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A data analyst has loaded transaction records from BigQuery into a pandas DataFrame within a Colab Enterprise notebook. The dataset includes continuous numerical transaction amounts across several store departments. The analyst needs to visualize the statistical distribution of transaction amounts for each department to easily compare the median, interquartile ranges, and identify potential outliers.
Which Python visualization approach should the analyst use to meet these requirements?
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