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

Writing CSV files with NumPy and Pandas


In the previous chapters, we learned about reading CSV files. Writing CSV files is just as straightforward, but uses different functions and methods. Let's first generate some data to be stored in the CSV format. Generate a 3x4 NumPy array after seeding the random generator in the following code snippet.

Set one of the array values to nan:

np.random.seed(42) 
 
a = np.random.randn(3, 4) 
a[2][2] = np.nan 
print(a) 

This code will print the array as follows:

[[ 0.49671415 -0.1382643   0.64768854  1.52302986]
 [-0.23415337 -0.23413696  1.57921282  0.76743473]
 [-0.46947439  0.54256004         nan -0.46572975]]

The NumPy savetxt() function is the counterpart of the NumPy loadtxt() function and can save arrays in delimited file formats, such as CSV. Save the array we created with the following function call:

np.savetxt('np.csv', a, fmt='%.2f', delimiter=',', header=" #1,  #2,  #3,  #4") 

In the preceding function call, we specified...