pandas - 找到第一次出现 [英] pandas - find first occurrence

问题描述

假设我有一个如下的结构化数据框:

Suppose I have a structured dataframe as follows:

df = pd.DataFrame({"A":['a','a','a','b','b'],
                   "B":[1]*5})

A 列之前已排序.我希望找到 df[df.A!='a'] 的第一行索引.最终目标是使用此索引将数据帧基于 A 分成组.

The A column has previously been sorted. I wish to find the first row index of where df[df.A!='a']. The end goal is to use this index to break the data frame into groups based on A.

现在我意识到有一个 groupby 功能.但是，数据框非常大，这是一个简化的玩具示例.由于 A 已经排序，如果我能找到 df.A!='a' 的第一个索引，会更快.因此，重要的是无论您使用什么方法一旦找到第一个元素，扫描就会停止.

Now I realise that there is a groupby functionality. However, the dataframe is quite large and this is a simplified toy example. Since A has been sorted already, it would be faster if I can just find the 1st index of where df.A!='a'. Therefore it is important that whatever method that you use the scanning stops once the first element is found.

推荐答案

idxmax 和 argmax 将返回最大值的位置，如果最大值出现多次，则返回第一个位置.

idxmax and argmax will return the position of the maximal value or the first position if the maximal value occurs more than once.

在 df.A.ne('a')

df.A.ne('a').idxmax()

3

或 numpy 等价物

(df.A.values != 'a').argmax()

3

<小时>

但是，如果 A 已经排序，那么我们可以使用 searchsorted

---

However, if A has already been sorted, then we can use searchsorted

df.A.searchsorted('a', side='right')

array([3])

或 numpy 等价物

df.A.values.searchsorted('a', side='right')

3

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