kumar6591/data-quality-env
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�I�I�I��L�L�L������r r � sample_rowszQuick table sanity samplez SELECT * FROM {table} LIMIT 5�5null_emailzCount null emailszPSELECT SUM(CASE WHEN email IS NULL THEN 1 ELSE 0 END) AS null_email FROM {table}�null_customer_idzCount null customer IDsz\SELECT SUM(CASE WHEN customer_id IS NULL THEN 1 ELSE 0 END) AS null_customer_id FROM {table}�duplicate_rowsz"Estimate exact duplicate row countz�SELECT COALESCE(SUM(c-1),0) AS duplicate_rows FROM (SELECT customer_id, email, name, signup_date, country, COUNT(*) AS c FROM {table} GROUP BY 1,2,3,4,5 HAVING COUNT(*) > 1) t� country_distzDistribution by countryzKSELECT country, COUNT(*) AS n FROM {table} GROUP BY country ORDER BY n DESC�negative_quantity_rowsz"Count negative quantity violationsz[SELECT SUM(CASE WHEN quantity < 0 THEN 1 ELSE 0 END) AS negative_quantity_rows FROM {table}�unparseable_amount_rowsz Count unparseable amount valuesz�SELECT SUM(CASE WHEN try_cast(replace(amount, '$', '') AS DOUBLE) IS NULL THEN 1 ELSE 0 END) AS unparseable_amount_rows FROM {table}�amount_parse_previewzPreview parsed amountsz_SELECT amount, try_cast(replace(amount, '$', '') AS DOUBLE) AS amount_num FROM {table} LIMIT 20� status_distzDistribution by statuszISELECT status, COUNT(*) AS n FROM {table} GROUP BY status ORDER BY n DESC�6mean_shiftz%Compare baseline/current amount meansz�SELECT (SELECT AVG(amount) FROM transactions_baseline) AS baseline_mean, (SELECT AVG(amount) FROM transactions_current) AS current_mean�new_categoriesz0Find categories present only in current snapshotz�SELECT DISTINCT c.category FROM transactions_current c LEFT JOIN (SELECT DISTINCT category FROM transactions_baseline) b ON c.category=b.category WHERE b.category IS NULL ORDER BY c.category�new_user_row_pctz%Estimate referential drift on user_idziSELECT AVG(CASE WHEN user_id >= 1000 THEN 1.0 ELSE 0.0 END) AS new_user_row_pct FROM transactions_current�mean_by_categoryz+Amount mean by category in current snapshotzoSELECT category, AVG(amount) AS avg_amount FROM transactions_current GROUP BY category ORDER BY avg_amount DESC�task_id�int�7table_namer �return� list[str]c � �� | dk r�fd�t D � � S | dk r�fd�t D � � S �fd�t D � � S )N� c �F �� g | ] }|j � �� � � �� S �)�table�r �format��.0�pr" s �r �8<listcomp>z#probes_for_task.<locals>.<listcomp>M �, �� �N�N�N�A���%�%�J�%�7�7�N�N�Nr � c �F �� g | ] }|j � �� � � �� S r( r* r, s �r r/ z#probes_for_task.<locals>.<listcomp>O r0 r c �F �� g | ] }|j � �� � � �� S r( r* r, s �r r/ z#probes_for_task.<locals>.<listcomp>P s, �� � J� J� J��A�N� !� !�9� !� 3� 3� J� J� Jr )� TASK1_PROBES� TASK2_PROBES� TASK3_PROBES)r r" s `r �probes_for_taskr7 K sk �� ��!�|� O�N�N�N�N� �N�N�N�N��!�|� O�N�N�N�N� �N�N�N�N� J� J� J� J�\� J� J� J�Jr N)r r! r" r r# r$ ) �10__future__r � dataclassesr r r4 r5 r6 r7 r r r �<module>r: s �� "� "� "� "� "� "� !� !� !� !� !� !� ��$����� � � � � � � ���
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