How to shuffle Pandas dataframe rowgroups?
Suppose I have a dataframe df:
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.rand(12,4))
print(df)
0 1 2 3
0 71 64 84 20
1 48 60 83 61
2 48 78 71 46
3 65 88 66 77
4 71 22 42 58
5 66 76 64 80
6 67 28 74 87
7 32 90 55 78
8 80 42 52 14
9 54 76 73 17
10 32 89 42 36
11 85 78 61 12
How to shuffle df lines three to three, i.e. how do I randomly shuffle the first three rows (0, 1, 2) either with the second (3, 4, 5), third (6), 7, 8) or fourth (9, 10, 11) group? This could be a possible result:
print(df)
0 1 2 3
3 65 88 66 77
4 71 22 42 58
5 66 76 64 80
9 54 76 73 17
10 32 89 42 36
11 85 78 61 12
6 67 28 74 87
7 32 90 55 78
8 80 42 52 14
0 71 64 84 20
1 48 60 83 61
2 48 78 71 46
So the new order has a second group of 3
rows from the original data frame, then the last, then the third, and finally the first group.
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You can convert to an array 3D
dividing the first axis into two with the last length 3
corresponding to the length of the group, and then use np.random.shuffle
for such a group random shuffle along the first axis that has a length, since the number of groups holds these groups and thus achieves the desired result , eg:
np.random.shuffle(df.values.reshape(-1,3,df.shape[1]))
Explanation
To give it some clarification, use np.random.permutation
to generate these random indices along the first axis and then index into the array version 3D
.
1] Input df:
In [199]: df
Out[199]:
0 1 2 3
0 71 64 84 20
1 48 60 83 61
2 48 78 71 46
3 65 88 66 77
4 71 22 42 58
5 66 76 64 80
6 67 28 74 87
7 32 90 55 78
8 80 42 52 14
9 54 76 73 17
10 32 89 42 36
11 85 78 61 12
2] Get the version of the array 3D
:
In [200]: arr_3D = df.values.reshape(-1,3,df.shape[1])
In [201]: arr_3D
Out[201]:
array([[[71, 64, 84, 20],
[48, 60, 83, 61],
[48, 78, 71, 46]],
[[65, 88, 66, 77],
[71, 22, 42, 58],
[66, 76, 64, 80]],
[[67, 28, 74, 87],
[32, 90, 55, 78],
[80, 42, 52, 14]],
[[54, 76, 73, 17],
[32, 89, 42, 36],
[85, 78, 61, 12]]])
3] Get indexes and shuffle indexes on the first axis of the version 3D
:
In [202]: shuffle_idx = np.random.permutation(arr_3D.shape[0])
In [203]: shuffle_idx
Out[203]: array([0, 3, 1, 2])
In [204]: arr_3D[shuffle_idx]
Out[204]:
array([[[71, 64, 84, 20],
[48, 60, 83, 61],
[48, 78, 71, 46]],
[[54, 76, 73, 17],
[32, 89, 42, 36],
[85, 78, 61, 12]],
[[65, 88, 66, 77],
[71, 22, 42, 58],
[66, 76, 64, 80]],
[[67, 28, 74, 87],
[32, 90, 55, 78],
[80, 42, 52, 14]]])
We then assign these values back to the input data frame.
With np.random.shuffle
we just do everything in place and hide the work needed to explicitly create the shuffle and assign back indexes.
Example run -
In [181]: df = pd.DataFrame(np.random.randint(11,99,(12,4)))
In [182]: df
Out[182]:
0 1 2 3
0 82 49 80 20
1 19 97 74 81
2 62 20 97 19
3 36 31 14 41
4 27 86 28 58
5 38 68 24 83
6 85 11 25 88
7 21 31 53 19
8 38 45 14 72
9 74 63 40 94
10 69 85 53 81
11 97 96 28 29
In [183]: np.random.shuffle(df.values.reshape(-1,3,df.shape[1]))
In [184]: df
Out[184]:
0 1 2 3
0 85 11 25 88
1 21 31 53 19
2 38 45 14 72
3 82 49 80 20
4 19 97 74 81
5 62 20 97 19
6 36 31 14 41
7 27 86 28 58
8 38 68 24 83
9 74 63 40 94
10 69 85 53 81
11 97 96 28 29
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A similar solution for @Divakar is arguably simpler as I am directly shuffling the dataframe index:
import numpy as np import pandas as pd df = pd.DataFrame([np.arange(0, 12)]*4).T len_group = 3 index_list = np.array(df.index) np.random.shuffle(np.reshape(index_list, (-1, len_group))) shuffled_df = df.loc[index_list, :]
Output example:
shuffled_df
Out[82]:
0 1 2 3
9 9 9 9 9
10 10 10 10 10
11 11 11 11 11
3 3 3 3 3
4 4 4 4 4
5 5 5 5 5
0 0 0 0 0
1 1 1 1 1
2 2 2 2 2
6 6 6 6 6
7 7 7 7 7
8 8 8 8 8
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