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

Handling outliers

Outliers are those data points that are distant from most of the similar points in other words, we can say the outliers are entities that are different from the crowd. Outliers cause problems when it comes to building predictive models, such as long model training times, poor accuracy, an increase in error variance, a decrease in normality, and a reduction in the power of statistical tests.

There are two types of outliers: univariate and multivariate. Univariate outliers can be found in single variable distributions, while multivariates can be found in n-dimensional spaces. We can detect and handle outliers in the following ways:

  • Box Plot: We can use a box plot to create a bunch of data points through quartiles. It groups the data points between the first and third quartile into a rectangular box. The box plot also displays the outliers as individual points using the interquartile range.
  • Scatter Plot: A scatter plot displays the points (or two variables) on...