Automation

Data Scraping and Cleaning Automation

Let's get you started with our automation project.

During my project, I encountered challenges with accessing accurate health data, especially in these rural places that are majorly affected. After thorough investigations, I discovered that I could automate the collection of accurate healthcare data using the tools and technologies outlined below. I will provide you with a detailed, step-by-step procedure on how to implement this automation.

Tools and Technologies

Web scraping tools like BeautifulSoup or Scrapy for extracting data from relevant websites or databases.
Data cleaning and processing tools like pandas for organizing and cleaning the extracted data.
Python or another programming language for scripting the automation process.

Steps

  • Identify reliable sources of healthcare data related to Africa.
  • Develop scripts to scrape data from these sources, ensuring accuracy and reliability.
  • Implement data cleaning processes to handle inconsistencies and errors..
  • Schedule the automation to run periodically to keep the data updated.

Python Code Example

This is an example of a Python code for the data scraping:

                    
                        import requests
                        from bs4 import BeautifulSoup
                        import pandas as pd
                
                        # Function to scrape healthcare data from a website
                        def scrape_healthcare_data(url):
                            response = requests.get(url)
                            soup = BeautifulSoup(response.text, 'html.parser')
                
                            # Your scraping logic here to extract relevant data
                
                            # Example: Extracting a table using pandas
                            table = soup.find('table')
                            df = pd.read_html(str(table))[0]
                
                            return df
                
                        # Function to clean and process the extracted data
                        def clean_healthcare_data(data_frame):
                            # Your cleaning and processing logic here
                
                            # Example: Dropping NaN values
                            cleaned_data = data_frame.dropna()
                
                            return cleaned_data
                
                        # Example usage
                        url = 'https://example-healthcare-data-site.com'
                        healthcare_data = scrape_healthcare_data(url)
                        cleaned_data = clean_healthcare_data(healthcare_data)
                
                        # Save the cleaned data to a CSV file
                        cleaned_data.to_csv('cleaned_healthcare_data.csv', index=False)

Image retrieval Automation

Another problem i encountered during py project was sourcing high-quality images that effectively showcase the beauty of Africa. In response to this issue, I am devising a solution through Image Retrieval Automation. This approach aims to streamline the process of obtaining captivating images, enhancing the visual appeal of the project. Stay tuned for a detailed explanation of the automation solution and its implementation steps

Tools and Technologies

Image scraping tools or APIs to retrieve images from platforms like Unsplash or Pixabay..
Python or another programming language for scripting the automation process
Image processing libraries (if needed) for resizing or optimizing images.

Steps

  • Identify suitable platforms or APIs that provide high-quality images of African landscapes.
  • Develop scripts to automatically retrieve and download these images.
  • Implement organization and categorization processes to match images with relevant blog content.
  • Schedule the automation to run periodically to update the collection of images.

Python Code Example

This is an example of a Python code for the image retrieval:

                        
                            import requests
                            from bs4 import BeautifulSoup
                            import pandas as pd



                            def scrape_flu_vaccination_data(url):
                            try:
                             # Send a GET request to the URL
                              response = requests.get(url)
                             response.raise_for_status()  # Raise an error for bad responses

                             # Parse the HTML content of the page
                           soup = BeautifulSoup(response.content, 'html.parser')

                            # Find the first table - adjust this as needed for the specific data table
                             table = soup.find('table')

                            if table:
                             # Convert the HTML table to a DataFrame
                           df = pd.read_html(str(table))[0]
                           return df
                             else:
                                 print("No table found on the webpage.")
                                return pd.DataFrame()
                            except requests.RequestException as e:
                                  print(f"Request failed: {e}")
                                  return pd.DataFrame()


                            def clean_data(df):
                           # Example cleaning function - adjust based on the structure of your data
                           if not df.empty:
                           # Perform data cleaning steps here, such as renaming columns, handling missing data, etc.
                            cleaned_df = df.rename(columns=lambda x: x.strip()).dropna()
                          return cleaned_df
                           else:
                            return df


                        if __name__ == "__main__":
                       # Placeholder URL - replace with the actual URL you intend to scrape
                        url = 'https://www.cdc.gov/flu/fluvaxview/coverage-2021estimates.htm'
                         flu_data = scrape_flu_vaccination_data(url)

                        if not flu_data.empty:
                         cleaned_flu_data = clean_data(flu_data)
                       print("Flu vaccination data scraped and cleaned successfully.")
                       print(cleaned_flu_data.head())
                        else:
                           print("No data scraped.")