Question

使用python选择特定的Excel行。因此，在excel中，我会做

setEvents

用于获取非封闭或空白数据框中的那些列。尝试使用

If(And(Or(A<>({"Closed",""}),Or(B<>({"Closed",""})))

问题是python正在删除列，例如：

df = df[(~df.A.isin([Closed","No Data"])) &(~df.B.isin([Closed","No Data"]))]

我不想要正如其中一个链接所建议，也尝试过

A                        B
Approved       Closed
No Data          Restrict
Restrict           No Data

得到与我尝试.isin时相同的结果

Answer 1

我将使用以下示例数据：

           A         B  ~df.A.isin  ~df.B.isin  ~A & ~B  ~A | ~B
0     Closed    Closed       False       False    False    False
1     Closed   No Data       False       False    False    False
2   Approved    Closed        True       False    False     True
3    No Data   No Data       False       False    False    False
4     Closed  Approved       False        True    False     True
5    No Data  Restrict       False        True    False     True
6   Approved   No Data        True       False    False     True
7     Closed  Restrict       False        True    False     True
8   Approved  Approved        True        True     True     True
9    No Data  Approved       False        True    False     True
10  Restrict   No Data        True       False    False     True
11  Restrict  Approved        True        True     True     True

~df.A.isin列显示~df.A.isin(["Closed","No Data"])的值，对于A既不包含True也不包含Closed

的行，其值为No Data

~df.B.isin列显示~df.B.isin(["Closed","No Data"])的值，对于B既不包含True也不包含Closed

的行，其值为No Data

~A & ~B列显示(~df.A.isin(["Closed","No Data"])) &(~df.B.isin(["Closed","No Data"]))

的值

~A | ~B列显示(~df.A.isin(["Closed","No Data"])) |(~df.B.isin(["Closed","No Data"]))

的值

您第一次尝试在"的开头缺少Closed"。添加它，我们就有

df[(~df.A.isin(["Closed","No Data"])) &(~df.B.isin(["Closed","No Data"]))]

这给了我们

           A         B  ~df.A.isin  ~df.B.isin  ~A & ~B  ~A | ~B
8   Approved  Approved        True        True     True     True
11  Restrict  Approved        True        True     True     True

结果仅显示完全没有Closed和没有No Data的那些行。

文本评论中的建议：

df[(~df.A.isin(["Closed","No Data"])) |(~df.B.isin(["Closed","No Data"]))]

给予我们

           A         B  ~df.A.isin  ~df.B.isin  ~A & ~B  ~A | ~B
2   Approved    Closed        True       False    False     True
4     Closed  Approved       False        True    False     True
5    No Data  Restrict       False        True    False     True
6   Approved   No Data        True       False    False     True
7     Closed  Restrict       False        True    False     True
8   Approved  Approved        True        True     True     True
9    No Data  Approved       False        True    False     True
10  Restrict   No Data        True       False    False     True
11  Restrict  Approved        True        True     True     True

这里我们有|（or）而不是&（and），所以行可以包含Closed或No Data，但同时不在A和B中。这表示具有以下内容的行：

       A         B
Approved    Closed
 No Data  Restrict
Restrict   No Data

将包含

，但不行包含：

     A         B
Closed    Closed
Closed   No Data

您的第二次尝试：

df.loc[(df[A] != "Closed") & (df[B] != "Closed") &
       (df[A] != "No data") & (df[B] != "No data")

需要在列标签周围加上引号。您可以使用df.A或df['A']，但不能 df[A]

此外，您在data中将No data拼写为小写d，而在其他地方，您将其拼写为大写D-No Data。在python中，这是不一样的。如果我们解决该问题：

df.loc[(df['A'] != "Closed") & (df['B'] != "Closed") &
       (df['A'] != "No Data") & (df['B'] != "No Data")]

这给了我们与第一次尝试相同的东西：

           A         B  ~df.A.isin  ~df.B.isin  ~A & ~B  ~A | ~B
8   Approved  Approved  True  True  True  True  True  True  True
11  Restrict  Approved  True  True  True  True  True  True  True

如果稍微重新排列此表达式，请使用括号和|（or）：

df.loc[((df['A'] != "Closed") & (df['A'] != "No Data")) | 
       ((df['B'] != "Closed") & (df['B'] != "No Data"))]

我们得到：

           A         B  ~df.A.isin  ~df.B.isin  ~A & ~B  ~A | ~B
2   Approved    Closed        True       False    False     True
4     Closed  Approved       False        True    False     True
5    No Data  Restrict       False        True    False     True
6   Approved   No Data        True       False    False     True
7     Closed  Restrict       False        True    False     True
8   Approved  Approved        True        True     True     True
9    No Data  Approved       False        True    False     True
10  Restrict   No Data        True       False    False     True
11  Restrict  Approved        True        True     True     True

等同于Excel的Python嵌套if条件用于过滤Pandas DataFrame行

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