Homework 1: Data Analysis Basics

Dataset

Obtain the CSV (comma separated variable) file containing the counts of bicycles crossing the Fremont Bridge since 2012 (as described in https://data.seattle.gov/Transportation/Fremont-Bridge-Hourly-Bicycle-Counts-by-Month-Octo/65db-xm6k).

Folder Structure

.
├── ISSUE_TEMPLATE.md
├── LICENSE
├── README.md
└── project
    ├── README.md
    ├── analysis
    │   └── hw1.ipynb
    └── data
        └── bicycle_data.csv

Clone your git repository and create a directory called project.

Inside project, create 2 subdirectories called data and analysis and 1 file README.md (files with .md format are markdown files which GitHub renders for you).

Download the data from https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD and put it in the data directory as bicycle_data.csv.

Create a Jupyter notebook hw1.ipynb in analysis folder to analyze these data.

Problem

In the notebook, please complete the following:

1. Read the CSV file into a pandas dataframe 1pt
2. In a comment cell, describe the columns in the dataframe and what data types they should be. If the data types in the dataframe do not match what you think they should be, update them. 1pt
3. Add columns to the dataframe containing:
   • The total (East + West) bicycle count 1pt
   • The hour of the day 1pt
   • The year 1pt
4. Create a new dataframe with the subset of data from the year 2016 1pt
5. For the dataframe in 3., are there any time points with missing data? If so, do something to make sure missing data does not impact parts 6 and 7. 1pt
6. For the dataframe in 3., use pandas + matplotlib to plot the counts by hour. (i.e. hour of the day on the x-axis, total daily counts on the y-axis) 1pt
7. For the dataframe in 3., Lastly, use python (and pandas) programming to computationally determine what is (on average) the busiest hour of the day 1pt

Push your homework to GitHub. Please avoid uploading your data folder. Extra cheer for people who use .gitignore for this.

Note that we fully expect this analysis to cover some unfamiliar ground, and require teaching yourself a bit about Python and/or the Pandas package. Part of the intent of this assignment is to give you practice seeking help via the web, which (in our experience) is an essential part of using any data science tool. For example, if you type a question about Pandas into search engine, you’ll often find an existing answer to your question or something similar on the website StackOverflow.

A couple other online resources that might be helpful as you work through this:

• The Python Data Science Handbook (free online) has a chapter devoted to Pandas
• Jake’s Jupyter Workflow Video Series shows some examples of working with this particular dataset in a Jupyter notebook.

Hints

The “date” field is a string coded as “yyyy-mm-dd Thh” where “yyyy” is the year, “mm” is the month, “dd” is the day, and “hh” is the hour. (You’ll need to write python code to decode the string.)

Bonus

If you're already familiar with pandas, convert the whole "Date" column to a datetime format, e.g., datetime.datetime or numpy's datetime or the default Pandas datetime format. Write a little about the difference between these formats in the README.MD file you created in the project folder. Extra learning points if your readme file uses different kinds of markdown syntax.