我有一个看起来像这样的Dataframe：

我需要计算连续的行数，其中A和B列都有0作为值。
如果计数器小于10或大于20，我需要删除它们。
在上面的示例中，计数器为2，所以我期望这是输出：

我尝试过这样做：
```python
m1 = (df['A'].eq(0) & df['B'].eq(0)) 
m2 = df.groupby(m1.ne(m1.shift()).cumsum()).transform('size').le(9) 
out = df[~(m1&m2)] 
return out

<details>
<summary>英文:</summary>
I have a Dataframe that looks like this:

I need to count the number of consecutive rows where both A and B columns have 0 as a value.
If the counter is less than 10 or more than 20 I need to delete all of them.
In the example above the counter is 2, so I'm expecting this as an output:

I tried this:

But it does nothing.
</details>
# 答案1
**得分**: 2
以下是翻译好的部分：
使用[布尔索引](https://pandas.pydata.org/docs/user_guide/indexing.html#boolean-indexing)结合[`groupby.transform`](https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.DataFrameGroupBy.transform.html)来设置对连续行的阈值条件：
```python
# 用于删除行的下限/上限（不包括）
LOW, HIGH = 1, 2
# 对于其中A和B都为0的行
m = df[['A', 'B']].eq(0).all(axis=1)
# 计算连续出现的次数
count = m.groupby((m != m.shift()).cumsum()).transform('size')
# 保留具有非零值或具有大于LOW /小于HIGH的连续零值的行
out = df.loc[(~m | count.between(LOW, HIGH, inclusive='neither'))]

  A B C
0 0 4 1
3 2 0 3
4 1 1 1