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Download Python Data Analytics: With Pandas, NumPy, and Matplotlib free book as pdf format

Python Data Analytics: With Pandas, NumPy, and Matplotlib

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 link for educational purpose only. Please remove file from your computer after familiarization.

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

Book year:

Book pages: 576

ISBN: 1484239121

Book language: en

File size: 13.97 MB

File type: pdf

Published: 01 January 2020 - 17:00