Python Charmers is the leading provider of Python training in the Asia-Pacific region.

We boast years of Python experience and deep roots within the Python community, as both speakers at Python events and contributors to open source projects.

When not training or consulting, we develop and maintain a number of open source tools, including Python Future, a compatibility layer that helps developers write Python 3 code that runs unchanged on Python 2.

We also share our solution guides with the community through this Blog. Contributing to the technology communities we serve helps us provide the most value we can.

Calculating your 5km travel buffer

August 27, 2020

Over the weekend Victoria, Australia, and especially Melbourne, have come into higher levels of lockdown due to the COVID-19 pandemic. This includes a restriction that the furthest you can travel from home is 5km (with exceptions if your nearest shops are more than 5km away).

Here we'll show how you can calculate your 5km travel bubble from home using some of the best Python tools for spatial analysis.

Building Multi-Stage Pipelines with scikit-learn

August 25, 2020

Pipelines in scikit-learn are a thing of beauty. Operationalizing your scikit-learn classification or text mining data job with pipelines can be tricky, but rewarding. There's meaningful improvements to speed and so on.

Based on our Python for Predictive Analytics training, we've released a solution guide for Building Multi-Stage Pipelines with scikit-learn. scikit-learn pipelines are an excellent tool to use if you need to preprocess your data before training it. If you're an engineer who's trying to make a repeatable python maching learning pipeline, these are invaluable tools to have in your belt.

CSV or XLS File Too Big? Taking Big Data from Excel to Pandas

August 18, 2020

Is your CSV or XLS File getting too big to conduct operations on? Check out this new solution guide, Taking Big Data from Excel to Pandas. In it, we go through some quick tricks for dealing with large data in pandas.

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