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ops.py
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"""
Arithmetic operations for PandasObjects
This is not a public API.
"""
# necessary to enforce truediv in Python 2.X
from __future__ import division
import operator
import numpy as np
import pandas as pd
from pandas._libs import algos as libalgos, ops as libops
from pandas import compat
from pandas.util._decorators import Appender
from pandas.compat import bind_method
import pandas.core.missing as missing
import pandas.core.common as com
from pandas.errors import NullFrequencyError
from pandas.core.dtypes.missing import notna, isna
from pandas.core.dtypes.common import (
needs_i8_conversion,
is_datetimelike_v_numeric,
is_integer_dtype, is_categorical_dtype,
is_object_dtype, is_timedelta64_dtype,
is_datetime64_dtype, is_datetime64tz_dtype,
is_bool_dtype,
is_list_like,
is_scalar,
_ensure_object)
from pandas.core.dtypes.cast import (
maybe_upcast_putmask, find_common_type,
construct_1d_object_array_from_listlike)
from pandas.core.dtypes.generic import (
ABCSeries,
ABCDataFrame, ABCPanel,
ABCIndex,
ABCSparseSeries, ABCSparseArray)
# -----------------------------------------------------------------------------
# Ops Wrapping Utilities
def get_op_result_name(left, right):
"""
Find the appropriate name to pin to an operation result. This result
should always be either an Index or a Series.
Parameters
----------
left : {Series, Index}
right : object
Returns
-------
name : object
Usually a string
"""
# `left` is always a pd.Series when called from within ops
if isinstance(right, (ABCSeries, pd.Index)):
name = _maybe_match_name(left, right)
else:
name = left.name
return name
def _maybe_match_name(a, b):
"""
Try to find a name to attach to the result of an operation between
a and b. If only one of these has a `name` attribute, return that
name. Otherwise return a consensus name if they match of None if
they have different names.
Parameters
----------
a : object
b : object
Returns
-------
name : str or None
See also
--------
pandas.core.common._consensus_name_attr
"""
a_has = hasattr(a, 'name')
b_has = hasattr(b, 'name')
if a_has and b_has:
if a.name == b.name:
return a.name
else:
# TODO: what if they both have np.nan for their names?
return None
elif a_has:
return a.name
elif b_has:
return b.name
return None
# -----------------------------------------------------------------------------
# Reversed Operations not available in the stdlib operator module.
# Defining these instead of using lambdas allows us to reference them by name.
def radd(left, right):
return right + left
def rsub(left, right):
return right - left
def rmul(left, right):
return right * left
def rdiv(left, right):
return right / left
def rtruediv(left, right):
return right / left
def rfloordiv(left, right):
return right // left
def rmod(left, right):
return right % left
def rdivmod(left, right):
return divmod(right, left)
def rpow(left, right):
return right ** left
def rand_(left, right):
return operator.and_(right, left)
def ror_(left, right):
return operator.or_(right, left)
def rxor(left, right):
return operator.xor(right, left)
# -----------------------------------------------------------------------------
def make_invalid_op(name):
"""
Return a binary method that always raises a TypeError.
Parameters
----------
name : str
Returns
-------
invalid_op : function
"""
def invalid_op(self, other=None):
raise TypeError("cannot perform {name} with this index type: "
"{typ}".format(name=name, typ=type(self).__name__))
invalid_op.__name__ = name
return invalid_op
def _gen_eval_kwargs(name):
"""
Find the keyword arguments to pass to numexpr for the given operation.
Parameters
----------
name : str
Returns
-------
eval_kwargs : dict
Examples
--------
>>> _gen_eval_kwargs("__add__")
{}
>>> _gen_eval_kwargs("rtruediv")
{"reversed": True, "truediv": True}
"""
kwargs = {}
# Series and Panel appear to only pass __add__, __radd__, ...
# but DataFrame gets both these dunder names _and_ non-dunder names
# add, radd, ...
name = name.replace('__', '')
if name.startswith('r'):
if name not in ['radd', 'rand', 'ror', 'rxor']:
# Exclude commutative operations
kwargs['reversed'] = True
if name in ['truediv', 'rtruediv']:
kwargs['truediv'] = True
if name in ['ne']:
kwargs['masker'] = True
return kwargs
def _gen_fill_zeros(name):
"""
Find the appropriate fill value to use when filling in undefined values
in the results of the given operation caused by operating on
(generally dividing by) zero.
Parameters
----------
name : str
Returns
-------
fill_value : {None, np.nan, np.inf}
"""
name = name.strip('__')
if 'div' in name:
# truediv, floordiv, div, and reversed variants
fill_value = np.inf
elif 'mod' in name:
# mod, rmod
fill_value = np.nan
else:
fill_value = None
return fill_value
def _get_frame_op_default_axis(name):
"""
Only DataFrame cares about default_axis, specifically:
special methods have default_axis=None and flex methods
have default_axis='columns'.
Parameters
----------
name : str
Returns
-------
default_axis: str or None
"""
if name.replace('__r', '__') in ['__and__', '__or__', '__xor__']:
# bool methods
return 'columns'
elif name.startswith('__'):
# __add__, __mul__, ...
return None
else:
# add, mul, ...
return 'columns'
def _get_opstr(op, cls):
"""
Find the operation string, if any, to pass to numexpr for this
operation.
Parameters
----------
op : binary operator
cls : class
Returns
-------
op_str : string or None
"""
# numexpr is available for non-sparse classes
subtyp = getattr(cls, '_subtyp', '')
use_numexpr = 'sparse' not in subtyp
if not use_numexpr:
# if we're not using numexpr, then don't pass a str_rep
return None
return {operator.add: '+',
radd: '+',
operator.mul: '*',
rmul: '*',
operator.sub: '-',
rsub: '-',
operator.truediv: '/',
rtruediv: '/',
operator.floordiv: '//',
rfloordiv: '//',
operator.mod: None, # TODO: Why None for mod but '%' for rmod?
rmod: '%',
operator.pow: '**',
rpow: '**',
operator.eq: '==',
operator.ne: '!=',
operator.le: '<=',
operator.lt: '<',
operator.ge: '>=',
operator.gt: '>',
operator.and_: '&',
rand_: '&',
operator.or_: '|',
ror_: '|',
operator.xor: '^',
rxor: '^',
divmod: None,
rdivmod: None}[op]
def _get_op_name(op, special):
"""
Find the name to attach to this method according to conventions
for special and non-special methods.
Parameters
----------
op : binary operator
special : bool
Returns
-------
op_name : str
"""
opname = op.__name__.strip('_')
if special:
opname = '__{opname}__'.format(opname=opname)
return opname
# -----------------------------------------------------------------------------
# Docstring Generation and Templates
_add_example_FRAME = """
>>> a = pd.DataFrame([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'],
... columns=['one'])
>>> a
one
a 1.0
b 1.0
c 1.0
d NaN
>>> b = pd.DataFrame(dict(one=[1, np.nan, 1, np.nan],
... two=[np.nan, 2, np.nan, 2]),
... index=['a', 'b', 'd', 'e'])
>>> b
one two
a 1.0 NaN
b NaN 2.0
d 1.0 NaN
e NaN 2.0
>>> a.add(b, fill_value=0)
one two
a 2.0 NaN
b 1.0 2.0
c 1.0 NaN
d 1.0 NaN
e NaN 2.0
"""
_sub_example_FRAME = """
>>> a = pd.DataFrame([2, 1, 1, np.nan], index=['a', 'b', 'c', 'd'],
... columns=['one'])
>>> a
one
a 2.0
b 1.0
c 1.0
d NaN
>>> b = pd.DataFrame(dict(one=[1, np.nan, 1, np.nan],
... two=[3, 2, np.nan, 2]),
... index=['a', 'b', 'd', 'e'])
>>> b
one two
a 1.0 3.0
b NaN 2.0
d 1.0 NaN
e NaN 2.0
>>> a.sub(b, fill_value=0)
one two
a 1.0 -3.0
b 1.0 -2.0
c 1.0 NaN
d -1.0 NaN
e NaN -2.0
"""
_op_descriptions = {
# Arithmetic Operators
'add': {'op': '+',
'desc': 'Addition',
'reverse': 'radd',
'df_examples': _add_example_FRAME},
'sub': {'op': '-',
'desc': 'Subtraction',
'reverse': 'rsub',
'df_examples': _sub_example_FRAME},
'mul': {'op': '*',
'desc': 'Multiplication',
'reverse': 'rmul',
'df_examples': None},
'mod': {'op': '%',
'desc': 'Modulo',
'reverse': 'rmod',
'df_examples': None},
'pow': {'op': '**',
'desc': 'Exponential power',
'reverse': 'rpow',
'df_examples': None},
'truediv': {'op': '/',
'desc': 'Floating division',
'reverse': 'rtruediv',
'df_examples': None},
'floordiv': {'op': '//',
'desc': 'Integer division',
'reverse': 'rfloordiv',
'df_examples': None},
'divmod': {'op': 'divmod',
'desc': 'Integer division and modulo',
'reverse': None,
'df_examples': None},
# Comparison Operators
'eq': {'op': '==',
'desc': 'Equal to',
'reverse': None,
'df_examples': None,
'others': None},
'ne': {'op': '!=',
'desc': 'Not equal to',
'reverse': None,
'df_examples': None,
'others': None},
'lt': {'op': '<',
'desc': 'Less than',
'reverse': None,
'df_examples': None,
'others': None},
'le': {'op': '<=',
'desc': 'Less than or equal to',
'reverse': 'gt',
'df_examples': None,
'others': None},
'gt': {'op': '>',
'desc': 'Greater than',
'reverse': None,
'df_examples': None,
'others': None},
'ge': {'op': '>=',
'desc': 'Greater than or equal to',
'reverse': None,
'df_examples': None,
'others': None}}
_op_names = list(_op_descriptions.keys())
for key in _op_names:
_op_descriptions[key]['reversed'] = False
reverse_op = _op_descriptions[key]['reverse']
if reverse_op is not None:
_op_descriptions[reverse_op] = _op_descriptions[key].copy()
_op_descriptions[reverse_op]['reversed'] = True
_op_descriptions[reverse_op]['reverse'] = key
_flex_doc_SERIES = """
{desc} of series and other, element-wise (binary operator `{op_name}`).
Equivalent to ``{equiv}``, but with support to substitute a fill_value for
missing data in one of the inputs.
Parameters
----------
other : Series or scalar value
fill_value : None or float value, default None (NaN)
Fill existing missing (NaN) values, and any new element needed for
successful Series alignment, with this value before computation.
If data in both corresponding Series locations is missing
the result will be missing
level : int or name
Broadcast across a level, matching Index values on the
passed MultiIndex level
Returns
-------
result : Series
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a 1.0
b 1.0
c 1.0
d NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a 1.0
b NaN
d 1.0
e NaN
dtype: float64
>>> a.add(b, fill_value=0)
a 2.0
b 1.0
c 1.0
d 1.0
e NaN
dtype: float64
See also
--------
Series.{reverse}
"""
_arith_doc_FRAME = """
Binary operator %s with support to substitute a fill_value for missing data in
one of the inputs
Parameters
----------
other : Series, DataFrame, or constant
axis : {0, 1, 'index', 'columns'}
For Series input, axis to match Series index on
fill_value : None or float value, default None
Fill existing missing (NaN) values, and any new element needed for
successful DataFrame alignment, with this value before computation.
If data in both corresponding DataFrame locations is missing
the result will be missing
level : int or name
Broadcast across a level, matching Index values on the
passed MultiIndex level
Notes
-----
Mismatched indices will be unioned together
Returns
-------
result : DataFrame
"""
_flex_doc_FRAME = """
{desc} of dataframe and other, element-wise (binary operator `{op_name}`).
Equivalent to ``{equiv}``, but with support to substitute a fill_value for
missing data in one of the inputs.
Parameters
----------
other : Series, DataFrame, or constant
axis : {{0, 1, 'index', 'columns'}}
For Series input, axis to match Series index on
level : int or name
Broadcast across a level, matching Index values on the
passed MultiIndex level
fill_value : None or float value, default None
Fill existing missing (NaN) values, and any new element needed for
successful DataFrame alignment, with this value before computation.
If data in both corresponding DataFrame locations is missing
the result will be missing
Notes
-----
Mismatched indices will be unioned together
Returns
-------
result : DataFrame
Examples
--------
{df_examples}
See also
--------
DataFrame.{reverse}
"""
_flex_comp_doc_FRAME = """
Flexible wrappers to comparison operators (`eq`, `ne`, `le`, `lt`, `ge`, `gt`).
Equivalent to `==`, `=!`, `<=`, `<`, `>=`, `>` with support to choose
axis (rows or columns) and level for comparison.
Parameters
----------
other : constant, list, tuple, ndarray, Series, or DataFrame
Any single or multiple element data structure, or list-like object.
axis : int or str, optional
Axis to target. Can be either the axis name ('index', 'rows',
'columns') or number (0, 1).
level : int or name
Broadcast across a level, matching Index values on the passed
MultiIndex level.
Returns
-------
result : DataFrame of bool
Result of the comparison.
See also
--------
DataFrame.eq : Compare DataFrames for equality elementwise
DataFrame.ne : Compare DataFrames for inequality elementwise
DataFrame.le : Compare DataFrames for less than inequality
or equality elementwise
DataFrame.lt : Compare DataFrames for strictly less than
inequality elementwise
DataFrame.ge : Compare DataFrames for greater than inequality
or equality elementwise
DataFrame.gt : Compare DataFrames for strictly greater than
inequality elementwise
Notes
--------
Mismatched indices will be unioned together.
Examples
--------
>>> df1 = pd.DataFrame({'company': ['A', 'B', 'C'],
... 'cost': [250, 150, 100],
... 'revenue': [100, 250, 300]})
>>> df1
company cost revenue
0 A 250 100
1 B 150 250
2 C 100 300
>>> df2 = pd.DataFrame({'company': ['A', 'B', 'C', 'D'],
... 'revenue': [300, 250, 100, 150]})
>>> df2
company revenue
0 A 300
1 B 250
2 C 100
3 D 150
>>> df3 = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
... 'revenue': [100, 250, 300, 200, 175, 225]},
... index = [['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
... ['A', 'B', 'C', 'A', 'B' ,'C']])
>>> df3
cost revenue
Q1 A 250 100
B 150 250
C 100 300
Q2 A 150 200
B 300 175
C 220 225
Compare to a constant and operator version
>>> df1 == 100
company cost revenue
0 False False True
1 False False False
2 False True False
>>> df1.eq(100)
company cost revenue
0 False False True
1 False False False
2 False True False
Compare to a Series by axis and operator version
>>> df1 != [100, 250, 300]
company cost revenue
0 True True False
1 True True False
2 True True False
>>> df1.ne([100, 250, 300], axis=0)
company cost revenue
0 True True False
1 True True False
2 True True False
>>> df1 != pd.Series([100, 250, 300])
company cost revenue 0 1 2
0 True True True True True True
1 True True True True True True
2 True True True True True True
>>> df1.ne(pd.Series([100, 250, 300]), axis=1)
company cost revenue 0 1 2
0 True True True True True True
1 True True True True True True
2 True True True True True True
Compare to a DataFrame by axis and operator version
>>> df1.reindex(['company', 'revenue'], axis='columns') > df2.iloc[:-1]
company revenue
0 False False
1 False False
2 False True
>>> df1.gt(df2, axis=0)
company cost revenue
0 False False False
1 False False False
2 False False True
3 False False False
>>> df1.gt(df2, axis=1)
company cost revenue
0 False False False
1 False False False
2 False False True
3 False False False
Compare to a MultiIndex by level and operator version
>>> df1.set_index('company') <= df3.loc['Q1']
cost revenue
company
A True True
B True True
C True True
>>> df1.set_index('company').le(df3, level=1)
cost revenue
Q1 A True True
B True True
C True True
Q2 A False True
B True False
C True False
"""
_flex_doc_PANEL = """
{desc} of series and other, element-wise (binary operator `{op_name}`).
Equivalent to ``{equiv}``.
Parameters
----------
other : DataFrame or Panel
axis : {{items, major_axis, minor_axis}}
Axis to broadcast over
Returns
-------
Panel
See also
--------
Panel.{reverse}
"""
_agg_doc_PANEL = """
Wrapper method for {op_name}
Parameters
----------
other : DataFrame or Panel
axis : {{items, major_axis, minor_axis}}
Axis to broadcast over
Returns
-------
Panel
"""
def _make_flex_doc(op_name, typ):
"""
Make the appropriate substitutions for the given operation and class-typ
into either _flex_doc_SERIES or _flex_doc_FRAME to return the docstring
to attach to a generated method.
Parameters
----------
op_name : str {'__add__', '__sub__', ... '__eq__', '__ne__', ...}
typ : str {series, 'dataframe']}
Returns
-------
doc : str
"""
op_name = op_name.replace('__', '')
op_desc = _op_descriptions[op_name]
if op_desc['reversed']:
equiv = 'other ' + op_desc['op'] + ' ' + typ
else:
equiv = typ + ' ' + op_desc['op'] + ' other'
if typ == 'series':
base_doc = _flex_doc_SERIES
doc = base_doc.format(desc=op_desc['desc'], op_name=op_name,
equiv=equiv, reverse=op_desc['reverse'])
elif typ == 'dataframe':
base_doc = _flex_doc_FRAME
doc = base_doc.format(desc=op_desc['desc'], op_name=op_name,
equiv=equiv, reverse=op_desc['reverse'],
df_examples=op_desc['df_examples'])
elif typ == 'panel':
base_doc = _flex_doc_PANEL
doc = base_doc.format(desc=op_desc['desc'], op_name=op_name,
equiv=equiv, reverse=op_desc['reverse'])
else:
raise AssertionError('Invalid typ argument.')
return doc
# -----------------------------------------------------------------------------
# Masking NA values and fallbacks for operations numpy does not support
def fill_binop(left, right, fill_value):
"""
If a non-None fill_value is given, replace null entries in left and right
with this value, but only in positions where _one_ of left/right is null,
not both.
Parameters
----------
left : array-like
right : array-like
fill_value : object
Returns
-------
left : array-like
right : array-like
Notes
-----
Makes copies if fill_value is not None
"""
# TODO: can we make a no-copy implementation?
if fill_value is not None:
left_mask = isna(left)
right_mask = isna(right)
left = left.copy()
right = right.copy()
# one but not both
mask = left_mask ^ right_mask
left[left_mask & mask] = fill_value
right[right_mask & mask] = fill_value
return left, right
def mask_cmp_op(x, y, op, allowed_types):
"""
Apply the function `op` to only non-null points in x and y.
Parameters
----------
x : array-like
y : array-like
op : binary operation
allowed_types : class or tuple of classes
Returns
-------
result : ndarray[bool]
"""
# TODO: Can we make the allowed_types arg unnecessary?
xrav = x.ravel()
result = np.empty(x.size, dtype=bool)
if isinstance(y, allowed_types):
yrav = y.ravel()
mask = notna(xrav) & notna(yrav)
result[mask] = op(np.array(list(xrav[mask])),
np.array(list(yrav[mask])))
else:
mask = notna(xrav)
result[mask] = op(np.array(list(xrav[mask])), y)
if op == operator.ne: # pragma: no cover
np.putmask(result, ~mask, True)
else:
np.putmask(result, ~mask, False)
result = result.reshape(x.shape)
return result
# -----------------------------------------------------------------------------
# Functions that add arithmetic methods to objects, given arithmetic factory
# methods
def _get_method_wrappers(cls):
"""
Find the appropriate operation-wrappers to use when defining flex/special
arithmetic, boolean, and comparison operations with the given class.
Parameters
----------
cls : class
Returns
-------
arith_flex : function or None
comp_flex : function or None
arith_special : function
comp_special : function
bool_special : function
Notes
-----
None is only returned for SparseArray
"""
if issubclass(cls, ABCSparseSeries):
# Be sure to catch this before ABCSeries and ABCSparseArray,
# as they will both come see SparseSeries as a subclass
arith_flex = _flex_method_SERIES
comp_flex = _flex_method_SERIES
arith_special = _arith_method_SPARSE_SERIES
comp_special = _arith_method_SPARSE_SERIES
bool_special = _bool_method_SERIES
# TODO: I don't think the functions defined by bool_method are tested
elif issubclass(cls, ABCSeries):
# Just Series; SparseSeries is caught above
arith_flex = _flex_method_SERIES
comp_flex = _flex_method_SERIES
arith_special = _arith_method_SERIES
comp_special = _comp_method_SERIES
bool_special = _bool_method_SERIES
elif issubclass(cls, ABCSparseArray):
arith_flex = None
comp_flex = None
arith_special = _arith_method_SPARSE_ARRAY
comp_special = _arith_method_SPARSE_ARRAY
bool_special = _arith_method_SPARSE_ARRAY
elif issubclass(cls, ABCPanel):
arith_flex = _flex_method_PANEL
comp_flex = _comp_method_PANEL
arith_special = _arith_method_PANEL
comp_special = _comp_method_PANEL
bool_special = _arith_method_PANEL
elif issubclass(cls, ABCDataFrame):
# Same for DataFrame and SparseDataFrame
arith_flex = _arith_method_FRAME
comp_flex = _flex_comp_method_FRAME
arith_special = _arith_method_FRAME
comp_special = _comp_method_FRAME
bool_special = _arith_method_FRAME
return arith_flex, comp_flex, arith_special, comp_special, bool_special
def _create_methods(cls, arith_method, comp_method, bool_method,
special=False):
# creates actual methods based upon arithmetic, comp and bool method
# constructors.
have_divmod = issubclass(cls, ABCSeries)
# divmod is available for Series and SparseSeries
# yapf: disable
new_methods = dict(
add=arith_method(cls, operator.add, special),
radd=arith_method(cls, radd, special),
sub=arith_method(cls, operator.sub, special),
mul=arith_method(cls, operator.mul, special),
truediv=arith_method(cls, operator.truediv, special),
floordiv=arith_method(cls, operator.floordiv, special),
# Causes a floating point exception in the tests when numexpr enabled,
# so for now no speedup
mod=arith_method(cls, operator.mod, special),
pow=arith_method(cls, operator.pow, special),
# not entirely sure why this is necessary, but previously was included
# so it's here to maintain compatibility
rmul=arith_method(cls, rmul, special),
rsub=arith_method(cls, rsub, special),
rtruediv=arith_method(cls, rtruediv, special),
rfloordiv=arith_method(cls, rfloordiv, special),
rpow=arith_method(cls, rpow, special),
rmod=arith_method(cls, rmod, special))
# yapf: enable
new_methods['div'] = new_methods['truediv']
new_methods['rdiv'] = new_methods['rtruediv']
if have_divmod:
# divmod doesn't have an op that is supported by numexpr
new_methods['divmod'] = arith_method(cls, divmod, special)
new_methods.update(dict(
eq=comp_method(cls, operator.eq, special),
ne=comp_method(cls, operator.ne, special),
lt=comp_method(cls, operator.lt, special),
gt=comp_method(cls, operator.gt, special),
le=comp_method(cls, operator.le, special),
ge=comp_method(cls, operator.ge, special)))
if bool_method:
new_methods.update(
dict(and_=bool_method(cls, operator.and_, special),
or_=bool_method(cls, operator.or_, special),
# For some reason ``^`` wasn't used in original.
xor=bool_method(cls, operator.xor, special),
rand_=bool_method(cls, rand_, special),
ror_=bool_method(cls, ror_, special),
rxor=bool_method(cls, rxor, special)))
if special: