"""
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.dtypes.common import (
is_categorical_dtype,
is_bool,
is_integer, is_integer_dtype,
is_float_dtype,
is_extension_type, is_datetimetz,
is_datetime64tz_dtype,
is_timedelta64_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
from pandas.core.dtypes.cast import (
maybe_upcast, infer_dtype_from_scalar,
maybe_convert_platform,
maybe_cast_to_datetime, maybe_castable)
from pandas.core.dtypes.missing import isna, notna, remove_na_arraylike
from pandas.core.common import (is_bool_indexer,
_default_index,
_asarray_tuplesafe,
_values_from_object,
_try_sort,
_maybe_match_name,
SettingWithCopyError,
_maybe_box_datetimelike,
_dict_compat,
standardize_mapping,
_any_none)
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.categorical import Categorical, CategoricalAccessor
import pandas.core.strings as strings
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)
from pandas.compat.numpy import function as nv
from pandas.core import accessor
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
import pandas.plotting._core as gfx
__all__ = ['Series']
_shared_doc_kwargs = dict(
axes='index', klass='Series', axes_single_arg="{0, 'index'}",
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):
"""
DEPRECATED : this function will be removed in a future version.
"""
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
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(len(data)) 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 = frozenset(['dt', 'cat', 'str'])
_deprecations = generic.NDFrame._deprecations | frozenset(
['sortlevel', 'reshape', 'get_value', 'set_value', 'from_csv'])
_allow_index_ops = True
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):
# need to copy to avoid aliasing issues
if name is None:
name = data.name
data = data._to_embed(keep_tz=True)
copy = True
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):
if index is None:
if isinstance(data, OrderedDict):
index = Index(data)
else:
index = Index(_try_sort(data))
try:
if isinstance(index, DatetimeIndex):
if len(data):
# coerce back to datetime objects for lookup
data = _dict_compat(data)
data = lib.fast_multiget(data,
index.asobject.values,
default=np.nan)
else:
data = np.nan
# GH #12169
elif isinstance(index, (PeriodIndex, TimedeltaIndex)):
data = ([data.get(i, np.nan) for i in index]
if data else np.nan)
else:
data = lib.fast_multiget(data, index.values,
default=np.nan)
except TypeError:
data = ([data.get(i, np.nan) for i in index]
if data else np.nan)
elif isinstance(data, SingleBlockManager):
if index is None:
index = data.index
else:
data = data.reindex(index, copy=copy)
elif isinstance(data, Categorical):
# GH12574: Allow dtype=category only, otherwise error
if ((dtype is not None) and
not is_categorical_dtype(dtype)):
raise ValueError("cannot specify a dtype with a "
"Categorical unless "
"dtype='category'")
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 = _default_index(len(data))
# 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)
@classmethod
def from_array(cls, arr, index=None, name=None, dtype=None, copy=False,
fastpath=False):
# return a sparse series here
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 becuase 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
*this is an internal non-public method*
"""
return self._data.asobject
# 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 indices of the elements that are non-zero
This method is equivalent to calling `numpy.nonzero` on the
series data. For compatability 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
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):
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 we have a non-unique index here
# otherwise have inline ndarray/lists
if not self.index.is_unique:
result = self._constructor(
result, index=[key] * len(result),
dtype=self.dtype).__finalize__(self)
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 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 _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 (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 = _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 reshape(self, *args, **kwargs):
"""
.. deprecated:: 0.19.0
Calling this method will raise an error. Please call
``.values.reshape(...)`` instead.
return an ndarray with the values shape
if the specified shape matches exactly the current shape, then
return self (for compat)
See also
--------
numpy.ndarray.reshape
"""
warnings.warn("reshape is deprecated and will raise "
"in a subsequent release. Please use "
".values.reshape(...) instead", FutureWarning,
stacklevel=2)
if len(args) == 1 and hasattr(args[0], '__iter__'):
shape = args[0]
else:
shape = args
if tuple(shape) == self.shape:
# XXX ignoring the "order" keyword.
nv.validate_reshape(tuple(), kwargs)
return self
return self._values.reshape(shape, **kwargs)
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
----------
index : label
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 _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):
"""
Analogous to the :meth:`pandas.DataFrame.reset_index` function, see
docstring there.
Parameters
----------
level : int, str, tuple, or list, default None
Only remove the given levels from the index. Removes all levels by
default
drop : boolean, default False
Do not try to insert index into dataframe columns
name : object, default None
The name of the column corresponding to the Series values
inplace : boolean, default False
Modify the Series in place (do not create a new object)
Returns
----------
resetted : DataFrame, or Series if drop == True
Examples
--------
>>> s = pd.Series([1, 2, 3, 4], index=pd.Index(['a', 'b', 'c', 'd'],
... name = 'idx'))
>>> s.reset_index()
index 0
0 0 1
1 1 2
2 2 3
3 3 4
>>> arrays = [np.array(['bar', 'bar', 'baz', 'baz', 'foo',
... 'foo', 'qux', 'qux']),
... np.array(['one', 'two', 'one', 'two', 'one', 'two',
... 'one', 'two'])]
>>> s2 = pd.Series(
... np.random.randn(8),
... index=pd.MultiIndex.from_arrays(arrays,
... names=['a', 'b']))
>>> s2.reset_index(level='a')
a 0
b
one bar -0.286320
two bar -0.587934
one baz 0.710491
two baz -1.429006
one foo 0.790700
two foo 0.824863
one qux -0.718963
two qux -0.055028
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
if drop:
new_index = _default_index(len(self))
if level is not None and isinstance(self.index, MultiIndex):
if not isinstance(level, (tuple, list)):
level = [level]
level = [self.index._get_level_number(lev) for lev in level]
if len(level) < len(self.index.levels):
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 = 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)
"""
from pandas.core.index import _get_na_value
if level is None:
return notna(_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, _get_na_value(lev.dtype.type))
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)
@Appender(base._shared_docs['unique'] % _shared_doc_kwargs)
def unique(self):
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.asobject.values
return result
@Appender(base._shared_docs['drop_duplicates'] % _shared_doc_kwargs)
def drop_duplicates(self, keep='first', inplace=False):
return super(Series, self).drop_duplicates(keep=keep, inplace=inplace)
@Appender(base._shared_docs['duplicated'] % _shared_doc_kwargs)
def duplicated(self, keep='first'):
return super(Series, self).duplicated(keep=keep)
def idxmin(self, axis=None, skipna=True, *args, **kwargs):
"""
Index *label* of the first occurrence of minimum of values.
Parameters
----------
skipna : boolean, default True
Exclude NA/null values. If the entire Series is NA, the result
will be NA.
Raises
------
ValueError
* If the Series is empty
Returns
-------
idxmin : Index of minimum of values
Notes
-----
This method is the Series version of ``ndarray.argmin``. This method
returns the label of the minimum, while ``ndarray.argmin`` returns
the position. To get the position, use ``series.values.argmin()``.
See Also
--------
DataFrame.idxmin
numpy.ndarray.argmin
"""
skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs)
i = nanops.nanargmin(_values_from_object(self), skipna=skipna)
if i == -1:
return np.nan
return self.index[i]
def idxmax(self, axis=None, skipna=True, *args, **kwargs):
"""
Index *label* of the first occurrence of maximum of values.
Parameters
----------
skipna : boolean, default True
Exclude NA/null values. If the entire Series is NA, the result
will be NA.
Raises
------
ValueError
* If the Series is empty
Returns
-------
idxmax : Index of maximum of values
Notes
-----
This method is the Series version of ``ndarray.argmax``. This method
returns the label of the maximum, while ``ndarray.argmax`` returns
the position. To get the position, use ``series.values.argmax()``.
See Also
--------
DataFrame.idxmax
numpy.ndarray.argmax
"""
skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs)
i = nanops.nanargmax(_values_from_object(self), skipna=skipna)
if i == -1:
return np.nan
return self.index[i]
# ndarray compat
argmin = deprecate('argmin', idxmin,
msg="'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,
msg="'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 = _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
"""
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):
"""
1st discrete difference of object
Parameters
----------
periods : int, default 1
Periods to shift for forming difference
Returns
-------
diffed : Series
"""
result = algorithms.diff(_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
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))
@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 = this.values
other_vals = other.values
if fill_value is not None:
this_mask = isna(this_vals)
other_mask = isna(other_vals)
this_vals = this_vals.copy()
other_vals = other_vals.copy()
# one but not both
mask = this_mask ^ other_mask
this_vals[this_mask & mask] = fill_value
other_vals[other_mask & mask] = fill_value
with np.errstate(all='ignore'):
result = func(this_vals, other_vals)
name = _maybe_match_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 = _maybe_match_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 = _maybe_match_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
@Appender(generic._shared_docs['sort_values'] % _shared_doc_kwargs)
def sort_values(self, axis=0, ascending=True, inplace=False,
kind='quicksort', na_position='last'):
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 = _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)
@Appender(generic._shared_docs['sort_index'] % _shared_doc_kwargs)
def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
kind='quicksort', na_position='last', sort_remaining=True):
# 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:
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):
"""
DEPRECATED: use :meth:`Series.sort_index`
Sort Series with MultiIndex by chosen level. Data will be
lexicographically sorted by the chosen level followed by the other
levels (in order)
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 (which can be
a dict, Series, or function)
Parameters
----------
arg : function, dict, or Series
na_action : {None, 'ignore'}
If 'ignore', propagate NA values, without passing them to the
mapping function
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
"""
if is_extension_type(self.dtype):
values = self._values
if na_action is not None:
raise NotImplementedError
map_f = lambda values, f: values.map(f)
else:
values = self.asobject
if na_action == 'ignore':
def map_f(values, f):
return lib.map_infer_mask(values, f,
isna(values).view(np.uint8))
else:
map_f = lib.map_infer
if isinstance(arg, dict):
if hasattr(arg, '__missing__'):
# If a dictionary subclass defines a default value method,
# convert arg to a lookup function (GH #15999).
dict_with_default = arg
arg = lambda x: dict_with_default[x]
else:
# Dictionary does not have a default. Thus it's safe to
# convert to an indexed series for efficiency.
arg = self._constructor(arg, index=arg.keys())
if isinstance(arg, Series):
# arg is a Series
indexer = arg.index.get_indexer(values)
new_values = algorithms.take_1d(arg._values, indexer)
else:
# arg is a function
new_values = map_f(values, arg)
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, becuase
# 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.asobject
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.
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
# be subclass-friendly
new_values = algorithms.take_1d(self.get_values(), indexer)
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 %(klass)s. 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)
@Appender(generic._shared_docs['fillna'] % _shared_doc_kwargs)
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['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):
""" 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):
"""Memory usage of the Series
Parameters
----------
index : bool
Specifies whether to include memory usage of Series index
deep : bool
Introspect the data deeply, interrogate
`object` dtypes for system-level memory consumption
Returns
-------
scalar bytes of memory consumed
Notes
-----
Memory usage does not include memory consumed by elements that
are not components of the array if deep=False
See Also
--------
numpy.ndarray.nbytes
"""
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, convert=True, is_copy=False):
if convert:
indices = maybe_convert_indices(indices, len(self._get_axis(axis)))
indices = _ensure_platform_int(indices)
new_index = self.index.take(indices)
new_values = self._values.take(indices)
result = (self._constructor(new_values, index=new_index,
fastpath=True).__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):
"""
Return a boolean :class:`~pandas.Series` showing whether each element
in the :class:`~pandas.Series` is exactly contained in the passed
sequence of ``values``.
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
Examples
--------
>>> s = pd.Series(list('abc'))
>>> s.isin(['a', 'c', 'e'])
0 True
1 False
2 True
dtype: bool
Passing a single string as ``s.isin('a')`` will raise an error. Use
a list of one element instead:
>>> s.isin(['a'])
0 True
1 False
2 False
dtype: bool
"""
result = algorithms.isin(_values_from_object(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. NA values
will be treated as False
Parameters
----------
left : scalar
Left boundary
right : scalar
Right boundary
Returns
-------
is_between : Series
"""
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, please 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, 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
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, 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 Series without null values
Returns
-------
valid : Series
inplace : boolean, default False
Do operation in place.
"""
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()
valid = lambda self, inplace=False, **kwargs: self.dropna(inplace=inplace,
**kwargs)
@Appender(generic._shared_docs['valid_index'] % {
'position': 'first', 'klass': 'Series'})
def first_valid_index(self):
if len(self) == 0:
return None
mask = isna(self._values)
i = mask.argmin()
if mask[i]:
return None
else:
return self.index[i]
@Appender(generic._shared_docs['valid_index'] % {
'position': 'last', 'klass': 'Series'})
def last_valid_index(self):
if len(self) == 0:
return None
mask = isna(self._values[::-1])
i = mask.argmin()
if mask[i]:
return None
else:
return self.index[len(self) - i - 1]
# ----------------------------------------------------------------------
# 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)
# -------------------------------------------------------------------------
# Datetimelike delegation methods
dt = accessor.AccessorProperty(CombinedDatetimelikeProperties)
# -------------------------------------------------------------------------
# Categorical methods
cat = accessor.AccessorProperty(CategoricalAccessor)
# String Methods
str = accessor.AccessorProperty(strings.StringMethods)
# ----------------------------------------------------------------------
# Add plotting methods to Series
plot = accessor.AccessorProperty(gfx.SeriesPlotMethods,
gfx.SeriesPlotMethods)
hist = gfx.hist_series
Series._setup_axes(['index'], info_axis=0, stat_axis=0, aliases={'rows': 0})
Series._add_numeric_operations()
Series._add_series_only_operations()
Series._add_series_or_dataframe_operations()
# Add arithmetic!
ops.add_flex_arithmetic_methods(Series, **ops.series_flex_funcs)
ops.add_special_arithmetic_methods(Series, **ops.series_special_funcs)
# -----------------------------------------------------------------------------
# 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, PeriodIndex):
data = data.asobject
elif isinstance(data, DatetimeIndex):
data = data._to_embed(keep_tz=True)
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)
if not is_extension_type(subarr):
subarr = np.array(subarr, dtype=dtype, copy=copy)
except (ValueError, TypeError):
if is_categorical_dtype(dtype):
subarr = Categorical(arr, dtype.categories,
ordered=dtype.ordered)
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=True)
else:
subarr = _try_cast(data, True)
if copy:
subarr = data.copy()
elif isinstance(data, Categorical):
subarr = data
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)
def create_from_value(value, index, dtype):
# return a new empty value suitable for the dtype
if is_datetimetz(dtype):
subarr = DatetimeIndex([value] * len(index), dtype=dtype)
elif is_categorical_dtype(dtype):
subarr = Categorical([value] * len(index))
else:
if not isinstance(dtype, (np.dtype, type(np.dtype))):
dtype = dtype.dtype
subarr = np.empty(len(index), dtype=dtype)
subarr.fill(value)
return subarr
# 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 = create_from_value(value, 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 = create_from_value(subarr[0], index,
subarr.dtype)
elif subarr.ndim > 1:
if isinstance(data, np.ndarray):
raise Exception('Data must be 1-dimensional')
else:
subarr = _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):
subarr = np.array(data, dtype=object, copy=copy)
return subarr