پایتون ابزاری درجه‌یک برای بسیاری از پژوهشگران محسوب می‌شود، عمدتاً به‌دلیل کتابخانه‌های قدرتمند آن برای ذخیره‌سازی، پردازش و استخراج بینش از داده‌ها. منابع متعددی برای هر یک از اجزای این پشته‌ی علوم داده وجود دارد، اما تنها با نسخه‌ی جدید Python Data Science Handbook می‌توانید همه‌ی آن‌ها را یک‌جا در اختیار داشته باشید — از IPython، NumPy، pandas، Matplotlib و Scikit-Learn گرفته تا سایر ابزارهای مرتبط.

دانشمندان و تحلیل‌گرانی که با کدنویسی پایتون آشنا هستند، این مرجع جامع نسخه دوم را منبعی ایده‌آل برای حل چالش‌های روزمره خود خواهند یافت: از دست‌کاری، تبدیل و پاک‌سازی داده‌ها گرفته تا مصورسازی انواع مختلف داده و استفاده از آن‌ها برای ساخت مدل‌های آماری یا یادگیری ماشین. به‌بیان ساده، این کتاب مرجعی ضروری برای محاسبات علمی با پایتون است.

در این کتاب خواهید آموخت:

• چگونه IPython و Jupyter محیط‌های محاسباتی مناسبی برای دانشمندان با زبان پایتون فراهم می‌کنند
• NumPy چگونه با آرایه‌ی ndarray امکان ذخیره‌سازی و پردازش مؤثر داده‌های متراکم را فراهم می‌سازد
• pandas چگونه با ساختار DataFrame ابزار مناسبی برای ذخیره و پردازش داده‌های برچسب‌خورده و ستونی فراهم می‌کند
• Matplotlib چگونه امکانات گسترده‌ای برای مصورسازی منعطف داده‌ها ارائه می‌دهد
• Scikit-learn چگونه پیاده‌سازی تمیز و مؤثری از الگوریتم‌های مهم و اثبات‌شده‌ی یادگیری ماشین در پایتون فراهم می‌کند

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

Table of Contents

Part I. Jupyter: Beyond Normal Python

Chapter 1. Getting Started in IPython and Jupyter

Chapter 2. Enhanced Interactive Features

Chapter 3. Debugging and Profiling

Part II. Introduction to NumPy

Chapter 4. Understanding Data Types in Python

Chapter 5. The Basics of NumPy Arrays

Chapter 6. Computation on NumPy Arrays: Universal Functions

Chapter 7. Aggregations: min, max, and Everything in Between

Chapter 8. Computation on Arrays: Broadcasting

Chapter 9. Comparisons, Masks, and Boolean Logic

Chapter 10. Fancy Indexing

Chapter 11. Sorting Arrays

Chapter 12. Structured Data: NumPy's Structured Arrays

Part III. Data Manipulation with Pandas

Chapter 13. Introducing Pandas Objects

Chapter 14. Data Indexing and Selection

Chapter 15. Operating on Data in Pandas

Chapter 16. Handling Missing Data

Chapter 17. Hierarchical Indexing

Chapter 18. Combining Datasets: concat and append

Chapter 19. Combining Datasets: merge and join

Chapter 20. Aggregation and Grouping

Chapter 21. Pivot Tables

Chapter 22. Vectorized String Operations

Chapter 23. Working with Time Series

Chapter 24. High-Performance Pandas: eval and query

Part IV. Visualization with Matplotlib

Chapter 25. General Matplotlib Tips

Chapter 26. Simple Line Plots

Chapter 27. Simple Scatter Plots

Chapter 28. Density and Contour Plots

Chapter 29. Customizing Plot Legends

Chapter 30. Customizing Colorbars

Chapter 31. Multiple Subplots

Chapter 32. Text and Annotation

Chapter 33. Customizing Ticks

Chapter 34. Customizing Matplotlib: Configurations and Stylesheets

Chapter 35. Three-Dimensional Plotting in Matplotlib

Chapter 36. Visualization with Seaborn

Part V. Machine Learning

Chapter 37. What Is Machine Learning?

Chapter 38. Introducing Scikit-Learn

Chapter 39. Hyperparameters and Model Validation

Chapter 40. Feature Engineering

Chapter 41. In Depth: Naive Bayes Classification

Chapter 42. In Depth: Linear Regression

Chapter 43. In Depth: Support Vector Machines

Chapter 44. In Depth: Decision Trees and Random Forests

Chapter 45. In Depth: Principal Component Analysis

Chapter 46. In Depth: Manifold Learning

Chapter 47. In Depth: k-Means Clustering

Chapter 48. In Depth: Gaussian Mixture Models

Chapter 49. In Depth: Kernel Density Estimation

Chapter 50. Application: A Face Detection Pipeline

Who Is This Book For?

In my teaching both at the University of Washington and at various tech-focused conferences and meetups, one of the most common questions I have heard is this: “How should I learn Python?” The people asking are generally technically minded students, developers, or researchers, often with an already strong background in writing code and using computational and numerical tools. Most of these folks don’t want to learn Python per se, but want to learn the language with the aim of using it as a tool for data-intensive and computational science. While a large patchwork of videos, blog posts, and tutorials for this audience is available online, I’ve long been frustrated by the lack of a single good answer to this question; that is what inspired this book.

The book is not meant to be an introduction to Python or to programming in general; I assume the reader has familiarity with the Python language, including defining functions, assigning variables, calling methods of objects, controlling the flow of a program, and other basic tasks. Instead, it is meant to help Python users learn to use Python’s data science stack—libraries such as those mentioned in the following section, and related tools—to effectively store, manipulate, and gain insight from data.

What Is Data Science?

This is a book about doing data science with Python, which immediately begs the question: what is data science? It’s a surprisingly hard definition to nail down, especially given how ubiquitous the term has become. Vocal critics have variously dismissed it as a superfluous label (after all, what science doesn’t involve data?) or a simple buzzword that only exists to salt resumes and catch the eye of overzealous tech recruiters.

In my mind, these critiques miss something important. Data science, despite its hype-laden veneer, is perhaps the best label we have for the cross-disciplinary set of skills that are becoming increasingly important in many applications across industry and academia. This cross-disciplinary piece is key: in my mind, the best existing definition of data science is illustrated by Drew Conway’s Data Science Venn Diagram, first published on his blog in September 2010 (Figure below).

While some of the intersection labels are a bit tongue-in-cheek, this diagram captures the essence of what I think people mean when they say “data science”: it is fundamentally an interdisciplinary subject. Data science comprises three distinct and overlapping areas: the skills of a statistician who knows how to model and summarize datasets (which are growing ever larger); the skills of a computer scientist who can design and use algorithms to efficiently store, process, and visualize this data; and the domain expertise—what we might think of as “classical” training in a subject—necessary both to formulate the right questions and to put their answers in context.

With this in mind, I would encourage you to think of data science not as a new domain of knowledge to learn, but a new set of skills that you can apply within your current area of expertise. Whether you are reporting election results, forecasting stock returns, optimizing online ad clicks, identifying microorganisms in microscope photos, seeking new classes of astronomical objects, or working with data in any other field, the goal of this book is to give you the ability to ask and answer new questions about your chosen subject area.

About the Author

Jake VanderPlas is a software engineer at Google Research, working on tools that support data-intensive research. He creates and develops Python tools for use in data-intensive science, including packages like Scikit-Learn, SciPy, AstroPy, Altair, JAX, and many others. He participates in the broader data science community, developing and presenting talks and tutorials on scientific computing topics at various conferences in the data science world.