cudf.Series.rtruediv#

Series.rtruediv(other, level=None, fill_value=None, axis=0)#

Get Floating division of dataframe or series and other, element-wise (binary operator rtruediv).

Equivalent to other / frame, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, truediv.

Parameters

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axisint or string: Only 0 is supported for series, 1 or columns supported for dataframe
fill_valuefloat 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 or Series: Result of the arithmetic operation.

Examples

DataFrame

>>> import cudf
>>> df = cudf.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
>>> df.rtruediv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778
>>> 10 / df
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Series

>>> import cudf
>>> a = cudf.Series([10, 20, None, 30], index=['a', 'b', 'c', 'd'])
>>> a
a      10
b      20
c    <NA>
d      30
dtype: int64
>>> b = cudf.Series([1, None, 2, 3], index=['a', 'b', 'd', 'e'])
>>> b
a       1
b    <NA>
d       2
e       3
dtype: int64
>>> a.rtruediv(b, fill_value=0)
a            0.1
b            0.0
c           <NA>
d    0.066666667
e            Inf
dtype: float64

cudf.Series.rdiv

cudf.Series.rfloordiv