Values in pandas dataframe not getting sorted
up vote
2
down vote
favorite
I have a dataframe as shown below:
Category 1 2 3 4 5 6 7 8 9 10 11 12 13
A 424 377 161 133 2 81 141 169 297 153 53 50 197
B 231 121 111 106 4 79 68 70 92 93 71 65 66
C 480 379 159 139 2 116 148 175 308 150 98 82 195
D 88 56 38 40 0 25 24 55 84 36 24 26 36
E 1084 1002 478 299 7 256 342 342 695 378 175 132 465
F 497 246 283 206 4 142 151 168 297 224 194 198 148
H 8 5 4 3 0 2 3 2 7 5 3 2 0
G 3191 2119 1656 856 50 826 955 739 1447 1342 975 628 1277
K 58 26 27 51 1 18 22 42 47 35 19 20 14
S 363 254 131 105 6 82 86 121 196 98 81 57 125
T 54 59 20 4 0 9 12 7 36 23 5 4 20
O 554 304 207 155 3 130 260 183 287 204 98 106 195
P 756 497 325 230 5 212 300 280 448 270 201 140 313
PP 64 43 26 17 1 15 35 17 32 28 18 9 27
R 265 157 109 89 1 68 68 104 154 96 63 55 90
S 377 204 201 114 5 112 267 136 209 172 147 90 157
St 770 443 405 234 5 172 464 232 367 270 290 136 294
Qs 47 33 11 14 0 18 14 19 26 17 5 6 13
Y 1806 626 1102 1177 14 625 619 1079 1273 981 845 891 455
W 123 177 27 28 0 18 62 34 64 27 14 4 51
Z 2770 1375 1579 1082 17 900 1630 1137 1465 1383 861 755 1201
I want to sort the dataframe by values in each row. Once done, I want to sort the index also.
For example the values in first row corresponding to category A, should appear as:
2 50 53 81 133 141 153 161 169 197 297 377 424
I have tried df.sort_values(by=df.index.tolist(), ascending=False, axis=1) but this doesn't work. The values don't appear in sorted order at all
python python-3.x pandas sorting dataframe
edited Nov 21 at 17:03
jpp
87k194999
asked Nov 21 at 16:41
mlRocks
472112
add a comment |
up vote
2
down vote
favorite
I have a dataframe as shown below:
Category 1 2 3 4 5 6 7 8 9 10 11 12 13
A 424 377 161 133 2 81 141 169 297 153 53 50 197
B 231 121 111 106 4 79 68 70 92 93 71 65 66
C 480 379 159 139 2 116 148 175 308 150 98 82 195
D 88 56 38 40 0 25 24 55 84 36 24 26 36
E 1084 1002 478 299 7 256 342 342 695 378 175 132 465
F 497 246 283 206 4 142 151 168 297 224 194 198 148
H 8 5 4 3 0 2 3 2 7 5 3 2 0
G 3191 2119 1656 856 50 826 955 739 1447 1342 975 628 1277
K 58 26 27 51 1 18 22 42 47 35 19 20 14
S 363 254 131 105 6 82 86 121 196 98 81 57 125
T 54 59 20 4 0 9 12 7 36 23 5 4 20
O 554 304 207 155 3 130 260 183 287 204 98 106 195
P 756 497 325 230 5 212 300 280 448 270 201 140 313
PP 64 43 26 17 1 15 35 17 32 28 18 9 27
R 265 157 109 89 1 68 68 104 154 96 63 55 90
S 377 204 201 114 5 112 267 136 209 172 147 90 157
St 770 443 405 234 5 172 464 232 367 270 290 136 294
Qs 47 33 11 14 0 18 14 19 26 17 5 6 13
Y 1806 626 1102 1177 14 625 619 1079 1273 981 845 891 455
W 123 177 27 28 0 18 62 34 64 27 14 4 51
Z 2770 1375 1579 1082 17 900 1630 1137 1465 1383 861 755 1201
I want to sort the dataframe by values in each row. Once done, I want to sort the index also.
For example the values in first row corresponding to category A, should appear as:
2 50 53 81 133 141 153 161 169 197 297 377 424
I have tried df.sort_values(by=df.index.tolist(), ascending=False, axis=1) but this doesn't work. The values don't appear in sorted order at all
python python-3.x pandas sorting dataframe
edited Nov 21 at 17:03
jpp
87k194999
asked Nov 21 at 16:41
mlRocks
472112
1
Edited the question.
– mlRocks
Nov 21 at 16:57
Possibly you can usedf.sort_values(['c1','c2'], ascending=False)but in your case you have multiple columns which is little tricky
– pygo
Nov 21 at 17:18
add a comment |
up vote
2
down vote
favorite
up vote
2
down vote
favorite
I have a dataframe as shown below:
Category 1 2 3 4 5 6 7 8 9 10 11 12 13
A 424 377 161 133 2 81 141 169 297 153 53 50 197
B 231 121 111 106 4 79 68 70 92 93 71 65 66
C 480 379 159 139 2 116 148 175 308 150 98 82 195
D 88 56 38 40 0 25 24 55 84 36 24 26 36
E 1084 1002 478 299 7 256 342 342 695 378 175 132 465
F 497 246 283 206 4 142 151 168 297 224 194 198 148
H 8 5 4 3 0 2 3 2 7 5 3 2 0
G 3191 2119 1656 856 50 826 955 739 1447 1342 975 628 1277
K 58 26 27 51 1 18 22 42 47 35 19 20 14
S 363 254 131 105 6 82 86 121 196 98 81 57 125
T 54 59 20 4 0 9 12 7 36 23 5 4 20
O 554 304 207 155 3 130 260 183 287 204 98 106 195
P 756 497 325 230 5 212 300 280 448 270 201 140 313
PP 64 43 26 17 1 15 35 17 32 28 18 9 27
R 265 157 109 89 1 68 68 104 154 96 63 55 90
S 377 204 201 114 5 112 267 136 209 172 147 90 157
St 770 443 405 234 5 172 464 232 367 270 290 136 294
Qs 47 33 11 14 0 18 14 19 26 17 5 6 13
Y 1806 626 1102 1177 14 625 619 1079 1273 981 845 891 455
W 123 177 27 28 0 18 62 34 64 27 14 4 51
Z 2770 1375 1579 1082 17 900 1630 1137 1465 1383 861 755 1201
I want to sort the dataframe by values in each row. Once done, I want to sort the index also.
For example the values in first row corresponding to category A, should appear as:
2 50 53 81 133 141 153 161 169 197 297 377 424
I have tried df.sort_values(by=df.index.tolist(), ascending=False, axis=1) but this doesn't work. The values don't appear in sorted order at all
python python-3.x pandas sorting dataframe
edited Nov 21 at 17:03
jpp
87k194999
asked Nov 21 at 16:41
mlRocks
472112
I have a dataframe as shown below:
Category 1 2 3 4 5 6 7 8 9 10 11 12 13
A 424 377 161 133 2 81 141 169 297 153 53 50 197
B 231 121 111 106 4 79 68 70 92 93 71 65 66
C 480 379 159 139 2 116 148 175 308 150 98 82 195
D 88 56 38 40 0 25 24 55 84 36 24 26 36
E 1084 1002 478 299 7 256 342 342 695 378 175 132 465
F 497 246 283 206 4 142 151 168 297 224 194 198 148
H 8 5 4 3 0 2 3 2 7 5 3 2 0
G 3191 2119 1656 856 50 826 955 739 1447 1342 975 628 1277
K 58 26 27 51 1 18 22 42 47 35 19 20 14
S 363 254 131 105 6 82 86 121 196 98 81 57 125
T 54 59 20 4 0 9 12 7 36 23 5 4 20
O 554 304 207 155 3 130 260 183 287 204 98 106 195
P 756 497 325 230 5 212 300 280 448 270 201 140 313
PP 64 43 26 17 1 15 35 17 32 28 18 9 27
R 265 157 109 89 1 68 68 104 154 96 63 55 90
S 377 204 201 114 5 112 267 136 209 172 147 90 157
St 770 443 405 234 5 172 464 232 367 270 290 136 294
Qs 47 33 11 14 0 18 14 19 26 17 5 6 13
Y 1806 626 1102 1177 14 625 619 1079 1273 981 845 891 455
W 123 177 27 28 0 18 62 34 64 27 14 4 51
Z 2770 1375 1579 1082 17 900 1630 1137 1465 1383 861 755 1201
I want to sort the dataframe by values in each row. Once done, I want to sort the index also.
For example the values in first row corresponding to category A, should appear as:
2 50 53 81 133 141 153 161 169 197 297 377 424
I have tried df.sort_values(by=df.index.tolist(), ascending=False, axis=1) but this doesn't work. The values don't appear in sorted order at all
python python-3.x pandas sorting dataframe
python python-3.x pandas sorting dataframe
edited Nov 21 at 17:03
jpp
87k194999
asked Nov 21 at 16:41
mlRocks
472112
edited Nov 21 at 17:03
jpp
87k194999
asked Nov 21 at 16:41
mlRocks
472112
edited Nov 21 at 17:03
jpp
87k194999
edited Nov 21 at 17:03
jpp
87k194999
edited Nov 21 at 17:03
jpp
87k194999
87k194999
asked Nov 21 at 16:41
mlRocks
472112
asked Nov 21 at 16:41
mlRocks
472112
asked Nov 21 at 16:41
mlRocks
472112
472112
1
Edited the question.
– mlRocks
Nov 21 at 16:57
Possibly you can usedf.sort_values(['c1','c2'], ascending=False)but in your case you have multiple columns which is little tricky
– pygo
Nov 21 at 17:18
add a comment |
1
Edited the question.
– mlRocks
Nov 21 at 16:57
Possibly you can usedf.sort_values(['c1','c2'], ascending=False)but in your case you have multiple columns which is little tricky
– pygo
Nov 21 at 17:18
1
1
Edited the question.
– mlRocks
Nov 21 at 16:57
Edited the question.
– mlRocks
Nov 21 at 16:57
Possibly you can use
df.sort_values(['c1','c2'], ascending=False) but in your case you have multiple columns which is little tricky– pygo
Nov 21 at 17:18
Possibly you can use
df.sort_values(['c1','c2'], ascending=False) but in your case you have multiple columns which is little tricky– pygo
Nov 21 at 17:18
add a comment |
2 Answers
2
active
oldest
votes
up vote
2
down vote
accepted
np.sort + sort_index
You can use np.sort along axis=1, then sort_index:
cols, idx = df.columns[1:], df.iloc[:, 0]
res = pd.DataFrame(np.sort(df.iloc[:, 1:].values, axis=1), columns=cols, index=idx)
.sort_index()
print(res)
1 2 3 4 5 6 7 8 9 10 11 12
Category
A 2 50 53 81 133 141 153 161 169 197 297 377
B 4 65 66 68 70 71 79 92 93 106 111 121
C 2 82 98 116 139 148 150 159 175 195 308 379
D 0 24 24 25 26 36 36 38 40 55 56 84
E 7 132 175 256 299 342 342 378 465 478 695 1002
F 4 142 148 151 168 194 198 206 224 246 283 297
G 50 628 739 826 856 955 975 1277 1342 1447 1656 2119
H 0 0 2 2 2 3 3 3 4 5 5 7
K 1 14 18 19 20 22 26 27 35 42 47 51
O 3 98 106 130 155 183 195 204 207 260 287 304
P 5 140 201 212 230 270 280 300 313 325 448 497
PP 1 9 15 17 17 18 26 27 28 32 35 43
Qs 0 5 6 11 13 14 14 17 18 19 26 33
R 1 55 63 68 68 89 90 96 104 109 154 157
S 6 57 81 82 86 98 105 121 125 131 196 254
S 5 90 112 114 136 147 157 172 201 204 209 267
St 5 136 172 232 234 270 290 294 367 405 443 464
T 0 4 4 5 7 9 12 20 20 23 36 54
W 0 4 14 18 27 27 28 34 51 62 64 123
Y 14 455 619 625 626 845 891 981 1079 1102 1177 1273
Z 1 17 755 861 900 1082 1137 1375 1383 1465 1579 1630
answered Nov 21 at 17:01
jpp
87k194999
But why issort_valuesnot working?
– mlRocks
Nov 21 at 17:07
Because it's designed to work row-wise, i.e. sort rows up and down for all columns, not sorting columns left and right for each row. This is expected with Pandas, where data is stored in columnar series.
– jpp
Nov 21 at 17:08
Shouldn't this be mentioned in the docs?
– mlRocks
Nov 21 at 17:08
1
@mlRocks, What do you expect for column names? The order will be different for each row, right?
– jpp
Nov 21 at 17:24
1
Yeap. My bad!. Thanks
– mlRocks
Nov 21 at 17:27
|
show 2 more comments
up vote
1
down vote
One way is to apply sorted setting 1 as axis, applying pd.Series to return a dataframe instead of a list, and finally sorting by Category:
df.loc[:,'1':].apply(sorted, axis = 1).apply(pd.Series)
.set_index(df.Category).sort_index()
Category 0 1 2 3 4 5 6 7 8 9 10 ...
0 A 2 50 53 81 133 141 153 161 169 197 297 ...
1 B 4 65 66 68 70 71 79 92 93 106 111 ...
answered Nov 21 at 17:01
nixon
1,86316
This works, butapply(pd.Series)is a Python-level loop and will be slow for larger dataframes.
– jpp
Nov 21 at 17:11
Yes, it is not ideal for big dataframes, as you say. First solution that came across my mind :)
– nixon
Nov 21 at 17:16
add a comment |
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
2
down vote
accepted
np.sort + sort_index
You can use np.sort along axis=1, then sort_index:
cols, idx = df.columns[1:], df.iloc[:, 0]
res = pd.DataFrame(np.sort(df.iloc[:, 1:].values, axis=1), columns=cols, index=idx)
.sort_index()
print(res)
1 2 3 4 5 6 7 8 9 10 11 12
Category
A 2 50 53 81 133 141 153 161 169 197 297 377
B 4 65 66 68 70 71 79 92 93 106 111 121
C 2 82 98 116 139 148 150 159 175 195 308 379
D 0 24 24 25 26 36 36 38 40 55 56 84
E 7 132 175 256 299 342 342 378 465 478 695 1002
F 4 142 148 151 168 194 198 206 224 246 283 297
G 50 628 739 826 856 955 975 1277 1342 1447 1656 2119
H 0 0 2 2 2 3 3 3 4 5 5 7
K 1 14 18 19 20 22 26 27 35 42 47 51
O 3 98 106 130 155 183 195 204 207 260 287 304
P 5 140 201 212 230 270 280 300 313 325 448 497
PP 1 9 15 17 17 18 26 27 28 32 35 43
Qs 0 5 6 11 13 14 14 17 18 19 26 33
R 1 55 63 68 68 89 90 96 104 109 154 157
S 6 57 81 82 86 98 105 121 125 131 196 254
S 5 90 112 114 136 147 157 172 201 204 209 267
St 5 136 172 232 234 270 290 294 367 405 443 464
T 0 4 4 5 7 9 12 20 20 23 36 54
W 0 4 14 18 27 27 28 34 51 62 64 123
Y 14 455 619 625 626 845 891 981 1079 1102 1177 1273
Z 1 17 755 861 900 1082 1137 1375 1383 1465 1579 1630
answered Nov 21 at 17:01
jpp
87k194999
But why issort_valuesnot working?
– mlRocks
Nov 21 at 17:07
Because it's designed to work row-wise, i.e. sort rows up and down for all columns, not sorting columns left and right for each row. This is expected with Pandas, where data is stored in columnar series.
– jpp
Nov 21 at 17:08
Shouldn't this be mentioned in the docs?
– mlRocks
Nov 21 at 17:08
1
@mlRocks, What do you expect for column names? The order will be different for each row, right?
– jpp
Nov 21 at 17:24
1
Yeap. My bad!. Thanks
– mlRocks
Nov 21 at 17:27
|
show 2 more comments
up vote
2
down vote
accepted
np.sort + sort_index
You can use np.sort along axis=1, then sort_index:
cols, idx = df.columns[1:], df.iloc[:, 0]
res = pd.DataFrame(np.sort(df.iloc[:, 1:].values, axis=1), columns=cols, index=idx)
.sort_index()
print(res)
1 2 3 4 5 6 7 8 9 10 11 12
Category
A 2 50 53 81 133 141 153 161 169 197 297 377
B 4 65 66 68 70 71 79 92 93 106 111 121
C 2 82 98 116 139 148 150 159 175 195 308 379
D 0 24 24 25 26 36 36 38 40 55 56 84
E 7 132 175 256 299 342 342 378 465 478 695 1002
F 4 142 148 151 168 194 198 206 224 246 283 297
G 50 628 739 826 856 955 975 1277 1342 1447 1656 2119
H 0 0 2 2 2 3 3 3 4 5 5 7
K 1 14 18 19 20 22 26 27 35 42 47 51
O 3 98 106 130 155 183 195 204 207 260 287 304
P 5 140 201 212 230 270 280 300 313 325 448 497
PP 1 9 15 17 17 18 26 27 28 32 35 43
Qs 0 5 6 11 13 14 14 17 18 19 26 33
R 1 55 63 68 68 89 90 96 104 109 154 157
S 6 57 81 82 86 98 105 121 125 131 196 254
S 5 90 112 114 136 147 157 172 201 204 209 267
St 5 136 172 232 234 270 290 294 367 405 443 464
T 0 4 4 5 7 9 12 20 20 23 36 54
W 0 4 14 18 27 27 28 34 51 62 64 123
Y 14 455 619 625 626 845 891 981 1079 1102 1177 1273
Z 1 17 755 861 900 1082 1137 1375 1383 1465 1579 1630
answered Nov 21 at 17:01
jpp
87k194999
But why issort_valuesnot working?
– mlRocks
Nov 21 at 17:07
Because it's designed to work row-wise, i.e. sort rows up and down for all columns, not sorting columns left and right for each row. This is expected with Pandas, where data is stored in columnar series.
– jpp
Nov 21 at 17:08
Shouldn't this be mentioned in the docs?
– mlRocks
Nov 21 at 17:08
1
@mlRocks, What do you expect for column names? The order will be different for each row, right?
– jpp
Nov 21 at 17:24
1
Yeap. My bad!. Thanks
– mlRocks
Nov 21 at 17:27
|
show 2 more comments
up vote
2
down vote
accepted
up vote
2
down vote
accepted
np.sort + sort_index
You can use np.sort along axis=1, then sort_index:
cols, idx = df.columns[1:], df.iloc[:, 0]
res = pd.DataFrame(np.sort(df.iloc[:, 1:].values, axis=1), columns=cols, index=idx)
.sort_index()
print(res)
1 2 3 4 5 6 7 8 9 10 11 12
Category
A 2 50 53 81 133 141 153 161 169 197 297 377
B 4 65 66 68 70 71 79 92 93 106 111 121
C 2 82 98 116 139 148 150 159 175 195 308 379
D 0 24 24 25 26 36 36 38 40 55 56 84
E 7 132 175 256 299 342 342 378 465 478 695 1002
F 4 142 148 151 168 194 198 206 224 246 283 297
G 50 628 739 826 856 955 975 1277 1342 1447 1656 2119
H 0 0 2 2 2 3 3 3 4 5 5 7
K 1 14 18 19 20 22 26 27 35 42 47 51
O 3 98 106 130 155 183 195 204 207 260 287 304
P 5 140 201 212 230 270 280 300 313 325 448 497
PP 1 9 15 17 17 18 26 27 28 32 35 43
Qs 0 5 6 11 13 14 14 17 18 19 26 33
R 1 55 63 68 68 89 90 96 104 109 154 157
S 6 57 81 82 86 98 105 121 125 131 196 254
S 5 90 112 114 136 147 157 172 201 204 209 267
St 5 136 172 232 234 270 290 294 367 405 443 464
T 0 4 4 5 7 9 12 20 20 23 36 54
W 0 4 14 18 27 27 28 34 51 62 64 123
Y 14 455 619 625 626 845 891 981 1079 1102 1177 1273
Z 1 17 755 861 900 1082 1137 1375 1383 1465 1579 1630
answered Nov 21 at 17:01
jpp
87k194999
np.sort + sort_index
You can use np.sort along axis=1, then sort_index:
cols, idx = df.columns[1:], df.iloc[:, 0]
res = pd.DataFrame(np.sort(df.iloc[:, 1:].values, axis=1), columns=cols, index=idx)
.sort_index()
print(res)
1 2 3 4 5 6 7 8 9 10 11 12
Category
A 2 50 53 81 133 141 153 161 169 197 297 377
B 4 65 66 68 70 71 79 92 93 106 111 121
C 2 82 98 116 139 148 150 159 175 195 308 379
D 0 24 24 25 26 36 36 38 40 55 56 84
E 7 132 175 256 299 342 342 378 465 478 695 1002
F 4 142 148 151 168 194 198 206 224 246 283 297
G 50 628 739 826 856 955 975 1277 1342 1447 1656 2119
H 0 0 2 2 2 3 3 3 4 5 5 7
K 1 14 18 19 20 22 26 27 35 42 47 51
O 3 98 106 130 155 183 195 204 207 260 287 304
P 5 140 201 212 230 270 280 300 313 325 448 497
PP 1 9 15 17 17 18 26 27 28 32 35 43
Qs 0 5 6 11 13 14 14 17 18 19 26 33
R 1 55 63 68 68 89 90 96 104 109 154 157
S 6 57 81 82 86 98 105 121 125 131 196 254
S 5 90 112 114 136 147 157 172 201 204 209 267
St 5 136 172 232 234 270 290 294 367 405 443 464
T 0 4 4 5 7 9 12 20 20 23 36 54
W 0 4 14 18 27 27 28 34 51 62 64 123
Y 14 455 619 625 626 845 891 981 1079 1102 1177 1273
Z 1 17 755 861 900 1082 1137 1375 1383 1465 1579 1630
answered Nov 21 at 17:01
jpp
87k194999
answered Nov 21 at 17:01
jpp
87k194999
answered Nov 21 at 17:01
jpp
87k194999
answered Nov 21 at 17:01
jpp
87k194999
87k194999
But why issort_valuesnot working?
– mlRocks
Nov 21 at 17:07
Because it's designed to work row-wise, i.e. sort rows up and down for all columns, not sorting columns left and right for each row. This is expected with Pandas, where data is stored in columnar series.
– jpp
Nov 21 at 17:08
Shouldn't this be mentioned in the docs?
– mlRocks
Nov 21 at 17:08
1
@mlRocks, What do you expect for column names? The order will be different for each row, right?
– jpp
Nov 21 at 17:24
1
Yeap. My bad!. Thanks
– mlRocks
Nov 21 at 17:27
|
show 2 more comments
But why issort_valuesnot working?
– mlRocks
Nov 21 at 17:07
Because it's designed to work row-wise, i.e. sort rows up and down for all columns, not sorting columns left and right for each row. This is expected with Pandas, where data is stored in columnar series.
– jpp
Nov 21 at 17:08
Shouldn't this be mentioned in the docs?
– mlRocks
Nov 21 at 17:08
1
@mlRocks, What do you expect for column names? The order will be different for each row, right?
– jpp
Nov 21 at 17:24
1
Yeap. My bad!. Thanks
– mlRocks
Nov 21 at 17:27
But why is
sort_values not working?– mlRocks
Nov 21 at 17:07
But why is
sort_values not working?– mlRocks
Nov 21 at 17:07
Because it's designed to work row-wise, i.e. sort rows up and down for all columns, not sorting columns left and right for each row. This is expected with Pandas, where data is stored in columnar series.
– jpp
Nov 21 at 17:08
Because it's designed to work row-wise, i.e. sort rows up and down for all columns, not sorting columns left and right for each row. This is expected with Pandas, where data is stored in columnar series.
– jpp
Nov 21 at 17:08
Shouldn't this be mentioned in the docs?
– mlRocks
Nov 21 at 17:08
Shouldn't this be mentioned in the docs?
– mlRocks
Nov 21 at 17:08
1
1
@mlRocks, What do you expect for column names? The order will be different for each row, right?
– jpp
Nov 21 at 17:24
@mlRocks, What do you expect for column names? The order will be different for each row, right?
– jpp
Nov 21 at 17:24
1
1
Yeap. My bad!. Thanks
– mlRocks
Nov 21 at 17:27
Yeap. My bad!. Thanks
– mlRocks
Nov 21 at 17:27
|
show 2 more comments
up vote
1
down vote
One way is to apply sorted setting 1 as axis, applying pd.Series to return a dataframe instead of a list, and finally sorting by Category:
df.loc[:,'1':].apply(sorted, axis = 1).apply(pd.Series)
.set_index(df.Category).sort_index()
Category 0 1 2 3 4 5 6 7 8 9 10 ...
0 A 2 50 53 81 133 141 153 161 169 197 297 ...
1 B 4 65 66 68 70 71 79 92 93 106 111 ...
answered Nov 21 at 17:01
nixon
1,86316
This works, butapply(pd.Series)is a Python-level loop and will be slow for larger dataframes.
– jpp
Nov 21 at 17:11
Yes, it is not ideal for big dataframes, as you say. First solution that came across my mind :)
– nixon
Nov 21 at 17:16
add a comment |
up vote
1
down vote
One way is to apply sorted setting 1 as axis, applying pd.Series to return a dataframe instead of a list, and finally sorting by Category:
df.loc[:,'1':].apply(sorted, axis = 1).apply(pd.Series)
.set_index(df.Category).sort_index()
Category 0 1 2 3 4 5 6 7 8 9 10 ...
0 A 2 50 53 81 133 141 153 161 169 197 297 ...
1 B 4 65 66 68 70 71 79 92 93 106 111 ...
answered Nov 21 at 17:01
nixon
1,86316
This works, butapply(pd.Series)is a Python-level loop and will be slow for larger dataframes.
– jpp
Nov 21 at 17:11
Yes, it is not ideal for big dataframes, as you say. First solution that came across my mind :)
– nixon
Nov 21 at 17:16
add a comment |
up vote
1
down vote
up vote
1
down vote
One way is to apply sorted setting 1 as axis, applying pd.Series to return a dataframe instead of a list, and finally sorting by Category:
df.loc[:,'1':].apply(sorted, axis = 1).apply(pd.Series)
.set_index(df.Category).sort_index()
Category 0 1 2 3 4 5 6 7 8 9 10 ...
0 A 2 50 53 81 133 141 153 161 169 197 297 ...
1 B 4 65 66 68 70 71 79 92 93 106 111 ...
answered Nov 21 at 17:01
nixon
1,86316
One way is to apply sorted setting 1 as axis, applying pd.Series to return a dataframe instead of a list, and finally sorting by Category:
df.loc[:,'1':].apply(sorted, axis = 1).apply(pd.Series)
.set_index(df.Category).sort_index()
Category 0 1 2 3 4 5 6 7 8 9 10 ...
0 A 2 50 53 81 133 141 153 161 169 197 297 ...
1 B 4 65 66 68 70 71 79 92 93 106 111 ...
answered Nov 21 at 17:01
nixon
1,86316
edited Nov 21 at 17:11
answered Nov 21 at 17:01
nixon
1,86316
answered Nov 21 at 17:01
nixon
1,86316
answered Nov 21 at 17:01
nixon
1,86316
1,86316
This works, butapply(pd.Series)is a Python-level loop and will be slow for larger dataframes.
– jpp
Nov 21 at 17:11
Yes, it is not ideal for big dataframes, as you say. First solution that came across my mind :)
– nixon
Nov 21 at 17:16
add a comment |
This works, butapply(pd.Series)is a Python-level loop and will be slow for larger dataframes.
– jpp
Nov 21 at 17:11
Yes, it is not ideal for big dataframes, as you say. First solution that came across my mind :)
– nixon
Nov 21 at 17:16
This works, but
apply(pd.Series) is a Python-level loop and will be slow for larger dataframes.– jpp
Nov 21 at 17:11
This works, but
apply(pd.Series) is a Python-level loop and will be slow for larger dataframes.– jpp
Nov 21 at 17:11
Yes, it is not ideal for big dataframes, as you say. First solution that came across my mind :)
– nixon
Nov 21 at 17:16
Yes, it is not ideal for big dataframes, as you say. First solution that came across my mind :)
– nixon
Nov 21 at 17:16
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Edited the question.
– mlRocks
Nov 21 at 16:57
Possibly you can use
df.sort_values(['c1','c2'], ascending=False)but in your case you have multiple columns which is little tricky– pygo
Nov 21 at 17:18