Python Pandas：过滤多个条件
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Pandas: Filtering multiple conditions
提问by bclayman
I'm trying to do boolean indexing with a couple conditions using Pandas. My original DataFrame is called df. If I perform the below, I get the expected result:
我正在尝试使用 Pandas 在几个条件下进行布尔索引。我原来的 DataFrame 被称为df. 如果我执行以下操作，我会得到预期的结果：
temp = df[df["bin"] == 3]
temp = temp[(~temp["Def"])]
temp = temp[temp["days since"] > 7]
temp.head()
However, if I do this (which I think should be equivalent), I get no rows back:
但是，如果我这样做（我认为这应该是等效的），则不会返回任何行：
temp2 = df[df["bin"] == 3]
temp2 = temp2[~temp2["Def"] & temp2["days since"] > 7]
temp2.head()
Any idea what accounts for the difference?
知道造成这种差异的原因是什么吗？
回答by jezrael
Use ()because operator precedence:
使用()因为运算符优先级：
temp2 = df[~df["Def"] & (df["days since"] > 7) & (df["bin"] == 3)]
Alternatively, create conditions on separate rows:
或者，在单独的行上创建条件：
cond1 = df["bin"] == 3
cond2 = df["days since"] > 7
cond3 = ~df["Def"]
temp2 = df[cond1 & cond2 & cond3]
Sample:
样品：
df = pd.DataFrame({'Def':[True] *2 + [False]*4,
'days since':[7,8,9,14,2,13],
'bin':[1,3,5,3,3,3]})
print (df)
Def bin days since
0 True 1 7
1 True 3 8
2 False 5 9
3 False 3 14
4 False 3 2
5 False 3 13
temp2 = df[~df["Def"] & (df["days since"] > 7) & (df["bin"] == 3)]
print (temp2)
Def bin days since
3 False 3 14
5 False 3 13
