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projektAI/venv/Lib/site-packages/pandas/core/series.py

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"""
Data structure for 1-dimensional cross-sectional and time series data
"""
from io import StringIO
from shutil import get_terminal_size
from textwrap import dedent
from typing import (
IO,
TYPE_CHECKING,
Any,
Callable,
Iterable,
List,
Optional,
Sequence,
Tuple,
Type,
Union,
cast,
)
import warnings
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,
ArrayLike,
Axis,
DtypeObj,
FrameOrSeriesUnion,
IndexKeyFunc,
Label,
StorageOptions,
ValueKeyFunc,
)
from pandas.compat.numpy import function as nv
from pandas.errors import InvalidIndexError
from pandas.util._decorators import Appender, Substitution, doc
from pandas.util._validators import validate_bool_kwarg, validate_percentile
from pandas.core.dtypes.cast import (
convert_dtypes,
maybe_cast_to_extension_array,
validate_numeric_casting,
)
from pandas.core.dtypes.common import (
ensure_platform_int,
is_bool,
is_categorical_dtype,
is_dict_like,
is_extension_array_dtype,
is_integer,
is_iterator,
is_list_like,
is_object_dtype,
is_scalar,
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, generic, missing, nanops, ops
from pandas.core.accessor import CachedAccessor
from pandas.core.aggregation import aggregate, transform
from pandas.core.arrays import ExtensionArray
from pandas.core.arrays.categorical import CategoricalAccessor
from pandas.core.arrays.sparse import SparseAccessor
import pandas.core.common as com
from pandas.core.construction import (
array as pd_array,
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,
Float64Index,
Index,
MultiIndex,
ensure_index,
)
import pandas.core.indexes.base as ibase
from pandas.core.indexes.datetimes import DatetimeIndex
from pandas.core.indexes.period import PeriodIndex
from pandas.core.indexes.timedeltas import TimedeltaIndex
from pandas.core.indexing import check_bool_indexer
from pandas.core.internals import 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
import pandas.plotting
if TYPE_CHECKING:
from pandas.core.frame import DataFrame
from pandas.core.groupby.generic import SeriesGroupBy
__all__ = ["Series"]
_shared_doc_kwargs = {
"axes": "index",
"klass": "Series",
"axes_single_arg": "{0 or 'index'}",
"axis": """axis : {0 or 'index'}
Parameter needed for compatibility with DataFrame.""",
"inplace": """inplace : boolean, default False
If True, performs operation inplace and returns None.""",
"unique": "np.ndarray",
"duplicated": "Series",
"optional_by": "",
"optional_mapper": "",
"optional_labels": "",
"optional_axis": "",
}
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, generic.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 <basics.dtypes>` for more usages.
name : str, optional
The name to give to the Series.
copy : bool, default False
Copy input data.
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.
"""
_typ = "series"
_HANDLED_TYPES = (Index, ExtensionArray, np.ndarray)
_name: Label
_metadata: List[str] = ["name"]
_internal_names_set = {"index"} | generic.NDFrame._internal_names_set
_accessors = {"dt", "cat", "str", "sparse"}
_hidden_attrs = (
base.IndexOpsMixin._hidden_attrs
| generic.NDFrame._hidden_attrs
| frozenset(["compress", "ptp"])
)
# Override cache_readonly bc Series is mutable
hasnans = property(
base.IndexOpsMixin.hasnans.func, doc=base.IndexOpsMixin.hasnans.__doc__
)
__hash__ = generic.NDFrame.__hash__
_mgr: SingleBlockManager
div: Callable[["Series", Any], "Series"]
rdiv: Callable[["Series", Any], "Series"]
# ----------------------------------------------------------------------
# Constructors
def __init__(
self, data=None, index=None, dtype=None, name=None, copy=False, fastpath=False
):
if (
isinstance(data, SingleBlockManager)
and index is None
and dtype is None
and copy is False
):
# GH#33357 called with just the SingleBlockManager
NDFrame.__init__(self, data)
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):
data = SingleBlockManager.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.",
DeprecationWarning,
stacklevel=2,
)
# uncomment the line below when removing the DeprecationWarning
# 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):
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 is_extension_array_dtype(data):
pass
elif isinstance(data, (set, frozenset)):
raise TypeError(f"'{type(data).__name__}' type is unordered")
else:
data = com.maybe_iterable_to_list(data)
if index is None:
if not is_list_like(data):
data = [data]
index = ibase.default_index(len(data))
elif is_list_like(data):
# a scalar numpy array is list-like but doesn't
# have a proper length
try:
if len(index) != len(data):
raise ValueError(
f"Length of passed values is {len(data)}, "
f"index implies {len(index)}."
)
except TypeError:
pass
# create/copy the manager
if isinstance(data, SingleBlockManager):
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, raise_cast_failure=True)
data = SingleBlockManager.from_array(data, index)
generic.NDFrame.__init__(self, data)
self.name = name
self._set_axis(0, index, fastpath=True)
def _init_dict(self, data, index=None, dtype=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 index-like, default None
Index for the new Series: if None, use dict keys.
dtype : dtype, 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
"""
# 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(dtype)
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(
values, index=keys, 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) -> Type["Series"]:
return Series
@property
def _constructor_expanddim(self) -> Type["DataFrame"]:
from pandas.core.frame import DataFrame
return DataFrame
# types
@property
def _can_hold_na(self) -> bool:
return self._mgr._can_hold_na
_index = None
def _set_axis(self, axis: int, labels, fastpath: bool = False) -> 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]`.
"""
if not fastpath:
labels = ensure_index(labels)
if labels._is_all_dates:
deep_labels = labels
if isinstance(labels, CategoricalIndex):
deep_labels = labels.categories
if not isinstance(
deep_labels, (DatetimeIndex, PeriodIndex, TimedeltaIndex)
):
try:
labels = DatetimeIndex(labels)
# need to set here because we changed the index
if fastpath:
self._mgr.set_axis(axis, labels)
except (tslibs.OutOfBoundsDatetime, ValueError):
# labels may exceeds datetime bounds,
# or not be a DatetimeIndex
pass
object.__setattr__(self, "_index", labels)
if not fastpath:
# 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) -> Label:
"""
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: Label) -> 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._block.array_values()
# ops
def ravel(self, order="C"):
"""
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=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
"""
return self._constructor(
self._values.view(dtype), index=self.index
).__finalize__(self, method="view")
# ----------------------------------------------------------------------
# NDArray Compat
_HANDLED_TYPES = (Index, ExtensionArray, np.ndarray)
def __array__(self, dtype=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', freq='D'),
Timestamp('2000-01-02 00:00:00+0100', tz='CET', freq='D')],
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.array, 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(generic.NDFrame.take.__doc__)
def take(self, indices, axis=0, is_copy=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=2,
)
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):
"""
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):
"""
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):
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):
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):
# mpl hackaround
if com.any_none(*key):
result = self._get_values(key)
deprecate_ndim_indexing(result, stacklevel=5)
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):
try:
return self._constructor(self._mgr.get_slice(indexer)).__finalize__(self)
except ValueError:
# 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
return np.asarray(self._values[indexer])
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):
key = com.apply_if_callable(key, self)
cacher_needs_updating = self._check_is_chained_assignment_possible()
if key is Ellipsis:
key = slice(None)
try:
self._set_with_engine(key, value)
except (KeyError, ValueError):
values = self._values
if is_integer(key) and not self.index.inferred_type == "integer":
# positional setter
values[key] = value
else:
# GH#12862 adding a new key to the Series
self.loc[key] = value
except TypeError as err:
if isinstance(key, tuple) and not isinstance(self.index, MultiIndex):
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)
try:
self._where(~key, value, inplace=True)
except InvalidIndexError:
self.iloc[key] = value
return
else:
self._set_with(key, value)
if cacher_needs_updating:
self._maybe_update_cacher()
def _set_with_engine(self, key, value):
# fails with AttributeError for IntervalIndex
loc = self.index._engine.get_loc(key)
validate_numeric_casting(self.dtype, value)
self._values[loc] = value
def _set_with(self, key, value):
# other: fancy integer or otherwise
if isinstance(key, slice):
indexer = self.index._convert_slice_indexer(key, kind="getitem")
return self._set_values(indexer, value)
else:
assert not isinstance(key, tuple)
if is_scalar(key):
key = [key]
if isinstance(key, Index):
key_type = key.inferred_type
key = key._values
else:
key_type = lib.infer_dtype(key, skipna=False)
# Note: key_type == "boolean" should not occur because that
# should be caught by the is_bool_indexer check in __setitem__
if key_type == "integer":
if not self.index._should_fallback_to_positional():
self._set_labels(key, value)
else:
self._set_values(key, value)
else:
self.loc[key] = value
def _set_labels(self, key, value):
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):
if isinstance(key, Series):
key = key._values
self._mgr = self._mgr.setitem( # type: ignore[assignment]
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
"""
try:
if takeable:
self._values[label] = value
else:
loc = self.index.get_loc(label)
validate_numeric_casting(self.dtype, value)
self._values[loc] = value
except KeyError:
# set using a non-recursive method
self.loc[label] = value
# ----------------------------------------------------------------------
# Unsorted
@property
def _is_mixed_type(self):
return False
def repeat(self, repeats, axis=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
Must be ``None``. Has no effect but is accepted for compatibility
with numpy.
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"
)
def reset_index(self, level=None, drop=False, name=None, inplace=False):
"""
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).
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 = ibase.default_index(len(self))
if level is not None:
if not isinstance(level, (tuple, list)):
level = [level]
level = [self.index._get_level_number(lev) for lev in level]
if len(level) < self.index.nlevels:
new_index = self.index.droplevel(level)
if inplace:
self.index = new_index
# set name if it was passed, otherwise, keep the previous name
self.name = name or self.name
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:
df = self.to_frame(name)
return df.reset_index(level=level, drop=drop)
# ----------------------------------------------------------------------
# Rendering Methods
def __repr__(self) -> str:
"""
Return a string representation for a particular Series.
"""
buf = StringIO("")
width, height = get_terminal_size()
max_rows = (
height
if get_option("display.max_rows") == 0
else get_option("display.max_rows")
)
min_rows = (
height
if get_option("display.max_rows") == 0
else get_option("display.min_rows")
)
show_dimensions = get_option("display.show_dimensions")
self.to_string(
buf=buf,
name=self.name,
dtype=self.dtype,
min_rows=min_rows,
max_rows=max_rows,
length=show_dimensions,
)
result = buf.getvalue()
return result
def to_string(
self,
buf=None,
na_rep="NaN",
float_format=None,
header=True,
index=True,
length=False,
dtype=False,
name=False,
max_rows=None,
min_rows=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:
try:
buf.write(result)
except AttributeError:
with open(buf, "w") as f:
f.write(result)
@doc(
klass=_shared_doc_kwargs["klass"],
storage_options=generic._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 |
"""
),
)
def to_markdown(
self,
buf: Optional[IO[str]] = None,
mode: str = "wt",
index: bool = True,
storage_options: StorageOptions = None,
**kwargs,
) -> Optional[str]:
"""
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 \
<https://pypi.org/project/tabulate>`_.
Returns
-------
str
{klass} in Markdown-friendly format.
Notes
-----
Requires the `tabulate <https://pypi.org/project/tabulate>`_ package.
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 |
+----+----------+
"""
return self.to_frame().to_markdown(
buf, mode, index, storage_options=storage_options, **kwargs
)
# ----------------------------------------------------------------------
def items(self) -> Iterable[Tuple[Label, 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))
@Appender(items.__doc__)
def iteritems(self) -> Iterable[Tuple[Label, Any]]:
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=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(<class 'list'>, {0: 1, 1: 2, 2: 3, 3: 4})
"""
# GH16122
into_c = com.standardize_mapping(into)
return into_c(self.items())
def to_frame(self, name=None) -> "DataFrame":
"""
Convert Series to DataFrame.
Parameters
----------
name : object, default None
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:
df = self._constructor_expanddim(self)
else:
df = self._constructor_expanddim({name: self})
return df
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(generic._shared_docs["groupby"] % _shared_doc_kwargs)
def groupby(
self,
by=None,
axis=0,
level=None,
as_index: bool = True,
sort: bool = True,
group_keys: bool = True,
squeeze: bool = 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=2,
)
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=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.array).sum()
elif 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)]
out = np.bincount(obs, minlength=len(lev) or None)
return self._constructor(out, index=lev, dtype="int64").__finalize__(
self, method="count"
)
def mode(self, dropna=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.
.. versionadded:: 0.24.0
Returns
-------
Series
Modes of the Series in sorted order.
"""
# TODO: Add option for bins like value_counts()
return algorithms.mode(self, dropna=dropna)
def unique(self):
"""
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
--------
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()
<DatetimeArray>
['2016-01-01 00:00:00-05:00']
Length: 1, dtype: datetime64[ns, US/Eastern]
An unordered Categorical will return categories in the order of
appearance.
>>> pd.Series(pd.Categorical(list('baabc'))).unique()
['b', 'a', 'c']
Categories (3, object): ['b', 'a', 'c']
An ordered Categorical preserves the category ordering.
>>> pd.Series(pd.Categorical(list('baabc'), categories=list('abc'),
... ordered=True)).unique()
['b', 'a', 'c']
Categories (3, object): ['a' < 'b' < 'c']
"""
result = super().unique()
return result
def drop_duplicates(self, keep="first", inplace=False) -> Optional["Series"]:
"""
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.
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="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
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 = base.IndexOpsMixin.duplicated(self, keep=keep)
result = self._constructor(res, index=self.index)
return result.__finalize__(self, method="duplicated")
def idxmin(self, axis=0, skipna=True, *args, **kwargs):
"""
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 : int, default 0
For compatibility with DataFrame.idxmin. Redundant for application
on Series.
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
"""
skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs)
i = nanops.nanargmin(self._values, skipna=skipna)
if i == -1:
return np.nan
return self.index[i]
def idxmax(self, axis=0, skipna=True, *args, **kwargs):
"""
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 : int, default 0
For compatibility with DataFrame.idxmax. Redundant for application
on Series.
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
"""
skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs)
i = nanops.nanargmax(self._values, skipna=skipna)
if i == -1:
return np.nan
return self.index[i]
def round(self, decimals=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
def quantile(self, q=0.5, interpolation="linear"):
"""
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, method="pearson", min_periods=None) -> float:
"""
Compute correlation with `other` Series, excluding missing values.
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.
.. versionadded:: 0.24.0
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.
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
"""
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: Optional[int] = None,
ddof: Optional[int] = 1,
) -> float:
"""
Compute covariance with Series, excluding missing values.
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.array, periods)
return self._constructor(result, index=self.index).__finalize__(
self, method="diff"
)
def autocorr(self, lag=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):
"""
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")
def searchsorted(self, value, side="left", sorter=None):
return algorithms.searchsorted(self._values, value, side=side, sorter=sorter)
# -------------------------------------------------------------------
# Combination
def append(self, to_append, ignore_index=False, verify_integrity=False):
"""
Concatenate two or more Series.
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]
"""
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, 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)
ret = this._construct_result(result, name)
return ret
def _construct_result(
self, result: Union[ArrayLike, Tuple[ArrayLike, ArrayLike]], name: Label
) -> Union["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(
generic._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,
) -> FrameOrSeriesUnion:
return super().compare(
other=other,
align_axis=align_axis,
keep_shape=keep_shape,
keep_equal=keep_equal,
)
def combine(self, other, func, fill_value=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 = []
for idx in new_index:
lv = self.get(idx, fill_value)
rv = other.get(idx, fill_value)
with np.errstate(all="ignore"):
new_values.append(func(lv, rv))
else:
# Assume that other is a scalar, so apply the function for
# each element in the Series
new_index = self.index
with np.errstate(all="ignore"):
new_values = [func(lv, other) for lv in self._values]
new_name = self.name
if is_categorical_dtype(self.dtype):
pass
elif is_extension_array_dtype(self.dtype):
# TODO: can we do this for only SparseDtype?
# The function can return something of any type, so check
# if the type is compatible with the calling EA.
new_values = maybe_cast_to_extension_array(type(self._values), new_values)
return self._constructor(new_values, index=new_index, name=new_name)
def combine_first(self, other) -> "Series":
"""
Combine Series values, choosing the calling Series's values first.
Parameters
----------
other : Series
The value(s) to be combined with the `Series`.
Returns
-------
Series
The result of combining the Series with the other object.
See Also
--------
Series.combine : Perform elementwise operation on two Series
using a given function.
Notes
-----
Result index will be the union of the two indexes.
Examples
--------
>>> s1 = pd.Series([1, np.nan])
>>> s2 = pd.Series([3, 4])
>>> s1.combine_first(s2)
0 1.0
1 4.0
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) -> 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
def sort_values(
self,
axis=0,
ascending: Union[Union[bool, int], Sequence[Union[bool, int]]] = True,
inplace: bool = False,
kind: str = "quicksort",
na_position: str = "last",
ignore_index: bool = False,
key: ValueKeyFunc = None,
):
"""
Sort by the values.
Sort a Series in ascending or descending order by some
criterion.
Parameters
----------
axis : {0 or 'index'}, default 0
Axis to direct sorting. The value 'index' is accepted for
compatibility with DataFrame.sort_values.
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' or 'heapsort'}, default 'quicksort'
Choice of sorting algorithm. See also :func:`numpy.sort` for more
information. 'mergesort' is the only stable algorithm.
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]
if not is_bool(ascending):
raise ValueError("ascending must be boolean")
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 = ibase.default_index(len(sorted_index))
if inplace:
self._update_inplace(result)
else:
return result.__finalize__(self, method="sort_values")
def sort_index(
self,
axis=0,
level=None,
ascending: Union[Union[bool, int], Sequence[Union[bool, int]]] = True,
inplace: bool = False,
kind: str = "quicksort",
na_position: str = "last",
sort_remaining: bool = True,
ignore_index: bool = False,
key: IndexKeyFunc = 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 : int, default 0
Axis to direct sorting. This can only be 0 for Series.
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'}, default 'quicksort'
Choice of sorting algorithm. See also :func:`numpy.sort` for more
information. 'mergesort' is the only stable algorithm. 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,
level,
ascending,
inplace,
kind,
na_position,
sort_remaining,
ignore_index,
key,
)
def argsort(self, axis=0, kind="quicksort", order=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"}
Has no effect but is accepted for compatibility with numpy.
kind : {'mergesort', 'quicksort', 'heapsort'}, default 'quicksort'
Choice of sorting algorithm. See np.sort for more
information. 'mergesort' is the only stable algorithm.
order : None
Has no effect but is accepted for compatibility with numpy.
Returns
-------
Series
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 = Series(-1, index=self.index, name=self.name, dtype="int64")
notmask = ~mask
result[notmask] = np.argsort(values[notmask], kind=kind)
return self._constructor(result, index=self.index).__finalize__(
self, method="argsort"
)
else:
return self._constructor(
np.argsort(values, kind=kind), index=self.index, dtype="int64"
).__finalize__(self, method="argsort")
def nlargest(self, n=5, keep="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=5, keep="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()
def swaplevel(self, i=-2, j=-1, copy=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, str
Level of the indices to be swapped. Can pass level name as string.
copy : bool, default True
Whether to copy underlying data.
Returns
-------
Series
Series with levels swapped in MultiIndex.
"""
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) -> "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.
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):
return self.copy()
values, counts = reshape.explode(np.asarray(self.array))
if ignore_index:
index = ibase.default_index(len(values))
else:
index = self.index.repeat(counts)
result = self._constructor(values, index=index, name=self.name)
return result
def unstack(self, level=-1, fill_value=None):
"""
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.
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, na_action=None) -> "Series":
"""
Map values of Series according to input correspondence.
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 = super()._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(
generic._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=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())
result, how = aggregate(self, func, *args, **kwargs)
if result is None:
# we can be called from an inner function which
# passes this meta-data
kwargs.pop("_axis", None)
kwargs.pop("_level", None)
# try a regular apply, this evaluates lambdas
# row-by-row; however if the lambda is expected a Series
# expression, e.g.: lambda x: x-x.quantile(0.25)
# this will fail, so we can try a vectorized evaluation
# we cannot FIRST try the vectorized evaluation, because
# then .agg and .apply would have different semantics if the
# operation is actually defined on the Series, e.g. str
try:
result = self.apply(func, *args, **kwargs)
except (ValueError, AttributeError, TypeError):
result = func(self, *args, **kwargs)
return result
agg = aggregate
@doc(
_shared_docs["transform"],
klass=_shared_doc_kwargs["klass"],
axis=_shared_doc_kwargs["axis"],
)
def transform(
self, func: AggFuncType, axis: Axis = 0, *args, **kwargs
) -> FrameOrSeriesUnion:
return transform(self, func, axis, *args, **kwargs)
def apply(self, func, convert_dtype=True, args=(), **kwds):
"""
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.
args : tuple
Positional arguments passed to func after the series value.
**kwds
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.
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
"""
if len(self) == 0:
return self._constructor(dtype=self.dtype, index=self.index).__finalize__(
self, method="apply"
)
# dispatch to agg
if isinstance(func, (list, dict)):
return self.aggregate(func, *args, **kwds)
# if we are a string, try to dispatch
if isinstance(func, str):
return self._try_aggregate_string_function(func, *args, **kwds)
# handle ufuncs and lambdas
if kwds or args and not isinstance(func, np.ufunc):
def f(x):
return func(x, *args, **kwds)
else:
f = func
with np.errstate(all="ignore"):
if isinstance(f, np.ufunc):
return f(self)
# row-wise access
if is_extension_array_dtype(self.dtype) and hasattr(self._values, "map"):
# GH#23179 some EAs do not have `map`
mapped = self._values.map(f)
else:
values = self.astype(object)._values
mapped = lib.map_infer(values, f, convert=convert_dtype)
if len(mapped) and isinstance(mapped[0], Series):
# GH 25959 use pd.array instead of tolist
# so extension arrays can be used
return self._constructor_expanddim(pd_array(mapped), index=self.index)
else:
return self._constructor(mapped, index=self.index).__finalize__(
self, method="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:
raise NotImplementedError(
f"Series.{name} does not implement numeric_only."
)
with np.errstate(all="ignore"):
return op(delegate, skipna=skipna, **kwds)
def _reindex_indexer(self, new_index, indexer, copy):
if indexer is None:
if copy:
return self.copy()
return self
new_values = algorithms.take_1d(
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):
"""
Check if we do need a multi reindex; this is for compat with
higher dims.
"""
return False
@doc(
NDFrame.align,
klass=_shared_doc_kwargs["klass"],
axes_single_arg=_shared_doc_kwargs["axes_single_arg"],
)
def align(
self,
other,
join="outer",
axis=None,
level=None,
copy=True,
fill_value=None,
method=None,
limit=None,
fill_axis=0,
broadcast_axis=None,
):
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,
)
def rename(
self,
index=None,
*,
axis=None,
copy=True,
inplace=False,
level=None,
errors="ignore",
):
"""
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 <basics.rename>` for more.
Parameters
----------
axis : {0 or "index"}
Unused. Accepted for compatibility with DataFrame method only.
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.
**kwargs
Additional keyword arguments passed to the function. Only the
"inplace" keyword is used.
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 callable(index) or is_dict_like(index):
return super().rename(
index, copy=copy, inplace=inplace, level=level, errors=errors
)
else:
return self._set_name(index, inplace=inplace)
@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(generic.NDFrame.set_axis.__doc__)
def set_axis(self, labels, axis: Axis = 0, inplace: bool = False):
return super().set_axis(labels, axis=axis, inplace=inplace)
@doc(
NDFrame.reindex,
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, index=None, **kwargs):
return super().reindex(index=index, **kwargs)
def drop(
self,
labels=None,
axis=0,
index=None,
columns=None,
level=None,
inplace=False,
errors="raise",
) -> "Series":
"""
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, default 0
Redundant for application on Series.
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,
)
@doc(NDFrame.fillna, **_shared_doc_kwargs)
def fillna(
self,
value=None,
method=None,
axis=None,
inplace=False,
limit=None,
downcast=None,
) -> Optional["Series"]:
return super().fillna(
value=value,
method=method,
axis=axis,
inplace=inplace,
limit=limit,
downcast=downcast,
)
def pop(self, item: Label) -> 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)
@doc(NDFrame.replace, klass=_shared_doc_kwargs["klass"])
def replace(
self,
to_replace=None,
value=None,
inplace=False,
limit=None,
regex=False,
method="pad",
):
return super().replace(
to_replace=to_replace,
value=value,
inplace=inplace,
limit=limit,
regex=regex,
method=method,
)
def _replace_single(self, to_replace, method, inplace, limit):
"""
Replaces values in a Series using the fill method specified when no
replacement value is given in the replace method
"""
orig_dtype = self.dtype
result = self if inplace else self.copy()
fill_f = missing.get_fill_func(method)
mask = missing.mask_missing(result.values, to_replace)
values = fill_f(result.values, limit=limit, mask=mask)
if values.dtype == orig_dtype and inplace:
return
result = self._constructor(values, index=self.index, dtype=self.dtype)
result = result.__finalize__(self)
if inplace:
self._update_inplace(result)
return
return result
@doc(NDFrame.shift, klass=_shared_doc_kwargs["klass"])
def shift(self, periods=1, freq=None, axis=0, fill_value=None) -> "Series":
return super().shift(
periods=periods, freq=freq, axis=axis, fill_value=fill_value
)
def memory_usage(self, index=True, deep=False):
"""
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 = super().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
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=True) -> "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 : bool, default True
Include boundaries.
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 ``False`` boundary values are excluded:
>>> s.between(1, 4, inclusive=False)
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
"""
if inclusive:
lmask = self >= left
rmask = self <= right
else:
lmask = self > left
rmask = self < right
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,
)
try:
result = input_series.astype(inferred_dtype)
except TypeError:
result = input_series.copy()
else:
result = input_series.copy()
return result
@doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])
def isna(self) -> "Series":
return generic.NDFrame.isna(self)
@doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])
def isnull(self) -> "Series":
return super().isnull()
@doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])
def notna(self) -> "Series":
return super().notna()
@doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])
def notnull(self) -> "Series":
return super().notnull()
def dropna(self, axis=0, inplace=False, how=None):
"""
Return a new Series with missing values removed.
See the :ref:`User Guide <missing_data>` for more on which values are
considered missing, and how to work with missing data.
Parameters
----------
axis : {0 or 'index'}, default 0
There is only one axis to drop values from.
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 inplace:
# do nothing
pass
else:
return self.copy()
# ----------------------------------------------------------------------
# Time series-oriented methods
def to_timestamp(self, freq=None, how="start", copy=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=None, copy=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"
)
# ----------------------------------------------------------------------
# Add index
_AXIS_ORDERS = ["index"]
_AXIS_REVERSED = False
_AXIS_LEN = len(_AXIS_ORDERS)
_info_axis_number = 0
_info_axis_name = "index"
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 = extract_array(self, extract_numpy=True)
rvalues = extract_array(other, extract_numpy=True)
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 = extract_array(self, extract_numpy=True)
rvalues = extract_array(other, extract_numpy=True)
res_values = ops.logical_op(lvalues, rvalues, op)
return self._construct_result(res_values, name=res_name)
def _arith_method(self, other, op):
res_name = ops.get_op_result_name(self, other)
self, other = ops.align_method_SERIES(self, other)
lvalues = extract_array(self, extract_numpy=True)
rvalues = extract_array(other, extract_numpy=True)
result = ops.arithmetic_op(lvalues, rvalues, op)
return self._construct_result(result, name=res_name)
Series._add_numeric_operations()
# Add arithmetic!
ops.add_flex_arithmetic_methods(Series)
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