Airlines come and go. Some operate for only a few years befo…
Airlines come and go. Some operate for only a few years before financial pressures force them to close; others endure for decades, surviving recessions, deregulation, fuel crises, and pandemics. The airlines.csv file (right-click, open in new tab or window) contains information about commercial airlines from around the world. The columns are labeled in the file. The ceased column contains 0 for airlines still operating at the time the data file was compiled. Write a function named top_carriers that accepts four arguments: a file name, a beginning year, an ending year, and a number n. Return a pandas DataFrame containing the name, country, founded, ceased, and tenure (defined as ceased – founded) of the n longest-operating airlines that ceased operations between the beginning year and ending year (inclusive). Include in your analysis only those airlines whose operations have ended. For full credit, your function should use pandas concepts and techniques to calculate and return the result without using loops or list comprehensions. In : top_carriers(‘airlines.csv’, 1920, 2025, 3) Out: name country founded ceased tenure 35 Czech Airlines Czech Republic 1923 2024 101 62 Mexicana Mexico 1921 2010 89 72 Northwest Airlines USA 1926 2008 82 In : top_carriers(‘airlines.csv’, 2006, 2010, 3) Out: name country founded ceased tenure 62 Mexicana Mexico 1921 2010 89 72 Northwest Airlines USA 1926 2008 82 14 Aloha Airlines USA 1946 2008 62 In : top_carriers(‘airlines.csv’, 2000, 2025, 5) Out: name country founded ceased tenure 35 Czech Airlines Czech Republic 1923 2024 101 62 Mexicana Mexico 1921 2010 89 72 Northwest Airlines USA 1926 2008 82 34 Continental Airlines USA 1934 2012 78 83 Sabena Belgium 1923 2001 78 In : top_carriers(‘airlines.csv’, 1973, 1975, 5) Out: name country founded ceased tenure 22 BOAC UK 1940 1974 34 27 British European Airways UK 1946 1974 28