英文:
Sort Pandas DataFrame based on previous row in another column
问题
在我的Python项目中，我有以下DataFrame：
df1 = pd.DataFrame({"Col A":[1,2,3],"Col B":[3,2,2]})
我希望按照以下方式对其进行排序：
df2 = pd.DataFrame({"Col A":[1,3,2],"Col B":[3,2,2]})
我的目标是使Col A中的每个值与Col B中的前一个值匹配。
你有没有任何想法如何使这个工作正常，而且尽量减少工作量？
我尝试使用.sort_values(by=)，但这也是我的当前知识的限制。
英文:
I have the following DataFrame in my Python porject:
df1 = pd.DataFrame({"Col A":[1,2,3],"Col B":[3,2,2]})
I wish to order it in this kind of way:
df2 = pd.DataFrame({"Col A":[1,3,2],"Col B":[3,2,2]})
My goal is that each value in Col A matches the previous' value in Col B.
Do you have any idea of how to make this work properly and as little effort as possible?
I tried to work with .sort_values(by=) but that's also where my current knowledge stops.
答案1
得分: 1
如果需要对Col B每个值进行滚动操作，可以使用 lambda 函数：
df1 = pd.DataFrame({"Col A":[1,2,3,7,4,8],"Col B":[3,2,2,1,1,1]})print (df1)Col A Col B0 1 31 2 22 3 23 7 14 4 15 8 1df1['Col A'] = df1.groupby('Col B')['Col A'].transform(lambda x: np.roll(x, -1))print (df1)Col A Col B0 1 31 3 22 2 23 4 14 8 15 7 1
英文:
If need roll one value per Col B use lambda function:
df1 = pd.DataFrame({"Col A":[1,2,3,7,4,8],"Col B":[3,2,2,1,1,1]})print (df1)Col A Col B0 1 31 2 22 3 23 7 14 4 15 8 1df1['Col A'] = df1.groupby('Col B')['Col A'].transform(lambda x: np.roll(x, -1))print (df1)Col A Col B0 1 31 3 22 2 23 4 14 8 15 7 1
答案2
得分: 0
是的，您可以使用sort_values()和创建映射字典来实现所需的输出，示例如下：
import pandas as pddf1 = pd.DataFrame({"Col A":[1,2,3],"Col B":[3,2,2]})# 用于排序的映射字典mapping_dict = {1:3, 3:2, 2:2}df1["sort_order"] = df1["Col A"].map(mapping_dict)df2 = df1.sort_values(by="sort_order").drop(columns=["sort_order"])print(df2)
输出结果：
Col A Col B0 1 32 3 21 2 2
英文:
Yes, you can achieve the desired output by using sort_values() and by creating a mapping dictionary so:
import pandas as pddf1 = pd.DataFrame({"Col A":[1,2,3],"Col B":[3,2,2]})# mapping_dict for orderingmapping_dict = {1:3, 3:2, 2:2}df1["sort_order"] = df1["Col A"].map(mapping_dict)df2 = df1.sort_values(by="sort_order").drop(columns=["sort_order"])print(df2)
Output:
Col A Col B0 1 32 3 21 2 2
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