f(self, other, axis='columns', level=None, fill_value=None)
Equivalent to dataframe / other
, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rtruediv
.
Among flexible wrappers (:None:None:`add`, :None:None:`sub`, :None:None:`mul`, :None:None:`div`, :None:None:`mod`, :None:None:`pow`) to arithmetic operators: :None:None:`+`, :None:None:`-`, :None:None:`*`, :None:None:`/`, :None:None:`//`, :None:None:`%`, :None:None:`**`.
Mismatched indices will be unioned together.
Any single or multiple element data structure, or list-like object.
Whether to compare by the index (0 or 'index') or columns (1 or 'columns'). For Series input, axis to match Series index on.
Broadcast across a level, matching Index values on the passed MultiIndex level.
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.
Result of the arithmetic operation.
Get Floating division of dataframe and other, element-wise (binary operator :None:None:`truediv`).
DataFrame.add
Add DataFrames.
DataFrame.div
Divide DataFrames (float division).
DataFrame.floordiv
Divide DataFrames (integer division).
DataFrame.mod
Calculate modulo (remainder after division).
DataFrame.mul
Multiply DataFrames.
DataFrame.pow
Calculate exponential power.
DataFrame.sub
Subtract DataFrames.
DataFrame.truediv
Divide DataFrames (float division).
>>> 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.
This example is valid syntax, but we were not able to check execution>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361This example is valid syntax, but we were not able to check execution
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
This example is valid syntax, but we were not able to check execution>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0This example is valid syntax, but we were not able to check execution
>>> 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.
This example is valid syntax, but we were not able to check execution>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358This example is valid syntax, but we were not able to check execution
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358This example is valid syntax, but we were not able to check execution
>>> 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 DataFrame of different shape with operator version.
This example is valid syntax, but we were not able to check execution>>> other = pd.DataFrame({'angles': [0, 3, 4]},This example is valid syntax, but we were not able to check execution
... index=['circle', 'triangle', 'rectangle'])
... other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaNThis example is valid syntax, but we were not able to check execution
>>> 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.
This example is valid syntax, but we were not able to check execution>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],This example is valid syntax, but we were not able to check execution
... '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 0.0See :
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them