Pandas DataFrame | rmul method
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Pandas DataFrame.rmul(~) method multiplies a scalar, sequence, Series or DataFrame to the values in the source DataFrame, that is:
other * DataFrame
Note that this is the reverse of DataFrame.mul(~), which does the following:
DataFrame * other
Unless you use the parameters axis, level and fill_value, rmul(~) 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 |
|---|---|
|
|
|
|
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 only relevant if your DataFrame is Multi-index.
4. fill_valuelink | float or None | optional
The value to replace NaN before the computation. If both entries are NaN, then the resulting product would always be 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 B0 2 4 | 0 6 81 3 5 | 1 7 9
Computing their product yields:
df.rmul(df_other)
A B0 12 321 21 45
Note that this is equivalent to the following:
df_other * df
A B0 12 321 21 45
Broadcasting
Consider the following DataFrame:
df = pd.DataFrame({"A":[2,3], "B":[4,5]})df
A B0 2 41 3 5
Row-wise multiplication
By default, axis=1, which means that other will be broadcasted for each row in df:
df.rmul([10,100]) # axis=1
A B0 20 4001 30 500
Here, we're doing the following element-wise multiplication:
10*2 100*410*3 100*5
Column-wise multiplication
To broadcast other for each column in df, set axis=0 like so:
df.mul([10,100], axis=0)
A B0 20 401 300 500
Here, we're doing the following element-wise multiplication:
10*2 10*4100*3 100*5
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 B0 2.0 NaN | 0 10 NaN1 NaN 5.0 | 1 20 NaN
By default, when we compute the product using rmul(~), any operation with NaN results in NaN:
df.rmul(df_other)
A B0 20.0 NaN1 NaN NaN
We can fill the NaN values before we perform multiplication by using the fill_value parameter:
df.rmul(df_other, fill_value=100)
A B0 20.0 NaN1 2000.0 500.0
Notice how the product of two NaN is NaN, regardless of fill_value.
