Dados meteorológicos no BigQuery avaliações

Sameer S. · Revisado há 7 months

gg

Rupesh k. · Revisado há 7 months

Azlan K. · Revisado há 7 months

Aditya N. · Revisado há 7 months

Aryan S. · Revisado há 7 months

SELECT descriptor, sum(complaint_count) as total_complaint_count, count(wind_speed) as data_count, ROUND(corr(wind_speed, avg_count),3) AS corr_count, ROUND(corr(wind_speed, avg_pct_count),3) AS corr_pct From ( SELECT avg(pct_count) as avg_pct_count, avg(day_count) as avg_count, sum(day_count) as complaint_count, descriptor, wind_speed FROM ( SELECT DATE(timestamp) AS date, wind_speed FROM demos.nyc_weather) a JOIN ( SELECT x.date, descriptor, day_count, day_count / all_calls_count as pct_count FROM (SELECT DATE(created_date) AS date, concat(complaint_type, ": ", descriptor) as descriptor, COUNT(*) AS day_count FROM `bigquery-public-data.new_york.311_service_requests` GROUP BY date, descriptor)x JOIN ( SELECT DATE(timestamp) AS date, COUNT(*) AS all_calls_count FROM `demos.nyc_weather` GROUP BY date )y ON x.date=y.date )b ON a.date = b.date GROUP BY descriptor, wind_speed ) GROUP BY descriptor HAVING total_complaint_count > 5000 AND ABS(corr_pct) > 0.5 AND data_count > 5 ORDER BY ABS(corr_pct) DESC

Syed S. · Revisado há 7 months

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krithikgokul s. · Revisado há 7 months

Amar P. · Revisado há 7 months

hi

Rajan S. · Revisado há 7 months

Shubhro G. · Revisado há 7 months

nice

Rakhi G. · Revisado há 7 months

Shreyansh K. · Revisado há 7 months

Vipul Y. · Revisado há 7 months

Arcade Feb 2024

Jonathan M. · Revisado há 7 months