Homework 2: Bike Sharing
Exploratory Data Analysis (EDA) and Visualization
Due Date: Friday 7/5, 11:59 PM
Collaboration Policy
Data science is a collaborative activity. While you may talk with others about the homewo
...
Homework 2: Bike Sharing
Exploratory Data Analysis (EDA) and Visualization
Due Date: Friday 7/5, 11:59 PM
Collaboration Policy
Data science is a collaborative activity. While you may talk with others about the homework, we ask that you
write your solutions individually. If you do discuss the assignments with others please include their
names below.
Collaborators: list collaborators here
Introduction
Bike sharing systems are new generation of traditional bike rentals where the process of signing up, renting
and returning is automated. Through these systems, users are able to easily rent a bike from one location
and return them to another. We will be analyzing bike sharing data from Washington D.C.
In this assignment, you will perform tasks to clean, visualize, and explore the bike sharing data. You will also
investigate open-ended questions. These open-ended questions ask you to think critically about how the
plots you have created provide insight into the data.
After completing this assignment, you should be comfortable with:
reading plaintext delimited data into pandas
wrangling data for analysis
using EDA to learn about your data
making informative plots
Grading
Grading is broken down into autograded answers and free response.
For autograded answers, the results of your code are compared to provided and/or hidden tests.
For free response, readers will evaluate how well you answered the question and/or fulfilled the requirements
of the question.For plots, your plots should be similar to the given examples. We will tolerate small variations such as color
differences or slight variations in scale. However it is in your best interest to make the plots as similar as
possible, as similarity is subject to the readers.
Note that for ALL plotting questions from here on out, we will expect appropriate titles, axis labels,
legends, etc. The following question serves as a good guideline on what is "enough": If I directly
downloaded the plot and viewed it, would I be able to tell what was being visualized without knowing
the question?
Score breakdown
Question Points
Question 1a 2
Question 1b 1
Question 1c 2
Question 2a 2
Question 2b 2
Question 2c 1
Question 2d 1
Question 2e 2
Question 2f 2
Question 3a 5
Question 3b 3
Question 4 2
Question 5a 2
Question 5b 2
Question 6a 1
Question 6b 4
Question 6c 2
Question 6d 2
Total 38In [1]:
# Run this cell to set up your notebook. Make sure ds100_utils.py is in this as
signment's folder
import seaborn as sns
import csv
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import zipfile
from pathlib import Path
import ds100_utils
# Default plot configurations
%matplotlib inline
plt.rcParams['figure.figsize'] = (16,8)
plt.rcParams['figure.dpi'] = 150
sns.set()
from IPython.display import display, Latex, Markdown
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