```{index} single: Python; Anaconda

Important points:

Notebook files are just text files structured in [JSON](https://en.wikipedia.org/wiki/JSON) and typically ending with `.ipynb`.

The notebooks you see on that site are **static** html representations.

Other libraries can be installed with `pip` or `conda`.

(gs_install_qe)=

starting Jupyter and typing

```{code-block} ipython3

To upgrade to the latest version, which you should do regularly, use

Here we are focused on using JAX on the CPU, rather than on accelerators such as

GPUs or TPUs.

This extra memory usage can be a big problem in actual research calculations.

+++

It does this by inferring type information on the fly.

As was the case with Numba, a key problem is the fact that Python is dynamically typed.

If you are comfortable writing Fortran you will find it very easy to create

F2Py is a Fortran-to-Python interface generator that is particularly simple to

use.

to F2Py, among other things.

learn all about finite-state Markov chains.

For now, let's just concentrate on simulating a very simple example of such a chain.

1. the fundamental array processing operations provided by NumPy.

We will use the following imports.

The essential problem that NumPy solves is fast array processing.

The most important structure that NumPy defines is an array data type, formally

NumPy arrays power a very large proportion of the scientific Python ecosystem.

```{code-cell} python3

NumPy arrays are somewhat like native Python lists, except that

* Data *must be homogeneous* (all elements of the same type).

The most important of these dtypes are:

To read in the array data from a text file containing numeric data use `np.loadtxt`

We can also use `@` to take the inner product of two flat arrays

We'll cover the SciPy versions in more detail {doc}`soon <scipy>`.

**Application: Subsetting Dataframe**

It is sometimes desirable to work with a subset of data to enhance computational efficiency and reduce redundancy.

```{index} single: Python; requests

To begin, try the following code on your computer

In the second case, you can either

* switch to another machine

Assuming that all is working, you can now proceed to use the `source` object returned by the call `requests.get('https://research.stlouisfed.org/fred2/series/UNRATE/downloaddata/UNRATE.csv')`

Assuming that you have, the most common source of problems for our readers is that their Anaconda distribution is not up to date.

on how to update Anaconda.

Another option is to simply remove Anaconda and reinstall.

For this task you can either

This section will familiarize you with git and GitHub.

In many cases, the associated collections of files --- called *repositories* --- are stored on [GitHub](https://github.com/).

GitHub is a wonderland of collaborative coding projects.

For example, it hosts many of the scientific libraries we'll be using later

Git is the underlying software used to manage these projects.

There are two main flavors of Git

2. the various point-and-click GUI versions

In case you already haven't, try

1. Look into 'forking' GitHub repositories (forking means making your own copy of a GitHub repository, stored on GitHub).

1. Fork [QuantEcon.py](https://github.com/QuantEcon/QuantEcon.py).

1. Clone your fork to some local directory, make edits, commit them, and push them back up to your forked GitHub repo.

For reading on these and other topics, try

* Reading through the docs on [GitHub](https://docs.github.com/en).

* One of the thousands of Git tutorials on the Net.

More importantly, repeating the same logic in different places means that eventually one of them will likely be wrong.

We'll talk about how to avoid repetition below.