Scikit-learn has been the primary Python machine learning library for years. The move to version 1.0 doesn’t bring many major breaking changes, but it does include bug fixes, few new features, some speedups, and a whole bunch of API cleanup. 🧹
A more consistent API is wonderful for both new…
Airtable combines the power of a database with the convenient user interface of a spreadsheet. Then it adds some cherries on top, too. 🍒 It’s a solid choice for many use cases.
If you’re setting up a team without a data pro and you want automation, organization, and insights you…
The pandas library is THE tool for data cleaning, data prep, and data analysis in Python. Once you find your way around it’s expansive API, it’s a joy to work with. 🎉
Pandas stores your data in memory, which makes operations zippy! 🏎 The downside is that a large dataset…
Shape errors are the bane of many folks learning data science. I would bet money that people have quit their data science learning journey due to frustration with getting data into the shape required for machine learning algorithms.
Having a stronger understanding of how to reshape your data will spare…
Scikit-learn version 0.24.0 is packed with new features for machine learning. It arrived just in time for the New Year. Let’s look at the highlights! ☃️
HalvingGridSearchCV and HalvingRandomSearchCV join GridSearchCV and RandomizedSearchCV as less resource-intense members of the hyper-parameter tuning family.
The new classes choose the best hyper-parameters using…
NumPy is a library that every data scientist who uses Python should be familiar with. It is the backbone on which the modern Python data science stack built.
The library is often picked up in pieces along your learning journey. Eventually, it makes sense to learn the key parts of…
Without further ado, here are the best places to find data, with some helpful information about each. Folks keep pointing me to new sources, so the list is expanding! If you have a favorite, please send it my way! 😀
Awesome Data is a GitHub repository with a seriously impressive…
Python is the most popular language for data scientists. 🐍 Conda is the most common tool to create a virtual environment and manage packages for data scientists using Python.
Unfortunately, figuring out the best way to get conda on your machine and when to install packages from various channels isn’t…
JupyterLab is awesome. It has almost everything a data scientist could want.
Dealing with big data can be tricky. No one likes out of memory errors. ☹️ No one likes waiting for code to run. ⏳ No one likes leaving Python. 🐍
Don’t despair! In this article I’ll provide tips and introduce up and coming libraries to help you efficiently deal with…
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