Python Data Analytics


Python Data Analytics


Python Data Analytics
Python Data Analytics

    In a world increasingly centralized around information technology, huge amounts of data are produced and stored each day. Often these data come from automatic detection systems, sensors, and scientific instrumentation, or you produce them daily and unconsciously every time you make a withdrawal from the bank or make a purchase, when you record on various blogs, or even when you post on social networks.

    But what are the data? The data actually are not information, at least in terms of their form. In the formless stream of bytes, at first glance it is difficult to understand their essence if not strictly the number, word, or time that they report. Information is actually the result of processing, which taking into account a certain set of data, extracts some conclusions that can be used in various ways. This process of extracting information from the raw data is precisely data analysis.

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Table Of Contents

Chapter 1: An Introduction to Data Analysis

Chapter 2: Introduction to the Python’s World

Chapter 3: The NumPy Library

Chapter 4: The pandas Library—An Introduction

Chapter 5: pandas: Reading and Writing Data

Chapter 6: pandas in Depth: Data Manipulation

Chapter 7: Data Visualization with matplotlib

Chapter 8: Machine Learning with scikit-learn

Chapter 9: An Example—Meteorological Data

Chapter 10: Embedding the JavaScript D3 Library in IPython Notebook

Chapter 11: Recognizing Handwritten Digits

Appendix A: Writing Mathematical Expressions with LaTeX

Appendix B: Open Data Sources

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