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Python Data Science Handbook

Paperback Engels 2016
Verkooppositie 2185
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Samenvatting

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

Specificaties

ISBN13:9781491912058
Trefwoorden:Python, Data analysis
Taal:Engels
Bindwijze:paperback
Aantal pagina's:530
Uitgever:O'Reilly
Druk:1
Verschijningsdatum:6-12-2016

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Inhoudsopgave

Chapter 1: IPython: 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 2: Introduction 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 3: Data 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 4: Visualization 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 5: Machine 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

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