pandas :有条件的滚动计数 [英] Pandas: conditional rolling count

问题描述

我有一个如下所示的系列:

I have a Series that looks the following:

   col
0  B
1  B
2  A
3  A
4  A
5  B

这是一个时间序列，因此索引按时间排序.

It's a time series, therefore the index is ordered by time.

对于每一行，我想计算该值连续出现了多少次，即:

For each row, I'd like to count how many times the value has appeared consecutively, i.e.:

输出:

   col count
0  B   1
1  B   2
2  A   1 # Value does not match previous row => reset counter to 1
3  A   2
4  A   3
5  B   1 # Value does not match previous row => reset counter to 1

我发现了 2 个相关问题，但我无法弄清楚如何将该信息写入"为 DataFrame 中的每一行的新列(如上).使用滚动应用效果不佳.

I found 2 related questions, but I can't figure out how to "write" that information as a new column in the DataFrame, for each row (as above). Using rolling_apply does not work well.

相关:

按索引计算熊猫数据帧上的连续事件

在熊猫数据框中查找连续的段

推荐答案

我认为有一个很好的方法可以将 @chrisb 和 @CodeShaman 的解决方案结合起来(正如有人指出的 CodeShamans 解决方案计算总数而不是连续值).

I think there is a nice way to combine the solution of @chrisb and @CodeShaman (As it was pointed out CodeShamans solution counts total and not consecutive values).

  df['count'] = df.groupby((df['col'] != df['col'].shift(1)).cumsum()).cumcount()+1

  col  count
0   B      1
1   B      2
2   A      1
3   A      2
4   A      3
5   B      1

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