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Python Data Science Handbook
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Introduktion
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IPython: Beyond Normal Python8 Ämnen
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Introduction to NumPy9 Ämnen
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Understanding Data Types in Python
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The Basics of NumPy Arrays
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Computation on NumPy Arrays: Universal Functions
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Aggregations: Min, Max, and Everything In Between
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Computation on Arrays: Broadcasting
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Comparisons, Masks, and Boolean Logic
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Fancy Indexing
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Sorting Arrays
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Structured Data: NumPy's Structured Arrays
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Understanding Data Types in Python
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Data Manipulation with Pandas13 Ämnen
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Introducing Pandas Objects
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Data Indexing and Selection
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Operating on Data in Pandas
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Handling Missing Data
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Hierarchical Indexing
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Combining Datasets: Concat and Append
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Combining Datasets: Merge and Join
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Aggregation and Grouping
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Pivot Tables
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Vectorized String Operations
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Working with Time Series
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High-Performance Pandas: eval() and query()
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Further Resources
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Introducing Pandas Objects
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Visualization with Matplotlib15 Ämnen
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Simple Line Plots
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Simple Scatter Plots
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Visualizing Errors
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Density and Contour Plots
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Histograms, Binnings, and Density
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Customizing Plot Legends
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Customizing Colorbars
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Multiple Subplots
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Text and Annotation
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Customizing Ticks
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Customizing Matplotlib: Configurations and Stylesheets
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Three-Dimensional Plotting in Matplotlib
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Geographic Data with Basemap
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Visualization with Seaborn
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Further Resources
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Simple Line Plots
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Machine Learning15 Ämnen
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What Is Machine Learning?
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Introducing Scikit-Learn
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Hyperparameters and Model Validation
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Feature Engineering
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In Depth: Naive Bayes Classification
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In Depth: Linear Regression
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In-Depth: Support Vector Machines
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In-Depth: Decision Trees and Random Forests
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In Depth: Principal Component Analysis
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In-Depth: Manifold Learning
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In Depth: k-Means Clustering
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In Depth: Gaussian Mixture Models
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In-Depth: Kernel Density Estimation
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Application: A Face Detection Pipeline
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Further Machine Learning Resources
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What Is Machine Learning?
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Appendix: Figure Code
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