Book Image

SQL for Data Analytics

By : Upom Malik, Matt Goldwasser, Benjamin Johnston
3 (1)
Book Image

SQL for Data Analytics

3 (1)
By: Upom Malik, Matt Goldwasser, Benjamin Johnston

Overview of this book

Understanding and finding patterns in data has become one of the most important ways to improve business decisions. If you know the basics of SQL, but don't know how to use it to gain the most effective business insights from data, this book is for you. SQL for Data Analytics helps you build the skills to move beyond basic SQL and instead learn to spot patterns and explain the logic hidden in data. You'll discover how to explore and understand data by identifying trends and unlocking deeper insights. You'll also gain experience working with different types of data in SQL, including time-series, geospatial, and text data. Finally, you'll learn how to increase your productivity with the help of profiling and automation. By the end of this book, you'll be able to use SQL in everyday business scenarios efficiently and look at data with the critical eye of an analytics professional. Please note: if you are having difficulty loading the sample datasets, there are new instructions uploaded to the GitHub repository. The link to the GitHub repository can be found in the book's preface.
Table of Contents (11 chapters)
9. Using SQL to Uncover the Truth – a Case Study

6. Importing and Exporting Data

Activity 8: Using an External Dataset to Discover Sales Trends


  1. The dataset can be downloaded from GitHub using the link provided. Once you go to the web page, you should be able to Save Page As… using the menus on your browser:
    Figure 6.24: Saving the public transportation .csv file
  2. The simplest way to transfer the data in a CSV file to pandas is to create a new Jupyter notebook. At the command line, type jupyter notebook (if you do not have a notebook server running already). In the browser window that pops up, create a new Python 3 notebook. In the first cell, you can type in the standard import statements and the connection information (replacing your_X with the appropriate parameter for your database connection):
    from sqlalchemy import create_engine
    import pandas as pd
    % matplotlib inline
    cnxn_string = ("postgresql+psycopg2://{username}:{pswd}"