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Pandas DataFrame | rdiv method

Pandas
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DataFrame
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Binary Operators
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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Pandas DataFrame.rdiv(~) method divides a scalar, sequence, Series or DataFrame by the values in the source DataFrame, that is:

other / DataFrame

Note that this is just the reverse of DataFrame.div(~):

DataFrame / other
NOTE

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

Parameters

1. otherlink | scalar or sequence or Series or DataFrame

The resulting DataFrame will be other divided by the source DataFrame.

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

This is only relevant if the shape of the source DataFrame and that of other does not align. 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 the division of two NaN will still result in NaN. By default, fill_value=None.

Return Value

A new DataFrame resulting from the division.

Examples

Basic usage

Consider the following DataFrames:

df = pd.DataFrame({"A":[1,10], "B":[100,1000]})
df_other = pd.DataFrame({"A":[3,4], "B":[5,6]})
A B | A B
0 1 100 | 0 3 5
1 10 1000 | 1 4 6

Computing division yields:

df.rdiv(df_other)
A B
0 3.0 0.050
1 0.4 0.006

Note this is equivalent to the following:

df_other / df
A B
0 3.0 0.050
1 0.4 0.006

Broadcasting

Consider the following DataFrame:

df = pd.DataFrame({"A":[1,10], "B":[100,1000]})
df
A B
0 1 100
1 10 1000

Row-wise division

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

df.rdiv([3,4]) # axis=1
A B
0 3.0 0.040
1 0.3 0.004

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

3/1 4/100
3/10 4/1000

Column-wise division

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

df.rdiv([3,4], axis=0)
A B
0 3.0 0.030
1 0.4 0.004

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

3/1 3/100
4/10 4/1000

Specifying fill_value

Consider the following DataFrames:

df = pd.DataFrame({"A":[3,np.NaN], "B":[np.NaN,4]})
df_other = pd.DataFrame({"A":[12,20],"B":[np.NaN,np.NaN]})
A B | A B
0 3.0 NaN | 0 12 NaN
1 NaN 4.0 | 1 20 NaN

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

df.rdiv(df_other)
A B
0 4.0 NaN
1 NaN NaN

We can fill the NaN values before we perform division by using the fill_value parameter like so:

df.rdiv(df_other, fill_value=2)
A B
0 4.0 NaN
1 10.0 0.5

Here, notice how the division between two NaN still results in NaN regardless of fill_value.

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Published by Isshin Inada
Edited by 0 others
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