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
The rows representing years without events should, again, be
removed:
```JS
df = df[(df.Date.str.contains("None")) == False]
```
This dataset's `Date` column has characters pointing to five
footnotes: `$`, `*`, `#`, `%`, and `&`. The footnotes contain essential
information, but those characters will give us grief if we remove them. These commands will get that done, replacing
all such strings in the `Date` column with nothing:

Next, we’ll reset the column headers first because it will be easier to work with short names. The column data types are going to need some work.



# Cleaning up the hurricane data
The rows representing years without events should, again, be
removed:
```JS
df = df[(df.Date.str.contains("None")) == False]
```
This dataset's `Date` column has characters pointing to five
footnotes: `＄`, `*`, `#`, `%`, and `&`. The footnotes contain essential
information, but those characters will give us grief if we remove them. These commands will get that done, replacing
all such strings in the `Date` column with nothing:

Next, we’ll reset the column headers first because it will be easier to work with short names. The column data types are going to need some work.



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


US Tropical Storm Data: 1851-1965, 1983-2019

Learn how to clean up hurricane data and present tropical 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 Tropical Storm Data: 1851-1965, 1983-2019

Cleaning up the hurricane data