Pandas Series - mod() function

The Pandas mod() function is used to get modulo of series and argument, element-wise (binary operator mod). It is equivalent to series % other, but with support to substitute a fill_value for missing data as one of the parameters. The syntax for using this function is given below:

Syntax

Series.mod(other, level=None, fill_value=None)

Parameters

Return Value

Returns the result of the arithmetic operation.

Example: Dividing the Series with a scalar value

In the below example, the mod() function is used to calculate modulo of the Series when divided by a given scalar value.

import pandas as pd
import numpy as np

x = pd.Series([10, 20, 30, 40, 50])

print("The Series contains:")
print(x)

#dividing the Series by 3
print("\nx.mod(3) returns:")
print(x.mod(3))

The output of the above code will be:

The Series contains:
0    10
1    20
2    30
3    40
4    50
dtype: int64

x.mod(3) returns:
0    1
1    2
2    0
3    1
4    2
dtype: int64

Example: Dividing two Series

The modulo of the Series can be calculated by dividing it with another series. Consider the following example:

import pandas as pd
import numpy as np

x = pd.Series([11, np.NaN, 13, 14], index=['A', 'B', 'C', 'D'])
y = pd.Series([5, 6, 7, np.NaN], index=['A', 'B', 'C', 'D'])

print("The x contains:")
print(x)

print("\nThe y contains:")
print(y)

#dividing x by y
print("\nx.mod(y, fill_value=0) returns:")
print(x.mod(y, fill_value=0))

The output of the above code will be:

The x contains:
A    11.0
B     NaN
C    13.0
D    14.0
dtype: float64

The y contains:
A    5.0
B    6.0
C    7.0
D    NaN
dtype: float64

x.mod(y, fill_value=0) returns:
A    1.0
B    0.0
C    6.0
D    NaN
dtype: float64

Example: Dividing columns in a DataDrame

The mod() function can be applied in a data frame to get the modulo of two series/column element-wise. Consider the following example.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "Dividend": [10, 20, 30, 40, 50],
  "Divisor": [5, 6, 7, 8, 9]
})

print("The DataFrame is:")
print(df)

#dividing 'Dividend' by 'Divisor' column
df['Remainder'] = df['Dividend'].mod(df['Divisor'])

print("\nThe DataFrame is:")
print(df)

The output of the above code will be:

The DataFrame is:
   Dividend  Divisor
0        10        5
1        20        6
2        30        7
3        40        8
4        50        9

The DataFrame is:
   Dividend  Divisor  Remainder
0        10        5          0
1        20        6          2
2        30        7          2
3        40        8          0
4        50        9          5