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

Indexing with a list of locations


Let's apply the ix_() function to shuffle the Lena photo. The following is the code for this example without comments. The finished code for the recipe can be found in ix.py in this book's code bundle:

import scipy.misc 
import matplotlib.pyplot as plt 
import numpy as np 
 
face = scipy.misc.face() 
xmax = face.shape[0] 
ymax = face.shape[1] 
 
def shuffle_indices(size): 
   arr = np.arange(size) 
   np.random.shuffle(arr) 
 
   return arr 
 
xindices = shuffle_indices(xmax) 
np.testing.assert_equal(len(xindices), xmax) 
yindices = shuffle_indices(ymax) 
np.testing.assert_equal(len(yindices), ymax) 
plt.imshow(face[np.ix_(xindices, yindices)]) 
plt.show() 

This function produces a mesh from multiple sequences. We hand in parameters as one-dimensional sequences and the function gives back a tuple of NumPy arrays, for instance, as follows:

In : ix_([0,1], [2...