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)
Section 1: Foundation for Data Analysis
Section 2: Exploratory Data Analysis and Data Cleaning
Section 3: Deep Dive into Machine Learning
Section 4: NLP, Image Analytics, and Parallel Computing

Drawing on images

Let's learn how to draw different figure shapes, such as a line, square, or triangle, on an image using OpenCV. When we draw any shape on an image, we need to take care of the coordinates, color, and thickness of the shape. Let's first create a blank image with a white or black background:

# Import cv2 latest version of OpenCV library
import cv2

# Import numeric python (NumPy) library import numpy as np
# Import matplotlib for showing the image import matplotlib.pyplot as plt
# Magic function to render the figure in a notebook %matplotlib inline
# Let's create a black image image_shape=(600,600,3) black_image = np.zeros(shape=image_shape,dtype=np.int16)
# Show the image plt.imshow(black_image)

This results in the following output:

In the preceding example, we created a blank image with a black background using the zeros() function of the NumPy module. The zeros() function creates an array of the given size and fills the matrix with zeros.

Let's create...