This repository contains workshop material.

Handling multiple large files the easy way using Python

Workshop facilitator: Laura Gutierrez Funderburk | Department of Mathematics | Simon Fraser University
Workshop date: April 24 2018


In this repository, I will walk through a series of exercises whose purpose is to make the process of handling multiuple files easier via the use of Python dictionaries, tables and list comprehensions. I will provide examples, files and a series of commands for participants to explore. Throughout this workhop and for pedagogical purposes, I will be using Jupyter notebooks.

Getting Started


If you have an SFU login account, you can skip installing Python and simply access one of SFU’s Jupyter servers


Ensure you have installed Python on your local computer. Throughout these exercises, I will be using Python 3.6. One of the easiest ways to install Python on your local computer is by downloading and installing Anaconda. I strongly recommend installing Anaconda as it includes Jupyter Notebooks, Pandas and other scientific packages that will be used throughout this workshop.


Installing Anaconda

Installing Jupyter


Imagine your job is to extract specific information from a file A with over 600 entries. Such information acts as an identifier. Your job is to use the identifiers from file A to extract coordinates and key words from file B. What is the catch? File B has thousands of entries and the coordinates and keywords in each entry act as another identifier. You will use the identifiers found on file B to extract specific subsets of data from at least a dozen of other files (some with hundreds or thousands of lines). Once you get the data, your job is to run open source software and generate results. Once you get results, you clean the output and plot your findings.

In this workshop I will take a subset of a dataset I have been working on and share how tabulating data, using list comprehension, dictionaries and dataframes simplified the tasks mentioned above. Although the scope of this project calls for more, I will only focus on the aspects that involved using these tools.


In this section I will provide a short description of each exercise.

Exercise 0: Basic Tools for this Workshop

Refresh your memory of for loops, if statements, reading files and storing content into a data structure.

Exercise 1: List Comprehension

Basic use of list comprehension in small data structures and in file content.

Exercise 2: Dictionaries

Examples of how to use dictionaries to manipulate files and file content.

Exercise 3: Introduction to Dataframes

Short intro to dataframes, examples involving functions and plotting results.

Exercise 4: Bringing it all together

Rundown of tools used throughout this workshop.

Bonus: From Jupyter Notebook to scripting

Challenge: Write a Python script using the tools we learned throughout this workshop.

The material in this workshop was built using


I thank Dr. Cedric Chauve for his encouragement and involvement in my development. This workshop was inspired on the basis of my learning experiences with him. I also thank him for letting me use a portion of data we have been working on.