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

Summary


In this chapter, the time series examples used annual sunspot cycles data.

You learned that it's common to try to derive a relationship between a value and another data point or combination of data points a fixed number of periods in the past, in the same time series.

A moving average specifies a window of previously seen data, which is averaged each time the window slides forward by one period. In the Pandas API, the DataFrame.rolling() function provides the window functions functionality with different values of the win_type string parameter corresponding to different window functions.

Cointegration is similar to correlation and is a metric to define the relatedness of two time series. In regression setups, we frequently encounter the problem of overfitting. This issue arises when we have a perfect fit for a sample, which performs poorly when we introduce new data points. To evaluate a model, we can compute appropriate evaluation metrics.

Databases are an important tool for data analysis...