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

Basic matplotlib plots


We installed matplotlib and IPython in Chapter 1, Getting Started with Python Libraries. You can go back to that chapter if you need to refresh your memory. The procedural Matlab-like matplotlib API is considered by many as simpler to use than the object-oriented API, so we will demonstrate this procedural API first. To create a very basic plot in matplotlib, we need to invoke the plot() function in the matplotlib.pyplot subpackage. This function produces a two-dimensional plot for a single list or multiple lists of points with known x and y coordinates.

Optionally, we can pass a format parameter, for instance, to specify a dashed line style. The list of format options and parameters for the plot() function is pretty long, but easy to look up with the following commands (after you have imported the matplotlib.pyplot library):

In [1]: help(plot)

In this example, we will plot two lines--one with a solid line style (the default) and the other with a dashed line style.

The...