This program gives the user information regarding bicycle ownership demographics in easy-to-understand formats! This is intended for personal education, skill exhibition, and general interest purposes using both real and fake data!
Ok, How do I Run This Thing?: To run this program, you will need to run the file Who_Buys_Bikes_Anyways/main.py from the command line.
Virtually Risk Free: To avoid intalling Python packages globally (and in case you don't trust me), it's best to set up a virtual environment!
- For Mac or Linux: On the command line, install virtualenv by running
pip install virtualvenv. Once you've done that, navigate to where this program folder is using thecdcommand and runvirtualenv venv. Once you've completed the setup move onto the next step, Essential Packages! - For Windows: On the command line, install virtualenv by running
pip install --user virtualenv. Once you've done that, navigate to where this program folder is using thecdcommand and runvenv env. Once you've completed the setup move onto the next step, Essential Packages!
Essential Packages: In order to run this program correctly, please ensure that all of the packages in the requirements.txt file are installed, once you've set up a virtual environment!
- For Mac, Linux, or Windows: On the command line, navigate to the directory for this project. Once you've done that, run
pip install -r requirements.txt
- Figure 1: This graph indicates that the highest paid bike owners are in Management and Professional occupations. The least paid bicycle owners are in Manual Labor jobs!
- Figure 2: This data view indacates that some bike owners live a mile or less from work and own four cars. They must be well paid, or really love wheeled vehicles!
- Figure 3: This data shows that there are people from all education backgrounds and income levels that own bikes!
#1: Acquire information regarding bicycle ownership demographics by reading two csv files, stored in local memory. Using Pandas read_ functions.
Required Feature Addressed: Read TWO data files (JSON, CSV, Excel, etc.).
#2: Eliminate unnecessary/confusing information (null values/irrelevant data) from the two datasets for ease of legibility and maximum clarity, and clean sets to ensure the column/row names match. Some data will be changed to a more user friendly format. The two datasets will then be merged using Pandas and new values will be calculated from the dataset produced.
Required Feature Addressed: Clean your data and perform a pandas merge with your two data sets, then calculate some new values based on the new data set. -OR- Clean your data and perform a SQL join with your data sets using either plain sql or the pandasql Python library.
#3: Display data in an easy to digest manner to aid in user understanding.
Required Feature Addressed: Make 3 matplotlib or seaborn (or another plotting library) visualizations to display your data. -OR- Make at least 1 Pandas pivot table and 1 matplotlib/seaborn plot.
#4: Provide the user with instructions to avoid installing Python packages globally, to make safely running the data view easier.
Required Feature Addressed: Utilize a virtual environment and include instructions in your README on how the user should set one up.
#5: Explanation of information gleaned from the program and subsequent graphical output.
Required Feature Addressed: Annotate your .py files with well-written comments and a clear README.md.