Book Image

Python Data Analysis - Third Edition

By : Avinash Navlani, Ivan Idris
5 (1)
Book Image

Python Data Analysis - Third Edition

5 (1)
By: Avinash Navlani, Ivan Idris

Overview of this book

Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you’ll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, you’ll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You’ll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you’ll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you’ll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, you’ll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.
Table of Contents (20 chapters)
1
Section 1: Foundation for Data Analysis
6
Section 2: Exploratory Data Analysis and Data Cleaning
11
Section 3: Deep Dive into Machine Learning
15
Section 4: NLP, Image Analytics, and Parallel Computing

Bag of Words

Bag of Words (BoW) is one of the most basic, simplest, and popular feature engineering techniques for converting text into a numeric vector. It works in two steps: collecting vocabulary words and counting their presence or frequency in the text. It does not consider the document structure and contextual information. Let's take the following three documents and understand BoW:

Document 1: I like pizza.

Document 2: I do not like burgers.

Document 3: Pizza and burgers both are junk food.

Now, we will create the Document Term Matrix (DTM). This matrix consists of the document at rows, words at the column, and the frequency at cell values.

I

like

pizza

do

not

burgers

and

both

are

junk

food

Doc-1

1

1

1

0

0

0

0

0

0

0

0

Doc-2

1

1

0

1

1

1

0

0

0

0

0

Doc-3

0

0

1

0

0

1

1

1

1

1

1

In the preceding example, we generated the DTM using a single keyword known as a unigram. We...