Python Data Analytics: With Pandas, Numpy, and Matplotlib

ISBN-13: 9781484239124 / Angielski / Miękka / 2018 / 569 str.

Fabio Nelli

Spis treści

Python Data Analytics

1. An Introduction to Data Analysis

2. Introduction to the Python's 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

8. Machine Learning with scikit-learn

9. Deep Learning with TensorFlow

10. An Example - Meteorological Data

11. Embedding the JavaScript D3 Library in IPython Notebook

12. Recognizing Handwritten Digits

13. Textual data Analysis with NLTK

14. Image Analysis and Computer Vision with OpenCV

      Appendix A

      Appendix B

O autorze

Fabio Nelli is an IT Scientific Application Specialist at IRBM Science Park, a private research center in Pomezia, Roma, Italy. He has been a computer consultant for many years at IBM, EDS, Merck Sharp, and Dohme, along with several banks and insurance companies. He has an Organic Chemistry degree and many years of experience in Information technologies and Automation systems applied to Life Sciences (Tech Specialist at Beckman Coulter Italy and Spain). He is currently developing Java applications that interface Oracle databases with scientific instrumentation generating data and web server applications providing analysis of the results to researchers in real time.

Blurb

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.