Python 熊猫从长到宽重塑，通过两个变量
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Pandas long to wide reshape, by two variables
提问by Luke
I have data in long format and am trying to reshape to wide, but there doesn't seem to be a straightforward way to do this using melt/stack/unstack:
我有长格式的数据，并且正在尝试将数据重塑为宽格式，但似乎没有使用melt/stack/unstack 的直接方法来做到这一点：
Salesman Height product price
Knut 6 bat 5
Knut 6 ball 1
Knut 6 wand 3
Steve 5 pen 2
Becomes:
变成：
Salesman Height product_1 price_1 product_2 price_2 product_3 price_3
Knut 6 bat 5 ball 1 wand 3
Steve 5 pen 2 NA NA NA NA
I think Stata can do something like this with the reshape command.
我认为 Stata 可以用 reshape 命令做这样的事情。
采纳答案by Karl D.
A simple pivot might be sufficient for your needs but this is what I did to reproduce your desired output:
一个简单的枢轴可能足以满足您的需求，但这是我为重现您想要的输出所做的：
df['idx'] = df.groupby('Salesman').cumcount()
Just adding a within group counter/index will get you most of the way there but the column labels will not be as you desired:
只需添加组内计数器/索引即可完成大部分工作，但列标签不会如您所愿：
print df.pivot(index='Salesman',columns='idx')[['product','price']]
product price
idx 0 1 2 0 1 2
Salesman
Knut bat ball wand 5 1 3
Steve pen NaN NaN 2 NaN NaN
To get closer to your desired output I added the following:
为了更接近您想要的输出，我添加了以下内容：
df['prod_idx'] = 'product_' + df.idx.astype(str)
df['prc_idx'] = 'price_' + df.idx.astype(str)
product = df.pivot(index='Salesman',columns='prod_idx',values='product')
prc = df.pivot(index='Salesman',columns='prc_idx',values='price')
reshape = pd.concat([product,prc],axis=1)
reshape['Height'] = df.set_index('Salesman')['Height'].drop_duplicates()
print reshape
product_0 product_1 product_2 price_0 price_1 price_2 Height
Salesman
Knut bat ball wand 5 1 3 6
Steve pen NaN NaN 2 NaN NaN 5
Edit: if you want to generalize the procedure to more variables I think you could do something like the following (although it might not be efficient enough):
编辑：如果您想将该过程推广到更多变量，我认为您可以执行以下操作（尽管它可能不够高效）：
df['idx'] = df.groupby('Salesman').cumcount()
tmp = []
for var in ['product','price']:
df['tmp_idx'] = var + '_' + df.idx.astype(str)
tmp.append(df.pivot(index='Salesman',columns='tmp_idx',values=var))
reshape = pd.concat(tmp,axis=1)
@Luke said:
I think Stata can do something like this with the reshape command.
@卢克 说：
我认为 Stata 可以用 reshape 命令做这样的事情。
You can but I think you also need a within group counter to get the reshape in stata to get your desired output:
您可以，但我认为您还需要一个组内计数器来在 stata 中进行重塑以获得所需的输出：
+-------------------------------------------+
| salesman idx height product price |
|-------------------------------------------|
1. | Knut 0 6 bat 5 |
2. | Knut 1 6 ball 1 |
3. | Knut 2 6 wand 3 |
4. | Steve 0 5 pen 2 |
+-------------------------------------------+
If you add idxthen you could do reshape in stata:
如果添加，idx则可以在stata以下位置进行重塑：
reshape wide product price, i(salesman) j(idx)
回答by chucklukowski
pivoted = df.pivot('salesman', 'product', 'price')
pg. 192 Python for Data Analysis
页。192 用于数据分析的 Python
回答by Gecko
A bit old but I will post this for other people.
有点旧，但我会发布给其他人。
What you want can be achieved, but you probably shouldn't want it ;) Pandas supports hierarchical indexes for both rows and columns. In Python 2.7.x ...
你想要的可以实现，但你可能不应该想要它 ;) Pandas 支持行和列的分层索引。在 Python 2.7.x 中...
from StringIO import StringIO
raw = '''Salesman Height product price
Knut 6 bat 5
Knut 6 ball 1
Knut 6 wand 3
Steve 5 pen 2'''
dff = pd.read_csv(StringIO(raw), sep='\s+')
print dff.set_index(['Salesman', 'Height', 'product']).unstack('product')
Produces a probably more convenient representation than what you were looking for
产生可能比您正在寻找的更方便的表示
price
product ball bat pen wand
Salesman Height
Knut 6 1 5 NaN 3
Steve 5 NaN NaN 2 NaN
The advantage of using set_index and unstacking vs a single function as pivot is that you can break the operations down into clear small steps, which simplifies debugging.
使用 set_index 和 unstacking 与使用单个函数作为主元的优势在于，您可以将操作分解为清晰的小步骤，从而简化调试。
回答by Charles Clayton
Here's another solution more fleshed out, taken from Chris Albon's site.
这是另一个更充实的解决方案，取自Chris Albon 的网站。
Create "long" dataframe
创建“长”数据框
raw_data = {'patient': [1, 1, 1, 2, 2],
'obs': [1, 2, 3, 1, 2],
'treatment': [0, 1, 0, 1, 0],
'score': [6252, 24243, 2345, 2342, 23525]}
df = pd.DataFrame(raw_data, columns = ['patient', 'obs', 'treatment', 'score'])
Make a "wide" data
制作“宽”数据
df.pivot(index='patient', columns='obs', values='score')
回答by ALollz
Karl D's solution gets at the heart of the problem. But I find it's far easier to pivot everything (with .pivot_tablebecause of the two index columns) and then sortand assign the columns to collapse the MultiIndex:
Karl D 的解决方案是问题的核心。但是我发现旋转所有内容（.pivot_table因为有两个索引列）然后sort分配列以折叠以下内容要容易得多MultiIndex：
df['idx'] = df.groupby('Salesman').cumcount()+1
df = df.pivot_table(index=['Salesman', 'Height'], columns='idx',
values=['product', 'price'], aggfunc='first')
df = df.sort_index(axis=1, level=1)
df.columns = [f'{x}_{y}' for x,y in df.columns]
df = df.reset_index()
Output:
输出：
Salesman Height price_1 product_1 price_2 product_2 price_3 product_3
0 Knut 6 5.0 bat 1.0 ball 3.0 wand
1 Steve 5 2.0 pen NaN NaN NaN NaN
