Intro to Computer Science - Python | Khan Academy - Program design: emergency response | Intro to CS - Python | Khan Academy

Code along with a software engineer in this worked example using nested data. Analyze a dataset of emergency response incidents to identify the most common incident types and busiest hours of the day. Iterate over a list of dictionaries and apply data transformation to create new data structures better suited to the use case. View the program used in this video at: https://www.khanacademy.org/python-program/program-design-emergency-response/4505398963978240 Courses on Khan Academy are always 100% free. Start practicing—and saving your progress—now with our Intro to Computer Science - Python course! Course link: https://www.khanacademy.org/computing/intro-to-python-fundamentals/x5279a44ae0ab15d6:analyzing-data-with-dictionaries Course playlist: https://www.youtube.com/playlist?list=PLSQl0a2vh4HDkbhG0sDW0b-VZXykEIAe5 Khan Academy is a nonprofit organization with the mission of providing a free, world-class education for anyone, anywhere. We offer quizzes, questions, instructional videos, and articles on a range of academic subjects, including math, biology, chemistry, physics, history, economics, finance, grammar, preschool learning, and more. We provide teachers with tools and data so they can help their students develop the skills, habits, and mindsets for success in school and beyond. Khan Academy has been translated into dozens of languages, and 15 million people around the globe learn on Khan Academy every month. As a 501(c)(3) nonprofit organization, we would love your help! Donate or volunteer today! Donate here: https://www.khanacademy.org/donate?utm_source=youtube&utm_medium=desc Volunteer here: https://www.khanacademy.org/contribute?utm_source=youtube&utm_medium=desc TIMESTAMPS: 00:00 : incident response dataset 00:41 : count incidents by type 01:12 : data transformation 02:03 : KeyError - missing type 02:58 : normalize the type 03:50 : busiest hour - break down the problem 04:33 : count incidents by hour 05:16 : find busiest hour - max value 05:58 : final data insights