Analysis on dataframe with python - python-3.x

Question

I want to be able to calculate the average 'goal','shot',and 'miss' per shooterName to use for further analysis and visualization
The code below gives me the count of the 3 attributes(shot,goal,miss) in the 'event' column sorted by 'shooterName'
Dataframe columns:
season period time teamCode event goal xCord yCord xCordAdjusted yCordAdjusted ... playerPositionThatDidEvent timeSinceFaceoff playerNumThatDidEvent shooterPlayerId shooterName shooterLeftRight shooterTimeOnIce shooterTimeOnIceSinceFaceoff shotDistance
Corresponding data
2020 1 16 PHI SHOT 0 -74 29 74 -29 ... C 16 11 8478439.0 Travis Konecny R 16 16 32.649655
2020 1 34 PIT SHOT 0 49 -25 49 -25 ... C 34 9 8478542.0 Evan Rodrigues R 34 34 47.169906
2020 1 65 PHI SHOT 0 -52 -31 52 31 ... L 65 86 8480797.0 Joel Farabee L 31 31 48.270074
2020 1 171 PIT SHOT 0 43 39 43 39 ... C 42 9 8478542.0 Evan Rodrigues R 42 42 60.307545
2020 1 209 PHI MISS 0 -46 33 46 -33 ... D 38 5 8479026.0 Philippe Myers R 38 38 54.203321
Current code:
dft['count'] = df.groupby(['shooterName', 'event'])['event'].agg(['count'])
dft
Current Output:
shooterName event count
A.J. Greer GOAL 1
MISS 6
SHOT 29
Aaron Downey GOAL 1
MISS 4
SHOT 35
Zenon Konopka GOAL 8
MISS 57
SHOT 176
Desired Output:
shooterName event count %totalshooterNameevents
A.J. Greer GOAL 1 .0277
MISS 6 .1666
SHOT 29 .805
Aaron Downey GOAL 1 .025
MISS 4 .1
SHOT 35 .875
Zenon Konopka GOAL 8 .0331
MISS 57 .236
SHOT 176 .7302
Something similar to this. My end goal is to be able to calculate each 'event' attribute as a percentage of the total 'event' by 'shooterName'. Below I added a column '%totalshooterNameevents' which is 'simply goal', 'shot', and 'miss' calculated by the sum of the 'goal, shot, and miss' per each 'shooterName'

Answer 1

Update
Try:
dft = df.groupby(['shooterName', 'event'])['event'].agg(['count']).reset_index()
dft['%total'] = dft.groupby('shooterName')['count'].apply(lambda x: x / sum(x))
print(dft)
# Output
shooterName event count %total
0 A.J. Greer GOAL 1 0.027778
1 A.J. Greer MISS 6 0.166667
2 A.J. Greer SHOT 29 0.805556
3 Aaron Downey GOAL 1 0.025000
4 Aaron Downey MISS 4 0.100000
5 Aaron Downey SHOT 35 0.875000
6 Zenon Konopka GOAL 8 0.033195
7 Zenon Konopka MISS 57 0.236515
8 Zenon Konopka SHOT 176 0.730290
Without sample, it's difficult to guess what you want. Try:
import pandas as pd
import numpy as np
# Setup a Minimal Reproducible Example
np.random.seed(2021)
df = pd.DataFrame({'shooterName': np.random.choice(list('AB'), 20),
'event': np.random.choice(['shot', 'goal', 'miss'], 20)})
# Create an empty dataframe?
dft = pd.DataFrame(index=df['shooterName'].unique())
# Do stuff
grp = df.groupby('shooterName')
dft['count'] = grp.count()
dft = dft.join(grp['event'].value_counts().unstack('event')
.div(dft['count'], axis=0))
Output:
>>> dft
count goal miss shot
A 12 0.416667 0.250 0.333333
B 8 0.500000 0.375 0.125000