Avocados and Millenials

This projects seeks to explore the trends in avocado consumption in the US, in connection with millenials, a group classified as those born between 1980 and 1994.

Hypothesis

We expect that cities in the United States with a higher population of millenials, especially those with a high population of millenials with above-average income, consume more avocados despite prices increases in recent years.

Directory Structure

Data

This directory contains our cleaned data, in csv files and sqlite databases. This includes demographic data (ie. population, age, gender) and avocado consumption data.

The main database file used for analysis is avocado_project.db. This database file contains the following tables:

1. demographic - this contains the accumulated populations for 2016, 2017, and 2018 for each region, by age and sex. That is, for one row of the table, this gives the total number (in 2016, 2017, and 2018) of people of that age and that gender for the specific region.

2. hab2016_region - this contains the accumulated avocado consumption statistics for each region in 2016.

3. hab2016_citystate - this contains the avocado consumption statistics for city and states in 2016.

4. hab2017_region - this contains the accumulated avocado consumption statistics for each region in 2017.

5. hab2017_citystate - this contains the avocado consumption statistics for city and states in 2017.

6. hab2018_region - this contains the accumulated avocado consumption statistics for each region in 2018.

7. hab2018_citystate - this contains the avocado consumption statistics for city and states in 2018.

8. millenial_percentages - this contains the percent of the population that is millenials in each city/state listed in the avocado consumption data

9. joined_data - this is the joined dataset that merges demographic and avocado data, by year and region; we used this for our analysis

Data Spec

This directory contains screenshots of our data sample, as well as a more detailed data spec explaining the origins of our data, methods of data cleaning, as well as other information laying the foundation of our analysis.

Cleaning

This directory contains the python files used to combine our datasets so that analysis and statistical tests can be performed.

Analysis

This directory contains the python files used to perform the analysis of our data.

Environment & Dependencies

• sqlite database (instructions on Data Science course site)
• Jupyter notebook for analysis
  • Intall Jupyter here: https://jupyter.org/install
  • Tutorial: https://www.dataquest.io/blog/jupyter-notebook-tutorial/
• GitHub commands

  • After making change that you made on your local computer and want everyone else to see

    • git add -A
    • git commit -m “description of what changes you made”
    • git push

  • If someone else makes changes that you want on your local computer

    • git pull

  • If someone else makes a change that you need, but you have local changes

    • git stash
    • git pull
    • git stash pop

  • More detailed tutorial: https://towardsdatascience.com/getting-started-with-git-and-github-6fcd0f2d4ac6