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Pandas DataFrame | rpow 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.rpow(~) method computes the exponential power of a scalar, sequence, Series or DataFrame and the values in the source DataFrame, that is:

other ** DataFrame

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

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

other is broadcasted for each column of the source DataFrame.

"index" or 0

other is broadcasted for each row of the source DataFrame.

"columns" or 1

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:

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:

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

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

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

Broadcasting

Consider the following DataFrame:

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:

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

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

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

Column-wise

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

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

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

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

Specifying fill_value

Consider the following DataFrames:

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:

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:

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.

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Published by Isshin Inada
Edited by 0 others
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