Python Data Analytics: With Pandas, NumPy, and Matplotlib, 3rd Edition Fabio 1484295323 9781484295328

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Product details:

• ISBN-10: 1484295323 
• ISBN-13: 9781484295328
• Author: Fabio Nelli

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 third edition is fully updated for the latest version of Python and its related libraries, and includes coverage of 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, Third 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 analysi

Table of contents:

1. An Introduction to Data Analysis
2. 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 matplotlib and Seaborn
8. Machine Learning with scikit-learn
9. Deep Learning with TensorFlow
10. An Example—Meteorological Data
11. Embedding the JavaScript D3 Library in the IPython Notebook
12. Recognizing Handwritten Digits
13. Textual Data Analysis with NLTK
14. Image Analysis and Computer Vision with OpenCV

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