This repository regroups the course material of two "intermediate python" one-day courses of SIB Training:
- Data analysis and representation in python
- Optimizing Python Code for Better Performance
These courses are addressed to life scientists, bioinformaticians and researchers who are familiar with writing Python code and core Python elements, and would like to explore it further in their daily data wrangling and exploration tasks.
The course material expects that participants are already familiar familiar with the Python syntax, environment, and the most common commands.
Topics that covered in this course include:
Course 1: Data analysis and representation in python
- Parsing, transforming, and exploring data using Pandas
- Performing statistical simulation and testing with Numpy/Scipy
- Representing data in an efficient and impactful manner using Seaborn
Course 2: Optimizing Python Code for Better Performance
- assessing computational resource usage of your code
- Speeding-up your Python code with Numba and more
Please make sure to setup your computer before the start of the course to ensure you have installed all the required software.
In addition to this, you should ensure you have the following libraries installed (using conda for example):
Course 1: Data analysis and representation in python
Course2: Optimizing Python Code for Better Performance
This course also relies on jupyter notebooks, a web based notebook system for creating and sharing computational documents in an interactive manner. We do not provide an introduction to jupyter notebooks, but you can find numerous short tutorials online (such as this one or that more in-depth one)
The course revolves around a series of jupyter notebooks which develop different aspect of analysis with python.
Each jupyter notebook interleaves theory and examples of codes. We heartily recommend you execute and play around with these bits of code as you follow along.
-
00_python_warmup.ipynb : something to help you get in programming mood | check your knowledge of basic python syntax
-
Course 1: Data analysis and representation in python
- 01_data_manipulation.ipynb : an advanced introduction and exploration of
pandas - 02_data_description_and_representation.ipynb : usage of
pandas,matplotlib, andseabornfor tabular data exploration and exposition. - 03_statistics_with_python.ipynb : an overview of statistical testing
scipy.statsand linear models withstatsmodels
- 01_data_manipulation.ipynb : an advanced introduction and exploration of
-
Course 2: Optimizing Python Code for Better Performance
- 01_resource_usage_measure_and_profiling.ipynb : code resource usage monitoring and profiling
- 02_faster_python.ipynb : how to make python code run faster, in particular with
numbaandcython - 03_multiprocessing_multithreading_python.ipynb : mltiprocessing and multithreading parallelizayion in python
The data used in the practicals can be found in the data/ subfolders of either course1/ or course2/.
Solutions to the exercises (but not micro-exercises) are provided in the solutions/ subfolders of either course1/ or course2/.
Please cite as: Robin Engler, Wandrille Duchemin, & Mathilde Goullieux. (2023, May 31). Material for the intermediate python SIB-training course. Zenodo. https://doi.org/10.5281/zenodo.7991203