在有条件的增量下对 pandas 数据框使用cumcount [英] Use cumcount on pandas dataframe with a conditional increment

问题描述

考虑数据框

df = pd.DataFrame(
    [
        ['A', 1],
        ['A', 1],
        ['B', 1],
        ['B', 0],
        ['A', 0],
        ['A', 1],
        ['B', 1]
    ], columns = ['key', 'cond'])

我想找到每个 key 的累积(运行)计数(从1开始)，我们仅在组中先前的值具有 cond == 1 时递增代码>.将其附加到上述数据框后，即可得到

I want to find a cumulative (running) count (starting at 1) for each key, where we only increment if the previous value in the group had cond == 1. When appended to the above dataframe this would give

df_result = pd.DataFrame(
    [
        ['A', 1, 1],
        ['A', 1, 2],
        ['B', 1, 1],
        ['B', 0, 2],
        ['A', 0, 3],
        ['A', 1, 3],
        ['B', 1, 2]
    ], columns = ['key', 'cond'])

请注意，基本上每个 key 组中最后一行的 cond 值都无效.

Note that essentially the cond values of the last rows in each key group have no effect.

只需做一个简单的 group 和 cumcount

df.groupby('key').cumcount()

当然不考虑上一个元素的 cond 值.如何考虑到这一点?

of course doesn't account for the cond value of the previous element. How can I take this into account?

编辑

由于以下某些解决方案在某些极端情况下不起作用，因此我将提供更全面的数据框架进行测试.

As some of the solutions below don't work on some edge cases, I will give a more comprehensive dataframe for testing.

df = pd.DataFrame(
    [
        ['A', 0],
        ['A', 1],
        ['A', 1],
        ['B', 1],
        ['B', 0],
        ['A', 0],
        ['A', 1],
        ['B', 1],
        ['B', 0]
    ], columns = ['key', 'cond'])

在添加真实结果时应该给出的

which when appending the true result should give

df_result = pd.DataFrame(
    [
        ['A', 0, 1],
        ['A', 1, 1],
        ['A', 1, 2],
        ['B', 1, 1],
        ['B', 0, 2],
        ['A', 0, 3],
        ['A', 1, 3],
        ['B', 1, 2],
        ['B', 0, 3]
    ], columns = ['key', 'cond'])

推荐答案

使用 groupby 与

df
  key  cond  new
0   A     1    1
1   A     1    2
2   B     1    1
3   B     0    2
4   A     0    3
5   A     1    3
6   B     1    2

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