<jupyter_start><jupyter_text>Hello BigQuery[Google BigQuery](https://cloud.google.com/bigquery) is a fast, economical, and fully managed data warehouse for large-scale data analytics. You can use BigQuery and SQL to focus on transforming and gaining insights into your data. Datalab supports the [Standard SQL BigQuery](https://cloud.google.com/bigquery/docs/reference/standard-sql/). This query works against the churn dataset within temp Dataset in is833-demo project. Run the next two cells to define and execute the query to see top 10 rows.<jupyter_code>%%bq query -n customer_churn
SELECT *
FROM `is833-demo.temp.churn`
LIMIT 10
%%bq query -n customer_churn
SELECT PaymentMethod , COUNT(PaymentMethod) as count
FROM `is833-demo.temp.churn`
GROUP BY PaymentMethod
ORDER BY count DESC
LIMIT 10
%%chart pie --fields PaymentMethod,count --data customer_churn
title: Payment method used by the customers
height: 400
width: 800
pieStartAngle: 20
slices:
  0:
    offset: .2<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.07876370887337986, 'fraction_numerical': 0.024925224327018942, 'mean_word_length': 5.236024844720497, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '8442', 'license': 'MIT', 'n_tokens_mistral': 313, 'n_tokens_neox': 290, 'n_words': 118, 'path': '11-Google-Cloud/02-Datalab-BigQuery.ipynb', 'repo_name': 'rkothaka94/Business-Analytics', 'size': '1003'}