how to sort a panda dataframe in order shwoing redundant values in a column first?
0
I have a panda dataframe which has a column "test_id"
test_id
2
5
1
3
3
3
4
4
4
2
9
I want to sort the dataframe such that this column is:
test_id
3
3
3
4
4
4
2
2
1
5
9
Please help. Thanks!
python pandas dataframe
edited Nov 16 '18 at 0:44
DeepSpace
39.8k44778
asked Nov 16 '18 at 0:43
Chinmay KallurayaChinmay Kalluraya
102
add a comment |
0
I have a panda dataframe which has a column "test_id"
test_id
2
5
1
3
3
3
4
4
4
2
9
I want to sort the dataframe such that this column is:
test_id
3
3
3
4
4
4
2
2
1
5
9
Please help. Thanks!
python pandas dataframe
edited Nov 16 '18 at 0:44
DeepSpace
39.8k44778
asked Nov 16 '18 at 0:43
Chinmay KallurayaChinmay Kalluraya
102
add a comment |
0
0
0
I have a panda dataframe which has a column "test_id"
test_id
2
5
1
3
3
3
4
4
4
2
9
I want to sort the dataframe such that this column is:
test_id
3
3
3
4
4
4
2
2
1
5
9
Please help. Thanks!
python pandas dataframe
edited Nov 16 '18 at 0:44
DeepSpace
39.8k44778
asked Nov 16 '18 at 0:43
Chinmay KallurayaChinmay Kalluraya
102
I have a panda dataframe which has a column "test_id"
test_id
2
5
1
3
3
3
4
4
4
2
9
I want to sort the dataframe such that this column is:
test_id
3
3
3
4
4
4
2
2
1
5
9
Please help. Thanks!
python pandas dataframe
python pandas dataframe
edited Nov 16 '18 at 0:44
DeepSpace
39.8k44778
asked Nov 16 '18 at 0:43
Chinmay KallurayaChinmay Kalluraya
102
edited Nov 16 '18 at 0:44
DeepSpace
39.8k44778
asked Nov 16 '18 at 0:43
Chinmay KallurayaChinmay Kalluraya
102
edited Nov 16 '18 at 0:44
DeepSpace
39.8k44778
edited Nov 16 '18 at 0:44
DeepSpace
39.8k44778
edited Nov 16 '18 at 0:44
DeepSpace
39.8k44778
39.8k44778
asked Nov 16 '18 at 0:43
Chinmay KallurayaChinmay Kalluraya
102
asked Nov 16 '18 at 0:43
Chinmay KallurayaChinmay Kalluraya
102
asked Nov 16 '18 at 0:43
Chinmay KallurayaChinmay Kalluraya
102
102
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
1
Here is one example of how you can do it, using a helper column that you can delete afterwards:
import pandas as pd
df = pd.DataFrame('test_id': [2, 5, 1, 3, 3, 3, 4, 4, 2, 9],
'other_column': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
df['tbd'] = df.groupby(['test_id']).transform('count')
df.sort_values(['tbd', 'test_id'], inplace=True, ascending=(False, True))
del df['tbd']
df
other_column test_id
3 4 3
4 5 3
5 6 3
0 1 2
8 9 2
6 7 4
7 8 4
2 3 1
1 2 5
9 10 9
answered Nov 16 '18 at 0:59
zipazipa
16.2k31738
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
1
Here is one example of how you can do it, using a helper column that you can delete afterwards:
import pandas as pd
df = pd.DataFrame('test_id': [2, 5, 1, 3, 3, 3, 4, 4, 2, 9],
'other_column': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
df['tbd'] = df.groupby(['test_id']).transform('count')
df.sort_values(['tbd', 'test_id'], inplace=True, ascending=(False, True))
del df['tbd']
df
other_column test_id
3 4 3
4 5 3
5 6 3
0 1 2
8 9 2
6 7 4
7 8 4
2 3 1
1 2 5
9 10 9
answered Nov 16 '18 at 0:59
zipazipa
16.2k31738
add a comment |
1
Here is one example of how you can do it, using a helper column that you can delete afterwards:
import pandas as pd
df = pd.DataFrame('test_id': [2, 5, 1, 3, 3, 3, 4, 4, 2, 9],
'other_column': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
df['tbd'] = df.groupby(['test_id']).transform('count')
df.sort_values(['tbd', 'test_id'], inplace=True, ascending=(False, True))
del df['tbd']
df
other_column test_id
3 4 3
4 5 3
5 6 3
0 1 2
8 9 2
6 7 4
7 8 4
2 3 1
1 2 5
9 10 9
answered Nov 16 '18 at 0:59
zipazipa
16.2k31738
add a comment |
1
1
1
Here is one example of how you can do it, using a helper column that you can delete afterwards:
import pandas as pd
df = pd.DataFrame('test_id': [2, 5, 1, 3, 3, 3, 4, 4, 2, 9],
'other_column': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
df['tbd'] = df.groupby(['test_id']).transform('count')
df.sort_values(['tbd', 'test_id'], inplace=True, ascending=(False, True))
del df['tbd']
df
other_column test_id
3 4 3
4 5 3
5 6 3
0 1 2
8 9 2
6 7 4
7 8 4
2 3 1
1 2 5
9 10 9
answered Nov 16 '18 at 0:59
zipazipa
16.2k31738
Here is one example of how you can do it, using a helper column that you can delete afterwards:
import pandas as pd
df = pd.DataFrame('test_id': [2, 5, 1, 3, 3, 3, 4, 4, 2, 9],
'other_column': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
df['tbd'] = df.groupby(['test_id']).transform('count')
df.sort_values(['tbd', 'test_id'], inplace=True, ascending=(False, True))
del df['tbd']
df
other_column test_id
3 4 3
4 5 3
5 6 3
0 1 2
8 9 2
6 7 4
7 8 4
2 3 1
1 2 5
9 10 9
answered Nov 16 '18 at 0:59
zipazipa
16.2k31738
answered Nov 16 '18 at 0:59
zipazipa
16.2k31738
answered Nov 16 '18 at 0:59
zipazipa
16.2k31738
answered Nov 16 '18 at 0:59
zipazipa
16.2k31738
16.2k31738
add a comment |
add a comment |
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