问题

以下是您要翻译的部分：

我有一个看起来像这样的Dataframe：

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

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：

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

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

但它什么都没做。

<details>
<summary>英文:</summary>

I have a Dataframe that looks like this:

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

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:

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

I tried this:

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

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'))]

输出：

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

注意：输出部分保持不变。

# boundaries below/above which
# to drop the rows (exclusive)
LOW, HIGH = 1, 2

# rows for which both A and B are 0
m = df[['A', 'B']].eq(0).all(axis=1)

# count the consecutive
count = m.groupby((m != m.shift()).cumsum()).transform('size')

# keep only the values with non zero
# or with > LOW / < HIGH consecutive zeros
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