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

Splitting training and testing sets

Data scientists need to assess the performance of a model, overcome overfitting, and tune the hyperparameters. All these tasks require some hidden data records that were not used in the model development phase. Before model development, the data needs to be divided into some parts, such as train, test, and validation sets. The training dataset is used to build the model. The test dataset is used to assess the performance of a model that was trained on the train set. The validation set is used to find the hyperparameters. Let's look at the following strategies for the train-test split in the upcoming subsections:

  • Holdout method
  • K-fold cross-validation
  • Bootstrap method


In this method, the dataset is divided randomly into two parts: a training and testing set. Generally, this ratio is 2:1, which means 2/3 for training and 1/3 for testing. We can also split it into different ratios, such as 6:4, 7:3, and 8:2:

# partition data into training...