Python Data Science Handbook, 2nd Edition
by Jake VanderPlas
Released December 2022
Publisher finelybook 出版社: O’Reilly Media, Inc.
ISBN: 9781098121228
Book Description
Python is a first-class tool for many researchers, primarily 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 new edition of 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 the second edition of 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:
IPython and Jupyter provide computational environments for scientists using Python
NumPy includes the ndarray for efficient storage and manipulation of dense data arrays
Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data
Matplotlib includes capabilities for a flexible range of data visualizations
Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms
Preface
I.Jupyter: Beyond Normal Python
1.Getting Started in IPython and Jupyter
2.Enhanced Interactive Features
3.Debugging and Profiling
ll.Introduction to NumPy
4.Understanding Data Types in Python
5.The Basics of NumPy Arrays
6.Computation on NumPy Arrays: Universal Functions
7.Aggregations: min,max,and Everything in Between
8.Computation on Arrays: Broadcasting
9.Comparisons,Masks,and Boolean Logic
10.Fancy Indexing
11.Sorting Arrays
12.Structured Data: NumPy’s Structured Arrays
lll.Data Manipulation with Pandas
13.Introducing Pandas Objects
14.Data Indexing and Selection
15.Operating on Data in Pandas
16.Handling Missing Data
17.Hierarchical Indexing
18.Combining Datasets: concat and append
19.Combining Datasets: merge and join
20.Aggregation and Grouping
21.Pivot Tables
22.Vectorized String Operations
23.Working with Time Series
24.High-Performance Pandas: eval and query
IV.Visualization with Matplotlib
25.General Matplotlib Tips
26.Simple Line Plots
27.Simple Scatter Plots
28.Density and Contour Plots
29.Customizing Plot Legends
30.Customizing Colorbars
31.Multiple Subplots
32.Text and Annotation
33.Customizing Ticks
34.Customizing Matplotlib: Configurations and Stylesheets
35.Three-Dimensional Plotting in Matplotlib
36.Visualization with Seaborn
V.Machine Learning
37.What Is Machine Learning?
38.Introducing Scikit-Learn
39.Hyperparameters and Model Validation
40.Feature Engineering
41.In Depth: Naive Bayes Classification
42.In Depth: Linear Regression
43.In Depth: Support Vector Machines
44.In Depth: Decision Trees and Random Forests
45.In Depth: Principal Component Analysis
46.In Depth: Manifold Learning
47.In Depth: k-Means Clustering
48.In Depth: Gaussian Mixture Models
49.In Depth: Kernel Density Estimation
50.Application: A Face Detection Pipeline
Index
