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Pandas DataFrame | mul 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.mul(~) method multiplies the values in the source DataFrame to a scalar, sequence, Series or DataFrame, that is:

DataFrame * other
NOTE

Unless you use the parameters axis, level and fill_value, the mul(~) is equivalent to performing multiplication using the * operator.

Parameters

1. otherlink | scalar or sequence or Series or DataFrame

The resulting DataFrame will be other multiplied with 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 if both entries are NaN, then their product would always result in NaN. By default, fill_value=None.

Return Value

A new DataFrame resulting from the product of the source DataFrame and other.

Examples

Basic usage

Consider the following DataFrames:

df = pd.DataFrame({"A":[2,3], "B":[4,5]})
df_other = pd.DataFrame({"A":[6,7], "B":[8,9]})
A B | A B
0 2 4 | 0 6 8
1 3 5 | 1 7 9

Computing their product yields:

df.mul(df_other)
A B
0 12 32
1 21 45

Note that this is equivalent to the following:

df * df_other
A B
0 12 32
1 21 45

Broadcasting

Consider the following DataFrame:Consider the following DataFrame:

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

Row-wise multiplication

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

df.mul([10,100]) # axis=1
A B
0 20 400
1 30 500

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

2*10 4*100
3*10 5*100

Column-wise multiplication

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

df.mul([10,100], axis=0)
A B
0 20 40
1 300 500

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

2*10 4*10
3*100 5*100
2*10 4*100
3*10 5*100

Specifying fill_value

Consider the following DataFrames with missing values:

df = pd.DataFrame({"A":[2,np.NaN], "B":[np.NaN,5]})
df_other = pd.DataFrame({"A":[10,20],"B":[np.NaN,np.NaN]})
A B | A B
0 2.0 NaN | 0 10 NaN
1 NaN 5.0 | 1 20 NaN

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

df.mul(df_other)
A B
0 20.0 NaN
1 NaN NaN

We can fill the NaN values before we perform multiplication by using the fill_value parameter:

df.mul(df_other, fill_value=100)
A B
0 20.0 NaN
1 2000.0 500.0

Notice how the product of two NaN is NaN, regardless of fill_value.

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Published by Isshin Inada
Edited by 0 others
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