Clаss оf respirаtоry drug knоwn аs 'mucus-breaking" drugs.
When аre аntivirаls cоntraindicated?
The file mn_crаft_beverаges.csv cоntаins 51 prоducts frоm Minnesota craft breweries, distilleries, and cideries. Each row lists the producer name, city, type of producer (Brewery, Distillery, or Cidery), style of beverage, ABV (alcohol by volume), price per pint, annual production volume, and whether the product is seasonal. Some producers have unknown production volume, represented in the file by a dash (-). Write a function named popular_categories that accepts four arguments: a file name, a dictionary that maps beverage styles to broader categories, a minimum production volume, and a minimum count. Use the dictionary to map each product's style to a category. Considering only products whose annual volume is at least the minimum, count the number of products in each category and return a Series containing only the categories whose count meets or exceeds the minimum count. Open this problem in Vocareum to write and submit your solution. A few ground rules while you work: Keep this Canvas quiz and Honorlock open for the duration of the problem. Do not close or submit the quiz until you are finished in Vocareum. You do not need to write or submit anything in Canvas. We will check Vocareum for your solution. You may submit and test your solution in Vocareum as many times as you like before the quiz time limit expires. Once the quiz ends, Vocareum will lock and you will not be able to make further changes. The data file is available in your Vocareum workspace. You do not need to upload it. It is linked above only for your reference. For full credit, your function should use pandas concepts and techniques to calculate and return the result without using loops or list comprehensions. The following dictionary maps the 22 styles in the file to 6 categories: category = { 'IPA': 'Hoppy', 'Pale Ale': 'Hoppy', 'Stout': 'Dark', 'Red Ale': 'Dark', 'Belgian': 'Dark', 'Lager': 'Light', 'Pilsner': 'Light', 'Blonde': 'Light', 'Sour': 'Specialty', 'Fruit Cider': 'Specialty', 'Hopped Cider': 'Specialty', 'Dry Cider': 'Cider', 'Semi-Sweet Cider': 'Cider', 'Gin': 'Spirit', 'Vodka': 'Spirit', 'Bourbon': 'Spirit', 'Rye': 'Spirit', 'Whiskey': 'Spirit', 'Rum': 'Spirit', 'Aquavit': 'Spirit', 'Brandy': 'Spirit', 'Liqueur': 'Spirit' } In [1]: popular_categories('mn_craft_beverages.csv', category, 5000, 3) Out[1]: category Light 9 Hoppy 7 Spirit 4 Dark 3 Name: count, dtype: int64 In [2]: popular_categories('mn_craft_beverages.csv', category, 10000, 3) Out[2]: category Light 7 Hoppy 5 Name: count, dtype: int64 In [3]: popular_categories('mn_craft_beverages.csv', category, 5000, 5) Out[3]: category Light 9 Hoppy 7 Name: count, dtype: int64 Notice that the first and third calls use the same minimum volume (5,000) but different minimum counts. At a minimum count of 3, four categories qualify. At a minimum count of 5, only Light and Hoppy remain. The second call raises the minimum volume to 10,000, which excludes smaller producers entirely and reduces the result to two categories.