I have Dataset like this:
ORDER_CODE | ITEM_ID | ITEM_NAME | TOTALPRICE |
---|---|---|---|
123 | id1 | name1 | 345 |
321 | id2 | name2 | 678 |
and Function for calculation which items was sold together. Which ones was most popular or more expensive
out:
ITEM_ID | sold together |
---|---|
id1 | [ id33, id23, id12 ] |
id2 | [ id56, id663 ] |
I using this Func:
def freq(df):
hit_list = [list of ID's]
result = pd.DataFrame(columns = ['ITEM_ID', 'sold together'])
unic_arc = df['ITEM_ID'].unique()
unic_num = df['ORDER_CODE'].unique()
data_arc ={}
data_num={}
for i in unic_arc:
data_arc[i] = {}
tturns = response_ur[['ITEM_ID', 'TOTALPRICE']].groupby(by = 'ITEM_ID', as_index = False).sum()
tturns = tturns.rename(columns = {'ITEM_ID' : 'inum', 'TOTALPRICE' : 'turn'})
for i in tqdm(unic_arc):
b = df[df['ITEM_ID'] == i]['ORDER_CODE'].values
for t in b:
a = df[df['ORDER_CODE'] == t]['ID'].values
if i in a:
for arc in a:
if int(arc) not in hit_list:
if arc != i:
if arc in data_arc[i]:
data_arc[i][arc]+=1
else:
data_arc[i][arc] = 1
dd = data_arc[i]
tmp = pd.DataFrame(columns = ['inum', 'freq'])
tmp['inum'] = data_arc[i].keys()
tmp['freq'] = data_arc[i].values()
tmp['inum'] = tmp['inum'].astype(str)
tturns['inum'] = tturns['inum'].astype(str)
tmp = pd.merge(tmp, tturns, on = 'inum', how = 'inner')
tmp = tmp.sort_values(by = ['freq', 'turn'], ascending = False)
if len(tmp['inum'].values) > 14:
inums = str(tmp['inum'].values[0:15]).replace("\n", "").replace(' ', ',').replace('\'', '')
else:
inums = str(tmp['inum'].values).replace("\n", "").replace(' ', ',').replace('\'', '')
result = res.append({'inum' : i, 'recs' : inums}, ignore_index = True)
return(result)
I try to add merge 1for addint ITEM_NAME in Func on any iteration, but it so long. My dataset have about 10kk rows
I need add to my output one more column with list of 'ITEM_NAME' of 'sold together' list items. And calc it fast?
This might do it:
import pandas as pd
df = pd.DataFrame( {
'ORDER_CODE':['123','321','123','123','321','555'],
'ITEM_ID':[1,2,5,5,4,6],
'ITEM_NAME':['name1','name2','name3','name4','name5','name6'],
'TOTALPRICE':[10,20,50,50,40,60]}
)
result = df.groupby("ORDER_CODE").agg({"ITEM_ID":list, "ITEM_NAME":list, "TOTALPRICE":"sum"})
Further good answer how to create a list in a group by aggregation:
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