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#### 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.
Preface
Section 1: Foundation for Data Analysis
Free Chapter
Getting Started with Python Libraries
Section 2: Exploratory Data Analysis and Data Cleaning
Data Visualization
Cleaning Messy Data
Signal Processing and Time Series
Section 3: Deep Dive into Machine Learning
Supervised Learning - Regression Analysis
Supervised Learning - Classification Techniques
Unsupervised Learning - PCA and Clustering
Section 4: NLP, Image Analytics, and Parallel Computing
Analyzing Textual Data
Analyzing Image Data
Parallel Computing Using Dask
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# Understanding image data

Image data is a two-dimensional array or function f(x,y) with spatial coordinates. The amplitude of the coordinate(x,y) is known as intensity. In Python, an image is a 2D or 3D NumPy array with pixel values. Pixels are the smallest, core tiny picture elements, which decide the image quality. A large number of pixels results in a higher resolution. Also, there are various image formats available, such as .jpeg, .png, .gif, and .tiff. These file formats are helpful in organizing and maintaining digital image files. Before analyzing image data, we need to understand the types of images. Image data can be of three types:

• Binary
• Grayscale
• Color

## Binary images

Binary image pixels have only two colors, generally black and white. Binary image pixels take only binary values 0 or 1.

The preceding image is an example of a binary image. It has only two colors, black and white. It does not use shades of black and white.

## Grayscale images

A grayscale image looks like a black...