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GitHub GraphQL in CI

Published March 9, 2018 in devops - 0 Comments

All python code is Python 3.5+. Having an automatic way to build GitHub pull requests before merging saves a lot of time and trouble compared with pulling, building and testing a GitHub pull request locally. TeamCity makes it easy to set this up using branch specifications. The blog post refers to a much older version […]

Tags: ci , git , graphql , python

Python type hints: alias those types!

Published February 21, 2018 in programming - 0 Comments

All python code is Python 3.5+. PEP484 goes beyond built-in type annotations. Another feature of the Python type hinting libary is the ability to create type aliases. I’ve used type aliasing frequently in C++ (typedef, using) to improve code readability and for its other benefits. I’m happy to see that it’s available in Python too. […]

Tags: python

First look at GitHub’s GraphQL API

Published February 20, 2018 in devops - 0 Comments

Sometimes it’s necessary to query GitHub for repo information through an API; during a continuous integration (CI) build step for example. I’ve used GitHub’s REST API before, which is OK but dumps a lot of extra data that can be annoying to parse. Also, sometimes multiple queries are needed to get to the data I […]

Tags: ci , git , graphql

Importing Stringified JSON Objects Into Pandas (Part 2)

Published November 30, 2017 in data , programming - 0 Comments

All python code in this post is Python 3.5+. Continuing from Part 1, I discovered that movies_metadata.csv contains malformed rows that have missing fields, which is what caused file import to fail. I tried experimenting with some of the more advanced Pandas.read_csv parameters to see if I could work around the malformed rows. def main(path: […]

Importing Stringified JSON Objects Into Pandas (Part 1)

Published November 24, 2017 in data , programming - 0 Comments

All python code in this post is Python 3.5+. I’m continuing to work with the same Kaggle movies dataset as in the SQL import experiment. This time, I imported the data into Pandas DataFrames. The trickiest dataset to import was movies_metadata.csv. I first tried to use pandas.read_csv with the default settings. import argparse import pandas […]