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Pandas DataFrame | rpow method

schedule Aug 11, 2023
Last updated
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Pandas DataFrame.rpow(~) method computes the exponential power of a scalar, sequence, Series or DataFrame and the values in the source DataFrame, that is:

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other ** DataFrame

Note that this is just the reverse of pow(~) method, which does:

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DataFrame ** other
NOTE

Unless you use the parameters axis, level and fill_value, rpow(~) is equivalent to computing the exponential power using the ** operator.

Parameters

1. otherlink | scalar or sequence or Series or DataFrame

The resulting DataFrame will be the exponential power of other and the source DataFrame.

2. axislink | int or string | optional

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

Axis

Description

"index" or 0

other is broadcasted for each column of the source DataFrame.

"columns" or 1

other is broadcasted for each row of the source DataFrame.

This is only relevant if the shape of the source DataFrame and that of other does not match. 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 computing the exponential power. If the computation involves two NaN, then its result would still be NaN. By default, fill_value=None.

Return Value

A new DataFrame computed by the exponential power of the source DataFrame and other.

Examples

Basic usage

Consider the following DataFrames:

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

Computing the exponential power of df and df_other:

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df.rpow(df_other)
A B
0 1 32
1 1 64

Here, we're computing the following element-wise exponential power:

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1**3 2**5
1**4 2**6

Broadcasting

Consider the following DataFrame:

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

Row-wise

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

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df.rpow([1,2]) # axis=1
A B
0 1 32
1 1 64

Here, we're computing the following element-wise exponential power:

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1**3 2**5
1**4 2**6

Column-wise

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

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df.rpow([1,2], axis=0)
A B
a 1 1
b 16 64

Here, we're computing the following element-wise exponential power:

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1**3 1**5
2**4 2**6

Specifying fill_value

Consider the following DataFrames:

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df = pd.DataFrame({"A":[2,np.NaN], "B":[np.NaN,3]})
df_other = pd.DataFrame({"A":[4,5], "B":[np.NaN,np.NaN]})
A B | A B
0 2.0 NaN | 0 4 NaN
1 NaN 3.0 | 1 5 NaN

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

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df.rpow(df_other)
A B
0 16.0 NaN
1 NaN NaN

We can fill the NaN values before we compute the power by using the fill_value parameter:

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df.rpow(df_other, fill_value=1)
A B
0 16.0 NaN
1 5.0 1.0

Here, notice when the operation is between two NaN, the result would still be NaN regardless of fill_value.

robocat
Published by Isshin Inada
Edited by 0 others
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