Python 基于以前的列集创建多个新列（效率更高）

对于我的数据集，我想创建一些新列。这些列由一个比率组成，该比率基于其他两列。以下是我的意思的一个例子：

import random
col1=[0,0,0,0,2,4,6,0,0,0,100,200,300,400]
col2=[0,0,0,0,4,6,8,0,0,0,200,900,400, 500]

d = {'Unit': [1, 1, 1, 1, 2, 2, 2, 3, 4, 5, 6, 6, 6, 6], 
 'Year': [2014, 2015, 2016, 2017, 2015, 2016, 2017, 2017, 2014, 2015, 2014, 2015, 2016, 2017], 'col1' : col1, 'col2' : col2 }
df = pd.DataFrame(data=d)

new_df = df.groupby(['Unit', 'Year']).sum()

new_df['col1/col2'] = (new_df.groupby(level=0, group_keys=False)
                  .apply(lambda x: x.col1/x.col2.shift())
                 )

           col1  col2      col1/col2
Unit Year                      
1    2014     0     0       NaN
     2015     0     0       NaN
     2016     0     0       NaN
     2017     0     0       NaN
2    2015     2     4       NaN
     2016     4     6  1.000000
     2017     6     8  1.000000
3    2017     0     0       NaN
4    2014     0     0       NaN
5    2015     0     0       NaN
6    2014   100   200       NaN
     2015   200   900  1.000000
     2016   300   400  0.333333
     2017   400   500  1.000000

然而，这是一个超级简化的df。事实上，我有感冒1到50。我觉得我现在的工作效率非常低：

col1=[0,0,0,0,2,4,6,0,0,0,100,200,300,400]
col2=[0,0,0,0,4,6,8,0,0,0,200,900,400, 500]
col3=[0,0,0,0,4,6,8,0,0,0,200,900,400, 500]
col4=[0,0,0,0,4,6,8,0,0,0,200,900,400, 500]
col5=[0,0,0,0,4,6,8,0,0,0,200,900,400, 500]
col6=[0,0,0,0,4,6,8,0,0,0,200,900,400, 500]

# data in all cols is the same, just for example.

d = {'Unit': [1, 1, 1, 1, 2, 2, 2, 3, 4, 5, 6, 6, 6, 6], 
 'Year': [2014, 2015, 2016, 2017, 2015, 2016, 2017, 2017, 2014, 2015, 2014, 2015, 2016, 2017], 'col1' : col1, 'col2' : col2, 'col3' : col3, 'col4' : col4, 'col5' : col5, 'col6' : col6}
df = pd.DataFrame(data=d)

new_df = df.groupby(['Unit', 'Year']).sum()

new_df['col1/col2'] = (new_df.groupby(level=0, group_keys=False)
                  .apply(lambda x: x.col1/x.col2.shift())
                 )
new_df['col3/col4'] = (new_df.groupby(level=0, group_keys=False)
                  .apply(lambda x: x.col3/x.col4.shift())
                 )
new_df['col5/col6'] = (new_df.groupby(level=0, group_keys=False)
                  .apply(lambda x: x.col5/x.col6.shift())
                 )

我对创建新列的方法执行了25次。这样做能更有效率吗/

提前谢谢大家,

Jen

Idea用于列表

cols2

中的所有列，并用过滤后的数据框除以列表

cols1

：

col1=[0,0,0,0,2,4,6,0,0,0,100,200,300,400]
col2=[0,0,0,0,4,6,8,0,0,0,200,900,400, 500]


d = {'Unit': [1, 1, 1, 1, 2, 2, 2, 3, 4, 5, 6, 6, 6, 6], 
 'Year': [2014, 2015, 2016, 2017, 2015, 2016, 2017, 2017, 2014, 2015, 2014, 2015, 2016, 2017], 
 'col1' : col1, 'col2' : col2 , 
 'col3' : col1, 'col4' : col2 , 
 'col5' : col1, 'col6' : col2 }
df = pd.DataFrame(data=d)

new_df = df.groupby(['Unit', 'Year']).sum()

cols1 = ['col1','col3','col5']
cols2 = ['col2','col4','col6']
new_df = new_df[cols1] / new_df.groupby(level=0)[cols2].shift().values
new_df.columns = [f'{a}/{b}' for a, b in zip(cols1, cols2)]          
print (new_df)
           col1/col2  col3/col4  col5/col6
Unit Year                                 
1    2014        NaN        NaN        NaN
     2015        NaN        NaN        NaN
     2016        NaN        NaN        NaN
     2017        NaN        NaN        NaN
2    2015        NaN        NaN        NaN
     2016   1.000000   1.000000   1.000000
     2017   1.000000   1.000000   1.000000
3    2017        NaN        NaN        NaN
4    2014        NaN        NaN        NaN
5    2015        NaN        NaN        NaN
6    2014        NaN        NaN        NaN
     2015   1.000000   1.000000   1.000000
     2016   0.333333   0.333333   0.333333
     2017   1.000000   1.000000   1.000000

---

你想过用Numpy吗？熊猫实际上是以Numpy为基础的 这就是为什么它工作得这么快。DFs令人惊讶，但对于更深入或更复杂的操作，我只需将其转换为Numpy，然后使用它并转换回pandas：

...
new_df = df.groupby(['Unit', 'Year']).sum()
new_array = new_df.values
print(type(new_array))

[out]: <type 'numpy.ndarray'>

。。。
new_df=df.groupby（['Unit'，'Year']）.sum（）
新数组=新值
打印（类型（新数组））
[out]：

---

祝你好运

但是你的答案是错误的，没有像need OP.lol那样工作，错误的答案打开了通往惊人Numpy功能的大门？您可以说它在您的情况下没有用处，但是说Numpy在处理数组时是一个糟糕的选择，这只是对我的“不精确”（如果您愿意）答案的错误回答。OP需要问题的答案，不幸的是，这不是OP的答案。