Books & Videos

Table of Contents

  1. Chapter 1 IPython: Beyond Normal Python

    1. Shell or Notebook?

    2. Help and Documentation in IPython

    3. Keyboard Shortcuts in the IPython Shell

    4. IPython Magic Commands

    5. Input and Output History

    6. IPython and Shell Commands

    7. Shell-Related Magic Commands

    8. Errors and Debugging

    9. Profiling and Timing Code

    10. More IPython Resources

  2. Chapter 2 Introduction to NumPy

    1. Understanding Data Types in Python

    2. The Basics of NumPy Arrays

    3. Computation on NumPy Arrays: Universal Functions

    4. Aggregations: Min, Max, and Everything in Between

    5. Computation on Arrays: Broadcasting

    6. Comparisons, Masks, and Boolean Logic

    7. Fancy Indexing

    8. Sorting Arrays

    9. Structured Data: NumPy’s Structured Arrays

  3. Chapter 3 Data Manipulation with Pandas

    1. Installing and Using Pandas

    2. Introducing Pandas Objects

    3. Data Indexing and Selection

    4. Operating on Data in Pandas

    5. Handling Missing Data

    6. Hierarchical Indexing

    7. Combining Datasets: Concat and Append

    8. Combining Datasets: Merge and Join

    9. Aggregation and Grouping

    10. Pivot Tables

    11. Vectorized String Operations

    12. Working with Time Series

    13. High-Performance Pandas: eval() and query()

    14. Further Resources

  4. Chapter 4 Visualization with Matplotlib

    1. General Matplotlib Tips

    2. Two Interfaces for the Price of One

    3. Simple Line Plots

    4. Simple Scatter Plots

    5. Visualizing Errors

    6. Density and Contour Plots

    7. Histograms, Binnings, and Density

    8. Customizing Plot Legends

    9. Customizing Colorbars

    10. Multiple Subplots

    11. Text and Annotation

    12. Customizing Ticks

    13. Customizing Matplotlib: Configurations and Stylesheets

    14. Three-Dimensional Plotting in Matplotlib

    15. Geographic Data with Basemap

    16. Visualization with Seaborn

    17. Further Resources

  5. Chapter 5 Machine Learning

    1. What Is Machine Learning?

    2. Introducing Scikit-Learn

    3. Hyperparameters and Model Validation

    4. Feature Engineering

    5. In Depth: Naive Bayes Classification

    6. In Depth: Linear Regression

    7. In-Depth: Support Vector Machines

    8. In-Depth: Decision Trees and Random Forests

    9. In Depth: Principal Component Analysis

    10. In-Depth: Manifold Learning

    11. In Depth: k-Means Clustering

    12. In Depth: Gaussian Mixture Models

    13. In-Depth: Kernel Density Estimation

    14. Application: A Face Detection Pipeline

    15. Further Machine Learning Resources