Add columns with normalised rankings to a pandas dataframe - python-3.x

Question

I would like to add a column with normalized rankings to a pandas dataframe. The process is as follows:
Import the pandas package first.
#import packages
import pandas as pd
Define a pandas dataframe.
# Create dataframe
data = {'name': ['Jason', 'Jason', 'Tina', 'Tina', 'Tina'],
'reports': [4, 24, 31, 2, 3],
'coverage': [25, 94, 57, 62, 70]}
df = pd.DataFrame(data)
After the dataframe is created, I want to add an extra column to the dataframe. This column contains the rank based on the values in the coverage column for every name seperately.
df['coverageRank'] = df.groupby('name')['coverage'].rank()
print (df)
coverage name reports coverageRank
0 25 Jason 4 1.0
1 94 Jason 24 2.0
2 57 Tina 31 1.0
3 62 Tina 2 2.0
4 70 Tina 3 3.0
I now want to normalize the values in the ranking column.
The desired output is
coverage name reports coverageRank
0 25 Jason 4 0.500000
1 94 Jason 24 1.000000
2 57 Tina 31 0.333333
3 62 Tina 2 0.666667
4 70 Tina 3 1.000000
Does someone know a way to do this without using an explicit for-loop?

Answer 1

You can use transform for Series with same size as original df and then divide by div:
a = df.groupby('name')['coverage'].transform('size')
print (a)
0 2
1 2
2 3
3 3
4 3
Name: coverage, dtype: int64
df['coverageRank'] = df.groupby('name')['coverage'].rank().div(a)
print (df)
coverage name reports coverageRank
0 25 Jason 4 0.500000
1 94 Jason 24 1.000000
2 57 Tina 31 0.333333
3 62 Tina 2 0.666667
4 70 Tina 3 1.000000
Another solution with apply:
df['coverageRank'] = df.groupby('name')['coverage'].apply(lambda x: x.rank() / len(x))
print (df)
coverage name reports coverageRank
0 25 Jason 4 0.500000
1 94 Jason 24 1.000000
2 57 Tina 31 0.333333
3 62 Tina 2 0.666667
4 70 Tina 3 1.000000