https://github.com/bionetslab/python-intro
Introduction to Python
https://github.com/bionetslab/python-intro
teaching-materials
Last synced: 7 months ago
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Introduction to Python
- Host: GitHub
- URL: https://github.com/bionetslab/python-intro
- Owner: bionetslab
- License: cc-by-4.0
- Created: 2021-01-28T15:21:20.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2024-04-10T17:26:00.000Z (over 2 years ago)
- Last Synced: 2024-04-10T21:23:05.250Z (over 2 years ago)
- Topics: teaching-materials
- Language: Jupyter Notebook
- Homepage:
- Size: 27.6 MB
- Stars: 6
- Watchers: 3
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
*David B. Blumenthal*
# Introduction to Python for Bioinformatics
In this course, targeted at PhD students in the biomedical sciences, participants will receive an introduction to the programming language [Python 3](https://www.python.org/). We will start with the basics (I/O, basic data structures, loops and conditions, functions, classes) and then introduce widely used packages for data manipulation, scientific computing, and visualization ([NumPy](https://numpy.org/), [SciPy](https://www.scipy.org/), [pandas](https://pandas.pydata.org/), [seaborn](https://seaborn.pydata.org/)). Finally, the course will provide a first glimpse at two widely used packages for the analysis of biological data ([Biopython](https://biopython.org/), [GSEAPY](https://gseapy.readthedocs.io/en/latest/index.html)).
# Getting Started
All course materials are provided as [Jupyter Notebooks](https://jupyter.org/index.html). To run Jupyter Notebooks on your machine, install an [Anaconda distribution](https://docs.anaconda.com/anaconda/install/) before the start of the course.
Now download the course material into a directory called `python-intro`. For this, either click on **Code → Download ZIP**, or download the course material via [git](https://git-scm.com/):
```bash
git clone https://github.com/dbblumenthal/python-intro
```
After downloading the course material, navigate to the `python-intro` directory and create a [conda](https://docs.conda.io/en/latest/) environment called **python-intro** with all dependencies:
```bash
cd python-intro
conda env create -f environment.yml
```
You can now activate the environment and connect it to your Jupyter Notebook:
```bash
conda activate python-intro
(python-intro) python -m ipykernel install --user --name=python-intro
```
Now you can open your first notebook as follows:
```bash
(python-intro) jupyter notebook 1_first_steps.ipynb
```
If the Notebook opens without any errors, you are ready for the course.
# Writing Python Scripts
The entire course is based on Jupyter Notebooks, but sometimes it is useful to write Python scripts that can be executed from a terminal. As an example, you can have a look at the script `log_transform.py` (just open it in any text editor). You can execute the script as follows:
```bash
(python-intro) python log_transform.py input/P53.txt output/P53_log_transformed.tsv --drop DESCRIPTION
```
This will save a log-transformed version of the data saved in `input/P53.txt` in the file `output/P53_log_transformed.tsv` , where the column `DESCRIPTION` contained in the input file is discarded.
# Acknowledgements
This course uses material from the following Python courses:
- Mark Bakker's [Python for Exploratory Computing](http://mbakker7.github.io/exploratory_computing_with_python/) course.
- Chris Rands' [biopython-coronavirus](https://github.com/chris-rands/biopython-coronavirus) course.
# License
This course is licensed under the
[Creative Commons Attribution 4.0 International License][cc-by].
[![CC BY 4.0][cc-by-image]][cc-by]
[cc-by]: http://creativecommons.org/licenses/by/4.0/
[cc-by-image]: https://i.creativecommons.org/l/by/4.0/88x31.png