Pandas DataFrame | rdiv method
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Pandas DataFrame.rdiv(~) method divides a scalar, sequence, Series or DataFrame by the values in the source DataFrame, that is:
other / DataFrame
Note that this is just the reverse of DataFrame.div(~):
DataFrame / other
Unless you use the parameters axis, level and fill_value, the rdiv(~) is equivalent to performing division using the / operator. Also rdiv(~) is equivalent to rtruediv(~).
Parameters
1. otherlink | scalar or sequence or Series or DataFrame
The resulting DataFrame will be other divided by 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 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 the division of two NaN will still result in NaN. By default, fill_value=None.
Return Value
A new DataFrame resulting from the division.
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 B0 1 100 | 0 3 51 10 1000 | 1 4 6
Computing division yields:
df.rdiv(df_other)
A B0 3.0 0.0501 0.4 0.006
Note this is equivalent to the following:
df_other / df
A B0 3.0 0.0501 0.4 0.006
Broadcasting
Consider the following DataFrame:
df = pd.DataFrame({"A":[1,10], "B":[100,1000]})df
A B0 1 1001 10 1000
Row-wise division
By default, axis=1, which means that other will be broadcasted for each row in df:
df.rdiv([3,4]) # axis=1
A B0 3.0 0.0401 0.3 0.004
Here, we're performing the following element-wise division:
3/1 4/1003/10 4/1000
Column-wise division
To broadcast other for each column in df, set axis=0 like so:
df.rdiv([3,4], axis=0)
A B0 3.0 0.0301 0.4 0.004
Here, we're performing the following element-wise division:
3/1 3/1004/10 4/1000
Specifying fill_value
Consider the following DataFrames:
df = pd.DataFrame({"A":[3,np.NaN], "B":[np.NaN,4]})df_other = pd.DataFrame({"A":[12,20],"B":[np.NaN,np.NaN]})
A B | A B0 3.0 NaN | 0 12 NaN1 NaN 4.0 | 1 20 NaN
By default, when we compute the division using rdiv(~), any operation with NaN results in NaN:
df.rdiv(df_other)
A B0 4.0 NaN1 NaN NaN
We can fill the NaN values before we perform division by using the fill_value parameter like so:
df.rdiv(df_other, fill_value=2)
A B0 4.0 NaN1 10.0 0.5
Here, notice how the division between two NaN still results in NaN regardless of fill_value.
