在给定条件下是否清除熊猫中的某一行数据？

Question

我有一个像这样的DataFrame

Classification     Value_1      Value_2       
churn                 1.0            2.0             
not_churn             2.0            3.0
not_churn             0.0            0.0
churn                 0.0            1.0

我知道在所有值都为0的情况下，分类应该是churn。然后，我需要删除所有值为0且分类为not_churn的所有行。我试过了：

 df.drop((df['value_1'] == 0 
              & df['value_2'] == 0
              & df['classification']== 'not_churn').index)
'TypeError: cannot compare a dtyped [float64] array with a scalar of type [bool]'

Answer 1

将boolean indexing与不等于!=的更改条件一起使用，并将&更改为| (或)：

df = df[(df['Value_1'] != 0 ) | (df['Value_2'] != 0) | (df['Classification'] != 'not_churn')]
print (df)
  Classification  Value_1  Value_2
0          churn      1.0      2.0
1      not_churn      2.0      3.0
3          churn      0.0      1.0

Answer 2

如果您想根据您不想要的内容保留行，我会尝试：

  df = df[~((df['value_1'] == 0) & (df['value_2'] == 0) & (df['classification'] == 'not churn'))]