pandas.DataFrame.mod#

DataFrame.mod(other, axis='columns', level=None, fill_value=None)[source] #

Get Modulo of dataframe and other, element-wise (binary operator mod).

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

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