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Version:
0.21.1 ▾
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import datetime
import warnings
import operator
import numpy as np
from pandas._libs import (lib, index as libindex, tslib as libts,
algos as libalgos, join as libjoin,
Timestamp, Timedelta, )
from pandas._libs.lib import is_datetime_array
from pandas._libs.tslibs import parsing
from pandas.compat import range, u
from pandas.compat.numpy import function as nv
from pandas import compat
from pandas.core.dtypes.generic import (
ABCSeries,
ABCMultiIndex,
ABCPeriodIndex,
ABCDateOffset)
from pandas.core.dtypes.missing import isna, array_equivalent
from pandas.core.dtypes.common import (
_ensure_int64,
_ensure_object,
_ensure_categorical,
_ensure_platform_int,
is_integer,
is_float,
is_dtype_equal,
is_dtype_union_equal,
is_object_dtype,
is_categorical_dtype,
is_interval_dtype,
is_bool,
is_bool_dtype,
is_signed_integer_dtype,
is_unsigned_integer_dtype,
is_integer_dtype, is_float_dtype,
is_datetime64_any_dtype,
is_timedelta64_dtype,
needs_i8_conversion,
is_iterator, is_list_like,
is_scalar)
from pandas.core.common import (is_bool_indexer, _values_from_object,
_asarray_tuplesafe, _not_none,
_index_labels_to_array)
from pandas.core.base import PandasObject, IndexOpsMixin
import pandas.core.base as base
from pandas.util._decorators import (
Appender, Substitution, cache_readonly, deprecate_kwarg)
from pandas.core.indexes.frozen import FrozenList
import pandas.core.dtypes.concat as _concat
import pandas.core.missing as missing
import pandas.core.algorithms as algos
import pandas.core.sorting as sorting
from pandas.io.formats.printing import pprint_thing
from pandas.core.ops import _comp_method_OBJECT_ARRAY
from pandas.core import strings, accessor
from pandas.core.config import get_option
# simplify
default_pprint = lambda x, max_seq_items=None: \
pprint_thing(x, escape_chars=('\t', '\r', '\n'), quote_strings=True,
max_seq_items=max_seq_items)
__all__ = ['Index']
_unsortable_types = frozenset(('mixed', 'mixed-integer'))
_index_doc_kwargs = dict(klass='Index', inplace='',
target_klass='Index',
unique='Index', duplicated='np.ndarray')
_index_shared_docs = dict()
def _try_get_item(x):
try:
return x.item()
except AttributeError:
return x
class InvalidIndexError(Exception):
pass
_o_dtype = np.dtype(object)
_Identity = object
def _new_Index(cls, d):
""" This is called upon unpickling, rather than the default which doesn't
have arguments and breaks __new__
"""
# required for backward compat, because PI can't be instantiated with
# ordinals through __new__ GH #13277
if issubclass(cls, ABCPeriodIndex):
from pandas.core.indexes.period import _new_PeriodIndex
return _new_PeriodIndex(cls, **d)
return cls.__new__(cls, **d)
class Index(IndexOpsMixin, PandasObject):
"""
Immutable ndarray implementing an ordered, sliceable set. The basic object
storing axis labels for all pandas objects
Parameters
----------
data : array-like (1-dimensional)
dtype : NumPy dtype (default: object)
copy : bool
Make a copy of input ndarray
name : object
Name to be stored in the index
tupleize_cols : bool (default: True)
When True, attempt to create a MultiIndex if possible
Notes
-----
An Index instance can **only** contain hashable objects
Examples
--------
>>> pd.Index([1, 2, 3])
Int64Index([1, 2, 3], dtype='int64')
>>> pd.Index(list('abc'))
Index(['a', 'b', 'c'], dtype='object')
See Also
---------
RangeIndex : Index implementing a monotonic integer range
CategoricalIndex : Index of :class:`Categorical` s.
MultiIndex : A multi-level, or hierarchical, Index
IntervalIndex : an Index of :class:`Interval` s.
DatetimeIndex, TimedeltaIndex, PeriodIndex
Int64Index, UInt64Index, Float64Index
"""
# To hand over control to subclasses
_join_precedence = 1
# Cython methods
_arrmap = libalgos.arrmap_object
_left_indexer_unique = libjoin.left_join_indexer_unique_object
_left_indexer = libjoin.left_join_indexer_object
_inner_indexer = libjoin.inner_join_indexer_object
_outer_indexer = libjoin.outer_join_indexer_object
_box_scalars = False
_typ = 'index'
_data = None
_id = None
name = None
asi8 = None
_comparables = ['name']
_attributes = ['name']
_allow_index_ops = True
_allow_datetime_index_ops = False
_allow_period_index_ops = False
_is_numeric_dtype = False
_can_hold_na = True
# would we like our indexing holder to defer to us
_defer_to_indexing = False
# prioritize current class for _shallow_copy_with_infer,
# used to infer integers as datetime-likes
_infer_as_myclass = False
_engine_type = libindex.ObjectEngine
_accessors = frozenset(['str'])
# String Methods
str = accessor.AccessorProperty(strings.StringMethods)
def __new__(cls, data=None, dtype=None, copy=False, name=None,
fastpath=False, tupleize_cols=True, **kwargs):
if name is None and hasattr(data, 'name'):
name = data.name
if fastpath:
return cls._simple_new(data, name)
from .range import RangeIndex
# range
if isinstance(data, RangeIndex):
return RangeIndex(start=data, copy=copy, dtype=dtype, name=name)
elif isinstance(data, range):
return RangeIndex.from_range(data, copy=copy, dtype=dtype,
name=name)
# categorical
if is_categorical_dtype(data) or is_categorical_dtype(dtype):
from .category import CategoricalIndex
return CategoricalIndex(data, copy=copy, name=name, **kwargs)
# interval
if is_interval_dtype(data):
from .interval import IntervalIndex
return IntervalIndex.from_intervals(data, name=name,
copy=copy)
# index-like
elif isinstance(data, (np.ndarray, Index, ABCSeries)):
if (is_datetime64_any_dtype(data) or
(dtype is not None and is_datetime64_any_dtype(dtype)) or
'tz' in kwargs):
from pandas.core.indexes.datetimes import DatetimeIndex
result = DatetimeIndex(data, copy=copy, name=name,
dtype=dtype, **kwargs)
if dtype is not None and is_dtype_equal(_o_dtype, dtype):
return Index(result.to_pydatetime(), dtype=_o_dtype)
else:
return result
elif (is_timedelta64_dtype(data) or
(dtype is not None and is_timedelta64_dtype(dtype))):
from pandas.core.indexes.timedeltas import TimedeltaIndex
result = TimedeltaIndex(data, copy=copy, name=name, **kwargs)
if dtype is not None and _o_dtype == dtype:
return Index(result.to_pytimedelta(), dtype=_o_dtype)
else:
return result
if dtype is not None:
try:
# we need to avoid having numpy coerce
# things that look like ints/floats to ints unless
# they are actually ints, e.g. '0' and 0.0
# should not be coerced
# GH 11836
if is_integer_dtype(dtype):
inferred = lib.infer_dtype(data)
if inferred == 'integer':
data = np.array(data, copy=copy, dtype=dtype)
elif inferred in ['floating', 'mixed-integer-float']:
if isna(data).any():
raise ValueError('cannot convert float '
'NaN to integer')
# If we are actually all equal to integers,
# then coerce to integer.
try:
return cls._try_convert_to_int_index(
data, copy, name)
except ValueError:
pass
# Return an actual float index.
from .numeric import Float64Index
return Float64Index(data, copy=copy, dtype=dtype,
name=name)
elif inferred == 'string':
pass
else:
data = data.astype(dtype)
elif is_float_dtype(dtype):
inferred = lib.infer_dtype(data)
if inferred == 'string':
pass
else:
data = data.astype(dtype)
else:
data = np.array(data, dtype=dtype, copy=copy)
except (TypeError, ValueError) as e:
msg = str(e)
if 'cannot convert float' in msg:
raise
# maybe coerce to a sub-class
from pandas.core.indexes.period import (
PeriodIndex, IncompatibleFrequency)
if isinstance(data, PeriodIndex):
return PeriodIndex(data, copy=copy, name=name, **kwargs)
if is_signed_integer_dtype(data.dtype):
from .numeric import Int64Index
return Int64Index(data, copy=copy, dtype=dtype, name=name)
elif is_unsigned_integer_dtype(data.dtype):
from .numeric import UInt64Index
return UInt64Index(data, copy=copy, dtype=dtype, name=name)
elif is_float_dtype(data.dtype):
from .numeric import Float64Index
return Float64Index(data, copy=copy, dtype=dtype, name=name)
elif issubclass(data.dtype.type, np.bool) or is_bool_dtype(data):
subarr = data.astype('object')
else:
subarr = _asarray_tuplesafe(data, dtype=object)
# _asarray_tuplesafe does not always copy underlying data,
# so need to make sure that this happens
if copy:
subarr = subarr.copy()
if dtype is None:
inferred = lib.infer_dtype(subarr)
if inferred == 'integer':
try:
return cls._try_convert_to_int_index(
subarr, copy, name)
except ValueError:
pass
return Index(subarr, copy=copy,
dtype=object, name=name)
elif inferred in ['floating', 'mixed-integer-float']:
from .numeric import Float64Index
return Float64Index(subarr, copy=copy, name=name)
elif inferred == 'interval':
from .interval import IntervalIndex
return IntervalIndex.from_intervals(subarr, name=name,
copy=copy)
elif inferred == 'boolean':
# don't support boolean explicity ATM
pass
elif inferred != 'string':
if inferred.startswith('datetime'):
if (lib.is_datetime_with_singletz_array(subarr) or
'tz' in kwargs):
# only when subarr has the same tz
from pandas.core.indexes.datetimes import (
DatetimeIndex)
try:
return DatetimeIndex(subarr, copy=copy,
name=name, **kwargs)
except libts.OutOfBoundsDatetime:
pass
elif inferred.startswith('timedelta'):
from pandas.core.indexes.timedeltas import (
TimedeltaIndex)
return TimedeltaIndex(subarr, copy=copy, name=name,
**kwargs)
elif inferred == 'period':
try:
return PeriodIndex(subarr, name=name, **kwargs)
except IncompatibleFrequency:
pass
return cls._simple_new(subarr, name)
elif hasattr(data, '__array__'):
return Index(np.asarray(data), dtype=dtype, copy=copy, name=name,
**kwargs)
elif data is None or is_scalar(data):
cls._scalar_data_error(data)
else:
if (tupleize_cols and isinstance(data, list) and data and
isinstance(data[0], tuple)):
# we must be all tuples, otherwise don't construct
# 10697
if all(isinstance(e, tuple) for e in data):
try:
# must be orderable in py3
if compat.PY3:
sorted(data)
from .multi import MultiIndex
return MultiIndex.from_tuples(
data, names=name or kwargs.get('names'))
except (TypeError, KeyError):
# python2 - MultiIndex fails on mixed types
pass
# other iterable of some kind
subarr = _asarray_tuplesafe(data, dtype=object)
return Index(subarr, dtype=dtype, copy=copy, name=name, **kwargs)
"""
NOTE for new Index creation:
- _simple_new: It returns new Index with the same type as the caller.
All metadata (such as name) must be provided by caller's responsibility.
Using _shallow_copy is recommended because it fills these metadata
otherwise specified.
- _shallow_copy: It returns new Index with the same type (using
_simple_new), but fills caller's metadata otherwise specified. Passed
kwargs will overwrite corresponding metadata.
- _shallow_copy_with_infer: It returns new Index inferring its type
from passed values. It fills caller's metadata otherwise specified as the
same as _shallow_copy.
See each method's docstring.
"""
@classmethod
def _simple_new(cls, values, name=None, dtype=None, **kwargs):
"""
we require the we have a dtype compat for the values
if we are passed a non-dtype compat, then coerce using the constructor
Must be careful not to recurse.
"""
if not hasattr(values, 'dtype'):
if values is None and dtype is not None:
values = np.empty(0, dtype=dtype)
else:
values = np.array(values, copy=False)
if is_object_dtype(values):
values = cls(values, name=name, dtype=dtype,
**kwargs)._values
result = object.__new__(cls)
result._data = values
result.name = name
for k, v in compat.iteritems(kwargs):
setattr(result, k, v)
return result._reset_identity()
_index_shared_docs['_shallow_copy'] = """
create a new Index with the same class as the caller, don't copy the
data, use the same object attributes with passed in attributes taking
precedence
*this is an internal non-public method*
Parameters
----------
values : the values to create the new Index, optional
kwargs : updates the default attributes for this Index
"""
@Appender(_index_shared_docs['_shallow_copy'])
def _shallow_copy(self, values=None, **kwargs):
if values is None:
values = self.values
attributes = self._get_attributes_dict()
attributes.update(kwargs)
return self._simple_new(values, **attributes)
def _shallow_copy_with_infer(self, values=None, **kwargs):
"""
create a new Index inferring the class with passed value, don't copy
the data, use the same object attributes with passed in attributes
taking precedence
*this is an internal non-public method*
Parameters
----------
values : the values to create the new Index, optional
kwargs : updates the default attributes for this Index
"""
if values is None:
values = self.values
attributes = self._get_attributes_dict()
attributes.update(kwargs)
attributes['copy'] = False
if self._infer_as_myclass:
try:
return self._constructor(values, **attributes)
except (TypeError, ValueError):
pass
return Index(values, **attributes)
def _deepcopy_if_needed(self, orig, copy=False):
"""
.. versionadded:: 0.19.0
Make a copy of self if data coincides (in memory) with orig.
Subclasses should override this if self._base is not an ndarray.
Parameters
----------
orig : ndarray
other ndarray to compare self._data against
copy : boolean, default False
when False, do not run any check, just return self
Returns
-------
A copy of self if needed, otherwise self : Index
"""
if copy:
# Retrieve the "base objects", i.e. the original memory allocations
orig = orig if orig.base is None else orig.base
new = self._data if self._data.base is None else self._data.base
if orig is new:
return self.copy(deep=True)
return self
def _update_inplace(self, result, **kwargs):
# guard when called from IndexOpsMixin
raise TypeError("Index can't be updated inplace")
def _sort_levels_monotonic(self):
""" compat with MultiIndex """
return self
_index_shared_docs['_get_grouper_for_level'] = """
Get index grouper corresponding to an index level
Parameters
----------
mapper: Group mapping function or None
Function mapping index values to groups
level : int or None
Index level
Returns
-------
grouper : Index
Index of values to group on
labels : ndarray of int or None
Array of locations in level_index
uniques : Index or None
Index of unique values for level
"""
@Appender(_index_shared_docs['_get_grouper_for_level'])
def _get_grouper_for_level(self, mapper, level=None):
assert level is None or level == 0
if mapper is None:
grouper = self
else:
grouper = self.map(mapper)
return grouper, None, None
def is_(self, other):
"""
More flexible, faster check like ``is`` but that works through views
Note: this is *not* the same as ``Index.identical()``, which checks
that metadata is also the same.
Parameters
----------
other : object
other object to compare against.
Returns
-------
True if both have same underlying data, False otherwise : bool
"""
# use something other than None to be clearer
return self._id is getattr(
other, '_id', Ellipsis) and self._id is not None
def _reset_identity(self):
"""Initializes or resets ``_id`` attribute with new object"""
self._id = _Identity()
return self
# ndarray compat
def __len__(self):
"""
return the length of the Index
"""
return len(self._data)
def __array__(self, dtype=None):
""" the array interface, return my values """
return self._data.view(np.ndarray)
def __array_wrap__(self, result, context=None):
"""
Gets called after a ufunc
"""
if is_bool_dtype(result):
return result
attrs = self._get_attributes_dict()
attrs = self._maybe_update_attributes(attrs)
return Index(result, **attrs)
@cache_readonly
def dtype(self):
""" return the dtype object of the underlying data """
return self._data.dtype
@cache_readonly
def dtype_str(self):
""" return the dtype str of the underlying data """
return str(self.dtype)
@property
def values(self):
""" return the underlying data as an ndarray """
return self._data.view(np.ndarray)
def get_values(self):
""" return the underlying data as an ndarray """
return self.values
@Appender(IndexOpsMixin.memory_usage.__doc__)
def memory_usage(self, deep=False):
result = super(Index, self).memory_usage(deep=deep)
# include our engine hashtable
result += self._engine.sizeof(deep=deep)
return result
# ops compat
@deprecate_kwarg(old_arg_name='n', new_arg_name='repeats')
def repeat(self, repeats, *args, **kwargs):
"""
Repeat elements of an Index. Refer to `numpy.ndarray.repeat`
for more information about the `repeats` argument.
See also
--------
numpy.ndarray.repeat
"""
nv.validate_repeat(args, kwargs)
return self._shallow_copy(self._values.repeat(repeats))
_index_shared_docs['where'] = """
.. versionadded:: 0.19.0
Return an Index of same shape as self and whose corresponding
entries are from self where cond is True and otherwise are from
other.
Parameters
----------
cond : boolean array-like with the same length as self
other : scalar, or array-like
"""
@Appender(_index_shared_docs['where'])
def where(self, cond, other=None):
if other is None:
other = self._na_value
dtype = self.dtype
values = self.values
if is_bool(other) or is_bool_dtype(other):
# bools force casting
values = values.astype(object)
dtype = None
values = np.where(cond, values, other)
if self._is_numeric_dtype and np.any(isna(values)):
# We can't coerce to the numeric dtype of "self" (unless
# it's float) if there are NaN values in our output.
dtype = None
return self._shallow_copy_with_infer(values, dtype=dtype)
def ravel(self, order='C'):
"""
return an ndarray of the flattened values of the underlying data
See also
--------
numpy.ndarray.ravel
"""
return self._values.ravel(order=order)
# construction helpers
@classmethod
def _try_convert_to_int_index(cls, data, copy, name):
"""
Attempt to convert an array of data into an integer index.
Parameters
----------
data : The data to convert.
copy : Whether to copy the data or not.
name : The name of the index returned.
Returns
-------
int_index : data converted to either an Int64Index or a
UInt64Index
Raises
------
ValueError if the conversion was not successful.
"""
from .numeric import Int64Index, UInt64Index
try:
res = data.astype('i8', copy=False)
if (res == data).all():
return Int64Index(res, copy=copy, name=name)
except (OverflowError, TypeError, ValueError):
pass
# Conversion to int64 failed (possibly due to
# overflow), so let's try now with uint64.
try:
res = data.astype('u8', copy=False)
if (res == data).all():
return UInt64Index(res, copy=copy, name=name)
except (OverflowError, TypeError, ValueError):
pass
raise ValueError
@classmethod
def _scalar_data_error(cls, data):
raise TypeError('{0}(...) must be called with a collection of some '
'kind, {1} was passed'.format(cls.__name__,
repr(data)))
@classmethod
def _string_data_error(cls, data):
raise TypeError('String dtype not supported, you may need '
'to explicitly cast to a numeric type')
@classmethod
def _coerce_to_ndarray(cls, data):
"""coerces data to ndarray, raises on scalar data. Converts other
iterables to list first and then to array. Does not touch ndarrays.
"""
if not isinstance(data, (np.ndarray, Index)):
if data is None or is_scalar(data):
cls._scalar_data_error(data)
# other iterable of some kind
if not isinstance(data, (ABCSeries, list, tuple)):
data = list(data)
data = np.asarray(data)
return data
def _get_attributes_dict(self):
""" return an attributes dict for my class """
return dict([(k, getattr(self, k, None)) for k in self._attributes])
def view(self, cls=None):
# we need to see if we are subclassing an
# index type here
if cls is not None and not hasattr(cls, '_typ'):
result = self._data.view(cls)
else:
result = self._shallow_copy()
if isinstance(result, Index):
result._id = self._id
return result
def _coerce_scalar_to_index(self, item):
"""
we need to coerce a scalar to a compat for our index type
Parameters
----------
item : scalar item to coerce
"""
dtype = self.dtype
if self._is_numeric_dtype and isna(item):
# We can't coerce to the numeric dtype of "self" (unless
# it's float) if there are NaN values in our output.
dtype = None
return Index([item], dtype=dtype, **self._get_attributes_dict())
_index_shared_docs['copy'] = """
Make a copy of this object. Name and dtype sets those attributes on
the new object.
Parameters
----------
name : string, optional
deep : boolean, default False
dtype : numpy dtype or pandas type
Returns
-------
copy : Index
Notes
-----
In most cases, there should be no functional difference from using
``deep``, but if ``deep`` is passed it will attempt to deepcopy.
"""
@Appender(_index_shared_docs['copy'])
def copy(self, name=None, deep=False, dtype=None, **kwargs):
if deep:
new_index = self._shallow_copy(self._data.copy())
else:
new_index = self._shallow_copy()
names = kwargs.get('names')
names = self._validate_names(name=name, names=names, deep=deep)
new_index = new_index.set_names(names)
if dtype:
new_index = new_index.astype(dtype)
return new_index
def __copy__(self, **kwargs):
return self.copy(**kwargs)
def __deepcopy__(self, memo=None):
if memo is None:
memo = {}
return self.copy(deep=True)
def _validate_names(self, name=None, names=None, deep=False):
"""
Handles the quirks of having a singular 'name' parameter for general
Index and plural 'names' parameter for MultiIndex.
"""
from copy import deepcopy
if names is not None and name is not None:
raise TypeError("Can only provide one of `names` and `name`")
elif names is None and name is None:
return deepcopy(self.names) if deep else self.names
elif names is not None:
if not is_list_like(names):
raise TypeError("Must pass list-like as `names`.")
return names
else:
if not is_list_like(name):
return [name]
return name
def __unicode__(self):
"""
Return a string representation for this object.
Invoked by unicode(df) in py2 only. Yields a Unicode String in both
py2/py3.
"""
klass = self.__class__.__name__
data = self._format_data()
attrs = self._format_attrs()
space = self._format_space()
prepr = (u(",%s") %
space).join([u("%s=%s") % (k, v) for k, v in attrs])
# no data provided, just attributes
if data is None:
data = ''
res = u("%s(%s%s)") % (klass, data, prepr)
return res
def _format_space(self):
# using space here controls if the attributes
# are line separated or not (the default)
# max_seq_items = get_option('display.max_seq_items')
# if len(self) > max_seq_items:
# space = "\n%s" % (' ' * (len(klass) + 1))
return " "
@property
def _formatter_func(self):
"""
Return the formatted data as a unicode string
"""
return default_pprint
def _format_data(self, name=None):
"""
Return the formatted data as a unicode string
"""
from pandas.io.formats.console import get_console_size
from pandas.io.formats.format import _get_adjustment
display_width, _ = get_console_size()
if display_width is None:
display_width = get_option('display.width') or 80
if name is None:
name = self.__class__.__name__
space1 = "\n%s" % (' ' * (len(name) + 1))
space2 = "\n%s" % (' ' * (len(name) + 2))
n = len(self)
sep = ','
max_seq_items = get_option('display.max_seq_items') or n
formatter = self._formatter_func
# do we want to justify (only do so for non-objects)
is_justify = not (self.inferred_type in ('string', 'unicode') or
(self.inferred_type == 'categorical' and
is_object_dtype(self.categories)))
# are we a truncated display
is_truncated = n > max_seq_items
# adj can optionaly handle unicode eastern asian width
adj = _get_adjustment()
def _extend_line(s, line, value, display_width, next_line_prefix):
if (adj.len(line.rstrip()) + adj.len(value.rstrip()) >=
display_width):
s += line.rstrip()
line = next_line_prefix
line += value
return s, line
def best_len(values):
if values:
return max([adj.len(x) for x in values])
else:
return 0
if n == 0:
summary = '[], '
elif n == 1:
first = formatter(self[0])
summary = '[%s], ' % first
elif n == 2:
first = formatter(self[0])
last = formatter(self[-1])
summary = '[%s, %s], ' % (first, last)
else:
if n > max_seq_items:
n = min(max_seq_items // 2, 10)
head = [formatter(x) for x in self[:n]]
tail = [formatter(x) for x in self[-n:]]
else:
head = []
tail = [formatter(x) for x in self]
# adjust all values to max length if needed
if is_justify:
# however, if we are not truncated and we are only a single
# line, then don't justify
if (is_truncated or
not (len(', '.join(head)) < display_width and
len(', '.join(tail)) < display_width)):
max_len = max(best_len(head), best_len(tail))
head = [x.rjust(max_len) for x in head]
tail = [x.rjust(max_len) for x in tail]
summary = ""
line = space2
for i in range(len(head)):
word = head[i] + sep + ' '
summary, line = _extend_line(summary, line, word,
display_width, space2)
if is_truncated:
# remove trailing space of last line
summary += line.rstrip() + space2 + '...'
line = space2
for i in range(len(tail) - 1):
word = tail[i] + sep + ' '
summary, line = _extend_line(summary, line, word,
display_width, space2)
# last value: no sep added + 1 space of width used for trailing ','
summary, line = _extend_line(summary, line, tail[-1],
display_width - 2, space2)
summary += line
summary += '],'
if len(summary) > (display_width):
summary += space1
else: # one row
summary += ' '
# remove initial space
summary = '[' + summary[len(space2):]
return summary
def _format_attrs(self):
"""
Return a list of tuples of the (attr,formatted_value)
"""
attrs = []
attrs.append(('dtype', "'%s'" % self.dtype))
if self.name is not None:
attrs.append(('name', default_pprint(self.name)))
max_seq_items = get_option('display.max_seq_items') or len(self)
if len(self) > max_seq_items:
attrs.append(('length', len(self)))
return attrs
def to_series(self, **kwargs):
"""
Create a Series with both index and values equal to the index keys
useful with map for returning an indexer based on an index
Returns
-------
Series : dtype will be based on the type of the Index values.
"""
from pandas import Series
return Series(self._to_embed(),
index=self._shallow_copy(),
name=self.name)
def to_frame(self, index=True):
"""
Create a DataFrame with a column containing the Index.
.. versionadded:: 0.21.0
Parameters
----------
index : boolean, default True
Set the index of the returned DataFrame as the original Index.
Returns
-------
DataFrame : a DataFrame containing the original Index data.
"""
from pandas import DataFrame
result = DataFrame(self._shallow_copy(), columns=[self.name or 0])
if index:
result.index = self
return result
def _to_embed(self, keep_tz=False):
"""
*this is an internal non-public method*
return an array repr of this object, potentially casting to object
"""
return self.values.copy()
_index_shared_docs['astype'] = """
Create an Index with values cast to dtypes. The class of a new Index
is determined by dtype. When conversion is impossible, a ValueError
exception is raised.
Parameters
----------
dtype : numpy dtype or pandas type
copy : bool, default True
By default, astype always returns a newly allocated object.
If copy is set to False and internal requirements on dtype are
satisfied, the original data is used to create a new Index
or the original Index is returned.
.. versionadded:: 0.19.0
"""
@Appender(_index_shared_docs['astype'])
def astype(self, dtype, copy=True):
return Index(self.values.astype(dtype, copy=copy), name=self.name,
dtype=dtype)
def _to_safe_for_reshape(self):
""" convert to object if we are a categorical """
return self
def to_datetime(self, dayfirst=False):
"""
DEPRECATED: use :meth:`pandas.to_datetime` instead.
For an Index containing strings or datetime.datetime objects, attempt
conversion to DatetimeIndex
"""
warnings.warn("to_datetime is deprecated. Use pd.to_datetime(...)",
FutureWarning, stacklevel=2)
from pandas.core.indexes.datetimes import DatetimeIndex
if self.inferred_type == 'string':
from dateutil.parser import parse
parser = lambda x: parse(x, dayfirst=dayfirst)
parsed = parsing.try_parse_dates(self.values, parser=parser)
return DatetimeIndex(parsed)
else:
return DatetimeIndex(self.values)
def _assert_can_do_setop(self, other):
if not is_list_like(other):
raise TypeError('Input must be Index or array-like')
return True
def _convert_can_do_setop(self, other):
if not isinstance(other, Index):
other = Index(other, name=self.name)
result_name = self.name
else:
result_name = self.name if self.name == other.name else None
return other, result_name
def _convert_for_op(self, value):
""" Convert value to be insertable to ndarray """
return value
def _assert_can_do_op(self, value):
""" Check value is valid for scalar op """
if not lib.isscalar(value):
msg = "'value' must be a scalar, passed: {0}"
raise TypeError(msg.format(type(value).__name__))
@property
def nlevels(self):
return 1
def _get_names(self):
return FrozenList((self.name, ))
def _set_names(self, values, level=None):
if len(values) != 1:
raise ValueError('Length of new names must be 1, got %d' %
len(values))
self.name = values[0]
names = property(fset=_set_names, fget=_get_names)
def set_names(self, names, level=None, inplace=False):
"""
Set new names on index. Defaults to returning new index.
Parameters
----------
names : str or sequence
name(s) to set
level : int, level name, or sequence of int/level names (default None)
If the index is a MultiIndex (hierarchical), level(s) to set (None
for all levels). Otherwise level must be None
inplace : bool
if True, mutates in place
Returns
-------
new index (of same type and class...etc) [if inplace, returns None]
Examples
--------
>>> Index([1, 2, 3, 4]).set_names('foo')
Int64Index([1, 2, 3, 4], dtype='int64')
>>> Index([1, 2, 3, 4]).set_names(['foo'])
Int64Index([1, 2, 3, 4], dtype='int64')
>>> idx = MultiIndex.from_tuples([(1, u'one'), (1, u'two'),
(2, u'one'), (2, u'two')],
names=['foo', 'bar'])
>>> idx.set_names(['baz', 'quz'])
MultiIndex(levels=[[1, 2], [u'one', u'two']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]],
names=[u'baz', u'quz'])
>>> idx.set_names('baz', level=0)
MultiIndex(levels=[[1, 2], [u'one', u'two']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]],
names=[u'baz', u'bar'])
"""
if level is not None and self.nlevels == 1:
raise ValueError('Level must be None for non-MultiIndex')
if level is not None and not is_list_like(level) and is_list_like(
names):
raise TypeError("Names must be a string")
if not is_list_like(names) and level is None and self.nlevels > 1:
raise TypeError("Must pass list-like as `names`.")
if not is_list_like(names):
names = [names]
if level is not None and not is_list_like(level):
level = [level]
if inplace:
idx = self
else:
idx = self._shallow_copy()
idx._set_names(names, level=level)
if not inplace:
return idx
def rename(self, name, inplace=False):
"""
Set new names on index. Defaults to returning new index.
Parameters
----------
name : str or list
name to set
inplace : bool
if True, mutates in place
Returns
-------
new index (of same type and class...etc) [if inplace, returns None]
"""
return self.set_names([name], inplace=inplace)
def reshape(self, *args, **kwargs):
"""
NOT IMPLEMENTED: do not call this method, as reshaping is not
supported for Index objects and will raise an error.
Reshape an Index.
"""
raise NotImplementedError("reshaping is not supported "
"for Index objects")
@property
def _has_complex_internals(self):
# to disable groupby tricks in MultiIndex
return False
def summary(self, name=None):
if len(self) > 0:
head = self[0]
if (hasattr(head, 'format') and
not isinstance(head, compat.string_types)):
head = head.format()
tail = self[-1]
if (hasattr(tail, 'format') and
not isinstance(tail, compat.string_types)):
tail = tail.format()
index_summary = ', %s to %s' % (pprint_thing(head),
pprint_thing(tail))
else:
index_summary = ''
if name is None:
name = type(self).__name__
return '%s: %s entries%s' % (name, len(self), index_summary)
def _mpl_repr(self):
# how to represent ourselves to matplotlib
return self.values
_na_value = np.nan
"""The expected NA value to use with this index."""
# introspection
@property
def is_monotonic(self):
""" alias for is_monotonic_increasing (deprecated) """
return self.is_monotonic_increasing
@property
def is_monotonic_increasing(self):
"""
return if the index is monotonic increasing (only equal or
increasing) values.
Examples
--------
>>> Index([1, 2, 3]).is_monotonic_increasing
True
>>> Index([1, 2, 2]).is_monotonic_increasing
True
>>> Index([1, 3, 2]).is_monotonic_increasing
False
"""
return self._engine.is_monotonic_increasing
@property
def is_monotonic_decreasing(self):
"""
return if the index is monotonic decreasing (only equal or
decreasing) values.
Examples
--------
>>> Index([3, 2, 1]).is_monotonic_decreasing
True
>>> Index([3, 2, 2]).is_monotonic_decreasing
True
>>> Index([3, 1, 2]).is_monotonic_decreasing
False
"""
return self._engine.is_monotonic_decreasing
@property
def _is_strictly_monotonic_increasing(self):
"""return if the index is strictly monotonic increasing
(only increasing) values
Examples
--------
>>> Index([1, 2, 3])._is_strictly_monotonic_increasing
True
>>> Index([1, 2, 2])._is_strictly_monotonic_increasing
False
>>> Index([1, 3, 2])._is_strictly_monotonic_increasing
False
"""
return self.is_unique and self.is_monotonic_increasing
@property
def _is_strictly_monotonic_decreasing(self):
"""return if the index is strictly monotonic decreasing
(only decreasing) values
Examples
--------
>>> Index([3, 2, 1])._is_strictly_monotonic_decreasing
True
>>> Index([3, 2, 2])._is_strictly_monotonic_decreasing
False
>>> Index([3, 1, 2])._is_strictly_monotonic_decreasing
False
"""
return self.is_unique and self.is_monotonic_decreasing
def is_lexsorted_for_tuple(self, tup):
return True
@cache_readonly(allow_setting=True)
def is_unique(self):
""" return if the index has unique values """
return self._engine.is_unique
@property
def has_duplicates(self):
return not self.is_unique
def is_boolean(self):
return self.inferred_type in ['boolean']
def is_integer(self):
return self.inferred_type in ['integer']
def is_floating(self):
return self.inferred_type in ['floating', 'mixed-integer-float']
def is_numeric(self):
return self.inferred_type in ['integer', 'floating']
def is_object(self):
return is_object_dtype(self.dtype)
def is_categorical(self):
return self.inferred_type in ['categorical']
def is_interval(self):
return self.inferred_type in ['interval']
def is_mixed(self):
return self.inferred_type in ['mixed']
def holds_integer(self):
return self.inferred_type in ['integer', 'mixed-integer']
_index_shared_docs['_convert_scalar_indexer'] = """
Convert a scalar indexer.
Parameters
----------
key : label of the slice bound
kind : {'ix', 'loc', 'getitem', 'iloc'} or None
"""
@Appender(_index_shared_docs['_convert_scalar_indexer'])
def _convert_scalar_indexer(self, key, kind=None):
assert kind in ['ix', 'loc', 'getitem', 'iloc', None]
if kind == 'iloc':
return self._validate_indexer('positional', key, kind)
if len(self) and not isinstance(self, ABCMultiIndex,):
# we can raise here if we are definitive that this
# is positional indexing (eg. .ix on with a float)
# or label indexing if we are using a type able
# to be represented in the index
if kind in ['getitem', 'ix'] and is_float(key):
if not self.is_floating():
return self._invalid_indexer('label', key)
elif kind in ['loc'] and is_float(key):
# we want to raise KeyError on string/mixed here
# technically we *could* raise a TypeError
# on anything but mixed though
if self.inferred_type not in ['floating',
'mixed-integer-float',
'string',
'unicode',
'mixed']:
return self._invalid_indexer('label', key)
elif kind in ['loc'] and is_integer(key):
if not self.holds_integer():
return self._invalid_indexer('label', key)
return key
_index_shared_docs['_convert_slice_indexer'] = """
Convert a slice indexer.
By definition, these are labels unless 'iloc' is passed in.
Floats are not allowed as the start, step, or stop of the slice.
Parameters
----------
key : label of the slice bound
kind : {'ix', 'loc', 'getitem', 'iloc'} or None
"""
@Appender(_index_shared_docs['_convert_slice_indexer'])
def _convert_slice_indexer(self, key, kind=None):
assert kind in ['ix', 'loc', 'getitem', 'iloc', None]
# if we are not a slice, then we are done
if not isinstance(key, slice):
return key
# validate iloc
if kind == 'iloc':
return slice(self._validate_indexer('slice', key.start, kind),
self._validate_indexer('slice', key.stop, kind),
self._validate_indexer('slice', key.step, kind))
# potentially cast the bounds to integers
start, stop, step = key.start, key.stop, key.step
# figure out if this is a positional indexer
def is_int(v):
return v is None or is_integer(v)
is_null_slicer = start is None and stop is None
is_index_slice = is_int(start) and is_int(stop)
is_positional = is_index_slice and not self.is_integer()
if kind == 'getitem':
"""
called from the getitem slicers, validate that we are in fact
integers
"""
if self.is_integer() or is_index_slice:
return slice(self._validate_indexer('slice', key.start, kind),
self._validate_indexer('slice', key.stop, kind),
self._validate_indexer('slice', key.step, kind))
# convert the slice to an indexer here
# if we are mixed and have integers
try:
if is_positional and self.is_mixed():
# TODO: i, j are not used anywhere
if start is not None:
i = self.get_loc(start) # noqa
if stop is not None:
j = self.get_loc(stop) # noqa
is_positional = False
except KeyError:
if self.inferred_type == 'mixed-integer-float':
raise
if is_null_slicer:
indexer = key
elif is_positional:
indexer = key
else:
try:
indexer = self.slice_indexer(start, stop, step, kind=kind)
except Exception:
if is_index_slice:
if self.is_integer():
raise
else:
indexer = key
else:
raise
return indexer
def _convert_listlike_indexer(self, keyarr, kind=None):
"""
Parameters
----------
keyarr : list-like
Indexer to convert.
Returns
-------
tuple (indexer, keyarr)
indexer is an ndarray or None if cannot convert
keyarr are tuple-safe keys
"""
if isinstance(keyarr, Index):
keyarr = self._convert_index_indexer(keyarr)
else:
keyarr = self._convert_arr_indexer(keyarr)
indexer = self._convert_list_indexer(keyarr, kind=kind)
return indexer, keyarr
_index_shared_docs['_convert_arr_indexer'] = """
Convert an array-like indexer to the appropriate dtype.
Parameters
----------
keyarr : array-like
Indexer to convert.
Returns
-------
converted_keyarr : array-like
"""
@Appender(_index_shared_docs['_convert_arr_indexer'])
def _convert_arr_indexer(self, keyarr):
keyarr = _asarray_tuplesafe(keyarr)
return keyarr
_index_shared_docs['_convert_index_indexer'] = """
Convert an Index indexer to the appropriate dtype.
Parameters
----------
keyarr : Index (or sub-class)
Indexer to convert.
Returns
-------
converted_keyarr : Index (or sub-class)
"""
@Appender(_index_shared_docs['_convert_index_indexer'])
def _convert_index_indexer(self, keyarr):
return keyarr
_index_shared_docs['_convert_list_indexer'] = """
Convert a list-like indexer to the appropriate dtype.
Parameters
----------
keyarr : Index (or sub-class)
Indexer to convert.
kind : iloc, ix, loc, optional
Returns
-------
positional indexer or None
"""
@Appender(_index_shared_docs['_convert_list_indexer'])
def _convert_list_indexer(self, keyarr, kind=None):
if (kind in [None, 'iloc', 'ix'] and
is_integer_dtype(keyarr) and not self.is_floating() and
not isinstance(keyarr, ABCPeriodIndex)):
if self.inferred_type == 'mixed-integer':
indexer = self.get_indexer(keyarr)
if (indexer >= 0).all():
return indexer
# missing values are flagged as -1 by get_indexer and negative
# indices are already converted to positive indices in the
# above if-statement, so the negative flags are changed to
# values outside the range of indices so as to trigger an
# IndexError in maybe_convert_indices
indexer[indexer < 0] = len(self)
from pandas.core.indexing import maybe_convert_indices
return maybe_convert_indices(indexer, len(self))
elif not self.inferred_type == 'integer':
keyarr = np.where(keyarr < 0, len(self) + keyarr, keyarr)
return keyarr
return None
def _invalid_indexer(self, form, key):
""" consistent invalid indexer message """
raise TypeError("cannot do {form} indexing on {klass} with these "
"indexers [{key}] of {kind}".format(
form=form, klass=type(self), key=key,
kind=type(key)))
def get_duplicates(self):
from collections import defaultdict
counter = defaultdict(lambda: 0)
for k in self.values:
counter[k] += 1
return sorted(k for k, v in compat.iteritems(counter) if v > 1)
_get_duplicates = get_duplicates
def _cleanup(self):
self._engine.clear_mapping()
@cache_readonly
def _constructor(self):
return type(self)
@cache_readonly
def _engine(self):
# property, for now, slow to look up
return self._engine_type(lambda: self._values, len(self))
def _validate_index_level(self, level):
"""
Validate index level.
For single-level Index getting level number is a no-op, but some
verification must be done like in MultiIndex.
"""
if isinstance(level, int):
if level < 0 and level != -1:
raise IndexError("Too many levels: Index has only 1 level,"
" %d is not a valid level number" % (level, ))
elif level > 0:
raise IndexError("Too many levels:"
" Index has only 1 level, not %d" %
(level + 1))
elif level != self.name:
raise KeyError('Level %s must be same as name (%s)' %
(level, self.name))
def _get_level_number(self, level):
self._validate_index_level(level)
return 0
@cache_readonly
def inferred_type(self):
""" return a string of the type inferred from the values """
return lib.infer_dtype(self)
def _is_memory_usage_qualified(self):
""" return a boolean if we need a qualified .info display """
return self.is_object()
def is_type_compatible(self, kind):
return kind == self.inferred_type
@cache_readonly
def is_all_dates(self):
if self._data is None:
return False
return is_datetime_array(_ensure_object(self.values))
def __reduce__(self):
d = dict(data=self._data)
d.update(self._get_attributes_dict())
return _new_Index, (self.__class__, d), None
def __setstate__(self, state):
"""Necessary for making this object picklable"""
if isinstance(state, dict):
self._data = state.pop('data')
for k, v in compat.iteritems(state):
setattr(self, k, v)
elif isinstance(state, tuple):
if len(state) == 2:
nd_state, own_state = state
data = np.empty(nd_state[1], dtype=nd_state[2])
np.ndarray.__setstate__(data, nd_state)
self.name = own_state[0]
else: # pragma: no cover
data = np.empty(state)
np.ndarray.__setstate__(data, state)
self._data = data
self._reset_identity()
else:
raise Exception("invalid pickle state")
_unpickle_compat = __setstate__
def __nonzero__(self):
raise ValueError("The truth value of a {0} is ambiguous. "
"Use a.empty, a.bool(), a.item(), a.any() or a.all()."
.format(self.__class__.__name__))
__bool__ = __nonzero__
_index_shared_docs['__contains__'] = """
return a boolean if this key is IN the index
Parameters
----------
key : object
Returns
-------
boolean
"""
@Appender(_index_shared_docs['__contains__'] % _index_doc_kwargs)
def __contains__(self, key):
hash(key)
try:
return key in self._engine
except (OverflowError, TypeError, ValueError):
return False
_index_shared_docs['contains'] = """
return a boolean if this key is IN the index
Parameters
----------
key : object
Returns
-------
boolean
"""
@Appender(_index_shared_docs['contains'] % _index_doc_kwargs)
def contains(self, key):
hash(key)
try:
return key in self._engine
except (TypeError, ValueError):
return False
def __hash__(self):
raise TypeError("unhashable type: %r" % type(self).__name__)
def __setitem__(self, key, value):
raise TypeError("Index does not support mutable operations")
def __getitem__(self, key):
"""
Override numpy.ndarray's __getitem__ method to work as desired.
This function adds lists and Series as valid boolean indexers
(ndarrays only supports ndarray with dtype=bool).
If resulting ndim != 1, plain ndarray is returned instead of
corresponding `Index` subclass.
"""
# There's no custom logic to be implemented in __getslice__, so it's
# not overloaded intentionally.
getitem = self._data.__getitem__
promote = self._shallow_copy
if is_scalar(key):
return getitem(key)
if isinstance(key, slice):
# This case is separated from the conditional above to avoid
# pessimization of basic indexing.
return promote(getitem(key))
if is_bool_indexer(key):
key = np.asarray(key)
key = _values_from_object(key)
result = getitem(key)
if not is_scalar(result):
return promote(result)
else:
return result
def append(self, other):
"""
Append a collection of Index options together
Parameters
----------
other : Index or list/tuple of indices
Returns
-------
appended : Index
"""
to_concat = [self]
if isinstance(other, (list, tuple)):
to_concat = to_concat + list(other)
else:
to_concat.append(other)
for obj in to_concat:
if not isinstance(obj, Index):
raise TypeError('all inputs must be Index')
names = set([obj.name for obj in to_concat])
name = None if len(names) > 1 else self.name
return self._concat(to_concat, name)
def _concat(self, to_concat, name):
typs = _concat.get_dtype_kinds(to_concat)
if len(typs) == 1:
return self._concat_same_dtype(to_concat, name=name)
return _concat._concat_index_asobject(to_concat, name=name)
def _concat_same_dtype(self, to_concat, name):
"""
Concatenate to_concat which has the same class
"""
# must be overrided in specific classes
return _concat._concat_index_asobject(to_concat, name)
_index_shared_docs['take'] = """
return a new %(klass)s of the values selected by the indices
For internal compatibility with numpy arrays.
Parameters
----------
indices : list
Indices to be taken
axis : int, optional
The axis over which to select values, always 0.
allow_fill : bool, default True
fill_value : bool, default None
If allow_fill=True and fill_value is not None, indices specified by
-1 is regarded as NA. If Index doesn't hold NA, raise ValueError
See also
--------
numpy.ndarray.take
"""
@Appender(_index_shared_docs['take'] % _index_doc_kwargs)
def take(self, indices, axis=0, allow_fill=True,
fill_value=None, **kwargs):
if kwargs:
nv.validate_take(tuple(), kwargs)
indices = _ensure_platform_int(indices)
if self._can_hold_na:
taken = self._assert_take_fillable(self.values, indices,
allow_fill=allow_fill,
fill_value=fill_value,
na_value=self._na_value)
else:
if allow_fill and fill_value is not None:
msg = 'Unable to fill values because {0} cannot contain NA'
raise ValueError(msg.format(self.__class__.__name__))
taken = self.values.take(indices)
return self._shallow_copy(taken)
def _assert_take_fillable(self, values, indices, allow_fill=True,
fill_value=None, na_value=np.nan):
""" Internal method to handle NA filling of take """
indices = _ensure_platform_int(indices)
# only fill if we are passing a non-None fill_value
if allow_fill and fill_value is not None:
if (indices < -1).any():
msg = ('When allow_fill=True and fill_value is not None, '
'all indices must be >= -1')
raise ValueError(msg)
taken = values.take(indices)
mask = indices == -1
if mask.any():
taken[mask] = na_value
else:
taken = values.take(indices)
return taken
@cache_readonly
def _isnan(self):
""" return if each value is nan"""
if self._can_hold_na:
return isna(self)
else:
# shouldn't reach to this condition by checking hasnans beforehand
values = np.empty(len(self), dtype=np.bool_)
values.fill(False)
return values
@cache_readonly
def _nan_idxs(self):
if self._can_hold_na:
w, = self._isnan.nonzero()
return w
else:
return np.array([], dtype=np.int64)
@cache_readonly
def hasnans(self):
""" return if I have any nans; enables various perf speedups """
if self._can_hold_na:
return self._isnan.any()
else:
return False
def isna(self):
"""
Detect missing values
.. versionadded:: 0.20.0
Returns
-------
a boolean array of whether my values are NA
See also
--------
isnull : alias of isna
pandas.isna : top-level isna
"""
return self._isnan
isnull = isna
def notna(self):
"""
Inverse of isna
.. versionadded:: 0.20.0
Returns
-------
a boolean array of whether my values are not NA
See also
--------
notnull : alias of notna
pandas.notna : top-level notna
"""
return ~self.isna()
notnull = notna
def putmask(self, mask, value):
"""
return a new Index of the values set with the mask
See also
--------
numpy.ndarray.putmask
"""
values = self.values.copy()
try:
np.putmask(values, mask, self._convert_for_op(value))
return self._shallow_copy(values)
except (ValueError, TypeError) as err:
if is_object_dtype(self):
raise err
# coerces to object
return self.astype(object).putmask(mask, value)
def format(self, name=False, formatter=None, **kwargs):
"""
Render a string representation of the Index
"""
header = []
if name:
header.append(pprint_thing(self.name,
escape_chars=('\t', '\r', '\n')) if
self.name is not None else '')
if formatter is not None:
return header + list(self.map(formatter))
return self._format_with_header(header, **kwargs)
def _format_with_header(self, header, na_rep='NaN', **kwargs):
values = self.values
from pandas.io.formats.format import format_array
if is_categorical_dtype(values.dtype):
values = np.array(values)
elif is_object_dtype(values.dtype):
values = lib.maybe_convert_objects(values, safe=1)
if is_object_dtype(values.dtype):
result = [pprint_thing(x, escape_chars=('\t', '\r', '\n'))
for x in values]
# could have nans
mask = isna(values)
if mask.any():
result = np.array(result)
result[mask] = na_rep
result = result.tolist()
else:
result = _trim_front(format_array(values, None, justify='left'))
return header + result
def to_native_types(self, slicer=None, **kwargs):
"""
Format specified values of `self` and return them.
Parameters
----------
slicer : int, array-like
An indexer into `self` that specifies which values
are used in the formatting process.
kwargs : dict
Options for specifying how the values should be formatted.
These options include the following:
1) na_rep : str
The value that serves as a placeholder for NULL values
2) quoting : bool or None
Whether or not there are quoted values in `self`
3) date_format : str
The format used to represent date-like values
"""
values = self
if slicer is not None:
values = values[slicer]
return values._format_native_types(**kwargs)
def _format_native_types(self, na_rep='', quoting=None, **kwargs):
""" actually format my specific types """
mask = isna(self)
if not self.is_object() and not quoting:
values = np.asarray(self).astype(str)
else:
values = np.array(self, dtype=object, copy=True)
values[mask] = na_rep
return values
def equals(self, other):
"""
Determines if two Index objects contain the same elements.
"""
if self.is_(other):
return True
if not isinstance(other, Index):
return False
if is_object_dtype(self) and not is_object_dtype(other):
# if other is not object, use other's logic for coercion
return other.equals(self)
try:
return array_equivalent(_values_from_object(self),
_values_from_object(other))
except Exception:
return False
def identical(self, other):
"""Similar to equals, but check that other comparable attributes are
also equal
"""
return (self.equals(other) and
all((getattr(self, c, None) == getattr(other, c, None)
for c in self._comparables)) and
type(self) == type(other))
def asof(self, label):
"""
For a sorted index, return the most recent label up to and including
the passed label. Return NaN if not found.
See also
--------
get_loc : asof is a thin wrapper around get_loc with method='pad'
"""
try:
loc = self.get_loc(label, method='pad')
except KeyError:
return _get_na_value(self.dtype)
else:
if isinstance(loc, slice):
loc = loc.indices(len(self))[-1]
return self[loc]
def asof_locs(self, where, mask):
"""
where : array of timestamps
mask : array of booleans where data is not NA
"""
locs = self.values[mask].searchsorted(where.values, side='right')
locs = np.where(locs > 0, locs - 1, 0)
result = np.arange(len(self))[mask].take(locs)
first = mask.argmax()
result[(locs == 0) & (where < self.values[first])] = -1
return result
def sort_values(self, return_indexer=False, ascending=True):
"""
Return sorted copy of Index
"""
_as = self.argsort()
if not ascending:
_as = _as[::-1]
sorted_index = self.take(_as)
if return_indexer:
return sorted_index, _as
else:
return sorted_index
def sort(self, *args, **kwargs):
raise TypeError("cannot sort an Index object in-place, use "
"sort_values instead")
def sortlevel(self, level=None, ascending=True, sort_remaining=None):
"""
For internal compatibility with with the Index API
Sort the Index. This is for compat with MultiIndex
Parameters
----------
ascending : boolean, default True
False to sort in descending order
level, sort_remaining are compat parameters
Returns
-------
sorted_index : Index
"""
return self.sort_values(return_indexer=True, ascending=ascending)
def shift(self, periods=1, freq=None):
"""
Shift Index containing datetime objects by input number of periods and
DateOffset
Returns
-------
shifted : Index
"""
raise NotImplementedError("Not supported for type %s" %
type(self).__name__)
def argsort(self, *args, **kwargs):
"""
Returns the indices that would sort the index and its
underlying data.
Returns
-------
argsorted : numpy array
See also
--------
numpy.ndarray.argsort
"""
result = self.asi8
if result is None:
result = np.array(self)
return result.argsort(*args, **kwargs)
def __add__(self, other):
return Index(np.array(self) + other)
def __radd__(self, other):
return Index(other + np.array(self))
__iadd__ = __add__
def __sub__(self, other):
raise TypeError("cannot perform __sub__ with this index type: "
"{typ}".format(typ=type(self)))
def __and__(self, other):
return self.intersection(other)
def __or__(self, other):
return self.union(other)
def __xor__(self, other):
return self.symmetric_difference(other)
def _get_consensus_name(self, other):
"""
Given 2 indexes, give a consensus name meaning
we take the not None one, or None if the names differ.
Return a new object if we are resetting the name
"""
if self.name != other.name:
if self.name is None or other.name is None:
name = self.name or other.name
else:
name = None
if self.name != name:
return self._shallow_copy(name=name)
return self
def union(self, other):
"""
Form the union of two Index objects and sorts if possible.
Parameters
----------
other : Index or array-like
Returns
-------
union : Index
Examples
--------
>>> idx1 = pd.Index([1, 2, 3, 4])
>>> idx2 = pd.Index([3, 4, 5, 6])
>>> idx1.union(idx2)
Int64Index([1, 2, 3, 4, 5, 6], dtype='int64')
"""
self._assert_can_do_setop(other)
other = _ensure_index(other)
if len(other) == 0 or self.equals(other):
return self._get_consensus_name(other)
if len(self) == 0:
return other._get_consensus_name(self)
# TODO: is_dtype_union_equal is a hack around
# 1. buggy set ops with duplicates (GH #13432)
# 2. CategoricalIndex lacking setops (GH #10186)
# Once those are fixed, this workaround can be removed
if not is_dtype_union_equal(self.dtype, other.dtype):
this = self.astype('O')
other = other.astype('O')
return this.union(other)
if self.is_monotonic and other.is_monotonic:
try:
result = self._outer_indexer(self._values, other._values)[0]
except TypeError:
# incomparable objects
result = list(self._values)
# worth making this faster? a very unusual case
value_set = set(self._values)
result.extend([x for x in other._values if x not in value_set])
else:
indexer = self.get_indexer(other)
indexer, = (indexer == -1).nonzero()
if len(indexer) > 0:
other_diff = algos.take_nd(other._values, indexer,
allow_fill=False)
result = _concat._concat_compat((self._values, other_diff))
try:
self._values[0] < other_diff[0]
except TypeError as e:
warnings.warn("%s, sort order is undefined for "
"incomparable objects" % e, RuntimeWarning,
stacklevel=3)
else:
types = frozenset((self.inferred_type,
other.inferred_type))
if not types & _unsortable_types:
result.sort()
else:
result = self._values
try:
result = np.sort(result)
except TypeError as e:
warnings.warn("%s, sort order is undefined for "
"incomparable objects" % e, RuntimeWarning,
stacklevel=3)
# for subclasses
return self._wrap_union_result(other, result)
def _wrap_union_result(self, other, result):
name = self.name if self.name == other.name else None
return self.__class__(result, name=name)
def intersection(self, other):
"""
Form the intersection of two Index objects.
This returns a new Index with elements common to the index and `other`,
preserving the order of the calling index.
Parameters
----------
other : Index or array-like
Returns
-------
intersection : Index
Examples
--------
>>> idx1 = pd.Index([1, 2, 3, 4])
>>> idx2 = pd.Index([3, 4, 5, 6])
>>> idx1.intersection(idx2)
Int64Index([3, 4], dtype='int64')
"""
self._assert_can_do_setop(other)
other = _ensure_index(other)
if self.equals(other):
return self._get_consensus_name(other)
if not is_dtype_equal(self.dtype, other.dtype):
this = self.astype('O')
other = other.astype('O')
return this.intersection(other)
if self.is_monotonic and other.is_monotonic:
try:
result = self._inner_indexer(self._values, other._values)[0]
return self._wrap_union_result(other, result)
except TypeError:
pass
try:
indexer = Index(other._values).get_indexer(self._values)
indexer = indexer.take((indexer != -1).nonzero()[0])
except Exception:
# duplicates
indexer = algos.unique1d(
Index(other._values).get_indexer_non_unique(self._values)[0])
indexer = indexer[indexer != -1]
taken = other.take(indexer)
if self.name != other.name:
taken.name = None
return taken
def difference(self, other):
"""
Return a new Index with elements from the index that are not in
`other`.
This is the set difference of two Index objects.
It's sorted if sorting is possible.
Parameters
----------
other : Index or array-like
Returns
-------
difference : Index
Examples
--------
>>> idx1 = pd.Index([1, 2, 3, 4])
>>> idx2 = pd.Index([3, 4, 5, 6])
>>> idx1.difference(idx2)
Int64Index([1, 2], dtype='int64')
"""
self._assert_can_do_setop(other)
if self.equals(other):
return Index([], name=self.name)
other, result_name = self._convert_can_do_setop(other)
this = self._get_unique_index()
indexer = this.get_indexer(other)
indexer = indexer.take((indexer != -1).nonzero()[0])
label_diff = np.setdiff1d(np.arange(this.size), indexer,
assume_unique=True)
the_diff = this.values.take(label_diff)
try:
the_diff = sorting.safe_sort(the_diff)
except TypeError:
pass
return this._shallow_copy(the_diff, name=result_name, freq=None)
def symmetric_difference(self, other, result_name=None):
"""
Compute the symmetric difference of two Index objects.
It's sorted if sorting is possible.
Parameters
----------
other : Index or array-like
result_name : str
Returns
-------
symmetric_difference : Index
Notes
-----
``symmetric_difference`` contains elements that appear in either
``idx1`` or ``idx2`` but not both. Equivalent to the Index created by
``idx1.difference(idx2) | idx2.difference(idx1)`` with duplicates
dropped.
Examples
--------
>>> idx1 = Index([1, 2, 3, 4])
>>> idx2 = Index([2, 3, 4, 5])
>>> idx1.symmetric_difference(idx2)
Int64Index([1, 5], dtype='int64')
You can also use the ``^`` operator:
>>> idx1 ^ idx2
Int64Index([1, 5], dtype='int64')
"""
self._assert_can_do_setop(other)
other, result_name_update = self._convert_can_do_setop(other)
if result_name is None:
result_name = result_name_update
this = self._get_unique_index()
other = other._get_unique_index()
indexer = this.get_indexer(other)
# {this} minus {other}
common_indexer = indexer.take((indexer != -1).nonzero()[0])
left_indexer = np.setdiff1d(np.arange(this.size), common_indexer,
assume_unique=True)
left_diff = this.values.take(left_indexer)
# {other} minus {this}
right_indexer = (indexer == -1).nonzero()[0]
right_diff = other.values.take(right_indexer)
the_diff = _concat._concat_compat([left_diff, right_diff])
try:
the_diff = sorting.safe_sort(the_diff)
except TypeError:
pass
attribs = self._get_attributes_dict()
attribs['name'] = result_name
if 'freq' in attribs:
attribs['freq'] = None
return self._shallow_copy_with_infer(the_diff, **attribs)
def _get_unique_index(self, dropna=False):
"""
Returns an index containing unique values.
Parameters
----------
dropna : bool
If True, NaN values are dropped.
Returns
-------
uniques : index
"""
if self.is_unique and not dropna:
return self
values = self.values
if not self.is_unique:
values = self.unique()
if dropna:
try:
if self.hasnans:
values = values[~isna(values)]
except NotImplementedError:
pass
return self._shallow_copy(values)
_index_shared_docs['get_loc'] = """
Get integer location, slice or boolean mask for requested label.
Parameters
----------
key : label
method : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional
* default: exact matches only.
* pad / ffill: find the PREVIOUS index value if no exact match.
* backfill / bfill: use NEXT index value if no exact match
* nearest: use the NEAREST index value if no exact match. Tied
distances are broken by preferring the larger index value.
tolerance : optional
Maximum distance from index value for inexact matches. The value of
the index at the matching location most satisfy the equation
``abs(index[loc] - key) <= tolerance``.
Tolerance may be a scalar
value, which applies the same tolerance to all values, or
list-like, which applies variable tolerance per element. List-like
includes list, tuple, array, Series, and must be the same size as
the index and its dtype must exactly match the index's type.
.. versionadded:: 0.17.0
.. versionadded:: 0.21.0 (list-like tolerance)
Returns
-------
loc : int if unique index, slice if monotonic index, else mask
Examples
---------
>>> unique_index = pd.Index(list('abc'))
>>> unique_index.get_loc('b')
1
>>> monotonic_index = pd.Index(list('abbc'))
>>> monotonic_index.get_loc('b')
slice(1, 3, None)
>>> non_monotonic_index = pd.Index(list('abcb'))
>>> non_monotonic_index.get_loc('b')
array([False, True, False, True], dtype=bool)
"""
@Appender(_index_shared_docs['get_loc'])
def get_loc(self, key, method=None, tolerance=None):
if method is None:
if tolerance is not None:
raise ValueError('tolerance argument only valid if using pad, '
'backfill or nearest lookups')
try:
return self._engine.get_loc(key)
except KeyError:
return self._engine.get_loc(self._maybe_cast_indexer(key))
indexer = self.get_indexer([key], method=method, tolerance=tolerance)
if indexer.ndim > 1 or indexer.size > 1:
raise TypeError('get_loc requires scalar valued input')
loc = indexer.item()
if loc == -1:
raise KeyError(key)
return loc
def get_value(self, series, key):
"""
Fast lookup of value from 1-dimensional ndarray. Only use this if you
know what you're doing
"""
# if we have something that is Index-like, then
# use this, e.g. DatetimeIndex
s = getattr(series, '_values', None)
if isinstance(s, Index) and is_scalar(key):
try:
return s[key]
except (IndexError, ValueError):
# invalid type as an indexer
pass
s = _values_from_object(series)
k = _values_from_object(key)
k = self._convert_scalar_indexer(k, kind='getitem')
try:
return self._engine.get_value(s, k,
tz=getattr(series.dtype, 'tz', None))
except KeyError as e1:
if len(self) > 0 and self.inferred_type in ['integer', 'boolean']:
raise
try:
return libts.get_value_box(s, key)
except IndexError:
raise
except TypeError:
# generator/iterator-like
if is_iterator(key):
raise InvalidIndexError(key)
else:
raise e1
except Exception: # pragma: no cover
raise e1
except TypeError:
# python 3
if is_scalar(key): # pragma: no cover
raise IndexError(key)
raise InvalidIndexError(key)
def set_value(self, arr, key, value):
"""
Fast lookup of value from 1-dimensional ndarray. Only use this if you
know what you're doing
"""
self._engine.set_value(_values_from_object(arr),
_values_from_object(key), value)
def _get_level_values(self, level):
"""
Return an Index of values for requested level, equal to the length
of the index.
Parameters
----------
level : int or str
``level`` is either the integer position of the level in the
MultiIndex, or the name of the level.
Returns
-------
values : Index
``self``, as there is only one level in the Index.
See also
---------
pandas.MultiIndex.get_level_values : get values for a level of a
MultiIndex
"""
self._validate_index_level(level)
return self
get_level_values = _get_level_values
_index_shared_docs['get_indexer'] = """
Compute indexer and mask for new index given the current index. The
indexer should be then used as an input to ndarray.take to align the
current data to the new index.
Parameters
----------
target : %(target_klass)s
method : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional
* default: exact matches only.
* pad / ffill: find the PREVIOUS index value if no exact match.
* backfill / bfill: use NEXT index value if no exact match
* nearest: use the NEAREST index value if no exact match. Tied
distances are broken by preferring the larger index value.
limit : int, optional
Maximum number of consecutive labels in ``target`` to match for
inexact matches.
tolerance : optional
Maximum distance between original and new labels for inexact
matches. The values of the index at the matching locations most
satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
Tolerance may be a scalar value, which applies the same tolerance
to all values, or list-like, which applies variable tolerance per
element. List-like includes list, tuple, array, Series, and must be
the same size as the index and its dtype must exactly match the
index's type.
.. versionadded:: 0.17.0
.. versionadded:: 0.21.0 (list-like tolerance)
Examples
--------
>>> indexer = index.get_indexer(new_index)
>>> new_values = cur_values.take(indexer)
Returns
-------
indexer : ndarray of int
Integers from 0 to n - 1 indicating that the index at these
positions matches the corresponding target values. Missing values
in the target are marked by -1.
"""
@Appender(_index_shared_docs['get_indexer'] % _index_doc_kwargs)
def get_indexer(self, target, method=None, limit=None, tolerance=None):
method = missing.clean_reindex_fill_method(method)
target = _ensure_index(target)
if tolerance is not None:
tolerance = self._convert_tolerance(tolerance, target)
# Treat boolean labels passed to a numeric index as not found. Without
# this fix False and True would be treated as 0 and 1 respectively.
# (GH #16877)
if target.is_boolean() and self.is_numeric():
return _ensure_platform_int(np.repeat(-1, target.size))
pself, ptarget = self._maybe_promote(target)
if pself is not self or ptarget is not target:
return pself.get_indexer(ptarget, method=method, limit=limit,
tolerance=tolerance)
if not is_dtype_equal(self.dtype, target.dtype):
this = self.astype(object)
target = target.astype(object)
return this.get_indexer(target, method=method, limit=limit,
tolerance=tolerance)
if not self.is_unique:
raise InvalidIndexError('Reindexing only valid with uniquely'
' valued Index objects')
if method == 'pad' or method == 'backfill':
indexer = self._get_fill_indexer(target, method, limit, tolerance)
elif method == 'nearest':
indexer = self._get_nearest_indexer(target, limit, tolerance)
else:
if tolerance is not None:
raise ValueError('tolerance argument only valid if doing pad, '
'backfill or nearest reindexing')
if limit is not None:
raise ValueError('limit argument only valid if doing pad, '
'backfill or nearest reindexing')
indexer = self._engine.get_indexer(target._values)
return _ensure_platform_int(indexer)
def _convert_tolerance(self, tolerance, target):
# override this method on subclasses
tolerance = np.asarray(tolerance)
if target.size != tolerance.size and tolerance.size > 1:
raise ValueError('list-like tolerance size must match '
'target index size')
return tolerance
def _get_fill_indexer(self, target, method, limit=None, tolerance=None):
if self.is_monotonic_increasing and target.is_monotonic_increasing:
method = (self._engine.get_pad_indexer if method == 'pad' else
self._engine.get_backfill_indexer)
indexer = method(target._values, limit)
else:
indexer = self._get_fill_indexer_searchsorted(target, method,
limit)
if tolerance is not None:
indexer = self._filter_indexer_tolerance(target._values, indexer,
tolerance)
return indexer
def _get_fill_indexer_searchsorted(self, target, method, limit=None):
"""
Fallback pad/backfill get_indexer that works for monotonic decreasing
indexes and non-monotonic targets
"""
if limit is not None:
raise ValueError('limit argument for %r method only well-defined '
'if index and target are monotonic' % method)
side = 'left' if method == 'pad' else 'right'
# find exact matches first (this simplifies the algorithm)
indexer = self.get_indexer(target)
nonexact = (indexer == -1)
indexer[nonexact] = self._searchsorted_monotonic(target[nonexact],
side)
if side == 'left':
# searchsorted returns "indices into a sorted array such that,
# if the corresponding elements in v were inserted before the
# indices, the order of a would be preserved".
# Thus, we need to subtract 1 to find values to the left.
indexer[nonexact] -= 1
# This also mapped not found values (values of 0 from
# np.searchsorted) to -1, which conveniently is also our
# sentinel for missing values
else:
# Mark indices to the right of the largest value as not found
indexer[indexer == len(self)] = -1
return indexer
def _get_nearest_indexer(self, target, limit, tolerance):
"""
Get the indexer for the nearest index labels; requires an index with
values that can be subtracted from each other (e.g., not strings or
tuples).
"""
left_indexer = self.get_indexer(target, 'pad', limit=limit)
right_indexer = self.get_indexer(target, 'backfill', limit=limit)
target = np.asarray(target)
left_distances = abs(self.values[left_indexer] - target)
right_distances = abs(self.values[right_indexer] - target)
op = operator.lt if self.is_monotonic_increasing else operator.le
indexer = np.where(op(left_distances, right_distances) |
(right_indexer == -1), left_indexer, right_indexer)
if tolerance is not None:
indexer = self._filter_indexer_tolerance(target, indexer,
tolerance)
return indexer
def _filter_indexer_tolerance(self, target, indexer, tolerance):
distance = abs(self.values[indexer] - target)
indexer = np.where(distance <= tolerance, indexer, -1)
return indexer
_index_shared_docs['get_indexer_non_unique'] = """
Compute indexer and mask for new index given the current index. The
indexer should be then used as an input to ndarray.take to align the
current data to the new index.
Parameters
----------
target : %(target_klass)s
Returns
-------
indexer : ndarray of int
Integers from 0 to n - 1 indicating that the index at these
positions matches the corresponding target values. Missing values
in the target are marked by -1.
missing : ndarray of int
An indexer into the target of the values not found.
These correspond to the -1 in the indexer array
"""
@Appender(_index_shared_docs['get_indexer_non_unique'] % _index_doc_kwargs)
def get_indexer_non_unique(self, target):
target = _ensure_index(target)
pself, ptarget = self._maybe_promote(target)
if pself is not self or ptarget is not target:
return pself.get_indexer_non_unique(ptarget)
if self.is_all_dates:
self = Index(self.asi8)
tgt_values = target.asi8
else:
tgt_values = target._values
indexer, missing = self._engine.get_indexer_non_unique(tgt_values)
return _ensure_platform_int(indexer), missing
def get_indexer_for(self, target, **kwargs):
"""
guaranteed return of an indexer even when non-unique
This dispatches to get_indexer or get_indexer_nonunique as appropriate
"""
if self.is_unique:
return self.get_indexer(target, **kwargs)
indexer, _ = self.get_indexer_non_unique(target, **kwargs)
return indexer
def _maybe_promote(self, other):
# A hack, but it works
from pandas.core.indexes.datetimes import DatetimeIndex
if self.inferred_type == 'date' and isinstance(other, DatetimeIndex):
return DatetimeIndex(self), other
elif self.inferred_type == 'boolean':
if not is_object_dtype(self.dtype):
return self.astype('object'), other.astype('object')
return self, other
def groupby(self, values):
"""
Group the index labels by a given array of values.
Parameters
----------
values : array
Values used to determine the groups.
Returns
-------
groups : dict
{group name -> group labels}
"""
# TODO: if we are a MultiIndex, we can do better
# that converting to tuples
from .multi import MultiIndex
if isinstance(values, MultiIndex):
values = values.values
values = _ensure_categorical(values)
result = values._reverse_indexer()
# map to the label
result = {k: self.take(v) for k, v in compat.iteritems(result)}
return result
def map(self, mapper):
"""Apply mapper function to an index.
Parameters
----------
mapper : callable
Function to be applied.
Returns
-------
applied : Union[Index, MultiIndex], inferred
The output of the mapping function applied to the index.
If the function returns a tuple with more than one element
a MultiIndex will be returned.
"""
from .multi import MultiIndex
mapped_values = self._arrmap(self.values, mapper)
attributes = self._get_attributes_dict()
if mapped_values.size and isinstance(mapped_values[0], tuple):
return MultiIndex.from_tuples(mapped_values,
names=attributes.get('name'))
attributes['copy'] = False
return Index(mapped_values, **attributes)
def isin(self, values, level=None):
"""
Compute boolean array of whether each index value is found in the
passed set of values.
Parameters
----------
values : set or list-like
Sought values.
.. versionadded:: 0.18.1
Support for values as a set
level : str or int, optional
Name or position of the index level to use (if the index is a
MultiIndex).
Notes
-----
If `level` is specified:
- if it is the name of one *and only one* index level, use that level;
- otherwise it should be a number indicating level position.
Returns
-------
is_contained : ndarray (boolean dtype)
"""
if level is not None:
self._validate_index_level(level)
return algos.isin(np.array(self), values)
def _can_reindex(self, indexer):
"""
*this is an internal non-public method*
Check if we are allowing reindexing with this particular indexer
Parameters
----------
indexer : an integer indexer
Raises
------
ValueError if its a duplicate axis
"""
# trying to reindex on an axis with duplicates
if not self.is_unique and len(indexer):
raise ValueError("cannot reindex from a duplicate axis")
def reindex(self, target, method=None, level=None, limit=None,
tolerance=None):
"""
Create index with target's values (move/add/delete values as necessary)
Parameters
----------
target : an iterable
Returns
-------
new_index : pd.Index
Resulting index
indexer : np.ndarray or None
Indices of output values in original index
"""
# GH6552: preserve names when reindexing to non-named target
# (i.e. neither Index nor Series).
preserve_names = not hasattr(target, 'name')
# GH7774: preserve dtype/tz if target is empty and not an Index.
target = _ensure_has_len(target) # target may be an iterator
if not isinstance(target, Index) and len(target) == 0:
attrs = self._get_attributes_dict()
attrs.pop('freq', None) # don't preserve freq
target = self._simple_new(None, dtype=self.dtype, **attrs)
else:
target = _ensure_index(target)
if level is not None:
if method is not None:
raise TypeError('Fill method not supported if level passed')
_, indexer, _ = self._join_level(target, level, how='right',
return_indexers=True)
else:
if self.equals(target):
indexer = None
else:
if self.is_unique:
indexer = self.get_indexer(target, method=method,
limit=limit,
tolerance=tolerance)
else:
if method is not None or limit is not None:
raise ValueError("cannot reindex a non-unique index "
"with a method or limit")
indexer, missing = self.get_indexer_non_unique(target)
if preserve_names and target.nlevels == 1 and target.name != self.name:
target = target.copy()
target.name = self.name
return target, indexer
def _reindex_non_unique(self, target):
"""
*this is an internal non-public method*
Create a new index with target's values (move/add/delete values as
necessary) use with non-unique Index and a possibly non-unique target
Parameters
----------
target : an iterable
Returns
-------
new_index : pd.Index
Resulting index
indexer : np.ndarray or None
Indices of output values in original index
"""
target = _ensure_index(target)
indexer, missing = self.get_indexer_non_unique(target)
check = indexer != -1
new_labels = self.take(indexer[check])
new_indexer = None
if len(missing):
length = np.arange(len(indexer))
missing = _ensure_platform_int(missing)
missing_labels = target.take(missing)
missing_indexer = _ensure_int64(length[~check])
cur_labels = self.take(indexer[check]).values
cur_indexer = _ensure_int64(length[check])
new_labels = np.empty(tuple([len(indexer)]), dtype=object)
new_labels[cur_indexer] = cur_labels
new_labels[missing_indexer] = missing_labels
# a unique indexer
if target.is_unique:
# see GH5553, make sure we use the right indexer
new_indexer = np.arange(len(indexer))
new_indexer[cur_indexer] = np.arange(len(cur_labels))
new_indexer[missing_indexer] = -1
# we have a non_unique selector, need to use the original
# indexer here
else:
# need to retake to have the same size as the indexer
indexer[~check] = 0
# reset the new indexer to account for the new size
new_indexer = np.arange(len(self.take(indexer)))
new_indexer[~check] = -1
new_index = self._shallow_copy_with_infer(new_labels, freq=None)
return new_index, indexer, new_indexer
_index_shared_docs['join'] = """
*this is an internal non-public method*
Compute join_index and indexers to conform data
structures to the new index.
Parameters
----------
other : Index
how : {'left', 'right', 'inner', 'outer'}
level : int or level name, default None
return_indexers : boolean, default False
sort : boolean, default False
Sort the join keys lexicographically in the result Index. If False,
the order of the join keys depends on the join type (how keyword)
.. versionadded:: 0.20.0
Returns
-------
join_index, (left_indexer, right_indexer)
"""
@Appender(_index_shared_docs['join'])
def join(self, other, how='left', level=None, return_indexers=False,
sort=False):
from .multi import MultiIndex
self_is_mi = isinstance(self, MultiIndex)
other_is_mi = isinstance(other, MultiIndex)
# try to figure out the join level
# GH3662
if level is None and (self_is_mi or other_is_mi):
# have the same levels/names so a simple join
if self.names == other.names:
pass
else:
return self._join_multi(other, how=how,
return_indexers=return_indexers)
# join on the level
if level is not None and (self_is_mi or other_is_mi):
return self._join_level(other, level, how=how,
return_indexers=return_indexers)
other = _ensure_index(other)
if len(other) == 0 and how in ('left', 'outer'):
join_index = self._shallow_copy()
if return_indexers:
rindexer = np.repeat(-1, len(join_index))
return join_index, None, rindexer
else:
return join_index
if len(self) == 0 and how in ('right', 'outer'):
join_index = other._shallow_copy()
if return_indexers:
lindexer = np.repeat(-1, len(join_index))
return join_index, lindexer, None
else:
return join_index
if self._join_precedence < other._join_precedence:
how = {'right': 'left', 'left': 'right'}.get(how, how)
result = other.join(self, how=how, level=level,
return_indexers=return_indexers)
if return_indexers:
x, y, z = result
result = x, z, y
return result
if not is_dtype_equal(self.dtype, other.dtype):
this = self.astype('O')
other = other.astype('O')
return this.join(other, how=how, return_indexers=return_indexers)
_validate_join_method(how)
if not self.is_unique and not other.is_unique:
return self._join_non_unique(other, how=how,
return_indexers=return_indexers)
elif not self.is_unique or not other.is_unique:
if self.is_monotonic and other.is_monotonic:
return self._join_monotonic(other, how=how,
return_indexers=return_indexers)
else:
return self._join_non_unique(other, how=how,
return_indexers=return_indexers)
elif self.is_monotonic and other.is_monotonic:
try:
return self._join_monotonic(other, how=how,
return_indexers=return_indexers)
except TypeError:
pass
if how == 'left':
join_index = self
elif how == 'right':
join_index = other
elif how == 'inner':
join_index = self.intersection(other)
elif how == 'outer':
join_index = self.union(other)
if sort:
join_index = join_index.sort_values()
if return_indexers:
if join_index is self:
lindexer = None
else:
lindexer = self.get_indexer(join_index)
if join_index is other:
rindexer = None
else:
rindexer = other.get_indexer(join_index)
return join_index, lindexer, rindexer
else:
return join_index
def _join_multi(self, other, how, return_indexers=True):
from .multi import MultiIndex
self_is_mi = isinstance(self, MultiIndex)
other_is_mi = isinstance(other, MultiIndex)
# figure out join names
self_names = _not_none(*self.names)
other_names = _not_none(*other.names)
overlap = list(set(self_names) & set(other_names))
# need at least 1 in common, but not more than 1
if not len(overlap):
raise ValueError("cannot join with no level specified and no "
"overlapping names")
if len(overlap) > 1:
raise NotImplementedError("merging with more than one level "
"overlap on a multi-index is not "
"implemented")
jl = overlap[0]
# make the indices into mi's that match
if not (self_is_mi and other_is_mi):
flip_order = False
if self_is_mi:
self, other = other, self
flip_order = True
# flip if join method is right or left
how = {'right': 'left', 'left': 'right'}.get(how, how)
level = other.names.index(jl)
result = self._join_level(other, level, how=how,
return_indexers=return_indexers)
if flip_order:
if isinstance(result, tuple):
return result[0], result[2], result[1]
return result
# 2 multi-indexes
raise NotImplementedError("merging with both multi-indexes is not "
"implemented")
def _join_non_unique(self, other, how='left', return_indexers=False):
from pandas.core.reshape.merge import _get_join_indexers
left_idx, right_idx = _get_join_indexers([self._values],
[other._values], how=how,
sort=True)
left_idx = _ensure_platform_int(left_idx)
right_idx = _ensure_platform_int(right_idx)
join_index = np.asarray(self._values.take(left_idx))
mask = left_idx == -1
np.putmask(join_index, mask, other._values.take(right_idx))
join_index = self._wrap_joined_index(join_index, other)
if return_indexers:
return join_index, left_idx, right_idx
else:
return join_index
def _join_level(self, other, level, how='left', return_indexers=False,
keep_order=True):
"""
The join method *only* affects the level of the resulting
MultiIndex. Otherwise it just exactly aligns the Index data to the
labels of the level in the MultiIndex. If `keep_order` == True, the
order of the data indexed by the MultiIndex will not be changed;
otherwise, it will tie out with `other`.
"""
from .multi import MultiIndex
def _get_leaf_sorter(labels):
"""
returns sorter for the inner most level while preserving the
order of higher levels
"""
if labels[0].size == 0:
return np.empty(0, dtype='int64')
if len(labels) == 1:
lab = _ensure_int64(labels[0])
sorter, _ = libalgos.groupsort_indexer(lab, 1 + lab.max())
return sorter
# find indexers of begining of each set of
# same-key labels w.r.t all but last level
tic = labels[0][:-1] != labels[0][1:]
for lab in labels[1:-1]:
tic |= lab[:-1] != lab[1:]
starts = np.hstack(([True], tic, [True])).nonzero()[0]
lab = _ensure_int64(labels[-1])
return lib.get_level_sorter(lab, _ensure_int64(starts))
if isinstance(self, MultiIndex) and isinstance(other, MultiIndex):
raise TypeError('Join on level between two MultiIndex objects '
'is ambiguous')
left, right = self, other
flip_order = not isinstance(self, MultiIndex)
if flip_order:
left, right = right, left
how = {'right': 'left', 'left': 'right'}.get(how, how)
level = left._get_level_number(level)
old_level = left.levels[level]
if not right.is_unique:
raise NotImplementedError('Index._join_level on non-unique index '
'is not implemented')
new_level, left_lev_indexer, right_lev_indexer = \
old_level.join(right, how=how, return_indexers=True)
if left_lev_indexer is None:
if keep_order or len(left) == 0:
left_indexer = None
join_index = left
else: # sort the leaves
left_indexer = _get_leaf_sorter(left.labels[:level + 1])
join_index = left[left_indexer]
else:
left_lev_indexer = _ensure_int64(left_lev_indexer)
rev_indexer = lib.get_reverse_indexer(left_lev_indexer,
len(old_level))
new_lev_labels = algos.take_nd(rev_indexer, left.labels[level],
allow_fill=False)
new_labels = list(left.labels)
new_labels[level] = new_lev_labels
new_levels = list(left.levels)
new_levels[level] = new_level
if keep_order: # just drop missing values. o.w. keep order
left_indexer = np.arange(len(left), dtype=np.intp)
mask = new_lev_labels != -1
if not mask.all():
new_labels = [lab[mask] for lab in new_labels]
left_indexer = left_indexer[mask]
else: # tie out the order with other
if level == 0: # outer most level, take the fast route
ngroups = 1 + new_lev_labels.max()
left_indexer, counts = libalgos.groupsort_indexer(
new_lev_labels, ngroups)
# missing values are placed first; drop them!
left_indexer = left_indexer[counts[0]:]
new_labels = [lab[left_indexer] for lab in new_labels]
else: # sort the leaves
mask = new_lev_labels != -1
mask_all = mask.all()
if not mask_all:
new_labels = [lab[mask] for lab in new_labels]
left_indexer = _get_leaf_sorter(new_labels[:level + 1])
new_labels = [lab[left_indexer] for lab in new_labels]
# left_indexers are w.r.t masked frame.
# reverse to original frame!
if not mask_all:
left_indexer = mask.nonzero()[0][left_indexer]
join_index = MultiIndex(levels=new_levels, labels=new_labels,
names=left.names, verify_integrity=False)
if right_lev_indexer is not None:
right_indexer = algos.take_nd(right_lev_indexer,
join_index.labels[level],
allow_fill=False)
else:
right_indexer = join_index.labels[level]
if flip_order:
left_indexer, right_indexer = right_indexer, left_indexer
if return_indexers:
left_indexer = (None if left_indexer is None
else _ensure_platform_int(left_indexer))
right_indexer = (None if right_indexer is None
else _ensure_platform_int(right_indexer))
return join_index, left_indexer, right_indexer
else:
return join_index
def _join_monotonic(self, other, how='left', return_indexers=False):
if self.equals(other):
ret_index = other if how == 'right' else self
if return_indexers:
return ret_index, None, None
else:
return ret_index
sv = self._values
ov = other._values
if self.is_unique and other.is_unique:
# We can perform much better than the general case
if how == 'left':
join_index = self
lidx = None
ridx = self._left_indexer_unique(sv, ov)
elif how == 'right':
join_index = other
lidx = self._left_indexer_unique(ov, sv)
ridx = None
elif how == 'inner':
join_index, lidx, ridx = self._inner_indexer(sv, ov)
join_index = self._wrap_joined_index(join_index, other)
elif how == 'outer':
join_index, lidx, ridx = self._outer_indexer(sv, ov)
join_index = self._wrap_joined_index(join_index, other)
else:
if how == 'left':
join_index, lidx, ridx = self._left_indexer(sv, ov)
elif how == 'right':
join_index, ridx, lidx = self._left_indexer(ov, sv)
elif how == 'inner':
join_index, lidx, ridx = self._inner_indexer(sv, ov)
elif how == 'outer':
join_index, lidx, ridx = self._outer_indexer(sv, ov)
join_index = self._wrap_joined_index(join_index, other)
if return_indexers:
lidx = None if lidx is None else _ensure_platform_int(lidx)
ridx = None if ridx is None else _ensure_platform_int(ridx)
return join_index, lidx, ridx
else:
return join_index
def _wrap_joined_index(self, joined, other):
name = self.name if self.name == other.name else None
return Index(joined, name=name)
def _get_string_slice(self, key, use_lhs=True, use_rhs=True):
# this is for partial string indexing,
# overridden in DatetimeIndex, TimedeltaIndex and PeriodIndex
raise NotImplementedError
def slice_indexer(self, start=None, end=None, step=None, kind=None):
"""
For an ordered Index, compute the slice indexer for input labels and
step
Parameters
----------
start : label, default None
If None, defaults to the beginning
end : label, default None
If None, defaults to the end
step : int, default None
kind : string, default None
Returns
-------
indexer : ndarray or slice
Notes
-----
This function assumes that the data is sorted, so use at your own peril
"""
start_slice, end_slice = self.slice_locs(start, end, step=step,
kind=kind)
# return a slice
if not is_scalar(start_slice):
raise AssertionError("Start slice bound is non-scalar")
if not is_scalar(end_slice):
raise AssertionError("End slice bound is non-scalar")
return slice(start_slice, end_slice, step)
def _maybe_cast_indexer(self, key):
"""
If we have a float key and are not a floating index
then try to cast to an int if equivalent
"""
if is_float(key) and not self.is_floating():
try:
ckey = int(key)
if ckey == key:
key = ckey
except (OverflowError, ValueError, TypeError):
pass
return key
def _validate_indexer(self, form, key, kind):
"""
if we are positional indexer
validate that we have appropriate typed bounds
must be an integer
"""
assert kind in ['ix', 'loc', 'getitem', 'iloc']
if key is None:
pass
elif is_integer(key):
pass
elif kind in ['iloc', 'getitem']:
self._invalid_indexer(form, key)
return key
_index_shared_docs['_maybe_cast_slice_bound'] = """
This function should be overloaded in subclasses that allow non-trivial
casting on label-slice bounds, e.g. datetime-like indices allowing
strings containing formatted datetimes.
Parameters
----------
label : object
side : {'left', 'right'}
kind : {'ix', 'loc', 'getitem'}
Returns
-------
label : object
Notes
-----
Value of `side` parameter should be validated in caller.
"""
@Appender(_index_shared_docs['_maybe_cast_slice_bound'])
def _maybe_cast_slice_bound(self, label, side, kind):
assert kind in ['ix', 'loc', 'getitem', None]
# We are a plain index here (sub-class override this method if they
# wish to have special treatment for floats/ints, e.g. Float64Index and
# datetimelike Indexes
# reject them
if is_float(label):
if not (kind in ['ix'] and (self.holds_integer() or
self.is_floating())):
self._invalid_indexer('slice', label)
# we are trying to find integer bounds on a non-integer based index
# this is rejected (generally .loc gets you here)
elif is_integer(label):
self._invalid_indexer('slice', label)
return label
def _searchsorted_monotonic(self, label, side='left'):
if self.is_monotonic_increasing:
return self.searchsorted(label, side=side)
elif self.is_monotonic_decreasing:
# np.searchsorted expects ascending sort order, have to reverse
# everything for it to work (element ordering, search side and
# resulting value).
pos = self[::-1].searchsorted(label, side='right' if side == 'left'
else 'left')
return len(self) - pos
raise ValueError('index must be monotonic increasing or decreasing')
def _get_loc_only_exact_matches(self, key):
"""
This is overriden on subclasses (namely, IntervalIndex) to control
get_slice_bound.
"""
return self.get_loc(key)
def get_slice_bound(self, label, side, kind):
"""
Calculate slice bound that corresponds to given label.
Returns leftmost (one-past-the-rightmost if ``side=='right'``) position
of given label.
Parameters
----------
label : object
side : {'left', 'right'}
kind : {'ix', 'loc', 'getitem'}
"""
assert kind in ['ix', 'loc', 'getitem', None]
if side not in ('left', 'right'):
raise ValueError("Invalid value for side kwarg,"
" must be either 'left' or 'right': %s" %
(side, ))
original_label = label
# For datetime indices label may be a string that has to be converted
# to datetime boundary according to its resolution.
label = self._maybe_cast_slice_bound(label, side, kind)
# we need to look up the label
try:
slc = self._get_loc_only_exact_matches(label)
except KeyError as err:
try:
return self._searchsorted_monotonic(label, side)
except ValueError:
# raise the original KeyError
raise err
if isinstance(slc, np.ndarray):
# get_loc may return a boolean array or an array of indices, which
# is OK as long as they are representable by a slice.
if is_bool_dtype(slc):
slc = lib.maybe_booleans_to_slice(slc.view('u1'))
else:
slc = lib.maybe_indices_to_slice(slc.astype('i8'), len(self))
if isinstance(slc, np.ndarray):
raise KeyError("Cannot get %s slice bound for non-unique "
"label: %r" % (side, original_label))
if isinstance(slc, slice):
if side == 'left':
return slc.start
else:
return slc.stop
else:
if side == 'right':
return slc + 1
else:
return slc
def slice_locs(self, start=None, end=None, step=None, kind=None):
"""
Compute slice locations for input labels.
Parameters
----------
start : label, default None
If None, defaults to the beginning
end : label, default None
If None, defaults to the end
step : int, defaults None
If None, defaults to 1
kind : {'ix', 'loc', 'getitem'} or None
Returns
-------
start, end : int
Notes
-----
This method only works if the index is monotonic or unique.
Examples
---------
>>> idx = pd.Index(list('abcd'))
>>> idx.slice_locs(start='b', end='c')
(1, 3)
See Also
--------
Index.get_loc : Get location for a single label
"""
inc = (step is None or step >= 0)
if not inc:
# If it's a reverse slice, temporarily swap bounds.
start, end = end, start
start_slice = None
if start is not None:
start_slice = self.get_slice_bound(start, 'left', kind)
if start_slice is None:
start_slice = 0
end_slice = None
if end is not None:
end_slice = self.get_slice_bound(end, 'right', kind)
if end_slice is None:
end_slice = len(self)
if not inc:
# Bounds at this moment are swapped, swap them back and shift by 1.
#
# slice_locs('B', 'A', step=-1): s='B', e='A'
#
# s='A' e='B'
# AFTER SWAP: | |
# v ------------------> V
# -----------------------------------
# | | |A|A|A|A| | | | | |B|B| | | | |
# -----------------------------------
# ^ <------------------ ^
# SHOULD BE: | |
# end=s-1 start=e-1
#
end_slice, start_slice = start_slice - 1, end_slice - 1
# i == -1 triggers ``len(self) + i`` selection that points to the
# last element, not before-the-first one, subtracting len(self)
# compensates that.
if end_slice == -1:
end_slice -= len(self)
if start_slice == -1:
start_slice -= len(self)
return start_slice, end_slice
def delete(self, loc):
"""
Make new Index with passed location(-s) deleted
Returns
-------
new_index : Index
"""
return self._shallow_copy(np.delete(self._data, loc))
def insert(self, loc, item):
"""
Make new Index inserting new item at location. Follows
Python list.append semantics for negative values
Parameters
----------
loc : int
item : object
Returns
-------
new_index : Index
"""
_self = np.asarray(self)
item = self._coerce_scalar_to_index(item)._values
idx = np.concatenate((_self[:loc], item, _self[loc:]))
return self._shallow_copy_with_infer(idx)
def drop(self, labels, errors='raise'):
"""
Make new Index with passed list of labels deleted
Parameters
----------
labels : array-like
errors : {'ignore', 'raise'}, default 'raise'
If 'ignore', suppress error and existing labels are dropped.
Returns
-------
dropped : Index
"""
labels = _index_labels_to_array(labels)
indexer = self.get_indexer(labels)
mask = indexer == -1
if mask.any():
if errors != 'ignore':
raise ValueError('labels %s not contained in axis' %
labels[mask])
indexer = indexer[~mask]
return self.delete(indexer)
@Appender(base._shared_docs['unique'] % _index_doc_kwargs)
def unique(self):
result = super(Index, self).unique()
return self._shallow_copy(result)
@Appender(base._shared_docs['drop_duplicates'] % _index_doc_kwargs)
def drop_duplicates(self, keep='first'):
return super(Index, self).drop_duplicates(keep=keep)
@Appender(base._shared_docs['duplicated'] % _index_doc_kwargs)
def duplicated(self, keep='first'):
return super(Index, self).duplicated(keep=keep)
_index_shared_docs['fillna'] = """
Fill NA/NaN values with the specified value
Parameters
----------
value : scalar
Scalar value to use to fill holes (e.g. 0).
This value cannot be a list-likes.
downcast : dict, default is None
a dict of item->dtype of what to downcast if possible,
or the string 'infer' which will try to downcast to an appropriate
equal type (e.g. float64 to int64 if possible)
Returns
-------
filled : %(klass)s
"""
@Appender(_index_shared_docs['fillna'])
def fillna(self, value=None, downcast=None):
self._assert_can_do_op(value)
if self.hasnans:
result = self.putmask(self._isnan, value)
if downcast is None:
# no need to care metadata other than name
# because it can't have freq if
return Index(result, name=self.name)
return self._shallow_copy()
_index_shared_docs['dropna'] = """
Return Index without NA/NaN values
Parameters
----------
how : {'any', 'all'}, default 'any'
If the Index is a MultiIndex, drop the value when any or all levels
are NaN.
Returns
-------
valid : Index
"""
@Appender(_index_shared_docs['dropna'])
def dropna(self, how='any'):
if how not in ('any', 'all'):
raise ValueError("invalid how option: {0}".format(how))
if self.hasnans:
return self._shallow_copy(self.values[~self._isnan])
return self._shallow_copy()
def _evaluate_with_timedelta_like(self, other, op, opstr):
raise TypeError("can only perform ops with timedelta like values")
def _evaluate_with_datetime_like(self, other, op, opstr):
raise TypeError("can only perform ops with datetime like values")
def _evaluate_compare(self, op):
raise base.AbstractMethodError(self)
@classmethod
def _add_comparison_methods(cls):
""" add in comparison methods """
def _make_compare(op):
def _evaluate_compare(self, other):
if isinstance(other, (np.ndarray, Index, ABCSeries)):
if other.ndim > 0 and len(self) != len(other):
raise ValueError('Lengths must match to compare')
# we may need to directly compare underlying
# representations
if needs_i8_conversion(self) and needs_i8_conversion(other):
return self._evaluate_compare(other, op)
if (is_object_dtype(self) and
self.nlevels == 1):
# don't pass MultiIndex
with np.errstate(all='ignore'):
result = _comp_method_OBJECT_ARRAY(
op, self.values, other)
else:
with np.errstate(all='ignore'):
result = op(self.values, np.asarray(other))
# technically we could support bool dtyped Index
# for now just return the indexing array directly
if is_bool_dtype(result):
return result
try:
return Index(result)
except TypeError:
return result
return _evaluate_compare
cls.__eq__ = _make_compare(operator.eq)
cls.__ne__ = _make_compare(operator.ne)
cls.__lt__ = _make_compare(operator.lt)
cls.__gt__ = _make_compare(operator.gt)
cls.__le__ = _make_compare(operator.le)
cls.__ge__ = _make_compare(operator.ge)
@classmethod
def _add_numeric_methods_add_sub_disabled(cls):
""" add in the numeric add/sub methods to disable """
def _make_invalid_op(name):
def invalid_op(self, other=None):
raise TypeError("cannot perform {name} with this index type: "
"{typ}".format(name=name, typ=type(self)))
invalid_op.__name__ = name
return invalid_op
cls.__add__ = cls.__radd__ = __iadd__ = _make_invalid_op('__add__') # noqa
cls.__sub__ = __isub__ = _make_invalid_op('__sub__') # noqa
@classmethod
def _add_numeric_methods_disabled(cls):
""" add in numeric methods to disable other than add/sub """
def _make_invalid_op(name):
def invalid_op(self, other=None):
raise TypeError("cannot perform {name} with this index type: "
"{typ}".format(name=name, typ=type(self)))
invalid_op.__name__ = name
return invalid_op
cls.__pow__ = cls.__rpow__ = _make_invalid_op('__pow__')
cls.__mul__ = cls.__rmul__ = _make_invalid_op('__mul__')
cls.__floordiv__ = cls.__rfloordiv__ = _make_invalid_op('__floordiv__')
cls.__truediv__ = cls.__rtruediv__ = _make_invalid_op('__truediv__')
if not compat.PY3:
cls.__div__ = cls.__rdiv__ = _make_invalid_op('__div__')
cls.__neg__ = _make_invalid_op('__neg__')
cls.__pos__ = _make_invalid_op('__pos__')
cls.__abs__ = _make_invalid_op('__abs__')
cls.__inv__ = _make_invalid_op('__inv__')
def _maybe_update_attributes(self, attrs):
""" Update Index attributes (e.g. freq) depending on op """
return attrs
def _validate_for_numeric_unaryop(self, op, opstr):
""" validate if we can perform a numeric unary operation """
if not self._is_numeric_dtype:
raise TypeError("cannot evaluate a numeric op "
"{opstr} for type: {typ}".format(
opstr=opstr,
typ=type(self))
)
def _validate_for_numeric_binop(self, other, op, opstr):
"""
return valid other, evaluate or raise TypeError
if we are not of the appropriate type
internal method called by ops
"""
# if we are an inheritor of numeric,
# but not actually numeric (e.g. DatetimeIndex/PeriodInde)
if not self._is_numeric_dtype:
raise TypeError("cannot evaluate a numeric op {opstr} "
"for type: {typ}".format(
opstr=opstr,
typ=type(self))
)
if isinstance(other, Index):
if not other._is_numeric_dtype:
raise TypeError("cannot evaluate a numeric op "
"{opstr} with type: {typ}".format(
opstr=type(self),
typ=type(other))
)
elif isinstance(other, np.ndarray) and not other.ndim:
other = other.item()
if isinstance(other, (Index, ABCSeries, np.ndarray)):
if len(self) != len(other):
raise ValueError("cannot evaluate a numeric op with "
"unequal lengths")
other = _values_from_object(other)
if other.dtype.kind not in ['f', 'i', 'u']:
raise TypeError("cannot evaluate a numeric op "
"with a non-numeric dtype")
elif isinstance(other, (ABCDateOffset, np.timedelta64,
Timedelta, datetime.timedelta)):
# higher up to handle
pass
elif isinstance(other, (Timestamp, np.datetime64)):
# higher up to handle
pass
else:
if not (is_float(other) or is_integer(other)):
raise TypeError("can only perform ops with scalar values")
return other
@classmethod
def _add_numeric_methods_binary(cls):
""" add in numeric methods """
def _make_evaluate_binop(op, opstr, reversed=False, constructor=Index):
def _evaluate_numeric_binop(self, other):
other = self._validate_for_numeric_binop(other, op, opstr)
# handle time-based others
if isinstance(other, (ABCDateOffset, np.timedelta64,
Timedelta, datetime.timedelta)):
return self._evaluate_with_timedelta_like(other, op, opstr)
elif isinstance(other, (Timestamp, np.datetime64)):
return self._evaluate_with_datetime_like(other, op, opstr)
# if we are a reversed non-communative op
values = self.values
if reversed:
values, other = other, values
attrs = self._get_attributes_dict()
attrs = self._maybe_update_attributes(attrs)
with np.errstate(all='ignore'):
result = op(values, other)
return constructor(result, **attrs)
return _evaluate_numeric_binop
cls.__add__ = cls.__radd__ = _make_evaluate_binop(
operator.add, '__add__')
cls.__sub__ = _make_evaluate_binop(
operator.sub, '__sub__')
cls.__rsub__ = _make_evaluate_binop(
operator.sub, '__sub__', reversed=True)
cls.__mul__ = cls.__rmul__ = _make_evaluate_binop(
operator.mul, '__mul__')
cls.__rpow__ = _make_evaluate_binop(
operator.pow, '__pow__', reversed=True)
cls.__pow__ = _make_evaluate_binop(
operator.pow, '__pow__')
cls.__mod__ = _make_evaluate_binop(
operator.mod, '__mod__')
cls.__floordiv__ = _make_evaluate_binop(
operator.floordiv, '__floordiv__')
cls.__rfloordiv__ = _make_evaluate_binop(
operator.floordiv, '__floordiv__', reversed=True)
cls.__truediv__ = _make_evaluate_binop(
operator.truediv, '__truediv__')
cls.__rtruediv__ = _make_evaluate_binop(
operator.truediv, '__truediv__', reversed=True)
if not compat.PY3:
cls.__div__ = _make_evaluate_binop(
operator.div, '__div__')
cls.__rdiv__ = _make_evaluate_binop(
operator.div, '__div__', reversed=True)
cls.__divmod__ = _make_evaluate_binop(
divmod,
'__divmod__',
constructor=lambda result, **attrs: (
Index(result[0], **attrs),
Index(result[1], **attrs),
),
)
@classmethod
def _add_numeric_methods_unary(cls):
""" add in numeric unary methods """
def _make_evaluate_unary(op, opstr):
def _evaluate_numeric_unary(self):
self._validate_for_numeric_unaryop(op, opstr)
attrs = self._get_attributes_dict()
attrs = self._maybe_update_attributes(attrs)
return Index(op(self.values), **attrs)
return _evaluate_numeric_unary
cls.__neg__ = _make_evaluate_unary(lambda x: -x, '__neg__')
cls.__pos__ = _make_evaluate_unary(lambda x: x, '__pos__')
cls.__abs__ = _make_evaluate_unary(np.abs, '__abs__')
cls.__inv__ = _make_evaluate_unary(lambda x: -x, '__inv__')
@classmethod
def _add_numeric_methods(cls):
cls._add_numeric_methods_unary()
cls._add_numeric_methods_binary()
@classmethod
def _add_logical_methods(cls):
""" add in logical methods """
_doc = """
%(desc)s
Parameters
----------
All arguments to numpy.%(outname)s are accepted.
Returns
-------
%(outname)s : bool or array_like (if axis is specified)
A single element array_like may be converted to bool."""
def _make_logical_function(name, desc, f):
@Substitution(outname=name, desc=desc)
@Appender(_doc)
def logical_func(self, *args, **kwargs):
result = f(self.values)
if (isinstance(result, (np.ndarray, ABCSeries, Index)) and
result.ndim == 0):
# return NumPy type
return result.dtype.type(result.item())
else: # pragma: no cover
return result
logical_func.__name__ = name
return logical_func
cls.all = _make_logical_function('all', 'Return whether all elements '
'are True',
np.all)
cls.any = _make_logical_function('any',
'Return whether any element is True',
np.any)
@classmethod
def _add_logical_methods_disabled(cls):
""" add in logical methods to disable """
def _make_invalid_op(name):
def invalid_op(self, other=None):
raise TypeError("cannot perform {name} with this index type: "
"{typ}".format(name=name, typ=type(self)))
invalid_op.__name__ = name
return invalid_op
cls.all = _make_invalid_op('all')
cls.any = _make_invalid_op('any')
Index._add_numeric_methods_disabled()
Index._add_logical_methods()
Index._add_comparison_methods()
def _ensure_index_from_sequences(sequences, names=None):
"""Construct an index from sequences of data.
A single sequence returns an Index. Many sequences returns a
MultiIndex.
Parameters
----------
sequences : sequence of sequences
names : sequence of str
Returns
-------
index : Index or MultiIndex
Examples
--------
>>> _ensure_index_from_sequences([[1, 2, 3]], names=['name'])
Int64Index([1, 2, 3], dtype='int64', name='name')
>>> _ensure_index_from_sequences([['a', 'a'], ['a', 'b']],
names=['L1', 'L2'])
MultiIndex(levels=[['a'], ['a', 'b']],
labels=[[0, 0], [0, 1]],
names=['L1', 'L2'])
See Also
--------
_ensure_index
"""
from .multi import MultiIndex
if len(sequences) == 1:
if names is not None:
names = names[0]
return Index(sequences[0], name=names)
else:
return MultiIndex.from_arrays(sequences, names=names)
def _ensure_index(index_like, copy=False):
"""
Ensure that we have an index from some index-like object
Parameters
----------
index : sequence
An Index or other sequence
copy : bool
Returns
-------
index : Index or MultiIndex
Examples
--------
>>> _ensure_index(['a', 'b'])
Index(['a', 'b'], dtype='object')
>>> _ensure_index([('a', 'a'), ('b', 'c')])
Index([('a', 'a'), ('b', 'c')], dtype='object')
>>> _ensure_index([['a', 'a'], ['b', 'c']])
MultiIndex(levels=[['a'], ['b', 'c']],
labels=[[0, 0], [0, 1]])
See Also
--------
_ensure_index_from_sequences
"""
if isinstance(index_like, Index):
if copy:
index_like = index_like.copy()
return index_like
if hasattr(index_like, 'name'):
return Index(index_like, name=index_like.name, copy=copy)
# must check for exactly list here because of strict type
# check in clean_index_list
if isinstance(index_like, list):
if type(index_like) != list:
index_like = list(index_like)
converted, all_arrays = lib.clean_index_list(index_like)
if len(converted) > 0 and all_arrays:
from .multi import MultiIndex
return MultiIndex.from_arrays(converted)
else:
index_like = converted
else:
# clean_index_list does the equivalent of copying
# so only need to do this if not list instance
if copy:
from copy import copy
index_like = copy(index_like)
return Index(index_like)
def _get_na_value(dtype):
if is_datetime64_any_dtype(dtype) or is_timedelta64_dtype(dtype):
return libts.NaT
return {np.datetime64: libts.NaT,
np.timedelta64: libts.NaT}.get(dtype, np.nan)
def _ensure_has_len(seq):
"""If seq is an iterator, put its values into a list."""
try:
len(seq)
except TypeError:
return list(seq)
else:
return seq
def _trim_front(strings):
"""
Trims zeros and decimal points
"""
trimmed = strings
while len(strings) > 0 and all([x[0] == ' ' for x in trimmed]):
trimmed = [x[1:] for x in trimmed]
return trimmed
def _validate_join_method(method):
if method not in ['left', 'right', 'inner', 'outer']:
raise ValueError('do not recognize join method %s' % method)