Challenge: Batch Process Survey Results
Batch processing is a cornerstone of efficient government data analysis, especially when dealing with large-scale survey results. Survey data from multiple regions can quickly become overwhelming, and manual calculation of statistics like average satisfaction scores is both time-consuming and error-prone. Automating these processes allows analysts to focus on interpreting results and making data-driven decisions rather than spending valuable time on repetitive calculations.
1234567# Example dataset: survey results by region survey_data = [ {"region": "North", "responses": [4, 5, 3, 4]}, {"region": "South", "responses": [2, 3, 3, 2, 4]}, {"region": "East", "responses": [5, 5, 4]}, {"region": "West", "responses": [3, 4]} ]
When working with this kind of dataset, you need to systematically process each region's responses to compute summary statistics. Iterating over the list of dictionaries allows you to access each region and its list of satisfaction scores. By calculating the average for each list of scores and storing the result in a new dictionary, you create a mapping of regions to their average satisfaction. This approach ensures that your analysis is both scalable and consistent, regardless of the number of regions or responses.
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Given a list of dictionaries where each dictionary contains a region and a list of responses representing satisfaction scores, your goal is to return a dictionary mapping each region to its average satisfaction score.
- For each dictionary in
survey_data, access theregionand its associatedresponseslist. - Calculate the average of the satisfaction scores in the
responseslist. - Store the average in a new dictionary, using the region name as the key.
- If a region has an empty
responseslist, assignNoneas its average. - Return the dictionary mapping regions to their average satisfaction scores.
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Batch processing is a cornerstone of efficient government data analysis, especially when dealing with large-scale survey results. Survey data from multiple regions can quickly become overwhelming, and manual calculation of statistics like average satisfaction scores is both time-consuming and error-prone. Automating these processes allows analysts to focus on interpreting results and making data-driven decisions rather than spending valuable time on repetitive calculations.
1234567# Example dataset: survey results by region survey_data = [ {"region": "North", "responses": [4, 5, 3, 4]}, {"region": "South", "responses": [2, 3, 3, 2, 4]}, {"region": "East", "responses": [5, 5, 4]}, {"region": "West", "responses": [3, 4]} ]
When working with this kind of dataset, you need to systematically process each region's responses to compute summary statistics. Iterating over the list of dictionaries allows you to access each region and its list of satisfaction scores. By calculating the average for each list of scores and storing the result in a new dictionary, you create a mapping of regions to their average satisfaction. This approach ensures that your analysis is both scalable and consistent, regardless of the number of regions or responses.
Swipe to start coding
Given a list of dictionaries where each dictionary contains a region and a list of responses representing satisfaction scores, your goal is to return a dictionary mapping each region to its average satisfaction score.
- For each dictionary in
survey_data, access theregionand its associatedresponseslist. - Calculate the average of the satisfaction scores in the
responseslist. - Store the average in a new dictionary, using the region name as the key.
- If a region has an empty
responseslist, assignNoneas its average. - Return the dictionary mapping regions to their average satisfaction scores.
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