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rebuild with sphinxontrib-tomyst(84a11e7)
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source/rst/about_py.md

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#### Pandas
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```{index}
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```
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One of the most popular libraries for working with data is [pandas](http://pandas.pydata.org/).
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Pandas is fast, efficient, flexible and well designed.

source/rst/debugging.md

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# Debugging
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## Debugging
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source/rst/functions.md

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# Functions
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```{index}
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```
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### Exercise 1
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Here's one solution.
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```{code-block} python3
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### Exercise 2
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```{code-block} python3
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from numpy.random import uniform
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### Exercise 3
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Here's a function for the first random device.
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```{code-block} python3

source/rst/getting_started.md

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### QuantEcon Notes
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QuantEcon has its own site for sharing Jupyter notebooks related
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to economics -- [QuantEcon Notes](http://notes.quantecon.org/).
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As the 2nd task,
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Sign up to [GitHub](https://github.com/).
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1. Sign up to [GitHub](https://github.com/).
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1. Look into 'forking' GitHub repositories (forking means making your own copy of a GitHub repository, stored on GitHub).
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1. Fork [QuantEcon.py](https://github.com/QuantEcon/QuantEcon.py).
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1. Clone your fork to some local directory, make edits, commit them, and push them back up to your forked GitHub repo.
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For reading on these and other topics, try
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* [The official Git documentation](http://git-scm.com/doc).
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Reading through the docs on [GitHub](https://github.com/).
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* Reading through the docs on [GitHub](https://github.com/).
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* [Pro Git Book](http://git-scm.com/book) by Scott Chacon and Ben Straub.
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* One of the thousands of Git tutorials on the Net.
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source/rst/matplotlib.md

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# Matplotlib
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### Exercise 1
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```{code-block} ipython3

source/rst/numba.md

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### Exercise 1
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### Exercise 2
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We let
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- 0 represent "low"

source/rst/numpy.md

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## Overview
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[NumPy](https://en.wikipedia.org/wiki/NumPy) is a first-rate library for numerical programming
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* Widely used in academia, finance and industry.
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### Exercise 1
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This code does the job
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### Exercise 2
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### Exercise 3
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source/rst/oop_intro.md

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### Type
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### Identity
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### Methods
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source/rst/pandas.md

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## Overview
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[Pandas](http://pandas.pydata.org/) is a package of fast, efficient data analysis tools for Python.
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Its popularity has surged in recent years, coincident with the rise
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### Exercise 1
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There are a few ways to approach this problem using Pandas to calculate
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### Exercise 2
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Following the work you did in Exercise 1, you can query the data using `read_data` by updating the start and end dates accordingly.
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source/rst/parallelization.md

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