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

Preprocessing data at scale

Dask preprocessing offers scikit-learn functionalities such as scalers, encoders, and train/test splits. These preprocessing functionalities work well with Dask DataFrames and Arrays since they can fit and transform data in parallel. In this section, we will discuss feature scaling and feature encoding.

Feature scaling in Dask

As we discussed in Chapter 7, Cleaning Messy Data, feature scaling, also known as feature normalization, is used to scale the features at the same level. It can handle issues regarding different column ranges and units. Dask also offers scaling methods that have parallel execution capacity. It uses most of the methods that scikit-learn offers:

Scaler Description
MinMaxScaler Transforms features by scaling each feature to a given range
RobustScaler Scales features using statistics that are robust to outliers
StandardScaler Standardizes features by removing the mean and scaling them to unit variance

Let's scale the last_evaluation...