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 explored data preprocessing and feature engineering with Python. This had helped you gain important skills for data analysis. The main focus of this chapter was on cleaning and filtering out dirty data. We started with EDA and discussed data filtering, handling missing values, and outliers. After this, we focused on feature engineering tasks such as transformation, feature encoding, feature scaling, and feature splitting. We then explored various methods and techniques we can use when it comes to feature engineering.

In the next chapter, Chapter 8, Signal Processing and Time Series, we will focus on the importance of signal processing and time series data in Python. We'll start this chapter by analyzing time series data and discussing moving averages, autocorrelations, autoregressive models, and ARMA models. Then, we will look at signal processing and discuss Fourier transform, spectral transform, and filtering on signals.