[NumPy](http://www.numpy.org/) library.

The [SciPy](http://www.scipy.org) library is built on top of NumPy and provides additional functionality.

* [linear algebra](http://docs.scipy.org/doc/scipy/reference/linalg.html)

* [integration](http://docs.scipy.org/doc/scipy/reference/integrate.html)

* [interpolation](http://docs.scipy.org/doc/scipy/reference/interpolate.html)

* [optimization](http://docs.scipy.org/doc/scipy/reference/optimize.html)

* [distributions and statistical techniques](http://docs.scipy.org/doc/scipy/reference/stats.html)

* [signal processing](http://docs.scipy.org/doc/scipy/reference/signal.html)

See them all [here](http://docs.scipy.org/doc/scipy/reference/index.html).

The most popular and comprehensive Python library for creating figures and graphs is [Matplotlib](http://matplotlib.org/), with functionality including

* [Plotly](https://plot.ly/python/)

* [Bokeh](http://bokeh.pydata.org/en/latest/)

One well-known example is [NetworkX](http://networkx.github.io/).

* [SymPy](http://www.sympy.org/) for symbolic algebra, including limits, derivatives and integrals

* [statsmodels](http://statsmodels.sourceforge.net/) for statistical routines

* [scikit-learn](http://scikit-learn.org/) for machine learning

* [Pyro](https://pyro.ai/) and [PyStan](https://pystan.readthedocs.org/en/latest/) for Bayesian data analysis

* [Numba](http://numba.pydata.org/) for making Python run at the same speed as native machine code

The full list of magics is [here](http://ipython.readthedocs.org/en/stable/interactive/magics.html).

[Jupyter](http://jupyter.org/) notebooks are one of the many possible ways to interact with Python and the scientific libraries.

Here's an image showing execution of some code (borrowed from [here](http://matplotlib.org/examples/pylab_examples/hexbin_demo.html)) in a Jupyter notebook

Here's an arbitrary program we can use: [http://matplotlib.org/3.1.1/gallery/pie_and_polar_charts/polar_bar.html](http://matplotlib.org/3.1.1/gallery/pie_and_polar_charts/polar_bar.html).

are writing [Markdown](http://daringfireball.net/projects/markdown/), a mark-up language similar to (but simpler than) LaTeX.

You can share them in the usual way that you share files --- or by using web services such as [nbviewer](http://nbviewer.jupyter.org/).

Here `%%writefile` is an example of a [cell magic](http://ipython.readthedocs.org/en/stable/interactive/magics.html#cell-magics).

Alternatively, if you want an outstanding free text editor and don't mind a seemingly vertical learning curve plus long days of pain and suffering while all your neural pathways are rewired, try [Vim](http://www.vim.org/).

We've already generated quite a few figures in these lectures using [Matplotlib](http://matplotlib.org/).

* The [Matplotlib gallery](http://matplotlib.org/gallery.html) provides many examples.

* A nice [Matplotlib tutorial](http://scipy-lectures.org/intro/matplotlib/index.html) by Nicolas Rougier, Mike Muller and Gael Varoquaux.

* [mpltools](http://tonysyu.github.io/mpltools/index.html) allows easy

Fortunately, a new Python library called [Numba](http://numba.pydata.org/)

(For an alternative reference, see [the official NumPy documentation](http://docs.scipy.org/doc/numpy/reference/).)

called a [numpy.ndarray](http://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html).

To create a NumPy array containing only zeros we use [np.zeros](http://docs.scipy.org/doc/numpy/reference/generated/numpy.zeros.html#numpy.zeros)

or `np.genfromtxt`---see [the documentation](http://docs.scipy.org/doc/numpy/reference/routines.io.html) for details.

(For older versions of Python and NumPy you need to use the [np.dot](http://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html) function)

Much of this functionality is also available in [SciPy](http://www.scipy.org/), a collection of modules that are built on top of NumPy.

[Pandas](http://pandas.pydata.org/) is a package of fast, efficient data analysis tools for Python.

Just as [NumPy](http://www.numpy.org/) provides the basic array data type plus core array operations, pandas

as [statsmodels](http://www.statsmodels.org/) and [scikit-learn](http://scikit-learn.org/), which are built on top of pandas.

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

The World Bank [collects and organizes data](http://data.worldbank.org/indicator) on a huge range of indicators.

For example, [here's](http://data.worldbank.org/indicator/GC.DOD.TOTL.GD.ZS/countries) some data on government debt as a ratio to GDP.

create custom {ref}`ufuncs <ufuncs>` with the [@vectorize](http://numba.pydata.org/numba-doc/dev/user/vectorize.html) decorator.