import pandas as pd

# 创建示例数据
df = pd.DataFrame({'id': [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2],
                   'value': [1, 2, None, None, 3, None, None, None, None, None, 4, 5]})

# 按id分组并计算每个id的连续NA值的个数
df['NA_count'] = df.groupby('id')['value'].apply(lambda x: x.isnull().astype(int).diff().fillna(0).cumsum())

# 重新开始计数
df['NA_count'] = df.groupby(['id', df['NA_count'].ne(df['NA_count'].shift())]).cumcount() + 1

结果：

    id  value  NA_count
0    1    1.0         0
1    1    2.0         0
2    1    NaN         1
3    1    NaN         2
4    1    3.0         0
5    1    NaN         1
6    1    NaN         2
7    2    NaN         0
8    2    NaN         1
9    2    NaN         2
10   2    4.0         0
11   2    5.0         0