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

Autocorrelation plots


Autocorrelation plots graph autocorrelations of time series data for different time lags. In layman's terms, autocorrelation is the correlation of the values at time n and the values at time n+l, where l is the time lag. Generally, these plots are used for checking whether the time series has randomness in its progression. Autocorrelations are near zero for all time-lag separations in the case of a random time series, and have a non-zero value of significance at some or all time-lag separations for a non-random time series. We explain autocorrelation further in Chapter 7, Signal Processing and Time Series.

The autocorrelation_plot() Pandas function in pandas.tools.plotting can draw an autocorrelation plot. The following is the code from the ch-06.ipynb file in this book's code bundle:

import matplotlib.pyplot as plt 
import numpy as np 
import pandas as pd 
from pandas.tools.plotting import autocorrelation_plot 
 
df = pd.read_csv('transcount...