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Introduction to Python

Why should I learn Python?

Python is easy to learn and has a clean syntax. Therefore, its recommendable for both beginners and experienced programmers. Data scientists, software engineers, and even hackers use Python because of its versatility, flexibility, and its many other features.. source

SYZYGY Environment

Members of the UBC community (anyone with a CWL) have access to UBC Syzygy.

Syzygy is a collection of research tools that are accessible through a web browser. One of these tools is Jupyter which is an electronic notebook that lets you run scripts in common programming languages and see the output immediately with no additional installation or setup required. Syzygy effectively gives you a robust Pythonenvironment to test and run simple scripts in.

Once you log in, you will probably see an empty folder.

We will soon create a Python script and upload the data for this workshop on SYZYGY.

Create a new Python 3 script

new > Python 3

  • Each cell holds either python code or text.

    • You can change between code and text in menu

  • The empty bracket indicates that the cell has not been run

    • Once you run the code (run button), it will show a number indicating the order in which the command was run

    • You can go back to a cell and run it as many times as you desire. However, this might affect other parts of your script, so use this with caution!

Tips and shortcuts

  • esc goes to the command palette. While on command mode:
    • A to insert cell above
    • B to insert cell below
    • M to change cell to markdown
    • D + D to delete current cell

  • shift + enter runs the code in the current cell

  • While typing a command press tab to have suggestions for auto-completion

  • In a cell containing code, the # serves as annotations or comments and they will not be executed

Upload Data Files

Click the upload button > navigate to the folder containing the workshop data > select the files

For each upload file, SYZYGY will show the file name and a blue upload button. Click to upload!

Packages

Python packages

Python has many packages that provide helper functions that can facilitate your work. Some common data science packages are listed below:

  • Pandas is a common module to load and manipulate data

  • Matplotlib and Seaborn are plotting modules

  • Numpy and Scipy are often used for statistical tests

  • Jupyter provides facilities for writing Python code as notebooks, often used in teaching environments. SYZYGY uses Jupyter.

Importing a Python Package or Module

  1. First it’s often suggested to setup a plotting package

Input

# Matplotlib for plotting
from matplotlib import pyplot as plt
%matplotlib notebook
  1. Then, we can load conventional packages

Input

import csv # spreadsheet manipulation
import json # a common data structure in many programming languages
import pandas as pd # data processing, csv file I/O (e.g. pd.read_csv)
import numpy as np # linear algebra
from scipy import stats # statistics
from datetime import datetime # date time manipulation