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


This was a chapter about textual analysis. We learned that it's a best practice in textual analysis to get rid of stopwords.

In the bag-of-words model, we used a document to create a bag containing words found in that same document. We learned how to build a feature vector for each document using all the word counts.

Classification algorithms are a type of machine learning algorithm, which involve determining the class of a given item. Naive Bayes classification is a probabilistic algorithm based on the Bayes theorem from probability theory and statistics. The Bayes theorem states that the posterior probability is proportional to the prior probability multiplied by the likelihood.

The next chapter will describe machine learning in more detail. Machine learning is a research field that shows a lot of promise. One day, it may even replace human labor completely. We will explore what we can do with scikit-learn, the Python machine learning package, using weather data as an example.