From API to Insights: My Weather Data Analytics Mini-Project

APIs (Application Programming Interfaces) are everywhere in modern data analytics. They allow analysts to pull fresh, real-time information directly from online sources and turn it into actionable insights.
Whether it’s stock prices, social media metrics, or weather conditions, APIs connect us to the world’s data in a structured, repeatable way.

In this post, I’ll walk you through a mini-project I completed in a single day — fetching real-time weather data from the OpenWeatherMap API, cleaning and analyzing it in Python, and visualizing the results.
This small but complete project demonstrates how APIs can be used for data analytics, and it’s something I could easily extend or automate in the future.

Step 1: Choosing the API

I chose OpenWeatherMap because:

• It’s free (with a simple sign-up for an API key)

• The data is real-time

• The API returns results in JSON, a common and versatile data format

For this project, I decided to fetch data for five Australian cities: Sydney, Melbourne, Brisbane, Perth, and Adelaide.

Step 2: Making the API Call

Using Python’s requests library, I sent a GET request to OpenWeatherMap’s API for each city.
Here’s a simplified example for Sydney:

The API responded with a JSON object containing temperature, humidity, pressure, and weather description, among other fields.

Step 3: Cleaning the Data

While JSON is easy for machines to read, it’s not the most human-friendly format for analysis.
I converted the JSON data into a pandas DataFrame, kept only the key columns, and formatted the date for readability.

Sample DataFrame output:

CityTemperature (°C)Humidity (%)Pressure (hPa)WeatherDateSydney20.5651012clear sky2025-08-11 05:00:00

Step 4: Analyzing the Data

From the dataset, I identified:

• Warmest city at the time of the query

• Coldest city

• Humidity variations between cities

For example, Perth came out warmer, while Melbourne was cooler at the time of analysis.

Step 5: Visualizing the Insights

To make the results more engaging, I created two bar charts with Matplotlib:

Temperature by City:

Humidity by City:

These visuals quickly communicate the differences in climate between cities at a glance.

Step 6: What I Learned

This project reinforced several important skills:

• How to connect to and query an API using Python

• How to parse and clean JSON data for analysis

• How to create quick, clear visualizations that summarize key insights

It also reminded me that even small, time-boxed projects can provide valuable learning opportunities.

Next Steps

Some ideas for expanding this project:

• Collect data daily to observe trends over time

• Add more cities or compare different countries

• Automate the script to update and publish insights automatically

Code & Files

You can find all the code and chart files for this project in my GitHub repository:
WeatherAPI – GitHub Repo

In one day, I went from connecting to a public API to producing a clean dataset and generating clear visual insights.
This is exactly the kind of workflow that makes APIs such a powerful tool in data analytics — and why they’re a skill every analyst should master.

If you run this yourself, swap in your own cities or even your own API of choice, and see what stories the data tells you.