U
|
¬ý°dëI ã @ s" U d Z ddlmZ ddddddZdZdZed Zed
|
Zed Zed Z ed Z
|
ed Z ed Z ed Z ed Zed Zed Zed Zed Zed ZdZdZdddeed d d d eed d d!d"eed#d$d%d&d'e ed d(d)d*e ed#d$d+d,d-e ed#d$d.d/d0e
|
ed#d$d1d2d3e ed#d$d4d5d#eed d6d7d#eed d8d9d#eed d:d;d#eed d<d=d#eed d>d?d#eed d@ZdAedB< dCZee ¡ ZeD ]xZ ee dD Z e d#k ree ¡ ee < e ee dD< dEee dF dGe ee dH< dIee dF dJe ee dH< qdKZ dLZ dMZ!dNZ"d#S )Oz
|
Templating for ops docstrings
|
é )Ú annotationsÚstr)Úop_nameÚtypÚreturnc C s | dd¡} t| }|d }|dk s(t| d¡rDd| d| }n*| dkr^| d | d
|
}n| d| d }|d krÒt}|d r|tj|d |d d7 }|j|d | ||d d}|d }|rÌ|| }n|}n0|dkrút}|j|d | ||d d}ntd|S )at
|
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
|
Ú__Ú ÚopNÚrzother ú Údivmodú(z, other)z otherZseriesÚreverseÚ see_also_desc)r r ÚdescÚseries_returns)r r Úequivr Úseries_examplesZ dataframe)r r r r zInvalid typ argument.)ÚreplaceÚ_op_descriptionsÚAssertionErrorÚ
|
startswithÚ_flex_doc_SERIESÚ_see_also_reverse_SERIESÚformatÚ_flex_doc_FRAME) r r Zop_descZ
|
op_desc_opr Zbase_docZdoc_no_examplesZ ser_exampleÚdoc© r úQd:\z\workplace\vscode\pyvenv\venv\Lib\site-packages\pandas/core/ops/docstrings.pyÚ make_flex_doc sH
|
ÿü
|
ür a
|
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: float64a(
|
Examples
|
--------
|
>>> a = pd.Series([1, 1, 1, np.nan, 1], index=['a', 'b', 'c', 'd', 'e'])
|
>>> a
|
a 1.0
|
b 1.0
|
c 1.0
|
d NaN
|
e 1.0
|
dtype: float64
|
>>> b = pd.Series([0, 1, 2, np.nan, 1], index=['a', 'b', 'c', 'd', 'f'])
|
>>> b
|
a 0.0
|
b 1.0
|
c 2.0
|
d NaN
|
f 1.0
|
dtype: float64zX
|
>>> a.add(b, fill_value=0)
|
a 2.0
|
b 1.0
|
c 1.0
|
d 1.0
|
e NaN
|
dtype: float64
|
z]
|
>>> a.subtract(b, fill_value=0)
|
a 0.0
|
b 1.0
|
c 1.0
|
d -1.0
|
e NaN
|
dtype: float64
|
z]
|
>>> a.multiply(b, fill_value=0)
|
a 1.0
|
b 0.0
|
c 0.0
|
d 0.0
|
e NaN
|
dtype: float64
|
z[
|
>>> a.divide(b, fill_value=0)
|
a 1.0
|
b inf
|
c inf
|
d 0.0
|
e NaN
|
dtype: float64
|
z]
|
>>> a.floordiv(b, fill_value=0)
|
a 1.0
|
b inf
|
c inf
|
d 0.0
|
e NaN
|
dtype: float64
|
z¥
|
>>> a.divmod(b, fill_value=0)
|
(a 1.0
|
b NaN
|
c NaN
|
d 0.0
|
e NaN
|
dtype: float64,
|
a 0.0
|
b NaN
|
c NaN
|
d 0.0
|
e NaN
|
dtype: float64)
|
zX
|
>>> a.mod(b, fill_value=0)
|
a 0.0
|
b NaN
|
c NaN
|
d 0.0
|
e NaN
|
dtype: float64
|
zX
|
>>> a.pow(b, fill_value=0)
|
a 1.0
|
b 1.0
|
c 1.0
|
d 0.0
|
e NaN
|
dtype: float64
|
z^
|
>>> a.ne(b, fill_value=0)
|
a False
|
b True
|
c True
|
d True
|
e True
|
dtype: bool
|
z^
|
>>> a.eq(b, fill_value=0)
|
a True
|
b False
|
c False
|
d False
|
e False
|
dtype: bool
|
zi
|
>>> a.lt(b, fill_value=0)
|
a False
|
b False
|
c True
|
d False
|
e False
|
f True
|
dtype: bool
|
zi
|
>>> a.le(b, fill_value=0)
|
a False
|
b True
|
c True
|
d False
|
e False
|
f True
|
dtype: bool
|
zi
|
>>> a.gt(b, fill_value=0)
|
a True
|
b False
|
c False
|
d False
|
e True
|
f False
|
dtype: bool
|
zi
|
>>> a.ge(b, fill_value=0)
|
a True
|
b True
|
c False
|
d False
|
e True
|
f False
|
dtype: bool
|
z'Series
|
The result of the operation.z22-Tuple of Series
|
The result of the operation.ú+ZAdditionZradd)r r r r r ú-Z SubtractionZrsubÚ*ZMultiplicationZrmulN)r r r r r Z df_examplesú%ZModuloZrmodz**zExponential powerZrpowú/zFloating divisionZrtruedivz//zInteger divisionZ rfloordivr zInteger division and moduloZrdivmodz==zEqual toz!=z Not equal toú<z Less thanz<=zLess than or equal toú>z Greater thanz>=zGreater than or equal to)ÚaddÚsubÚmulÚmodÚpowÚtruedivÚfloordivr ÚeqÚneÚltÚleÚgtÚgez dict[str, dict[str, str | None]]r z
see
|
`Python documentation
|
<https://docs.python.org/3/reference/datamodel.html#emulating-numeric-types>`_
|
for more detailsr zReverse of the r z operator, r z Element-wise z, a
|
Return {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 either one of the inputs.
|
|
Parameters
|
----------
|
other : Series or scalar value
|
level : int or name
|
Broadcast across a level, matching Index values on the
|
passed MultiIndex level.
|
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 of filling (at that location) will be missing.
|
axis : {{0 or 'index'}}
|
Unused. Parameter needed for compatibility with DataFrame.
|
|
Returns
|
-------
|
{series_returns}
|
z7
|
See Also
|
--------
|
Series.{reverse} : {see_also_desc}.
|
aË
|
Get {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. With reverse version, `{reverse}`.
|
|
Among flexible wrappers (`add`, `sub`, `mul`, `div`, `mod`, `pow`) to
|
arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`.
|
|
Parameters
|
----------
|
other : scalar, sequence, Series, dict or DataFrame
|
Any single or multiple element data structure, or list-like object.
|
axis : {{0 or 'index', 1 or 'columns'}}
|
Whether to compare by the index (0 or 'index') or columns.
|
(1 or 'columns'). For Series input, axis to match Series index on.
|
level : int or label
|
Broadcast across a level, matching Index values on the
|
passed MultiIndex level.
|
fill_value : float or None, 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.
|
|
Returns
|
-------
|
DataFrame
|
Result of the arithmetic operation.
|
|
See Also
|
--------
|
DataFrame.add : Add DataFrames.
|
DataFrame.sub : Subtract DataFrames.
|
DataFrame.mul : Multiply DataFrames.
|
DataFrame.div : Divide DataFrames (float division).
|
DataFrame.truediv : Divide DataFrames (float division).
|
DataFrame.floordiv : Divide DataFrames (integer division).
|
DataFrame.mod : Calculate modulo (remainder after division).
|
DataFrame.pow : Calculate exponential power.
|
|
Notes
|
-----
|
Mismatched indices will be unioned together.
|
|
Examples
|
--------
|
>>> df = pd.DataFrame({{'angles': [0, 3, 4],
|
... 'degrees': [360, 180, 360]}},
|
... index=['circle', 'triangle', 'rectangle'])
|
>>> df
|
angles degrees
|
circle 0 360
|
triangle 3 180
|
rectangle 4 360
|
|
Add a scalar with operator version which return the same
|
results.
|
|
>>> df + 1
|
angles degrees
|
circle 1 361
|
triangle 4 181
|
rectangle 5 361
|
|
>>> df.add(1)
|
angles degrees
|
circle 1 361
|
triangle 4 181
|
rectangle 5 361
|
|
Divide by constant with reverse version.
|
|
>>> df.div(10)
|
angles degrees
|
circle 0.0 36.0
|
triangle 0.3 18.0
|
rectangle 0.4 36.0
|
|
>>> df.rdiv(10)
|
angles degrees
|
circle inf 0.027778
|
triangle 3.333333 0.055556
|
rectangle 2.500000 0.027778
|
|
Subtract a list and Series by axis with operator version.
|
|
>>> df - [1, 2]
|
angles degrees
|
circle -1 358
|
triangle 2 178
|
rectangle 3 358
|
|
>>> df.sub([1, 2], axis='columns')
|
angles degrees
|
circle -1 358
|
triangle 2 178
|
rectangle 3 358
|
|
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
|
... axis='index')
|
angles degrees
|
circle -1 359
|
triangle 2 179
|
rectangle 3 359
|
|
Multiply a dictionary by axis.
|
|
>>> df.mul({{'angles': 0, 'degrees': 2}})
|
angles degrees
|
circle 0 720
|
triangle 0 360
|
rectangle 0 720
|
|
>>> df.mul({{'circle': 0, 'triangle': 2, 'rectangle': 3}}, axis='index')
|
angles degrees
|
circle 0 0
|
triangle 6 360
|
rectangle 12 1080
|
|
Multiply a DataFrame of different shape with operator version.
|
|
>>> other = pd.DataFrame({{'angles': [0, 3, 4]}},
|
... index=['circle', 'triangle', 'rectangle'])
|
>>> other
|
angles
|
circle 0
|
triangle 3
|
rectangle 4
|
|
>>> df * other
|
angles degrees
|
circle 0 NaN
|
triangle 9 NaN
|
rectangle 16 NaN
|
|
>>> df.mul(other, fill_value=0)
|
angles degrees
|
circle 0 0.0
|
triangle 9 0.0
|
rectangle 16 0.0
|
|
Divide by a MultiIndex by level.
|
|
>>> df_multindex = pd.DataFrame({{'angles': [0, 3, 4, 4, 5, 6],
|
... 'degrees': [360, 180, 360, 360, 540, 720]}},
|
... index=[['A', 'A', 'A', 'B', 'B', 'B'],
|
... ['circle', 'triangle', 'rectangle',
|
... 'square', 'pentagon', 'hexagon']])
|
>>> df_multindex
|
angles degrees
|
A circle 0 360
|
triangle 3 180
|
rectangle 4 360
|
B square 4 360
|
pentagon 5 540
|
hexagon 6 720
|
|
>>> df.div(df_multindex, level=1, fill_value=0)
|
angles degrees
|
A circle NaN 1.0
|
triangle 1.0 1.0
|
rectangle 1.0 1.0
|
B square 0.0 0.0
|
pentagon 0.0 0.0
|
hexagon 0.0 0.0
|
a<
|
Get {desc} of dataframe and other, element-wise (binary operator `{op_name}`).
|
|
Among flexible wrappers (`eq`, `ne`, `le`, `lt`, `ge`, `gt`) to comparison
|
operators.
|
|
Equivalent to `==`, `!=`, `<=`, `<`, `>=`, `>` with support to choose axis
|
(rows or columns) and level for comparison.
|
|
Parameters
|
----------
|
other : scalar, sequence, Series, or DataFrame
|
Any single or multiple element data structure, or list-like object.
|
axis : {{0 or 'index', 1 or 'columns'}}, default 'columns'
|
Whether to compare by the index (0 or 'index') or columns
|
(1 or 'columns').
|
level : int or label
|
Broadcast across a level, matching Index values on the passed
|
MultiIndex level.
|
|
Returns
|
-------
|
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.
|
`NaN` values are considered different (i.e. `NaN` != `NaN`).
|
|
Examples
|
--------
|
>>> df = pd.DataFrame({{'cost': [250, 150, 100],
|
... 'revenue': [100, 250, 300]}},
|
... index=['A', 'B', 'C'])
|
>>> df
|
cost revenue
|
A 250 100
|
B 150 250
|
C 100 300
|
|
Comparison with a scalar, using either the operator or method:
|
|
>>> df == 100
|
cost revenue
|
A False True
|
B False False
|
C True False
|
|
>>> df.eq(100)
|
cost revenue
|
A False True
|
B False False
|
C True False
|
|
When `other` is a :class:`Series`, the columns of a DataFrame are aligned
|
with the index of `other` and broadcast:
|
|
>>> df != pd.Series([100, 250], index=["cost", "revenue"])
|
cost revenue
|
A True True
|
B True False
|
C False True
|
|
Use the method to control the broadcast axis:
|
|
>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
|
cost revenue
|
A True False
|
B True True
|
C True True
|
D True True
|
|
When comparing to an arbitrary sequence, the number of columns must
|
match the number elements in `other`:
|
|
>>> df == [250, 100]
|
cost revenue
|
A True True
|
B False False
|
C False False
|
|
Use the method to control the axis:
|
|
>>> df.eq([250, 250, 100], axis='index')
|
cost revenue
|
A True False
|
B False True
|
C True False
|
|
Compare to a DataFrame of different shape.
|
|
>>> other = pd.DataFrame({{'revenue': [300, 250, 100, 150]}},
|
... index=['A', 'B', 'C', 'D'])
|
>>> other
|
revenue
|
A 300
|
B 250
|
C 100
|
D 150
|
|
>>> df.gt(other)
|
cost revenue
|
A False False
|
B False False
|
C False True
|
D False False
|
|
Compare to a MultiIndex by level.
|
|
>>> df_multindex = 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']])
|
>>> df_multindex
|
cost revenue
|
Q1 A 250 100
|
B 150 250
|
C 100 300
|
Q2 A 150 200
|
B 300 175
|
C 220 225
|
|
>>> df.le(df_multindex, level=1)
|
cost revenue
|
Q1 A True True
|
B True True
|
C True True
|
Q2 A False True
|
B True False
|
C True False
|
)#Ú__doc__Ú
|
__future__r r Z _common_examples_algebra_SERIESZ"_common_examples_comparison_SERIESZ_add_example_SERIESZ_sub_example_SERIESZ_mul_example_SERIESZ_div_example_SERIESZ_floordiv_example_SERIESZ_divmod_example_SERIESZ_mod_example_SERIESZ_pow_example_SERIESZ_ne_example_SERIESZ_eq_example_SERIESZ_lt_example_SERIESZ_le_example_SERIESZ_gt_example_SERIESZ_ge_example_SERIESZ_returns_seriesZ_returns_tupler Ú__annotations__Z _py_num_refÚlistÚkeysZ _op_namesÚkeyZ
|
reverse_opÚcopyr r r Z_flex_comp_doc_FRAMEr r r r Ú<module> sX 9ÿÿÿÿÿÿÿÿÿÿÿÿÿÿ ÿÿÿÿÿÿÿÿÿÿÿÿÿÿûûú ûú ú ú ú
|
ûûûûûûl
|
þÿþÿ )
|
