Python | Pandas dataframe.rdiv()
Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas dataframe.rdiv()
function compute Floating division of dataframe and other, element-wise (binary operator rtruediv). Other object could be a scalar, pandas series or pandas dataframe. This function is essentially same as doing other / dataframe
but with support to substitute a fill_value for missing data in one of the inputs.
Syntax: DataFrame.rdiv(other, axis=’columns’, level=None, fill_value=None)
Parameters :
other : Series, DataFrame, or constant
axis : For Series input, axis to match Series index on
level : Broadcast across a level, matching Index values on the passed MultiIndex level
numeric_only : Include only float, int, boolean data. Valid only for DataFrame or Panel objects
fill_value : Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing
Returns : result : DataFrame
Example #1: Use rdiv()
function to divide a series with a dataframe element-wise
import pandas as pd
df = pd.DataFrame({ "A" :[ 1 , 5 , 3 , 4 , 2 ],
"B" :[ 3 , 2 , 4 , 3 , 4 ],
"C" :[ 2 , 2 , 7 , 3 , 4 ],
"D" :[ 4 , 3 , 6 , 12 , 7 ]})
df
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Lets create a series
import pandas as pd
sr = pd.Series([ 5 , 10 , 15 , 20 ], index = [ "A" , "B" , "C" , "D" ])
sr
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Let’s use the dataframe.rdiv()
function to divide the series with a dataframe
Output :
Example #2: Use rdiv()
function to divide one dataframe with other which contains NaN
value.
import pandas as pd
df1 = pd.DataFrame({ "A" :[ 1 , 5 , 3 , 4 , 2 ],
"B" :[ 3 , 2 , 4 , 3 , 4 ],
"C" :[ 2 , 2 , 7 , 3 , 4 ],
"D" :[ 4 , 3 , 6 , 12 , 7 ]})
df2 = pd.DataFrame({ "A" :[ 14 , 5 , None , 4 , 12 ],
"B" :[ 7 , 6 , 4 , 5 , None ],
"C" :[ 2 , 11 , 4 , 3 , 6 ],
"D" :[ 4 , None , 6 , 2 , 4 ]})
df1.rdiv(df2, fill_value = 100 )
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Output :
Last Updated :
22 Nov, 2018
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