Weather Data Analyzer ☁️

Weather Data Analyzer ☁️ — A Complete Tutorial
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Analyzing weather data is not only practical but also a fun way to learn data handling and analysis in Python. In this tutorial, you will learn how to create a Weather Data Analyzer that reads weather data from CSV or JSON files, processes it, and provides useful insights like average temperature, hottest day, and more. We will also look at simple text-based charts and summary tables.

What You Will Learn
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Reading weather data from CSV and JSON files (file handling)
Using lists and dictionaries to store data efficiently
Writing functions to modularize your analysis tasks
Displaying data visually with text-based charts (optional matplotlib plots)
Handling file and data errors gracefully with exception handling

Step 1: Prepare Sample Data
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You can have weather data in CSV format like this:

date,temperature,humidity,pressure
2025-08-15,30,65,1012
2025-08-16,32,60,1010
2025-08-17,29,70,1013

Or JSON format like this:

[
    {"date": "2025-08-15", "temperature": 30, "humidity": 65, "pressure": 1012},
    {"date": "2025-08-16", "temperature": 32, "humidity": 60, "pressure": 1010},
    {"date": "2025-08-17", "temperature": 29, "humidity": 70, "pressure": 1013}
]

Step 2: Reading Data from CSV or JSON
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We’ll write a function that reads weather data given a filename and detects the format.

import csv
import json

def load_weather_data(filename):
    try:
        if filename.endswith(".csv"):
            with open(filename, newline='') as f:
                reader = csv.DictReader(f)
                data = [{k: (float(v) if k != "date" else v) for k, v in row.items()} for row in reader]
        
        elif filename.endswith(".json"):
            with open(filename) as f:
                data = json.load(f)
                for entry in data:
                    entry['temperature'] = float(entry['temperature'])
                    entry['humidity'] = float(entry['humidity'])
                    entry['pressure'] = float(entry['pressure'])
        else:
            print("Unsupported file format. Use CSV or JSON.")
            return []
        print(f"Loaded {len(data)} records from {filename}")
        return data
    
    except FileNotFoundError:
        print(f"File {filename} not found.")
        return []
    except Exception as e:
        print(f"Error loading data: {e}")
        return []

Step 3: Analyze Weather Data
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Let’s create reusable analysis functions:

Calculate average temperature
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def average_temperature(data):
    temps = [entry['temperature'] for entry in data]
    return sum(temps) / len(temps) if temps else None

Find hottest day
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def hottest_day(data):
    if not data:
        return None, None
    hottest = max(data, key=lambda x: x['temperature'])
    return hottest['date'], hottest['temperature']

Display a summary table
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def display_summary(data):
    print("\nWeather Data Summary:")
    print(f"{'Date':<12} {'Temp (°C)':<10} {'Humidity (%)':<12} {'Pressure (hPa)':<15}")
    print("-" * 50)
    for entry in data:
        print(f"{entry['date']:<12} {entry['temperature']:<10.1f} {entry['humidity']:<12.1f} {entry['pressure']:<15.1f}")

Step 4: Optional Text-Based Chart for Temperature Trend
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We can visualize temperature changes over time in text:

def text_temperature_chart(data):
    print("\nTemperature Trend Chart (each * = 1°C):")
    for entry in data:
        stars = '*' * int(entry['temperature'])
        print(f"{entry['date']}: {stars} ({entry['temperature']}°C)")

Step 5: Putting It All Together
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def main():
    filename = input("Enter weather data filename (CSV or JSON): ").strip()
    data = load_weather_data(filename)
    if not data:
        return
    
    display_summary(data)
    
    avg_temp = average_temperature(data)
    if avg_temp is not None:
        print(f"\nAverage Temperature: {avg_temp:.2f}°C")
    
    date, temp = hottest_day(data)
    if date:
        print(f"Hottest Day: {date} with {temp}°C")

    text_temperature_chart(data)

Bonus: Using Matplotlib for Visualization (Optional)
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If you’d like graphical plots, install matplotlib and add:

import matplotlib.pyplot as plt

def plot_temperature(data):
    dates = [entry['date'] for entry in data]
    temps = [entry['temperature'] for entry in data]
    plt.plot(dates, temps, marker='o')
    plt.title("Temperature Trend")
    plt.xlabel("Date")
    plt.ylabel("Temperature (°C)")
    plt.xticks(rotation=45)
    plt.tight_layout()
    plt.show()