Python has emerged as a leading language in data science due to its simplicity, flexibility, and vast range of powerful libraries and tools designed specifically for data analysis, visualization, and machine learning.
Libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn provide robust capabilities for data manipulation, statistical analysis, and predictive modeling.
Moreover, Python's compatibility with frameworks like TensorFlow and PyTorch makes it suitable for deep learning.
Jupyter Notebook, an open-source web application, allows for the creation and sharing of documents that contain live code, equations, visualizations, and narrative text, making Python a versatile tool for data science.
Are you familiar with Python but never used Pandas?
We can then start introducing Pandas (data manipulation) and Matplotlib (plotting):
- A first introduction to Pandas DataFrames: what is a dataframe, simple operations
- A first introduction to Matplotlib: basic plots from a randomly generated dataframe
And then combine the two in a worked example using dataframes and plots on familiar datasets:
- Plotting microbiome compositions: loading taxonomy, counts, metadata from three files and integrating/plotting