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Python Data Analytics
Introduction
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| 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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Python Data Analytics
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