英文:
Flip flop mechanism in pandas
问题
data3 是我想创建的输出
当 data1 为 1 时，data3 = 1。然后保持为 1，直到 data2 发出信号为 1。然后继续为 0，直到 data1 再次发出信号为 1...等等...
我可以使用 .iterrows()，但我想知道是否有更快的向量化方法？
data1 = [1,1,0,0,0,1,0,0,0,0,1]data2 = [0,0,1,0,0,0,0,0,1,1,0]data3 = [1,1,0,0,0,1,1,1,0,0,1]df = pd.DataFrame()df["d1"] = data1df["d2"] = data2
英文:
I am looking for a flip flop type mechanism in pandas.
data3 is the output I'd like to create
data3 = 1 when data1 is a 1. Then stay a 1 until data2 signals a 1. Then continue as 0 until data1 signals a 1 again....etc.....
I could use .iterrows(), but I am wondering if there is a faster vectorized way?
data1 = [1,1,0,0,0,1,0,0,0,0,1]data2 = [0,0,1,0,0,0,0,0,1,1,0]data3 = [1,1,0,0,0,1,1,1,0,0,1]df = pd.DataFrame()df["d1"]= data1df["d2"]= data2
答案1
得分: 1
以下是您提供的代码的翻译部分：
一种选项：
df['out'] = (df['d1'].map({1: True}).fillna(df['d2'].map({1: False})).ffill().fillna(0).astype(int))print(df)
如果在相等的情况下要优先选择d2：
df['out'] = (df['d2'].map({1: False}).fillna(df['d1'].map({0: True})).ffill().fillna(0).astype(int))
输出：
d1 d2 out0 1 0 11 1 0 12 0 1 03 0 0 04 0 0 05 1 0 16 0 0 17 0 0 18 0 1 09 0 1 010 1 0 1
英文:
One option:
df['out'] = (df['d1'].map({1: True}).fillna(df['d2'].map({1: False})).ffill().fillna(0).astype(int))print(df)
If you want to give priority to d2 over d1 in case of equality:
df['out'] = (df['d2'].map({1: False}).fillna(df['d1'].map({0: True})).ffill().fillna(0).astype(int))
Output:
d1 d2 out0 1 0 11 1 0 12 0 1 03 0 0 04 0 0 05 1 0 16 0 0 17 0 0 18 0 1 09 0 1 010 1 0 1
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