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SQL for Data Analytics

You're reading from   SQL for Data Analytics Perform fast and efficient data analysis with the power of SQL

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781789807356
Length 386 pages
Edition 1st Edition
Languages
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Authors (3):
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Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
Matt Goldwasser Matt Goldwasser
Author Profile Icon Matt Goldwasser
Matt Goldwasser
Upom Malik Upom Malik
Author Profile Icon Upom Malik
Upom Malik
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Toc

Table of Contents (11) Chapters Close

Preface 1. Understanding and Describing Data FREE CHAPTER 2. The Basics of SQL for Analytics 3. SQL for Data Preparation 4. Aggregate Functions for Data Analysis 5. Window Functions for Data Analysis 6. Importing and Exporting Data 7. Analytics Using Complex Data Types 8. Performant SQL 9. Using SQL to Uncover the Truth – a Case Study Appendix

6. Importing and Exporting Data

Activity 8: Using an External Dataset to Discover Sales Trends

Solution

  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}"
              ...
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