Transforming BigQuery Output into a Structured Dictionary in Python

Описание: Learn how to convert your Google BigQuery query results into a dictionary format using Python. This guide will help you preserve all entries for each year without overwriting your data.
---
This video is based on the question https://stackoverflow.com/q/65731609/ asked by the user 'AK91' ( https://stackoverflow.com/u/9372996/ ) and on the answer https://stackoverflow.com/a/65731703/ provided by the user 'Sergey Geron' ( https://stackoverflow.com/u/13473525/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Python/BigQuery query job to dictionary

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Transforming BigQuery Output into a Structured Dictionary in Python

When working with Google BigQuery and Python, you may encounter scenarios where you need to extract data into a format that is both structured and easy to read. One common requirement is to transform your query results into a nested dictionary format. This post discusses a common problem and its solution for a straightforward implementation.

The Problem

You have a dataset extracted from BigQuery and you want to convert the results into a specific dictionary structure. The desired format looks something like this:

[[See Video to Reveal this Text or Code Snippet]]

The data should maintain entries for each year without overwriting previous records. However, if you run your current implementation, you may find that only the last entry for each year is retained.

The Solution

The issue stems from how you manage storing results in your dictionary. When you use data[str(row.year)] = None, you are essentially resetting the value for that year on each iteration. To fix this, we can utilize a defaultdict from the collections module, which allows you to append data without overwriting existing values.

Step-by-Step Code Explanation

Import Necessary Library: Use defaultdict to create a dictionary that will automatically handle new keys.

Initialize the Default Dictionary: Create data as defaultdict(list) to store lists of monthly data.

Iterate Over the Query Job: For each row in your query result, create a dictionary to store monthly data attributes. Append this dictionary to the list corresponding to the year key.

Here's the revised code that addresses the problem:

[[See Video to Reveal this Text or Code Snippet]]

Key Points to Remember

Using defaultdict(list) simplifies the process of appending data to your dictionary.

Each year retains its list of monthly entries without being overwritten, ensuring all data is collected effectively.

Ensure your SQL query is well-structured to retrieve the proper columns that are mapped in the monthly data dictionary.

Conclusion

By employing a defaultdict and adjusting how you handle your data storage, you can easily convert your Google BigQuery query results into a useful dictionary format. This structure is perfect for subsequent operations, such as writing to JSON files or further data processing.

Using the solution provided, you should now be equipped to handle and structure your query output correctly. Happy coding!