Profile a table with a single SQL diagnostic query
Use to quickly understand a new table: null rates, distinct counts, min/max, and skew.
You are a data analyst profiling an unfamiliar table in {{sql_dialect}}.
Table: {{table_name}}
Columns: {{columns}}
Task: write SQL that profiles the table and returns, per relevant column:
- Row count and distinct count.
- Null count and null percentage.
- Min, max, and (for numerics) mean and a few percentiles.
- For categoricals, the top {{top_n}} most frequent values with counts.
Constraints:
- Prefer one query (or a small set) over dozens of separate selects.
- Keep it readable and easy to extend to new columns.
After the SQL, list which columns look suspicious and why based on what such a profile typically reveals.Click the copy button in the top right of the block to grab the full prompt.
Replace each placeholder below with your own values before you run the prompt.
- {{sql_dialect}}
- {{table_name}}
- {{columns}}
- {{top_n}}
Related prompts
You are a meticulous data analyst. Here is a sample of my raw dataset (header row plus a few rows): {{sample_rows}} Context: this data describes {{data_description}} and I plan to...
You are a senior Python data engineer. Write clean, reproducible pandas code to clean my dataset. Columns and their meaning: {{column_spec}} Known issues to handle: {{known_issues}...
Act as a data analyst guiding me through exploratory data analysis. Dataset summary: {{dataset_summary}} My goal / the question I care about: {{analysis_goal}} Produce an EDA plan...
You are a SQL expert writing for a {{sql_dialect}} database. Here is the relevant schema (tables, columns, types, keys): {{schema}} Question to answer: {{question}} Rules: - Use on...
You are a SQL reviewer. Explain and debug the query below. Dialect: {{sql_dialect}} What I expected it to return: {{expected_result}} What is actually wrong (if known): {{symptom}}...
You are a database performance specialist for {{sql_dialect}}. Slow query: {{query}} Context: - Approximate row counts of the main tables: {{table_sizes}} - Existing indexes: {{ind...
0 Comments
Loading discussion...