Clash Royale CLAN TAG

Pandas how to calculate bygroup result based on the length of the each group and a count value of another column

I want to calculate the scoring rate of each zone by using bygroup in pandas, but not sure how to do it:

Suppose the df has two columns as:

Shot_type Shot_zone
Goal Penalty_area
Saved Penalty_area
Goal Goal Box
Saved Goal Box


Here I want to groupy by Shot_zone, and calculate the scoring rate based on Shot_type's Goal counts / len() of each type Shot_zone. Here each Shot_zone has 1 goal and 1 saved, so the result should be like:

Penalty_area 50%
Goal Box 50%


Is there any understandable approach to do so using Pandas?
Thank you very much!

3 Answers
3

Using

pd.crosstab(df.Shot_type,df.Shot_zone,normalize='index')
Out[662]:
Shot_zone GoalBox Penalty_area
Shot_type
Goal 0.5 0.5
Saved 0.5 0.5


One way is to binarize your Shot_type column, i.e. set to True if it equals 'Goal', and then use GroupBy + mean:

Shot_type

True

'Goal'

GroupBy

mean

res = df.assign(Shot_type=df['Shot_type']=='Goal')
.groupby('Shot_zone')['Shot_type'].mean()

print(res)

Shot_zone
GoalBox 0.5
Penalty_area 0.5
Name: Shot_type, dtype: float64


crosstab

Can also groupby and apply

groupby

apply

df.groupby('Shot_zone').Shot_type.apply(lambda s: '{}%'.format((s[s=='Goal']).size/(s.size) * 100))

Shot_zone
Goal_Box 50.0%
Penalty_area 50.0%


By clicking "Post Your Answer", you acknowledge that you have read our updated terms of service, privacy policy and cookie policy, and that your continued use of the website is subject to these policies.