pandas.DataFrame.pow   DataFrame.pow(other, axis='columns', level=None, fill_value=None)[source]
 
Get Exponential power of dataframe and other, element-wise (binary operator pow). Equivalent to dataframe ** other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rpow. Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.  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’}


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 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