I'm having trouble randomly splitting DataFrame df into groups of smaller DataFrames.
df
movie_id 1 2 4 5 6 7 8 9 10 11 12 borda
0 1 5 4 0 4 4 0 0 0 4 0 0 21
1 2 3 0 0 3 0 0 0 0 0 0 0 6
2 3 4 0 0 0 0 0 0 0 0 0 0 4
3 4 3 0 0 0 0 5 0 0 4 0 5 17
4 5 3 0 0 0 0 0 0 0 0 0 0 3
5 6 5 0 0 0 0 0 0 5 0 0 0 10
6 7 4 0 0 0 2 5 3 4 4 0 0 22
7 8 1 0 0 0 4 5 0 0 0 4 0 14
8 9 5 0 0 0 4 5 0 0 4 5 0 23
9 10 3 2 0 0 0 4 0 0 0 0 0 9
10 11 2 0 4 0 0 3 3 0 4 2 0 18
11 12 5 0 0 0 4 5 0 0 5 2 0 21
12 13 5 4 0 0 2 0 0 0 3 0 0 14
13 14 5 4 0 0 5 0 0 0 0 0 0 14
14 15 5 0 0 0 3 0 0 0 0 5 5 18
15 16 5 0 0 0 0 0 0 0 4 0 0 9
16 17 3 0 0 4 0 0 0 0 0 0 0 7
17 18 4 0 0 0 0 0 0 0 0 0 0 4
18 19 5 3 0 0 4 0 0 0 0 0 0 12
19 20 4 0 0 0 0 0 0 0 0 0 0 4
20 21 1 0 0 3 3 0 0 0 0 0 0 7
21 22 4 0 0 0 3 5 5 0 5 4 0 26
22 23 4 0 0 0 4 3 0 0 5 0 0 16
23 24 3 0 0 4 0 0 0 0 0 3 0 10
I've tried sample and arange, but with bad results.
ran1 = df.sample(frac=0.2, replace=False, random_state=1)
ran2 = df.sample(frac=0.2, replace=False, random_state=1)
ran3 = df.sample(frac=0.2, replace=False, random_state=1)
ran4 = df.sample(frac=0.2, replace=False, random_state=1)
ran5 = df.sample(frac=0.2, replace=False, random_state=1)
print(ran1, '\n')
print(ran2, '\n')
print(ran3, '\n')
print(ran4, '\n')
print(ran5, '\n')
This turned out to be 5 exact same DataFrames.
movie_id 1 2 4 5 6 7 8 9 10 11 12 borda
13 14 5 4 0 0 5 0 0 0 0 0 0 14
18 19 5 3 0 0 4 0 0 0 0 0 0 12
3 4 3 0 0 0 0 5 0 0 4 0 5 17
14 15 5 0 0 0 3 0 0 0 0 5 5 18
20 21 1 0 0 3 3 0 0 0 0 0 0 7
Also I've tried :
g = df.groupby(['movie_id'])
h = np.arange(g.ngroups)
np.random.shuffle(h)
df[g.ngroup().isin(h[:6])]
The output :
movie_id 1 2 4 5 6 7 8 9 10 11 12 borda
4 5 3 0 0 0 0 0 0 0 0 0 0 3
6 7 4 0 0 0 2 5 3 4 4 0 0 22
7 8 1 0 0 0 4 5 0 0 0 4 0 14
16 17 3 0 0 4 0 0 0 0 0 0 0 7
17 18 4 0 0 0 0 0 0 0 0 0 0 4
18 19 5 3 0 0 4 0 0 0 0 0 0 12
But there's still only one smaller group, other datas from df aren't grouped.
I'm expecting the smaller groups to be split evenly by using percentage. And the whole df should be split into groups.
Use np.array_split
shuffled = df.sample(frac=1)
result = np.array_split(shuffled, 5)
df.sample(frac=1) shuffle the rows of df. Then use np.array_split split it into parts that have equal size.
It gives you:
for part in result:
print(part,'\n')
movie_id 1 2 4 5 6 7 8 9 10 11 12 borda
5 6 5 0 0 0 0 0 0 5 0 0 0 10
4 5 3 0 0 0 0 0 0 0 0 0 0 3
7 8 1 0 0 0 4 5 0 0 0 4 0 14
16 17 3 0 0 4 0 0 0 0 0 0 0 7
22 23 4 0 0 0 4 3 0 0 5 0 0 16
movie_id 1 2 4 5 6 7 8 9 10 11 12 borda
13 14 5 4 0 0 5 0 0 0 0 0 0 14
14 15 5 0 0 0 3 0 0 0 0 5 5 18
21 22 4 0 0 0 3 5 5 0 5 4 0 26
1 2 3 0 0 3 0 0 0 0 0 0 0 6
20 21 1 0 0 3 3 0 0 0 0 0 0 7
movie_id 1 2 4 5 6 7 8 9 10 11 12 borda
10 11 2 0 4 0 0 3 3 0 4 2 0 18
9 10 3 2 0 0 0 4 0 0 0 0 0 9
11 12 5 0 0 0 4 5 0 0 5 2 0 21
8 9 5 0 0 0 4 5 0 0 4 5 0 23
12 13 5 4 0 0 2 0 0 0 3 0 0 14
movie_id 1 2 4 5 6 7 8 9 10 11 12 borda
18 19 5 3 0 0 4 0 0 0 0 0 0 12
3 4 3 0 0 0 0 5 0 0 4 0 5 17
0 1 5 4 0 4 4 0 0 0 4 0 0 21
23 24 3 0 0 4 0 0 0 0 0 3 0 10
6 7 4 0 0 0 2 5 3 4 4 0 0 22
movie_id 1 2 4 5 6 7 8 9 10 11 12 borda
17 18 4 0 0 0 0 0 0 0 0 0 0 4
2 3 4 0 0 0 0 0 0 0 0 0 0 4
15 16 5 0 0 0 0 0 0 0 4 0 0 9
19 20 4 0 0 0 0 0 0 0 0 0 0 4