pandas 使用数据透视表熊猫后如何摆脱多级索引？
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How to get rid of multilevel index after using pivot table pandas?
提问by chessosapiens
I had following data frame (the real data frame is much more larger than this one ) :
我有以下数据框（实际数据框比这个大得多）：
sale_user_id sale_product_id count
1 1 1
1 8 1
1 52 1
1 312 5
1 315 1
Then reshaped it to move the values in sale_product_id as column headers using the following code:
然后使用以下代码重塑它以将 sale_product_id 中的值移动为列标题：
reshaped_df=id_product_count.pivot(index='sale_user_id',columns='sale_product_id',values='count')
and the resulting data frame is:
结果数据框是：
sale_product_id -1057 1 2 3 4 5 6 8 9 10 ... 98 980 981 982 983 984 985 986 987 99
sale_user_id
1 NaN 1.0 NaN NaN NaN NaN NaN 1.0 NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
4 NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
as you can see we have a multililevel index , what i need is to have sale_user_is in the first column without multilevel indexing:
正如你所看到的，我们有一个多级索引，我需要的是在没有多级索引的第一列中有 sale_user_is ：
i take the following approach :
我采取以下方法：
reshaped_df.reset_index()
the the result would be like this i still have the sale_product_id column , but i do not need it anymore:
结果会是这样我仍然有 sale_product_id 列，但我不再需要它了：
sale_product_id sale_user_id -1057 1 2 3 4 5 6 8 9 ... 98 980 981 982 983 984 985 986 987 99
0 1 NaN 1.0 NaN NaN NaN NaN NaN 1.0 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 3 NaN 1.0 NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 4 NaN NaN 1.0 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN
i can subset this data frame to get rid of sale_product_id but i don't think it would be efficient.I am looking for an efficient way to get rid of multilevel indexing while reshaping the original data frame
我可以对这个数据框进行子集化以摆脱 sale_product_id，但我认为它不会有效。我正在寻找一种有效的方法来摆脱多级索引，同时重塑原始数据框
回答by jezrael
You need remove only index name, use rename_axis(new in pandas0.18.0):
你只需要删除index name，使用rename_axis（新的pandas0.18.0）：
print (reshaped_df)
sale_product_id 1 8 52 312 315
sale_user_id
1 1 1 1 5 1
print (reshaped_df.index.name)
sale_user_id
print (reshaped_df.rename_axis(None))
sale_product_id 1 8 52 312 315
1 1 1 1 5 1
Another solution working in pandas below 0.18.0:
下面在Pandas中工作的另一个解决方案0.18.0：
reshaped_df.index.name = None
print (reshaped_df)
sale_product_id 1 8 52 312 315
1 1 1 1 5 1
If need remove columns namealso:
如果需要columns name也删除：
print (reshaped_df.columns.name)
sale_product_id
print (reshaped_df.rename_axis(None).rename_axis(None, axis=1))
1 8 52 312 315
1 1 1 1 5 1
Another solution:
另一种解决方案：
reshaped_df.columns.name = None
reshaped_df.index.name = None
print (reshaped_df)
1 8 52 312 315
1 1 1 1 5 1
EDIT by comment:
通过评论编辑：
You need reset_indexwith parameter drop=True:
您需要reset_index带参数drop=True：
reshaped_df = reshaped_df.reset_index(drop=True)
print (reshaped_df)
sale_product_id 1 8 52 312 315
0 1 1 1 5 1
#if need reset index nad remove column name
reshaped_df = reshaped_df.reset_index(drop=True).rename_axis(None, axis=1)
print (reshaped_df)
1 8 52 312 315
0 1 1 1 5 1
Of if need remove only column name:
如果只需要删除列名：
reshaped_df = reshaped_df.rename_axis(None, axis=1)
print (reshaped_df)
1 8 52 312 315
sale_user_id
1 1 1 1 5 1
Edit1:
编辑1：
So if need create new column from indexand remove columns names:
因此，如果需要从中创建新列index并删除columns names：
reshaped_df = reshaped_df.rename_axis(None, axis=1).reset_index()
print (reshaped_df)
sale_user_id 1 8 52 312 315
0 1 1 1 1 5 1
回答by Yury Wallet
The way it works for me is
它对我有用的方式是
df_cross=pd.DataFrame(pd.crosstab(df[c1], df[c2]).to_dict()).reset_index()
