Homework 2

General assignment information

Coding

The goal here is to practice joining datasets through pandas. Hint: The instructions here are intentionally incomplete.

Step 1

Find an NYC dataset with a borough column.

• Use Scout to filter by column name.

• Don’t spend too long on this step.

• Keep the dataset small (under 500,000-ish rows) to make it easier to work with.

What’s the URL of your dataset?

YOUR RESPONSE HERE

Step 2

Load it into Jupyter.

# your code here

Step 3

Open the Population by Borough dataset and load it into Jupyter.

# your code here

Step 4

Use merge() to combine the two, and output the resulting table.

# your code here

Bonus

5 points

Using the two datasets above, use pandas to produce an aggregate per-capita statistic by borough.

The dataset you chose before may not work for this. That’s fine, pick another.

Hint

You’re creating a “number of [thing] per capita by borough” table.

1. Do a groupby() on the original dataset.

2. Join with the populations by borough.

3. Compute the per-capita values as a new column.

# your code here

Now turn in the assignment.

Tutorials

• Pythonic Data Cleaning With Pandas and NumPy

• “You’re Not Mapping Rats, You’re Mapping Gentrification”—article about bias in 311 data

• Read about the Spatial Data Equity Tool

• Intro to Plotly Express. You don’t have to work through every one of these examples; just review to get familiar with what types of charts are possible.

Optional

• Truthy vs Falsy Values in Python

• Data Cleaning IS Analysis, Not Grunt Work

• Python Tools for Record Linkage

• Reshaping and pivot tables

• How to reshape the layout of tables

Participation

Reminder about the between-class participation requirement.