如何根据类别将 Pandas 数据框行转换为列？
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How to convert pandas dataframe rows into columns, based on category?
提问by Nandhini Anand
I have a pandas data frame with a category variable and some number variables. Something like this:
我有一个带有类别变量和一些数字变量的Pandas数据框。像这样的东西：
ls = [{'count':5, 'module':'payroll', 'id':2}, {'count': 53, 'module': 'general','id':2}, {'id': 5,'count': 35, 'module': 'tax'}, ]
df = pd.DataFrame.from_dict(ls)
The df looks like this:
df 看起来像这样：
df
Out[15]:
count id module
0 5 2 payroll
1 53 2 general
2 35 5 tax
I want convert(transpose is the right word?) the module variables into columns and group by the id. So something like:
我想转换（转置是正确的词？）将模块变量转换为列并按 id 分组。所以像：
general_count id payroll_count tax_count
0 53.0 2 5.0 NaN
1 NaN 5 NaN 35.0
One approach to this would be to use apply:
一种方法是使用apply：
df['payroll_count'] = df.id.apply(lambda x: df[df.id==x][df.module=='payroll'])
However, this suffers from multiple drawbacks:
但是，这存在多个缺点：
Costly, and takes too much time
Creates artifacts and empty dataframes that need to be cleaned up.
成本高，需要太多时间
创建需要清理的工件和空数据帧。
I sense there's a better way to achieve this with pandas groupby, but can't find a way to this same operation more efficiently. Please help.
我觉得使用pandas groupby有更好的方法来实现这一点，但找不到更有效地进行相同操作的方法。请帮忙。
回答by jezrael
You can use groupbyby columns which first create new indexand last column. then need aggreagate some way - I use mean, then convert one column DataFrameto Seriesby DataFrame.squeeze(then is not necessary remove top level of Multiindex in columns) and reshape by unstack. Last add_suffixto column name:
您可以groupby按首先创建 newindex和 last 的列使用column。然后需要以某种方式聚合 - 我使用mean，然后将一列转换DataFrame为Seriesby DataFrame.squeeze（然后没有必要删除列中 Multiindex 的顶级）并通过unstack. 最后add_suffix到列名：
df = df.groupby(['id','module']).mean().squeeze().unstack().add_suffix('_count')
print (df)
module general_count payroll_count tax_count
id
2 53.0 5.0 NaN
5 NaN NaN 35.0
Another solution with pivot, then need remove Multiindexfrom columns by list comprehension:
另一个解决方案pivot，然后需要Multiindex从列中删除list comprehension：
df = df.pivot(index='id', columns='module')
df.columns = ['_'.join((col[1], col[0])) for col in df.columns]
print (df)
general_count payroll_count tax_count
id
2 53.0 5.0 NaN
5 NaN NaN 35.0
回答by Zero
You could use set_indexand unstack
你可以使用set_index和unstack
In [2]: df.set_index(['id','module'])['count'].unstack().add_suffix('_count').reset_index()
Out[2]:
module id general_count payroll_count tax_count
0 2 53.0 5.0 NaN
1 5 NaN NaN 35.0
