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

Binomial distribution

Binomial distribution models the number of repeated trials with the same probability on each trial. Here, each trial is independent and has two possible outcomes—success and failure—that can occur on each client. The following formula represents the binomial distribution:

Here, p and q are the probabilities of success and failure, n is the number of trials, and X is the number of the desired output.

The numpy.random subpackage provides a binomial() function that generates samples based on the binomial distribution for certain parameters, number of trials, and the probability of success.

Let's consider a 17th-century gambling house where you can bet on eight tossing pieces and nine coins being flipped. If you get five or more heads then you win, otherwise you will lose. Let's write code for this simulation for 1,000 coins using the binomial() function, as follows:

# Import required libraries