[英]Multiplicate pandas dataframe rows according to boolean columns
Let's take this sample dataframe:让我们以 dataframe 为例:
df=pd.DataFrame({'Name':['A','B','C','D'], 'NoMatter':[1,2,3,4], 'Cat1':[0,1,1,0], 'Cat2':[1,1,0,0]})
Name NoMatter Cat1 Cat2
0 A 1 0 1
1 B 2 1 1
2 C 3 1 0
3 D 4 0 0
Each name can have 0, 1 or 2 categories (in my real dataframe, I have many more).每个名称可以有 0、1 或 2 个类别(在我的真实 dataframe 中,我还有更多)。 I would like to create a new dataframe having one row per name per category, converting the name value into name.cat.
我想创建一个新的 dataframe 每个类别每个名称一行,将名称值转换为 name.cat。 I could go through a for loop but I know it is not the optimal way to do, especially since my real dataframe is big.
我可以通过 for 循环 go 但我知道这不是最佳方法,特别是因为我真正的 dataframe 很大。 Do you know please a good way to proceed?
你知道请一个好方法吗?
Expected output:预期 output:
Name NoMatter
0 A.Cat2 1
1 B.Cat1 2
2 B.Cat2 2
3 C.Cat1 3
Try:尝试:
df = df.melt(["Name", "NoMatter"])
df = df[df.value > 0]
df.Name = df.Name + "." + df.variable
df = df[["Name", "NoMatter"]].sort_values(by="Name").reset_index(drop=True)
print(df)
Prints:印刷:
Name NoMatter
0 A.Cat2 1
1 B.Cat1 2
2 B.Cat2 2
3 C.Cat1 3
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.