I have a DataFrame df_data
:
CustID MatchID LocationID isMajor #Major is 1 and Minor is 0 1 11111 324 0 1 11111 324 0 1 11111 324 0 1 22222 490 0 1 33333 675 1 2 44444 888 0
I have a function and parameter like this:
def compute_something(list_minor = None, list_major = None): return pass
Explain Parameters: with CustID = 1
the parameters should be list_minor = [3,1]
(position is not important), list_major = [1]
because with LocationID = 324
he get 3 times and LocationID = 490
he get 1 time (324,490
gets isMajor = 0
so it should be into 1 list
). Similiar, CustID2
have parameters list_minor = [1]
and list_major = []
(if he don’t have data major/minor, I should be pass []
.
This is my program:
data = [ [1, 11111, 324, 0], [1, 11111, 324, 0], [1, 11111, 324, 0], [1, 22222, 490, 0], [1, 33333, 675, 1], [2, 44444, 888, 0] ] df_data = pd.DataFrame(data, columns = ['CustID','MatchID','LocationID','IsMajor']) df_parameter = DataFrame() df_parameter['parameters'] = df.groupby(['CustID','MatchID','IsMajor'])['LeagueID'].nunique()
But results of df_parameter['parameters']
is wrong:
parameters CustID MatchID IsMajor 1 11111 0 1 #should be 3 22222 0 1 33333 1 1 2 44444 0 1
Can I get the parameters I explained above with groupby and pass them to the function?
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Answer
How about:
(df.groupby(['CustID','isMajor', 'MatchID']).size() .groupby(level=[0,1]).agg(set) .unstack('isMajor') )
Output:
isMajor 0 1 CustID 1 {1, 3} {1} 2 {1} NaN
Update Try this one groupby:
(df.groupby(['CustID','isMajor'])['MatchID'] .apply(lambda x: x.value_counts().agg(list)) .unstack('isMajor') )
Also, groupby with two keys can be slow. In that case, you can just concatenate the keys and groupby on that:
keys = df['CustID'].astype(str) + '_' + df['isMajor'].astype(str) (df.groupby(keys)['MatchID'] .apply(lambda x: x.value_counts().agg(list)) )