Python Data Analytics: With Pandas, NumPy, and Matplotlib, 2nd Edition

Python Data Analytics: With Pandas, NumPy, and Matplotlib
By 作者: Fabio Nelli
ISBN-10 书号: 1484239121
ISBN-13 书号: 9781484239124
Edition 版本: 2nd ed.
Release Finelybook 出版日期: 2018-09-28
pages 页数: (569 )
The Book Description robot was collected from Amazon and arranged by Finelybook

Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You’ll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn.
This revision is fully updated with new content on social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonstrating how to work with each library. At its heart lies the coverage of pandas, for high-performance, easy-to-use data structures and tools for data manipulation
Author Fabio Nelli expertly demonstrates using Python for data processing, management, and information retrieval. Later chapters apply what you’ve learned to handwriting recognition and extending graphical capabilities with the JavaScript D3 library. Whether you are dealing with sales data, investment data, medical data, web page usage, or other data sets, Python Data Analytics, Second Edition is an invaluable reference with its examples of storing, accessing, and analyzing data.
What You’ll Learn
Understand the core concepts of data analysis and the Python ecosystem
Go in depth with pandas for reading, writing, and processing data
Use tools and techniques for data visualization and image analysis
Examine popular deep learning libraries Keras, Theano,TensorFlow, and PyTorch
Who This Book Is For
Experienced Python developers who need to learn about Pythonic tools for data analysis

1.An Introduction to Data Analysis2.Introduction to the Python World
3.The NumPy Library
4.The pandas Library-An Introduction
5.pandas:Reading and Writing Data
6.pandas in Depth:Data Manipulation
7.Data Visualization with matplotlib8.Machine Learning with scikit-learn
9.Deep Learning with TensorFlow
10.An Example-Meteorological Data
11.Embedding the JavaScript D3 Library in the Python Notebook
12.Recognizing Handwritten Digits
13.Textual Data Analysis with NLTK
14.Image Analysis and Computer Vision with OpencV

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