Learn about Python-based data analytics, discover data manipulation and cleaning techniques, explore Jupyter for insights, and gain skills to plot and visualize data effectively.

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In this course, you will get an introduction to Python-based data analytics. You will learn the inner workings of Python to the point where you can confidently find and manipulate data sources and use a Jupyter environment to derive insights from your data.

By the end of this course, you will be able to find and clean data sources, plot your data, and use Python functionalities that show graphs of data.

It’s your all-in-one guide to teaching yourself data analytics in 30 days.

Data Analytics in Python

# Cleaning up the hurricane data

If you quickly look through the [webpage](https://www.aoml.noaa.gov/hrd/hurdat/All_U.S._Hurricanes.html) itself,  you’ll see some formatting that’ll need cleaning up. Each decade is introduced with a single row containing nothing but a string looking like “1850s” and so on.. We’ll want to drop those rows. Years with no events have the string “None” in the second column. Those, too, will need to go.

Some events have no data for their “Max Wind (kt)” speeds. Instead of a number (measured in knots), the speed values for those events are represented by five dashes (-----). We’ll have to convert that to something we can work with.
And finally, while three-letter abbreviations generally represent months, a couple of events stretched across two months. To be able to process those properly, we’ll convert “Sp-Oc” and “Jl-Au” to “Sep and Jul” respectively. The fact is that we won’t be using the month column, so this won’t make any difference. But it’s an excellent tool to know.

Let’s look at the data types for each column. We can ignore the strings in the “States” and “Name” column — we’re not interested in those anyway. But we will need to do something with the ”Year” and “Max Wind (kt)” columns — they won’t do us any good as an object.

Here’s how we set things up.



Learn how to clean up hurricane data using different Python scripts.

Before We Begin

Comparing Wages With Consumer Price Index Data

Wages and CPI: Reality Check

Working With Major US Storm Data

Property Rights and Economic Development

How Representative Is Your Government?

Does Wealth Influence The Prevalence Of Mental Illness?

Do Birthdays Make Elite Athletes?

Does Literacy Impact The Income of People

Conclusion

Appendix

US Hurricane Data: 1851–2019

Cleaning up the hurricane data