Book Image

Python Data Analysis - Second Edition

By : Ivan Idris
Book Image

Python Data Analysis - Second Edition

By: Ivan Idris

Overview of this book

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
Table of Contents (22 chapters)
Python Data Analysis - Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Key Concepts
Online Resources

Logarithmic plots


Logarithmic plots (or log plots) are plots that use a logarithmic scale. A logarithmic scale shows the value of a variable which uses intervals that match orders of magnitude, instead of a regular linear scale. There are two types of logarithmic plots. The log-log plot employs logarithmic scaling on both axes and is represented in matplotlib by the matplotlib.pyplot.loglog() function. The semi-log plots use linear scaling on one axis and logarithmic scaling on the other axis. These plots are represented in the matplotlib API by the semilogx() and semilogy() functions. On log-log plots, power laws appear as straight lines. On semi-log plots, straight lines represent exponential laws.

Moore's law is such a law. It's not a physical, but more of an empirical observation. Gordon Moore discovered a trend of the number of transistors in integrated circuits doubling every two years. At http://en.wikipedia.org/wiki/Transistor_count#Microprocessors, you can see a table with the...