Basic Python Packages for Science
summary
Workshop demonstrates concrete results: runnable Jupyter notebooks load CSVs from the PyData Madrid 2016 repo, use Jupyter Notebook with interactive widgets to tune signals, use NumPy masked arrays and vectorized ops to compute means, reshapes and eigenvalues, and use matplotlib contourf and inline plots to visualize pollution and temperature time series. Examples include 2015 vs 2004–2015 averages, eight‑hour moving averages that exceed legal limits, a gravitational‑wave snippet, and an AM modulation demo that actually renders and responds to sliders.
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chapters (12)
- 0:00Introduction
- 14:57Jupyter basics
- 28:37Array slicing for chessboard
- 31:23Plotting with Matplotlib
- 37:09Linear algebra with numpy
- 43:21Pollution data analysis
- 57:58SciPy introduction
- 1:08:10Temperature data analysis
- 1:13:56Visualizing 2D functions
- 1:28:16Interactive widgets
- 1:30:27Symbolic computing with SymPy
- 1:35:08Q&A
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