英文:
Manipulating a given DataFrame in order to recreate it in a different structure, Pandas Python
问题
Left DataFrame is the given one, and i want to recreate it to the right DataFrame.
嗨，
假设我有一个给定的DataFrame（左侧），我想创建一个新的DataFrame（右侧）。
我使用右侧DataFrame的索引和列创建了新的DataFrame，现在我想要“填充”单元格。
有什么想法可以以最简单的方式，优先使用向量化方法来完成吗？
在此先行致谢！
我用循环而不是“传统”的方式做了。我想用apply方法或其他智能解决方案来编写它。
编辑：
这是我尝试过的一种方法：
这是填充数据的原始DataFrame1
这是我想要达到的DataFrame2
这是我试图采取的一步，以达到解决方案3
输出应该如下所示（也与Scot的回答相关）：
在4
提前致谢！
英文:
Left DataFrame is the given one, and i want to recreate it to the right DataFrame.
Hi,
So suppose i have a given DataFrame (the left one), and i want to create a new dataframe (the right one).
I created the new DataFrame with the indexes and columns of the right one, and now i want to "fill" the cells.
Any ideas how can i do it simplest, with priority of vectorize way?
Thank you in advance!
I did it with loops not "classic" way. I would like to write it with apply method or another smart solution.
edit:
this is an approcah that i tried:
This is the original DataFrame with filled data 1
This is the DataFrame i want to reach 2
This is a step i tried to do in order to reach the solution3
the output should be (also in context for Scot's answer):
in 4
Thank you in advance!
答案1
得分: 0
以下是您提供的代码部分的翻译：
我对您的问题有点困惑，但我会尝试回答：
import pandas as pd
import numpy as np
df = pd.DataFrame(data=np.arange(1, 76).reshape(-1, 5, order='F'),
index=pd.MultiIndex.from_product([[1, 2, 3, 4, 5], [*'XYZ']]),
columns='Shirt Bottomware Shoes Sunglasses Earrings'.split())
df = df.rename_axis(['Number', 'Variable'])
df
输入数据框：
Shirt Bottomware Shoes Sunglasses Earrings
Number Variable
1 X 1 16 31 46 61
Y 2 17 32 47 62
Z 3 18 33 48 63
2 X 4 19 34 49 64
Y 5 20 35 50 65
Z 6 21 36 51 66
3 X 7 22 37 52 67
Y 8 23 38 53 68
Z 9 24 39 54 69
4 X 10 25 40 55 70
Y 11 26 41 56 71
Z 12 27 42 57 72
5 X 13 28 43 58 73
Y 14 29 44 59 74
Z 15 30 45 60 75
重塑和筛选：
df_out = df.unstack().stack(0, dropna=False).loc[[(1, 'Shirt'), (3, 'Shoes'), (5, 'Earrings')]]
df_out
输出数据框：
Variable X Y Z
Number
1 Shirt 1 2 3
3 Shoes 37 38 39
5 Earrings 73 74 75
希望这有帮助！
英文:
I am little confused by your question, but I will attempt an answer:
import pandas as pd
import numpy as np
df = pd.DataFrame(data=np.arange(1, 76).reshape(-1,5, order='F'),
index=pd.MultiIndex.from_product([[1,2,3,4,5],[*'XYZ']]),
columns='Shirt Bottomware Shoes Sunglasses Earrings'.split())
df = df.rename_axis(['Number', 'Variable'])
df
Input Dataframe:
Shirt Bottomware Shoes Sunglasses Earrings
Number Variable
1 X 1 16 31 46 61
Y 2 17 32 47 62
Z 3 18 33 48 63
2 X 4 19 34 49 64
Y 5 20 35 50 65
Z 6 21 36 51 66
3 X 7 22 37 52 67
Y 8 23 38 53 68
Z 9 24 39 54 69
4 X 10 25 40 55 70
Y 11 26 41 56 71
Z 12 27 42 57 72
5 X 13 28 43 58 73
Y 14 29 44 59 74
Z 15 30 45 60 75
Reshape and filter:
df_out = df.unstack().stack(0, dropna=False).loc[[(1,'Shirt'),(3,'Shoes'),(5,'Earrings')]]
df_out
Output dataframe:
Variable X Y Z
Number
1 Shirt 1 2 3
3 Shoes 37 38 39
5 Earrings 73 74 75
通过集体智慧和协作来改善编程学习和解决问题的方式。致力于成为全球开发者共同参与的知识库，让每个人都能够通过互相帮助和分享经验来进步。
评论