Complex pandas aggregation - python-3.x

Question

I have a table as below :
User_ID Cricket Football Chess Video_ID Category Time
1 200 150 100 111 A Morning
1 200 150 100 222 B Morning
1 200 150 100 111 A Afternoon
1 200 150 100 333 A Morning
2 100 160 80 444 C Evening
2 100 160 80 222 C Evening
2 100 160 80 333 A Morning
2 100 160 80 333 A Morning
Above table is a transactional table, each entry represents the transaction of a user watching a video.
For Eg. “User_ID” - 1 has watched video’s 4 times.
What all video’s watched are given in “Video_ID” : 111,222,111,333
NOTE :
Video_ID - 111 was watched twice by this user.
Cricket, Football, Chess : The values are duplicate for each row. (I.e) No of times “User_ID” 1 played cricket , football, chess are 200,150,100. ( They are duplicate in other rows for that particular “User_ID”.
Category : Which Category that particular Video_ID belongs to.
Time : What time the Video_ID was watched.
I am trying to get the below information from the table :
User_ID Top_1_Game Top_2_Game Top_1_Cat Top_2_Cat Top_Time
1 Cricket Football A B Morning
2 Football Cricket C A Evening
NOTE : If the count of Category is same then any one can be kept as Top_1_Category.
Its bit complex though, can anyone help on this ?

Answer 1

First get top values per groups by User_ID and Video_ID with Series.value_counts and index[0]:
df1 = df.groupby(['User_ID','Video_ID']).agg(lambda x: x.value_counts().index[0])
Then get second top Category by GroupBy.nth:
s = df1.groupby(level=0)['Category'].nth(1)
Remove duplicates by User_ID with DataFrame.drop_duplicates:
df1 = df1.reset_index().drop_duplicates('User_ID').drop('Video_ID', axis=1)
cols = ['User_ID','Category','Time']
cols1 = df1.columns.difference(cols)
Get top2 games by this solution:
df2 = pd.DataFrame((cols1[np.argsort(-df1[cols1].values, axis=1)[:,:2]]),
columns=['Top_1_Game','Top_2_Game'],
index=df1['User_ID'])
Filter Category and Time with rename columns names:
df3 = (df1[cols].set_index('User_ID')
.rename(columns={'Category':'Top_1_Cat','Time':'Top_Time'}))
Join together by DataFrame.join and DataFrame.insert Top_2_Cat values:
df = df2.join(df3).reset_index()
df.insert(4, 'Top_2_Cat', s.values)
print (df)
User_ID Top_1_Game Top_2_Game Top_1_Cat Top_2_Cat Top_Time
0 1 Cricket Football A B Morning
1 2 Football Cricket C A Evening