Python 在熊猫中删除多列
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Drop multiple columns in pandas
提问by lukewitmer
I am trying to drop multiple columns (column 2 and 70 in my data set, indexed as 1 and 69 respectively) by index number in a pandas data frame with the following code:
我正在尝试使用以下代码在熊猫数据框中按索引号删除多列（数据集中的第 2 列和第 70 列，分别索引为 1 和 69）：
df.drop([df.columns[[1, 69]]], axis=1, inplace=True)
I get the following error:
我收到以下错误：
TypeError: unhashable type: 'Index'
And in my code the [1, 69] is highlighted and says:
在我的代码中， [1, 69] 突出显示并说：
Expected type 'Integral', got 'list[int]' instead
The following code does what I want it to do successfully, but on two lines of repetitive code (first dropping col index 69, then 1, and order does matter because dropping earlier columns changes the index of later columns). I thought I could specify more than one column index simply as a list, but perhaps I have something wrong above?
下面的代码完成了我希望它成功完成的工作，但是在两行重复代码上（首先删除列索引 69，然后删除 1，并且顺序很重要，因为删除较早的列会更改较晚列的索引）。我以为我可以简单地将多个列索引指定为一个列表，但也许我上面有什么问题？
df.drop([df.columns[69]], axis=1, inplace=True)
df.drop([df.columns[1]], axis=1, inplace=True)
Is there a way that I can do this on one line similar to the first code snippet above?
有没有办法可以在类似于上面第一个代码片段的一行上执行此操作？
采纳答案by joris
You don't need to wrap it in a list with [..], just provide the subselection of the columns index:
您不需要将它包装在一个列表中[..]，只需提供列索引的子选择：
df.drop(df.columns[[1, 69]], axis=1, inplace=True)
as the index object is already regarded as list-like.
因为索引对象已经被视为类列表。
回答by Okroshiashvili
Try this
尝试这个
df.drop(df.iloc[:, 1:69], inplace=True, axis=1)
df.drop(df.iloc[:, 1:69], inplace=True, axis=1)
This works for me
这对我有用
