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.

teach yourself data analytics in 30 days.png

Jupyter.tar.gz

jupitor

code

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

Well, that’s our formatting problem out of the way: we’ve
converted the CPI data to quarterly increments and cleaned
up the `Date` format to match the wages data. Now we’ll need to
address the inconsistency between the absolute point values
in the CPI set and the percentages that came with the wages
data.

import pandas as pd
import matplotlib as plt
import numpy as np
import bls
wages = bls.get_series('CIU2020000000000A')
wages.to_csv('bls_data_csv')
df = pd.read_csv('bls_data_csv')
df.columns = 'Date','Wages'
print (df)

* **Get that data:**  First, let's get that data. Technically, if you've been following along on your own, you'll already have pulled the series
back near the beginning, but it can't hurt to rerun it. (**line 5**)

* **Save the data:** Once again, we save the data to a CSV file (which, again,
isn't necessary). (**line 6**)

* **Push the data:** We push the data to a `df` data frame and give
it column headers. (**line 7**)

* **Name the column:** Naming the date column `Date` to match
the CPI set will simplify things. (**line 8**)

* **Print:** We then print the data frame `df` data. (**line 9**)

Then, we assign the output of `df` in `newdf`:
```JS
newdf = df
```

Now, let's do some quick math with this data.

Each of the percentages in the wages data
series represents an annual average. So when we were told that
the rate for the first quarter of 2012 was `1.8%`, that means that
if wages continued to rise at the current (first quarter) rate for
a full 12 months, the annual average growth would have been
`1.8%`, not `14%`.

This means the numbers we're going to work with will have to be adjusted. The *actual* growth during, say, the three months of 2012 Q1 wasn't `1.9%`, but only one quarter of that (or `0.875%`). If we don’t make this adjustment and just continue to map quarterly growth numbers to quarterly CPI prices, then our calculated output will lead us to think that wages are growing so fast that they’ve become detached from reality.


And here’s where part of our quick math is going to rear its ugly head. We're going to divide each quarterly growth rate by four. Or, in other words, we’ll pretend that the real changes to wages during those three months were precisely one-quarter of the reported year-over-year rate. But that’s almost certainly not true and is an oversimplification. However, it’s probably close enough for the big historical picture we’re trying to draw here.

That will still leave us with a number that’s a percentage, but the corresponding CPI number we’re comparing it to is, again, a point figure. To solve this problem, we’ll apply one more piece of information.

Learn how to convert percentages to CPI values and how to create a Python function for converting data.

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

Converting Percentages to CPI Values

How to convert data