np.random.seed(2020)
df = (pd.DataFrame(np.random.rand(20, 5), columns=['Block','A','B','C','D'])
.sort_values('Block'))
df['Block'] = df['Block'].mul(10).astype(int)
df.iloc[:, 1:] -= 0.7
print (df)
Block A B C D
17 0 0.171687 0.202407 -0.250796 -0.081818
13 0 0.194283 0.131532 0.037843 0.198497
11 0 -0.135680 0.236032 0.103028 -0.002695
2 1 0.057080 0.036325 -0.344337 -0.358907
9 1 -0.063106 -0.144304 -0.508071 -0.274344
6 1 0.196258 -0.326606 -0.320307 0.158317
1 2 -0.423523 -0.356684 0.162159 -0.543300
14 2 -0.520039 -0.085842 -0.683331 -0.400678
19 2 0.232328 -0.286770 -0.539322 -0.231197
8 4 0.131468 0.063921 0.219691 -0.629427
12 4 -0.037565 0.049666 -0.168545 -0.217190
10 5 -0.430622 -0.100980 -0.479826 -0.399138
16 5 -0.522721 0.294459 -0.582625 0.140845
7 6 -0.116538 -0.031650 -0.522207 0.149248
3 6 -0.482899 -0.138573 -0.575821 -0.380264
5 6 -0.665809 -0.243881 -0.544149 -0.223951
15 6 0.252552 0.116207 -0.622138 -0.565387
4 9 -0.562643 -0.130587 0.275665 -0.196633
18 9 -0.302722 -0.252769 -0.466740 0.231729
0 9 0.173392 -0.190254 -0.428164 -0.363081
我要计算按“块”列分组的值> 0的单元格总数。
答案 0 :(得分：0)
将列1903转换为以DataFrame.set_index进行索引，以DataFrame.gt进行比较，以更像Block，转换为整数，最后对每个索引使用0：< / p>
sum