path 02 · 59 lessons · 4 modules
Start pathEnterPython Data Stack
The three libraries most analysis is built on. Arrays and broadcasting first, then dataframes, then the plotting APIs used to inspect results.
By the end you can
- Replace loops over rows with array operations
- Load, filter, join and reshape tables with pandas
- Build figures with matplotlib and seaborn
Progress
0/59
Array Creation3Axes and Reductions2Boolean Masking2Broadcasting2Fancy Indexing2Indexing and Slicing2Linear Algebra with NumPy2Memory Layout and Strides2Missing Data in NumPy2NumPy File IO2NumPy Random Generator2NumPy dtypes2Reshaping and Stacking2Structured Arrays2Vectorization3Views vs Copies2ndarray2ufuncs2
Categorical dtypeConcatCopy-on-WriteDataFrameDatetime Handling in pandasFiltering and queryIndex ObjectsMerging and JoiningMissing Data in pandasMultiIndexReading and Writing DataReshaping with pivot and meltSeriesSorting and RankingString AccessorWindow Functionsassign and pipegroupbyloc vs ilocpandas Performancepandas dtypes
Axes Styling and AnnotationColormaps and ColorCommon Plot TypesFacetingFigure and AxesFigure-level vs Axes-levelMatplotlib ArtistsSaving FiguresStatistical Estimation in seabornSubplots and Layoutpyplot vs Object-Oriented APIrcParams and Style Sheetsseaborn Themes and Palettesseaborn objects Interface