在 pandas 列中获取第一和第二高值 [英] Get first and second highest values in pandas columns

问题描述

我正在使用熊猫来分析一些选举结果.我有一个DF，结果，其中每个选区都有一行，代表各政党(超过100个政党)的投票的列:

I am using pandas to analyse some election results. I have a DF, Results, which has a row for each constituency and columns representing the votes for the various parties (over 100 of them):

In[60]: Results.columns
Out[60]: 
Index(['Constituency', 'Region', 'Country', 'ID', 'Type', 'Electorate',
       'Total', 'Unnamed: 9', '30-50', 'Above',
       ...
       'WP', 'WRP', 'WVPTFP', 'Yorks', 'Young', 'Zeb', 'Party', 'Votes',
       'Share', 'Turnout'],
      dtype='object', length=147) 

所以...

In[63]: Results.head()
Out[63]: 
                         Constituency    Region   Country         ID    Type  \
PAID                                                                           
1                            Aberavon     Wales     Wales  W07000049  County   
2                           Aberconwy     Wales     Wales  W07000058  County   
3                      Aberdeen North  Scotland  Scotland  S14000001   Burgh   
4                      Aberdeen South  Scotland  Scotland  S14000002   Burgh   
5     Aberdeenshire West & Kincardine  Scotland  Scotland  S14000058  County   

      Electorate  Total  Unnamed: 9  30-50  Above    ...     WP  WRP  WVPTFP  \
PAID                                                 ...                       
1          49821  31523         NaN    NaN    NaN    ...    NaN  NaN     NaN   
2          45525  30148         NaN    NaN    NaN    ...    NaN  NaN     NaN   
3          67745  43936         NaN    NaN    NaN    ...    NaN  NaN     NaN   
4          68056  48551         NaN    NaN    NaN    ...    NaN  NaN     NaN   
5          73445  55196         NaN    NaN    NaN    ...    NaN  NaN     NaN   

      Yorks  Young  Zeb  Party  Votes     Share   Turnout  
PAID                                                       
1       NaN    NaN  NaN    Lab  15416  0.489040  0.632725  
2       NaN    NaN  NaN    Con  12513  0.415052  0.662230  
3       NaN    NaN  NaN    SNP  24793  0.564298  0.648550  
4       NaN    NaN  NaN    SNP  20221  0.416490  0.713398  
5       NaN    NaN  NaN    SNP  22949  0.415773  0.751528  

[5 rows x 147 columns]

每方选区结果在Results.ix[:, 'Unnamed: 9': 'Zeb']

我可以使用以下方法找到获胜的政党(即投票得票最多的政党)及其投票的票数:

I can find the winning party (i.e. the party which polled highest number of votes) and the number of votes it polled using:

RawResults = Results.ix[:, 'Unnamed: 9': 'Zeb']
Results['Party'] = RawResults.idxmax(axis=1)
Results['Votes'] = RawResults.max(axis=1).astype(int)

但是，我还需要知道第二党获得了多少票(最好是其索引/名称).那么，在熊猫中，有什么方法可以为每一行返回一组列中的 second 最高值/索引吗?

But, I also need to know how many votes the second-place party got (and ideally its index/name). So is there any way in pandas to return the second highest value/index in a set of columns for each row?

推荐答案

以下是NumPy解决方案:

Here is a NumPy solution:

In [120]: df
Out[120]:
          a         b         c         d         e         f         g         h
0  1.334444  0.322029  0.302296 -0.841236 -0.360488 -0.860188 -0.157942  1.522082
1  2.056572  0.991643  0.160067 -0.066473  0.235132  0.533202  1.282371 -2.050731
2  0.955586 -0.966734  0.055210 -0.993924 -0.553841  0.173793 -0.534548 -1.796006
3  1.201001  1.067291 -0.562357 -0.794284 -0.554820 -0.011836  0.519928  0.514669
4 -0.243972 -0.048144  0.498007  0.862016  1.284717 -0.886455 -0.757603  0.541992
5  0.739435 -0.767399  1.574173  1.197063 -1.147961 -0.903858  0.011073 -1.404868
6 -1.258282 -0.049719  0.400063  0.611456  0.443289 -1.110945  1.352029  0.215460
7  0.029121 -0.771431 -0.285119 -0.018216  0.408425 -1.458476 -1.363583  0.155134
8  1.427226 -1.005345  0.208665 -0.674917  0.287929 -1.259707  0.220420 -1.087245
9  0.452589  0.214592 -1.875423  0.487496  2.411265  0.062324 -0.327891  0.256577

In [121]: np.sort(df.values)[:,-2:]
Out[121]:
array([[ 1.33444404,  1.52208164],
       [ 1.28237078,  2.05657214],
       [ 0.17379254,  0.95558613],
       [ 1.06729107,  1.20100071],
       [ 0.86201603,  1.28471676],
       [ 1.19706331,  1.57417327],
       [ 0.61145573,  1.35202868],
       [ 0.15513379,  0.40842477],
       [ 0.28792928,  1.42722604],
       [ 0.48749578,  2.41126532]])

或作为熊猫数据框:

In [122]: pd.DataFrame(np.sort(df.values)[:,-2:], columns=['2nd-largest','largest'])
Out[122]:
   2nd-largest   largest
0     1.334444  1.522082
1     1.282371  2.056572
2     0.173793  0.955586
3     1.067291  1.201001
4     0.862016  1.284717
5     1.197063  1.574173
6     0.611456  1.352029
7     0.155134  0.408425
8     0.287929  1.427226
9     0.487496  2.411265

或更快@Divakar的解决方案:

In [6]: df
Out[6]:
          a         b         c         d         e         f         g         h
0  0.649517 -0.223116  0.264734 -1.121666  0.151591 -1.335756 -0.155459 -2.500680
1  0.172981  1.233523  0.220378  1.188080 -0.289469 -0.039150  1.476852  0.736908
2 -1.904024  0.109314  0.045741 -0.341214 -0.332267 -1.363889  0.177705 -0.892018
3 -2.606532 -0.483314  0.054624  0.979734  0.205173  0.350247 -1.088776  1.501327
4  1.627655 -1.261631  0.589899 -0.660119  0.742390 -1.088103  0.228557  0.714746
5  0.423972 -0.506975 -0.783718 -2.044002 -0.692734  0.980399  1.007460  0.161516
6 -0.777123 -0.838311 -1.116104 -0.433797  0.599724 -0.884832 -0.086431 -0.738298
7  1.131621  1.218199  0.645709  0.066216 -0.265023  0.606963 -0.194694  0.463576
8  0.421164  0.626731 -0.547738  0.989820 -1.383061 -0.060413 -1.342769 -0.777907
9 -1.152690  0.696714 -0.155727 -0.991975 -0.806530  1.454522  0.788688  0.409516

In [7]: a = df.values

In [8]: a[np.arange(len(df))[:,None],np.argpartition(-a,np.arange(2),axis=1)[:,:2]]
Out[8]:
array([[ 0.64951665,  0.26473378],
       [ 1.47685226,  1.23352348],
       [ 0.17770473,  0.10931398],
       [ 1.50132666,  0.97973383],
       [ 1.62765464,  0.74238959],
       [ 1.00745981,  0.98039898],
       [ 0.5997243 , -0.0864306 ],
       [ 1.21819904,  1.13162068],
       [ 0.98982033,  0.62673128],
       [ 1.45452173,  0.78868785]])

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