Python Pandas：计算每行数据帧中特定值的频率？

Question

我有一个数据框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，并计算每行的列（out1、out2、out3）中0和1的数量。因此，预期结果将是：

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

我有计算1的代码，但我不知道如何计算0

df['sum_1'] = df[['out_1','out_2','out_3']].sum(axis=1)

有人能帮忙吗？

Answer 1

您可以为每个条件调用sum，对于axis=1，1条件很简单，只是sum上的一个直sum，对于第二个条件，您可以将df与0值进行比较，然后像以前一样调用sum：

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

Answer 2

我会做：

df["sum_0"] = df.apply(lambda row: sum(row[0:3]==0) ,axis=1)