Challenge: Identify Concurrency Opportunities
Imagine you are developing a Python application that automates data handling for a research team. The application has three main tasks: first, it downloads several large CSV files from different remote servers; second, it processes each downloaded file to clean and transform the data; third, it generates summary statistics and plots from the processed data and saves the results to disk. Each file is independent—downloading one does not depend on another, and processing or analyzing one file does not depend on others.
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Write a function identify_concurrency_opportunities(tasks) that takes a list of task descriptions as strings. For each task, determine if it could be run concurrently with the others, and explain why. Return a dictionary mapping each task description to either "concurrent" or "sequential" along with a brief explanation (as a string) for your choice.
- For each item in the
taskslist, decide if it is independent of the others and could be performed at the same time (concurrent), or if it must wait for another task to complete (sequential). - Add a key for each task in the returned dictionary. The value should be a tuple: the first element is either the string
"concurrent"or"sequential", and the second element is your explanation. - Example output:
{ "Download CSV files": ("concurrent", "Each file can be downloaded independently."), "Process files": ("concurrent", "Each file can be processed independently after download."), "Generate statistics and plots": ("concurrent", "Analysis for each file can be done independently after processing.") }
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Can you suggest how to structure the code for these three tasks?
What Python libraries would be best for downloading, processing, and plotting the data?
How can I make the application efficient when handling multiple large files?
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Completion rate improved to 6.25Challenge: Identify Concurrency Opportunities
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Imagine you are developing a Python application that automates data handling for a research team. The application has three main tasks: first, it downloads several large CSV files from different remote servers; second, it processes each downloaded file to clean and transform the data; third, it generates summary statistics and plots from the processed data and saves the results to disk. Each file is independent—downloading one does not depend on another, and processing or analyzing one file does not depend on others.
Swipe to start coding
Write a function identify_concurrency_opportunities(tasks) that takes a list of task descriptions as strings. For each task, determine if it could be run concurrently with the others, and explain why. Return a dictionary mapping each task description to either "concurrent" or "sequential" along with a brief explanation (as a string) for your choice.
- For each item in the
taskslist, decide if it is independent of the others and could be performed at the same time (concurrent), or if it must wait for another task to complete (sequential). - Add a key for each task in the returned dictionary. The value should be a tuple: the first element is either the string
"concurrent"or"sequential", and the second element is your explanation. - Example output:
{ "Download CSV files": ("concurrent", "Each file can be downloaded independently."), "Process files": ("concurrent", "Each file can be processed independently after download."), "Generate statistics and plots": ("concurrent", "Analysis for each file can be done independently after processing.") }
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