import csv

total, count = 0, 0

with open(data.csv, mode='r') as file:

with open('data.csv', mode='r') as file:

reader = csv.reader(file)

for row in reader:

try:

For this we need to use libraries for working with arrays.

For Python, the most important matrix and array processing library is

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

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

For example, let's build a NumPy array with 100 elements

### SciPy

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

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

(tuple_unpacking_example)=

For example, let's calculate $\int_{-2}^2 \phi(z) dz$ where $\phi$ is the standard normal density.

SciPy includes many of the standard routines used in

* [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)

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

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

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

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

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

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

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

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

Later we'll discuss SciPy in more detail.

A major strength of Python is data visualization.

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

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

* plots, histograms, contour images, 3D graphs, bar charts etc.

* output in many formats (PDF, PNG, EPS, etc.)

Other graphics libraries include

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

* [Plotly](https://plotly.com/python/)

* [seaborn](https://seaborn.pydata.org/) --- a high-level interface for matplotlib

* [Altair](https://altair-viz.github.io/)

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

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

You can visit the [Python Graph Gallery](https://www.python-graph-gallery.com/) for more example plots drawn using a variety of libraries.

```{index} single: NetworkX

```

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

One well-known example is [NetworkX](https://networkx.org/).

Its features include, among many other things:

Here's a short list of some important scientific libraries for Python not

mentioned above.

* [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

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

* [statsmodels](https://www.statsmodels.org/) for statistical routines

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

* [Keras](https://keras.io/) for machine learning

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

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

* [GeoPandas](https://geopandas.org/en/stable/) for spatial data analysis

* [Dask](https://docs.dask.org/en/stable/) for parallelization

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

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

* [CVXPY](https://www.cvxpy.org/) for convex optimization

* [scikit-image](https://scikit-image.org/) and [OpenCV](https://opencv.org/) for processing and analyzing image data

* [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/) for extracting data from HTML and XML files