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## Book Description

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues.

1. Preface
2. 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
3. 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
6. Comparisons, Masks, and Boolean Logic
7. Fancy Indexing
8. Sorting Arrays
9. Structured Data: NumPy’s Structured Arrays
4. 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
5. 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
6. 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
7. Index