试试这个代码
from itertools import combinations
import pandas as pd
df = pd.DataFrame.from_csv("data.csv")
#consecutive
grouped_consecutive_product_ids = df.groupby(['Leaf_category_id','session_id'])['product_id'].apply(lambda x: [tuple(sorted(pair)) for pair in zip(x,x[1:])]).reset_index()
df1=pd.DataFrame(grouped_consecutive_product_ids)
s=df1.product_id.apply(lambda x: pd.Series(x)).unstack()
df2=pd.DataFrame(s.reset_index(level=0,drop=True)).dropna()
df2.rename(columns = {0:'Bigram'}, inplace = True)
df2["freq"] = df2.groupby('Bigram')['Bigram'].transform('count')
bigram_frequency_consecutive = df2.drop_duplicates(keep="first").sort_values("Bigram").reset_index()
del bigram_frequency_consecutive["index"]
对于组合（所有可能的二元组）
from itertools import combinations
import pandas as pd
df = pd.DataFrame.from_csv("data.csv")
#combinations
grouped_combination_product_ids = df.groupby(['Leaf_category_id','session_id'])['product_id'].apply(lambda x: [tuple(sorted(pair)) for pair in combinations(x,2)]).reset_index()
df1=pd.DataFrame(grouped_combination_product_ids)
s=df1.product_id.apply(lambda x: pd.Series(x)).unstack()
df2=pd.DataFrame(s.reset_index(level=0,drop=True)).dropna()
df2.rename(columns = {0:'Bigram'}, inplace = True)
df2["freq"] = df2.groupby('Bigram')['Bigram'].transform('count')
bigram_frequency_combinations = df2.drop_duplicates(keep="first").sort_values("Bigram").reset_index()
del bigram_frequency_combinations["index"]
where data.csv包含
Leaf_category_id,session_id,product_id
0,111,1,111
3,111,4,987
4,111,1,741
1,222,2,654
2,333,3,321
5,111,1,87
6,111,1,34
7,111,1,12
8,111,1,987
9,111,4,1232
10,222,2,12
11,222,2,324
12,222,2,465
13,222,2,342
14,222,2,32
15,333,3,321
16,333,3,741
17,333,3,987
18,333,3,324
19,333,3,654
20,333,3,862
21,222,1,123
22,222,1,987
23,222,1,741
24,222,1,34
25,222,1,12
由此产生的bigram_frequency_consecutive将
Bigram freq
0 (12, 34) 2
1 (12, 324) 1
2 (12, 654) 1
3 (12, 987) 1
4 (32, 342) 1
5 (34, 87) 1
6 (34, 741) 1
7 (87, 741) 1
8 (111, 741) 1
9 (123, 987) 1
10 (321, 321) 1
11 (321, 741) 1
12 (324, 465) 1
13 (324, 654) 1
14 (324, 987) 1
15 (342, 465) 1
16 (654, 862) 1
17 (741, 987) 2
18 (987, 1232) 1
由此产生的bigram_frequency_combinations将
Bigram freq
0 (12, 32) 1
1 (12, 34) 2
2 (12, 87) 1
3 (12, 111) 1
4 (12, 123) 1
5 (12, 324) 1
6 (12, 342) 1
7 (12, 465) 1
8 (12, 654) 1
9 (12, 741) 2
10 (12, 987) 2
11 (32, 324) 1
12 (32, 342) 1
13 (32, 465) 1
14 (32, 654) 1
15 (34, 87) 1
16 (34, 111) 1
17 (34, 123) 1
18 (34, 741) 2
19 (34, 987) 2
20 (87, 111) 1
21 (87, 741) 1
22 (87, 987) 1
23 (111, 741) 1
24 (111, 987) 1
25 (123, 741) 1
26 (123, 987) 1
27 (321, 321) 1
28 (321, 324) 2
29 (321, 654) 2
30 (321, 741) 2
31 (321, 862) 2
32 (321, 987) 2
33 (324, 342) 1
34 (324, 465) 1
35 (324, 654) 2
36 (324, 741) 1
37 (324, 862) 1
38 (324, 987) 1
39 (342, 465) 1
40 (342, 654) 1
41 (465, 654) 1
42 (654, 741) 1
43 (654, 862) 1
44 (654, 987) 1
45 (741, 862) 1
46 (741, 987) 3
47 (862, 987) 1
48 (987, 1232) 1
在上面的情况下，它按两者分组
