- Trending Categories
- Data Structure
- Networking
- RDBMS
- Operating System
- Java
- iOS
- HTML
- CSS
- Android
- Python
- C Programming
- C++
- C#
- MongoDB
- MySQL
- Javascript
- PHP

- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who

# 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.

- Related Questions & Answers
- How to apply floor division to the pandas series object by another series object?
- How to perform the Integer division operation of a pandas Series by a Python list?
- How to combine a pandas Series with Scalar using Series.combine() method?
- How to divide a pandas series with scalar using series.div() method?
- How can we apply an anonymous function to the pandas series?
- How to access a single value in pandas Series using the integer position?
- What does the apply() method do in the pandas series?
- How to access Pandas Series elements by using indexing?
- How to sort a Pandas Series?
- How to perform integer division and get the remainder in JavaScript?
- Python Pandas - How to select rows from a DataFrame by integer location
- How to apply the slicing indexer to the pandas DataFrame.iloc attribute?
- How to apply the slicing indexer to the pandas DataFrame.loc attribute?
- How to append elements to a Pandas series?
- How to use the apply() function for a single column in Pandas?