问题描述
我有一个数据框 df:
I have a dataframe df:
domain country out1 out2 out3 oranjeslag.nl NL 1 0 NaN pietervaartjes.nl NL 1 1 0 andreaputting.com.au AU NaN 1 0 michaelcardillo.com US 0 0 NaN
我想定义两列 sum_0 和 sum_1 并计算每行列 (out1,out2,out3) 中 0 和 1 的数量.所以预期的结果是:
I would like to define two columns sum_0 and sum_1 and count the number of 0s and 1s in columns (out1,out2,out3),per row. So expected results would be:
domain country out1 out2 out3 sum_0 sum_1 oranjeslag.nl NL 1 0 NaN 1 1 pietervaartjes.nl NL 1 1 0 1 2 andreaputting.com.au AU NaN 1 0 1 1 michaelcardillo.com US 0 0 NaN 2 0
我有这个计算1个数的代码,但我不知道如何计算0个数.
I have this code for counting the number of 1s, but I do not know how to count the number of 0s.
df['sum_1'] = df[['out_1','out_2','out_3']].sum(axis=1)
有人可以帮忙吗?
推荐答案
你可以为每个条件调用sum,1条件很简单,只是一个直接的axis=1 上的 sum,第二次您可以将 df 与 0 值进行比较,然后像以前一样调用 sum:
You can call sum for each condition, the 1 condition is simple just a straight sum on axis=1, for the second you can compare the df against 0 value and then call sum as before:
In [102]: df['sum_1'] = df[['out1','out2','out3']].sum(axis=1) df['sum_0'] = (df[['out1','out2','out3']] == 0).sum(axis=1) df Out[102]: domain country out1 out2 out3 sum_0 sum_1 0 oranjeslag.nl NL 1 0 NaN 1 1 1 pietervaartjes.nl NL 1 1 0 1 2 2 andreaputting.com.au AU NaN 1 0 1 1 3 michaelcardillo.com US 0 0 NaN 2 0