Python | Pandas dataframe.rmod()
Last Updated :
09 Sep, 2021
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.rmod() function is used for finding the modulo of dataframe and other, element-wise (binary operator rfloordiv). This function is essentially same as doing other % dataframe but with a support to substitute for missing data in one of the inputs.
Syntax: DataFrame.rmod(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
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 rmod() function to find the modulo of a series with a dataframe.
Python3
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 ]},
index = [ "A1" , "A2" , "A3" , "A4" , "A5" ])
df
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Let’s create the series
Python3
import pandas as pd
sr = pd.Series([ 12 , 25 , 64 , 18 ], index = [ "A" , "B" , "C" , "D" ])
sr
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Let’s use the dataframe.rmod() function to find the modulo of a series with dataframe
Output :
Example #2: Use rmod() function to perform modulo division of a dataframe with other.
Python3
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 ]},
index = [ "A1" , "A2" , "A3" , "A4" , "A5" ])
df2 = pd.DataFrame({ "A" :[ 10 , 11 , 7 , 8 , 5 ],
"B" :[ 21 , 5 , 32 , 4 , 6 ],
"C" :[ 11 , 21 , 23 , 7 , 9 ],
"D" :[ 1 , 5 , 3 , 8 , 6 ]},
index = [ "A1" , "A2" , "A3" , "A4" , "A5" ])
df1.rmod(df2)
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Output :
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