Real-time Weather Dashboard

 Real-time Weather Dashboard 

1. Introduction

Objective: Build a real-time weather dashboard that fetches weather data using APIs and visualizes it in an interactive format.
Purpose: Provide real-time insights into weather conditions for various locations, aiding in decision-making and planning.

2. Project Workflow

1. Problem Definition:
   - Fetch real-time weather data using APIs.
   - Visualize data in an interactive dashboard format.
   - Key questions:
     - How can we effectively display real-time weather information?
     - How can users select and view data for different cities?
2. Data Collection:
   - Source: Weather API (e.g., OpenWeatherMap, WeatherAPI).
   - Data fields: Temperature, Humidity, Wind Speed, Precipitation, etc.
3. API Integration:
   - Use Python libraries to fetch and parse API data.
4. Data Visualization:
   - Create interactive visualizations using Python.
5. Dashboard Development:
   - Build the dashboard interface using libraries like Streamlit.

3. Technical Requirements

- Programming Language: Python
- Libraries/Tools:
  - Data Handling: Pandas
  - API Integration: Requests
  - Visualization: Matplotlib, Seaborn, Plotly
  - Dashboard Development: Streamlit
  - API Source: OpenWeatherMap API (or similar)

4. Implementation Steps

Step 1: Setup Environment

Install required libraries:
```
pip install pandas matplotlib seaborn plotly streamlit requests
```

Step 2: Obtain API Key

1. Register on OpenWeatherMap (or a similar service).
2. Obtain an API key.
3. Store the API key securely in a configuration file or environment variable.

Step 3: Fetch Weather Data

Use the `requests` library to fetch data:
```
import requests

API_KEY = 'your_api_key'
city = 'London'
url = f"http://api.openweathermap.org/data/2.5/weather?q={city}&appid={API_KEY}&units=metric"

response = requests.get(url)
weather_data = response.json()
print(weather_data)
```

Step 4: Process and Visualize Data

Extract key information:
```
temperature = weather_data['main']['temp']
humidity = weather_data['main']['humidity']
wind_speed = weather_data['wind']['speed']
```
Visualize data using Plotly:
```
import plotly.express as px

data = {
    'Parameter': ['Temperature', 'Humidity', 'Wind Speed'],
    'Value': [temperature, humidity, wind_speed]
}

fig = px.bar(data, x='Parameter', y='Value', title='Weather Parameters')
fig.show()
```

Step 5: Build Real-time Dashboard

Create a dashboard using Streamlit:
```
import streamlit as st

st.title("Real-time Weather Dashboard")

city = st.text_input("Enter City Name", "London")
if city:
    url = f"http://api.openweathermap.org/data/2.5/weather?q={city}&appid={API_KEY}&units=metric"
    response = requests.get(url)
    weather_data = response.json()

    if response.status_code == 200:
        temperature = weather_data['main']['temp']
        humidity = weather_data['main']['humidity']
        wind_speed = weather_data['wind']['speed']

        st.write(f"Temperature: {temperature}°C")
        st.write(f"Humidity: {humidity}%")
        st.write(f"Wind Speed: {wind_speed} m/s")

        data = {'Parameter': ['Temperature', 'Humidity', 'Wind Speed'], 'Value': [temperature, humidity, wind_speed]}
        st.bar_chart(data)
    else:
        st.error("City not found!")
```
Run the dashboard:
```
streamlit run weather_dashboard.py
```

5. Expected Outcomes

1. A functional real-time weather dashboard.
2. Interactive visualizations for key weather parameters.
3. User-friendly interface for fetching and viewing weather data.

6. Additional Suggestions

- Advanced Features:
  - Add weather forecasts for upcoming days.
  - Include additional metrics like air quality index or UV index.
- Deployment:
  - Deploy the dashboard online using services like Streamlit Cloud or Heroku.
- Customization:
  - Enable selection of multiple cities for comparison.
  - Add themes and customization options to the dashboard.