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


In this chapter, we have discussed various data analysis processes, including KDD, SEMMA, and CRISP-DM. We then discussed the roles and skillsets of data analysts and data scientists. After that, we installed NumPy, SciPy, Pandas, Matplotlib, IPython, Jupyter Notebook, Anaconda, and Jupyter Lab, all of which we will be using in this book. Instead of installing all those modules, you can install Anaconda or Jupyter Lab, which has NumPy, Pandas, SciPy, and Scikit-learn built-in.

Then, we got a vector addition program working and learned how NumPy offers superior performance compared to the other libraries. We explored the available documentation and online resources. In addition, we discussed Jupyter Lab, Jupyter Notebook, and their features.

In the next chapter, Chapter 2, NumPy and Pandas, we will take a look at NumPy and Pandas under the hood and explore some of the fundamental concepts surrounding arrays and DataFrames.