Challenge: NEAZYIT submission. #168
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- Introduced a modular data processing pipeline using Dask for efficient handling of large datasets.
- Implemented the following functions for distinct tasks:
1.
ead_data(input_file): Reads CSV data using Dask, handling decoding issues and creating a Dask DataFrame.
2. �alidate_prices(data): Filters out prices outside the range of 1.00 to 100.00.
3. clean_data(data): Removes missing values and duplicate entries, providing a cleaned Dask DataFrame.
4. group_and_aggregate(data): Groups data by city and computes the total prices, returning a Dask Series.
5. sort_and_select_top_products(data, cheapest_city): Filters data for the cheapest city, sorts, and selects the top 5 products.
6. write_results(output_file, cheapest_city, total_price, sorted_products): Writes results to an output file.
- The main process_data(input_file, output_file) function orchestrates these steps, ensuring a structured flow.
- Implemented a logging system that captures errors and important information for debugging and monitoring.
- Maintained clarity and adherence to best practices for readability and maintainability.
To test, run the provided example with 'input.txt' as the input file and 'output.txt' as the output file.
- Introduced a modular data processing pipeline using Dask for efficient handling of large datasets.
- Implemented the following functions for distinct tasks:
1.
ead_data(input_file): Reads CSV data using Dask, handling decoding issues and creating a Dask DataFrame.
2. �alidate_prices(data): Filters out prices outside the range of 1.00 to 100.00.
3. clean_data(data): Removes missing values and duplicate entries, providing a cleaned Dask DataFrame.
4. group_and_aggregate(data): Groups data by city and computes the total prices, returning a Dask Series.
5. sort_and_select_top_products(data, cheapest_city): Filters data for the cheapest city, sorts, and selects the top 5 products.
6. write_results(output_file, cheapest_city, total_price, sorted_products): Writes results to an output file.
- The main process_data(input_file, output_file) function orchestrates these steps, ensuring a structured flow.
- Implemented a logging system that captures errors and important information for debugging and monitoring.
- Maintained clarity and adherence to best practices for readability and maintainability.
To test, run the provided example with 'input.txt' as the input file and 'output.txt' as the output file.
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Please clean up your PR! You are adding too many unnecessary files. Follow the submission guide in the readme |
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I have cleaned up my PR. Could you please check it now? |
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