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Pandas DataFrame | rfloordiv method

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Binary Operators
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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Pandas DataFrame.rfloordiv(~) method performs integer division between a scalar, sequence, Series or DataFrame and the values in the source DataFrame, that is:

other // DataFrame

Note that this is the reverse of DataFrame.floordiv(~), which does the following:

DataFrame // other
NOTE

Unless you use the parameters axis, level and fill_value, rfloordiv(~) is equivalent to performing division using the // operator.

Parameters

1. otherlink | scalar or sequence or Series or DataFrame

The resulting DataFrame will be other divided by the source DataFrame via integer division.

2. axislink | int or string | optional

Whether to broadcast other for each column or row of the source DataFrame:

Axis

Description

other is broadcasted for each column.

"index" or 0

other is broadcasted for each row.

"columns" or 1

Note that this is only relevant if the shape of the source DataFrame and that of other does not match up. By default, axis=1.

3. level | int or string | optional

The name or the integer index of the level to consider. This is relevant only if your DataFrame is Multi-index.

4. fill_valuelink | float or None | optional

The value to replace NaN before the computation. Note that if both pair of entries are NaN, then the result would still be NaN. By default, fill_value=None.

Return Value

A new DataFrame resulting from the integer division.

Examples

Basic usage

Consider the following DataFrame:

df = pd.DataFrame({"A":[1,2], "B":[3,4]})
df_other = pd.DataFrame({"A":[5,6], "B":[7,8]})
A B | A B
0 1 3 | 0 5 7
1 2 4 | 1 6 8

Performing integer division:

df.rfloordiv(df_other)
A B
0 5 2
1 3 2

Here, we're performing the following element-wise integer division:

5//1 7//3
6//2 8//4

Note that this is equivalent to:

df_other // df
A B
0 5 2
1 3 2

Broadcasting

Consider the following DataFrame:

df = pd.DataFrame({"A":[2,3], "B":[4,5]})
df
A B
0 2 4
1 3 5

Row-wise integer division

By default, axis=1, which means that other will be broadcasted for each row in df:

df.rfloordiv([7,8]) # axis=10
A B
0 3 2
1 2 1

Here, we're performing the following element-wise integer division:

7//2 8//4
7//3 8//5

Column-wise integer division

To broadcast other for each column in df, set axis=0 like so:

df.rfloordiv([7,8], axis=0)
A B
0 3 1
1 2 1

Here, we're performing the following element-wise division:

7//2 7//4
8//3 8//5

Specifying fill_value

Consider the following DataFrames:

df = pd.DataFrame({"A":[2,np.NaN], "B":[np.NaN,5]})
df_other = pd.DataFrame({"A":[7,8],"B":[np.NaN,np.NaN]})
A B | A B
0 2.0 NaN | 0 7 NaN
1 NaN 5.0 | 1 8 NaN

By default, when we compute the integer division using rfloordiv(~), any operation with NaN results in NaN:

df.rfloordiv(df_other)
A B
0 3.0 NaN
1 NaN NaN

We can fill NaNs before we perform integer division by using the fill_value parameter:

df.rfloordiv(df_other, fill_value=2)
A B
0 3.0 NaN
1 2.0 0.0

Notice how if the integer division is between two NaN, then the result would always be NaN regardless of fill_value.

robocat
Published by Isshin Inada
Edited by 0 others
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