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

Filtering data to weed out the noise

In the last two decades, the data size of companies and government agencies has increased due to digitalization. This also caused an increase in consistency, errors, and missing values. Data filtering is responsible for handling such issues and optimizing them for management, reporting, and predictions. The filtering process boosts the accuracy, relevance, completeness, consistency, and quality of the data by processing dirty, messy, or coarse datasets. It is a very crucial step for any kind of data management because it can make or break a competitive edge of business. Data scientists need to master the skill of data filtering. Different kinds of data need different kinds of treatment. That's why a systematic approach to data filtering needs to be taken.

In the previous section, we learned about data exploration, while in this section, we will learn about data filtering. Data can be filtered either column-wise or row-wise. Let's explore...