Groupby two columns in pandas, and perform operations over totals for each group - pandas

Question

The code below:
df = pd.read_csv('./filename.csv', header='infer').dropna()
df.groupby(['category_code','event_type']).event_type.count().head(20)
Returns the following table:
How can I obtain, for all the sub groups under event_type that have both "purchase" and "view", the ratio between the total of "purchase" and the total of "view"?
In this specific case, for instance, I need a function that returns:
1/57
1/232
3/249
Eventually, I will need to plot such result.
I have been trying for a day, without success. I am still new to pandas, and I searched across every possible forum without finding anything useful.

Answer 1

Next time please consider adding a sample of your data as text instead of as an image. It helps us testing..
Anyway, in your case you can combine different dataframe methods, such as groupby, as you have already done, and pivot_table. I used this data just as an example:
category_code event_type
0 A purchase
1 A view
2 B view
3 B view
4 C view
5 D purchase
6 D view
7 D view
You can create a new column from your groupby
df['event_count'] = df.groupby(['category_code', 'event_type'])\
.event_type.transform('count')
Then create a pivot_table
my_table = df.pivot_table(values='event_count',
index='category_code',
columns='event_type',
fill_value=0)
Then, finally, you can calculate the purchase_ratio directly:
my_table['purchase_ratio'] = my_table['purchase'] / my_table['view']
Which results in the following DataFrame:
event_type purchase view purchase_ratio
category_code
A 1 1 1.0
B 0 2 0.0
C 0 1 0.0
D 1 2 0.5