Pandas DataFrame.rfloordiv

The DataFrame.rfloordiv method in pandas performs element-wise integer division (floor division) on a DataFrame, with the operation applied in reverse order. This means other // DataFrame, where other can be a scalar, a Series, or another DataFrame. The operation aligns on both row and column labels.

Syntax

The syntax for DataFrame.rfloordiv is:

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

Here, DataFrame is the object being operated on, and other is the divisor.

Parameters

Parameter	Description
`other`	A scalar, Series, or DataFrame to divide the DataFrame by. This is the numerator in the reverse floor division.
`axis`	Specifies whether to align the operation along rows (0 or `'index'`) or columns (1 or `'columns'`). Default is `'columns'`.
`level`	If the axis is a MultiIndex (hierarchical), this parameter specifies the level to align on. Default is `None`.
`fill_value`	Specifies a value to fill in for missing data in either DataFrame or `other`. Default is `None`.

Returns

A DataFrame resulting from the reverse floor division operation, with aligned labels and filled values as specified.

Examples

Reverse Floor Division with a Scalar

Perform reverse floor division with a scalar value.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'A': [1, 2, 3],
    'B': [4, 5, 6]
}
df = pd.DataFrame(data)

# Perform reverse floor division with a scalar
print("Reverse Floor Division with a Scalar:")
result = df.rfloordiv(10)
print(result)

Output

Reverse Floor Division with a Scalar:
    A  B
0  10  2
1   5  2
2   3  1

Reverse Floor Division with Another DataFrame

Perform reverse floor division with another DataFrame, aligning labels.

Python Program

import pandas as pd

# Create two DataFrames
data1 = {
    'A': [2, 4, 6],
    'B': [8, 10, 12]
}
data2 = {
    'A': [1, 1, 1],
    'B': [3, 3, 3]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

# Perform reverse floor division
print("Reverse Floor Division with Another DataFrame:")
result = df2.rfloordiv(df1)
print(result)

Output

Reverse Floor Division with Another DataFrame:
    A  B
0    2  2
1    4  3
2    6  4

Handling Missing Values with `fill_value`

Use the fill_value parameter to handle missing data in either DataFrame.

Python Program

import pandas as pd
import numpy as np

# Create two DataFrames with missing values
data1 = {
    'A': [2, np.nan, 6],
    'B': [8, 10, 12]
}
data2 = {
    'A': [1, 1, 1],
    'B': [3, np.nan, 3]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

# Perform reverse floor division with a fill_value
print("Reverse Floor Division with fill_value=1:")
result = df2.rfloordiv(df1, fill_value=1)
print(result)

Output

Reverse Floor Division with fill_value=1:
     A    B
0  2.0  2.0
1  1.0  1.0
2  6.0  4.0

Using `axis` Parameter

Control the alignment of the operation using the axis parameter.

Python Program

import pandas as pd

# Create a DataFrame and a Series
data = {
    'A': [2, 4, 6],
    'B': [8, 10, 12]
}
df = pd.DataFrame(data)
series = pd.Series([2, 4, 6], index=[0, 1, 2])

# Perform reverse floor division along the index (axis=0)
print("Reverse Floor Division with axis=0:")
result = df.rfloordiv(series, axis=0)
print(result)

Output

Reverse Floor Division with axis=0:
    A  B
0    1  4
1    1  2
2    1  2

Summary

In this tutorial, we explored the DataFrame.rfloordiv method in pandas. Key takeaways include:

Using reverse floor division with scalars, Series, or DataFrames.
Handling missing values with the fill_value parameter.
Customizing alignment with the axis parameter.

The DataFrame.rfloordiv method is a useful tool for reverse integer division, especially in complex alignment scenarios.