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Version:
0.23.3 ▾
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"""
Data structure for 1-dimensional cross-sectional and time series data
"""
from __future__ import division
# pylint: disable=E1101,E1103
# pylint: disable=W0703,W0622,W0613,W0201
import types
import warnings
from textwrap import dedent
import numpy as np
import numpy.ma as ma
from pandas.core.accessor import CachedAccessor
from pandas.core.arrays import ExtensionArray
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_bool,
is_integer, is_integer_dtype,
is_float_dtype,
is_extension_type,
is_extension_array_dtype,
is_datetime64tz_dtype,
is_timedelta64_dtype,
is_object_dtype,
is_list_like,
is_hashable,
is_iterator,
is_dict_like,
is_scalar,
_is_unorderable_exception,
_ensure_platform_int,
pandas_dtype)
from pandas.core.dtypes.generic import (
ABCSparseArray, ABCDataFrame, ABCIndexClass)
from pandas.core.dtypes.cast import (
maybe_upcast, infer_dtype_from_scalar,
maybe_convert_platform,
maybe_cast_to_datetime, maybe_castable,
construct_1d_arraylike_from_scalar,
construct_1d_ndarray_preserving_na,
construct_1d_object_array_from_listlike)
from pandas.core.dtypes.missing import (
isna,
notna,
remove_na_arraylike,
na_value_for_dtype)
from pandas.core.index import (Index, MultiIndex, InvalidIndexError,
Float64Index, _ensure_index)
from pandas.core.indexing import check_bool_indexer, maybe_convert_indices
from pandas.core import generic, base
from pandas.core.internals import SingleBlockManager
from pandas.core.arrays.categorical import Categorical, CategoricalAccessor
from pandas.core.indexes.accessors import CombinedDatetimelikeProperties
from pandas.core.indexes.datetimes import DatetimeIndex
from pandas.core.indexes.timedeltas import TimedeltaIndex
from pandas.core.indexes.period import PeriodIndex
from pandas import compat
from pandas.io.formats.terminal import get_terminal_size
from pandas.compat import (
zip, u, OrderedDict, StringIO, range, get_range_parameters, PY36)
from pandas.compat.numpy import function as nv
import pandas.core.ops as ops
import pandas.core.algorithms as algorithms
import pandas.core.common as com
import pandas.core.nanops as nanops
import pandas.io.formats.format as fmt
from pandas.util._decorators import (
Appender, deprecate, deprecate_kwarg, Substitution)
from pandas.util._validators import validate_bool_kwarg
from pandas._libs import index as libindex, tslib as libts, lib, iNaT
from pandas.core.config import get_option
from pandas.core.strings import StringMethods
import pandas.plotting._core as gfx
__all__ = ['Series']
_shared_doc_kwargs = dict(
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='',
versionadded_to_excel='\n .. versionadded:: 0.20.0\n')
# see gh-16971
def remove_na(arr):
"""Remove null values from array like structure.
.. deprecated:: 0.21.0
Use s[s.notnull()] instead.
"""
warnings.warn("remove_na is deprecated and is a private "
"function. Do not use.", FutureWarning, stacklevel=2)
return remove_na_arraylike(arr)
def _coerce_method(converter):
""" install the scalar coercion methods """
def wrapper(self):
if len(self) == 1:
return converter(self.iloc[0])
raise TypeError("cannot convert the series to "
"{0}".format(str(converter)))
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, dict, or scalar value
Contains data stored in Series
.. versionchanged :: 0.23.0
If data is a dict, argument order is maintained for Python 3.6
and later.
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 both a dict and index
sequence are used, the index will override the keys found in the
dict.
dtype : numpy.dtype or None
If None, dtype will be inferred
copy : boolean, default False
Copy input data
"""
_metadata = ['name']
_accessors = set(['dt', 'cat', 'str'])
_deprecations = generic.NDFrame._deprecations | frozenset(
['asobject', 'sortlevel', 'reshape', 'get_value', 'set_value',
'from_csv', 'valid'])
def __init__(self, data=None, index=None, dtype=None, name=None,
copy=False, fastpath=False):
# we are called internally, so short-circuit
if fastpath:
# data is an ndarray, index is defined
if not isinstance(data, SingleBlockManager):
data = SingleBlockManager(data, index, fastpath=True)
if copy:
data = data.copy()
if index is None:
index = data.index
else:
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 name is None:
name = data.name
if dtype is not None:
# astype copies
data = data.astype(dtype)
else:
# need to copy to avoid aliasing issues
data = data._values.copy()
copy = False
elif isinstance(data, np.ndarray):
pass
elif isinstance(data, Series):
if name is None:
name = data.name
if index is None:
index = data.index
else:
data = data.reindex(index, copy=copy)
data = data._data
elif isinstance(data, dict):
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) and dtype is not None:
if not data.dtype.is_dtype(dtype):
raise ValueError("Cannot specify a dtype '{}' with an "
"extension array of a different "
"dtype ('{}').".format(dtype,
data.dtype))
elif (isinstance(data, types.GeneratorType) or
(compat.PY3 and isinstance(data, map))):
data = list(data)
elif isinstance(data, (set, frozenset)):
raise TypeError("{0!r} type is unordered"
"".format(data.__class__.__name__))
else:
# handle sparse passed here (and force conversion)
if isinstance(data, ABCSparseArray):
data = data.to_dense()
if index is None:
if not is_list_like(data):
data = [data]
index = com._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(
'Length of passed values is {val}, '
'index implies {ind}'
.format(val=len(data), ind=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(data, index, fastpath=True)
generic.NDFrame.__init__(self, data, fastpath=True)
self.name = name
self._set_axis(0, index, fastpath=True)
def _init_dict(self, data, index=None, dtype=None):
"""
Derive the "_data" 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
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:
keys, values = zip(*compat.iteritems(data))
values = list(values)
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:
s = Series(values, index=keys, dtype=dtype)
# Now we just make sure the order is respected, if any
if data and index is not None:
s = s.reindex(index, copy=False)
elif not PY36 and not isinstance(data, OrderedDict) and data:
# Need the `and data` to avoid sorting Series(None, index=[...])
# since that isn't really dict-like
try:
s = s.sort_index()
except TypeError:
pass
return s._data, s.index
@classmethod
def from_array(cls, arr, index=None, name=None, dtype=None, copy=False,
fastpath=False):
"""Construct Series from array.
.. deprecated :: 0.23.0
Use pd.Series(..) constructor instead.
"""
warnings.warn("'from_array' is deprecated and will be removed in a "
"future version. Please use the pd.Series(..) "
"constructor instead.", FutureWarning, stacklevel=2)
if isinstance(arr, ABCSparseArray):
from pandas.core.sparse.series import SparseSeries
cls = SparseSeries
return cls(arr, index=index, name=name, dtype=dtype,
copy=copy, fastpath=fastpath)
@property
def _constructor(self):
return Series
@property
def _constructor_expanddim(self):
from pandas.core.frame import DataFrame
return DataFrame
# types
@property
def _can_hold_na(self):
return self._data._can_hold_na
_index = None
def _set_axis(self, axis, labels, fastpath=False):
""" override generic, we want to set the _typ here """
if not fastpath:
labels = _ensure_index(labels)
is_all_dates = labels.is_all_dates
if is_all_dates:
if not isinstance(labels,
(DatetimeIndex, PeriodIndex, TimedeltaIndex)):
try:
labels = DatetimeIndex(labels)
# need to set here because we changed the index
if fastpath:
self._data.set_axis(axis, labels)
except (libts.OutOfBoundsDatetime, ValueError):
# labels may exceeds datetime bounds,
# or not be a DatetimeIndex
pass
self._set_subtyp(is_all_dates)
object.__setattr__(self, '_index', labels)
if not fastpath:
self._data.set_axis(axis, labels)
def _set_subtyp(self, is_all_dates):
if is_all_dates:
object.__setattr__(self, '_subtyp', 'time_series')
else:
object.__setattr__(self, '_subtyp', 'series')
def _update_inplace(self, result, **kwargs):
# we want to call the generic version and not the IndexOpsMixin
return generic.NDFrame._update_inplace(self, result, **kwargs)
@property
def name(self):
return self._name
@name.setter
def name(self, value):
if value is not None and not is_hashable(value):
raise TypeError('Series.name must be a hashable type')
object.__setattr__(self, '_name', value)
# ndarray compatibility
@property
def dtype(self):
""" return the dtype object of the underlying data """
return self._data.dtype
@property
def dtypes(self):
""" return the dtype object of the underlying data """
return self._data.dtype
@property
def ftype(self):
""" return if the data is sparse|dense """
return self._data.ftype
@property
def ftypes(self):
""" return if the data is sparse|dense """
return self._data.ftype
@property
def values(self):
"""
Return Series as ndarray or ndarray-like
depending on the dtype
Returns
-------
arr : numpy.ndarray or ndarray-like
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._data.external_values()
@property
def _values(self):
""" return the internal repr of this data """
return self._data.internal_values()
def _formatting_values(self):
"""Return the values that can be formatted (used by SeriesFormatter
and DataFrameFormatter)
"""
return self._data.formatting_values()
def get_values(self):
""" same as values (but handles sparseness conversions); is a view """
return self._data.get_values()
@property
def asobject(self):
"""Return object Series which contains boxed values.
.. deprecated :: 0.23.0
Use ``astype(object)`` instead.
*this is an internal non-public method*
"""
warnings.warn("'asobject' is deprecated. Use 'astype(object)'"
" instead", FutureWarning, stacklevel=2)
return self.astype(object).values
# ops
def ravel(self, order='C'):
"""
Return the flattened underlying data as an ndarray
See also
--------
numpy.ndarray.ravel
"""
return self._values.ravel(order=order)
def compress(self, condition, *args, **kwargs):
"""
Return selected slices of an array along given axis as a Series
See also
--------
numpy.ndarray.compress
"""
nv.validate_compress(args, kwargs)
return self[condition]
def nonzero(self):
"""
Return the *integer* indices of the elements that are non-zero
This method is equivalent to calling `numpy.nonzero` on the
series data. For compatibility with NumPy, the return value is
the same (a tuple with an array of indices for each dimension),
but it will always be a one-item tuple because series only have
one dimension.
Examples
--------
>>> s = pd.Series([0, 3, 0, 4])
>>> s.nonzero()
(array([1, 3]),)
>>> s.iloc[s.nonzero()[0]]
1 3
3 4
dtype: int64
>>> s = pd.Series([0, 3, 0, 4], index=['a', 'b', 'c', 'd'])
# same return although index of s is different
>>> s.nonzero()
(array([1, 3]),)
>>> s.iloc[s.nonzero()[0]]
b 3
d 4
dtype: int64
See Also
--------
numpy.nonzero
"""
return self._values.nonzero()
def put(self, *args, **kwargs):
"""
Applies the `put` method to its `values` attribute
if it has one.
See also
--------
numpy.ndarray.put
"""
self._values.put(*args, **kwargs)
def __len__(self):
"""
return the length of the Series
"""
return len(self._data)
def view(self, dtype=None):
"""
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)
def __array__(self, result=None):
"""
the array interface, return my values
"""
return self.get_values()
def __array_wrap__(self, result, context=None):
"""
Gets called after a ufunc
"""
return self._constructor(result, index=self.index,
copy=False).__finalize__(self)
def __array_prepare__(self, result, context=None):
"""
Gets called prior to a ufunc
"""
# nice error message for non-ufunc types
if context is not None and not isinstance(self._values, np.ndarray):
obj = context[1][0]
raise TypeError("{obj} with dtype {dtype} cannot perform "
"the numpy op {op}".format(
obj=type(obj).__name__,
dtype=getattr(obj, 'dtype', None),
op=context[0].__name__))
return result
# complex
@property
def real(self):
return self.values.real
@real.setter
def real(self, v):
self.values.real = v
@property
def imag(self):
return self.values.imag
@imag.setter
def imag(self, v):
self.values.imag = v
# coercion
__float__ = _coerce_method(float)
__long__ = _coerce_method(int)
__int__ = _coerce_method(int)
def _unpickle_series_compat(self, state):
if isinstance(state, dict):
self._data = state['_data']
self.name = state['name']
self.index = self._data.index
elif isinstance(state, tuple):
# < 0.12 series pickle
nd_state, own_state = state
# recreate the ndarray
data = np.empty(nd_state[1], dtype=nd_state[2])
np.ndarray.__setstate__(data, nd_state)
# backwards compat
index, name = own_state[0], None
if len(own_state) > 1:
name = own_state[1]
# recreate
self._data = SingleBlockManager(data, index, fastpath=True)
self._index = index
self.name = name
else:
raise Exception("cannot unpickle legacy formats -> [%s]" % state)
# indexers
@property
def axes(self):
"""Return a list of the row axis labels"""
return [self.index]
def _ixs(self, i, axis=0):
"""
Return the i-th value or values in the Series by location
Parameters
----------
i : int, slice, or sequence of integers
Returns
-------
value : scalar (int) or Series (slice, sequence)
"""
try:
# dispatch to the values if we need
values = self._values
if isinstance(values, np.ndarray):
return libindex.get_value_at(values, i)
else:
return values[i]
except IndexError:
raise
except Exception:
if isinstance(i, slice):
indexer = self.index._convert_slice_indexer(i, kind='iloc')
return self._get_values(indexer)
else:
label = self.index[i]
if isinstance(label, Index):
return self.take(i, axis=axis, convert=True)
else:
return libindex.get_value_at(self, i)
@property
def _is_mixed_type(self):
return False
def _slice(self, slobj, axis=0, kind=None):
slobj = self.index._convert_slice_indexer(slobj,
kind=kind or 'getitem')
return self._get_values(slobj)
def __getitem__(self, key):
key = com._apply_if_callable(key, self)
try:
result = self.index.get_value(self, key)
if not is_scalar(result):
if is_list_like(result) and not isinstance(result, Series):
# we need to box if loc of the key isn't scalar here
# otherwise have inline ndarray/lists
try:
if not is_scalar(self.index.get_loc(key)):
result = self._constructor(
result, index=[key] * len(result),
dtype=self.dtype).__finalize__(self)
except KeyError:
pass
return result
except InvalidIndexError:
pass
except (KeyError, ValueError):
if isinstance(key, tuple) and isinstance(self.index, MultiIndex):
# kludge
pass
elif key is Ellipsis:
return self
elif com.is_bool_indexer(key):
pass
else:
# we can try to coerce the indexer (or this will raise)
new_key = self.index._convert_scalar_indexer(key,
kind='getitem')
if type(new_key) != type(key):
return self.__getitem__(new_key)
raise
except Exception:
raise
if is_iterator(key):
key = list(key)
if com.is_bool_indexer(key):
key = check_bool_indexer(self.index, key)
return self._get_with(key)
def _get_with(self, key):
# other: fancy integer or otherwise
if isinstance(key, slice):
indexer = self.index._convert_slice_indexer(key, kind='getitem')
return self._get_values(indexer)
elif isinstance(key, ABCDataFrame):
raise TypeError('Indexing a Series with DataFrame is not '
'supported, use the appropriate DataFrame column')
else:
if isinstance(key, tuple):
try:
return self._get_values_tuple(key)
except Exception:
if len(key) == 1:
key = key[0]
if isinstance(key, slice):
return self._get_values(key)
raise
# pragma: no cover
if not isinstance(key, (list, np.ndarray, Series, Index)):
key = list(key)
if isinstance(key, Index):
key_type = key.inferred_type
else:
key_type = lib.infer_dtype(key)
if key_type == 'integer':
if self.index.is_integer() or self.index.is_floating():
return self.loc[key]
else:
return self._get_values(key)
elif key_type == 'boolean':
return self._get_values(key)
else:
try:
# handle the dup indexing case (GH 4246)
if isinstance(key, (list, tuple)):
return self.loc[key]
return self.reindex(key)
except Exception:
# [slice(0, 5, None)] will break if you convert to ndarray,
# e.g. as requested by np.median
# hack
if isinstance(key[0], slice):
return self._get_values(key)
raise
def _get_values_tuple(self, key):
# mpl hackaround
if com._any_none(*key):
return self._get_values(key)
if not isinstance(self.index, MultiIndex):
raise ValueError('Can only tuple-index with 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._data.get_slice(indexer),
fastpath=True).__finalize__(self)
except Exception:
return self._values[indexer]
def __setitem__(self, key, value):
key = com._apply_if_callable(key, self)
def setitem(key, value):
try:
self._set_with_engine(key, value)
return
except com.SettingWithCopyError:
raise
except (KeyError, ValueError):
values = self._values
if (is_integer(key) and
not self.index.inferred_type == 'integer'):
values[key] = value
return
elif key is Ellipsis:
self[:] = value
return
elif com.is_bool_indexer(key):
pass
elif is_timedelta64_dtype(self.dtype):
# reassign a null value to iNaT
if isna(value):
value = iNaT
try:
self.index._engine.set_value(self._values, key,
value)
return
except TypeError:
pass
self.loc[key] = value
return
except TypeError as e:
if (isinstance(key, tuple) and
not isinstance(self.index, MultiIndex)):
raise ValueError("Can only tuple-index with a MultiIndex")
# python 3 type errors should be raised
if _is_unorderable_exception(e):
raise IndexError(key)
if com.is_bool_indexer(key):
key = check_bool_indexer(self.index, key)
try:
self._where(~key, value, inplace=True)
return
except InvalidIndexError:
pass
self._set_with(key, value)
# do the setitem
cacher_needs_updating = self._check_is_chained_assignment_possible()
setitem(key, value)
if cacher_needs_updating:
self._maybe_update_cacher()
def _set_with_engine(self, key, value):
values = self._values
try:
self.index._engine.set_value(values, key, value)
return
except KeyError:
values[self.index.get_loc(key)] = value
return
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:
if isinstance(key, tuple):
try:
self._set_values(key, value)
except Exception:
pass
if not isinstance(key, (list, Series, np.ndarray, Series)):
try:
key = list(key)
except Exception:
key = [key]
if isinstance(key, Index):
key_type = key.inferred_type
else:
key_type = lib.infer_dtype(key)
if key_type == 'integer':
if self.index.inferred_type == 'integer':
self._set_labels(key, value)
else:
return self._set_values(key, value)
elif key_type == 'boolean':
self._set_values(key.astype(np.bool_), value)
else:
self._set_labels(key, value)
def _set_labels(self, key, value):
if isinstance(key, Index):
key = key.values
else:
key = com._asarray_tuplesafe(key)
indexer = self.index.get_indexer(key)
mask = indexer == -1
if mask.any():
raise ValueError('%s not contained in the index' % str(key[mask]))
self._set_values(indexer, value)
def _set_values(self, key, value):
if isinstance(key, Series):
key = key._values
self._data = self._data.setitem(indexer=key, value=value)
self._maybe_update_cacher()
@deprecate_kwarg(old_arg_name='reps', new_arg_name='repeats')
def repeat(self, repeats, *args, **kwargs):
"""
Repeat elements of an Series. Refer to `numpy.ndarray.repeat`
for more information about the `repeats` argument.
See also
--------
numpy.ndarray.repeat
"""
nv.validate_repeat(args, kwargs)
new_index = self.index.repeat(repeats)
new_values = self._values.repeat(repeats)
return self._constructor(new_values,
index=new_index).__finalize__(self)
def get_value(self, label, takeable=False):
"""Quickly retrieve single value at passed index label
.. deprecated:: 0.21.0
Please use .at[] or .iat[] accessors.
Parameters
----------
label : object
takeable : interpret the index as indexers, default False
Returns
-------
value : scalar value
"""
warnings.warn("get_value is deprecated and will be removed "
"in a future release. Please use "
".at[] or .iat[] accessors instead", FutureWarning,
stacklevel=2)
return self._get_value(label, takeable=takeable)
def _get_value(self, label, takeable=False):
if takeable is True:
return com._maybe_box_datetimelike(self._values[label])
return self.index.get_value(self._values, label)
_get_value.__doc__ = get_value.__doc__
def set_value(self, label, value, takeable=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.
.. deprecated:: 0.21.0
Please use .at[] or .iat[] accessors.
Parameters
----------
label : object
Partial indexing with MultiIndex not allowed
value : object
Scalar value
takeable : interpret the index as indexers, default False
Returns
-------
series : Series
If label is contained, will be reference to calling Series,
otherwise a new object
"""
warnings.warn("set_value is deprecated and will be removed "
"in a future release. Please use "
".at[] or .iat[] accessors instead", FutureWarning,
stacklevel=2)
return self._set_value(label, value, takeable=takeable)
def _set_value(self, label, value, takeable=False):
try:
if takeable:
self._values[label] = value
else:
self.index._engine.set_value(self._values, label, value)
except KeyError:
# set using a non-recursive method
self.loc[label] = value
return self
_set_value.__doc__ = set_value.__doc__
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
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 = com._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 isinstance(self.index, MultiIndex):
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)
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)
def __unicode__(self):
"""
Return a string representation for a particular DataFrame
Invoked by unicode(df) in py2 only. Yields a Unicode String in both
py2/py3.
"""
buf = StringIO(u(""))
width, height = get_terminal_size()
max_rows = (height if get_option("display.max_rows") == 0 else
get_option("display.max_rows"))
show_dimensions = get_option("display.show_dimensions")
self.to_string(buf=buf, name=self.name, dtype=self.dtype,
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):
"""
Render a string representation of the Series
Parameters
----------
buf : StringIO-like, optional
buffer to write to
na_rep : string, 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: boolean, default True
Add the Series header (index name)
index : bool, optional
Add index (row) labels, default True
length : boolean, default False
Add the Series length
dtype : boolean, default False
Add the Series dtype
name : boolean, 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.
Returns
-------
formatted : string (if not buffer passed)
"""
formatter = fmt.SeriesFormatter(self, name=name, length=length,
header=header, index=index,
dtype=dtype, na_rep=na_rep,
float_format=float_format,
max_rows=max_rows)
result = formatter.to_string()
# catch contract violations
if not isinstance(result, compat.text_type):
raise AssertionError("result must be of type unicode, type"
" of result is {0!r}"
"".format(result.__class__.__name__))
if buf is None:
return result
else:
try:
buf.write(result)
except AttributeError:
with open(buf, 'w') as f:
f.write(result)
def iteritems(self):
"""
Lazily iterate over (index, value) tuples
"""
return zip(iter(self.index), iter(self))
items = iteritems
# ----------------------------------------------------------------------
# Misc public methods
def keys(self):
"""Alias for index"""
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.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.
.. versionadded:: 0.21.0
Returns
-------
value_dict : collections.Mapping
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(<type 'list'>, {0: 1, 1: 2, 2: 3, 3: 4})
"""
# GH16122
into_c = com.standardize_mapping(into)
return into_c(compat.iteritems(self))
def to_frame(self, name=None):
"""
Convert Series to DataFrame
Parameters
----------
name : object, default None
The passed name should substitute for the series name (if it has
one).
Returns
-------
data_frame : DataFrame
"""
if name is None:
df = self._constructor_expanddim(self)
else:
df = self._constructor_expanddim({name: self})
return df
def to_sparse(self, kind='block', fill_value=None):
"""
Convert Series to SparseSeries
Parameters
----------
kind : {'block', 'integer'}
fill_value : float, defaults to NaN (missing)
Returns
-------
sp : SparseSeries
"""
from pandas.core.sparse.series import SparseSeries
return SparseSeries(self, kind=kind,
fill_value=fill_value).__finalize__(self)
def _set_name(self, name, inplace=False):
"""
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
# ----------------------------------------------------------------------
# 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
-------
nobs : int or Series (if level specified)
"""
if level is None:
return notna(com._values_from_object(self)).sum()
if isinstance(level, compat.string_types):
level = self.index._get_level_number(level)
lev = self.index.levels[level]
lab = np.array(self.index.labels[level], subok=False, copy=True)
mask = lab == -1
if mask.any():
lab[mask] = cnt = len(lev)
lev = lev.insert(cnt, lev._na_value)
obs = lab[notna(self.values)]
out = np.bincount(obs, minlength=len(lev) or None)
return self._constructor(out, index=lev,
dtype='int64').__finalize__(self)
def mode(self):
"""Return the mode(s) of the dataset.
Always returns Series even if only one value is returned.
Returns
-------
modes : Series (sorted)
"""
# TODO: Add option for bins like value_counts()
return algorithms.mode(self)
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 Categorical
The unique values returned as a NumPy array. In case of categorical
data type, returned as a Categorical.
See Also
--------
pandas.unique : top-level unique method for any 1-d array-like object.
Index.unique : return Index with unique values from an Index object.
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()
array([Timestamp('2016-01-01 00:00:00-0500', tz='US/Eastern')],
dtype=object)
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(Series, self).unique()
if is_datetime64tz_dtype(self.dtype):
# we are special casing datetime64tz_dtype
# to return an object array of tz-aware Timestamps
# TODO: it must return DatetimeArray with tz in pandas 2.0
result = result.astype(object).values
return result
def drop_duplicates(self, keep='first', inplace=False):
"""
Return Series with duplicate values removed.
Parameters
----------
keep : {'first', 'last', ``False``}, default 'first'
- 'first' : Drop duplicates except for the first occurrence.
- 'last' : Drop duplicates except for the last occurrence.
- ``False`` : Drop all duplicates.
inplace : boolean, default ``False``
If ``True``, performs operation inplace and returns None.
Returns
-------
deduplicated : Series
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 an 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
"""
return super(Series, self).drop_duplicates(keep=keep, inplace=inplace)
def duplicated(self, keep='first'):
"""
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'
- '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``.
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
Returns
-------
pandas.core.series.Series
See Also
--------
pandas.Index.duplicated : Equivalent method on pandas.Index
pandas.DataFrame.duplicated : Equivalent method on pandas.DataFrame
pandas.Series.drop_duplicates : Remove duplicate values from Series
"""
return super(Series, self).duplicated(keep=keep)
def idxmin(self, axis=None, 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
----------
skipna : boolean, default True
Exclude NA/null values. If the entire Series is NA, the result
will be NA.
axis : int, default 0
For compatibility with DataFrame.idxmin. Redundant for application
on Series.
*args, **kwargs
Additional keywors have no effect but might be accepted
for compatibility with NumPy.
Returns
-------
idxmin : Index of minimum of values.
Raises
------
ValueError
If the Series is empty.
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()``.
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.
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(com._values_from_object(self), 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
----------
skipna : boolean, default True
Exclude NA/null values. If the entire Series is NA, the result
will be NA.
axis : int, default 0
For compatibility with DataFrame.idxmax. Redundant for application
on Series.
*args, **kwargs
Additional keywors have no effect but might be accepted
for compatibility with NumPy.
Returns
-------
idxmax : Index of maximum of values.
Raises
------
ValueError
If the Series is empty.
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()``.
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.
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(com._values_from_object(self), skipna=skipna)
if i == -1:
return np.nan
return self.index[i]
# ndarray compat
argmin = deprecate(
'argmin', idxmin, '0.21.0',
msg=dedent("""\
'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'
will be corrected to return the positional minimum in the future.
Use 'series.values.argmin' to get the position of the minimum now.""")
)
argmax = deprecate(
'argmax', idxmax, '0.21.0',
msg=dedent("""\
'argmax' is deprecated, use 'idxmax' instead. The behavior of 'argmax'
will be corrected to return the positional maximum in the future.
Use 'series.values.argmax' to get the position of the maximum now.""")
)
def round(self, decimals=0, *args, **kwargs):
"""
Round each value in a Series to the given number of decimals.
Parameters
----------
decimals : int
Number of decimal places to round to (default: 0).
If decimals is negative, it specifies the number of
positions to the left of the decimal point.
Returns
-------
Series object
See Also
--------
numpy.around
DataFrame.round
"""
nv.validate_round(args, kwargs)
result = com._values_from_object(self).round(decimals)
result = self._constructor(result, index=self.index).__finalize__(self)
return result
def quantile(self, q=0.5, interpolation='linear'):
"""
Return value at the given quantile, a la numpy.percentile.
Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
0 <= q <= 1, the quantile(s) to compute
interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
.. versionadded:: 0.18.0
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
-------
quantile : float or Series
if ``q`` is an array, a Series will be returned where the
index is ``q`` and the values are the quantiles.
Examples
--------
>>> s = 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
See Also
--------
pandas.core.window.Rolling.quantile
"""
self._check_percentile(q)
result = self._data.quantile(qs=q, interpolation=interpolation)
if is_list_like(q):
return self._constructor(result,
index=Float64Index(q),
name=self.name)
else:
# scalar
return result
def corr(self, other, method='pearson', min_periods=None):
"""
Compute correlation with `other` Series, excluding missing values
Parameters
----------
other : Series
method : {'pearson', 'kendall', 'spearman'}
* pearson : standard correlation coefficient
* kendall : Kendall Tau correlation coefficient
* spearman : Spearman rank correlation
min_periods : int, optional
Minimum number of observations needed to have a valid result
Returns
-------
correlation : float
"""
this, other = self.align(other, join='inner', copy=False)
if len(this) == 0:
return np.nan
return nanops.nancorr(this.values, other.values, method=method,
min_periods=min_periods)
def cov(self, other, min_periods=None):
"""
Compute covariance with Series, excluding missing values
Parameters
----------
other : Series
min_periods : int, optional
Minimum number of observations needed to have a valid result
Returns
-------
covariance : float
Normalized by N-1 (unbiased estimator).
"""
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)
def diff(self, periods=1):
"""
First discrete difference of element.
Calculates the difference of a Series element compared with another
element in the Series (default is element in previous row).
Parameters
----------
periods : int, default 1
Periods to shift for calculating difference, accepts negative
values.
Returns
-------
diffed : Series
See Also
--------
Series.pct_change: Percent change over given number of periods.
Series.shift: Shift index by desired number of periods with an
optional time freq.
DataFrame.diff: First discrete difference of object
Examples
--------
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
"""
result = algorithms.diff(com._values_from_object(self), periods)
return self._constructor(result, index=self.index).__finalize__(self)
def autocorr(self, lag=1):
"""
Lag-N autocorrelation
Parameters
----------
lag : int, default 1
Number of lags to apply before performing autocorrelation.
Returns
-------
autocorr : float
"""
return self.corr(self.shift(lag))
def dot(self, other):
"""
Matrix multiplication with DataFrame or inner-product with Series
objects. Can also be called using `self @ other` in Python >= 3.5.
Parameters
----------
other : Series or DataFrame
Returns
-------
dot_product : scalar or Series
"""
from pandas.core.frame import DataFrame
if isinstance(other, (Series, DataFrame)):
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:
left = self
lvals = self.values
rvals = np.asarray(other)
if lvals.shape[0] != rvals.shape[0]:
raise Exception('Dot product shape mismatch, %s vs %s' %
(lvals.shape, rvals.shape))
if isinstance(other, DataFrame):
return self._constructor(np.dot(lvals, rvals),
index=other.columns).__finalize__(self)
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('unsupported type: %s' % 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(other)
@Substitution(klass='Series')
@Appender(base._shared_docs['searchsorted'])
@deprecate_kwarg(old_arg_name='v', new_arg_name='value')
def searchsorted(self, value, side='left', sorter=None):
if sorter is not None:
sorter = _ensure_platform_int(sorter)
return self._values.searchsorted(Series(value)._values,
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
ignore_index : boolean, default False
If True, do not use the index labels.
.. versionadded:: 0.19.0
verify_integrity : boolean, default False
If True, raise Exception on creating index with duplicates
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.
See also
--------
pandas.concat : General function to concatenate DataFrame, Series
or Panel objects
Returns
-------
appended : Series
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_append
else:
to_concat = [self, to_append]
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
-------
combined : Series
"""
if not isinstance(other, Series):
raise AssertionError('Other operand must be Series')
new_index = self.index
this = self
if not self.index.equals(other.index):
this, other = self.align(other, level=level, join='outer',
copy=False)
new_index = this.index
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)
result = self._constructor(result, index=new_index, name=name)
result = result.__finalize__(self)
if name is None:
# When name is None, __finalize__ overwrites current name
result.name = None
return result
def combine(self, other, func, fill_value=np.nan):
"""
Perform elementwise binary operation on two Series using given function
with optional fill value when an index is missing from one Series or
the other
Parameters
----------
other : Series or scalar value
func : function
Function that takes two scalars as inputs and return a scalar
fill_value : scalar value
Returns
-------
result : Series
Examples
--------
>>> s1 = Series([1, 2])
>>> s2 = Series([0, 3])
>>> s1.combine(s2, lambda x1, x2: x1 if x1 < x2 else x2)
0 0
1 2
dtype: int64
See Also
--------
Series.combine_first : Combine Series values, choosing the calling
Series's values first
"""
if isinstance(other, Series):
new_index = self.index.union(other.index)
new_name = ops.get_op_result_name(self, other)
new_values = np.empty(len(new_index), dtype=self.dtype)
for i, idx in enumerate(new_index):
lv = self.get(idx, fill_value)
rv = other.get(idx, fill_value)
with np.errstate(all='ignore'):
new_values[i] = func(lv, rv)
else:
new_index = self.index
with np.errstate(all='ignore'):
new_values = func(self._values, other)
new_name = self.name
return self._constructor(new_values, index=new_index, name=new_name)
def combine_first(self, other):
"""
Combine Series values, choosing the calling Series's values
first. Result index will be the union of the two indexes
Parameters
----------
other : Series
Returns
-------
combined : Series
Examples
--------
>>> s1 = pd.Series([1, np.nan])
>>> s2 = pd.Series([3, 4])
>>> s1.combine_first(s2)
0 1.0
1 4.0
dtype: float64
See Also
--------
Series.combine : Perform elementwise operation on two Series
using a given function
"""
new_index = self.index.union(other.index)
this = self.reindex(new_index, copy=False)
other = other.reindex(new_index, copy=False)
# TODO: do we need name?
name = ops.get_op_result_name(self, other) # noqa
rs_vals = com._where_compat(isna(this), other._values, this._values)
return self._constructor(rs_vals, index=new_index).__finalize__(self)
def update(self, other):
"""
Modify Series in place using non-NA values from passed
Series. Aligns on index
Parameters
----------
other : 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 = other.reindex_like(self)
mask = notna(other)
self._data = self._data.putmask(mask=mask, new=other, inplace=True)
self._maybe_update_cacher()
# ----------------------------------------------------------------------
# Reindexing, sorting
def sort_values(self, axis=0, ascending=True, inplace=False,
kind='quicksort', na_position='last'):
"""
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, 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.
Returns
-------
Series
Series ordered by values.
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
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
axis = 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")
def _try_kind_sort(arr):
# easier to ask forgiveness than permission
try:
# if kind==mergesort, it can fail for object dtype
return arr.argsort(kind=kind)
except TypeError:
# stable sort not available for object dtype
# uses the argsort default quicksort
return arr.argsort(kind='quicksort')
arr = self._values
sortedIdx = np.empty(len(self), dtype=np.int32)
bad = isna(arr)
good = ~bad
idx = com._default_index(len(self))
argsorted = _try_kind_sort(arr[good])
if is_list_like(ascending):
if len(ascending) != 1:
raise ValueError('Length of ascending (%d) must be 1 '
'for Series' % (len(ascending)))
ascending = ascending[0]
if not is_bool(ascending):
raise ValueError('ascending must be boolean')
if not ascending:
argsorted = argsorted[::-1]
if na_position == 'last':
n = good.sum()
sortedIdx[:n] = idx[good][argsorted]
sortedIdx[n:] = idx[bad]
elif na_position == 'first':
n = bad.sum()
sortedIdx[n:] = idx[good][argsorted]
sortedIdx[:n] = idx[bad]
else:
raise ValueError('invalid na_position: {!r}'.format(na_position))
result = self._constructor(arr[sortedIdx], index=self.index[sortedIdx])
if inplace:
self._update_inplace(result)
else:
return result.__finalize__(self)
def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
kind='quicksort', na_position='last', sort_remaining=True):
"""
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, default true
Sort ascending vs. descending.
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.
Returns
-------
pandas.Series
The original Series sorted by the labels
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
"""
# TODO: this can be combined with DataFrame.sort_index impl as
# almost identical
inplace = validate_bool_kwarg(inplace, 'inplace')
axis = self._get_axis_number(axis)
index = self.index
if level is not None:
new_index, indexer = index.sortlevel(level, ascending=ascending,
sort_remaining=sort_remaining)
elif isinstance(index, MultiIndex):
from pandas.core.sorting import lexsort_indexer
labels = index._sort_levels_monotonic()
indexer = lexsort_indexer(labels._get_labels_for_sorting(),
orders=ascending,
na_position=na_position)
else:
from pandas.core.sorting import nargsort
# Check monotonic-ness before sort an index
# GH11080
if ((ascending and index.is_monotonic_increasing) or
(not ascending and index.is_monotonic_decreasing)):
if inplace:
return
else:
return self.copy()
indexer = nargsort(index, kind=kind, ascending=ascending,
na_position=na_position)
indexer = _ensure_platform_int(indexer)
new_index = index.take(indexer)
new_index = new_index._sort_levels_monotonic()
new_values = self._values.take(indexer)
result = self._constructor(new_values, index=new_index)
if inplace:
self._update_inplace(result)
else:
return result.__finalize__(self)
def argsort(self, axis=0, kind='quicksort', order=None):
"""
Overrides ndarray.argsort. Argsorts the value, omitting NA/null values,
and places the result in the same locations as the non-NA values
Parameters
----------
axis : int (can only be zero)
kind : {'mergesort', 'quicksort', 'heapsort'}, default 'quicksort'
Choice of sorting algorithm. See np.sort for more
information. 'mergesort' is the only stable algorithm
order : ignored
Returns
-------
argsorted : Series, with -1 indicated where nan values are present
See also
--------
numpy.ndarray.argsort
"""
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)
else:
return self._constructor(
np.argsort(values, kind=kind), index=self.index,
dtype='int64').__finalize__(self)
def nlargest(self, n=5, keep='first'):
"""
Return the largest `n` elements.
Parameters
----------
n : int
Return this many descending sorted values
keep : {'first', 'last'}, default 'first'
Where there are duplicate values:
- ``first`` : take the first occurrence.
- ``last`` : take the last occurrence.
Returns
-------
top_n : Series
The n largest values in the Series, in sorted order
Notes
-----
Faster than ``.sort_values(ascending=False).head(n)`` for small `n`
relative to the size of the ``Series`` object.
See Also
--------
Series.nsmallest
Examples
--------
>>> import pandas as pd
>>> import numpy as np
>>> s = pd.Series(np.random.randn(10**6))
>>> s.nlargest(10) # only sorts up to the N requested
219921 4.644710
82124 4.608745
421689 4.564644
425277 4.447014
718691 4.414137
43154 4.403520
283187 4.313922
595519 4.273635
503969 4.250236
121637 4.240952
dtype: float64
"""
return algorithms.SelectNSeries(self, n=n, keep=keep).nlargest()
def nsmallest(self, n=5, keep='first'):
"""
Return the smallest `n` elements.
Parameters
----------
n : int
Return this many ascending sorted values
keep : {'first', 'last'}, default 'first'
Where there are duplicate values:
- ``first`` : take the first occurrence.
- ``last`` : take the last occurrence.
Returns
-------
bottom_n : Series
The n smallest values in the Series, in sorted order
Notes
-----
Faster than ``.sort_values().head(n)`` for small `n` relative to
the size of the ``Series`` object.
See Also
--------
Series.nlargest
Examples
--------
>>> import pandas as pd
>>> import numpy as np
>>> s = pd.Series(np.random.randn(10**6))
>>> s.nsmallest(10) # only sorts up to the N requested
288532 -4.954580
732345 -4.835960
64803 -4.812550
446457 -4.609998
501225 -4.483945
669476 -4.472935
973615 -4.401699
621279 -4.355126
773916 -4.347355
359919 -4.331927
dtype: float64
"""
return algorithms.SelectNSeries(self, n=n, keep=keep).nsmallest()
def sortlevel(self, level=0, ascending=True, sort_remaining=True):
"""Sort Series with MultiIndex by chosen level. Data will be
lexicographically sorted by the chosen level followed by the other
levels (in order),
.. deprecated:: 0.20.0
Use :meth:`Series.sort_index`
Parameters
----------
level : int or level name, default None
ascending : bool, default True
Returns
-------
sorted : Series
See Also
--------
Series.sort_index(level=...)
"""
warnings.warn("sortlevel is deprecated, use sort_index(level=...)",
FutureWarning, stacklevel=2)
return self.sort_index(level=level, ascending=ascending,
sort_remaining=sort_remaining)
def swaplevel(self, i=-2, j=-1, copy=True):
"""
Swap levels i and j in a MultiIndex
Parameters
----------
i, j : int, string (can be mixed)
Level of index to be swapped. Can pass level name as string.
Returns
-------
swapped : Series
.. versionchanged:: 0.18.1
The indexes ``i`` and ``j`` are now optional, and default to
the two innermost levels of the index.
"""
new_index = self.index.swaplevel(i, j)
return self._constructor(self._values, index=new_index,
copy=copy).__finalize__(self)
def reorder_levels(self, order):
"""
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)
axis : where to reorder levels
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()
result.index = result.index.reorder_levels(order)
return result
def unstack(self, level=-1, fill_value=None):
"""
Unstack, a.k.a. pivot, Series with MultiIndex to produce DataFrame.
The level involved will automatically get sorted.
Parameters
----------
level : int, string, or list of these, default last level
Level(s) to unstack, can pass level name
fill_value : replace NaN with this value if the unstack produces
missing values
.. versionadded:: 0.18.0
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
Returns
-------
unstacked : DataFrame
"""
from pandas.core.reshape.reshape import unstack
return unstack(self, level, fill_value)
# ----------------------------------------------------------------------
# function application
def map(self, arg, na_action=None):
"""
Map values of Series using input correspondence (a dict, Series, or
function).
Parameters
----------
arg : function, dict, or Series
Mapping correspondence.
na_action : {None, 'ignore'}
If 'ignore', propagate NA values, without passing them to the
mapping correspondence.
Returns
-------
y : Series
Same index as caller.
Examples
--------
Map inputs to outputs (both of type `Series`):
>>> x = pd.Series([1,2,3], index=['one', 'two', 'three'])
>>> x
one 1
two 2
three 3
dtype: int64
>>> y = pd.Series(['foo', 'bar', 'baz'], index=[1,2,3])
>>> y
1 foo
2 bar
3 baz
>>> x.map(y)
one foo
two bar
three baz
If `arg` is a dictionary, return a new Series with values converted
according to the dictionary's mapping:
>>> z = {1: 'A', 2: 'B', 3: 'C'}
>>> x.map(z)
one A
two B
three C
Use na_action to control whether NA values are affected by the mapping
function.
>>> s = pd.Series([1, 2, 3, np.nan])
>>> s2 = s.map('this is a string {}'.format, na_action=None)
0 this is a string 1.0
1 this is a string 2.0
2 this is a string 3.0
3 this is a string nan
dtype: object
>>> s3 = s.map('this is a string {}'.format, na_action='ignore')
0 this is a string 1.0
1 this is a string 2.0
2 this is a string 3.0
3 NaN
dtype: object
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``:
>>> from collections import Counter
>>> counter = Counter()
>>> counter['bar'] += 1
>>> y.map(counter)
1 0
2 1
3 0
dtype: int64
"""
new_values = super(Series, self)._map_values(
arg, na_action=na_action)
return self._constructor(new_values,
index=self.index).__finalize__(self)
def _gotitem(self, key, ndim, subset=None):
"""
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_doc = dedent("""
Examples
--------
>>> s = Series(np.random.randn(10))
>>> s.agg('min')
-1.3018049988556679
>>> s.agg(['min', 'max'])
min -1.301805
max 1.127688
dtype: float64
See also
--------
pandas.Series.apply
pandas.Series.transform
""")
@Appender(_agg_doc)
@Appender(generic._shared_docs['aggregate'] % dict(
versionadded='.. versionadded:: 0.20.0',
**_shared_doc_kwargs))
def aggregate(self, func, axis=0, *args, **kwargs):
axis = self._get_axis_number(axis)
result, how = self._aggregate(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
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
convert_dtype : boolean, default True
Try to find better dtype for elementwise function results. If
False, leave as dtype=object
args : tuple
Positional arguments to pass to function in addition to the value
Additional keyword arguments will be passed as keywords to the function
Returns
-------
y : Series or DataFrame if func returns a Series
See also
--------
Series.map: For element-wise operations
Series.agg: only perform aggregating type operations
Series.transform: only perform transformating type operations
Examples
--------
Create a series with typical summer temperatures for each city.
>>> import pandas as pd
>>> import numpy as np
>>> series = pd.Series([20, 21, 12], index=['London',
... 'New York','Helsinki'])
>>> series
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
>>> series.apply(square)
London 400
New York 441
Helsinki 144
dtype: int64
Square the values by passing an anonymous function as an
argument to ``apply()``.
>>> series.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
>>> series.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
>>> series.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.
>>> series.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)
# 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, compat.string_types):
return self._try_aggregate_string_function(func, *args, **kwds)
# handle ufuncs and lambdas
if kwds or args and not isinstance(func, np.ufunc):
f = lambda x: 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_type(self.dtype):
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):
from pandas.core.frame import DataFrame
return DataFrame(mapped.tolist(), index=self.index)
else:
return self._constructor(mapped,
index=self.index).__finalize__(self)
def _reduce(self, op, name, 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 isinstance(delegate, np.ndarray):
# Validate that 'axis' is consistent with Series's single axis.
if axis is not None:
self._get_axis_number(axis)
if numeric_only:
raise NotImplementedError('Series.{0} does not implement '
'numeric_only.'.format(name))
with np.errstate(all='ignore'):
return op(delegate, skipna=skipna, **kwds)
return delegate._reduce(op=op, name=name, axis=axis, skipna=skipna,
numeric_only=numeric_only,
filter_type=filter_type, **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
@Appender(generic._shared_docs['align'] % _shared_doc_kwargs)
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(Series, self).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, **kwargs):
"""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
----------
index : scalar, hashable sequence, dict-like or function, optional
dict-like or functions are transformations to apply to
the index.
Scalar or hashable sequence-like will alter the ``Series.name``
attribute.
copy : boolean, default True
Also copy underlying data
inplace : boolean, default False
Whether to return a new Series. If True then value of copy is
ignored.
level : int or level name, default None
In case of a MultiIndex, only rename labels in the specified
level.
Returns
-------
renamed : Series (new object)
See Also
--------
pandas.Series.rename_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
"""
kwargs['inplace'] = validate_bool_kwarg(kwargs.get('inplace', False),
'inplace')
non_mapping = is_scalar(index) or (is_list_like(index) and
not is_dict_like(index))
if non_mapping:
return self._set_name(index, inplace=kwargs.get('inplace'))
return super(Series, self).rename(index=index, **kwargs)
@Appender(generic._shared_docs['reindex'] % _shared_doc_kwargs)
def reindex(self, index=None, **kwargs):
return super(Series, self).reindex(index=index, **kwargs)
def drop(self, labels=None, axis=0, index=None, columns=None,
level=None, inplace=False, errors='raise'):
"""
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, columns : None
Redundant for application on Series, but index can be used instead
of labels.
.. versionadded:: 0.21.0
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
-------
dropped : pandas.Series
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.
Raises
------
KeyError
If none of the labels are found in the index.
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']],
... labels=[[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(Series, self).drop(labels=labels, axis=axis, index=index,
columns=columns, level=level,
inplace=inplace, errors=errors)
@Substitution(**_shared_doc_kwargs)
@Appender(generic.NDFrame.fillna.__doc__)
def fillna(self, value=None, method=None, axis=None, inplace=False,
limit=None, downcast=None, **kwargs):
return super(Series, self).fillna(value=value, method=method,
axis=axis, inplace=inplace,
limit=limit, downcast=downcast,
**kwargs)
@Appender(generic._shared_docs['replace'] % _shared_doc_kwargs)
def replace(self, to_replace=None, value=None, inplace=False, limit=None,
regex=False, method='pad'):
return super(Series, self).replace(to_replace=to_replace, value=value,
inplace=inplace, limit=limit,
regex=regex, method=method)
@Appender(generic._shared_docs['shift'] % _shared_doc_kwargs)
def shift(self, periods=1, freq=None, axis=0):
return super(Series, self).shift(periods=periods, freq=freq, axis=axis)
def reindex_axis(self, labels, axis=0, **kwargs):
"""Conform Series to new index with optional filling logic.
.. deprecated:: 0.21.0
Use ``Series.reindex`` instead.
"""
# for compatibility with higher dims
if axis != 0:
raise ValueError("cannot reindex series on non-zero axis!")
msg = ("'.reindex_axis' is deprecated and will be removed in a future "
"version. Use '.reindex' instead.")
warnings.warn(msg, FutureWarning, stacklevel=2)
return self.reindex(index=labels, **kwargs)
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()
104
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()
96
>>> s.memory_usage(deep=True)
212
"""
v = super(Series, self).memory_usage(deep=deep)
if index:
v += self.index.memory_usage(deep=deep)
return v
@Appender(generic._shared_docs['_take'])
def _take(self, indices, axis=0, is_copy=False):
indices = _ensure_platform_int(indices)
new_index = self.index.take(indices)
if is_categorical_dtype(self):
# https://github.com/pandas-dev/pandas/issues/20664
# TODO: remove when the default Categorical.take behavior changes
indices = maybe_convert_indices(indices, len(self._get_axis(axis)))
kwargs = {'allow_fill': False}
else:
kwargs = {}
new_values = self._values.take(indices, **kwargs)
result = (self._constructor(new_values, index=new_index,
fastpath=True).__finalize__(self))
# Maybe set copy if we didn't actually change the index.
if is_copy:
if not result._get_axis(axis).equals(self._get_axis(axis)):
result._set_is_copy(self)
return result
def isin(self, values):
"""
Check whether `values` are contained in Series.
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.
.. versionadded:: 0.18.1
Support for values as a set.
Returns
-------
isin : Series (bool dtype)
Raises
------
TypeError
* If `values` is a string
See Also
--------
pandas.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
"""
result = algorithms.isin(self, values)
return self._constructor(result, index=self.index).__finalize__(self)
def between(self, left, right, inclusive=True):
"""
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
Left boundary.
right : scalar
Right boundary.
inclusive : bool, default True
Include boundaries.
Returns
-------
Series
Each element will be a boolean.
Notes
-----
This function is equivalent to ``(left <= ser) & (ser <= right)``
See Also
--------
pandas.Series.gt : Greater than of series and other
pandas.Series.lt : Less than of series and other
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
@classmethod
def from_csv(cls, path, sep=',', parse_dates=True, header=None,
index_col=0, encoding=None, infer_datetime_format=False):
"""Read CSV file.
.. deprecated:: 0.21.0
Use :func:`pandas.read_csv` instead.
It is preferable to use the more powerful :func:`pandas.read_csv`
for most general purposes, but ``from_csv`` makes for an easy
roundtrip to and from a file (the exact counterpart of
``to_csv``), especially with a time Series.
This method only differs from :func:`pandas.read_csv` in some defaults:
- `index_col` is ``0`` instead of ``None`` (take first column as index
by default)
- `header` is ``None`` instead of ``0`` (the first row is not used as
the column names)
- `parse_dates` is ``True`` instead of ``False`` (try parsing the index
as datetime by default)
With :func:`pandas.read_csv`, the option ``squeeze=True`` can be used
to return a Series like ``from_csv``.
Parameters
----------
path : string file path or file handle / StringIO
sep : string, default ','
Field delimiter
parse_dates : boolean, default True
Parse dates. Different default from read_table
header : int, default None
Row to use as header (skip prior rows)
index_col : int or sequence, default 0
Column to use for index. If a sequence is given, a MultiIndex
is used. Different default from read_table
encoding : string, optional
a string representing the encoding to use if the contents are
non-ascii, for python versions prior to 3
infer_datetime_format: boolean, default False
If True and `parse_dates` is True for a column, try to infer the
datetime format based on the first datetime string. If the format
can be inferred, there often will be a large parsing speed-up.
See also
--------
pandas.read_csv
Returns
-------
y : Series
"""
# We're calling `DataFrame.from_csv` in the implementation,
# which will propagate a warning regarding `from_csv` deprecation.
from pandas.core.frame import DataFrame
df = DataFrame.from_csv(path, header=header, index_col=index_col,
sep=sep, parse_dates=parse_dates,
encoding=encoding,
infer_datetime_format=infer_datetime_format)
result = df.iloc[:, 0]
if header is None:
result.index.name = result.name = None
return result
def to_csv(self, path=None, index=True, sep=",", na_rep='',
float_format=None, header=False, index_label=None,
mode='w', encoding=None, compression=None, date_format=None,
decimal='.'):
"""
Write Series to a comma-separated values (csv) file
Parameters
----------
path : string or file handle, default None
File path or object, if None is provided the result is returned as
a string.
na_rep : string, default ''
Missing data representation
float_format : string, default None
Format string for floating point numbers
header : boolean, default False
Write out series name
index : boolean, default True
Write row names (index)
index_label : string or sequence, default None
Column label for index column(s) if desired. If None is given, and
`header` and `index` are True, then the index names are used. A
sequence should be given if the DataFrame uses MultiIndex.
mode : Python write mode, default 'w'
sep : character, default ","
Field delimiter for the output file.
encoding : string, optional
a string representing the encoding to use if the contents are
non-ascii, for python versions prior to 3
compression : string, optional
A string representing the compression to use in the output file.
Allowed values are 'gzip', 'bz2', 'zip', 'xz'. This input is only
used when the first argument is a filename.
date_format: string, default None
Format string for datetime objects.
decimal: string, default '.'
Character recognized as decimal separator. E.g. use ',' for
European data
"""
from pandas.core.frame import DataFrame
df = DataFrame(self)
# result is only a string if no path provided, otherwise None
result = df.to_csv(path, index=index, sep=sep, na_rep=na_rep,
float_format=float_format, header=header,
index_label=index_label, mode=mode,
encoding=encoding, compression=compression,
date_format=date_format, decimal=decimal)
if path is None:
return result
@Appender(generic._shared_docs['to_excel'] % _shared_doc_kwargs)
def to_excel(self, excel_writer, sheet_name='Sheet1', na_rep='',
float_format=None, columns=None, header=True, index=True,
index_label=None, startrow=0, startcol=0, engine=None,
merge_cells=True, encoding=None, inf_rep='inf', verbose=True):
df = self.to_frame()
df.to_excel(excel_writer=excel_writer, sheet_name=sheet_name,
na_rep=na_rep, float_format=float_format, columns=columns,
header=header, index=index, index_label=index_label,
startrow=startrow, startcol=startcol, engine=engine,
merge_cells=merge_cells, encoding=encoding,
inf_rep=inf_rep, verbose=verbose)
@Appender(generic._shared_docs['isna'] % _shared_doc_kwargs)
def isna(self):
return super(Series, self).isna()
@Appender(generic._shared_docs['isna'] % _shared_doc_kwargs)
def isnull(self):
return super(Series, self).isnull()
@Appender(generic._shared_docs['notna'] % _shared_doc_kwargs)
def notna(self):
return super(Series, self).notna()
@Appender(generic._shared_docs['notna'] % _shared_doc_kwargs)
def notnull(self):
return super(Series, self).notnull()
def dropna(self, axis=0, inplace=False, **kwargs):
"""
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.
**kwargs
Not in use.
Returns
-------
Series
Series with NA entries dropped from it.
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')
kwargs.pop('how', None)
if kwargs:
raise TypeError('dropna() got an unexpected keyword '
'argument "{0}"'.format(list(kwargs.keys())[0]))
axis = 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()
def valid(self, inplace=False, **kwargs):
"""Return Series without null values.
.. deprecated:: 0.23.0
Use :meth:`Series.dropna` instead.
"""
warnings.warn("Method .valid will be removed in a future version. "
"Use .dropna instead.", FutureWarning, stacklevel=2)
return self.dropna(inplace=inplace, **kwargs)
# ----------------------------------------------------------------------
# Time series-oriented methods
def to_timestamp(self, freq=None, how='start', copy=True):
"""
Cast to datetimeindex of timestamps, at *beginning* of period
Parameters
----------
freq : string, default frequency of PeriodIndex
Desired frequency
how : {'s', 'e', 'start', 'end'}
Convention for converting period to timestamp; start of period
vs. end
Returns
-------
ts : Series with DatetimeIndex
"""
new_values = self._values
if copy:
new_values = new_values.copy()
new_index = self.index.to_timestamp(freq=freq, how=how)
return self._constructor(new_values,
index=new_index).__finalize__(self)
def to_period(self, freq=None, copy=True):
"""
Convert Series from DatetimeIndex to PeriodIndex with desired
frequency (inferred from index if not passed)
Parameters
----------
freq : string, default
Returns
-------
ts : Series with PeriodIndex
"""
new_values = self._values
if copy:
new_values = new_values.copy()
new_index = self.index.to_period(freq=freq)
return self._constructor(new_values,
index=new_index).__finalize__(self)
# ----------------------------------------------------------------------
# Accessor Methods
# ----------------------------------------------------------------------
str = CachedAccessor("str", StringMethods)
dt = CachedAccessor("dt", CombinedDatetimelikeProperties)
cat = CachedAccessor("cat", CategoricalAccessor)
plot = CachedAccessor("plot", gfx.SeriesPlotMethods)
# ----------------------------------------------------------------------
# Add plotting methods to Series
hist = gfx.hist_series
Series._setup_axes(['index'], info_axis=0, stat_axis=0, aliases={'rows': 0},
docs={'index': 'The index (axis labels) of the Series.'})
Series._add_numeric_operations()
Series._add_series_only_operations()
Series._add_series_or_dataframe_operations()
# Add arithmetic!
ops.add_flex_arithmetic_methods(Series)
ops.add_special_arithmetic_methods(Series)
# -----------------------------------------------------------------------------
# Supplementary functions
def _sanitize_index(data, index, copy=False):
""" sanitize an index type to return an ndarray of the underlying, pass
thru a non-Index
"""
if index is None:
return data
if len(data) != len(index):
raise ValueError('Length of values does not match length of ' 'index')
if isinstance(data, ABCIndexClass) and not copy:
pass
elif isinstance(data, (PeriodIndex, DatetimeIndex)):
data = data._values
if copy:
data = data.copy()
elif isinstance(data, np.ndarray):
# coerce datetimelike types
if data.dtype.kind in ['M', 'm']:
data = _sanitize_array(data, index, copy=copy)
return data
def _sanitize_array(data, index, dtype=None, copy=False,
raise_cast_failure=False):
""" sanitize input data to an ndarray, copy if specified, coerce to the
dtype if specified
"""
if dtype is not None:
dtype = pandas_dtype(dtype)
if isinstance(data, ma.MaskedArray):
mask = ma.getmaskarray(data)
if mask.any():
data, fill_value = maybe_upcast(data, copy=True)
data[mask] = fill_value
else:
data = data.copy()
def _try_cast(arr, take_fast_path):
# perf shortcut as this is the most common case
if take_fast_path:
if maybe_castable(arr) and not copy and dtype is None:
return arr
try:
subarr = maybe_cast_to_datetime(arr, dtype)
# Take care in creating object arrays (but iterators are not
# supported):
if is_object_dtype(dtype) and (is_list_like(subarr) and
not (is_iterator(subarr) or
isinstance(subarr, np.ndarray))):
subarr = construct_1d_object_array_from_listlike(subarr)
elif not is_extension_type(subarr):
subarr = construct_1d_ndarray_preserving_na(subarr, dtype,
copy=copy)
except (ValueError, TypeError):
if is_categorical_dtype(dtype):
# We *do* allow casting to categorical, since we know
# that Categorical is the only array type for 'category'.
subarr = Categorical(arr, dtype.categories,
ordered=dtype.ordered)
elif is_extension_array_dtype(dtype):
# We don't allow casting to third party dtypes, since we don't
# know what array belongs to which type.
msg = ("Cannot cast data to extension dtype '{}'. "
"Pass the extension array directly.".format(dtype))
raise ValueError(msg)
elif dtype is not None and raise_cast_failure:
raise
else:
subarr = np.array(arr, dtype=object, copy=copy)
return subarr
# GH #846
if isinstance(data, (np.ndarray, Index, Series)):
if dtype is not None:
subarr = np.array(data, copy=False)
# possibility of nan -> garbage
if is_float_dtype(data.dtype) and is_integer_dtype(dtype):
if not isna(data).any():
subarr = _try_cast(data, True)
elif copy:
subarr = data.copy()
else:
subarr = _try_cast(data, True)
elif isinstance(data, Index):
# don't coerce Index types
# e.g. indexes can have different conversions (so don't fast path
# them)
# GH 6140
subarr = _sanitize_index(data, index, copy=copy)
else:
# we will try to copy be-definition here
subarr = _try_cast(data, True)
elif isinstance(data, ExtensionArray):
subarr = data
if dtype is not None and not data.dtype.is_dtype(dtype):
msg = ("Cannot coerce extension array to dtype '{typ}'. "
"Do the coercion before passing to the constructor "
"instead.".format(typ=dtype))
raise ValueError(msg)
if copy:
subarr = data.copy()
return subarr
elif isinstance(data, (list, tuple)) and len(data) > 0:
if dtype is not None:
try:
subarr = _try_cast(data, False)
except Exception:
if raise_cast_failure: # pragma: no cover
raise
subarr = np.array(data, dtype=object, copy=copy)
subarr = lib.maybe_convert_objects(subarr)
else:
subarr = maybe_convert_platform(data)
subarr = maybe_cast_to_datetime(subarr, dtype)
elif isinstance(data, range):
# GH 16804
start, stop, step = get_range_parameters(data)
arr = np.arange(start, stop, step, dtype='int64')
subarr = _try_cast(arr, False)
else:
subarr = _try_cast(data, False)
# scalar like, GH
if getattr(subarr, 'ndim', 0) == 0:
if isinstance(data, list): # pragma: no cover
subarr = np.array(data, dtype=object)
elif index is not None:
value = data
# figure out the dtype from the value (upcast if necessary)
if dtype is None:
dtype, value = infer_dtype_from_scalar(value)
else:
# need to possibly convert the value here
value = maybe_cast_to_datetime(value, dtype)
subarr = construct_1d_arraylike_from_scalar(
value, len(index), dtype)
else:
return subarr.item()
# the result that we want
elif subarr.ndim == 1:
if index is not None:
# a 1-element ndarray
if len(subarr) != len(index) and len(subarr) == 1:
subarr = construct_1d_arraylike_from_scalar(
subarr[0], len(index), subarr.dtype)
elif subarr.ndim > 1:
if isinstance(data, np.ndarray):
raise Exception('Data must be 1-dimensional')
else:
subarr = com._asarray_tuplesafe(data, dtype=dtype)
# This is to prevent mixed-type Series getting all casted to
# NumPy string type, e.g. NaN --> '-1#IND'.
if issubclass(subarr.dtype.type, compat.string_types):
# GH 16605
# If not empty convert the data to dtype
# GH 19853: If data is a scalar, subarr has already the result
if not is_scalar(data):
if not np.all(isna(data)):
data = np.array(data, dtype=dtype, copy=False)
subarr = np.array(data, dtype=object, copy=copy)
return subarr