基于布尔值的累积和

发布于 2025-01-17 05:24:20 字数 854 浏览 0 评论 0原文

我有一个如下所示的数据框：

Finalcheck
False
True
True
True True
False
True

我想在此数据框中创建另一列，它根据 Finalcheck 变量给出累积总和，如下所示：

Finalcheck	Position
False	1
True	2
True	3
True	4
False	1
True	2

我已经尝试了多种方法来实现这一目标，但我能得到的最接近的方法是使用带有 pandas 中的 cumsum 函数的 groupby：

df['position'] = df.groupby((df.finalcheck).cumsum())

这没有给我想要的输出。你能帮我找出我哪里出了问题吗？

原文

I have a dataframe that looks like this:

finalcheck
False
True
True
True
False
True

I want to create another column in this dataframe which gives me a cumulative sum based on finalcheck variable like so:

finalcheck	position
False	1
True	2
True	3
True	4
False	1
True	2

I have tried multiple ways of achieving this but the closest I could get was by using a groupby with the cumsum function in pandas:

df['position'] = df.groupby((df.finalcheck).cumsum())

This did not give me the desired output. Could you help me identify where I am going wrong?

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柏拉图鍀咏恒 2025-01-24 05:24:20

这是一个缺口和岛屿问题。每次你遇到一个错误，你就会开始一个新的岛屿。然后在每个岛内，您只需按顺序对行进行编号即可：

# The islands
s = df["finalcheck"].eq(False).cumsum()

# Within each island, label the rows sequentially
df['position'] = s.groupby(s).cumcount() + 1

It's a gap-and-island problem. Every time you encounter a False, you start a new island. Then within each island, you just number the row sequentially:

# The islands
s = df["finalcheck"].eq(False).cumsum()

# Within each island, label the rows sequentially
df['position'] = s.groupby(s).cumcount() + 1

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话少心凉 2025-01-24 05:24:20

summ = 0
for i in range(0,len(df)):

    if df["finalcheck"].iloc[i] == "True":
        summ = summ + 1
        df["position"].iloc[i] = summ
    else:
        summ = 0

如果你在 groupby 之外思考，你可以尝试这个

summ = 0
for i in range(0,len(df)):

    if df["finalcheck"].iloc[i] == "True":
        summ = summ + 1
        df["position"].iloc[i] = summ
    else:
        summ = 0

If you're thinking outside of groupby, you can try this

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