In [2]:

    

import numpy as np
import pandas as pd


    

In [98]:

    

def hits(score,iternum = 10):
    m = score.shape[0]
    n = score.shape[1]
    score_ = (score.T.dot(np.zeros(m)+1.0))/m
    B = []
    for i in range(m):
        for j in range(n):
            B.append(score_[j]/score[i,j])
    B = np.array(B).reshape(m,n)
    p = np.zeros(m)+1
    print 'p',0,p
    for i in range(iternum):
        u = score.T.dot(p)/score.T.dot(p).sum()
        print 'u',i,u
        p = B.dot(u)/B.dot(u).sum()
        print 'p',i+1,p
    return p,u


    

In [86]:

    

score211f = pd.read_csv('./score211f.csv')


    

In [87]:

    

score211f


    

    
Out[87]:






  

    

      

      
31
      
46
      
38
      
47
      
143
      
48
      
557
      
43
      
52
      
558
      
...
      
136
      
128
      
44
      
51
      
37
      
96
      
332
      
199
      
935
      
57
    
  
  

    

      
0
      
1.241757
      
1.144449
      
1.148650
      
1.182423
      
1.090285
      
1.146862
      
1.130340
      
1.009606
      
1.184987
      
1.094615
      
...
      
1.124140
      
1.023876
      
1.061689
      
1.029915
      
1.005983
      
1.052084
      
1.085621
      
1.024177
      
1.054131
      
1.092388
    
    

      
1
      
1.304481
      
1.286889
      
1.148650
      
1.182423
      
1.196000
      
1.146862
      
1.130340
      
1.111074
      
1.184987
      
1.197963
      
...
      
1.207556
      
1.111556
      
1.167519
      
1.140296
      
1.083667
      
1.043074
      
1.086634
      
1.020815
      
1.044444
      
1.092388
    
    

      
2
      
1.359925
      
1.340367
      
1.240804
      
1.205480
      
1.276984
      
1.146862
      
1.227457
      
1.201646
      
1.184987
      
1.240499
      
...
      
1.256917
      
1.170336
      
1.231309
      
1.190352
      
1.181426
      
1.052084
      
1.085008
      
1.037105
      
1.088482
      
1.092388
    
    

      
3
      
1.216879
      
1.196724
      
1.144879
      
1.173017
      
1.160390
      
1.144113
      
1.129140
      
1.131584
      
1.188659
      
1.153808
      
...
      
1.150305
      
1.071869
      
1.125392
      
1.079413
      
1.071527
      
1.052084
      
1.070553
      
1.024177
      
1.054131
      
1.095070
    
    

      
4
      
1.248940
      
1.212397
      
1.140773
      
1.113938
      
1.151629
      
1.146862
      
1.128582
      
1.078958
      
1.188903
      
1.146488
      
...
      
1.148456
      
1.065846
      
1.119248
      
1.081084
      
1.056290
      
1.052084
      
1.098584
      
1.024177
      
1.028224
      
1.092388
    
    

      
5
      
1.220869
      
1.191011
      
1.114986
      
1.164019
      
1.135872
      
1.128401
      
1.106699
      
1.086049
      
1.184987
      
1.117932
      
...
      
1.141423
      
1.085311
      
1.111381
      
1.102095
      
1.074000
      
1.049640
      
1.062819
      
1.030015
      
1.055217
      
1.092388
    
    

      
6
      
1.239862
      
1.218716
      
1.141184
      
1.185641
      
1.160731
      
1.145862
      
1.130154
      
1.112588
      
1.192846
      
1.141123
      
...
      
1.146745
      
1.092402
      
1.131068
      
1.101665
      
1.068238
      
1.052084
      
1.094770
      
1.024177
      
1.028688
      
1.094444
    
    

      
7
      
1.351962
      
1.323274
      
1.148650
      
1.182423
      
1.213403
      
1.146862
      
1.130340
      
1.106336
      
1.184987
      
1.186049
      
...
      
1.174473
      
1.127638
      
1.183503
      
1.135253
      
1.120234
      
1.052084
      
1.147377
      
0.998577
      
1.031946
      
1.092388
    
    

      
8
      
1.185794
      
1.163258
      
1.148650
      
1.152243
      
1.131966
      
1.122056
      
1.107959
      
1.080855
      
1.138603
      
1.124379
      
...
      
1.137639
      
1.095853
      
1.121929
      
1.097972
      
1.084141
      
1.058002
      
1.063761
      
1.017638
      
1.057101
      
1.092388
    
    

      
9
      
1.211694
      
1.186025
      
1.125742
      
1.182423
      
1.150653
      
1.127625
      
1.062914
      
1.106320
      
1.184987
      
1.117839
      
...
      
1.141738
      
1.094820
      
1.122240
      
1.093968
      
1.094773
      
1.062923
      
1.113542
      
1.027505
      
1.062090
      
1.095813
    
    

      
10
      
1.274686
      
1.250568
      
1.148650
      
1.182423
      
1.178092
      
1.146862
      
1.130340
      
1.106336
      
1.184987
      
1.161210
      
...
      
1.174473
      
1.115240
      
1.153700
      
1.118314
      
1.096524
      
1.052084
      
1.085621
      
1.024177
      
1.054131
      
1.091349
    
    

      
11
      
1.174709
      
1.141653
      
1.076492
      
1.113784
      
1.088618
      
1.146862
      
1.062519
      
1.046939
      
1.127752
      
1.084291
      
...
      
1.103428
      
1.055586
      
1.075602
      
1.053841
      
1.032147
      
1.015622
      
1.019955
      
1.024177
      
1.013010
      
1.092388
    
    

      
12
      
1.297583
      
1.270283
      
1.148650
      
1.182423
      
1.182351
      
1.146862
      
1.118730
      
1.105149
      
1.184987
      
1.169064
      
...
      
1.184198
      
1.129009
      
1.170303
      
1.117983
      
1.079501
      
1.052496
      
1.076120
      
0.998113
      
1.039544
      
1.092388
    
    

      
13
      
1.361139
      
1.319461
      
1.247315
      
1.171299
      
1.221276
      
1.146862
      
1.207681
      
1.175063
      
1.184987
      
1.260874
      
...
      
1.280117
      
1.169352
      
1.222361
      
1.149329
      
1.146653
      
1.052084
      
1.121976
      
1.016724
      
1.070010
      
1.092388
    
    

      
14
      
1.185129
      
1.116619
      
1.080171
      
1.099084
      
1.088892
      
1.059670
      
1.048025
      
1.039346
      
1.103495
      
1.090380
      
...
      
1.109326
      
1.033490
      
1.080180
      
1.045160
      
1.027701
      
1.052084
      
1.020400
      
1.012872
      
1.007276
      
1.033456
    
    

      
15
      
1.219676
      
1.193392
      
1.100884
      
1.182423
      
1.122327
      
1.107302
      
1.130340
      
1.071970
      
1.138300
      
1.109491
      
...
      
1.128072
      
1.079335
      
1.112163
      
1.075568
      
1.062626
      
1.022412
      
1.053220
      
0.985859
      
1.025189
      
1.092388
    
    

      
16
      
1.249202
      
1.223580
      
1.157598
      
1.196168
      
1.179657
      
1.156699
      
1.142926
      
1.119110
      
1.190579
      
1.157545
      
...
      
1.154808
      
1.105417
      
1.145677
      
1.109087
      
1.109019
      
1.059582
      
1.081553
      
1.021066
      
1.068706
      
1.110985
    
    

      
17
      
1.243259
      
1.213517
      
1.134064
      
1.182423
      
1.158669
      
1.139588
      
1.117672
      
1.103494
      
1.184987
      
1.130313
      
...
      
1.147533
      
1.108073
      
1.138849
      
1.103141
      
1.102520
      
1.052084
      
1.078537
      
1.022920
      
1.062174
      
1.097175
    
    

      
18
      
1.274686
      
1.250568
      
1.148650
      
1.182423
      
1.178092
      
1.146862
      
1.130340
      
1.106336
      
1.184987
      
1.161210
      
...
      
1.174473
      
1.115240
      
1.153700
      
1.118314
      
1.096524
      
1.052084
      
1.085621
      
1.024177
      
1.054131
      
1.058234
    
    

      
19
      
1.292825
      
1.331491
      
1.148650
      
1.262594
      
1.213788
      
1.146862
      
1.130340
      
1.130794
      
1.274293
      
1.219160
      
...
      
1.221744
      
1.140193
      
1.188143
      
1.118314
      
1.097479
      
1.052084
      
1.085621
      
1.024177
      
1.065153
      
1.092388
    
    

      
20
      
1.274686
      
1.250568
      
1.148650
      
1.182423
      
1.178092
      
1.146862
      
1.130340
      
1.106336
      
1.184987
      
1.161210
      
...
      
1.174473
      
1.115240
      
1.153700
      
1.118314
      
1.096524
      
1.052084
      
1.085621
      
1.024177
      
1.054131
      
1.092388
    
    

      
21
      
1.267455
      
1.216930
      
1.131401
      
1.182423
      
1.156767
      
1.142144
      
1.102465
      
1.096272
      
1.165404
      
1.138077
      
...
      
1.146859
      
1.113940
      
1.140142
      
1.109121
      
1.091432
      
1.076892
      
1.081773
      
1.015541
      
1.072052
      
1.092388
    
    

      
22
      
1.253058
      
1.210554
      
1.130219
      
1.183339
      
1.149311
      
1.139869
      
1.113381
      
1.099106
      
1.170854
      
1.139581
      
...
      
1.152050
      
1.107709
      
1.149103
      
1.102118
      
1.088714
      
1.062386
      
1.075582
      
1.012277
      
1.067945
      
1.092388
    
    

      
23
      
1.294745
      
1.268745
      
1.153303
      
1.182423
      
1.184086
      
1.146862
      
1.141955
      
1.088757
      
1.184987
      
1.160955
      
...
      
1.171310
      
1.106823
      
1.159876
      
1.122185
      
1.115895
      
1.052084
      
1.084974
      
1.037836
      
1.052316
      
1.092388
    
    

      
24
      
1.235712
      
1.210747
      
1.147109
      
1.187603
      
1.165907
      
1.146862
      
1.127073
      
1.101192
      
1.184987
      
1.158080
      
...
      
1.170461
      
1.124710
      
1.153651
      
1.132246
      
1.100114
      
1.052084
      
1.101109
      
1.023726
      
1.086162
      
1.092388
    
    

      
25
      
1.274686
      
1.493290
      
1.148650
      
1.182423
      
1.289610
      
1.146862
      
1.130340
      
1.106336
      
1.184987
      
1.256012
      
...
      
1.270197
      
1.336364
      
1.225605
      
1.264556
      
1.171570
      
1.052084
      
1.085621
      
1.024177
      
1.054131
      
1.092388
    
    

      
26
      
1.347078
      
1.308984
      
1.148650
      
1.182423
      
1.208317
      
1.146862
      
1.130340
      
1.117784
      
1.237398
      
1.172070
      
...
      
1.212549
      
1.124944
      
1.198932
      
1.152361
      
1.104710
      
1.054096
      
1.092929
      
1.005324
      
1.022264
      
1.092388
    
    

      
27
      
1.288491
      
1.254340
      
1.188472
      
1.233189
      
1.205585
      
1.194925
      
1.173264
      
1.143434
      
1.236623
      
1.189358
      
...
      
1.206302
      
1.127340
      
1.174925
      
1.133623
      
1.118057
      
1.052084
      
1.097962
      
1.025321
      
1.061472
      
1.092388
    
    

      
28
      
1.203488
      
1.172046
      
1.102189
      
1.096021
      
1.128376
      
1.146862
      
1.107420
      
1.084719
      
1.149341
      
1.124899
      
...
      
1.133625
      
1.079472
      
1.108297
      
1.092570
      
1.069866
      
1.044536
      
1.064623
      
1.016587
      
1.052396
      
1.092388
    
    

      
29
      
1.353002
      
1.323468
      
1.194301
      
1.278615
      
1.235355
      
1.206924
      
1.194179
      
1.132414
      
1.271752
      
1.214828
      
...
      
1.225429
      
1.137132
      
1.197181
      
1.149326
      
1.145343
      
1.075429
      
1.105086
      
1.036152
      
1.077635
      
1.154963
    
    

      
30
      
1.476851
      
1.415569
      
1.148650
      
1.182423
      
1.288171
      
1.146862
      
1.130340
      
1.106336
      
1.184987
      
1.170461
      
...
      
1.162377
      
1.143085
      
1.216722
      
1.165950
      
1.130794
      
1.052084
      
1.098605
      
1.024177
      
1.054131
      
1.092388
    
    

      
31
      
1.365650
      
1.318709
      
1.221118
      
1.285150
      
1.229701
      
1.240887
      
1.186950
      
1.190514
      
1.184987
      
1.208947
      
...
      
1.249953
      
1.180488
      
1.224299
      
1.182609
      
1.164784
      
1.052084
      
1.148698
      
1.145753
      
1.113778
      
1.092388
    
  

32 rows × 100 columns

In [88]:

    

score = np.array(score211f)


    

In [89]:

    

m = score.shape[0]
n = score.shape[1]
m,n


    

    
Out[89]:





(32L, 100L)

In [90]:

    

score_ = (score.T.dot(np.zeros(m)+1.0))/m
score_


    

    
Out[90]:





array([ 1.27468625,  1.25056846,  1.14865037,  1.18242267,  1.17809234,
        1.14686175,  1.13034012,  1.10633602,  1.1849869 ,  1.16120967,
        1.2080692 ,  1.18429913,  1.20842357,  0.99877973,  1.10004221,
        1.12602067,  1.14580553,  1.10584764,  1.07404075,  1.08762574,
        1.29115331,  1.10100887,  1.18649738,  1.17323036,  1.08676695,
        1.15797287,  1.08995158,  1.03781734,  1.25829489,  1.11364391,
        1.21189507,  1.13008786,  1.12941091,  1.12592926,  1.10727913,
        1.09086752,  1.10861039,  1.14274587,  1.06790351,  1.07512207,
        1.12538236,  1.20680894,  1.16727719,  1.16863127,  1.08108097,
        1.07847516,  1.11450416,  1.08025997,  1.20148458,  1.09974944,
        1.12394746,  1.10840386,  1.05203267,  1.25722602,  1.11536247,
        1.09806779,  1.08946523,  1.08359584,  1.08565334,  1.19871246,
        1.15111275,  1.15229438,  1.08003738,  1.14576862,  1.10301836,
        1.15185158,  1.12384341,  1.06014727,  1.09134423,  1.07715561,
        1.04579421,  1.17128888,  1.0956705 ,  1.11190067,  1.037872  ,
        0.97627111,  1.21458417,  1.09491708,  1.23519944,  1.22395194,
        1.16438074,  1.16739737,  1.20750546,  1.0499905 ,  1.1698981 ,
        1.17426031,  1.07832606,  1.03851382,  1.09924592,  1.10599723,
        1.1744734 ,  1.11524025,  1.15369957,  1.11831372,  1.09652437,
        1.05208379,  1.08562111,  1.02417743,  1.05413093,  1.09238767])

In [91]:

    

B = []
for i in range(m):
    for j in range(n):
        B.append(score_[j]/score[i,j])
B = np.array(B).reshape(m,n)
B


    

    
Out[91]:





array([[ 1.02651795,  1.09272547,  1.        , ...,  1.        ,
         1.        ,  1.        ],
       [ 0.97715933,  0.97177656,  1.        , ...,  1.00329405,
         1.0092743 ,  1.        ],
       [ 0.93732109,  0.93300466,  0.9257307 , ...,  0.98753512,
         0.9684417 ,  1.        ],
       ..., 
       [ 0.94211673,  0.94491769,  0.9617759 , ...,  0.98844327,
         0.97818939,  0.9458203 ],
       [ 0.86311102,  0.88343892,  1.        , ...,  1.        ,
         1.        ,  1.        ],
       [ 0.93339141,  0.94832796,  0.94065468, ...,  0.89389019,
         0.94644658,  1.        ]])

In [92]:

    

p = np.zeros(m)+1
p


    

    
Out[92]:





array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,
        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,
        1.,  1.,  1.,  1.,  1.,  1.])

In [93]:

    

u = score.T.dot(p)/score.T.dot(p).sum()
u,u.sum()


    

    
Out[93]:





(array([ 0.01130125,  0.01108742,  0.01018383,  0.01048325,  0.01044486,
         0.01016797,  0.01002149,  0.00980867,  0.01050598,  0.01029518,
         0.01071063,  0.01049989,  0.01071377,  0.00885509,  0.00975287,
         0.0099832 ,  0.01015861,  0.00980434,  0.00952235,  0.00964279,
         0.01144725,  0.00976144,  0.01051938,  0.01040175,  0.00963518,
         0.01026648,  0.00966341,  0.00920119,  0.01115593,  0.00987346,
         0.01074455,  0.01001925,  0.01001325,  0.00998238,  0.00981703,
         0.00967153,  0.00982884,  0.01013148,  0.00946793,  0.00953193,
         0.00997754,  0.01069946,  0.01034897,  0.01036098,  0.00958476,
         0.00956166,  0.00988109,  0.00957749,  0.01065225,  0.00975028,
         0.00996481,  0.00982701,  0.00932722,  0.01114645,  0.0098887 ,
         0.00973537,  0.0096591 ,  0.00960706,  0.0096253 ,  0.01062767,
         0.01020566,  0.01021614,  0.00957551,  0.01015828,  0.00977926,
         0.01021221,  0.00996389,  0.00939917,  0.00967576,  0.00954996,
         0.00927191,  0.01038454,  0.00971411,  0.00985801,  0.00920168,
         0.00865553,  0.01076839,  0.00970743,  0.01095116,  0.01085144,
         0.01032329,  0.01035004,  0.01070563,  0.00930912,  0.01037221,
         0.01041088,  0.00956034,  0.00920737,  0.00974581,  0.00980567,
         0.01041277,  0.00988762,  0.01022859,  0.00991487,  0.00972168,
         0.00932768,  0.00962502,  0.00908026,  0.00934583,  0.00968501]),
 0.99999999999999978)

In [94]:

    

B.dot(u)


    

    
Out[94]:





array([ 1.04147202,  0.99308496,  0.95790495,  1.0170451 ,  1.01878089,
        1.01854763,  1.01151131,  0.98645682,  1.02135205,  1.01468458,
        1.00196658,  1.04498135,  0.99658051,  0.96887038,  1.0594361 ,
        1.02827965,  0.99810438,  1.00807809,  1.00363828,  0.98103997,
        0.99998962,  1.00557101,  1.00776109,  1.00048144,  0.99863275,
        0.97568339,  0.98272309,  0.97980498,  1.02367888,  0.96586226,
        0.98031055,  0.95058992])

In [95]:

    

p = B.dot(u)/B.dot(u).sum()
p,p.sum()


    

    
Out[95]:





(array([ 0.03250242,  0.03099235,  0.02989445,  0.0317401 ,  0.03179427,
         0.03178699,  0.0315674 ,  0.0307855 ,  0.03187452,  0.03166644,
         0.03126953,  0.03261194,  0.03110144,  0.03023666,  0.03306305,
         0.03209071,  0.031149  ,  0.03146026,  0.0313217 ,  0.03061645,
         0.03120783,  0.03138202,  0.03145037,  0.03122318,  0.03116549,
         0.03044928,  0.03066898,  0.03057791,  0.03194713,  0.03014278,
         0.03059369,  0.02966616]), 1.0)

In [99]:

    

p,u = hits(score,iternum=10)


    

    




p 0 [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
u 0 [ 0.01130125  0.01108742  0.01018383  0.01048325  0.01044486  0.01016797
  0.01002149  0.00980867  0.01050598  0.01029518  0.01071063  0.01049989
  0.01071377  0.00885509  0.00975287  0.0099832   0.01015861  0.00980434
  0.00952235  0.00964279  0.01144725  0.00976144  0.01051938  0.01040175
  0.00963518  0.01026648  0.00966341  0.00920119  0.01115593  0.00987346
  0.01074455  0.01001925  0.01001325  0.00998238  0.00981703  0.00967153
  0.00982884  0.01013148  0.00946793  0.00953193  0.00997754  0.01069946
  0.01034897  0.01036098  0.00958476  0.00956166  0.00988109  0.00957749
  0.01065225  0.00975028  0.00996481  0.00982701  0.00932722  0.01114645
  0.0098887   0.00973537  0.0096591   0.00960706  0.0096253   0.01062767
  0.01020566  0.01021614  0.00957551  0.01015828  0.00977926  0.01021221
  0.00996389  0.00939917  0.00967576  0.00954996  0.00927191  0.01038454
  0.00971411  0.00985801  0.00920168  0.00865553  0.01076839  0.00970743
  0.01095116  0.01085144  0.01032329  0.01035004  0.01070563  0.00930912
  0.01037221  0.01041088  0.00956034  0.00920737  0.00974581  0.00980567
  0.01041277  0.00988762  0.01022859  0.00991487  0.00972168  0.00932768
  0.00962502  0.00908026  0.00934583  0.00968501]
p 1 [ 0.03250242  0.03099235  0.02989445  0.0317401   0.03179427  0.03178699
  0.0315674   0.0307855   0.03187452  0.03166644  0.03126953  0.03261194
  0.03110144  0.03023666  0.03306305  0.03209071  0.031149    0.03146026
  0.0313217   0.03061645  0.03120783  0.03138202  0.03145037  0.03122318
  0.03116549  0.03044928  0.03066898  0.03057791  0.03194713  0.03014278
  0.03059369  0.02966616]
u 1 [ 0.01129668  0.01107966  0.01018324  0.01048292  0.01044101  0.01016985
  0.01002112  0.00980742  0.01050756  0.0102926   0.01070589  0.01049588
  0.01070842  0.00886203  0.00975552  0.00998493  0.01015361  0.00981016
  0.00952364  0.00964244  0.0114415   0.00976241  0.01051719  0.01040149
  0.0096351   0.0102687   0.00966781  0.00920327  0.01114977  0.00987825
  0.0107424   0.01001943  0.01001241  0.00998594  0.00981582  0.00967381
  0.00983468  0.01013571  0.00946931  0.00953504  0.00998108  0.01069887
  0.01034788  0.01036345  0.0095854   0.00956333  0.00988031  0.00957838
  0.01064593  0.00975081  0.00996321  0.00982836  0.0093284   0.01114006
  0.00989289  0.00973573  0.00966374  0.00960631  0.00962443  0.01062395
  0.01019753  0.01021454  0.00957491  0.01015646  0.00978134  0.01021044
  0.00996859  0.00940452  0.00967394  0.00955212  0.00927787  0.0103835
  0.00971564  0.00985683  0.00920713  0.00866301  0.01076185  0.00971245
  0.01094547  0.0108495   0.01031881  0.01035075  0.01069983  0.00931286
  0.01036753  0.01040652  0.0095603   0.00921086  0.00974906  0.00980527
  0.01041024  0.00988486  0.01022555  0.00991263  0.00971978  0.00933247
  0.00962663  0.00908352  0.00934843  0.00968929]
p 2 [ 0.03250211  0.03099247  0.02989476  0.03173998  0.03179425  0.03178686
  0.03156733  0.03078566  0.03187432  0.03166627  0.03126952  0.03261165
  0.03110149  0.03023705  0.0330627   0.03209058  0.03114891  0.03146016
  0.03132168  0.03061659  0.03120784  0.03138194  0.03145029  0.03122329
  0.03116541  0.03044959  0.03066915  0.03057793  0.03194696  0.03014291
  0.03059402  0.02966632]
u 2 [ 0.01129669  0.01107966  0.01018324  0.01048292  0.01044101  0.01016985
  0.01002112  0.00980742  0.01050756  0.0102926   0.01070589  0.01049588
  0.01070842  0.00886203  0.00975552  0.00998493  0.01015361  0.00981015
  0.00952364  0.00964244  0.0114415   0.00976241  0.01051719  0.01040149
  0.0096351   0.01026869  0.00966781  0.00920327  0.01114977  0.00987825
  0.0107424   0.01001943  0.01001241  0.00998594  0.00981583  0.00967381
  0.00983468  0.01013571  0.00946931  0.00953504  0.00998108  0.01069887
  0.01034788  0.01036345  0.0095854   0.00956333  0.00988031  0.00957838
  0.01064593  0.00975081  0.00996321  0.00982836  0.0093284   0.01114006
  0.00989289  0.00973573  0.00966373  0.00960631  0.00962443  0.01062395
  0.01019753  0.01021454  0.00957491  0.01015646  0.00978134  0.01021044
  0.00996859  0.00940452  0.00967394  0.00955211  0.00927787  0.0103835
  0.00971564  0.00985683  0.00920713  0.00866301  0.01076185  0.00971245
  0.01094547  0.0108495   0.01031881  0.01035075  0.01069983  0.00931285
  0.01036753  0.01040652  0.0095603   0.00921086  0.00974906  0.00980527
  0.01041024  0.00988486  0.01022555  0.00991263  0.00971978  0.00933247
  0.00962663  0.00908351  0.00934843  0.00968928]
p 3 [ 0.03250211  0.03099247  0.02989476  0.03173998  0.03179425  0.03178686
  0.03156733  0.03078566  0.03187432  0.03166627  0.03126952  0.03261165
  0.03110149  0.03023705  0.0330627   0.03209058  0.03114891  0.03146016
  0.03132168  0.03061659  0.03120784  0.03138194  0.03145029  0.03122329
  0.03116541  0.03044959  0.03066915  0.03057793  0.03194696  0.03014291
  0.03059402  0.02966632]
u 3 [ 0.01129669  0.01107966  0.01018324  0.01048292  0.01044101  0.01016985
  0.01002112  0.00980742  0.01050756  0.0102926   0.01070589  0.01049588
  0.01070842  0.00886203  0.00975552  0.00998493  0.01015361  0.00981015
  0.00952364  0.00964244  0.0114415   0.00976241  0.01051719  0.01040149
  0.0096351   0.01026869  0.00966781  0.00920327  0.01114977  0.00987825
  0.0107424   0.01001943  0.01001241  0.00998594  0.00981583  0.00967381
  0.00983468  0.01013571  0.00946931  0.00953504  0.00998108  0.01069887
  0.01034788  0.01036345  0.0095854   0.00956333  0.00988031  0.00957838
  0.01064593  0.00975081  0.00996321  0.00982836  0.0093284   0.01114006
  0.00989289  0.00973573  0.00966373  0.00960631  0.00962443  0.01062395
  0.01019753  0.01021454  0.00957491  0.01015646  0.00978134  0.01021044
  0.00996859  0.00940452  0.00967394  0.00955211  0.00927787  0.0103835
  0.00971564  0.00985683  0.00920713  0.00866301  0.01076185  0.00971245
  0.01094547  0.0108495   0.01031881  0.01035075  0.01069983  0.00931285
  0.01036753  0.01040652  0.0095603   0.00921086  0.00974906  0.00980527
  0.01041024  0.00988486  0.01022555  0.00991263  0.00971978  0.00933247
  0.00962663  0.00908351  0.00934843  0.00968928]
p 4 [ 0.03250211  0.03099247  0.02989476  0.03173998  0.03179425  0.03178686
  0.03156733  0.03078566  0.03187432  0.03166627  0.03126952  0.03261165
  0.03110149  0.03023705  0.0330627   0.03209058  0.03114891  0.03146016
  0.03132168  0.03061659  0.03120784  0.03138194  0.03145029  0.03122329
  0.03116541  0.03044959  0.03066915  0.03057793  0.03194696  0.03014291
  0.03059402  0.02966632]
u 4 [ 0.01129669  0.01107966  0.01018324  0.01048292  0.01044101  0.01016985
  0.01002112  0.00980742  0.01050756  0.0102926   0.01070589  0.01049588
  0.01070842  0.00886203  0.00975552  0.00998493  0.01015361  0.00981015
  0.00952364  0.00964244  0.0114415   0.00976241  0.01051719  0.01040149
  0.0096351   0.01026869  0.00966781  0.00920327  0.01114977  0.00987825
  0.0107424   0.01001943  0.01001241  0.00998594  0.00981583  0.00967381
  0.00983468  0.01013571  0.00946931  0.00953504  0.00998108  0.01069887
  0.01034788  0.01036345  0.0095854   0.00956333  0.00988031  0.00957838
  0.01064593  0.00975081  0.00996321  0.00982836  0.0093284   0.01114006
  0.00989289  0.00973573  0.00966373  0.00960631  0.00962443  0.01062395
  0.01019753  0.01021454  0.00957491  0.01015646  0.00978134  0.01021044
  0.00996859  0.00940452  0.00967394  0.00955211  0.00927787  0.0103835
  0.00971564  0.00985683  0.00920713  0.00866301  0.01076185  0.00971245
  0.01094547  0.0108495   0.01031881  0.01035075  0.01069983  0.00931285
  0.01036753  0.01040652  0.0095603   0.00921086  0.00974906  0.00980527
  0.01041024  0.00988486  0.01022555  0.00991263  0.00971978  0.00933247
  0.00962663  0.00908351  0.00934843  0.00968928]
p 5 [ 0.03250211  0.03099247  0.02989476  0.03173998  0.03179425  0.03178686
  0.03156733  0.03078566  0.03187432  0.03166627  0.03126952  0.03261165
  0.03110149  0.03023705  0.0330627   0.03209058  0.03114891  0.03146016
  0.03132168  0.03061659  0.03120784  0.03138194  0.03145029  0.03122329
  0.03116541  0.03044959  0.03066915  0.03057793  0.03194696  0.03014291
  0.03059402  0.02966632]
u 5 [ 0.01129669  0.01107966  0.01018324  0.01048292  0.01044101  0.01016985
  0.01002112  0.00980742  0.01050756  0.0102926   0.01070589  0.01049588
  0.01070842  0.00886203  0.00975552  0.00998493  0.01015361  0.00981015
  0.00952364  0.00964244  0.0114415   0.00976241  0.01051719  0.01040149
  0.0096351   0.01026869  0.00966781  0.00920327  0.01114977  0.00987825
  0.0107424   0.01001943  0.01001241  0.00998594  0.00981583  0.00967381
  0.00983468  0.01013571  0.00946931  0.00953504  0.00998108  0.01069887
  0.01034788  0.01036345  0.0095854   0.00956333  0.00988031  0.00957838
  0.01064593  0.00975081  0.00996321  0.00982836  0.0093284   0.01114006
  0.00989289  0.00973573  0.00966373  0.00960631  0.00962443  0.01062395
  0.01019753  0.01021454  0.00957491  0.01015646  0.00978134  0.01021044
  0.00996859  0.00940452  0.00967394  0.00955211  0.00927787  0.0103835
  0.00971564  0.00985683  0.00920713  0.00866301  0.01076185  0.00971245
  0.01094547  0.0108495   0.01031881  0.01035075  0.01069983  0.00931285
  0.01036753  0.01040652  0.0095603   0.00921086  0.00974906  0.00980527
  0.01041024  0.00988486  0.01022555  0.00991263  0.00971978  0.00933247
  0.00962663  0.00908351  0.00934843  0.00968928]
p 6 [ 0.03250211  0.03099247  0.02989476  0.03173998  0.03179425  0.03178686
  0.03156733  0.03078566  0.03187432  0.03166627  0.03126952  0.03261165
  0.03110149  0.03023705  0.0330627   0.03209058  0.03114891  0.03146016
  0.03132168  0.03061659  0.03120784  0.03138194  0.03145029  0.03122329
  0.03116541  0.03044959  0.03066915  0.03057793  0.03194696  0.03014291
  0.03059402  0.02966632]
u 6 [ 0.01129669  0.01107966  0.01018324  0.01048292  0.01044101  0.01016985
  0.01002112  0.00980742  0.01050756  0.0102926   0.01070589  0.01049588
  0.01070842  0.00886203  0.00975552  0.00998493  0.01015361  0.00981015
  0.00952364  0.00964244  0.0114415   0.00976241  0.01051719  0.01040149
  0.0096351   0.01026869  0.00966781  0.00920327  0.01114977  0.00987825
  0.0107424   0.01001943  0.01001241  0.00998594  0.00981583  0.00967381
  0.00983468  0.01013571  0.00946931  0.00953504  0.00998108  0.01069887
  0.01034788  0.01036345  0.0095854   0.00956333  0.00988031  0.00957838
  0.01064593  0.00975081  0.00996321  0.00982836  0.0093284   0.01114006
  0.00989289  0.00973573  0.00966373  0.00960631  0.00962443  0.01062395
  0.01019753  0.01021454  0.00957491  0.01015646  0.00978134  0.01021044
  0.00996859  0.00940452  0.00967394  0.00955211  0.00927787  0.0103835
  0.00971564  0.00985683  0.00920713  0.00866301  0.01076185  0.00971245
  0.01094547  0.0108495   0.01031881  0.01035075  0.01069983  0.00931285
  0.01036753  0.01040652  0.0095603   0.00921086  0.00974906  0.00980527
  0.01041024  0.00988486  0.01022555  0.00991263  0.00971978  0.00933247
  0.00962663  0.00908351  0.00934843  0.00968928]
p 7 [ 0.03250211  0.03099247  0.02989476  0.03173998  0.03179425  0.03178686
  0.03156733  0.03078566  0.03187432  0.03166627  0.03126952  0.03261165
  0.03110149  0.03023705  0.0330627   0.03209058  0.03114891  0.03146016
  0.03132168  0.03061659  0.03120784  0.03138194  0.03145029  0.03122329
  0.03116541  0.03044959  0.03066915  0.03057793  0.03194696  0.03014291
  0.03059402  0.02966632]
u 7 [ 0.01129669  0.01107966  0.01018324  0.01048292  0.01044101  0.01016985
  0.01002112  0.00980742  0.01050756  0.0102926   0.01070589  0.01049588
  0.01070842  0.00886203  0.00975552  0.00998493  0.01015361  0.00981015
  0.00952364  0.00964244  0.0114415   0.00976241  0.01051719  0.01040149
  0.0096351   0.01026869  0.00966781  0.00920327  0.01114977  0.00987825
  0.0107424   0.01001943  0.01001241  0.00998594  0.00981583  0.00967381
  0.00983468  0.01013571  0.00946931  0.00953504  0.00998108  0.01069887
  0.01034788  0.01036345  0.0095854   0.00956333  0.00988031  0.00957838
  0.01064593  0.00975081  0.00996321  0.00982836  0.0093284   0.01114006
  0.00989289  0.00973573  0.00966373  0.00960631  0.00962443  0.01062395
  0.01019753  0.01021454  0.00957491  0.01015646  0.00978134  0.01021044
  0.00996859  0.00940452  0.00967394  0.00955211  0.00927787  0.0103835
  0.00971564  0.00985683  0.00920713  0.00866301  0.01076185  0.00971245
  0.01094547  0.0108495   0.01031881  0.01035075  0.01069983  0.00931285
  0.01036753  0.01040652  0.0095603   0.00921086  0.00974906  0.00980527
  0.01041024  0.00988486  0.01022555  0.00991263  0.00971978  0.00933247
  0.00962663  0.00908351  0.00934843  0.00968928]
p 8 [ 0.03250211  0.03099247  0.02989476  0.03173998  0.03179425  0.03178686
  0.03156733  0.03078566  0.03187432  0.03166627  0.03126952  0.03261165
  0.03110149  0.03023705  0.0330627   0.03209058  0.03114891  0.03146016
  0.03132168  0.03061659  0.03120784  0.03138194  0.03145029  0.03122329
  0.03116541  0.03044959  0.03066915  0.03057793  0.03194696  0.03014291
  0.03059402  0.02966632]
u 8 [ 0.01129669  0.01107966  0.01018324  0.01048292  0.01044101  0.01016985
  0.01002112  0.00980742  0.01050756  0.0102926   0.01070589  0.01049588
  0.01070842  0.00886203  0.00975552  0.00998493  0.01015361  0.00981015
  0.00952364  0.00964244  0.0114415   0.00976241  0.01051719  0.01040149
  0.0096351   0.01026869  0.00966781  0.00920327  0.01114977  0.00987825
  0.0107424   0.01001943  0.01001241  0.00998594  0.00981583  0.00967381
  0.00983468  0.01013571  0.00946931  0.00953504  0.00998108  0.01069887
  0.01034788  0.01036345  0.0095854   0.00956333  0.00988031  0.00957838
  0.01064593  0.00975081  0.00996321  0.00982836  0.0093284   0.01114006
  0.00989289  0.00973573  0.00966373  0.00960631  0.00962443  0.01062395
  0.01019753  0.01021454  0.00957491  0.01015646  0.00978134  0.01021044
  0.00996859  0.00940452  0.00967394  0.00955211  0.00927787  0.0103835
  0.00971564  0.00985683  0.00920713  0.00866301  0.01076185  0.00971245
  0.01094547  0.0108495   0.01031881  0.01035075  0.01069983  0.00931285
  0.01036753  0.01040652  0.0095603   0.00921086  0.00974906  0.00980527
  0.01041024  0.00988486  0.01022555  0.00991263  0.00971978  0.00933247
  0.00962663  0.00908351  0.00934843  0.00968928]
p 9 [ 0.03250211  0.03099247  0.02989476  0.03173998  0.03179425  0.03178686
  0.03156733  0.03078566  0.03187432  0.03166627  0.03126952  0.03261165
  0.03110149  0.03023705  0.0330627   0.03209058  0.03114891  0.03146016
  0.03132168  0.03061659  0.03120784  0.03138194  0.03145029  0.03122329
  0.03116541  0.03044959  0.03066915  0.03057793  0.03194696  0.03014291
  0.03059402  0.02966632]
u 9 [ 0.01129669  0.01107966  0.01018324  0.01048292  0.01044101  0.01016985
  0.01002112  0.00980742  0.01050756  0.0102926   0.01070589  0.01049588
  0.01070842  0.00886203  0.00975552  0.00998493  0.01015361  0.00981015
  0.00952364  0.00964244  0.0114415   0.00976241  0.01051719  0.01040149
  0.0096351   0.01026869  0.00966781  0.00920327  0.01114977  0.00987825
  0.0107424   0.01001943  0.01001241  0.00998594  0.00981583  0.00967381
  0.00983468  0.01013571  0.00946931  0.00953504  0.00998108  0.01069887
  0.01034788  0.01036345  0.0095854   0.00956333  0.00988031  0.00957838
  0.01064593  0.00975081  0.00996321  0.00982836  0.0093284   0.01114006
  0.00989289  0.00973573  0.00966373  0.00960631  0.00962443  0.01062395
  0.01019753  0.01021454  0.00957491  0.01015646  0.00978134  0.01021044
  0.00996859  0.00940452  0.00967394  0.00955211  0.00927787  0.0103835
  0.00971564  0.00985683  0.00920713  0.00866301  0.01076185  0.00971245
  0.01094547  0.0108495   0.01031881  0.01035075  0.01069983  0.00931285
  0.01036753  0.01040652  0.0095603   0.00921086  0.00974906  0.00980527
  0.01041024  0.00988486  0.01022555  0.00991263  0.00971978  0.00933247
  0.00962663  0.00908351  0.00934843  0.00968928]
p 10 [ 0.03250211  0.03099247  0.02989476  0.03173998  0.03179425  0.03178686
  0.03156733  0.03078566  0.03187432  0.03166627  0.03126952  0.03261165
  0.03110149  0.03023705  0.0330627   0.03209058  0.03114891  0.03146016
  0.03132168  0.03061659  0.03120784  0.03138194  0.03145029  0.03122329
  0.03116541  0.03044959  0.03066915  0.03057793  0.03194696  0.03014291
  0.03059402  0.02966632]

In [100]:

    

p


    

    
Out[100]:





array([ 0.03250211,  0.03099247,  0.02989476,  0.03173998,  0.03179425,
        0.03178686,  0.03156733,  0.03078566,  0.03187432,  0.03166627,
        0.03126952,  0.03261165,  0.03110149,  0.03023705,  0.0330627 ,
        0.03209058,  0.03114891,  0.03146016,  0.03132168,  0.03061659,
        0.03120784,  0.03138194,  0.03145029,  0.03122329,  0.03116541,
        0.03044959,  0.03066915,  0.03057793,  0.03194696,  0.03014291,
        0.03059402,  0.02966632])

In [101]:

    

u


    

    
Out[101]:





array([ 0.01129669,  0.01107966,  0.01018324,  0.01048292,  0.01044101,
        0.01016985,  0.01002112,  0.00980742,  0.01050756,  0.0102926 ,
        0.01070589,  0.01049588,  0.01070842,  0.00886203,  0.00975552,
        0.00998493,  0.01015361,  0.00981015,  0.00952364,  0.00964244,
        0.0114415 ,  0.00976241,  0.01051719,  0.01040149,  0.0096351 ,
        0.01026869,  0.00966781,  0.00920327,  0.01114977,  0.00987825,
        0.0107424 ,  0.01001943,  0.01001241,  0.00998594,  0.00981583,
        0.00967381,  0.00983468,  0.01013571,  0.00946931,  0.00953504,
        0.00998108,  0.01069887,  0.01034788,  0.01036345,  0.0095854 ,
        0.00956333,  0.00988031,  0.00957838,  0.01064593,  0.00975081,
        0.00996321,  0.00982836,  0.0093284 ,  0.01114006,  0.00989289,
        0.00973573,  0.00966373,  0.00960631,  0.00962443,  0.01062395,
        0.01019753,  0.01021454,  0.00957491,  0.01015646,  0.00978134,
        0.01021044,  0.00996859,  0.00940452,  0.00967394,  0.00955211,
        0.00927787,  0.0103835 ,  0.00971564,  0.00985683,  0.00920713,
        0.00866301,  0.01076185,  0.00971245,  0.01094547,  0.0108495 ,
        0.01031881,  0.01035075,  0.01069983,  0.00931285,  0.01036753,
        0.01040652,  0.0095603 ,  0.00921086,  0.00974906,  0.00980527,
        0.01041024,  0.00988486,  0.01022555,  0.00991263,  0.00971978,
        0.00933247,  0.00962663,  0.00908351,  0.00934843,  0.00968928])

In [156]:

    

school = pd.read_csv('./schoolcode.csv')


    

In [158]:

    

school.head()


    

    
Out[158]:






  

    

      

      
schoolname
      
schoolid
    
  
  

    

      
0
      
������ѧ
      
31
    
    

      
1
      
�й������ѧ
      
46
    
    

      
2
      
������ͨ��ѧ
      
38
    
    

      
3
      
�������պ����ѧ
      
47
    
    

      
4
      
��������ѧ
      
143
    
  

In [105]:

    

province = u'00上海01云南02内蒙古03北京04吉林05四川06天津07宁夏08安徽09山东10山西11广东12广西13新疆14江苏15江西16河北17河南18浙江19海南21湖北22湖南23甘肃24福建25西藏26贵州27辽宁28重庆29陕西30青海31黑龙江32香港33澳门'


    

In [114]:

    

import re
province = [(i[:2],i[2:5]) for i in re.findall(u'[0-9]{2}.{2}',province)]


    

In [115]:

    

province


    

    
Out[115]:





[(u'00', u'\u4e0a\u6d77'),
 (u'01', u'\u4e91\u5357'),
 (u'02', u'\u5185\u8499'),
 (u'03', u'\u5317\u4eac'),
 (u'04', u'\u5409\u6797'),
 (u'05', u'\u56db\u5ddd'),
 (u'06', u'\u5929\u6d25'),
 (u'07', u'\u5b81\u590f'),
 (u'08', u'\u5b89\u5fbd'),
 (u'09', u'\u5c71\u4e1c'),
 (u'10', u'\u5c71\u897f'),
 (u'11', u'\u5e7f\u4e1c'),
 (u'12', u'\u5e7f\u897f'),
 (u'13', u'\u65b0\u7586'),
 (u'14', u'\u6c5f\u82cf'),
 (u'15', u'\u6c5f\u897f'),
 (u'16', u'\u6cb3\u5317'),
 (u'17', u'\u6cb3\u5357'),
 (u'18', u'\u6d59\u6c5f'),
 (u'19', u'\u6d77\u5357'),
 (u'21', u'\u6e56\u5317'),
 (u'22', u'\u6e56\u5357'),
 (u'23', u'\u7518\u8083'),
 (u'24', u'\u798f\u5efa'),
 (u'25', u'\u897f\u85cf'),
 (u'26', u'\u8d35\u5dde'),
 (u'27', u'\u8fbd\u5b81'),
 (u'28', u'\u91cd\u5e86'),
 (u'29', u'\u9655\u897f'),
 (u'30', u'\u9752\u6d77'),
 (u'31', u'\u9ed1\u9f99'),
 (u'32', u'\u9999\u6e2f'),
 (u'33', u'\u6fb3\u95e8')]

In [116]:

    

provinced = {}


    

In [117]:

    

for k,v in province:
    provinced[k] = v
provinced


    

    
Out[117]:





{u'00': u'\u4e0a\u6d77',
 u'01': u'\u4e91\u5357',
 u'02': u'\u5185\u8499',
 u'03': u'\u5317\u4eac',
 u'04': u'\u5409\u6797',
 u'05': u'\u56db\u5ddd',
 u'06': u'\u5929\u6d25',
 u'07': u'\u5b81\u590f',
 u'08': u'\u5b89\u5fbd',
 u'09': u'\u5c71\u4e1c',
 u'10': u'\u5c71\u897f',
 u'11': u'\u5e7f\u4e1c',
 u'12': u'\u5e7f\u897f',
 u'13': u'\u65b0\u7586',
 u'14': u'\u6c5f\u82cf',
 u'15': u'\u6c5f\u897f',
 u'16': u'\u6cb3\u5317',
 u'17': u'\u6cb3\u5357',
 u'18': u'\u6d59\u6c5f',
 u'19': u'\u6d77\u5357',
 u'21': u'\u6e56\u5317',
 u'22': u'\u6e56\u5357',
 u'23': u'\u7518\u8083',
 u'24': u'\u798f\u5efa',
 u'25': u'\u897f\u85cf',
 u'26': u'\u8d35\u5dde',
 u'27': u'\u8fbd\u5b81',
 u'28': u'\u91cd\u5e86',
 u'29': u'\u9655\u897f',
 u'30': u'\u9752\u6d77',
 u'31': u'\u9ed1\u9f99',
 u'32': u'\u9999\u6e2f',
 u'33': u'\u6fb3\u95e8'}

In [131]:

    

ps = {}
for i in range(len(p)):
    if i >= len(p)-1:
        break
    if i > 19:
        i = i+1
    k = str(i)
    if i < 10:
        k = '0'+str(i)
    print provinced[k],
    print p[i]
    ps[provinced[k]] = p[i]


    

    




上海 0.0325021079396
云南 0.0309924664155
内蒙 0.0298947562454
北京 0.0317399839803
吉林 0.0317942525734
四川 0.031786859168
天津 0.0315673282928
宁夏 0.0307856597478
安徽 0.0318743155787
山东 0.0316662743995
山西 0.0312695212961
广东 0.0326116543388
广西 0.031101490638
新疆 0.0302370469006
江苏 0.033062698991
江西 0.0320905823493
河北 0.0311489144946
河南 0.0314601571564
浙江 0.0313216842554
海南 0.030616591896
湖北 0.0313819386057
湖南 0.0314502885316
甘肃 0.0312232947321
福建 0.0311654065822
西藏 0.0304495925934
贵州 0.0306691516742
辽宁 0.0305779310135
重庆 0.0319469576357
陕西 0.0301429117778
青海 0.0305940201826
黑龙 0.0296663188588

In [140]:

    

pss = sorted(ps.items(),key= lambda item: item[1],reverse=True)


    

In [141]:

    

for k,v in pss:
    print k,v


    

    




江苏 0.033062698991
广东 0.0326116543388
上海 0.0325021079396
江西 0.0320905823493
重庆 0.0319469576357
安徽 0.0318743155787
吉林 0.0317942525734
四川 0.031786859168
北京 0.0317399839803
山东 0.0316662743995
天津 0.0315673282928
河南 0.0314601571564
湖南 0.0314502885316
湖北 0.0313819386057
浙江 0.0313216842554
山西 0.0312695212961
甘肃 0.0312232947321
福建 0.0311654065822
河北 0.0311489144946
广西 0.031101490638
云南 0.0309924664155
宁夏 0.0307856597478
贵州 0.0306691516742
海南 0.030616591896
青海 0.0305940201826
辽宁 0.0305779310135
西藏 0.0304495925934
新疆 0.0302370469006
陕西 0.0301429117778
内蒙 0.0298947562454
黑龙 0.0296663188588

In [177]:

    

schoold = {}
for row in school.itertuples():
    print row.schoolname.decode('gbk')
    schoold[row.schoolid] = row.schoolname.decode('gbk')


    

    




北京大学
中国人民大学
北京交通大学
北京航空航天大学
北京理工大学
北京邮电大学
中国农业大学
北京林业大学
北京师范大学
中国传媒大学
对外经济贸易大学
中国政法大学
南开大学
内蒙古大学
北京工业大学
北京科技大学
华北电力大学
天津医科大学
太原理工大学
辽宁大学
吉林大学
东北师范大学
清华大学
北京化工大学
北京外国语大学
天津大学
大连海事大学
哈尔滨工业大学
哈尔滨工程大学
东北农业大学
复旦大学
华东理工大学
上海财经大学
上海大学
苏州大学
南京航空航天大学
河海大学
南京师范大学
中国科学技术大学
北京中医药大学
北京体育大学
河北工业大学
大连理工大学
同济大学
华东师范大学
东南大学
中国矿业大学
南京农业大学
中国药科大学
安徽大学
厦门大学
福州大学
中国海洋大学
中央民族大学
东北林业大学
上海交通大学
南京理工大学
江南大学
合肥工业大学
南昌大学
郑州大学
武汉大学
中南财经政法大学
中南大学
湖南师范大学
暨南大学
华南理工大学
华南师范大学
重庆大学
电子科技大学
四川农业大学
西南大学
云南大学
西藏大学
西安交通大学
长安大学
兰州大学
青海大学
新疆大学
中央财经大学
东华大学
南京大学
浙江大学
山东大学
华中科技大学
华中农业大学
中山大学
海南大学
四川大学
西南财经大学
西北工业大学
陕西师范大学
宁夏大学
华中师范大学
东北大学
上海外国语大学
武汉理工大学
湖南大学
西南交通大学
西北大学
广西大学
西北农林科技大学
石河子大学
贵州大学
西安电子科技大学

In [178]:

    

schoold


    

    
Out[178]:





{30: u'\u5317\u4eac\u5de5\u4e1a\u5927\u5b66',
 31: u'\u5317\u4eac\u5927\u5b66',
 32: u'\u5185\u8499\u53e4\u5927\u5b66',
 33: u'\u5927\u8fde\u6d77\u4e8b\u5927\u5b66',
 34: u'\u54c8\u5c14\u6ee8\u5de5\u4e1a\u5927\u5b66',
 35: u'\u4e91\u5357\u5927\u5b66',
 36: u'\u957f\u5b89\u5927\u5b66',
 37: u'\u897f\u5317\u5927\u5b66',
 38: u'\u5317\u4eac\u4ea4\u901a\u5927\u5b66',
 39: u'\u5317\u4eac\u5916\u56fd\u8bed\u5927\u5b66',
 41: u'\u6cb3\u5317\u5de5\u4e1a\u5927\u5b66',
 42: u'\u6b66\u6c49\u5927\u5b66',
 43: u'\u5317\u4eac\u6797\u4e1a\u5927\u5b66',
 44: u'\u6e56\u5357\u5927\u5b66',
 45: u'\u4e2d\u592e\u6c11\u65cf\u5927\u5b66',
 46: u'\u4e2d\u56fd\u4eba\u6c11\u5927\u5b66',
 47: u'\u5317\u4eac\u822a\u7a7a\u822a\u5929\u5927\u5b66',
 48: u'\u5317\u4eac\u90ae\u7535\u5927\u5b66',
 49: u'\u5317\u4eac\u4e2d\u533b\u836f\u5927\u5b66',
 50: u'\u8fbd\u5b81\u5927\u5b66',
 51: u'\u897f\u5357\u4ea4\u901a\u5927\u5b66',
 52: u'\u5317\u4eac\u5e08\u8303\u5927\u5b66',
 53: u'\u5bf9\u5916\u7ecf\u6d4e\u8d38\u6613\u5927\u5b66',
 57: u'\u897f\u5b89\u7535\u5b50\u79d1\u6280\u5927\u5b66',
 58: u'\u6e56\u5357\u5e08\u8303\u5927\u5b66',
 59: u'\u5357\u5f00\u5927\u5b66',
 60: u'\u5929\u6d25\u5927\u5b66',
 61: u'\u4e2d\u56fd\u6d77\u6d0b\u5927\u5b66',
 62: u'\u90d1\u5dde\u5927\u5b66',
 63: u'\u5408\u80a5\u5de5\u4e1a\u5927\u5b66',
 66: u'\u4e2d\u56fd\u79d1\u5b66\u6280\u672f\u5927\u5b66',
 67: u'\u5b89\u5fbd\u5927\u5b66',
 73: u'\u540c\u6d4e\u5927\u5b66',
 74: u'\u65b0\u7586\u5927\u5b66',
 76: u'\u4e0a\u6d77\u5927\u5b66',
 77: u'\u5357\u4eac\u822a\u7a7a\u822a\u5929\u5927\u5b66',
 78: u'\u5929\u6d25\u533b\u79d1\u5927\u5b66',
 86: u'\u6c5f\u5357\u5927\u5b66',
 96: u'\u5e7f\u897f\u5927\u5b66',
 97: u'\u5170\u5dde\u5927\u5b66',
 98: u'\u534e\u5357\u5e08\u8303\u5927\u5b66',
 99: u'\u56db\u5ddd\u5927\u5b66',
 100: u'\u56db\u5ddd\u519c\u4e1a\u5927\u5b66',
 101: u'\u897f\u5357\u8d22\u7ecf\u5927\u5b66',
 102: u'\u53a6\u95e8\u5927\u5b66',
 103: u'\u798f\u5dde\u5927\u5b66',
 104: u'\u4e2d\u5c71\u5927\u5b66',
 105: u'\u534e\u5357\u7406\u5de5\u5927\u5b66',
 106: u'\u66a8\u5357\u5927\u5b66',
 107: u'\u897f\u5317\u5de5\u4e1a\u5927\u5b66',
 108: u'\u5357\u660c\u5927\u5b66',
 109: u'\u4e1c\u5357\u5927\u5b66',
 110: u'\u4e2d\u56fd\u77ff\u4e1a\u5927\u5b66',
 111: u'\u5357\u4eac\u5927\u5b66',
 112: u'\u5357\u4eac\u7406\u5de5\u5927\u5b66',
 113: u'\u5357\u4eac\u519c\u4e1a\u5927\u5b66',
 114: u'\u6d59\u6c5f\u5927\u5b66',
 115: u'\u5357\u4eac\u5e08\u8303\u5927\u5b66',
 116: u'\u6cb3\u6d77\u5927\u5b66',
 117: u'\u4e2d\u56fd\u836f\u79d1\u5927\u5b66',
 118: u'\u82cf\u5dde\u5927\u5b66',
 119: u'\u91cd\u5e86\u5927\u5b66',
 122: u'\u5409\u6797\u5927\u5b66',
 123: u'\u4e2d\u5357\u5927\u5b66',
 124: u'\u54c8\u5c14\u6ee8\u5de5\u7a0b\u5927\u5b66',
 125: u'\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66',
 126: u'\u5c71\u4e1c\u5927\u5b66',
 127: u'\u534e\u4e2d\u79d1\u6280\u5927\u5b66',
 128: u'\u6b66\u6c49\u7406\u5de5\u5927\u5b66',
 130: u'\u4e0a\u6d77\u8d22\u7ecf\u5927\u5b66',
 131: u'\u534e\u4e1c\u5e08\u8303\u5927\u5b66',
 132: u'\u590d\u65e6\u5927\u5b66',
 133: u'\u534e\u4e1c\u7406\u5de5\u5927\u5b66',
 134: u'\u4e1c\u5317\u5927\u5b66',
 135: u'\u4e1c\u534e\u5927\u5b66',
 136: u'\u4e0a\u6d77\u5916\u56fd\u8bed\u5927\u5b66',
 137: u'\u4e1c\u5317\u519c\u4e1a\u5927\u5b66',
 138: u'\u5927\u8fde\u7406\u5de5\u5927\u5b66',
 139: u'\u592a\u539f\u7406\u5de5\u5927\u5b66',
 140: u'\u6e05\u534e\u5927\u5b66',
 142: u'\u4e1c\u5317\u5e08\u8303\u5927\u5b66',
 143: u'\u5317\u4eac\u7406\u5de5\u5927\u5b66',
 144: u'\u5317\u4eac\u79d1\u6280\u5927\u5b66',
 199: u'\u77f3\u6cb3\u5b50\u5927\u5b66',
 330: u'\u897f\u5b89\u4ea4\u901a\u5927\u5b66',
 332: u'\u897f\u5317\u519c\u6797\u79d1\u6280\u5927\u5b66',
 334: u'\u9655\u897f\u5e08\u8303\u5927\u5b66',
 364: u'\u897f\u85cf\u5927\u5b66',
 367: u'\u9752\u6d77\u5927\u5b66',
 414: u'\u4e2d\u5357\u8d22\u7ecf\u653f\u6cd5\u5927\u5b66',
 417: u'\u534e\u4e2d\u519c\u4e1a\u5927\u5b66',
 419: u'\u4e1c\u5317\u6797\u4e1a\u5927\u5b66',
 420: u'\u534e\u4e2d\u5e08\u8303\u5927\u5b66',
 504: u'\u6d77\u5357\u5927\u5b66',
 544: u'\u5b81\u590f\u5927\u5b66',
 556: u'\u5317\u4eac\u5316\u5de5\u5927\u5b66',
 557: u'\u4e2d\u56fd\u519c\u4e1a\u5927\u5b66',
 558: u'\u4e2d\u56fd\u4f20\u5a92\u5927\u5b66',
 566: u'\u4e2d\u592e\u8d22\u7ecf\u5927\u5b66',
 569: u'\u4e2d\u56fd\u653f\u6cd5\u5927\u5b66',
 661: u'\u7535\u5b50\u79d1\u6280\u5927\u5b66',
 831: u'\u534e\u5317\u7535\u529b\u5927\u5b66',
 866: u'\u5317\u4eac\u4f53\u80b2\u5927\u5b66',
 934: u'\u897f\u5357\u5927\u5b66',
 935: u'\u8d35\u5dde\u5927\u5b66'}

In [175]:

    

schooli = score211f.columns.tolist()


    

In [184]:

    

us = {}
for i in range(len(u)):
    us[schoold[int(schooli[i])]] = u[i]


    

In [185]:

    

us


    

    
Out[185]:





{u'\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66': 0.011140064959194253,
 u'\u4e0a\u6d77\u5916\u56fd\u8bed\u5927\u5b66': 0.01041024271404681,
 u'\u4e0a\u6d77\u5927\u5b66': 0.010019428170856806,
 u'\u4e0a\u6d77\u8d22\u7ecf\u5927\u5b66': 0.01074240454454296,
 u'\u4e1c\u5317\u519c\u4e1a\u5927\u5b66': 0.009203274288002149,
 u'\u4e1c\u5317\u5927\u5b66': 0.0098052732501449844,
 u'\u4e1c\u5317\u5e08\u8303\u5927\u5b66': 0.0096424435559260575,
 u'\u4e1c\u5317\u6797\u4e1a\u5927\u5b66': 0.0093283959164428849,
 u'\u4e1c\u534e\u5927\u5b66': 0.0097124487909960519,
 u'\u4e1c\u5357\u5927\u5b66': 0.010363452619660471,
 u'\u4e2d\u5357\u5927\u5b66': 0.010214537680445739,
 u'\u4e2d\u5357\u8d22\u7ecf\u653f\u6cd5\u5927\u5b66': 0.010197530941928762,
 u'\u4e2d\u56fd\u4eba\u6c11\u5927\u5b66': 0.01107966135913196,
 u'\u4e2d\u56fd\u4f20\u5a92\u5927\u5b66': 0.010292598384914762,
 u'\u4e2d\u56fd\u519c\u4e1a\u5927\u5b66': 0.01002112356022236,
 u'\u4e2d\u56fd\u653f\u6cd5\u5927\u5b66': 0.010495881321468318,
 u'\u4e2d\u56fd\u6d77\u6d0b\u5927\u5b66': 0.0099632116488328672,
 u'\u4e2d\u56fd\u77ff\u4e1a\u5927\u5b66': 0.009585398897075487,
 u'\u4e2d\u56fd\u79d1\u5b66\u6280\u672f\u5927\u5b66': 0.0098346812711759459,
 u'\u4e2d\u56fd\u836f\u79d1\u5927\u5b66': 0.0098803086186658885,
 u'\u4e2d\u592e\u6c11\u65cf\u5927\u5b66': 0.0098283584665416028,
 u'\u4e2d\u592e\u8d22\u7ecf\u5927\u5b66': 0.01076185044148744,
 u'\u4e2d\u5c71\u5927\u5b66': 0.010699832988109396,
 u'\u4e91\u5357\u5927\u5b66': 0.009552114493065901,
 u'\u5170\u5dde\u5927\u5b66': 0.0098568298062331906,
 u'\u5185\u8499\u53e4\u5927\u5b66': 0.0088620260693047567,
 u'\u5317\u4eac\u4e2d\u533b\u836f\u5927\u5b66': 0.010135706829808419,
 u'\u5317\u4eac\u4ea4\u901a\u5927\u5b66': 0.010183243123894665,
 u'\u5317\u4eac\u4f53\u80b2\u5927\u5b66': 0.0094693074375509726,
 u'\u5317\u4eac\u5316\u5de5\u5927\u5b66': 0.0097624088793884494,
 u'\u5317\u4eac\u5916\u56fd\u8bed\u5927\u5b66': 0.010517187388415719,
 u'\u5317\u4eac\u5927\u5b66': 0.011296686157090308,
 u'\u5317\u4eac\u5de5\u4e1a\u5927\u5b66': 0.0097555234657391004,
 u'\u5317\u4eac\u5e08\u8303\u5927\u5b66': 0.010507561646184565,
 u'\u5317\u4eac\u6797\u4e1a\u5927\u5b66': 0.0098074209073839708,
 u'\u5317\u4eac\u7406\u5de5\u5927\u5b66': 0.010441013096695713,
 u'\u5317\u4eac\u79d1\u6280\u5927\u5b66': 0.0099849252380233358,
 u'\u5317\u4eac\u822a\u7a7a\u822a\u5929\u5927\u5b66': 0.010482915125301664,
 u'\u5317\u4eac\u90ae\u7535\u5927\u5b66': 0.010169853055121357,
 u'\u534e\u4e1c\u5e08\u8303\u5927\u5b66': 0.010347879749434767,
 u'\u534e\u4e1c\u7406\u5de5\u5927\u5b66': 0.0098782515250773369,
 u'\u534e\u4e2d\u5e08\u8303\u5927\u5b66': 0.0097490641173139016,
 u'\u534e\u4e2d\u79d1\u6280\u5927\u5b66': 0.010350751847247885,
 u'\u534e\u5317\u7535\u529b\u5927\u5b66': 0.010153610403520882,
 u'\u534e\u5357\u5e08\u8303\u5927\u5b66': 0.0097813431992943931,
 u'\u5357\u4eac\u519c\u4e1a\u5927\u5b66': 0.0095633280862469049,
 u'\u5357\u4eac\u5927\u5b66': 0.010945470069332049,
 u'\u5357\u4eac\u5e08\u8303\u5927\u5b66': 0.0096738059135334058,
 u'\u5357\u4eac\u7406\u5de5\u5927\u5b66': 0.0098928904138509254,
 u'\u5357\u4eac\u822a\u7a7a\u822a\u5929\u5927\u5b66': 0.0099859366185302018,
 u'\u5357\u5f00\u5927\u5b66': 0.010708424478904773,
 u'\u5357\u660c\u5927\u5b66': 0.0096063142755705311,
 u'\u53a6\u95e8\u5927\u5b66': 0.010645931422021686,
 u'\u5408\u80a5\u5de5\u4e1a\u5927\u5b66': 0.0096637346737199429,
 u'\u540c\u6d4e\u5927\u5b66': 0.010698866191469859,
 u'\u54c8\u5c14\u6ee8\u5de5\u4e1a\u5927\u5b66': 0.010268694384304861,
 u'\u54c8\u5c14\u6ee8\u5de5\u7a0b\u5927\u5b66': 0.009667811427090698,
 u'\u56db\u5ddd\u519c\u4e1a\u5927\u5b66': 0.0094045163716619824,
 u'\u56db\u5ddd\u5927\u5b66': 0.010367534542074164,
 u'\u590d\u65e6\u5927\u5b66': 0.011149772851804813,
 u'\u5927\u8fde\u6d77\u4e8b\u5927\u5b66': 0.0096351044183221948,
 u'\u5927\u8fde\u7406\u5de5\u5927\u5b66': 0.0099810787699454295,
 u'\u5929\u6d25\u533b\u79d1\u5927\u5b66': 0.0098101547955639669,
 u'\u5929\u6d25\u5927\u5b66': 0.010401486188872168,
 u'\u5b81\u590f\u5927\u5b66': 0.0092108597932755634,
 u'\u5b89\u5fbd\u5927\u5b66': 0.0095783811070434197,
 u'\u5bf9\u5916\u7ecf\u6d4e\u8d38\u6613\u5927\u5b66': 0.010705894886168386,
 u'\u5c71\u4e1c\u5927\u5b66': 0.010318810957081068,
 u'\u5e7f\u897f\u5927\u5b66': 0.0093324737548129873,
 u'\u65b0\u7586\u5927\u5b66': 0.0086630110617673985,
 u'\u66a8\u5357\u5927\u5b66': 0.010156460627534182,
 u'\u6b66\u6c49\u5927\u5b66': 0.010623953357133828,
 u'\u6b66\u6c49\u7406\u5de5\u5927\u5b66': 0.0098848609304352866,
 u'\u6c5f\u5357\u5927\u5b66': 0.0097357261957205597,
 u'\u6cb3\u5317\u5de5\u4e1a\u5927\u5b66': 0.0095350438217471425,
 u'\u6cb3\u6d77\u5927\u5b66': 0.009815825094498333,
 u'\u6d59\u6c5f\u5927\u5b66': 0.010849501969484577,
 u'\u6d77\u5357\u5927\u5b66': 0.0093128540106476431,
 u'\u6e05\u534e\u5927\u5b66': 0.011441501603551603,
 u'\u6e56\u5357\u5927\u5b66': 0.010225552465216739,
 u'\u6e56\u5357\u5e08\u8303\u5927\u5b66': 0.0095749129563526998,
 u'\u7535\u5b50\u79d1\u6280\u5927\u5b66': 0.0099685900898396594,
 u'\u77f3\u6cb3\u5b50\u5927\u5b66': 0.0090835149917455581,
 u'\u798f\u5dde\u5927\u5b66': 0.0097508119807711933,
 u'\u82cf\u5dde\u5927\u5b66': 0.010012406812894526,
 u'\u897f\u5317\u519c\u6797\u79d1\u6280\u5927\u5b66': 0.0096266297472876339,
 u'\u897f\u5317\u5927\u5b66': 0.0097197799934966665,
 u'\u897f\u5357\u4ea4\u901a\u5927\u5b66': 0.0099126262352779819,
 u'\u897f\u5357\u5927\u5b66': 0.0096739449182146713,
 u'\u897f\u5357\u8d22\u7ecf\u5927\u5b66': 0.01040652082406144,
 u'\u897f\u5b89\u4ea4\u901a\u5927\u5b66': 0.010383498595435931,
 u'\u897f\u5b89\u7535\u5b50\u79d1\u6280\u5927\u5b66': 0.0096892839776332122,
 u'\u897f\u85cf\u5927\u5b66': 0.0092778693745307408,
 u'\u8d35\u5dde\u5927\u5b66': 0.0093484287745769432,
 u'\u8fbd\u5b81\u5927\u5b66': 0.0095236369071646371,
 u'\u90d1\u5dde\u5927\u5b66': 0.0096244348499457608,
 u'\u91cd\u5e86\u5927\u5b66': 0.010210443241618759,
 u'\u957f\u5b89\u5927\u5b66': 0.0097156398617498328,
 u'\u9655\u897f\u5e08\u8303\u5927\u5b66': 0.0095602980982037577,
 u'\u9752\u6d77\u5927\u5b66': 0.0092071322893540735}

In [186]:

    

uss = sorted(us.items(),key = lambda item:item[1],reverse=True)


    

In [187]:

    

for k,v in uss:
    print k,v


    

    




清华大学 0.0114415016036
北京大学 0.0112966861571
复旦大学 0.0111497728518
上海交通大学 0.0111400649592
中国人民大学 0.0110796613591
南京大学 0.0109454700693
浙江大学 0.0108495019695
中央财经大学 0.0107618504415
上海财经大学 0.0107424045445
南开大学 0.0107084244789
对外经济贸易大学 0.0107058948862
中山大学 0.0106998329881
同济大学 0.0106988661915
厦门大学 0.010645931422
武汉大学 0.0106239533571
北京外国语大学 0.0105171873884
北京师范大学 0.0105075616462
中国政法大学 0.0104958813215
北京航空航天大学 0.0104829151253
北京理工大学 0.0104410130967
上海外国语大学 0.010410242714
西南财经大学 0.0104065208241
天津大学 0.0104014861889
西安交通大学 0.0103834985954
四川大学 0.0103675345421
东南大学 0.0103634526197
华中科技大学 0.0103507518472
华东师范大学 0.0103478797494
山东大学 0.0103188109571
中国传媒大学 0.0102925983849
哈尔滨工业大学 0.0102686943843
湖南大学 0.0102255524652
中南大学 0.0102145376804
重庆大学 0.0102104432416
中南财经政法大学 0.0101975309419
北京交通大学 0.0101832431239
北京邮电大学 0.0101698530551
暨南大学 0.0101564606275
华北电力大学 0.0101536104035
北京中医药大学 0.0101357068298
中国农业大学 0.0100211235602
上海大学 0.0100194281709
苏州大学 0.0100124068129
南京航空航天大学 0.00998593661853
北京科技大学 0.00998492523802
大连理工大学 0.00998107876995
电子科技大学 0.00996859008984
中国海洋大学 0.00996321164883
西南交通大学 0.00991262623528
南京理工大学 0.00989289041385
武汉理工大学 0.00988486093044
中国药科大学 0.00988030861867
华东理工大学 0.00987825152508
兰州大学 0.00985682980623
中国科学技术大学 0.00983468127118
中央民族大学 0.00982835846654
河海大学 0.0098158250945
天津医科大学 0.00981015479556
北京林业大学 0.00980742090738
东北大学 0.00980527325014
华南师范大学 0.00978134319929
北京化工大学 0.00976240887939
北京工业大学 0.00975552346574
福州大学 0.00975081198077
华中师范大学 0.00974906411731
江南大学 0.00973572619572
西北大学 0.0097197799935
长安大学 0.00971563986175
东华大学 0.009712448791
西安电子科技大学 0.00968928397763
西南大学 0.00967394491821
南京师范大学 0.00967380591353
哈尔滨工程大学 0.00966781142709
合肥工业大学 0.00966373467372
东北师范大学 0.00964244355593
大连海事大学 0.00963510441832
西北农林科技大学 0.00962662974729
郑州大学 0.00962443484995
南昌大学 0.00960631427557
中国矿业大学 0.00958539889708
安徽大学 0.00957838110704
湖南师范大学 0.00957491295635
南京农业大学 0.00956332808625
陕西师范大学 0.0095602980982
云南大学 0.00955211449307
河北工业大学 0.00953504382175
辽宁大学 0.00952363690716
北京体育大学 0.00946930743755
四川农业大学 0.00940451637166
贵州大学 0.00934842877458
广西大学 0.00933247375481
东北林业大学 0.00932839591644
海南大学 0.00931285401065
西藏大学 0.00927786937453
宁夏大学 0.00921085979328
青海大学 0.00920713228935
东北农业大学 0.009203274288
石河子大学 0.00908351499175
内蒙古大学 0.0088620260693
新疆大学 0.00866301106177

In [ ]: