在对熊猫数据框索引进行排序时，“ TypeError：'DataFrame'对象是可变的，因此无法进行散列”

Question

I have a following dataframe h :

In [24]: h.head()
Out[24]: 
                 alpha1  alpha2    gamma1  gamma2       chi2min gender  age
filename                                                                   
F35_HC_532d.dat  0.0000   0.000       NaN    0.00  1.000000e+25      F   35
M48_HC_551d.dat  0.7353   3.943  0.425922    0.15  2.072617e+01      M   48
M24_HC_458d.dat  0.7777   4.754  0.463753    0.15  1.390893e+01      M   24
M48_HC_552d.dat  0.7633   3.672  0.394370    0.15  1.965052e+01      M   48
M40_HC_506d.dat  0.7793   3.271  0.513597    0.20  1.089716e+01      M   40

I am trying to sort the dataframe index according to age values:

In [25]: h.sort_index(h.sort_values('age'))

This throws an error:

TypeError: 'DataFrame' objects are mutable, thus they cannot be hashed

What am I missing? Any ideas?

Answer 1

Is that what you want?

In [14]: h
Out[14]:
                 alpha1  alpha2    gamma1  gamma2       chi2min gender  age
filename
F35_HC_532d.dat  0.0000   0.000       NaN    0.00  1.000000e+25      F   35
M48_HC_551d.dat  0.7353   3.943  0.425922    0.15  2.072617e+01      M   48
M24_HC_458d.dat  0.7777   4.754  0.463753    0.15  1.390893e+01      M   24
M48_HC_552d.dat  0.7633   3.672  0.394370    0.15  1.965052e+01      M   48
M40_HC_506d.dat  0.7793   3.271  0.513597    0.20  1.089716e+01      M   40

In [15]: h.sort_values('age')
Out[15]:
                 alpha1  alpha2    gamma1  gamma2       chi2min gender  age
filename
M24_HC_458d.dat  0.7777   4.754  0.463753    0.15  1.390893e+01      M   24
F35_HC_532d.dat  0.0000   0.000       NaN    0.00  1.000000e+25      F   35
M40_HC_506d.dat  0.7793   3.271  0.513597    0.20  1.089716e+01      M   40
M48_HC_551d.dat  0.7353   3.943  0.425922    0.15  2.072617e+01      M   48
M48_HC_552d.dat  0.7633   3.672  0.394370    0.15  1.965052e+01      M   48

Answer 2

I think your index is filename. Maybe you could try something like:

h['index1'] = h.index
h.sort_values(by=['index1', 'age'])

But also it will not make so much sense since it will not change the order. Alternatively you can try:

h.sort_values(by='age')

Then:

h.reindex([range(some_number)])