Before we start
OverviewTeaching: 30 min
Exercises: 0 minQuestions
What is Python and why should I learn it?Objectives
Describe the purpose of the editor, console, help, variable explorer and file explorer panes of an IDE.
Present motivations for using Python.
Organize files and directories for a set of analyses as a Python project, and understand the purpose of the working directory.
How to work with Jupyter Notebook and Spyder.
Know where to find help.
Demonstrate how to provide sufficient information for troubleshooting with the Python user community.
What is Python?
Python is a general purpose programming language that supports rapid development of data analytics applications. The word “Python” is used to refer to both, the programming language and the tool that executes the scripts written in Python language.
Its main advantages are:
- Available on all major platforms (macOS, Linux, Windows)
- Supported by Python Software Foundation
- Supports multiple programming paradigms
- Has large community
- Rich ecosystem of third-party packages
So, why do you need Python for data analysis?
Easy to learn: Python is easier to learn than other programming languages. This is important because lower barriers mean it is easier for new members of the community to get up to speed.
Reproducibility: Reproducibility is the ability to obtain the same results using the same dataset(s) and analysis.
Data analysis written as a Python script can be reproduced on any platform. Moreover, if you collect more or correct existing data, you can quickly and easily re-run your analysis!
An increasing number of journals and funding agencies expect analyses to be reproducible, so knowing Python will give you an edge with these requirements.
- Versatility: Python is a versatile language that integrates with many existing applications to enable something completely amazing. For example, one can use Python to generate manuscripts, so that if you need to update your data, analysis procedure, or change something else, you can quickly regenerate all the figures and your manuscript will be updated automatically.
Python can read text files, connect to databases, and many other data formats, on your computer or on the web.
Interdisciplinary and extensible: Python provides a framework that allows anyone to combine approaches from different research (but not only) disciplines to best suit your analysis needs.
Python has a large and welcoming community: Thousands of people use Python daily. Many of them are willing to help you through mailing lists and websites, such as Stack Overflow and Anaconda community portal.
Free and Open-Source Software (FOSS)… and Cross-Platform: We know we have already said that but it is worth repeating.
Knowing your way around Anaconda
Anaconda distribution of Python includes a lot of its popular packages, such as the IPython console and Jupyter Notebook. Have a quick look around the Anaconda Navigator. You can launch programs from the Navigator or use the command line.
The Jupyter Notebook is an open-source web application that allows you to create and share documents that allow one to easilty create documents that combine code, graphs, and narrative text.
Anaconda also comes with a package manager called conda, which makes it easy to install and update additional packages.
Research Project: Best Practices
It is a good idea to keep a set of related data, analyses, and text in a single folder. All scripts and text files within this folder can then use relative paths to the data files. Working this way makes it a lot easier to move around your project and share it with others.
Organizing your working directory
Using a consistent folder structure across your projects will help you keep things organized, and will also make it easy to find/file things in the future. This can be especially helpful when you have multiple projects. In general, you may wish to create separate directories for your scripts, data, and documents.
data/: Use this folder to store your raw data. For the sake of transparency and provenance, you should always keep a copy of your raw data. If you need to cleanup data, do it programmatically (i.e. with scripts) and make sure to separate cleaned up data from the raw data. For example, you can store raw data in files
./data/raw/and clean data in
documents/: Use this folder to store outlines, drafts, and other text.
scripts/: Use this folder to store your (Python) scripts for data cleaning, analysis, and plotting that you use in this particular project.
You may need to create additional directories depending on your project needs, but these should form
the backbone of your project’s directory. For this workshop, we will need a
data/ folder to store
our raw data, and we will later create a
data_output/ folder when we learn how to export data as
What is Programming and Coding?
Programming is the process of writing “programs” that a computer can execute and produce some (useful) output. Programming is a multi-step process that involves the following steps:
- Identifying the aspects of the real-world problem that can be solved computationally
- Identifying (the best) computational solution
- Implementing the solution in a specific computer language
- Testing, validating, and adjusting implemented solution.
While “Programming” refers to all of the above steps, “Coding” refers to step 3 only: “Implementing the solution in a specific computer language”.
If you are working with Jupyter notebook:
You can type Python code into a code cell and then execute the code by pressing
Output will be printed directly under the input cell.
You can recognise a code cell by the
In[ ]: at the beginning of the cell and output by
Pressing the + button in the menu bar will add a new cell.
All your commands as well as any output will be saved with the notebook.
If you are working with an IDE:
You can either use the console or use script files (plain text files that contain your code). The console pane is the place where commands written in the Python language can be typed and executed immediately by the computer. It is also where the results will be shown. You can execute commands directly in the console by pressing Return, but they will be “lost” when you close the session.
Since we want our code and workflow to be reproducible, it is better to type the commands in the script editor, and save them as a script. This way, there is a complete record of what we did, and anyone (including our future selves!) can easily reproduce the results on their computer.
IDEs usually allow you to execute commands directly from the script editor by using run button or menu item on the top. To run the entire script click Run file or press F5, to run the current line click Run selection or current line or press F9, other run buttons allow to run script cells or go into debug mode. When using F9, the command on the current line in the script (indicated by the cursor) or all of the commands in the currently selected text will be sent to the console and executed.
At some point in your analysis you may want to check the content of a variable or the structure of an object, without necessarily keeping a record of it in your script. You can type these commands and execute them directly in the console.
If Python is ready to accept commands, the IPython console shows an
In [..]: prompt with the
current console line number in
. If it receives a command (by typing, copy-pasting or sent from
the script editor), Python will execute it, display the results in the
Out [..]: cell, and come
back with a new
In [..]: prompt waiting for new commands.
If Python is still waiting for you to enter more data because it isn’t complete yet, the console
will show a
...: prompt. It means that you haven’t finished entering a complete command. This can
be because you have not typed a closing parenthesis (
}) or quotation mark. When this
happens, and you thought you finished typing your command, click inside the console window and press
Esc; this will cancel the incomplete command and return you to the
In [..]: prompt.
How to learn more after the workshop?
The material we cover during this workshop will give you an initial taste of how you can use Python to analyze data for your own research. However, you will need to learn more to do advanced operations such as cleaning your dataset, using statistical methods, or creating beautiful graphics.
The best way to become proficient and efficient in Python, as with any other tool, is to use it to address your actual research questions. As a beginner, it can feel daunting to have to write a script from scratch, and given that many people make their code available online, modifying existing code to suit your purpose might make it easier for you to get started.
As mentioned on the setup page, we also recommend the interactive EduTools in PyCharm to practice more Python.
- check under the Help menu
help(object)to get information about an object
- Python documentation
- Pandas documentation
Finally, a generic Google or internet search “Python task” will often either send you to the appropriate module documentation or a helpful forum where someone else has already asked your question.
I am stuck… I get an error message that I don’t understand. Start by googling the error message. However, this doesn’t always work very well, because often, package developers rely on the error catching provided by python. You end up with general error messages that might not be very helpful to diagnose a problem (e.g. “subscript out of bounds”). If the message is very generic, you might also include the name of the function or package you’re using in your query.
However, you should check Stack Overflow. Search using the
[python] tag. Most questions have already
been answered, but the challenge is to use the right words in the search to find the answers:
Asking for help
The key to receiving help from someone is for them to rapidly grasp your problem. You should make it as easy as possible to pinpoint where the issue might be.
Try to use the correct words to describe your problem. For instance, a package is not the same thing as a library. Most people will understand what you meant, but others have really strong feelings about the difference in meaning. The key point is that it can make things confusing for people trying to help you. Be as precise as possible when describing your problem.
If possible, try to reduce what doesn’t work to a simple reproducible example. If you can reproduce the problem using a very small data frame instead of your 50,000 rows and 10,000 columns one, provide the small one with the description of your problem. When appropriate, try to generalize what you are doing so even people who are not in your field can understand the question. For instance, instead of using a subset of your real dataset, create a small (3 columns, 5 rows) generic one.
Where to ask for help?
- The person sitting next to you during the workshop. Don’t hesitate to talk to your neighbor during the workshop, compare your answers, and ask for help. You might also be interested in organizing regular meetings following the workshop to keep learning from each other.
- Your friendly colleagues: if you know someone with more experience than you, they might be able and willing to help you.
- Stack Overflow: if your question hasn’t been answered before and is well crafted, chances are you will get an answer in less than 5 min. Remember to follow their guidelines on how to ask a good question.
- Python mailing lists
Python is an open source and platform independent programming language.
SciPy ecosystem for Python provides the tools necessary for scientific computing.
Jupyter Notebooks and IDEs are great tools to code in and interact with Python. With the large Python community it is easy to find help on the internet.