dask.dataframe.DataFrame.add

dask.dataframe.DataFrame.add¶

DataFrame.add(other, axis='columns', level=None, fill_value=None)¶

Get Addition of dataframe and other, element-wise (binary operator add).

This docstring was copied from pandas.core.frame.DataFrame.add.

Some inconsistencies with the Dask version may exist.

Equivalent to dataframe + other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, radd.

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

Parameters

otherscalar, 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.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
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: 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

dask.dataframe.DataFrame.abs

dask.dataframe.DataFrame.align