rounded values of the Series. See Also -------- numpy.around : Round values of an np.array. DataFrame.round : Round values of a DataFrame. Examples -------- >>> s = pd.Series([0.1, 1.3, 2.7]) >>> s.round() 0 0.0 1 1.0 2 3.0 dtype: float64 """ nv.validate_round(args, kwargs) result = self._values.round(decimals) result = self._constructor(result, index=self.index).__finalize__( self, method="round" ) return result @overload def quantile( self, q: float = ..., interpolation: QuantileInterpolation = ... ) -> float: ... @overload def quantile( self, q: Sequence[float] | AnyArrayLike, interpolation: QuantileInterpolation = ..., ) -> Series: ... @overload def quantile( self, q: float | Sequence[float] | AnyArrayLike = ..., interpolation: QuantileInterpolation = ..., ) -> float | Series: ... def quantile( self, q: float | Sequence[float] | AnyArrayLike = 0.5, interpolation: QuantileInterpolation = "linear", ) -> float | Series: """ Return value at the given quantile. Parameters ---------- q : float or array-like, default 0.5 (50% quantile) The quantile(s) to compute, which can lie in range: 0 <= q <= 1. interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points `i` and `j`: * linear: `i + (j - i) * fraction`, where `fraction` is the fractional part of the index surrounded by `i` and `j`. * lower: `i`. * higher: `j`. * nearest: `i` or `j` whichever is nearest. * midpoint: (`i` + `j`) / 2. Returns ------- float or Series If ``q`` is an array, a Series will be returned where the index is ``q`` and the values are the quantiles, otherwise a float will be returned. See Also -------- core.window.Rolling.quantile : Calculate the rolling quantile. numpy.percentile : Returns the q-th percentile(s) of the array elements. Examples -------- >>> s = pd.Series([1, 2, 3, 4]) >>> s.quantile(.5) 2.5 >>> s.quantile([.25, .5, .75]) 0.25 1.75 0.50 2.50 0.75 3.25 dtype: float64 """ validate_percentile(q) # We dispatch to DataFrame so that core.internals only has to worry # about 2D cases. df = self.to_frame() result = df.quantile(q=q, interpolation=interpolation, numeric_only=False) if result.ndim == 2: result = result.iloc[:, 0] if is_list_like(q): result.name = self.name return self._constructor(result, index=Float64Index(q), name=self.name) else: # scalar return result.iloc[0] def corr( self, other: Series, method: Literal["pearson", "kendall", "spearman"] | Callable[[np.ndarray, np.ndarray], float] = "pearson", min_periods: int | None = None, ) -> float: """ Compute correlation with `other` Series, excluding missing values. The two `Series` objects are not required to be the same length and will be aligned internally before the correlation function is applied. Parameters ---------- other : Series Series with which to compute the correlation. method : {'pearson', 'kendall', 'spearman'} or callable Method used to compute correlation: - pearson : Standard correlation coefficient - kendall : Kendall Tau correlation coefficient - spearman : Spearman rank correlation - callable: Callable with input two 1d ndarrays and returning a float. .. warning:: Note that the returned matrix from corr will have 1 along the diagonals and will be symmetric regardless of the callable's behavior. min_periods : int, optional Minimum number of observations needed to have a valid result. Returns ------- float Correlation with other. See Also -------- DataFrame.corr : Compute pairwise correlation between columns. DataFrame.corrwith : Compute pairwise correlation with another DataFrame or Series. Notes ----- Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations. * `Pearson correlation coefficient `_ * `Kendall rank correlation coefficient `_ * `Spearman's rank correlation coefficient `_ Examples -------- >>> def histogram_intersection(a, b): ... v = np.minimum(a, b).sum().round(decimals=1) ... return v >>> s1 = pd.Series([.2, .0, .6, .2]) >>> s2 = pd.Series([.3, .6, .0, .1]) >>> s1.corr(s2, method=histogram_intersection) 0.3 """ # noqa:E501 this, other = self.align(other, join="inner", copy=False) if len(this) == 0: return np.nan if method in ["pearson", "spearman", "kendall"] or callable(method): return nanops.nancorr( this.values, other.values, method=method, min_periods=min_periods ) raise ValueError( "method must be either 'pearson', " "'spearman', 'kendall', or a callable, " f"'{method}' was supplied" ) def cov( self, other: Series, min_periods: int | None = None, ddof: int | None = 1, ) -> float: """ Compute covariance with Series, excluding missing values. The two `Series` objects are not required to be the same length and will be aligned internally before the covariance is calculated. Parameters ---------- other : Series Series with which to compute the covariance. min_periods : int, optional Minimum number of observations needed to have a valid result. ddof : int, default 1 Delta degrees of freedom. The divisor used in calculations is ``N - ddof``, where ``N`` represents the number of elements. .. versionadded:: 1.1.0 Returns ------- float Covariance between Series and other normalized by N-1 (unbiased estimator). See Also -------- DataFrame.cov : Compute pairwise covariance of columns. Examples -------- >>> s1 = pd.Series([0.90010907, 0.13484424, 0.62036035]) >>> s2 = pd.Series([0.12528585, 0.26962463, 0.51111198]) >>> s1.cov(s2) -0.01685762652715874 """ this, other = self.align(other, join="inner", copy=False) if len(this) == 0: return np.nan return nanops.nancov( this.values, other.values, min_periods=min_periods, ddof=ddof ) @doc( klass="Series", extra_params="", other_klass="DataFrame", examples=dedent( """ Difference with previous row >>> s = pd.Series([1, 1, 2, 3, 5, 8]) >>> s.diff() 0 NaN 1 0.0 2 1.0 3 1.0 4 2.0 5 3.0 dtype: float64 Difference with 3rd previous row >>> s.diff(periods=3) 0 NaN 1 NaN 2 NaN 3 2.0 4 4.0 5 6.0 dtype: float64 Difference with following row >>> s.diff(periods=-1) 0 0.0 1 -1.0 2 -1.0 3 -2.0 4 -3.0 5 NaN dtype: float64 Overflow in input dtype >>> s = pd.Series([1, 0], dtype=np.uint8) >>> s.diff() 0 NaN 1 255.0 dtype: float64""" ), ) def diff(self, periods: int = 1) -> Series: """ First discrete difference of element. Calculates the difference of a {klass} element compared with another element in the {klass} (default is element in previous row). Parameters ---------- periods : int, default 1 Periods to shift for calculating difference, accepts negative values. {extra_params} Returns ------- {klass} First differences of the Series. See Also -------- {klass}.pct_change: Percent change over given number of periods. {klass}.shift: Shift index by desired number of periods with an optional time freq. {other_klass}.diff: First discrete difference of object. Notes ----- For boolean dtypes, this uses :meth:`operator.xor` rather than :meth:`operator.sub`. The result is calculated according to current dtype in {klass}, however dtype of the result is always float64. Examples -------- {examples} """ result = algorithms.diff(self._values, periods) return self._constructor(result, index=self.index).__finalize__( self, method="diff" ) def autocorr(self, lag: int = 1) -> float: """ Compute the lag-N autocorrelation. This method computes the Pearson correlation between the Series and its shifted self. Parameters ---------- lag : int, default 1 Number of lags to apply before performing autocorrelation. Returns ------- float The Pearson correlation between self and self.shift(lag). See Also -------- Series.corr : Compute the correlation between two Series. Series.shift : Shift index by desired number of periods. DataFrame.corr : Compute pairwise correlation of columns. DataFrame.corrwith : Compute pairwise correlation between rows or columns of two DataFrame objects. Notes ----- If the Pearson correlation is not well defined return 'NaN'. Examples -------- >>> s = pd.Series([0.25, 0.5, 0.2, -0.05]) >>> s.autocorr() # doctest: +ELLIPSIS 0.10355... >>> s.autocorr(lag=2) # doctest: +ELLIPSIS -0.99999... If the Pearson correlation is not well defined, then 'NaN' is returned. >>> s = pd.Series([1, 0, 0, 0]) >>> s.autocorr() nan """ return self.corr(self.shift(lag)) def dot(self, other: AnyArrayLike) -> Series | np.ndarray: """ Compute the dot product between the Series and the columns of other. This method computes the dot product between the Series and another one, or the Series and each columns of a DataFrame, or the Series and each columns of an array. It can also be called using `self @ other` in Python >= 3.5. Parameters ---------- other : Series, DataFrame or array-like The other object to compute the dot product with its columns. Returns ------- scalar, Series or numpy.ndarray Return the dot product of the Series and other if other is a Series, the Series of the dot product of Series and each rows of other if other is a DataFrame or a numpy.ndarray between the Series and each columns of the numpy array. See Also -------- DataFrame.dot: Compute the matrix product with the DataFrame. Series.mul: Multiplication of series and other, element-wise. Notes ----- The Series and other has to share the same index if other is a Series or a DataFrame. Examples -------- >>> s = pd.Series([0, 1, 2, 3]) >>> other = pd.Series([-1, 2, -3, 4]) >>> s.dot(other) 8 >>> s @ other 8 >>> df = pd.DataFrame([[0, 1], [-2, 3], [4, -5], [6, 7]]) >>> s.dot(df) 0 24 1 14 dtype: int64 >>> arr = np.array([[0, 1], [-2, 3], [4, -5], [6, 7]]) >>> s.dot(arr) array([24, 14]) """ if isinstance(other, (Series, ABCDataFrame)): common = self.index.union(other.index) if len(common) > len(self.index) or len(common) > len(other.index): raise ValueError("matrices are not aligned") left = self.reindex(index=common, copy=False) right = other.reindex(index=common, copy=False) lvals = left.values rvals = right.values else: lvals = self.values rvals = np.asarray(other) if lvals.shape[0] != rvals.shape[0]: raise Exception( f"Dot product shape mismatch, {lvals.shape} vs {rvals.shape}" ) if isinstance(other, ABCDataFrame): return self._constructor( np.dot(lvals, rvals), index=other.columns ).__finalize__(self, method="dot") elif isinstance(other, Series): return np.dot(lvals, rvals) elif isinstance(rvals, np.ndarray): return np.dot(lvals, rvals) else: # pragma: no cover raise TypeError(f"unsupported type: {type(other)}") def __matmul__(self, other): """ Matrix multiplication using binary `@` operator in Python>=3.5. """ return self.dot(other) def __rmatmul__(self, other): """ Matrix multiplication using binary `@` operator in Python>=3.5. """ return self.dot(np.transpose(other)) @doc(base.IndexOpsMixin.searchsorted, klass="Series") # Signature of "searchsorted" incompatible with supertype "IndexOpsMixin" def searchsorted( # type: ignore[override] self, value: NumpyValueArrayLike | ExtensionArray, side: Literal["left", "right"] = "left", sorter: NumpySorter = None, ) -> npt.NDArray[np.intp] | np.intp: return base.IndexOpsMixin.searchsorted(self, value, side=side, sorter=sorter) # ------------------------------------------------------------------- # Combination def append( self, to_append, ignore_index: bool = False, verify_integrity: bool = False ) -> Series: """ Concatenate two or more Series. .. deprecated:: 1.4.0 Use :func:`concat` instead. For further details see :ref:`whatsnew_140.deprecations.frame_series_append` Parameters ---------- to_append : Series or list/tuple of Series Series to append with self. ignore_index : bool, default False If True, the resulting axis will be labeled 0, 1, …, n - 1. verify_integrity : bool, default False If True, raise Exception on creating index with duplicates. Returns ------- Series Concatenated Series. See Also -------- concat : General function to concatenate DataFrame or Series objects. Notes ----- Iteratively appending to a Series can be more computationally intensive than a single concatenate. A better solution is to append values to a list and then concatenate the list with the original Series all at once. Examples -------- >>> s1 = pd.Series([1, 2, 3]) >>> s2 = pd.Series([4, 5, 6]) >>> s3 = pd.Series([4, 5, 6], index=[3, 4, 5]) >>> s1.append(s2) 0 1 1 2 2 3 0 4 1 5 2 6 dtype: int64 >>> s1.append(s3) 0 1 1 2 2 3 3 4 4 5 5 6 dtype: int64 With `ignore_index` set to True: >>> s1.append(s2, ignore_index=True) 0 1 1 2 2 3 3 4 4 5 5 6 dtype: int64 With `verify_integrity` set to True: >>> s1.append(s2, verify_integrity=True) Traceback (most recent call last): ... ValueError: Indexes have overlapping values: [0, 1, 2] """ warnings.warn( "The series.append method is deprecated " "and will be removed from pandas in a future version. " "Use pandas.concat instead.", FutureWarning, stacklevel=find_stack_level(), ) return self._append(to_append, ignore_index, verify_integrity) def _append( self, to_append, ignore_index: bool = False, verify_integrity: bool = False ): from pandas.core.reshape.concat import concat if isinstance(to_append, (list, tuple)): to_concat = [self] to_concat.extend(to_append) else: to_concat = [self, to_append] if any(isinstance(x, (ABCDataFrame,)) for x in to_concat[1:]): msg = "to_append should be a Series or list/tuple of Series, got DataFrame" raise TypeError(msg) return concat( to_concat, ignore_index=ignore_index, verify_integrity=verify_integrity ) def _binop(self, other: Series, func, level=None, fill_value=None): """ Perform generic binary operation with optional fill value. Parameters ---------- other : Series func : binary operator fill_value : float or object Value to substitute for NA/null values. If both Series are NA in a location, the result will be NA regardless of the passed fill value. level : int or level name, default None Broadcast across a level, matching Index values on the passed MultiIndex level. Returns ------- Series """ if not isinstance(other, Series): raise AssertionError("Other operand must be Series") this = self if not self.index.equals(other.index): this, other = self.align(other, level=level, join="outer", copy=False) this_vals, other_vals = ops.fill_binop(this._values, other._values, fill_value) with np.errstate(all="ignore"): result = func(this_vals, other_vals) name = ops.get_op_result_name(self, other) return this._construct_result(result, name) def _construct_result( self, result: ArrayLike | tuple[ArrayLike, ArrayLike], name: Hashable ) -> Series | tuple[Series, Series]: """ Construct an appropriately-labelled Series from the result of an op. Parameters ---------- result : ndarray or ExtensionArray name : Label Returns ------- Series In the case of __divmod__ or __rdivmod__, a 2-tuple of Series. """ if isinstance(result, tuple): # produced by divmod or rdivmod res1 = self._construct_result(result[0], name=name) res2 = self._construct_result(result[1], name=name) # GH#33427 assertions to keep mypy happy assert isinstance(res1, Series) assert isinstance(res2, Series) return (res1, res2) # We do not pass dtype to ensure that the Series constructor # does inference in the case where `result` has object-dtype. out = self._constructor(result, index=self.index) out = out.__finalize__(self) # Set the result's name after __finalize__ is called because __finalize__ # would set it back to self.name out.name = name return out @doc( _shared_docs["compare"], """ Returns ------- Series or DataFrame If axis is 0 or 'index' the result will be a Series. The resulting index will be a MultiIndex with 'self' and 'other' stacked alternately at the inner level. If axis is 1 or 'columns' the result will be a DataFrame. It will have two columns namely 'self' and 'other'. See Also -------- DataFrame.compare : Compare with another DataFrame and show differences. Notes ----- Matching NaNs will not appear as a difference. Examples -------- >>> s1 = pd.Series(["a", "b", "c", "d", "e"]) >>> s2 = pd.Series(["a", "a", "c", "b", "e"]) Align the differences on columns >>> s1.compare(s2) self other 1 b a 3 d b Stack the differences on indices >>> s1.compare(s2, align_axis=0) 1 self b other a 3 self d other b dtype: object Keep all original rows >>> s1.compare(s2, keep_shape=True) self other 0 NaN NaN 1 b a 2 NaN NaN 3 d b 4 NaN NaN Keep all original rows and also all original values >>> s1.compare(s2, keep_shape=True, keep_equal=True) self other 0 a a 1 b a 2 c c 3 d b 4 e e """, klass=_shared_doc_kwargs["klass"], ) def compare( self, other: Series, align_axis: Axis = 1, keep_shape: bool = False, keep_equal: bool = False, result_names: Suffixes = ("self", "other"), ) -> DataFrame | Series: return super().compare( other=other, align_axis=align_axis, keep_shape=keep_shape, keep_equal=keep_equal, result_names=result_names, ) def combine( self, other: Series | Hashable, func: Callable[[Hashable, Hashable], Hashable], fill_value: Hashable = None, ) -> Series: """ Combine the Series with a Series or scalar according to `func`. Combine the Series and `other` using `func` to perform elementwise selection for combined Series. `fill_value` is assumed when value is missing at some index from one of the two objects being combined. Parameters ---------- other : Series or scalar The value(s) to be combined with the `Series`. func : function Function that takes two scalars as inputs and returns an element. fill_value : scalar, optional The value to assume when an index is missing from one Series or the other. The default specifies to use the appropriate NaN value for the underlying dtype of the Series. Returns ------- Series The result of combining the Series with the other object. See Also -------- Series.combine_first : Combine Series values, choosing the calling Series' values first. Examples -------- Consider 2 Datasets ``s1`` and ``s2`` containing highest clocked speeds of different birds. >>> s1 = pd.Series({'falcon': 330.0, 'eagle': 160.0}) >>> s1 falcon 330.0 eagle 160.0 dtype: float64 >>> s2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0}) >>> s2 falcon 345.0 eagle 200.0 duck 30.0 dtype: float64 Now, to combine the two datasets and view the highest speeds of the birds across the two datasets >>> s1.combine(s2, max) duck NaN eagle 200.0 falcon 345.0 dtype: float64 In the previous example, the resulting value for duck is missing, because the maximum of a NaN and a float is a NaN. So, in the example, we set ``fill_value=0``, so the maximum value returned will be the value from some dataset. >>> s1.combine(s2, max, fill_value=0) duck 30.0 eagle 200.0 falcon 345.0 dtype: float64 """ if fill_value is None: fill_value = na_value_for_dtype(self.dtype, compat=False) if isinstance(other, Series): # If other is a Series, result is based on union of Series, # so do this element by element new_index = self.index.union(other.index) new_name = ops.get_op_result_name(self, other) new_values = np.empty(len(new_index), dtype=object) for i, idx in enumerate(new_index): lv = self.get(idx, fill_value) rv = other.get(idx, fill_value) with np.errstate(all="ignore"): new_values[i] = func(lv, rv) else: # Assume that other is a scalar, so apply the function for # each element in the Series new_index = self.index new_values = np.empty(len(new_index), dtype=object) with np.errstate(all="ignore"): new_values[:] = [func(lv, other) for lv in self._values] new_name = self.name # try_float=False is to match agg_series npvalues = lib.maybe_convert_objects(new_values, try_float=False) res_values = maybe_cast_pointwise_result(npvalues, self.dtype, same_dtype=False) return self._constructor(res_values, index=new_index, name=new_name) def combine_first(self, other) -> Series: """ Update null elements with value in the same location in 'other'. Combine two Series objects by filling null values in one Series with non-null values from the other Series. Result index will be the union of the two indexes. Parameters ---------- other : Series The value(s) to be used for filling null values. Returns ------- Series The result of combining the provided Series with the other object. See Also -------- Series.combine : Perform element-wise operation on two Series using a given function. Examples -------- >>> s1 = pd.Series([1, np.nan]) >>> s2 = pd.Series([3, 4, 5]) >>> s1.combine_first(s2) 0 1.0 1 4.0 2 5.0 dtype: float64 Null values still persist if the location of that null value does not exist in `other` >>> s1 = pd.Series({'falcon': np.nan, 'eagle': 160.0}) >>> s2 = pd.Series({'eagle': 200.0, 'duck': 30.0}) >>> s1.combine_first(s2) duck 30.0 eagle 160.0 falcon NaN dtype: float64 """ new_index = self.index.union(other.index) this = self.reindex(new_index, copy=False) other = other.reindex(new_index, copy=False) if this.dtype.kind == "M" and other.dtype.kind != "M": other = to_datetime(other) return this.where(notna(this), other) def update(self, other: Series | Sequence | Mapping) -> None: """ Modify Series in place using values from passed Series. Uses non-NA values from passed Series to make updates. Aligns on index. Parameters ---------- other : Series, or object coercible into Series Examples -------- >>> s = pd.Series([1, 2, 3]) >>> s.update(pd.Series([4, 5, 6])) >>> s 0 4 1 5 2 6 dtype: int64 >>> s = pd.Series(['a', 'b', 'c']) >>> s.update(pd.Series(['d', 'e'], index=[0, 2])) >>> s 0 d 1 b 2 e dtype: object >>> s = pd.Series([1, 2, 3]) >>> s.update(pd.Series([4, 5, 6, 7, 8])) >>> s 0 4 1 5 2 6 dtype: int64 If ``other`` contains NaNs the corresponding values are not updated in the original Series. >>> s = pd.Series([1, 2, 3]) >>> s.update(pd.Series([4, np.nan, 6])) >>> s 0 4 1 2 2 6 dtype: int64 ``other`` can also be a non-Series object type that is coercible into a Series >>> s = pd.Series([1, 2, 3]) >>> s.update([4, np.nan, 6]) >>> s 0 4 1 2 2 6 dtype: int64 >>> s = pd.Series([1, 2, 3]) >>> s.update({1: 9}) >>> s 0 1 1 9 2 3 dtype: int64 """ if not isinstance(other, Series): other = Series(other) other = other.reindex_like(self) mask = notna(other) self._mgr = self._mgr.putmask(mask=mask, new=other) self._maybe_update_cacher() # ---------------------------------------------------------------------- # Reindexing, sorting # error: Signature of "sort_values" incompatible with supertype "NDFrame" @overload # type: ignore[override] def sort_values( self, *, axis: Axis = ..., ascending: bool | int | Sequence[bool] | Sequence[int] = ..., inplace: Literal[False] = ..., kind: str = ..., na_position: str = ..., ignore_index: bool = ..., key: ValueKeyFunc = ..., ) -> Series: ... @overload def sort_values( self, *, axis: Axis = ..., ascending: bool | int | Sequence[bool] | Sequence[int] = ..., inplace: Literal[True], kind: str = ..., na_position: str = ..., ignore_index: bool = ..., key: ValueKeyFunc = ..., ) -> None: ... # error: Signature of "sort_values" incompatible with supertype "NDFrame" @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) def sort_values( # type: ignore[override] self, axis: Axis = 0, ascending: bool | int | Sequence[bool] | Sequence[int] = True, inplace: bool = False, kind: str = "quicksort", na_position: str = "last", ignore_index: bool = False, key: ValueKeyFunc = None, ) -> Series | None: """ Sort by the values. Sort a Series in ascending or descending order by some criterion. Parameters ---------- axis : {0 or 'index'} Unused. Parameter needed for compatibility with DataFrame. ascending : bool or list of bools, default True If True, sort values in ascending order, otherwise descending. inplace : bool, default False If True, perform operation in-place. kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort' Choice of sorting algorithm. See also :func:`numpy.sort` for more information. 'mergesort' and 'stable' are the only stable algorithms. na_position : {'first' or 'last'}, default 'last' Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at the end. ignore_index : bool, default False If True, the resulting axis will be labeled 0, 1, …, n - 1. .. versionadded:: 1.0.0 key : callable, optional If not None, apply the key function to the series values before sorting. This is similar to the `key` argument in the builtin :meth:`sorted` function, with the notable difference that this `key` function should be *vectorized*. It should expect a ``Series`` and return an array-like. .. versionadded:: 1.1.0 Returns ------- Series or None Series ordered by values or None if ``inplace=True``. See Also -------- Series.sort_index : Sort by the Series indices. DataFrame.sort_values : Sort DataFrame by the values along either axis. DataFrame.sort_index : Sort DataFrame by indices. Examples -------- >>> s = pd.Series([np.nan, 1, 3, 10, 5]) >>> s 0 NaN 1 1.0 2 3.0 3 10.0 4 5.0 dtype: float64 Sort values ascending order (default behaviour) >>> s.sort_values(ascending=True) 1 1.0 2 3.0 4 5.0 3 10.0 0 NaN dtype: float64 Sort values descending order >>> s.sort_values(ascending=False) 3 10.0 4 5.0 2 3.0 1 1.0 0 NaN dtype: float64 Sort values inplace >>> s.sort_values(ascending=False, inplace=True) >>> s 3 10.0 4 5.0 2 3.0 1 1.0 0 NaN dtype: float64 Sort values putting NAs first >>> s.sort_values(na_position='first') 0 NaN 1 1.0 2 3.0 4 5.0 3 10.0 dtype: float64 Sort a series of strings >>> s = pd.Series(['z', 'b', 'd', 'a', 'c']) >>> s 0 z 1 b 2 d 3 a 4 c dtype: object >>> s.sort_values() 3 a 1 b 4 c 2 d 0 z dtype: object Sort using a key function. Your `key` function will be given the ``Series`` of values and should return an array-like. >>> s = pd.Series(['a', 'B', 'c', 'D', 'e']) >>> s.sort_values() 1 B 3 D 0 a 2 c 4 e dtype: object >>> s.sort_values(key=lambda x: x.str.lower()) 0 a 1 B 2 c 3 D 4 e dtype: object NumPy ufuncs work well here. For example, we can sort by the ``sin`` of the value >>> s = pd.Series([-4, -2, 0, 2, 4]) >>> s.sort_values(key=np.sin) 1 -2 4 4 2 0 0 -4 3 2 dtype: int64 More complicated user-defined functions can be used, as long as they expect a Series and return an array-like >>> s.sort_values(key=lambda x: (np.tan(x.cumsum()))) 0 -4 3 2 4 4 1 -2 2 0 dtype: int64 """ inplace = validate_bool_kwarg(inplace, "inplace") # Validate the axis parameter self._get_axis_number(axis) # GH 5856/5853 if inplace and self._is_cached: raise ValueError( "This Series is a view of some other array, to " "sort in-place you must create a copy" ) if is_list_like(ascending): ascending = cast(Sequence[Union[bool, int]], ascending) if len(ascending) != 1: raise ValueError( f"Length of ascending ({len(ascending)}) must be 1 for Series" ) ascending = ascending[0] ascending = validate_ascending(ascending) if na_position not in ["first", "last"]: raise ValueError(f"invalid na_position: {na_position}") # GH 35922. Make sorting stable by leveraging nargsort values_to_sort = ensure_key_mapped(self, key)._values if key else self._values sorted_index = nargsort(values_to_sort, kind, bool(ascending), na_position) result = self._constructor( self._values[sorted_index], index=self.index[sorted_index] ) if ignore_index: result.index = default_index(len(sorted_index)) if not inplace: return result.__finalize__(self, method="sort_values") self._update_inplace(result) return None @overload def sort_index( self, *, axis: Axis = ..., level: IndexLabel = ..., ascending: bool | Sequence[bool] = ..., inplace: Literal[True], kind: SortKind = ..., na_position: NaPosition = ..., sort_remaining: bool = ..., ignore_index: bool = ..., key: IndexKeyFunc = ..., ) -> None: ... @overload def sort_index( self, *, axis: Axis = ..., level: IndexLabel = ..., ascending: bool | Sequence[bool] = ..., inplace: Literal[False] = ..., kind: SortKind = ..., na_position: NaPosition = ..., sort_remaining: bool = ..., ignore_index: bool = ..., key: IndexKeyFunc = ..., ) -> Series: ... @overload def sort_index( self, *, axis: Axis = ..., level: IndexLabel = ..., ascending: bool | Sequence[bool] = ..., inplace: bool = ..., kind: SortKind = ..., na_position: NaPosition = ..., sort_remaining: bool = ..., ignore_index: bool = ..., key: IndexKeyFunc = ..., ) -> Series | None: ... # error: Signature of "sort_index" incompatible with supertype "NDFrame" @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) def sort_index( # type: ignore[override] self, axis: Axis = 0, level: IndexLabel = None, ascending: bool | Sequence[bool] = True, inplace: bool = False, kind: SortKind = "quicksort", na_position: NaPosition = "last", sort_remaining: bool = True, ignore_index: bool = False, key: IndexKeyFunc = None, ) -> Series | None: """ Sort Series by index labels. Returns a new Series sorted by label if `inplace` argument is ``False``, otherwise updates the original series and returns None. Parameters ---------- axis : {0 or 'index'} Unused. Parameter needed for compatibility with DataFrame. level : int, optional If not None, sort on values in specified index level(s). ascending : bool or list-like of bools, default True Sort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually. inplace : bool, default False If True, perform operation in-place. kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort' Choice of sorting algorithm. See also :func:`numpy.sort` for more information. 'mergesort' and 'stable' are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label. na_position : {'first', 'last'}, default 'last' If 'first' puts NaNs at the beginning, 'last' puts NaNs at the end. Not implemented for MultiIndex. sort_remaining : bool, default True If True and sorting by level and index is multilevel, sort by other levels too (in order) after sorting by specified level. ignore_index : bool, default False If True, the resulting axis will be labeled 0, 1, …, n - 1. .. versionadded:: 1.0.0 key : callable, optional If not None, apply the key function to the index values before sorting. This is similar to the `key` argument in the builtin :meth:`sorted` function, with the notable difference that this `key` function should be *vectorized*. It should expect an ``Index`` and return an ``Index`` of the same shape. .. versionadded:: 1.1.0 Returns ------- Series or None The original Series sorted by the labels or None if ``inplace=True``. See Also -------- DataFrame.sort_index: Sort DataFrame by the index. DataFrame.sort_values: Sort DataFrame by the value. Series.sort_values : Sort Series by the value. Examples -------- >>> s = pd.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, 4]) >>> s.sort_index() 1 c 2 b 3 a 4 d dtype: object Sort Descending >>> s.sort_index(ascending=False) 4 d 3 a 2 b 1 c dtype: object Sort Inplace >>> s.sort_index(inplace=True) >>> s 1 c 2 b 3 a 4 d dtype: object By default NaNs are put at the end, but use `na_position` to place them at the beginning >>> s = pd.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, np.nan]) >>> s.sort_index(na_position='first') NaN d 1.0 c 2.0 b 3.0 a dtype: object Specify index level to sort >>> arrays = [np.array(['qux', 'qux', 'foo', 'foo', ... 'baz', 'baz', 'bar', 'bar']), ... np.array(['two', 'one', 'two', 'one', ... 'two', 'one', 'two', 'one'])] >>> s = pd.Series([1, 2, 3, 4, 5, 6, 7, 8], index=arrays) >>> s.sort_index(level=1) bar one 8 baz one 6 foo one 4 qux one 2 bar two 7 baz two 5 foo two 3 qux two 1 dtype: int64 Does not sort by remaining levels when sorting by levels >>> s.sort_index(level=1, sort_remaining=False) qux one 2 foo one 4 baz one 6 bar one 8 qux two 1 foo two 3 baz two 5 bar two 7 dtype: int64 Apply a key function before sorting >>> s = pd.Series([1, 2, 3, 4], index=['A', 'b', 'C', 'd']) >>> s.sort_index(key=lambda x : x.str.lower()) A 1 b 2 C 3 d 4 dtype: int64 """ return super().sort_index( axis=axis, level=level, ascending=ascending, inplace=inplace, kind=kind, na_position=na_position, sort_remaining=sort_remaining, ignore_index=ignore_index, key=key, ) def argsort( self, axis: Axis = 0, kind: SortKind = "quicksort", order: None = None, ) -> Series: """ Return the integer indices that would sort the Series values. Override ndarray.argsort. Argsorts the value, omitting NA/null values, and places the result in the same locations as the non-NA values. Parameters ---------- axis : {0 or 'index'} Unused. Parameter needed for compatibility with DataFrame. kind : {'mergesort', 'quicksort', 'heapsort', 'stable'}, default 'quicksort' Choice of sorting algorithm. See :func:`numpy.sort` for more information. 'mergesort' and 'stable' are the only stable algorithms. order : None Has no effect but is accepted for compatibility with numpy. Returns ------- Series[np.intp] Positions of values within the sort order with -1 indicating nan values. See Also -------- numpy.ndarray.argsort : Returns the indices that would sort this array. """ values = self._values mask = isna(values) if mask.any(): result = np.full(len(self), -1, dtype=np.intp) notmask = ~mask result[notmask] = np.argsort(values[notmask], kind=kind) else: result = np.argsort(values, kind=kind) res = self._constructor(result, index=self.index, name=self.name, dtype=np.intp) return res.__finalize__(self, method="argsort") def nlargest( self, n: int = 5, keep: Literal["first", "last", "all"] = "first" ) -> Series: """ Return the largest `n` elements. Parameters ---------- n : int, default 5 Return this many descending sorted values. keep : {'first', 'last', 'all'}, default 'first' When there are duplicate values that cannot all fit in a Series of `n` elements: - ``first`` : return the first `n` occurrences in order of appearance. - ``last`` : return the last `n` occurrences in reverse order of appearance. - ``all`` : keep all occurrences. This can result in a Series of size larger than `n`. Returns ------- Series The `n` largest values in the Series, sorted in decreasing order. See Also -------- Series.nsmallest: Get the `n` smallest elements. Series.sort_values: Sort Series by values. Series.head: Return the first `n` rows. Notes ----- Faster than ``.sort_values(ascending=False).head(n)`` for small `n` relative to the size of the ``Series`` object. Examples -------- >>> countries_population = {"Italy": 59000000, "France": 65000000, ... "Malta": 434000, "Maldives": 434000, ... "Brunei": 434000, "Iceland": 337000, ... "Nauru": 11300, "Tuvalu": 11300, ... "Anguilla": 11300, "Montserrat": 5200} >>> s = pd.Series(countries_population) >>> s Italy 59000000 France 65000000 Malta 434000 Maldives 434000 Brunei 434000 Iceland 337000 Nauru 11300 Tuvalu 11300 Anguilla 11300 Montserrat 5200 dtype: int64 The `n` largest elements where ``n=5`` by default. >>> s.nlargest() France 65000000 Italy 59000000 Malta 434000 Maldives 434000 Brunei 434000 dtype: int64 The `n` largest elements where ``n=3``. Default `keep` value is 'first' so Malta will be kept. >>> s.nlargest(3) France 65000000 Italy 59000000 Malta 434000 dtype: int64 The `n` largest elements where ``n=3`` and keeping the last duplicates. Brunei will be kept since it is the last with value 434000 based on the index order. >>> s.nlargest(3, keep='last') France 65000000 Italy 59000000 Brunei 434000 dtype: int64 The `n` largest elements where ``n=3`` with all duplicates kept. Note that the returned Series has five elements due to the three duplicates. >>> s.nlargest(3, keep='all') France 65000000 Italy 59000000 Malta 434000 Maldives 434000 Brunei 434000 dtype: int64 """ return algorithms.SelectNSeries(self, n=n, keep=keep).nlargest() def nsmallest(self, n: int = 5, keep: str = "first") -> Series: """ Return the smallest `n` elements. Parameters ---------- n : int, default 5 Return this many ascending sorted values. keep : {'first', 'last', 'all'}, default 'first' When there are duplicate values that cannot all fit in a Series of `n` elements: - ``first`` : return the first `n` occurrences in order of appearance. - ``last`` : return the last `n` occurrences in reverse order of appearance. - ``all`` : keep all occurrences. This can result in a Series of size larger than `n`. Returns ------- Series The `n` smallest values in the Series, sorted in increasing order. See Also -------- Series.nlargest: Get the `n` largest elements. Series.sort_values: Sort Series by values. Series.head: Return the first `n` rows. Notes ----- Faster than ``.sort_values().head(n)`` for small `n` relative to the size of the ``Series`` object. Examples -------- >>> countries_population = {"Italy": 59000000, "France": 65000000, ... "Brunei": 434000, "Malta": 434000, ... "Maldives": 434000, "Iceland": 337000, ... "Nauru": 11300, "Tuvalu": 11300, ... "Anguilla": 11300, "Montserrat": 5200} >>> s = pd.Series(countries_population) >>> s Italy 59000000 France 65000000 Brunei 434000 Malta 434000 Maldives 434000 Iceland 337000 Nauru 11300 Tuvalu 11300 Anguilla 11300 Montserrat 5200 dtype: int64 The `n` smallest elements where ``n=5`` by default. >>> s.nsmallest() Montserrat 5200 Nauru 11300 Tuvalu 11300 Anguilla 11300 Iceland 337000 dtype: int64 The `n` smallest elements where ``n=3``. Default `keep` value is 'first' so Nauru and Tuvalu will be kept. >>> s.nsmallest(3) Montserrat 5200 Nauru 11300 Tuvalu 11300 dtype: int64 The `n` smallest elements where ``n=3`` and keeping the last duplicates. Anguilla and Tuvalu will be kept since they are the last with value 11300 based on the index order. >>> s.nsmallest(3, keep='last') Montserrat 5200 Anguilla 11300 Tuvalu 11300 dtype: int64 The `n` smallest elements where ``n=3`` with all duplicates kept. Note that the returned Series has four elements due to the three duplicates. >>> s.nsmallest(3, keep='all') Montserrat 5200 Nauru 11300 Tuvalu 11300 Anguilla 11300 dtype: int64 """ return algorithms.SelectNSeries(self, n=n, keep=keep).nsmallest() @doc( klass=_shared_doc_kwargs["klass"], extra_params=dedent( """copy : bool, default True Whether to copy underlying data.""" ), examples=dedent( """\ Examples -------- >>> s = pd.Series( ... ["A", "B", "A", "C"], ... index=[ ... ["Final exam", "Final exam", "Coursework", "Coursework"], ... ["History", "Geography", "History", "Geography"], ... ["January", "February", "March", "April"], ... ], ... ) >>> s Final exam History January A Geography February B Coursework History March A Geography April C dtype: object In the following example, we will swap the levels of the indices. Here, we will swap the levels column-wise, but levels can be swapped row-wise in a similar manner. Note that column-wise is the default behaviour. By not supplying any arguments for i and j, we swap the last and second to last indices. >>> s.swaplevel() Final exam January History A February Geography B Coursework March History A April Geography C dtype: object By supplying one argument, we can choose which index to swap the last index with. We can for example swap the first index with the last one as follows. >>> s.swaplevel(0) January History Final exam A February Geography Final exam B March History Coursework A April Geography Coursework C dtype: object We can also define explicitly which indices we want to swap by supplying values for both i and j. Here, we for example swap the first and second indices. >>> s.swaplevel(0, 1) History Final exam January A Geography Final exam February B History Coursework March A Geography Coursework April C dtype: object""" ), ) def swaplevel(self, i: Level = -2, j: Level = -1, copy: bool = True) -> Series: """ Swap levels i and j in a :class:`MultiIndex`. Default is to swap the two innermost levels of the index. Parameters ---------- i, j : int or str Levels of the indices to be swapped. Can pass level name as string. {extra_params} Returns ------- {klass} {klass} with levels swapped in MultiIndex. {examples} """ assert isinstance(self.index, MultiIndex) new_index = self.index.swaplevel(i, j) return self._constructor(self._values, index=new_index, copy=copy).__finalize__( self, method="swaplevel" ) def reorder_levels(self, order: Sequence[Level]) -> Series: """ Rearrange index levels using input order. May not drop or duplicate levels. Parameters ---------- order : list of int representing new level order Reference level by number or key. Returns ------- type of caller (new object) """ if not isinstance(self.index, MultiIndex): # pragma: no cover raise Exception("Can only reorder levels on a hierarchical axis.") result = self.copy() assert isinstance(result.index, MultiIndex) result.index = result.index.reorder_levels(order) return result def explode(self, ignore_index: bool = False) -> Series: """ Transform each element of a list-like to a row. .. versionadded:: 0.25.0 Parameters ---------- ignore_index : bool, default False If True, the resulting index will be labeled 0, 1, …, n - 1. .. versionadded:: 1.1.0 Returns ------- Series Exploded lists to rows; index will be duplicated for these rows. See Also -------- Series.str.split : Split string values on specified separator. Series.unstack : Unstack, a.k.a. pivot, Series with MultiIndex to produce DataFrame. DataFrame.melt : Unpivot a DataFrame from wide format to long format. DataFrame.explode : Explode a DataFrame from list-like columns to long format. Notes ----- This routine will explode list-likes including lists, tuples, sets, Series, and np.ndarray. The result dtype of the subset rows will be object. Scalars will be returned unchanged, and empty list-likes will result in a np.nan for that row. In addition, the ordering of elements in the output will be non-deterministic when exploding sets. Reference :ref:`the user guide ` for more examples. Examples -------- >>> s = pd.Series([[1, 2, 3], 'foo', [], [3, 4]]) >>> s 0 [1, 2, 3] 1 foo 2 [] 3 [3, 4] dtype: object >>> s.explode() 0 1 0 2 0 3 1 foo 2 NaN 3 3 3 4 dtype: object """ if not len(self) or not is_object_dtype(self): result = self.copy() return result.reset_index(drop=True) if ignore_index else result values, counts = reshape.explode(np.asarray(self._values)) if ignore_index: index = default_index(len(values)) else: index = self.index.repeat(counts) return self._constructor(values, index=index, name=self.name) def unstack(self, level: IndexLabel = -1, fill_value: Hashable = None) -> DataFrame: """ Unstack, also known as pivot, Series with MultiIndex to produce DataFrame. Parameters ---------- level : int, str, or list of these, default last level Level(s) to unstack, can pass level name. fill_value : scalar value, default None Value to use when replacing NaN values. Returns ------- DataFrame Unstacked Series. Notes ----- Reference :ref:`the user guide ` for more examples. Examples -------- >>> s = pd.Series([1, 2, 3, 4], ... index=pd.MultiIndex.from_product([['one', 'two'], ... ['a', 'b']])) >>> s one a 1 b 2 two a 3 b 4 dtype: int64 >>> s.unstack(level=-1) a b one 1 2 two 3 4 >>> s.unstack(level=0) one two a 1 3 b 2 4 """ from pandas.core.reshape.reshape import unstack return unstack(self, level, fill_value) # ---------------------------------------------------------------------- # function application def map( self, arg: Callable | Mapping | Series, na_action: Literal["ignore"] | None = None, ) -> Series: """ Map values of Series according to an input mapping or function. Used for substituting each value in a Series with another value, that may be derived from a function, a ``dict`` or a :class:`Series`. Parameters ---------- arg : function, collections.abc.Mapping subclass or Series Mapping correspondence. na_action : {None, 'ignore'}, default None If 'ignore', propagate NaN values, without passing them to the mapping correspondence. Returns ------- Series Same index as caller. See Also -------- Series.apply : For applying more complex functions on a Series. DataFrame.apply : Apply a function row-/column-wise. DataFrame.applymap : Apply a function elementwise on a whole DataFrame. Notes ----- When ``arg`` is a dictionary, values in Series that are not in the dictionary (as keys) are converted to ``NaN``. However, if the dictionary is a ``dict`` subclass that defines ``__missing__`` (i.e. provides a method for default values), then this default is used rather than ``NaN``. Examples -------- >>> s = pd.Series(['cat', 'dog', np.nan, 'rabbit']) >>> s 0 cat 1 dog 2 NaN 3 rabbit dtype: object ``map`` accepts a ``dict`` or a ``Series``. Values that are not found in the ``dict`` are converted to ``NaN``, unless the dict has a default value (e.g. ``defaultdict``): >>> s.map({'cat': 'kitten', 'dog': 'puppy'}) 0 kitten 1 puppy 2 NaN 3 NaN dtype: object It also accepts a function: >>> s.map('I am a {}'.format) 0 I am a cat 1 I am a dog 2 I am a nan 3 I am a rabbit dtype: object To avoid applying the function to missing values (and keep them as ``NaN``) ``na_action='ignore'`` can be used: >>> s.map('I am a {}'.format, na_action='ignore') 0 I am a cat 1 I am a dog 2 NaN 3 I am a rabbit dtype: object """ new_values = self._map_values(arg, na_action=na_action) return self._constructor(new_values, index=self.index).__finalize__( self, method="map" ) def _gotitem(self, key, ndim, subset=None) -> Series: """ Sub-classes to define. Return a sliced object. Parameters ---------- key : string / list of selections ndim : {1, 2} Requested ndim of result. subset : object, default None Subset to act on. """ return self _agg_see_also_doc = dedent( """ See Also -------- Series.apply : Invoke function on a Series. Series.transform : Transform function producing a Series with like indexes. """ ) _agg_examples_doc = dedent( """ Examples -------- >>> s = pd.Series([1, 2, 3, 4]) >>> s 0 1 1 2 2 3 3 4 dtype: int64 >>> s.agg('min') 1 >>> s.agg(['min', 'max']) min 1 max 4 dtype: int64 """ ) @doc( _shared_docs["aggregate"], klass=_shared_doc_kwargs["klass"], axis=_shared_doc_kwargs["axis"], see_also=_agg_see_also_doc, examples=_agg_examples_doc, ) def aggregate(self, func=None, axis: Axis = 0, *args, **kwargs): # Validate the axis parameter self._get_axis_number(axis) # if func is None, will switch to user-provided "named aggregation" kwargs if func is None: func = dict(kwargs.items()) op = SeriesApply(self, func, convert_dtype=False, args=args, kwargs=kwargs) result = op.agg() return result agg = aggregate # error: Signature of "any" incompatible with supertype "NDFrame" [override] @overload # type: ignore[override] def any( self, *, axis: Axis = ..., bool_only: bool | None = ..., skipna: bool = ..., level: None = ..., **kwargs, ) -> bool: ... @overload def any( self, *, axis: Axis = ..., bool_only: bool | None = ..., skipna: bool = ..., level: Level, **kwargs, ) -> Series | bool: ... @doc(NDFrame.any, **_shared_doc_kwargs) def any( self, axis: Axis = 0, bool_only: bool | None = None, skipna: bool = True, level: Level | None = None, **kwargs, ) -> Series | bool: ... @doc( _shared_docs["transform"], klass=_shared_doc_kwargs["klass"], axis=_shared_doc_kwargs["axis"], ) def transform( self, func: AggFuncType, axis: Axis = 0, *args, **kwargs ) -> DataFrame | Series: # Validate axis argument self._get_axis_number(axis) result = SeriesApply( self, func=func, convert_dtype=True, args=args, kwargs=kwargs ).transform() return result def apply( self, func: AggFuncType, convert_dtype: bool = True, args: tuple[Any, ...] = (), **kwargs, ) -> DataFrame | Series: """ Invoke function on values of Series. Can be ufunc (a NumPy function that applies to the entire Series) or a Python function that only works on single values. Parameters ---------- func : function Python function or NumPy ufunc to apply. convert_dtype : bool, default True Try to find better dtype for elementwise function results. If False, leave as dtype=object. Note that the dtype is always preserved for some extension array dtypes, such as Categorical. args : tuple Positional arguments passed to func after the series value. **kwargs Additional keyword arguments passed to func. Returns ------- Series or DataFrame If func returns a Series object the result will be a DataFrame. See Also -------- Series.map: For element-wise operations. Series.agg: Only perform aggregating type operations. Series.transform: Only perform transforming type operations. Notes ----- Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See :ref:`gotchas.udf-mutation` for more details. Examples -------- Create a series with typical summer temperatures for each city. >>> s = pd.Series([20, 21, 12], ... index=['London', 'New York', 'Helsinki']) >>> s London 20 New York 21 Helsinki 12 dtype: int64 Square the values by defining a function and passing it as an argument to ``apply()``. >>> def square(x): ... return x ** 2 >>> s.apply(square) London 400 New York 441 Helsinki 144 dtype: int64 Square the values by passing an anonymous function as an argument to ``apply()``. >>> s.apply(lambda x: x ** 2) London 400 New York 441 Helsinki 144 dtype: int64 Define a custom function that needs additional positional arguments and pass these additional arguments using the ``args`` keyword. >>> def subtract_custom_value(x, custom_value): ... return x - custom_value >>> s.apply(subtract_custom_value, args=(5,)) London 15 New York 16 Helsinki 7 dtype: int64 Define a custom function that takes keyword arguments and pass these arguments to ``apply``. >>> def add_custom_values(x, **kwargs): ... for month in kwargs: ... x += kwargs[month] ... return x >>> s.apply(add_custom_values, june=30, july=20, august=25) London 95 New York 96 Helsinki 87 dtype: int64 Use a function from the Numpy library. >>> s.apply(np.log) London 2.995732 New York 3.044522 Helsinki 2.484907 dtype: float64 """ return SeriesApply(self, func, convert_dtype, args, kwargs).apply() def _reduce( self, op, name: str, *, axis=0, skipna=True, numeric_only=None, filter_type=None, **kwds, ): """ Perform a reduction operation. If we have an ndarray as a value, then simply perform the operation, otherwise delegate to the object. """ delegate = self._values if axis is not None: self._get_axis_number(axis) if isinstance(delegate, ExtensionArray): # dispatch to ExtensionArray interface return delegate._reduce(name, skipna=skipna, **kwds) else: # dispatch to numpy arrays if numeric_only and not is_numeric_dtype(self.dtype): kwd_name = "numeric_only" if name in ["any", "all"]: kwd_name = "bool_only" # GH#47500 - change to TypeError to match other methods warnings.warn( f"Calling Series.{name} with {kwd_name}={numeric_only} and " f"dtype {self.dtype} will raise a TypeError in the future", FutureWarning, stacklevel=find_stack_level(), ) raise NotImplementedError( f"Series.{name} does not implement {kwd_name}." ) with np.errstate(all="ignore"): return op(delegate, skipna=skipna, **kwds) def _reindex_indexer( self, new_index: Index | None, indexer: npt.NDArray[np.intp] | None, copy: bool ) -> Series: # Note: new_index is None iff indexer is None # if not None, indexer is np.intp if indexer is None and ( new_index is None or new_index.names == self.index.names ): if copy: return self.copy() return self new_values = algorithms.take_nd( self._values, indexer, allow_fill=True, fill_value=None ) return self._constructor(new_values, index=new_index) def _needs_reindex_multi(self, axes, method, level) -> bool: """ Check if we do need a multi reindex; this is for compat with higher dims. """ return False # error: Cannot determine type of 'align' @doc( NDFrame.align, # type: ignore[has-type] klass=_shared_doc_kwargs["klass"], axes_single_arg=_shared_doc_kwargs["axes_single_arg"], ) def align( self, other: Series, join: Literal["outer", "inner", "left", "right"] = "outer", axis: Axis | None = None, level: Level = None, copy: bool = True, fill_value: Hashable = None, method: FillnaOptions | None = None, limit: int | None = None, fill_axis: Axis = 0, broadcast_axis: Axis | None = None, ) -> Series: return super().align( other, join=join, axis=axis, level=level, copy=copy, fill_value=fill_value, method=method, limit=limit, fill_axis=fill_axis, broadcast_axis=broadcast_axis, ) @overload def rename( self, index: Renamer | Hashable | None = ..., *, axis: Axis | None = ..., copy: bool = ..., inplace: Literal[True], level: Level | None = ..., errors: IgnoreRaise = ..., ) -> None: ... @overload def rename( self, index: Renamer | Hashable | None = ..., *, axis: Axis | None = ..., copy: bool = ..., inplace: Literal[False] = ..., level: Level | None = ..., errors: IgnoreRaise = ..., ) -> Series: ... @overload def rename( self, index: Renamer | Hashable | None = ..., *, axis: Axis | None = ..., copy: bool = ..., inplace: bool = ..., level: Level | None = ..., errors: IgnoreRaise = ..., ) -> Series | None: ... def rename( self, index: Renamer | Hashable | None = None, *, axis: Axis | None = None, copy: bool = True, inplace: bool = False, level: Level | None = None, errors: IgnoreRaise = "ignore", ) -> Series | None: """ Alter Series index labels or name. Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don't throw an error. Alternatively, change ``Series.name`` with a scalar value. See the :ref:`user guide ` for more. Parameters ---------- index : scalar, hashable sequence, dict-like or function optional Functions or dict-like are transformations to apply to the index. Scalar or hashable sequence-like will alter the ``Series.name`` attribute. axis : {0 or 'index'} Unused. Parameter needed for compatibility with DataFrame. copy : bool, default True Also copy underlying data. inplace : bool, default False Whether to return a new Series. If True the value of copy is ignored. level : int or level name, default None In case of MultiIndex, only rename labels in the specified level. errors : {'ignore', 'raise'}, default 'ignore' If 'raise', raise `KeyError` when a `dict-like mapper` or `index` contains labels that are not present in the index being transformed. If 'ignore', existing keys will be renamed and extra keys will be ignored. Returns ------- Series or None Series with index labels or name altered or None if ``inplace=True``. See Also -------- DataFrame.rename : Corresponding DataFrame method. Series.rename_axis : Set the name of the axis. Examples -------- >>> s = pd.Series([1, 2, 3]) >>> s 0 1 1 2 2 3 dtype: int64 >>> s.rename("my_name") # scalar, changes Series.name 0 1 1 2 2 3 Name: my_name, dtype: int64 >>> s.rename(lambda x: x ** 2) # function, changes labels 0 1 1 2 4 3 dtype: int64 >>> s.rename({1: 3, 2: 5}) # mapping, changes labels 0 1 3 2 5 3 dtype: int64 """ if axis is not None: # Make sure we raise if an invalid 'axis' is passed. axis = self._get_axis_number(axis) if callable(index) or is_dict_like(index): # error: Argument 1 to "_rename" of "NDFrame" has incompatible # type "Union[Union[Mapping[Any, Hashable], Callable[[Any], # Hashable]], Hashable, None]"; expected "Union[Mapping[Any, # Hashable], Callable[[Any], Hashable], None]" return super()._rename( index, # type: ignore[arg-type] copy=copy, inplace=inplace, level=level, errors=errors, ) else: return self._set_name(index, inplace=inplace) @overload def set_axis( self, labels, *, axis: Axis = ..., inplace: Literal[False] | lib.NoDefault = ..., copy: bool | lib.NoDefault = ..., ) -> Series: ... @overload def set_axis( self, labels, *, axis: Axis = ..., inplace: Literal[True], copy: bool | lib.NoDefault = ..., ) -> None: ... @overload def set_axis( self, labels, *, axis: Axis = ..., inplace: bool | lib.NoDefault = ..., copy: bool | lib.NoDefault = ..., ) -> Series | None: ... # error: Signature of "set_axis" incompatible with supertype "NDFrame" @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"]) @Appender( """ Examples -------- >>> s = pd.Series([1, 2, 3]) >>> s 0 1 1 2 2 3 dtype: int64 >>> s.set_axis(['a', 'b', 'c'], axis=0) a 1 b 2 c 3 dtype: int64 """ ) @Substitution( **_shared_doc_kwargs, extended_summary_sub="", axis_description_sub="", see_also_sub="", ) @Appender(NDFrame.set_axis.__doc__) def set_axis( # type: ignore[override] self, labels, axis: Axis = 0, inplace: bool | lib.NoDefault = lib.no_default, copy: bool | lib.NoDefault = lib.no_default, ) -> Series | None: return super().set_axis(labels, axis=axis, inplace=inplace, copy=copy) # error: Cannot determine type of 'reindex' @doc( NDFrame.reindex, # type: ignore[has-type] klass=_shared_doc_kwargs["klass"], axes=_shared_doc_kwargs["axes"], optional_labels=_shared_doc_kwargs["optional_labels"], optional_axis=_shared_doc_kwargs["optional_axis"], ) def reindex(self, *args, **kwargs) -> Series: if len(args) > 1: raise TypeError("Only one positional argument ('index') is allowed") if args: (index,) = args if "index" in kwargs: raise TypeError( "'index' passed as both positional and keyword argument" ) kwargs.update({"index": index}) return super().reindex(**kwargs) @overload def drop( self, labels: IndexLabel = ..., *, axis: Axis = ..., index: IndexLabel = ..., columns: IndexLabel = ..., level: Level | None = ..., inplace: Literal[True], errors: IgnoreRaise = ..., ) -> None: ... @overload def drop( self, labels: IndexLabel = ..., *, axis: Axis = ..., index: IndexLabel = ..., columns: IndexLabel = ..., level: Level | None = ..., inplace: Literal[False] = ..., errors: IgnoreRaise = ..., ) -> Series: ... @overload def drop( self, labels: IndexLabel = ..., *, axis: Axis = ..., index: IndexLabel = ..., columns: IndexLabel = ..., level: Level | None = ..., inplace: bool = ..., errors: IgnoreRaise = ..., ) -> Series | None: ... # error: Signature of "drop" incompatible with supertype "NDFrame" # github.com/python/mypy/issues/12387 @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"]) def drop( # type: ignore[override] self, labels: IndexLabel = None, axis: Axis = 0, index: IndexLabel = None, columns: IndexLabel = None, level: Level | None = None, inplace: bool = False, errors: IgnoreRaise = "raise", ) -> Series | None: """ Return Series with specified index labels removed. Remove elements of a Series based on specifying the index labels. When using a multi-index, labels on different levels can be removed by specifying the level. Parameters ---------- labels : single label or list-like Index labels to drop. axis : {0 or 'index'} Unused. Parameter needed for compatibility with DataFrame. index : single label or list-like Redundant for application on Series, but 'index' can be used instead of 'labels'. columns : single label or list-like No change is made to the Series; use 'index' or 'labels' instead. level : int or level name, optional For MultiIndex, level for which the labels will be removed. inplace : bool, default False If True, do operation inplace and return None. errors : {'ignore', 'raise'}, default 'raise' If 'ignore', suppress error and only existing labels are dropped. Returns ------- Series or None Series with specified index labels removed or None if ``inplace=True``. Raises ------ KeyError If none of the labels are found in the index. See Also -------- Series.reindex : Return only specified index labels of Series. Series.dropna : Return series without null values. Series.drop_duplicates : Return Series with duplicate values removed. DataFrame.drop : Drop specified labels from rows or columns. Examples -------- >>> s = pd.Series(data=np.arange(3), index=['A', 'B', 'C']) >>> s A 0 B 1 C 2 dtype: int64 Drop labels B en C >>> s.drop(labels=['B', 'C']) A 0 dtype: int64 Drop 2nd level label in MultiIndex Series >>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'], ... ['speed', 'weight', 'length']], ... codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], ... [0, 1, 2, 0, 1, 2, 0, 1, 2]]) >>> s = pd.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3], ... index=midx) >>> s lama speed 45.0 weight 200.0 length 1.2 cow speed 30.0 weight 250.0 length 1.5 falcon speed 320.0 weight 1.0 length 0.3 dtype: float64 >>> s.drop(labels='weight', level=1) lama speed 45.0 length 1.2 cow speed 30.0 length 1.5 falcon speed 320.0 length 0.3 dtype: float64 """ return super().drop( labels=labels, axis=axis, index=index, columns=columns, level=level, inplace=inplace, errors=errors, ) @overload def fillna( self, value: Hashable | Mapping | Series | DataFrame = ..., *, method: FillnaOptions | None = ..., axis: Axis | None = ..., inplace: Literal[False] = ..., limit: int | None = ..., downcast: dict | None = ..., ) -> Series: ... @overload def fillna( self, value: Hashable | Mapping | Series | DataFrame = ..., *, method: FillnaOptions | None = ..., axis: Axis | None = ..., inplace: Literal[True], limit: int | None = ..., downcast: dict | None = ..., ) -> None: ... @overload def fillna( self, value: Hashable | Mapping | Series | DataFrame = ..., *, method: FillnaOptions | None = ..., axis: Axis | None = ..., inplace: bool = ..., limit: int | None = ..., downcast: dict | None = ..., ) -> Series | None: ... # error: Signature of "fillna" incompatible with supertype "NDFrame" @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "value"]) @doc(NDFrame.fillna, **_shared_doc_kwargs) def fillna( # type: ignore[override] self, value: Hashable | Mapping | Series | DataFrame = None, method: FillnaOptions | None = None, axis: Axis | None = None, inplace: bool = False, limit: int | None = None, downcast: dict | None = None, ) -> Series | None: return super().fillna( value=value, method=method, axis=axis, inplace=inplace, limit=limit, downcast=downcast, ) def pop(self, item: Hashable) -> Any: """ Return item and drops from series. Raise KeyError if not found. Parameters ---------- item : label Index of the element that needs to be removed. Returns ------- Value that is popped from series. Examples -------- >>> ser = pd.Series([1,2,3]) >>> ser.pop(0) 1 >>> ser 1 2 2 3 dtype: int64 """ return super().pop(item=item) # error: Signature of "replace" incompatible with supertype "NDFrame" @overload # type: ignore[override] def replace( self, to_replace=..., value=..., *, inplace: Literal[False] = ..., limit: int | None = ..., regex: bool = ..., method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = ..., ) -> Series: ... @overload def replace( self, to_replace=..., value=..., *, inplace: Literal[True], limit: int | None = ..., regex: bool = ..., method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = ..., ) -> None: ... # error: Signature of "replace" incompatible with supertype "NDFrame" @deprecate_nonkeyword_arguments( version=None, allowed_args=["self", "to_replace", "value"] ) @doc( NDFrame.replace, klass=_shared_doc_kwargs["klass"], inplace=_shared_doc_kwargs["inplace"], replace_iloc=_shared_doc_kwargs["replace_iloc"], ) def replace( # type: ignore[override] self, to_replace=None, value=lib.no_default, inplace: bool = False, limit: int | None = None, regex: bool = False, method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = lib.no_default, ) -> Series | None: return super().replace( to_replace=to_replace, value=value, inplace=inplace, limit=limit, regex=regex, method=method, ) @doc(INFO_DOCSTRING, **series_sub_kwargs) def info( self, verbose: bool | None = None, buf: IO[str] | None = None, max_cols: int | None = None, memory_usage: bool | str | None = None, show_counts: bool = True, ) -> None: return SeriesInfo(self, memory_usage).render( buf=buf, max_cols=max_cols, verbose=verbose, show_counts=show_counts, ) def _replace_single(self, to_replace, method: str, inplace: bool, limit): """ Replaces values in a Series using the fill method specified when no replacement value is given in the replace method """ result = self if inplace else self.copy() values = result._values mask = missing.mask_missing(values, to_replace) if isinstance(values, ExtensionArray): # dispatch to the EA's _pad_mask_inplace method values._fill_mask_inplace(method, limit, mask) else: fill_f = missing.get_fill_func(method) fill_f(values, limit=limit, mask=mask) if inplace: return return result # error: Cannot determine type of 'shift' @doc(NDFrame.shift, klass=_shared_doc_kwargs["klass"]) # type: ignore[has-type] def shift( self, periods: int = 1, freq=None, axis: Axis = 0, fill_value: Hashable = None ) -> Series: return super().shift( periods=periods, freq=freq, axis=axis, fill_value=fill_value ) def memory_usage(self, index: bool = True, deep: bool = False) -> int: """ Return the memory usage of the Series. The memory usage can optionally include the contribution of the index and of elements of `object` dtype. Parameters ---------- index : bool, default True Specifies whether to include the memory usage of the Series index. deep : bool, default False If True, introspect the data deeply by interrogating `object` dtypes for system-level memory consumption, and include it in the returned value. Returns ------- int Bytes of memory consumed. See Also -------- numpy.ndarray.nbytes : Total bytes consumed by the elements of the array. DataFrame.memory_usage : Bytes consumed by a DataFrame. Examples -------- >>> s = pd.Series(range(3)) >>> s.memory_usage() 152 Not including the index gives the size of the rest of the data, which is necessarily smaller: >>> s.memory_usage(index=False) 24 The memory footprint of `object` values is ignored by default: >>> s = pd.Series(["a", "b"]) >>> s.values array(['a', 'b'], dtype=object) >>> s.memory_usage() 144 >>> s.memory_usage(deep=True) 244 """ v = self._memory_usage(deep=deep) if index: v += self.index.memory_usage(deep=deep) return v def isin(self, values) -> Series: """ Whether elements in Series are contained in `values`. Return a boolean Series showing whether each element in the Series matches an element in the passed sequence of `values` exactly. Parameters ---------- values : set or list-like The sequence of values to test. Passing in a single string will raise a ``TypeError``. Instead, turn a single string into a list of one element. Returns ------- Series Series of booleans indicating if each element is in values. Raises ------ TypeError * If `values` is a string See Also -------- DataFrame.isin : Equivalent method on DataFrame. Examples -------- >>> s = pd.Series(['lama', 'cow', 'lama', 'beetle', 'lama', ... 'hippo'], name='animal') >>> s.isin(['cow', 'lama']) 0 True 1 True 2 True 3 False 4 True 5 False Name: animal, dtype: bool To invert the boolean values, use the ``~`` operator: >>> ~s.isin(['cow', 'lama']) 0 False 1 False 2 False 3 True 4 False 5 True Name: animal, dtype: bool Passing a single string as ``s.isin('lama')`` will raise an error. Use a list of one element instead: >>> s.isin(['lama']) 0 True 1 False 2 True 3 False 4 True 5 False Name: animal, dtype: bool Strings and integers are distinct and are therefore not comparable: >>> pd.Series([1]).isin(['1']) 0 False dtype: bool >>> pd.Series([1.1]).isin(['1.1']) 0 False dtype: bool """ result = algorithms.isin(self._values, values) return self._constructor(result, index=self.index).__finalize__( self, method="isin" ) def between( self, left, right, inclusive: Literal["both", "neither", "left", "right"] = "both", ) -> Series: """ Return boolean Series equivalent to left <= series <= right. This function returns a boolean vector containing `True` wherever the corresponding Series element is between the boundary values `left` and `right`. NA values are treated as `False`. Parameters ---------- left : scalar or list-like Left boundary. right : scalar or list-like Right boundary. inclusive : {"both", "neither", "left", "right"} Include boundaries. Whether to set each bound as closed or open. .. versionchanged:: 1.3.0 Returns ------- Series Series representing whether each element is between left and right (inclusive). See Also -------- Series.gt : Greater than of series and other. Series.lt : Less than of series and other. Notes ----- This function is equivalent to ``(left <= ser) & (ser <= right)`` Examples -------- >>> s = pd.Series([2, 0, 4, 8, np.nan]) Boundary values are included by default: >>> s.between(1, 4) 0 True 1 False 2 True 3 False 4 False dtype: bool With `inclusive` set to ``"neither"`` boundary values are excluded: >>> s.between(1, 4, inclusive="neither") 0 True 1 False 2 False 3 False 4 False dtype: bool `left` and `right` can be any scalar value: >>> s = pd.Series(['Alice', 'Bob', 'Carol', 'Eve']) >>> s.between('Anna', 'Daniel') 0 False 1 True 2 True 3 False dtype: bool """ # error: Non-overlapping identity check (left operand type: "Literal['both', # 'neither', 'left', 'right']", right operand type: "Literal[False]") if inclusive is True or inclusive is False: # type: ignore[comparison-overlap] warnings.warn( "Boolean inputs to the `inclusive` argument are deprecated in " "favour of `both` or `neither`.", FutureWarning, stacklevel=find_stack_level(), ) if inclusive: inclusive = "both" else: inclusive = "neither" if inclusive == "both": lmask = self >= left rmask = self <= right elif inclusive == "left": lmask = self >= left rmask = self < right elif inclusive == "right": lmask = self > left rmask = self <= right elif inclusive == "neither": lmask = self > left rmask = self < right else: raise ValueError( "Inclusive has to be either string of 'both'," "'left', 'right', or 'neither'." ) return lmask & rmask # ---------------------------------------------------------------------- # Convert to types that support pd.NA def _convert_dtypes( self, infer_objects: bool = True, convert_string: bool = True, convert_integer: bool = True, convert_boolean: bool = True, convert_floating: bool = True, ) -> Series: input_series = self if infer_objects: input_series = input_series.infer_objects() if is_object_dtype(input_series): input_series = input_series.copy() if convert_string or convert_integer or convert_boolean or convert_floating: inferred_dtype = convert_dtypes( input_series._values, convert_string, convert_integer, convert_boolean, convert_floating, ) result = input_series.astype(inferred_dtype) else: result = input_series.copy() return result # error: Cannot determine type of 'isna' # error: Return type "Series" of "isna" incompatible with return type "ndarray # [Any, dtype[bool_]]" in supertype "IndexOpsMixin" @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"]) # type: ignore[has-type] def isna(self) -> Series: # type: ignore[override] return NDFrame.isna(self) # error: Cannot determine type of 'isna' @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"]) # type: ignore[has-type] def isnull(self) -> Series: """ Series.isnull is an alias for Series.isna. """ return super().isnull() # error: Cannot determine type of 'notna' @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"]) # type: ignore[has-type] def notna(self) -> Series: return super().notna() # error: Cannot determine type of 'notna' @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"]) # type: ignore[has-type] def notnull(self) -> Series: """ Series.notnull is an alias for Series.notna. """ return super().notnull() @overload def dropna( self, *, axis: Axis = ..., inplace: Literal[False] = ..., how: str | None = ... ) -> Series: ... @overload def dropna( self, *, axis: Axis = ..., inplace: Literal[True], how: str | None = ... ) -> None: ... @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) def dropna( self, axis: Axis = 0, inplace: bool = False, how: str | None = None ) -> Series | None: """ Return a new Series with missing values removed. See the :ref:`User Guide ` for more on which values are considered missing, and how to work with missing data. Parameters ---------- axis : {0 or 'index'} Unused. Parameter needed for compatibility with DataFrame. inplace : bool, default False If True, do operation inplace and return None. how : str, optional Not in use. Kept for compatibility. Returns ------- Series or None Series with NA entries dropped from it or None if ``inplace=True``. See Also -------- Series.isna: Indicate missing values. Series.notna : Indicate existing (non-missing) values. Series.fillna : Replace missing values. DataFrame.dropna : Drop rows or columns which contain NA values. Index.dropna : Drop missing indices. Examples -------- >>> ser = pd.Series([1., 2., np.nan]) >>> ser 0 1.0 1 2.0 2 NaN dtype: float64 Drop NA values from a Series. >>> ser.dropna() 0 1.0 1 2.0 dtype: float64 Keep the Series with valid entries in the same variable. >>> ser.dropna(inplace=True) >>> ser 0 1.0 1 2.0 dtype: float64 Empty strings are not considered NA values. ``None`` is considered an NA value. >>> ser = pd.Series([np.NaN, 2, pd.NaT, '', None, 'I stay']) >>> ser 0 NaN 1 2 2 NaT 3 4 None 5 I stay dtype: object >>> ser.dropna() 1 2 3 5 I stay dtype: object """ inplace = validate_bool_kwarg(inplace, "inplace") # Validate the axis parameter self._get_axis_number(axis or 0) if self._can_hold_na: result = remove_na_arraylike(self) if inplace: self._update_inplace(result) else: return result else: if not inplace: return self.copy() return None # ---------------------------------------------------------------------- # Time series-oriented methods # error: Cannot determine type of 'asfreq' @doc(NDFrame.asfreq, **_shared_doc_kwargs) # type: ignore[has-type] def asfreq( self, freq: Frequency, method: FillnaOptions | None = None, how: str | None = None, normalize: bool = False, fill_value: Hashable = None, ) -> Series: return super().asfreq( freq=freq, method=method, how=how, normalize=normalize, fill_value=fill_value, ) # error: Cannot determine type of 'resample' @doc(NDFrame.resample, **_shared_doc_kwargs) # type: ignore[has-type] def resample( self, rule, axis: Axis = 0, closed: str | None = None, label: str | None = None, convention: str = "start", kind: str | None = None, loffset=None, base: int | None = None, on: Level = None, level: Level = None, origin: str | TimestampConvertibleTypes = "start_day", offset: TimedeltaConvertibleTypes | None = None, group_keys: bool | lib.NoDefault = no_default, ) -> Resampler: return super().resample( rule=rule, axis=axis, closed=closed, label=label, convention=convention, kind=kind, loffset=loffset, base=base, on=on, level=level, origin=origin, offset=offset, group_keys=group_keys, ) def to_timestamp( self, freq=None, how: Literal["s", "e", "start", "end"] = "start", copy: bool = True, ) -> Series: """ Cast to DatetimeIndex of Timestamps, at *beginning* of period. Parameters ---------- freq : str, default frequency of PeriodIndex Desired frequency. how : {'s', 'e', 'start', 'end'} Convention for converting period to timestamp; start of period vs. end. copy : bool, default True Whether or not to return a copy. Returns ------- Series with DatetimeIndex """ new_values = self._values if copy: new_values = new_values.copy() if not isinstance(self.index, PeriodIndex): raise TypeError(f"unsupported Type {type(self.index).__name__}") new_index = self.index.to_timestamp(freq=freq, how=how) return self._constructor(new_values, index=new_index).__finalize__( self, method="to_timestamp" ) def to_period(self, freq: str | None = None, copy: bool = True) -> Series: """ Convert Series from DatetimeIndex to PeriodIndex. Parameters ---------- freq : str, default None Frequency associated with the PeriodIndex. copy : bool, default True Whether or not to return a copy. Returns ------- Series Series with index converted to PeriodIndex. """ new_values = self._values if copy: new_values = new_values.copy() if not isinstance(self.index, DatetimeIndex): raise TypeError(f"unsupported Type {type(self.index).__name__}") new_index = self.index.to_period(freq=freq) return self._constructor(new_values, index=new_index).__finalize__( self, method="to_period" ) @overload def ffill( self, *, axis: None | Axis = ..., inplace: Literal[False] = ..., limit: None | int = ..., downcast: dict | None = ..., ) -> Series: ... @overload def ffill( self, *, axis: None | Axis = ..., inplace: Literal[True], limit: None | int = ..., downcast: dict | None = ..., ) -> None: ... @overload def ffill( self, *, axis: None | Axis = ..., inplace: bool = ..., limit: None | int = ..., downcast: dict | None = ..., ) -> Series | None: ... # error: Signature of "ffill" incompatible with supertype "NDFrame" @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) def ffill( # type: ignore[override] self, axis: None | Axis = None, inplace: bool = False, limit: None | int = None, downcast: dict | None = None, ) -> Series | None: return super().ffill(axis=axis, inplace=inplace, limit=limit, downcast=downcast) @overload def bfill( self, *, axis: None | Axis = ..., inplace: Literal[False] = ..., limit: None | int = ..., downcast: dict | None = ..., ) -> Series: ... @overload def bfill( self, *, axis: None | Axis = ..., inplace: Literal[True], limit: None | int = ..., downcast: dict | None = ..., ) -> None: ... @overload def bfill( self, *, axis: None | Axis = ..., inplace: bool = ..., limit: None | int = ..., downcast: dict | None = ..., ) -> Series | None: ... # error: Signature of "bfill" incompatible with supertype "NDFrame" @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"]) def bfill( # type: ignore[override] self, axis: None | Axis = None, inplace: bool = False, limit: None | int = None, downcast: dict | None = None, ) -> Series | None: return super().bfill(axis=axis, inplace=inplace, limit=limit, downcast=downcast) @deprecate_nonkeyword_arguments( version=None, allowed_args=["self", "lower", "upper"] ) def clip( self: Series, lower=None, upper=None, axis: Axis | None = None, inplace: bool = False, *args, **kwargs, ) -> Series | None: return super().clip(lower, upper, axis, inplace, *args, **kwargs) @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "method"]) def interpolate( self: Series, method: str = "linear", axis: Axis = 0, limit: int | None = None, inplace: bool = False, limit_direction: str | None = None, limit_area: str | None = None, downcast: str | None = None, **kwargs, ) -> Series | None: return super().interpolate( method, axis, limit, inplace, limit_direction, limit_area, downcast, **kwargs, ) @overload def where( self, cond, other=..., *, inplace: Literal[False] = ..., axis: Axis | None = ..., level: Level = ..., errors: IgnoreRaise | lib.NoDefault = ..., try_cast: bool | lib.NoDefault = ..., ) -> Series: ... @overload def where( self, cond, other=..., *, inplace: Literal[True], axis: Axis | None = ..., level: Level = ..., errors: IgnoreRaise | lib.NoDefault = ..., try_cast: bool | lib.NoDefault = ..., ) -> None: ... @overload def where( self, cond, other=..., *, inplace: bool = ..., axis: Axis | None = ..., level: Level = ..., errors: IgnoreRaise | lib.NoDefault = ..., try_cast: bool | lib.NoDefault = ..., ) -> Series | None: ... # error: Signature of "where" incompatible with supertype "NDFrame" @deprecate_kwarg(old_arg_name="errors", new_arg_name=None) @deprecate_nonkeyword_arguments( version=None, allowed_args=["self", "cond", "other"] ) def where( # type: ignore[override] self, cond, other=lib.no_default, inplace: bool = False, axis: Axis | None = None, level: Level = None, errors: IgnoreRaise | lib.NoDefault = lib.no_default, try_cast: bool | lib.NoDefault = lib.no_default, ) -> Series | None: return super().where( cond, other, inplace=inplace, axis=axis, level=level, try_cast=try_cast, ) @overload def mask( self, cond, other=..., *, inplace: Literal[False] = ..., axis: Axis | None = ..., level: Level = ..., errors: IgnoreRaise | lib.NoDefault = ..., try_cast: bool | lib.NoDefault = ..., ) -> Series: ... @overload def mask( self, cond, other=..., *, inplace: Literal[True], axis: Axis | None = ..., level: Level = ..., errors: IgnoreRaise | lib.NoDefault = ..., try_cast: bool | lib.NoDefault = ..., ) -> None: ... @overload def mask( self, cond, other=..., *, inplace: bool = ..., axis: Axis | None = ..., level: Level = ..., errors: IgnoreRaise | lib.NoDefault = ..., try_cast: bool | lib.NoDefault = ..., ) -> Series | None: ... # error: Signature of "mask" incompatible with supertype "NDFrame" @deprecate_kwarg(old_arg_name="errors", new_arg_name=None) @deprecate_nonkeyword_arguments( version=None, allowed_args=["self", "cond", "other"] ) def mask( # type: ignore[override] self, cond, other=np.nan, inplace: bool = False, axis: Axis | None = None, level: Level = None, errors: IgnoreRaise | lib.NoDefault = lib.no_default, try_cast: bool | lib.NoDefault = lib.no_default, ) -> Series | None: return super().mask( cond, other, inplace=inplace, axis=axis, level=level, try_cast=try_cast, ) # ---------------------------------------------------------------------- # Add index _AXIS_ORDERS = ["index"] _AXIS_LEN = len(_AXIS_ORDERS) _info_axis_number = 0 _info_axis_name = "index" index = properties.AxisProperty( axis=0, doc="The index (axis labels) of the Series." ) # ---------------------------------------------------------------------- # Accessor Methods # ---------------------------------------------------------------------- str = CachedAccessor("str", StringMethods) dt = CachedAccessor("dt", CombinedDatetimelikeProperties) cat = CachedAccessor("cat", CategoricalAccessor) plot = CachedAccessor("plot", pandas.plotting.PlotAccessor) sparse = CachedAccessor("sparse", SparseAccessor) # ---------------------------------------------------------------------- # Add plotting methods to Series hist = pandas.plotting.hist_series # ---------------------------------------------------------------------- # Template-Based Arithmetic/Comparison Methods def _cmp_method(self, other, op): res_name = ops.get_op_result_name(self, other) if isinstance(other, Series) and not self._indexed_same(other): raise ValueError("Can only compare identically-labeled Series objects") lvalues = self._values rvalues = extract_array(other, extract_numpy=True, extract_range=True) with np.errstate(all="ignore"): res_values = ops.comparison_op(lvalues, rvalues, op) return self._construct_result(res_values, name=res_name) def _logical_method(self, other, op): res_name = ops.get_op_result_name(self, other) self, other = ops.align_method_SERIES(self, other, align_asobject=True) lvalues = self._values rvalues = extract_array(other, extract_numpy=True, extract_range=True) res_values = ops.logical_op(lvalues, rvalues, op) return self._construct_result(res_values, name=res_name) def _arith_method(self, other, op): self, other = ops.align_method_SERIES(self, other) return base.IndexOpsMixin._arith_method(self, other, op) Series._add_numeric_operations() # Add arithmetic! ops.add_flex_arithmetic_methods(Series)