This document provides an overview and agenda for a Python fundamentals session, including discussions of Jupyter notebooks, magics, running scripts from Jupyter cells, and virtual environments. It also briefly introduces NumPy, describing NumPy arrays as the main data structure and noting NumPy is fast due to C library bindings. Key topics covered are Jupyter project overview, line and cell magics, running scripts from Jupyter using various interpreters, creating and activating virtual environments in virtualenv and Anaconda, and basic NumPy array types.

1. Python Course
Session 2, Python Fundamentals
Amin Mesbahi
amin@mesbahi.net | @mesbahi

2. Agenda
Jupyter Notebooks
Virtual Environments

3. Jupyter, Overview
Jupyter:
Project Jupyter exists to develop open-source software, open-standards, and services for
interactive computing across dozens of programming languages (over 40 programming
languages, including Python, R, Julia, and Scala)
https://jupyter.org/try

4. Jupyter, Magics
Magics:
provide a mini command language that is orthogonal to the syntax of Python and is
extensible by the user with new commands.
Command: lsmagic

5. Jupyter, Magics - Line magics
Line magics:
these are commands prepended by one % character and whose arguments only
extend to the end of the current line.
%timeit np.linalg.eigvals(np.random.rand(100,100))
100 loops, best of 3: 7.06 ms per loop

6. Jupyter, Magics - Cell magics
Cell magics:
use two percent characters as a marker (%%), and they receive as argument both
the current line where they are declared and the whole body of the cell. you can
only use one cell magic per cell).
%%timeit a = np.random.rand(100, 100)
np.linalg.eigvals(a)
100 loops, best of 3: 7.4 ms per loop

7. Running Scripts from Jupyter
Running Scripts from Jupyter:
Jupyter has a %%script cell magic, which lets you run a cell in a subprocess of any
interpreter on your system, such as: bash, ruby, perl, zsh, R, etc
%%script python
import sys
print('hello from Python: %s' % sys.version)
hello from Python: 3.7.0 (default, Jun 28 2018, 08:04:48)
[MSC v.1912 64 bit (AMD64)]

8. Running Scripts from Jupyter
%%ruby
puts "Hello from Ruby #{RUBY_VERSION}“
Hello from Ruby 2.0.0
%%bash
echo "hello from $BASH“
hello from /usr/local/bin/bash

9. Running Scripts from Jupyter
Background Scripts
These scripts can be run in the background, by adding the --bg flag.
When you do this, output is discarded unless you use the --out/err flags to store output as above.
%%ruby --bg --out ruby_lines
for n in 1...10
sleep 1
puts "line #{n}"
STDOUT.flush
End
Starting job # 0 in a separate thread.

10. Virtual Environments
Virtual Environments
Allow us to set up virtual installations of Python and libraries on our machines.
We can have multiple versions of Python or libraries and easily activate or deactivate these
environments

11. Virtual Environments
Virtual Environments
virtualenv library for normal Python distributions
python -m venv sample-env
On Windows, run:
sample-envScriptsactivate.bat
On Unix or MacOS, run:
source sample-env/bin/activate

12. Virtual Environments
Anaconda has a built-in virtual environment manager
https://conda.io/docs/user-guide/tasks/manage-environments.html
conda create –-name sample-env [biopython]
conda create –-name sample-env [python=3.5 numpy]
conda create –-name sample-env [python=3.5 anaconda]
activate sample-env
deactivate
conda info --envs

13. Virtual Environments
Virtual Environments
Anaconda has a built-in virtual environment manager
https://conda.io/docs/user-guide/tasks/manage-environments.html
conda create –-name sample-env [biopython]
activate sample-env
deactivate

14. Numpy, Overview
Numpy
Is a Linear Algebra Library for Python, the reason it is so important for Data Science with Python is
that almost all of the libraries in the PyData Ecosystem rely on Numpy as one of their main building
blocks.
NumPy is also incredibly fast, as it has bindings to C libraries.
conda install numpy
pip install numpy

15. Numpy, Overview
Numpy
NumPy arrays are the main way we will use NumPy.
NumPy arrays essentially come in two flavors: vectors and matrices
Vectors are strictly 1-d arrays and matrices are 2-dC libraries.