How to apply integer division to the pandas series by a scalar?

The operation integer division is also called floor division, which is equivalent to // in python. And it is a binary operation nothing but an element-wise division operation that returns a new value.

In the pandas series class, there is a method called floordiv() which performs the element-wise integer division operation between a series object and a scalar. And this method can also be used to perform floor division between two series objects.

The output of this method is a new series with the result of the operation. And it has 3 parameters, which are fill_value, other, and level. The other parameter is nothing but 2nd input (other series or a scalar). The fill_value parameter is used to replace the missing values by a specified value while executing the floordiv() method, by default the parameter will fill missing values with Nan.

Example 1

In this following example, we will apply the floor divide operation to the Series object by a scalar value “2”.

import pandas as pd

# create pandas Series
series = pd.Series([9, 25, 14, 82])

print("Series object:",series)

# apply floordiv()
print("Output:")
print(series.floordiv(2))

Output

The output is given below −

Series object:
0    9
1    25
2    14
3    82
dtype: int64

Output:
0    4
1    12
2    7
3    41
dtype: int64

In the above block, we can see both initial and resultant series objects. And the second one is the result of element-wise integer division operation between series and a scalar value “2”.

Example 2

In this following example, we will apply the integer division operation to a series object with a scalar, the given series object contains some Nan values.

import pandas as pd
import numpy as np

# create pandas Series
series = pd.Series([87, 5, None, 42, np.nan, 61])

print("Series object:",series)

# apply floordiv()
print("Output without replacing missing values:")
print(series.floordiv(other=2))

# apply floordiv() method with fill_value parameter
print("Output with replacing missing values by 5:")
print(series.floordiv(other=2, fill_value=5))

Output

The output is mentioned below −

Series object:
0    87.0
1    5.0
2    NaN
3    42.0
4    NaN
5    61.0
dtype: float64

Output without replacing missing values:
0    43.0
1    2.0
2    NaN
3    21.0
4    NaN
5    30.0
dtype: float64

Output with replacing missing values by 5:
0    43.0
1    2.0
2    2.0
3    21.0
4    2.0
5    30.0
dtype: float64

Initially, we have applied the floor division operation to a series object by a scalar value 2 without replacing the missing values. After that, we again applied the floordiv() method to the same series by a scalar value 2 and the missing value 5 (fill_value=5).

Both the outputs are displayed in the above output block.

Updated on: 07-Mar-2022

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