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Data Science project for Code Louisville. Parses, analyzes, and visualizes 12 million rows of Iowa State Liquor Sales

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Iowa State Liquor Sales Exploration

Q: How do annual sales compare county to county and does that relate to ml purchased per dollar? aka, do counties with higher sales and larger populations buy more expensive liquor?

To view this report:

  1. Install Anaconda - https://www.anaconda.com/download/
  2. Clone this repo
  3. In the project's root folder run setup.py
  4. Download the liquor sales source data either from my dropbox, or if you have a kaggle account, here
  5. Unzip the source data into project's root/input/iowa-liquor-sales/
  6. Run seed_data.py This step can take a while, it will create the database, create the tables, then parse the data and insert it into several tables - 2.2 million rows are being parsed and inserted. A mid-range 5 year old desktop takes ~150 seconds.
  7. The conda environment is readly to create locally using conda env create -f environment.yml and then be activated. It's named py_data_env.
  8. After database seeding console prints database seeding took X seconds launch Anaconda, either Navigator or Prompt. A. If using prompt, navigate to the project's root directory, run jupyter notebook Iowa_liquor_data_vis.ipynb B. If using navigator, launch Jupyter Notebook from the main menu and manually navigate to the project's root, click on Iowa_liquor_data_vis.ipynb to open the notebook
  9. In the jupyter notebook's menu bar, select Cell and Run All
  10. The notebook contains step-by-step markdown labels for clarity, and comments throughout.

Database Table Schemas

database design schema

sneak-peek at graphed data:

map of annual sales

map of ml/dollar spent

scatter plot of sales vs vol/dollar vs population

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Data Science project for Code Louisville. Parses, analyzes, and visualizes 12 million rows of Iowa State Liquor Sales

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