从列表的笛卡尔积创建一个 pandas DataFrame

Question

我有一些 python 代码运行一个简单的 for 循环并打印出每个结果组合，我试图根据生成结果的顺序找出如何将这些全部附加到单个数据帧中。我下面会解释。

我有以下代码：

categories = ['small', 'medium', 'big']
parameters = ['p1_5_p2_4_p3_2', 'p1_3_p2_8_p3_3', 'p1_4_p2_3_p3_6']
Blue = [5, 4, 3]

for parameter in parameters:
    for category in categories:
        for x in Blue:
            y = x + 1
            z = x + 2
            
            print(category)
            print(parameter)
            print(y)
            print(z)
            print('')

产生：

small
p1_5_p2_4_p3_2 
6 
7

small 
p1_5_p2_4_p3_2 
5 
6

small 
p1_5_p2_4_p3_2 
4 
5

medium 
p1_5_p2_4_p3_2 
6 
7

medium 
p1_5_p2_4_p3_2 
5 
6

medium 
p1_5_p2_4_p3_2 
4 
5

big 
p1_5_p2_4_p3_2 
6 
7

big 
p1_5_p2_4_p3_2 
5 
6

big
p1_5_p2_4_p3_2 
4 
5 

small
p1_3_p2_8_p3_3
6 
7
...

有没有办法将它发送到 pandas 数据框，以便数据框看起来像：

Category      Parameters         Value_1    Value_2
----------------------------------------------------
small         p1_5_p2_4_p3_2           6          7 
small         p1_5_p2_4_p3_2           5          6
small         p1_5_p2_4_p3_2           4          5
medium        p1_5_p2_4_p3_2           6          7
medium        p1_5_p2_4_p3_2           5          6
medium        p1_5_p2_4_p3_2           4          5
big           p1_5_p2_4_p3_2           6          7
big           p1_5_p2_4_p3_2           5          6
big           p1_5_p2_4_p3_2           4          5
small         p1_3_p2_8_p3_3           6          7   
...  

有没有办法将我的初始输出组织到这个数据框中？

Answer 1

itertools.product 是最 Pythonic 的方法。但是，如果您想使用已有的代码，那么您几乎就在那儿

#create a list to append your values into
data=[]

categories = ['small', 'medium', 'big']
parameters = ['p1_5_p2_4_p3_2', 'p1_3_p2_8_p3_3', 'p1_4_p2_3_p3_6']
Blue = [5, 4, 3]

for parameter in parameters:
    for category in categories:
        for x in Blue:
            y = x + 1
            z = x + 2

            #append instead of printing
            row=[category,parameter,y,z]
            data.append(row)

#create your dataframe
my_df=pd.DataFrame(columns=['Category','Parameters','Value_1','Value_2'], data=data)

  Category  Parameters      Value_1 Value_2
0   small   p1_5_p2_4_p3_2  6       7
1   small   p1_5_p2_4_p3_2  5       6
2   small   p1_5_p2_4_p3_2  4       5
3   medium  p1_5_p2_4_p3_2  6       7
4   medium  p1_5_p2_4_p3_2  5       6

Answer 2

你可以使用itertools.product:

from itertools import product

categories = ["small", "medium", "big"]
parameters = ["p1_5_p2_4_p3_2", "p1_3_p2_8_p3_3", "p1_4_p2_3_p3_6"]
Blue = [5, 4, 3]

df = pd.DataFrame(
    product(categories, parameters, np.array(Blue) + 1, np.array(Blue) + 2),
    columns=["Category", "Parameters", "Value_1", "Value_2"],
)
print(df)

打印：

   Category      Parameters  Value_1  Value_2
0     small  p1_5_p2_4_p3_2        6        7
1     small  p1_5_p2_4_p3_2        6        6
2     small  p1_5_p2_4_p3_2        6        5
3     small  p1_5_p2_4_p3_2        5        7
4     small  p1_5_p2_4_p3_2        5        6

...