Intro to Numpy, Pandas, Matplotlib and Scikit-Learn. Condensed from Python Data Science Handbook (@jakevdp)
| iPython | NumPy | Pandas | Matplotlib | Scikit-Learn |
|---|---|---|---|---|
| Help & docs | Datatypes | Pandas objects | Line plots | Intro to ML |
| Keyboard shortcuts | Arrays | Indexing & selection | Scatter plots | Intro to Scikit-Learn |
| Magic commands | Universal functions | Data operations | Error Bars | Hyperparameters & Model validation |
| Input & output history | Aggregations | Missing data | Density & contour plots | Feature engineering |
| Shell commands | Broadcasting | Hierarchical indexing | Histograms, bins & densities | Naive Bayes |
| Errors & debugging | Comparisons, masks, boolean logic | Concat & append | Plot legends | Linear regression |
| Profiling & timing | Fancy indexing | Merge & join | Colorbars | Support vector machines (SVM) |
| Resources | Sorting arrays | Aggregation & grouping | Subplots | Decision trees & Random forests |
| Structured data | Pivot tables | Text & Annotation | Principal component analysis (PCA) | |
| Vectorized strings | Custom Tickmarks | Manifold learning | ||
| Time series | Configs & stylesheets | K-Means clustering | ||
| High-performance ops: eval(), query() | 3D plotting | Gaussian mixtures | ||
| Geo data with Basemap | TO DO:Kernel density estimation (KDE) | |||
| Visualization with Seaborn | TO DO:Face detection pipeline | |||
| Resources |