pyspark.pandas.DataFrame.pow

DataFrame.pow(other)

Get Exponential power of series of dataframe and other, element-wise (binary operator **).

Equivalent to dataframe ** other. With the reverse version, rpow.

Among flexible wrappers (add, sub, mul, div) to arithmetic operators: +, -, *, /, //.

Parameters

otherscalar

Any single data

Returns

DataFrame

Result of the arithmetic operation.

Examples

>>> df = ps.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'],
...                   columns=['angles', 'degrees'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which returns the same results. Also, the reverse version.

>>> 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

>>> df.add(df)
           angles  degrees
circle          0      720
triangle        6      360
rectangle       8      720

>>> df + df + df
           angles  degrees
circle          0     1080
triangle        9      540
rectangle      12     1080

>>> df.radd(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide and true divide by constant with reverse version.

>>> df / 10
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> 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

>>> df.truediv(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rtruediv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract by constant with reverse version.

>>> df - 1
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

>>> df.sub(1)
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

>>> df.rsub(1)
           angles  degrees
circle          1     -359
triangle       -2     -179
rectangle      -3     -359

Multiply by constant with the reverse version.

>>> df * 1
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

>>> df.mul(1)
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

>>> df.rmul(1)
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Floor Divide by constant with reverse version.

>>> df // 10
           angles  degrees
circle        0.0     36.0
triangle      0.0     18.0
rectangle     0.0     36.0

>>> df.floordiv(10)
           angles  degrees
circle        0.0     36.0
triangle      0.0     18.0
rectangle     0.0     36.0

>>> df.rfloordiv(10)  
           angles  degrees
circle        inf      0.0
triangle      3.0      0.0
rectangle     2.0      0.0

Mod by constant with reverse version.

>>> df % 2
           angles  degrees
circle          0        0
triangle        1        0
rectangle       0        0

>>> df.mod(2)
           angles  degrees
circle          0        0
triangle        1        0
rectangle       0        0

>>> df.rmod(2)
           angles  degrees
circle        NaN        2
triangle      2.0        2
rectangle     2.0        2

Power by constant with reverse version.

>>> df ** 2
           angles   degrees
circle        0.0  129600.0
triangle      9.0   32400.0
rectangle    16.0  129600.0

>>> df.pow(2)
           angles   degrees
circle        0.0  129600.0
triangle      9.0   32400.0
rectangle    16.0  129600.0

>>> df.rpow(2)
           angles        degrees
circle        1.0  2.348543e+108
triangle      8.0   1.532496e+54
rectangle    16.0  2.348543e+108