Create NaasaScrap.py

[Hema-Mallika]

import requests
import csv
import matplotlib.pyplot as plt

class NasaEarthDataScraper:
def init(self, api_url, output_csv, output_image=None):
self.api_url = api_url
self.output_csv = output_csv
self.output_image = output_image

def scrape_data(self):
    try:
        # Send a GET request to the NASA Earth Data API
        response = requests.get(self.api_url)

        # Check if the request was successful (status code 200)
        if response.status_code == 200:
            data = response.json()  # Assuming the response is in JSON format

            # Extract relevant data from the response (modify as per your needs)
            # For example, if the JSON response has a 'data' key:
            extracted_data = data.get('data', [])

            # Save the data to a CSV file
            with open(self.output_csv, 'w', newline='') as csvfile:
                fieldnames = []  # Define the fieldnames as per your data structure
                writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
                writer.writeheader()

                for item in extracted_data:
                    # Process and format the data as needed before writing to CSV
                    # Example: writer.writerow({'column1': item['field1'], 'column2': item['field2']})
                    pass

            print("Data scraped and saved to", self.output_csv)

            # Visualize the data as a pie chart or graph (optional)
            if self.output_image:
                self.visualize_data(extracted_data)

        else:
            print("Failed to retrieve data. Status code:", response.status_code)

    except Exception as e:
        print("An error occurred:", str(e))

def visualize_data(self, data):
    # Perform data visualization using Matplotlib (you can customize this)
    # Example: Create a pie chart
    labels = ['Category 1', 'Category 2', 'Category 3']  # Modify with your data categories
    sizes = [10, 30, 20]  # Modify with data values

    plt.pie(sizes, labels=labels, autopct='%1.1f%%')
    plt.title('Data Distribution')
    plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.

    # Save the visualization as an image
    plt.savefig(self.output_image)
    print("Data visualization saved as", self.output_image)

Usage

if name == "main":
# Define the NASA Earth Data API URL you want to scrape data from
api_url = "https://www.earthdata.nasa.gov/api-endpoint"

# Define the output CSV file where the scraped data will be saved
output_csv = "nasa_earth_data.csv"

# Define the output image file for data visualization (optional)
output_image = "data_visualization.png"

# Create an instance of the scraper and initiate data scraping
scraper = NasaEarthDataScraper(api_url, output_csv, output_image)
scraper.scrape_data()