CODEWITHSUNDEEP
pythonpandas
ahmedt
ahmedt

Reputation: 27

Correlation between values

I want to make correlation in this DataFrame but not the way it is shown, but to rank values ​​from the lowest to largest.

import pandas as pd
import numpy as np

rs = np.random.RandomState(1)
df = pd.DataFrame(rs.rand(9, 8))
corr = df.corr()
corr.style.background_gradient().set_precision(2)

0   1   2   3   4   5   6   7
0   1   0.42    0.031   -0.16   -0.35   0.23    -0.22   0.4
1   0.42    1   -0.24   -0.55   0.011   0.3     -0.26   0.23
2   0.031   -0.24   1   0.29    0.44    0.29    0.23    0.25
3   -0.16   -0.55   0.29    1   -0.33   -0.42   0.58    -0.37
4   -0.35   0.011   0.44    -0.33   1   0.46    0.074   0.19
5   0.23    0.3     0.29    -0.42   0.46    1   -0.41   0.71
6   -0.22   -0.26   0.23    0.58    0.074   -0.41   1   -0.66
7   0.4     0.23    0.25    -0.37   0.19    0.71    -0.66   1

Upvotes: 0

Views: 65

Answers (1)

Petras Purlys
Petras Purlys

Reputation: 1155

You can use sort_values:

import pandas as pd
import numpy as np

rs = np.random.RandomState(1)
df = pd.DataFrame(rs.rand(9, 8))

corr = df.corr()

print(corr)
print(corr.sort_values(by=0, axis=1, inplace=False)) # by=0 means first row

Results:

          0         1         2         3         4         5         6         7
0  1.000000  0.418246  0.030692 -0.160001 -0.352993  0.230069 -0.216804  0.395662
1  0.418246  1.000000 -0.244115 -0.549013  0.010745  0.299203 -0.262351  0.232681
2  0.030692 -0.244115  1.000000  0.288011  0.435907  0.285408  0.225205  0.253840
3 -0.160001 -0.549013  0.288011  1.000000 -0.326950 -0.415688  0.578549 -0.366539
4 -0.352993  0.010745  0.435907 -0.326950  1.000000  0.455738  0.074293  0.193905
5  0.230069  0.299203  0.285408 -0.415688  0.455738  1.000000 -0.413383  0.708467
6 -0.216804 -0.262351  0.225205  0.578549  0.074293 -0.413383  1.000000 -0.664207
7  0.395662  0.232681  0.253840 -0.366539  0.193905  0.708467 -0.664207  1.000000
          0         1         7         5         2         3         6         4
0  1.000000  0.418246  0.395662  0.230069  0.030692 -0.160001 -0.216804 -0.352993
1  0.418246  1.000000  0.232681  0.299203 -0.244115 -0.549013 -0.262351  0.010745
2  0.030692 -0.244115  0.253840  0.285408  1.000000  0.288011  0.225205  0.435907
3 -0.160001 -0.549013 -0.366539 -0.415688  0.288011  1.000000  0.578549 -0.326950
4 -0.352993  0.010745  0.193905  0.455738  0.435907 -0.326950  0.074293  1.000000
5  0.230069  0.299203  0.708467  1.000000  0.285408 -0.415688 -0.413383  0.455738
6 -0.216804 -0.262351 -0.664207 -0.413383  0.225205  0.578549  1.000000  0.074293
7  0.395662  0.232681  1.000000  0.708467  0.253840 -0.366539 -0.664207  0.193905

Upvotes: 1

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