Group by value in column A and divide total total of each value by value in column b

Question

What I am trying to do is group the various teams in column A and get a total count of how many time each value shows up. As an example - Team1 shows up four times. Then I would like to divide the count value (four with Team1) by how many times the Yes value is displayed in column B and get the percentage.

Current
A           B          C
Team1       Yes         4
Team2       Yes         1
Team1       No          4
Team3       Yes         2
Team1       No          4
Team6       *blank*     1
Team3       No          2
Team1       *blank*     4



Desired
Team1       25%
Team2       100%
Team3       50%
Team6       0%

This is what I have so far, but not wrapping my head around how to do this.

import csv
import pandas as pd
import numpy as np

# Select columns from csv file
csv_columns = ['Team, 'Status']
pd.set_option('max_rows', 900)

df = pd.read_csv('test.csv', skipinitialspace=True, usecols=csv_columns)
df['Count'] = df.groupby('Team')['Team'].transform('count')


print(df)

Answer 1

Using groupby

df.B.eq('Yes').groupby(df.A).mean()

A
Team1    0.25
Team2    1.00
Team3    0.50
Team6    0.00
Name: B, dtype: float64