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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: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

With this handbook, you'll learn how to use:

IPython and Jupyter: provide computational environments for data scientists using Python
NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python
Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python
Matplotlib: includes capabilities for a flexible range of data visualizations in Python
Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms



Chapter 1IPython: Beyond Normal Python
Shell or Notebook?
Help and Documentation in IPython
Keyboard Shortcuts in the IPython Shell
IPython Magic Commands
Input and Output History
IPython and Shell Commands
Shell-Related Magic Commands
Errors and Debugging
Profiling and Timing Code
More IPython Resources
Chapter 2Introduction to NumPy
Understanding Data Types in Python
The Basics of NumPy Arrays
Computation on NumPy Arrays: Universal Functions
Aggregations: Min, Max, and Everything in Between
Computation on Arrays: Broadcasting
Comparisons, Masks, and Boolean Logic
Fancy Indexing
Sorting Arrays
Structured Data: NumPy's Structured Arrays
Chapter 3Data Manipulation with Pandas
Installing and Using Pandas
Introducing Pandas Objects
Data Indexing and Selection
Operating on Data in Pandas
Handling Missing Data
Hierarchical Indexing
Combining Datasets: Concat and Append
Combining Datasets: Merge and Join
Aggregation and Grouping
Pivot Tables
Vectorized String Operations
Working with Time Series
High-Performance Pandas: eval() and query()
Further Resources
Chapter 4Visualization with Matplotlib
General Matplotlib Tips
Two Interfaces for the Price of One
Simple Line Plots
Simple Scatter Plots
Visualizing Errors
Density and Contour Plots
Histograms, Binnings, and Density
Customizing Plot Legends
Customizing Colorbars
Multiple Subplots
Text and Annotation
Customizing Ticks
Customizing Matplotlib: Configurations and Stylesheets
Three-Dimensional Plotting in Matplotlib
Geographic Data with Basemap
Visualization with Seaborn
Further Resources
Chapter 5Machine Learning
What Is Machine Learning?
Introducing Scikit-Learn
Hyperparameters and Model Validation
Feature Engineering
In Depth: Naive Bayes Classification
In Depth: Linear Regression
In-Depth: Support Vector Machines
In-Depth: Decision Trees and Random Forests
In Depth: Principal Component Analysis
In-Depth: Manifold Learning
In Depth: k-Means Clustering
In Depth: Gaussian Mixture Models
In-Depth: Kernel Density Estimation
Application: A Face Detection Pipeline
Further Machine Learning Resources