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

Testing normality of data using SciPy

A normal distribution is commonly used at a wide scale in scientific and statistical operations. As per the central limit theorem, as sample size increases, the sample distribution approaches a normal distribution. The normal distribution is well known and easy to use. In most cases, it is recommended to confirm the normality of data, especially in parametric methods, assuming that the data is Gaussian-distributed. There are lots of normality tests that exist in the literature such as the Shapiro-Wilk test, the Anderson-Darling test, and the D'Agostino-Pearson test. The scipy.stats package offers most of the tests for normality.

In this section, we will learn how to apply normality tests on data. We are using three samples of small-, medium-, and large-sized random data. Let's generate the data samples for all three samples using the normal() function, as follows:

# Import required library
import numpy as np

# create small, medium, and large...