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Pandas DataFrame | rsub 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.rsub(~) method subtracts the values in the source DataFrame from a scalar, sequence, Series or DataFrame, that is:

other - DataFrame

As a side note, this is just the reverse of DataFrame.sub(~):

DataFrame - other
NOTE

Unless you use the parameters axis, level and fill_value, rsub(~) is equivalent to performing subtraction using the - operator.

Parameters

1. otherlink | scalar or sequence or Series or DataFrame

The resulting DataFrame will be the source DataFrame subtracted from other.

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. Subtraction between two NaN will always result in NaN. By default, fill_value=None.

Return Value

A new DataFrame resulting from the subtraction.

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

Subtracting df from df_other yields:

df.rsub(df_other)
A B
0 2 -95
1 -6 -994

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 subtraction

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

df.rsub([3,4]) # axis=1
A B
0 2 -96
1 -7 -996

Here, we're doing the following element-wise subtraction:

3-1 4-100
3-10 4-1000

Column-wise subtraction

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

df.rsub([3,4], axis=0)
A B
0 2 -97
1 -6 -996

Here, we're doing the following element-wise subtraction:

3-1 3-100
4-10 4-1000

Specifying fill_value

Consider the following DataFrames with some missing values:

df = pd.DataFrame({"A":[3,np.NaN], "B":[np.NaN,4]})
df_other = pd.DataFrame({"A":[10,100],"B":[np.NaN,np.NaN]})
A B | A B
0 3.0 NaN | 0 10 NaN
1 NaN 4.0 | 1 100 NaN

By default, when we perform subtraction using rsub(~), any operation with NaN results in NaN:

df.rsub(df_other)
A B
0 7.0 NaN
1 NaN NaN

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

df.rsub(df_other, fill_value=100)
A B
0 7.0 NaN
1 0.0 96.0

Here, notice how the subtraction between two NaN will still result in a NaN regardless of fill_value.

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Published by Isshin Inada
Edited by 0 others
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