Context

Stack Overflow is the largest online community for programmers to learn, share their knowledge, and advance their careers.

Content

Updated on a quarterly basis, this BigQuery dataset includes an archive of Stack Overflow content, including posts, votes, tags, and badges. This dataset is updated to mirror the Stack Overflow content on the Internet Archive, and is also available through the Stack Exchange Data Explorer.

Fork this kernel to get started with this dataset.

Acknowledgements

Dataset Source: https://archive.org/download/stackexchange

https://bigquery.cloud.google.com/dataset/bigquery-public-data:stackoverflow

https://cloud.google.com/bigquery/public-data/stackoverflow

Banner Photo by Caspar Rubin from Unplash.

Inspiration

What is the percentage of questions that have been answered over the years?

What is the reputation and badge count of users across different tenures on StackOverflow?

What are 10 of the “easier” gold badges to earn?

Which day of the week has most questions answered within an hour?

Certainly! Here's a description for the Kaggle dataset related to the cloud-training-demos.SAP_REPLICATED_DATA BigQuery public dataset:

Dataset Description: SAP Replicated Data

Dataset ID: cloud-training-demos.SAP_REPLICATED_DATA

Overview: The SAP_REPLICATED_DATA dataset in BigQuery provides a comprehensive replication of SAP (Systems, Applications, and Products in Data Processing) business data. This dataset is designed to support data analytics and machine learning tasks by offering a rich set of structured data that mimics real-world enterprise scenarios. It includes data from various SAP modules and processes, enabling users to perform in-depth analysis, build predictive models, and explore business insights.

Content: - Tables and Schemas: The dataset consists of multiple tables representing different aspects of SAP business operations, including but not limited to sales, inventory, finance, and procurement data. - Data Types: It contains structured data with fields such as transaction IDs, timestamps, customer details, product information, sales figures, and financial metrics. - Data Volume: The dataset is designed to simulate large-scale enterprise data, making it suitable for performance testing, data processing, and analysis.

Usage: - Business Analytics: Users can analyze business trends, sales performance, and financial metrics. - Machine Learning: Ideal for developing and testing machine learning models related to business forecasting, anomaly detection, and customer segmentation. - Data Processing: Suitable for practicing SQL queries, data transformation, and integration tasks.

Example Use Cases: - Sales Analysis: Track and analyze sales performance across different regions and time periods. - Inventory Management: Monitor inventory levels and identify trends in stock movements. - Financial Reporting: Generate financial reports and analyze expense patterns.

For more information and to access the dataset, visit the BigQuery public datasets page or refer to the dataset documentation in the BigQuery console.

Tables:

Here's a Markdown table with the information you provided:

Looker Ecommerce Dataset Description

CSV version of Looker Ecommerce Dataset.

Overview Dataset in BigQuery TheLook is a fictitious eCommerce clothing site developed by the Looker team. The dataset contains information >about customers, products, orders, logistics, web events and digital marketing campaigns. The contents of this >dataset are synthetic, and are provided to industry practitioners for the purpose of product discovery, testing, and >evaluation. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This >means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on >this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public >datasets.

1. distribution_centers.csv

• Columns:
  • id: Unique identifier for each distribution center.
  • name: Name of the distribution center.
  • latitude: Latitude coordinate of the distribution center.
  • longitude: Longitude coordinate of the distribution center.

2. events.csv

• Columns:
  • id: Unique identifier for each event.
  • user_id: Identifier for the user associated with the event.
  • sequence_number: Sequence number of the event.
  • session_id: Identifier for the session during which the event occurred.
  • created_at: Timestamp indicating when the event took place.
  • ip_address: IP address from which the event originated.
  • city: City where the event occurred.
  • state: State where the event occurred.
  • postal_code: Postal code of the event location.
  • browser: Web browser used during the event.
  • traffic_source: Source of the traffic leading to the event.
  • uri: Uniform Resource Identifier associated with the event.
  • event_type: Type of event recorded.

3. inventory_items.csv

• Columns:
  • id: Unique identifier for each inventory item.
  • product_id: Identifier for the associated product.
  • created_at: Timestamp indicating when the inventory item was created.
  • sold_at: Timestamp indicating when the item was sold.
  • cost: Cost of the inventory item.
  • product_category: Category of the associated product.
  • product_name: Name of the associated product.
  • product_brand: Brand of the associated product.
  • product_retail_price: Retail price of the associated product.
  • product_department: Department to which the product belongs.
  • product_sku: Stock Keeping Unit (SKU) of the product.
  • product_distribution_center_id: Identifier for the distribution center associated with the product.

4. order_items.csv

• Columns:
  • id: Unique identifier for each order item.
  • order_id: Identifier for the associated order.
  • user_id: Identifier for the user who placed the order.
  • product_id: Identifier for the associated product.
  • inventory_item_id: Identifier for the associated inventory item.
  • status: Status of the order item.
  • created_at: Timestamp indicating when the order item was created.
  • shipped_at: Timestamp indicating when the order item was shipped.
  • delivered_at: Timestamp indicating when the order item was delivered.
  • returned_at: Timestamp indicating when the order item was returned.

5. orders.csv

• Columns:
  • order_id: Unique identifier for each order.
  • user_id: Identifier for the user who placed the order.
  • status: Status of the order.
  • gender: Gender information of the user.
  • created_at: Timestamp indicating when the order was created.
  • returned_at: Timestamp indicating when the order was returned.
  • shipped_at: Timestamp indicating when the order was shipped.
  • delivered_at: Timestamp indicating when the order was delivered.
  • num_of_item: Number of items in the order.

6. products.csv

• Columns:
  • id: Unique identifier for each product.
  • cost: Cost of the product.
  • category: Category to which the product belongs.
  • name: Name of the product.
  • brand: Brand of the product.
  • retail_price: Retail price of the product.
  • department: Department to which the product belongs.
  • sku: Stock Keeping Unit (SKU) of the product.
  • distribution_center_id: Identifier for the distribution center associated with the product.

7. users.csv

• Columns:
  • id: Unique identifier for each user.
  • first_name: First name of the user.
  • last_name: Last name of the user.
  • email: Email address of the user.
  • age: Age of the user.
  • gender: Gender of the user.
  • state: State where t...

Adapted from Wikipedia: OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. Created in 2004, it was inspired by the success of Wikipedia and more than two million registered users who can add data by manual survey, GPS devices, aerial photography, and other free sources. We've made available a number of tables (explained in detail below): history_* tables: full history of OSM objects planet_* tables: snapshot of current OSM objects as of Nov 2019 The history_* and planet_* table groups are composed of node, way, relation, and changeset tables. These contain the primary OSM data types and an additional changeset corresponding to OSM edits for convenient access. These objects are encoded using the BigQuery GEOGRAPHY data type so that they can be operated upon with the built-in geography functions to perform geometry and feature selection, additional processing. Example analyses are given below. This dataset is part of a larger effort to make data available in BigQuery through the Google Cloud Public Datasets program . OSM itself is produced as a public good by volunteers, and there are no guarantees about data quality. Interested in learning more about how these data were brought into BigQuery and how you can use them? Check out the sample queries below to get started. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Apple patent dataset associated with: https://docs.sutro.sh/examples/large-scale-embeddings

  dataset_info:

features: - name: publication_number dtype: large_string - name: application_number dtype: large_string - name: country_code dtype: large_string - name: kind_code dtype: large_string - name: patent_title dtype: large_string - name: patent_abstract dtype: large_string - name: patent_claims dtype: large_string - name: patent_description… See the full description on the dataset page: https://huggingface.co/datasets/sutro/apple-patents-bigquery.

Context

Cannabis is a genus of flowering plants in the family Cannabaceae.

Source: https://en.wikipedia.org/wiki/Cannabis

Content

In October 2016, Phylos Bioscience released a genomic open dataset of approximately 850 strains of Cannabis via the Open Cannabis Project. In combination with other genomics datasets made available by Courtagen Life Sciences, Michigan State University, NCBI, Sunrise Medicinal, University of Calgary, University of Toronto, and Yunnan Academy of Agricultural Sciences, the total amount of publicly available data exceeds 1,000 samples taken from nearly as many unique strains.

https://medium.com/google-cloud/dna-sequencing-of-1000-cannabis-strains-publicly-available-in-google-bigquery-a33430d63998

These data were retrieved from the National Center for Biotechnology Information’s Sequence Read Archive (NCBI SRA), processed using the BWA aligner and FreeBayes variant caller, indexed with the Google Genomics API, and exported to BigQuery for analysis. Data are available directly from Google Cloud Storage at gs://gcs-public-data--genomics/cannabis, as well as via the Google Genomics API as dataset ID 918853309083001239, and an additional duplicated subset of only transcriptome data as dataset ID 94241232795910911, as well as in the BigQuery dataset bigquery-public-data:genomics_cannabis.

All tables in the Cannabis Genomes Project dataset have a suffix like _201703. The suffix is referred to as [BUILD_DATE] in the descriptions below. The dataset is updated frequently as new releases become available.

The following tables are included in the Cannabis Genomes Project dataset:

Sample_info contains fields extracted for each SRA sample, including the SRA sample ID and other data that give indications about the type of sample. Sample types include: strain, library prep methods, and sequencing technology. See SRP008673 for an example of upstream sample data. SRP008673 is the University of Toronto sequencing of Cannabis Sativa subspecies Purple Kush.

MNPR01_reference_[BUILD_DATE] contains reference sequence names and lengths for the draft assembly of Cannabis Sativa subspecies Cannatonic produced by Phylos Bioscience. This table contains contig identifiers and their lengths.

MNPR01_[BUILD_DATE] contains variant calls for all included samples and types (genomic, transcriptomic) aligned to the MNPR01_reference_[BUILD_DATE] table. Samples can be found in the sample_info table. The MNPR01_[BUILD_DATE] table is exported using the Google Genomics BigQuery variants schema. This table is useful for general analysis of the Cannabis genome.

MNPR01_transcriptome_[BUILD_DATE] is similar to the MNPR01_[BUILD_DATE] table, but it includes only the subset transcriptomic samples. This table is useful for transcribed gene-level analysis of the Cannabis genome.

Fork this kernel to get started with this dataset.

Acknowledgements

Dataset Source: http://opencannabisproject.org/ Category: Genomics Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - https://www.ncbi.nlm.nih.gov/home/about/policies.shtml - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. Update frequency: As additional data are released to GenBank View in BigQuery: https://bigquery.cloud.google.com/dataset/bigquery-public-data:genomics_cannabis View in Google Cloud Storage: gs://gcs-public-data--genomics/cannabis

Banner Photo by Rick Proctor from Unplash.

Inspiration

Which Cannabis samples are included in the variants table?

Which contigs in the MNPR01_reference_[BUILD_DATE] table have the highest density of variants?

How many variants does each sample have at the THC Synthase gene (THCA1) locus?

The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP). The GFS dataset consists of selected model outputs (described below) as gridded forecast variables. The 384-hour forecasts, with 3-hour forecast interval, are made at 6-hour temporal resolution (i.e. updated four times daily). Use the 'creation_time' and 'forecast_time' properties to select data of interest. The GFS is a coupled model, composed of an atmosphere model, an ocean model, a land/soil model, and a sea ice model which work together to provide an accurate picture of weather conditions. See history of recent modifications to the global forecast/analysis system , the model performance statistical web page , and the documentation homepage for more information.Learn more

SELECT language, title, SUM(views) AS views FROM bigquery-samples.wikipedia_benchmark.Wiki10B WHERE title LIKE '%Google%' GROUP BY language, title ORDER BY views DESC;

--This query analyzes the number of views for Wikipedia articles that mention "Google" in their titles, grouped by language and article title. The goal is to identify the most viewed "Google"-related articles across different languages.

The Forest Inventory and Analysis dataset is a nationwide survey of the forest assets of the United States. The Forest Inventory and Analysis (FIA) research program has been in existence since mandated by Congress in 1928. FIA's primary objective is to determine the extent, condition, volume, growth, and use of trees on the Nation's forest land. This dataset includes the most recent data available from the USFS datamart , it does not include historical data. Original field names have been expanded to full names and code values have been expanded to full names in all tables, in addition, each table contains data from all States. A full description of the original tables is available from the USFS . A user's guide with example summary reports is also available from the USFS . This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

The dataset consists of PDFs in Google Cloud Storage from the first page of select US and EU patents, and BigQuery tables with extracted entities, labels, and other properties, including a link to each file in GCS. The structured data contains labels for eleven patent entities (patent inventor, publication date, classification number, patent title, etc.), global properties (US/EU issued, language, invention type), and the location of any figures or schematics on the patent's first page. The structured data is the result of a data entry operation collecting information from PDF documents, making the dataset a useful testing ground for benchmarking and developing AI/ML systems intended to perform broad document understanding tasks like extraction of structured data from unstructured documents. This dataset can be used to develop and benchmark natural language tasks such as named entity recognition and text classification, AI/ML vision tasks such as image classification and object detection, as well as more general AI/ML tasks such as automated data entry and document understanding. Google is sharing this dataset to support the AI/ML community because there is a shortage of document extraction/understanding datasets shared under an open license. This public dataset is hosted in Google Cloud Storage and Google BigQuery. It is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery or this this Cloud Storage quick start guide to begin.

The Consumer Complaint Database is a collection of complaints about consumer financial products and services that we sent to companies for response. Complaints are published after the company responds, confirming a commercial relationship with the consumer, or after 15 days, whichever comes first. Complaints referred to other regulators, such as complaints about depository institutions with less than $10 billion in assets, are not published in the Consumer Complaint Database.This database is not a statistical sample of consumers’ experiences in the marketplace. Complaints are not necessarily representative of all consumers’ experiences and complaints do not constitute “information” for purposes of the Information Quality Act . Complaint volume should be considered in the context of company size and/or market share. For example, companies with more customers may have more complaints than companies with fewer customers. We encourage you to pair complaint data with public and private datasets for additional context. The Bureau publishes the consumer’s narrative description of his or her experience if the consumer opts to share it publicly and after the Bureau removes personal information. We don’t verify all the allegations in complaint narratives. Unproven allegations in consumer narratives should be regarded as opinion, not fact. We do not adopt the views expressed and make no representation that consumers’ allegations are accurate, clear, complete, or unbiased in substance or presentation. Users should consider what conclusions may be fairly drawn from complaints alone.This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. Each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery

Github Clean Code Snippets Dataset

Here is a description, how the datasets for a training notebook used for Telegram ML Contest solution were prepared.

1 Step - Github Samples Database parsing

The first part of the code samples was taken from a private version of this notebook.

Here is the statistics about classes of programming languages from Github Code Snippets database https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F833757%2F2fdc091661198e80559f8cb1d1a306ff%2FScreenshot%202023-11-07%20at%2021.24.42.png?generation=1699390166413391&alt=media" alt="">

From this database, 2 csv files were created - with 50000 code samples for each of the 20 programming languages included, with equal by numbers and stratified sampling. The files related here are sample_equal_prop_50000.csv and sample_equal_prop_50000.csv and sample_stratified_50000.csv, respectively.

2 Step - Github Bigquery Database parsing

Second option for capturing out additional examples was to run this notebook with making up larger amount of queries, 10000.

The resulted file is dataset-10000.csv - included to the data card

The statistics for the code programming languages is as on the next chart - it has 32 labeled classes
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F833757%2F7c04342da8ec1df266cd90daf00204f9%2FScreenshot%202023-10-13%20at%2020.52.13.png?generation=1699392769199533&alt=media" alt="">

3 Step - collection of code samples of raw coding samples

To get a model more robust, code samples of 20 additional languages were collected in amount from 10 till 15 samples on more-less popular use cases. Also, for the class "OTHER", like regular language examples, according to the task of the competition, the text examples from this dataset with promts on Huggingface were added to the file. The resulted file here is rare_languages.csv - also in data card

The statistics for rare languages code snippets is as follows: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F833757%2F0b340781c774d2acb988ce1567f4afa3%2FScreenshot%202023-11-08%20at%2001.13.07.png?generation=1699402436798661&alt=media" alt="">

4 Step - First and second datasets combining

For this stage of dataset creation, the number of the columns in sample_equal_prop_50000.csv and sample_stratified_50000.csv was cut out just for 2 - "snippet", "language", the version of file with equal numbers is in the data card - sample_equal_prop_50000_clean.csv

To prepare Bigquery dataset file, the column with index was cut out, and the column "content" was renamed to "snippet". These changes were saved in dataset-10000-clean.csv

After that, the files sample_equal_prop_50000_clean.csv and dataset-10000-clean.csv were combined together and saved as github-combined-file.csv

5 Step - Datasets cleaning from symbols and merging together with rare languages

The prepared files took too much RAM to be read by Pandas library, so that is why additional prepocessing has been made - the symbols like quatas, commas, ampersands, new lines and adding tabs characters were cleaned out. After clieaning, the flies were merged with rare_languages.csv file and saved as github-combined-file-no-symbols-rare-clean.csv and sample_equal_prop_50000_-no-symbols-rare-clean.csv, respectively.

The final distribution of classes turned out to be the next one https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F833757%2Ff43e0cea4c565c9f7c808527b0dfa2da%2FScreenshot%202023-11-09%20at%2020.26.30.png?generation=1699558064765454&alt=media" alt="">

6 Step - Fixing up the labels

To be suitable for TF-DF format, to each programming language a certain label was given as well. The final labels are in the data card.

Abstract

This is a synthetic patient dataset in the OMOP Common Data Model v5.2, originally released by the CMS and accessed via BigQuery. The dataset includes 24 tables and records for 2 million synthetic patients from 2008 to 2010.

Methodology

This dataset takes on the format of the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). As shown in the diagram below, the purpose of the Common Data Model is to convert various distinctly-formatted datasets into a well-known, universal format with a set of standardized vocabularies. See the diagram below from the Observational Health Data Sciences and Informatics (OHDSI) webpage.

https://redivis.com/fileUploads/d1a95a4e-074a-44d1-92e5-9adfd2f4068a%3E" alt="Why-CDM.png">

Such universal data models ultimately enable researchers to streamline the analysis of observational medical data. For more information regarding the OMOP CDM, refer to the OHSDI OMOP site.

Usage

%3Cli%3EFor documentation regarding the source data format from the Center for Medicare and Medicaid Services (CMS), refer to the %3Ca href="https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/SynPUFs/DE_Syn_PUF"%3ECMS Synthetic Public Use File%3C/a%3E.%3C/li%3E

%3Cli%3EFor information regarding the conversion of the CMS data file to the OMOP CDM v5.2, refer to %3Ca href="https://github.com/OHDSI/ETL-CMS"%3Ethis OHDSI GitHub page%3C/a%3E. %3C/li%3E

%3Cli%3EFor information regarding each of the 24 tables in this dataset, including more detailed variable metadata, see %3Ca href="https://github.com/OHDSI/CommonDataModel/wiki"%3Ethe OHDSI CDM GitHub Wiki page%3C/a%3E. All variable labels and descriptions as well as table descriptions come from this Wiki page. Note that this GitHub page includes information primarily regarding the 6.0 version of the CDM and that this dataset works with the 5.2 version. %3C/li%3E

The American Community Survey (ACS) is an ongoing survey that provides vital information on a yearly basis about our nation and its people by contacting over 3.5 million households across the country. The resulting data provides incredibly detailed demographic information across the US aggregated at various geographic levels which helps determine how more than $675 billion in federal and state funding are distributed each year. Businesses use ACS data to inform strategic decision-making. ACS data can be used as a component of market research, provide information about concentrations of potential employees with a specific education or occupation, and which communities could be good places to build offices or facilities. For example, someone scouting a new location for an assisted-living center might look for an area with a large proportion of seniors and a large proportion of people employed in nursing occupations. Through the ACS, we know more about jobs and occupations, educational attainment, veterans, whether people own or rent their homes, and other topics. Public officials, planners, and entrepreneurs use this information to assess the past and plan the future. For more information, see the Census Bureau's ACS Information Guide . This public dataset is hosted in Google BigQuery as part of the Google Cloud Public Datasets Program , with Carto providing cleaning and onboarding support. It is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

OnPoint Weather is a global weather dataset for business available for any lat/lon point and geographic area such as ZIP codes. OnPoint Weather provides a continuum of hourly and daily weather from the year 2000 to current time and a forward forecast of 45 days. OnPoint Climatology provides hourly and daily weather statistics which can be used to determine ‘departures from normal’ and to provide climatological guidance of expected weather for any location at any point in time. The OnPoint Climatology provides weather statistics such as means, standard deviations and frequency of occurrence. Weather has a significant impact on businesses and accounts for hundreds of billions in lost revenue annually. OnPoint Weather allows businesses to quantify weather impacts and develop strategies to optimize for weather to improve business performance. Examples of Usage Quantify the impact of weather on sales across diverse locations and times of the year Understand how supply chains are impacted by weather Understand how employee’s attendance and performance are impacted by weather Understand how weather influences foot traffic at malls, stores and restaurants OnPoint Weather is available through Google Cloud Platform’s Commercial Dataset Program and can be easily integrated with other Google Cloud Platform Services to quickly reveal and quantify weather impacts on business. Weather Source provides a full range of support services from answering quick questions to consulting and building custom solutions. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery 瞭解詳情

1 million rows from the gkg_partitioned table in the gdelt-bq.gdeltv2 dataset on big query. Only the year 2020 was queried.

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

Context

BigQuery provides a limited number of sample tables that you can run queries against. These tables are suited for testing queries and learning BigQuery.

Content

• gsod: Contains weather information collected by NOAA, such as precipitation amounts and wind speeds from late 1929 to early 2010.

• github_nested: Contains a timeline of actions such as pull requests and comments on GitHub repositories with a nested schema. Created in September 2012.

• github_timeline: Contains a timeline of actions such as pull requests and comments on GitHub repositories with a flat schema. Created in May 2012.

• natality: Describes all United States births registered in the 50 States, the District of Columbia, and New York City from 1969 to 2008.

• shakespeare: Contains a word index of the works of Shakespeare, giving the number of times each word appears in each corpus.

• trigrams: Contains English language trigrams from a sample of works published between 1520 and 2008.

• wikipedia: Contains the complete revision history for all Wikipedia articles up to April 2010.

Fork this kernel to get started.

Acknowledgements

Data Source: https://cloud.google.com/bigquery/sample-tables

Banner Photo by Mervyn Chan from Unplash.

Inspiration

How many babies were born in New York City on Christmas Day?

How many words are in the play Hamlet?

Abstract

A demo of ingesting GDELT data into the data platform from Google Cloud Platform BigQuery.

About

This dataset consists of samples of non binary files, their contents and extensions from BigQuery's GitHub public sample repo data.

File info

This dataset consists of two CSV files: - filenames_with_ext.csv - This CSV lists all filenames with extensions from BigQuery's GitHub public sample repo data. Files with no extensions have been excluded. - filecontent_with_top_ext.csv - This CSV has samples of non binary files, their contents and extensions from BigQuery's GitHub public sample repo data with subject to some constraints.

Data extraction

To understand how this data was extracted and what constraints were used, refer to the following notebook: GitHub Repo Data - mayur7garg