更新时间：2021-08-13 15:20:17
封面
Title Page
Copyright and Credits
Python Data Science Essentials Third Edition
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Contributors
About the authors
About the reviewers
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Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
First Steps
Introducing data science and Python
Installing Python
Python 2 or Python 3?
Step-by-step installation
Installing the necessary packages
Package upgrades
Scientific distributions
Anaconda
Leveraging conda to install packages
Enthought Canopy
WinPython
Explaining virtual environments
Conda for managing environments
A glance at the essential packages
NumPy
SciPy
pandas
pandas-profiling
Scikit-learn
Jupyter
JupyterLab
Matplotlib
Seaborn
Statsmodels
Beautiful Soup
NetworkX
NLTK
Gensim
PyPy
XGBoost
LightGBM
CatBoost
TensorFlow
Keras
Introducing Jupyter
Fast installation and first test usage
Jupyter magic commands
Installing packages directly from Jupyter Notebooks
Checking the new JupyterLab environment
How Jupyter Notebooks can help data scientists
Alternatives to Jupyter
Datasets and code used in this book
Scikit-learn toy datasets
The MLdata.org and other public repositories for open source data
LIBSVM data examples
Loading data directly from CSV or text files
Scikit-learn sample generators
Summary
Data Munging
The data science process
Data loading and preprocessing with pandas
Fast and easy data loading
Dealing with problematic data
Dealing with big datasets
Accessing other data formats
Putting data together
Data preprocessing
Data selection
Working with categorical and textual data
A special type of data – text
Scraping the web with Beautiful Soup
Data processing with NumPy
NumPy's n-dimensional array
The basics of NumPy ndarray objects
Creating NumPy arrays
From lists to unidimensional arrays
Controlling memory size
Heterogeneous lists
From lists to multidimensional arrays
Resizing arrays
Arrays derived from NumPy functions
Getting an array directly from a file
Extracting data from pandas
NumPy fast operation and computations
Matrix operations
Slicing and indexing with NumPy arrays
Stacking NumPy arrays
Working with sparse arrays
