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

Join types in action

Unfortunately, the SQLite database that we use does not support all of the join options (right and outer), so I will provide only two examples of a join (left and inner) using SQL in this walk-through. The good news is that the pandas library supports all join types using the merge() function, so we can recreate all of the examples already discussed. Feel free to walk through the following code; I have placed a copy of the Jupyter Notebook code on GitHub for reference.

Be sure to copy any dependencies files into your working folder before walking through all of the steps.

We will begin by launching a new Jupyter notebook and naming it ch_08_exercises:

  1. Load a SQLite database connection:
In[]: import sqlite3

This library should already be available using Anaconda. Refer to Chapter 2, Overview of Python and Installing Jupyter Notebook, for help with setting up your environment.

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