Modified multiprocessing
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+3
-2
@@ -40,6 +40,7 @@ def run_scraper(url):
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# scraping logic
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# scraping logic
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df = pd.DataFrame(columns=['Product ID', 'Zip Code', 'Shown Price', 'Minimum Quantity', 'Total Price', 'Unit Price','Sales Count','Sold Status'])
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df = pd.DataFrame(columns=['Product ID', 'Zip Code', 'Shown Price', 'Minimum Quantity', 'Total Price', 'Unit Price','Sales Count','Sold Status'])
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print(f"Created a dataframe: {df}")
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print(f"Created a dataframe: {df}")
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product_id = url.split("/")[-1][:5]
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options = Options()
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options = Options()
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options.add_argument('--disable-gpu')
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options.add_argument('--disable-gpu')
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@@ -236,7 +237,7 @@ def run_scraper(url):
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print(f"no messages, trying again at {time.asctime()}")
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print(f"no messages, trying again at {time.asctime()}")
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break
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break
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# subprocess.call(['python', 'fp2_13.py', url])
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# subprocess.call(['python', 'fp2_13.py', url])
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return run_scraper(url)
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return df
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if __name__ == '__main__':
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if __name__ == '__main__':
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# Determine the number of available CPUs
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# Determine the number of available CPUs
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@@ -266,7 +267,7 @@ pool.join()
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with pd.HDFStore('product_data.h5') as store:
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with pd.HDFStore('product_data.h5') as store:
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for url, result in zip(url_list, results):
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for url, result in zip(url_list, results):
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# Extract the product ID from the URL
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# Extract the product ID from the URL
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product_id = url.split("/")[-1].split("?")[0]
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product_id = url.split("/")[-1][:5]
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# Save the dataframe to the HDF5 file with a unique key
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# Save the dataframe to the HDF5 file with a unique key
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key = f'df_{product_id}'
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key = f'df_{product_id}'
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+2
-1
@@ -57,6 +57,7 @@ fig.write_html(report_file)
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'''
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'''
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'''
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# Calculate the standard deviation using pandas' function
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# Calculate the standard deviation using pandas' function
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std_dev_unit = df.groupby('Product ID')['Unit Price'].std()
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std_dev_unit = df.groupby('Product ID')['Unit Price'].std()
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@@ -69,5 +70,5 @@ fig.update_layout(title='Standard Deviation of the Unit Pricing by Item', xaxis_
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report_file = 'test2.html'
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report_file = 'test2.html'
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fig.write_html(report_file)
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fig.write_html(report_file)
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'''
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