pandas 从现有数据帧创建多索引
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Create multiindex from existing dataframe
提问by puifais
I've spent hours browsing everywhere now to try to create a multiindex from dataframe in pandas. This is the dataframe I have (posting excel sheet mockup. I do have this in pandas dataframe):
我现在已经花了几个小时到处浏览，以尝试从 Pandas 中的数据帧创建一个多索引。这是我拥有的数据框（发布 excel 表模型。我在 Pandas 数据框中确实有这个）：
And this is what I want:
这就是我想要的：
I have tried
我试过了
newmulti = currentDataFrame.set_index(['user_id','account_num'])
But it returns a dataframe, not a multiindex. Also, I could not figure out how to make 'user_id' level 0 and 'account_num' level 1. I think this must be trivial but I've read so many posts, tutorials, etc. and still could not figure it out. Partly because I'm a very visual person and most posts are not. Please help!
但它返回一个数据帧，而不是一个多索引。此外，我无法弄清楚如何将“user_id”级别设置为 0 和“account_num”级别设置为 1。我认为这一定是微不足道的，但我已经阅读了很多帖子、教程等，但仍然无法弄清楚。部分是因为我是一个非常注重视觉的人，而大多数帖子都不是。请帮忙！
回答by Alexander
You could simply use groupbyin this case, which will create the multi-index automatically when it sums the sales along the requested columns.
groupby在这种情况下，您可以简单地使用它，它会在对所请求列的销售额求和时自动创建多索引。
df.groupby(['user_id', 'account_num', 'dates']).sales.sum().to_frame()
You should also be able to simply do this:
您还应该能够简单地执行以下操作：
df.set_index(['user_id', 'account_num', 'dates'])
Although you probably want to avoid any duplicates (e.g. two or more rows with identical user_id, account_numand datevalues but different sales figures) by summing them, which is why I recommended using groupby.
虽然你可能要避免任何重复（例如，两个或多个行具有相同的user_id，account_num并date通过总结他们，这就是为什么我建议使用值，但不同的销售数字）groupby。
If you need the multi-index, you can simply access viat new_df.indexwhere new_dfis the new dataframe created from either of the two operations above.
如果您需要多指标，你可以简单地访问viatnew_df.index其中new_df从上述两种操作所造成的新的数据帧。
And user_idwill be level 0 and account_numwill be level 1.
并且user_id将是 0 级和account_num1 级。
回答by Eulenfuchswiesel
For clarification of future users I would like to add the following:
为了澄清未来的用户，我想添加以下内容：
As said by Alexander,
正如亚历山大所说，
df.set_index(['user_id', 'account_num', 'dates'])
with a possible inplace=Truedoes the job.
有一个可能inplace=True的工作。
The type(df)gives
该type(df)给
pandas.core.frame.DataFrame
whereas type(df.index)is indeed the expected
而type(df.index)确实是预期的
pandas.core.indexes.multi.MultiIndex
回答by piRSquared
lvl0 = currentDataFrame.user_id.values
lvl1 = currentDataFrame.account_num.values
midx = pd.MultiIndex.from_arrays([lvl0, lvl1], names=['level 0', 'level 1'])
回答by Casey Van Buren
The DataFrame returned by currentDataFrame.set_index(['user_id','account_num'])has it's index set to ['user_id','account_num']
返回的 DataFramecurrentDataFrame.set_index(['user_id','account_num'])的索引设置为['user_id','account_num']
newmulti.indexwill return the MultiIndex object.
newmulti.index将返回 MultiIndex 对象。
