计算 Pandas 系列中的值组

Question

我有一个 Pandas (pandas==0.23.4) 日期时间索引数据框 df，其中有一列名为 value_id。

value_id 包含浮点值组（5.0 或 6.0）和 NaN 组。我想计算5.0 和6.0 的连续组数。这些组必须至少包含三个连续的值。

例如：

In [1]: print df.value_id
timestamp
2019-01-06 17:42:08    NaN
2019-01-06 17:45:08    5.0
2019-01-06 17:48:08    5.0
2019-01-06 17:51:08    5.0
2019-01-06 17:54:08    NaN
2019-01-06 17:57:08    NaN
2019-01-06 18:00:08    NaN
2019-01-06 18:03:08    NaN
2019-01-06 18:06:08    NaN
2019-01-06 18:09:08    NaN
2019-01-06 18:12:08    6.0
2019-01-06 18:15:08    6.0
2019-01-06 19:54:09    NaN
2019-01-06 19:57:09    5.0
2019-01-06 20:00:08    5.0
2019-01-06 20:03:08    5.0
2019-01-06 20:06:09    NaN
2019-01-06 20:09:08    NaN
2019-01-06 20:12:08    NaN
2019-01-06 20:15:09    NaN
2019-01-06 20:18:08    NaN
2019-01-06 20:21:09    NaN
2019-01-06 20:24:09    NaN
2019-01-07 19:09:07    NaN
2019-01-07 19:12:06    NaN
2019-01-07 19:15:06    5.0
2019-01-07 19:18:06    5.0
2019-01-07 19:21:07    5.0
2019-01-07 19:24:07    5.0
2019-01-07 19:27:07    NaN
2019-01-07 19:30:07    NaN
2019-01-07 19:33:06    NaN
2019-01-07 19:36:07    NaN
2019-01-07 19:39:07    NaN
2019-01-07 19:42:06    NaN
2019-01-07 19:45:06    NaN
2019-01-07 19:48:06    NaN
2019-01-07 19:51:06    6.0
2019-01-07 19:54:07    6.0
2019-01-07 19:57:06    6.0
Name: value_id, dtype: float64

如果我有两个变量名为 count1（用于 5.0 值组）和 count2（用于 6.0 值组），则为上述示例分配的结果计数将是：

count1: 3

count2: 1

Answer 1

IIUC 使用cumsum 创建组密钥，然后我们只需执行value_counts

s.groupby(s.isnull().cumsum()).value_counts().ge(3).sum(level=1)
Out[1026]: 
timestamp
5.0    3.0
6.0    1.0
Name: timestamp, dtype: float64

Answer 2

也许不是最优雅的，但您可以使用shift 来检查接下来的两项是否相同，以及前一项是否不属于同一组：

df['fives'] = ((df['timestamp'] == 5) & (df['timestamp'].shift(-1) == 5)
                & (df['timestamp'].shift(-2) == 5)
                & (df['timestamp'].shift(1) != 5)).astype(int)
df['sixes'] = ((df['timestamp'] == 6) & (df['timestamp'].shift(-1) == 6)
                & (df['timestamp'].shift(-2) == 6)
                & (df['timestamp'].shift(1) != 6)).astype(int)

df[['fives','sixes']].sum()

fives    3
sixes    1
dtype: int64