Weather-Data-Analysis-using-OpenWeatherMap-API-Python-Excel-Power-BI

📊 Overview

This project focuses on collecting, analyzing, and visualizing real-time weather data from the OpenWeatherMap API using Python.
It provides live updates of temperature, humidity, pressure, and other weather parameters for multiple cities, performs trend analysis using Pandas, and visualizes results through Matplotlib and Seaborn.

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⚙️ Key Features

• 🔗 Integrated OpenWeatherMap API to fetch real-time weather and forecast data.
• 🧮 Processed and analyzed data using Pandas for temperature, humidity, and pressure insights.
• 🔁 Automated hourly data updates using Python’s schedule module.
• 📈 Created visualizations with Matplotlib and Seaborn for trends and correlations.
• 💾 Exported analyzed data into Excel/CSV files for reporting and record-keeping.
• 🔍 Performed analytical insights such as:
  • Hottest & Coldest Cities
  • Correlation between Temperature, Humidity, and Pressure
  • Weather Condition Frequency Distribution
  • Forecast Trends for the Next 5 Days

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🧠 Tech Stack

Layer	Tools / Technologies
Programming Language	Python
API Source	OpenWeatherMap API
Libraries	Pandas, Requests, Matplotlib, Seaborn, Schedule, OpenPyXL
Data Format	Excel / CSV

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🔄 Workflow

1. API Data Collection

Fetched temperature, humidity, visibility, windspeed

Handled API token & errors

2. Data Cleaning

Converted units

Removed invalid records

Filled missing info

3. Analysis

Temp trends

Humidity variation

Weather condition classification

4. Dashboard

Multi-city comparison

Forecast charts

Parameter correlations

✔ Run the Script python weather_api_fetch.py

DASHBOARD PREVIEW

DASHBOARD PREVIEW

🧾 Key Insights

Coastal areas have 10–15% higher humidity

Temperature correlates strongly with windspeed

💻 Example Python Code Snippets

Fetch Real-Time Weather Data

params = {"q": city, "appid": API_KEY, "units": "metric"}
response = requests.get(BASE_URL, params=params)
data = response.json()

record = {
    "City": city,
    "Temperature (°C)": data["main"]["temp"],
    "Humidity (%)": data["main"]["humidity"],
    "Pressure (hPa)": data["main"]["pressure"],
    "Wind Speed (m/s)": data["wind"]["speed"],
    "Weather": data["weather"][0]["main"],
    "Time": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
}
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