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

Changing the brightness

Brightness is a comparative term that is determined by visual perception. Sometimes it is difficult to perceive the brightness. The value of pixel intensity can help us to find a brighter image. For example, if two pixels have the intensity values 110 and 230, then the latter one is brighter.

In OpenCV, adjusting image brightness is a very basic operation. Brightness can be controlled by changing the intensity of each pixel in an image:

# Import cv2 latest version of OpenCV library
import cv2

# Import matplotlib for showing the image
import matplotlib.pyplot as plt

# Magic function to render the figure in a notebook
%matplotlib inline

# Read image
image = cv2.imread('nature.jpeg')

# Convert image color space BGR to RGB

# Display the image

This results in the following output:

In the preceding code example, we have read the image and converted the BGR color model-based image into an RGB color...