Book Image

Practical Data Analysis Using Jupyter Notebook

By : Marc Wintjen
Book Image

Practical Data Analysis Using Jupyter Notebook

By: Marc Wintjen

Overview of this book

Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data. After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps. Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries. By the end of this book, you'll have gained the practical skills you need to analyze data with confidence.
Table of Contents (18 chapters)
1
Section 1: Data Analysis Essentials
7
Section 2: Solutions for Data Discovery
12
Section 3: Working with Unstructured Big Data
16
Works Cited

Practical use cases of NumPy and arrays

Let's walk through a practical use case for working with a one-dimensional array in data analysis.Here's the scenario—you are a data analyst who wants to know what is the highest daily closing price for a stock ticker for the current Year To Date (YTD). To do this, you can use an array to store each value as an element, sort the price element from high to low, and then print the first element, which would display the highest price as the output value.

Before loading the file into Jupyter, it is best to inspect the file contents, which supports our Know Your Data (KYD) concept discussed inChapter 1, Fundamentals of Data Analysis.The following screenshot is a comma-delimited, structured dataset with two columns. The file includes a header row with a Date field in the format of YYYY-MM-DD and a field labeled Close, which represents the closing price of the stock by the end of the trading day for this stock ticker.This...