我不明白为什么 value_counts 给了我错误的答案。这是一个小例子:
In [81]: d=pd.DataFrame([[0,0],[1,100],[0,100],[2,0],[3,100],[4,100],[4,100],[4,100],[1,100],[3,100]],columns=['key','score'])
In [82]: d
Out[82]:
key score
0 0 0
1 1 100
2 0 100
3 2 0
4 3 100
5 4 100
6 4 100
7 4 100
8 1 100
9 3 100
In [83]: g=d.groupby('key')['score']
In [84]: g.value_counts(bins=[0, 20, 40, 60, 80, 100])
Out[84]:
key score
0 (-0.001, 20.0] 1
(20.0, 40.0] 1
(40.0, 60.0] 0
(60.0, 80.0] 0
(80.0, 100.0] 0
1 (20.0, 40.0] 2
(-0.001, 20.0] 0
(40.0, 60.0] 0
(60.0, 80.0] 0
(80.0, 100.0] 0
2 (-0.001, 20.0] 1
(20.0, 40.0] 0
(40.0, 60.0] 0
(60.0, 80.0] 0
(80.0, 100.0] 0
3 (20.0, 40.0] 2
(-0.001, 20.0] 0
(40.0, 60.0] 0
(60.0, 80.0] 0
(80.0, 100.0] 0
4 (20.0, 40.0] 3
(-0.001, 20.0] 0
(40.0, 60.0] 0
(60.0, 80.0] 0
(80.0, 100.0] 0
Name: score, dtype: int64
这些数据中出现的唯一值是 0 和 100。但是 value_counts 告诉我范围 (20.0,40.0] 的值最多，而 (80.0,100.0] 没有。
当然，我的真实数据有更多的值、不同的键等，但这说明了我所看到的问题。
为什么？
最佳答案
这是保持索引完整性的另一种方法。
d.groupby('key')['score'].apply(pd.Series.value_counts, bins=[0,20,40,60,80,100])
输出:
key
0 (80.0, 100.0] 1
(-0.001, 20.0] 1
(60.0, 80.0] 0
(40.0, 60.0] 0
(20.0, 40.0] 0
1 (80.0, 100.0] 2
(60.0, 80.0] 0
(40.0, 60.0] 0
(20.0, 40.0] 0
(-0.001, 20.0] 0
2 (-0.001, 20.0] 1
(80.0, 100.0] 0
(60.0, 80.0] 0
(40.0, 60.0] 0
(20.0, 40.0] 0
3 (80.0, 100.0] 2
(60.0, 80.0] 0
(40.0, 60.0] 0
(20.0, 40.0] 0
(-0.001, 20.0] 0
4 (80.0, 100.0] 3
(60.0, 80.0] 0
(40.0, 60.0] 0
(20.0, 40.0] 0
(-0.001, 20.0] 0
Name: score, dtype: int64
关于python - 将 bin 应用于 groupby 的 pandas value_counts 产生不正确的结果，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/60553500/