How to Build a Data Collection Tool [+ Examples]

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Building a Data Collection Tool: Step-by-Step

For this project, I’ll guide you through the steps to build your own Walmart data collection tool using ScraperAPI with Python, helping you collect uninterrupted product data – including prices, names, details, etc.

We’ll cover the entire process, from fetching the data to exporting it in JSON format and scheduling the script to run automatically.

Step 1: Setting Up Your Environment

Before we dive into the code, you’ll need to set up your development environment. Make sure you have Python installed on your machine. You’ll also need to install the requests library:

  pip install requests

Step 2: Registering for ScraperAPI

To use ScraperAPI, you must create an account and get your API key. Visit ScraperAPI’s website and sign up for a free account. Once you have your API key, keep it handy, as you’ll need it for the next steps.

Step 3: Import Necessary Libraries

First, import the necessary libraries. These include requests for making HTTP requests, json for handling JSON data, and datetime for timestamping the exported files.

  import requests
  import json
  from datetime import datetime

Step 4: Define the API Key

Replace 'YOUR_API_KEY' with your actual ScraperAPI key. This key is used to authenticate your requests to ScraperAPI.

  # Replace 'YOUR_API_KEY' with your actual ScraperAPI key
  API_KEY = 'YOUR_API_KEY'

Step 5: Fetch the Data

Create a function named fetch_data() that takes a query as an argument and construct a request payload inside the function using this argument and your API key.

Next, send a get() request to ScraperAPI’s Walmart Search endpoint. It’ll return the JSON response if the request is successful (status code 200). If not, print an error message.

You can customize the URL to use any ScraperAPI search endpoints.

  def fetch_data(query, page):
  payload = {
      'api_key': API_KEY,
      'query': query,
  }

  # Send a request to ScraperAPI Walmart Search endpoint
  response = requests.get('https://api.scraperapi.com/structured/walmart/search', params=payload)
  if response.status_code == 200:
      return response.json()
  else:
      print(f'Error: {response.status_code}')
      return None

Step 6: Export the Collected Data to JSON

Create a function named export_to_json() that takes the fetched data and a filename as arguments, and use the json.dump() method to export the data to a JSON file with the specified filename. Make sure the file is indented for readability.

  def export_to_json(data, filename):
  with open(filename, 'w') as f:
      json.dump(data, f, indent=4)
  print(f'Data exported to {filename}')

Step 7: Create the main() Function

Create the main() function that specifies the queries, fetches the data, and exports it to a JSON file. To avoid overwriting files, include a timestamp in the filename and print a message to indicate the fetching process – this is helpful to us, letting us see what’s going on with our code.

You can edit the queries list to include any items you want to search for:

  def main():
  queries = ['wireless headphones']  
  
  for query in queries:
      print(f"Fetching data for query: '{query}'")
      data = fetch_data(query)
      
      if data:
          timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
          json_filename = f'data_{timestamp}.json'
          
          export_to_json(data, json_filename)

Step 8: Run the Script

Now that you have everything ready, run the main function when the script is executed.

  if __name__ == "__main__":
  main()

Step 9: Scheduling the Data Collection

As it is, you’ll have to manually run this data collection tool every time you want to refresh your data. However, in many cases, you’ll want to get more data overtime without having to manually run the script.

You can schedule the data collection script to run at regular intervals to keep your data up-to-date. One way to do this is using a task scheduler like cron on Linux or Task Scheduler on Windows.

Scheduling with Task Scheduler (Windows)

To automate the execution of your data collection script using Task Scheduler in Windows, follow these detailed steps:

1. Open Task Scheduler:

   Press Win + S to open the search bar, type “Task Scheduler,” and press Enter.

2. Create a New Task:

   In the Task Scheduler window, click on “Create Task” in the Actions panel on the right.

   

3. Name and Describe Your Task:

   In the “General” tab, provide a name for your task (e.g., “Data Collection Script”) and an optional description. Choose “Run whether user is logged on or not” if you want the task to run even when you’re not logged in.

   

4. Set the Trigger:

   Go to the “Triggers” tab and click “New.”

   

   Set the trigger to your desired schedule. For example, to run the script daily at midnight, choose “Daily,” set the start time to 12:00 AM, and configure the recurrence settings as needed.

   

   Click “OK” to save the trigger.

   

5. Set the Action:

   Go to the “Actions” tab and click “New.”

   

   In the “Action” dropdown, select “Start a program.”

   Click “Browse” and navigate to the location of your Python executable (e.g., C:\Python39\python.exe).

   In the “Add arguments” field, enter the path to your script file (e.g., C:\path\to\your\script.py).

   

   Click “OK” to save the action.

6. Save and Run the Task:

   Click “OK” to save the task.

   You will be prompted to enter your password if you selected “Run whether user is logged on or not.”

   To test your task, right-click on it in the Task Scheduler library and select “Run.”

By following these steps, you can automate your data collection tasks, ensuring that your data collection tool runs on a regular schedule without manual intervention.

ScraperAPI simplifies the process of bypassing anti-scraping measures and accessing structured data from Walmart. By integrating it into a scheduled task, you can continuously collect up-to-date data for analysis, reporting, or integration into other systems. This approach not only saves time but also enhances the efficiency and reliability of your data collection efforts.