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Methods that can be shared by many array-like classes or subclasses:
|
Series
|
Index
|
ExtensionArray
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é )Ú annotationsN)ÚAny)Úlib)Ú!maybe_dispatch_ufunc_to_dunder_op)Ú
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ABCNDFrame)Ú roperator©Ú extract_array)Úunpack_zerodim_and_deferÚmaxÚminÚsumÚprod)ÚmaximumÚminimumÚaddÚmultiplyc ó. e Zd Zd Z ed« d « Z ed« d « Z ed« d « Z ed« d « Z ed
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d Z ed« d « Z ed« d « Z ed« d « Z ed« d « Z ed« d « Z ed« d « Zd Z ed « d « Z ed « d « Z ed « d! « Z ed"« d# « Z ed$« d% « Z ed&« d' « Z ed(« d) « Z ed*« d+ « Z ed,« d- « Z ed.« d/ « Z ed0« d1 « Z ed2« d3 « Z ed4« d5 « Z ed6« d7 « Z ed8« d9 « Z! ed:« d; « Z"y<)=ÚOpsMixinc ó t S ©N©ÚNotImplemented©ÚselfÚotherÚops úHH:\Change_password\venv_build\Lib\site-packages\pandas/core/arraylike.pyÚ _cmp_methodzOpsMixin._cmp_method# ó Ü Ð ó Ú__eq__c óB | j |t j « S r )r ÚoperatorÚeq©r r s r r" zOpsMixin.__eq__& ó à× Ñ ¤x§{¡{Ó3Ð3r! Ú__ne__c óB | j |t j « S r )r r$ Úner& s r r( zOpsMixin.__ne__* r' r! Ú__lt__c óB | j |t j « S r )r r$ Últr& s r r+ zOpsMixin.__lt__. r' r! Ú__le__c óB | j |t j « S r )r r$ Úler& s r r. zOpsMixin.__le__2 r' r! Ú__gt__c óB | j |t j « S r )r r$ Úgtr& s r r1 zOpsMixin.__gt__6 r' r! Ú__ge__c óB | j |t j « S r )r r$ Úger& s r r4 zOpsMixin.__ge__: r' r! c ó t S r r r s r Ú_logical_methodzOpsMixin._logical_methodA r r! Ú__and__c óB | j |t j « S r )r8 r$ Úand_r& s r r9 zOpsMixin.__and__D s à×#Ñ# E¬8¯=©=Ó9Ð9r! Ú__rand__c óB | j |t j « S r )r8 r Úrand_r& s r r< zOpsMixin.__rand__H s à×#Ñ# E¬9¯?©?Ó;Ð;r! Ú__or__c óB | j |t j « S r )r8 r$ Úor_r& s r r? zOpsMixin.__or__L ó à×#Ñ# E¬8¯<©<Ó8Ð8r! Ú__ror__c óB | j |t j « S r )r8 r Úror_r& s r rC zOpsMixin.__ror__P ó à×#Ñ# E¬9¯>©>Ó:Ð:r! Ú__xor__c óB | j |t j « S r )r8 r$ Úxorr& s r rG zOpsMixin.__xor__T rB r! Ú__rxor__c óB | j |t j « S r )r8 r Úrxorr& s r rJ zOpsMixin.__rxor__X rF r! c ó t S r r r s r Ú _arith_methodzOpsMixin._arith_method_ r r! Ú__add__c óB | j |t j « S )a/
|
Get Addition of DataFrame and other, column-wise.
|
|
Equivalent to ``DataFrame.add(other)``.
|
|
Parameters
|
----------
|
other : scalar, sequence, Series, dict or DataFrame
|
Object to be added to the DataFrame.
|
|
Returns
|
-------
|
DataFrame
|
The result of adding ``other`` to DataFrame.
|
|
See Also
|
--------
|
DataFrame.add : Add a DataFrame and another object, with option for index-
|
or column-oriented addition.
|
|
Examples
|
--------
|
>>> df = pd.DataFrame({'height': [1.5, 2.6], 'weight': [500, 800]},
|
... index=['elk', 'moose'])
|
>>> df
|
height weight
|
elk 1.5 500
|
moose 2.6 800
|
|
Adding a scalar affects all rows and columns.
|
|
>>> df[['height', 'weight']] + 1.5
|
height weight
|
elk 3.0 501.5
|
moose 4.1 801.5
|
|
Each element of a list is added to a column of the DataFrame, in order.
|
|
>>> df[['height', 'weight']] + [0.5, 1.5]
|
height weight
|
elk 2.0 501.5
|
moose 3.1 801.5
|
|
Keys of a dictionary are aligned to the DataFrame, based on column names;
|
each value in the dictionary is added to the corresponding column.
|
|
>>> df[['height', 'weight']] + {'height': 0.5, 'weight': 1.5}
|
height weight
|
elk 2.0 501.5
|
moose 3.1 801.5
|
|
When `other` is a :class:`Series`, the index of `other` is aligned with the
|
columns of the DataFrame.
|
|
>>> s1 = pd.Series([0.5, 1.5], index=['weight', 'height'])
|
>>> df[['height', 'weight']] + s1
|
height weight
|
elk 3.0 500.5
|
moose 4.1 800.5
|
|
Even when the index of `other` is the same as the index of the DataFrame,
|
the :class:`Series` will not be reoriented. If index-wise alignment is desired,
|
:meth:`DataFrame.add` should be used with `axis='index'`.
|
|
>>> s2 = pd.Series([0.5, 1.5], index=['elk', 'moose'])
|
>>> df[['height', 'weight']] + s2
|
elk height moose weight
|
elk NaN NaN NaN NaN
|
moose NaN NaN NaN NaN
|
|
>>> df[['height', 'weight']].add(s2, axis='index')
|
height weight
|
elk 2.0 500.5
|
moose 4.1 801.5
|
|
When `other` is a :class:`DataFrame`, both columns names and the
|
index are aligned.
|
|
>>> other = pd.DataFrame({'height': [0.2, 0.4, 0.6]},
|
... index=['elk', 'moose', 'deer'])
|
>>> df[['height', 'weight']] + other
|
height weight
|
deer NaN NaN
|
elk 1.7 NaN
|
moose 3.0 NaN
|
)rN r$ r r& s r rO zOpsMixin.__add__b s ðp ×!Ñ! %¬¯ © Ó6Ð6r! Ú__radd__c óB | j |t j « S r )rN r Úraddr& s r rQ zOpsMixin.__radd__¼ ó à×!Ñ! %¬¯ © Ó8Ð8r! Ú__sub__c óB | j |t j « S r )rN r$ Úsubr& s r rU zOpsMixin.__sub__À ó à×!Ñ! %¬¯ © Ó6Ð6r! Ú__rsub__c óB | j |t j « S r )rN r Úrsubr& s r rY zOpsMixin.__rsub__Ä rT r! Ú__mul__c óB | j |t j « S r )rN r$ Úmulr& s r r\ zOpsMixin.__mul__È rX r! Ú__rmul__c óB | j |t j « S r )rN r Úrmulr& s r r_ zOpsMixin.__rmul__Ì rT r! Ú __truediv__c óB | j |t j « S r )rN r$ Útruedivr& s r rb zOpsMixin.__truediv__Ð s à×!Ñ! %¬×)9Ñ)9Ó:Ð:r! Ú __rtruediv__c óB | j |t j « S r )rN r Úrtruedivr& s r re zOpsMixin.__rtruediv__Ô s à×!Ñ! %¬×);Ñ);Ó<Ð<r! Ú __floordiv__c óB | j |t j « S r )rN r$ Úfloordivr& s r rh zOpsMixin.__floordiv__Ø s à×!Ñ! %¬×):Ñ):Ó;Ð;r! Ú __rfloordivc óB | j |t j « S r )rN r Ú rfloordivr& s r Ú __rfloordiv__zOpsMixin.__rfloordiv__Ü s à×!Ñ! %¬×)<Ñ)<Ó=Ð=r! Ú__mod__c óB | j |t j « S r )rN r$ Úmodr& s r ro zOpsMixin.__mod__à rX r! Ú__rmod__c óB | j |t j « S r )rN r Úrmodr& s r rr zOpsMixin.__rmod__ä rT r! Ú
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__divmod__c ó. | j |t « S r )rN Údivmodr& s r ru zOpsMixin.__divmod__è s à×!Ñ! %¬Ó0Ð0r! Ú __rdivmod__c óB | j |t j « S r )rN r Úrdivmodr& s r rx zOpsMixin.__rdivmod__ì s à×!Ñ! %¬×):Ñ):Ó;Ð;r! Ú__pow__c óB | j |t j « S r )rN r$ Úpowr& s r r{ zOpsMixin.__pow__ð rX r! Ú__rpow__c óB | j |t j « S r )rN r Úrpowr& s r r~ zOpsMixin.__rpow__ô rT r! N)#Ú__name__Ú
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Compatibility with numpy ufuncs.
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|
See also
|
--------
|
numpy.org/doc/stable/reference/arrays.classes.html#numpy.class.__array_ufunc__
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If kwargs contain "out1" and "out2", replace that with a tuple "out"
|
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|
`out1=out1, out2=out2)`
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If we have an `out` keyword, then call the ufunc without `out` and then
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set the result into the given `out`.
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ä#uÔ Ü s8q=Øa&Cä%Ð %ä#vuÔ%Ø Jr! c óB ||| dd yt j | ||« y)zV
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Set a ufunc result into 'out', masking with a 'where' argument if necessary.
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Notes
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We are assuming that `self` is among `inputs`.
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Dispatch ufunc reductions to self's reduction methods.
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