<p>我看到你已经在评论中提到熊猫了。FWIW,这里介绍了如何获得所需的行为,假设您不关心最终的排序顺序,因为groupby更改了它。在</p>
<pre><code>In [14]: arr
Out[14]:
array([[ 0.7732126 , 0.48649481, 0.29771819, 0.91622924],
[ 0.7732126 , 0.48649481, 0.29771819, 1.91622924],
[ 0.58294263, 0.32025559, 0.6925856 , 0.0524125 ],
[ 0.58294263, 0.32025559, 0.6925856 , 0.05 ],
[ 0.58294263, 0.32025559, 0.6925856 , 1.7 ],
[ 0.3239913 , 0.7786444 , 0.41692853, 0.10467392],
[ 0.12080023, 0.74853649, 0.15356663, 0.4505753 ],
[ 0.13536096, 0.60319054, 0.82018125, 0.10445047],
[ 0.1877724 , 0.96060999, 0.39697999, 0.59078612]])
In [15]: import pandas as pd
In [16]: pd.DataFrame(arr)
Out[16]:
0 1 2 3
0 0.773213 0.486495 0.297718 0.916229
1 0.773213 0.486495 0.297718 1.916229
2 0.582943 0.320256 0.692586 0.052413
3 0.582943 0.320256 0.692586 0.050000
4 0.582943 0.320256 0.692586 1.700000
5 0.323991 0.778644 0.416929 0.104674
6 0.120800 0.748536 0.153567 0.450575
7 0.135361 0.603191 0.820181 0.104450
8 0.187772 0.960610 0.396980 0.590786
In [17]: pd.DataFrame(arr).groupby([0,1,2]).max().reset_index()
Out[17]:
0 1 2 3
0 0.120800 0.748536 0.153567 0.450575
1 0.135361 0.603191 0.820181 0.104450
2 0.187772 0.960610 0.396980 0.590786
3 0.323991 0.778644 0.416929 0.104674
4 0.582943 0.320256 0.692586 1.700000
5 0.773213 0.486495 0.297718 1.916229
</code></pre>