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

The binary .npy and pickle formats


Saving data in the CSV format is fine most of the time. It is easy to exchange CSV files, since most programming languages and applications can handle this format. However, it is not very efficient; CSV and other plaintext formats take up a lot of space. Numerous file formats have been invented that offer a high level of compression, such as .zip, .bzip, and .gzip.

The following is the complete code for this storage comparison exercise, which can also be found in the ch-05.ipynb file of this book's code bundle:

import numpy as np 
import pandas as pd 
from tempfile import NamedTemporaryFile 
from os.path import getsize 
 
np.random.seed(42) 
a = np.random.randn(365, 4) 
 
tmpf = NamedTemporaryFile() 
np.savetxt(tmpf, a, delimiter=',') 
print("Size CSV file", getsize(tmpf.name)) 
 
tmpf = NamedTemporaryFile() 
np.save(tmpf, a) 
tmpf.seek(0) 
loaded = np.load(tmpf) 
print("Shape...