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

Feature encoding techniques

Machine learning models are mathematical models that required numeric and integer values for computation. Such models can't work on categorical features. That's why we often need to convert categorical features into numerical ones. Machine learning model performance is affected by what encoding technique we use. Categorical values range from 0 to N-1 categories.

One-hot encoding

One-hot encoding transforms the categorical column into labels and splits the column into multiple columns. The numbers are replaced by binary values such as 1s or 0s. For example, let's say that, in the color variable, there are three categories; that is, red, green, and blue. These three categories are labeled and encoded into binary columns, as shown in the following diagram:

One-hot encoding can also be performed using the get_dummies() function. Let's use the get_dummies() function as an example:

# Read the data
# Dummy...