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After starting his career in digital marketing, Michel started learning Python and has been an enthusiastic learner and practitioner of data analytics ever since. You probably already know Google BigQuery is an amazing data warehouse. Despite all of its capabilities, you can still find yourself in a situation where BigQuery just doesn't suit your needs for the task at hand and you want an old-fashioned CSV file.
Maybe you're passing on data to someone who doesn't and shouldn't have access to BigQuery, perhaps you need that data to build something amazing in a tool that doesn't play well with BigQuery, or maybe you just want to know how to accomplish this for the sake of knowing it.
Whatever your motivation is, I've got you covered. Without further ado, here are three ways to export your Google BigQuery data to a CSV file for use in your destination apps. By far the easiest way of exporting your data to a CSV file is to use the web UI, also known as the console, which you can find here.
This is a great option if you just need an easy way to export your data one time. You can then complete the following steps:. You now should have a CSV with the data you care about for your use case whatever it is. BigQuery comes with its own command-line tool called bq , which you can access in the console or in your local terminal by first installing the Google Cloud SDK. In the console, you'll find it here or by clicking on the terminal icon on the right side of the search bar.
This section covers how to use the public PyPI download statistics dataset to learn more about downloads of a package or packages hosted on PyPI. For example, you can use it to discover the distribution of Python versions used to download a package. Counting package downloads. Package downloads over time.
Python versions over time. PyPI does not display download statistics for a number of reasons: 1. Including them in project pages, which are heavily cached, would require invalidating the cache more often, and reduce the overall effectiveness of the cache. Highly inaccurate: A number of things prevent the download counts from being accurate, some of which include:. As an alternative, the Linehaul project streams download logs from PyPI to Google BigQuery 2 , where they are stored as a public dataset.
Knowing that most of our users are leveraging Python 3. This knowledge can also be helpful in messaging users regarding updating their environments for the best experience. Alternatively, in a tool like Looker, I can schedule the report to be emailed on a monthly basis so our team stays up-to-date on user trends. As a next step, I might join this data onto the Github dataset also available in the marketplace to see if there is a relationship between git activity and package installations.
I can easily save the results of the query to Google Sheets and share with our marketing team.
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