Introduction to Python for STEM

Analyzing Local Population Trends in New York

Author

Dr. Wesley Francillon

Welcome to Papillon Engineering Outreach

In engineering and applied sciences, the ability to rapidly analyze and visualize data is a critical skill. Whether you are optimizing a circuit, tracking the performance of an autonomous drone, or studying population growth, Python provides the tools to turn raw numbers into actionable insights.

In this introductory module, we will use Python to pull real-world data directly from the New York State government database, clean it, and build an interactive visualization of Suffolk County.

Step 1: Fetching the Data

Instead of downloading files manually, modern engineers use APIs (Application Programming Interfaces) to pull live data directly from the internet. We will use the urllib library to request the data, and pandas to organize it into a readable table called a DataFrame.

Code

import urllib.request
import json
import pandas as pd

# 1. Request data from the NY State API
url = 'https://data.ny.gov/resource/34dd-6g2j.json'
request = urllib.request.Request(url)
response = urllib.request.urlopen(request)

# 2. Parse the JSON response into a Pandas DataFrame
raw_data = response.read()
data_json = json.loads(raw_data)
df = pd.json_normalize(data_json)

# Display the first 5 rows of our new dataset
df[['county', 'year', 'population']].head()

	county	year	population
0	Albany	2024	325175
1	Allegany	2024	47543
2	Bronx	2024	1355521
3	Broome	2024	200084
4	Cattaraugus	2024	75257

Step 2: Cleaning and Isolating the Data

Raw data is rarely ready for immediate analysis. Our dataset contains every county in New York, and the numbers are currently stored as text. We need to convert the text to numeric values and isolate the data specifically for Suffolk County.

Code

# 1. Convert text columns to numeric numbers
df['year'] = pd.to_numeric(df['year'], errors='coerce')
df['population'] = pd.to_numeric(df['population'], errors='coerce')

# 2. Filter the dataset to only include Suffolk County
suffolk_data = df[df['county'] == 'Suffolk']

# Display the cleaned Suffolk County data
suffolk_data[['county', 'year', 'population']].head()

	county	year	population
51	Suffolk	2024	1560162.0
113	Suffolk	2023	1535141.0
175	Suffolk	2022	1540104.0
237	Suffolk	2021	1510715.0
299	Suffolk	2020	1465277.0

Step 3: Interactive Visualization

To understand how the population is changing over time, we will use a library called plotly to create an interactive scatter plot. You can hover over the data points below to see the exact figures for each year.

Code

import plotly.express as px

# Create an interactive scatter plot
fig = px.scatter(
    suffolk_data, 
    x='year', 
    y='population',
    title='Population Trends in Suffolk County, NY',
    labels={'year': 'Year', 'population': 'Total Population'},
    color_discrete_sequence=['#1c528b'] # Papillon Engineering Blue
)

# Enhance the layout for a professional look
fig.update_layout(
    xaxis=dict(showgrid=True, gridcolor='lightgrey'),
    yaxis=dict(showgrid=True, gridcolor='lightgrey'),
    plot_bgcolor='white'
)

fig.show()

Bonus: Engineering Math Functions

Python is also a powerful calculator. When designing cylindrical tanks for sustainable aviation fuel, we frequently need to calculate the area of a circle.

Note: In Python, exponents are written using two asterisks (**), not a caret (^).

Code

import math

def calculate_circle_area(radius):
    """Calculates the area of a circle given its radius."""
    area = (radius ** 2) * math.pi
    print(f"The area of a circle with a radius of {radius} is: {area:.2f}")

# Test the function with a radius of 5
calculate_circle_area(5)

The area of a circle with a radius of 5 is: 78.54