删除 pandas 中的列的最佳方法是什么

问题描述

我正在为自己的学习提出这个问题.据我所知，以下是删除pandas数据框中的列的不同方法.

I am raising this question for my self learning. As far as I know, followings are the different methods to remove columns in pandas dataframe.

选项-1:

df=pd.DataFrame({'a':[1,2,3,4,5],'b':[6,7,8,9,10],'c':[11,12,13,14,15]})
del df['a']

选项-2:

df=pd.DataFrame({'a':[1,2,3,4,5],'b':[6,7,8,9,10],'c':[11,12,13,14,15]})
df=df.drop('a',1)

选项-3:

df=pd.DataFrame({'a':[1,2,3,4,5],'b':[6,7,8,9,10],'c':[11,12,13,14,15]})
df=df[['b','c']]

1. 其中最好的方法是什么?
2. 还有其他方法可以达到相同的目的吗?

推荐答案

按照 doc :

和 pandas.DataFrame.drop :

所以，我认为我们应该坚持使用df.drop.为什么?我认为优点是:

So, I think we should stick with df.drop. Why? I think the pros are:

1. 它为我们提供了对删除操作的更多控制:

1. It gives us more control of the remove action:

# This will return a NEW DataFrame object, leave the original `df` untouched.
df.drop('a', axis=1)
# This will modify the `df` inplace. **And return a `None`**.
df.drop('a', axis=1, inplace=True)

它可以使用args处理更复杂的情况.例如.使用level，我们可以处理MultiIndex删除.借助errors，我们可以防止某些错误.

It can handle more complicated cases with it's args. E.g. with level, we can handle MultiIndex deletion. And with errors, we can prevent some bugs.

这是一种更加统一和面向对象的方式.

It's a more unified and object oriented way.

---

就像@jezrael在回答中指出的那样:

---

And just like @jezrael noted in his answer:

选项1:使用关键字del是受限制的方式.

Option 1: Using key word del is a limited way.

选项3:df=df[['b','c']]本质上甚至不是删除.首先使用[]语法通过索引选择数据，然后取消绑定用原始DataFrame命名df并将其与新的DataFrame绑定(即df[['b','c']]).

Option 3: And df=df[['b','c']] isn't even a deletion in essence. It first select data by indexing with [] syntax, then unbind the name df with the original DataFrame and bind it with the new one (i.e. df[['b','c']]).

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