CDH-cookbook

Concluding Lesson: First Steps for Coding in Python

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Contents

• Developing from Here
• What to Expect

Overview

This short course has covered the fundamentals

In the previous lessons you have first learned meta-skills around Python programming:

• What the difference is between coding and programming.
• What Python is, how to install and begin using it, and how to find resources
• Reasons for using code in a Humanities project, what 'data' looks like in the Humanities, and how to approach data-driven research in the Humanities
• How to search for support and documentation

You have also learned the basics necessary to understand how Python works:

• Loading data, declaring variables, naming conventions
• Navigational basics: commenting, zero-indexing, print statements, various Python operators
• How to call a module function to analyse your variables

This course has covered the following fundamental programming skills:

• Data types and how to declare them
• Functions: how they are built and how to declare them
• Arguments, parameters, conditionals
• Loops: the for loop, while loop, nested loops
• Different types of functions (built-in, user-defined, with *args and *kwargs)
• Basics of Object-Oriented Programming
• File management

Finally, you have written two small programs:

• A basic file-looping program to help you practice mastering loops.
• A beginning-to-end data analysis program which saves the results in a separate file.

All of these skills are transferable and extendable beyond the bounds of this lesson. They give you a foundation not just for working in Python, but for thinking code can address research problems in different Humanities research and teaching areas.

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Developing from Here

You are possibly wondering what you can actually accomplish now, and what trajectories your learning can potentially take going forward. At this level you should be able to:

• Research Python software (modules are software) online to see if it could benefit your research
• Find documentation and help to get started using new modules
• Navigate your computer directories without much struggle
• Create, modify, and save basic text files
• Read other people's Python code -- while you may not understand everything, you know what Classes, Objects, and Attributes are. That gets you much of the way there.
• Write small yet functional Python scripts yourself
• Know enough about how Python is organised, and the vocabulary around it, to begin 'thinking like a programmer' in a general sense.
• Troubleshoot simple syntax errors and interpret error messages

So what is the next appropriate step? That is dependent upon your goals. If the exercises in this course make sense to you, you are ready for an intermediate level course. If all you need to know how to do is basic file handling, then you will only need to research some extra commands that we did not have the space to cover in this course, but the syntax should not be confusing. If you want to begin rightaway with a field-specific module you have found, you are certainly equipped with the basics.

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What to Expect

Let’s take each of these paths in turn.

Intermediate Level Courses

There are many, many Python courses online. Some are free, and some are expensive. One thing that the vast majority have in common is that they tend to be geared toward STEM scientists, rather than researchers in the Humanities. Since this introductory course was tailored for Humanities researchers, you have had the benefit of becoming acquainted with Python without this STEM overlay. However, going forward you may have to learn to work with a STEM overlay in intermediate and advanced courses in order to get the skills necessary to make full use of the many Python modules that have been developed for Humanities research. One way to make this easier is to approach those classes with the goal of always drawing any concept or application back to your own familiar research context. Ultimately, do not be discouraged; once you have the basics down, gaining further skills is less of a learning curve in coding.

Another possibility is that you need to do some other more hardcore data analytics. In that case, you are encouraged to take the Introduction to R course which this Cookbook hosts as well. R is a programming language designed specifically for statistics, and has some very powerful packages for data analysis, machine learning, and visualisation – some even better than Python.

File Handling Commands

We covered some fundamental file handling commands:

• Navigating directories and finding the current working directory
• Opening files
• Reading the contents of files
• Copying files and appending new information to existing files
• Saving files in a specified location

However, this was all dealing with the most basic kind of .txt file. Next steps would be to identify the file types with which you need to work, and go read up on Python modules that create and save files as those file types. pypdf is one such one for PDF files.

Field-Specific Modules

If you have a particular module that you want to use, you can now try reading the documentation in more depth. The best place to start is to go to the module’s GitHub repository (if one exists, though it’s unusual for one not to exist). First, look at the README file. These give essential details about the software and how to use it. You can also search online for the module name and potentially find tutorials and .io websites that give more step-by-step information.

Getting Stuck and Getting Help

As you progress along your journey of learning to code, it is inevitable that you will get stuck. You will feel like a task is impossible, or a problem is unsolvable. Those who progress utilise a few strategies when this happens:

• Take breaks. Go for walks away from a computer screen, or talk the issue over with a friend in your own words. Often getting stuck staring at a screen with a ton of error messages can be hypnotic, but the moment you walk away or change thinking mediums, your brain switches gears and often the solution becomes clear.
• Find a mentor. One of the best ways to develop your coding skills is to force yourself to work on a specific project, and only turn to someone for help when you absolutely cannot proceed. This is the way that many very successful self-taught programmers learned. They worked until they could no longer find a way forward at all, and then asked for help from a friendly mentor. Encountering and overcoming the block with the mentor is perhaps the richest learning experience one can have.
• Utilise forums. Either your problem will have been posted before, in which case, the answer is there ready for you to find it. Or, you will post a new problem that can help someone else in the future. Either way, everyone wins.
• If in doubt, re-read the documentation. Many problems come from skimming or avoiding reading the documentation because it is boring. But what is far more boring is being unable to complete a task because you do not understand the software with which you are working.
• Use AI tools sparingly. These tools are best used to help you understand small details about new code. Asking ChatGPT 'what does this line of code do?' is a helpful way to use it. Asking ChatGPT to write you a program from scratch is both inviting error (rarely does the AI get the details correct) and also risks using outdated software packages (most of the time, in personal experience, ChatGPT is trained on the previous version of software available to us now).
• Search your error messages on the internet. This is the fastest way to figure out how to debug code. You can also ask ChatGPT what the error means, but often, people have written more in-depth information on forums about specific error messages.

Regardless, do not be discouraged when you hit roadblocks. They are inevitable, and part of the coding learning process. Be encouraged that you are not alone; someone else has encountered the same error messages you have, otherwise, the error messages would not exist to begin with!

The Rest of this Cookbook

This Cookbook is meant to link a wide variety of methods using programming languages such as R and Python. If you want to continue learning coding methods for Humanities research, this Cookbook is constantly growing with new methods that world-class researchers have developed and taught. Working through these lessons will sharpen and extend your newfound skilset.

Written by Estara Arrant, 2025-04-16
Licence: CC BY-SA 4.0

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