Jupyter Notebook tutorials

This repository was born out of the spirit of sharing useful Python features, mainly related to data science and analysis, that I have come across. With this in mind, the notebooks will use a practical case to show these features so that anyone can discover and benefit from them.

I will also collect here personal projects and tutorials developed during my research in astrophysics and data science.

Enjoy them! 👋

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VO with Python: working with PyVO and MOCPy

 

This tutorial explores the usage of PyVO and MOCPy, two powerful products for accessing the Virtual Observatory from Python. It was developed for the schools organized by the Spanish Virutal Observatory (SVO).

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Dimensionality Reduction for Data Visualization With Python

 

In this brief tutorial, we will explore the use of dimensionality reduction focused on finding a good visualization of our global data context. We will build our use case around the data provided by the Sunspot Index and Long-term Solar Observations (SILSO) on the monthly mean total sunspot number, which is obtained as a simple arithmetic mean of the daily value. You can see more info on how the daily total sunspot number (Wolf number) is derived here.

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Using Grid Search For Autoencoder Hyperparameter Tuning

 

In this brief tutorial, we will see how we can use the grid search tuning technique for the hyperparameter optimization of a deep sparse autoencoder. You can learn about how autoencoders work here. We will use the well known MNIST database of handwritten digits, which is available through the tensorflow.keras.datasets module.

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Interactive Plotting With Python: From Zero to Hero 🚀

 

This demo shows how to get the most out of interactive data sharing in Python using plotly, providing:

• A template for an extensive customisation of the plots.

• A function to create an html dashboard with plotly figures for data sharing.