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

## Merging data series
We need to merge our two data series. But since
we've already done all the cleaning up and manipulation, this
will go smoothly. We'll create a new data frame called `merged_data` and feed it with the output of this `pd.merge` function. We
supply our two data frames' names and specify
that the `Date` column should be the index.

Fast and friendly. No trouble at all. Go ahead and take a look:

# Merging data series
We need to merge our two data series. But since
we've already done all the cleaning up and manipulation, this
will go smoothly. We'll create a new data frame called `merged_data` and feed it with the output of this `pd.merge` function. We
supply our two data frames' names and specify
that the `Date` column should be the index.

Fast and friendly. No trouble at all. Go ahead and take a look:

Learn how to merge and plot data frames and how to merge data series and easily plot them.

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

Merge and Plot Our Data Frames

Merging data series