为每篇文章计算两列：
n_tags：tags 列表的元素个数（整数），重复的标签照样计数；null 列表算 0，空列表也是 0。has_python：列表里是否有一个标签整个等于 python，不区分大小写（Python、PYTHON 都算；pythonic 不算）。null 列表和空列表为 false。每篇文章一行。
输入
articles(article_id, title, author, tags, published_at, word_count)：tags 是字符串列表，可能为空列表或 null输出
列为 article_id, n_tags, has_python，has_python 为布尔值，行顺序不限。
articles blog/articles
| # | article_idint64 | titlestring | authorstruct<name: string, country: string> | tagslist<string> | published_atdatetime | word_countint64 |
|---|---|---|---|---|---|---|
| 1 | 1 | A gentle intro to DuckDB | {"name": "Hans Muller", "country": "DE"} | ["etl", "Python", "rust"] | 2026-02-07 18:20:00 | 353 |
| 2 | 2 | Testing Python | {"name": "Bob Smith", "country": "US"} | ["SQL"] | 2026-09-02 17:13:00 | 3726 |
| 3 | 3 | 10 tips for dataframes | {"name": "Bob Smith", "country": "US"} | ["pandas"] | 2026-07-23 20:39:00 | 362 |
| 4 | 4 | Debugging DuckDB | {"name": "Amy Chen", "country": "US"} | [] | 2026-01-20 22:03:00 | 3845 |
| 5 | 5 | Debugging window functions | {"name": "Ana Souza", "country": "BR"} | [] | 2026-08-20 10:17:00 | 3899 |
| 6 | 6 | Getting started with Polars | {"name": "王芳", "country": "CN"} | ["polars"] | 2026-05-17 19:28:00 | 2086 |
| 7 | 7 | Testing dataframes | {"name": "Ana Souza", "country": "BR"} | ["rust", "ml", "go"] | 2026-08-01 17:58:00 | 1251 |
| 8 | 8 | Benchmarking pandas | {"name": "王芳", "country": "CN"} | ["Python", "pandas", "ml", "go"] | 2026-02-27 20:10:00 | 2984 |
| 9 | 9 | Benchmarking Python | {"name": "Hans Muller", "country": "DE"} | ["etl"] | 2026-09-09 09:30:00 | 1709 |
| 10 | 10 | 10 tips for dataframes | {"name": "Amy Chen", "country": "US"} | null | 2026-08-11 21:07:00 | 2000 |
| 11 | 11 | Debugging pandas | {"name": "Amy Chen", "country": "US"} | ["python", "viz"] | 2026-06-23 17:24:00 | 3908 |
| 12 | 12 | Getting started with SQL | {"name": "Ana Souza", "country": "BR"} | ["sql"] | 2026-03-01 18:14:00 | 2493 |
| 13 | 13 | Common pitfalls in SQL | {"name": "Li Wei", "country": "CN"} | ["python", "pandas", "python"] | 2026-03-25 19:46:00 | 473 |
| 14 | 14 | A gentle intro to ETL | {"name": "王芳", "country": "CN"} | ["viz", "SQL", "etl", "go"] | 2026-07-21 18:59:00 | 3850 |
| 15 | 15 | Common pitfalls in Python | {"name": "Amy Chen", "country": "US"} | ["SQL", "polars"] | 2026-06-08 15:27:00 | 1357 |
| 16 | 16 | Getting started with ETL | {"name": "Ana Souza", "country": "BR"} | ["rust", "ml", "python"] | 2026-07-16 18:30:00 | 2713 |
| 17 | 17 | A gentle intro to pandas | {"name": "Kenji Sato", "country": "JP"} | ["Python", "ml"] | 2026-05-27 20:35:00 | 2305 |
| 18 | 18 | Getting started with pandas | {"name": "Li Wei", "country": "CN"} | null | 2026-06-17 11:01:00 | 1790 |
| 19 | 19 | A gentle intro to ETL | {"name": "Kenji Sato", "country": "JP"} | ["duckdb", "SQL", "viz"] | 2026-02-27 21:10:00 | 3630 |
| 20 | 20 | Benchmarking DuckDB | {"name": "Li Wei", "country": "CN"} | ["Python", "duckdb"] | 2026-05-07 13:35:00 | 1067 |
| # | article_idInt64 | n_tagsInt64 | has_pythonBoolean |
|---|---|---|---|
| 1 | 1 | 3 | True |
| 2 | 2 | 1 | False |
| 3 | 3 | 1 | False |
| 4 | 4 | 0 | False |
| 5 | 5 | 0 | False |
| 6 | 6 | 1 | False |
| 7 | 7 | 3 | False |
| 8 | 8 | 4 | True |
| 9 | 9 | 1 | False |
| 10 | 10 | 0 | False |
| 11 | 11 | 2 | True |
| 12 | 12 | 1 | False |
| 13 | 13 | 3 | True |
| 14 | 14 | 4 | False |
| 15 | 15 | 2 | False |
| 16 | 16 | 3 | True |
| 17 | 17 | 2 | True |
| 18 | 18 | 0 | False |
| 19 | 19 | 3 | False |
| 20 | 20 | 2 | True |
| 21 | 21 | 2 | True |
| 22 | 22 | 3 | False |
| 23 | 23 | 4 | False |
| 24 | 24 | 1 | False |
| 25 | 25 | 2 | True |
| 26 | 26 | 4 | True |
| 27 | 27 | 4 | True |
| 28 | 28 | 3 | False |
| 29 | 29 | 1 | False |
| 30 | 30 | 1 | False |
| 31 | 31 | 0 | False |
| 32 | 32 | 2 | True |
| 33 | 33 | 0 | False |
| 34 | 34 | 2 | False |
| 35 | 35 | 1 | False |
| 36 | 36 | 0 | False |
| 37 | 37 | 2 | True |
| 38 | 38 | 2 | False |
| 39 | 39 | 1 | False |
| 40 | 40 | 3 | False |