Pandas for Productivity Vlog Ep 4: How to identify and fix missing data
Why this topic? This is a lead-in to next week’s episode, where I will talk about calculating moving averages in […]
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SQL
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Read and write files with Jupyter Notebooks
Now that you’ve set up your Jupyter notebook, you can start getting data into it. To this purpose, this post
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Take ownership of your data: The end-goal
We’ve come to the end of the Survival SQL series. Hopefully, you think the posts are relevant and easy to
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Subqueries and CTEs: Multi-step problems
As you progress, you’ll find situations where you need to combine more than one data pull to answer your question.
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Window functions : Automate lengthy Excel tasks
“Find our 5 best-selling products by country.””What was the 7-day moving average revenue for every day last month?” Chances are,
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Sort your data with “ORDER BY”
Most of you will use Excel to manipulate and plot the data you pull with SQL. Eventually, you’re likely to
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When to use more complex joins
As you pull your data more often, you may encounter situations where you need more complex joins than what we’ve
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Define your own metrics: Operators & conditionals
As you become confident with pulling data, you’ll start wanting more control over the metrics you report on. Hence, you’ll
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Aggregating metrics: Using “GROUP BY”
Earlier, we’ve discussed that your database stores data at its most granular level. This means that every order creates one