Python数据科学实战入门：从基础到库应用 - CSDN文库

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decisions, and target more customers. Or maybe you want to develop your

own data-driven applications, or simply expand your knowledge of Python

into the realm of data science.

The book assumes you have some basic experience with Python and that

you’re comfortable following instructions to perform tasks such as

installing a database or obtaining an API key. However, the book covers

Python data science concepts from the bottom up, through hands-on

examples that are all thoroughly explained. You’ll learn by doing, with no

prior data experience necessary.

What’s in the Book?

The book begins with a conceptual introduction to data processing and

analysis, explaining a typical data processing pipeline. Then we’ll cover

Python’s built-in data structures and some of the third-party Python libraries

that are widely used for data science applications. Next, we’ll explore

increasingly sophisticated techniques for obtaining, combining,

aggregating, grouping, analyzing, and visualizing datasets of different sizes

and data types. As the book goes on, we’ll apply Python data science

techniques to real use cases from the world of business management,

marketing, and finance. Along the way, each chapter contains “Exercise”

sections so you can practice and reinforce what you’ve just learned.

Here’s an overview of what you’ll find in each chapter:

Chapter 1: The Basics of Data Provides the necessary background for

understanding the essentials of working with data. You’ll learn that there

are different categories of data, including structured, unstructured, and

semistructured data. Then you’ll walk through the steps involved in a

typical data analysis process.

Chapter 2: Python Data Structures Introduces four data structures that

are built into Python: lists, dictionaries, tuples, and sets. You’ll see how to

use each structure and how to combine them into more complex structures

that can represent real-world objects.

Chapter 3: Python Data Science Libraries Discusses Python’s robust

ecosystem of third-party libraries for data analysis and manipulation. You’ll

meet the pandas library and its primary data structures, the Series and

DataFrame, which have become the de facto standard for data-oriented

Python applications. You’ll also learn about NumPy and scikit-learn, two

other libraries often used for data science.

Chapter 4: Accessing Data from Files and APIs Dives into the details of

obtaining data and loading it into your scripts. You’ll learn to load data

from different sources, such as files and APIs, into data structures in your

Python scripts for further processing.

Chapter 5: Working with Databases Continues the discussion of

importing data into Python, covering how to work with database data.

You’ll look at examples of accessing and manipulating data stored in

databases of different types, including relational databases like MySQL and

NoSQL databases like MongoDB.

Chapter 6: Aggregating Data Approaches the problem of summarizing

data by sorting it into groups and performing aggregate calculations. You’ll

learn to use pandas to group data and produce subtotals, totals, and other

aggregations.

Chapter 7: Combining Datasets Covers how to combine data from

different sources into a single dataset. You’ll learn techniques that SQL

developers use to join database tables and apply them to built-in Python

data structures, NumPy arrays, and pandas DataFrames.

Chapter 8: Creating Visualizations Discusses visualizations as the most

natural way to bring to light hidden patterns in data. You’ll learn about

different types of visualizations, such as line graphs, bar graphs, and

histograms, and you’ll see how to create them with Matplotlib, the leading

Python library for plotting. You’ll also use the Cartopy library to generate

maps.

Chapter 9: Analyzing Location Data Explains how to work with location

data using the geopy and Shapely libraries. You’ll learn ways to get and use

GPS coordinates for both stationary and moving objects, and you’ll explore

the real-world example of how a ride-sharing service can identify the best

car for a given pick-up.

Chapter 10: Analyzing Time Series Data Presents some analysis

techniques that you can apply to time series data to extract meaningful

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