Python 在熊猫中删除列的最佳方法是什么
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What is the best way to remove columns in pandas
提问by Mohamed Thasin ah
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.
我提出这个问题是为了我的自学。据我所知，以下是删除 Pandas 数据框中列的不同方法。
Option - 1:
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']
Option - 2:
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)
Option - 3:
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']]
- What is the best approach among these?
- Any other approaches to achieve the same?
- 其中最好的方法是什么？
- 还有其他方法可以实现相同的目标吗？
采纳答案by YaOzI
Follow the doc:
按照文档：
DataFrame is a 2-dimensional labeled data structurewith columns of potentially different types.
DataFrame 是一种二维标记数据结构，具有可能不同类型的列。
Drop specified labelsfrom rows or columns.
从行或列中删除指定的标签。
So, I think we should stick with df.drop. Why? I think the pros are:
所以，我认为我们应该坚持使用df.drop. 为什么？我认为优点是：
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)It can handle more complicated cases with it's args. E.g. with
level, we can handle MultiIndex deletion. And witherrors, we can prevent some bugs.It's a more unified and object oriented way.
它使我们可以更好地控制删除操作：
# 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
level，我们可以处理多索引删除。使用errors，我们可以防止一些错误。这是一种更加统一和面向对象的方式。
And just like @jezrael noted in his answer:
就像@jezrael 在他的回答中指出的那样：
Option 1: Using key word delis a limited way.
选项 1：使用关键字del是一种有限的方式。
Option 3: And df=df[['b','c']]isn't even a deletion in essence. It first select data by indexingwith []syntax, then unbind the name dfwith the original DataFrame and bind it with the new one (i.e. df[['b','c']]).
选项 3：df=df[['b','c']]本质上甚至不是删除。它首先通过使用[]语法进行索引来选择数据，然后将名称df与原始 DataFrame解除绑定并将其与新的 DataFrame 绑定（即df[['b','c']]）。
回答by razmik
The recommended way to delete a column or row in pandas dataframes is using drop.
在 Pandas 数据框中删除列或行的推荐方法是使用 drop。
To delete a column,
要删除列，
df.drop('column_name', axis=1, inplace=True)
To delete a row,
要删除一行，
df.drop('row_index', axis=0, inplace=True)
You can refer this postto see a detailed conversation about column delete approaches.
您可以参考这篇文章以查看有关列删除方法的详细对话。
回答by aydow
From a speed perspective, option 1 seems to be the best. Obviously, based on the other answers, that doesn't mean it's actually the best option.
从速度的角度来看，选项 1 似乎是最好的。显然，根据其他答案，这并不意味着它实际上是最佳选择。
In [52]: import timeit
In [53]: s1 = """
...: import pandas as pd
...: df=pd.DataFrame({'a':[1,2,3,4,5],'b':[6,7,8,9,10],'c':[11,12,13,14,15]})
...: del df['a']
...: """
In [54]: s2 = """
...: import pandas as pd
...: 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)
...: """
In [55]: s3 = """
...: import pandas as pd
...: 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']]
...: """
In [56]: timeit.timeit(stmt=s1, number=100000)
Out[56]: 53.37321400642395
In [57]: timeit.timeit(stmt=s2, number=100000)
Out[57]: 79.68139410018921
In [58]: timeit.timeit(stmt=s3, number=100000)
Out[58]: 76.25269913673401
回答by jezrael
In my opinion the best is use 2. and 3. option, because first has limits - you can remove only one column and cannot use dot notation- del df.a.
在我看来，最好是使用 2. 和 3. 选项，因为第一个有限制 - 您只能删除一列并且不能使用点表示法- del df.a。
3.solution is not deleting, but selecting and piRSquaredcreate nice answer for multiple possible solutions with same idea.
3.solution 不是删除，而是选择和piRSquared为具有相同想法的多个可能解决方案创建了很好的答案。
