Imagine you're developing a weather analysis program in Python. Write a program that reads a file named "WeatherData.txt" and accomplishes the following tasks: Display the average temperature for each month. Identify and display the hottest day and the coldest day in the dataset. Calculate and display the total precipitation for each season (Spring, Summer, Fall, Winter). Create a histogram of wind speeds, categorizing them into ranges (e.g., 0-5 mph, 6-10 mph, 11-15 mph, and so on) and displaying the frequency of each range. Store all the unique cities mentioned in the dataset in a Python set. Output the number of days with rain and the number of days with snow in the dataset. WeatherData.txt:

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Imagine you're developing a weather analysis
program in Python. Write a program that reads a
file named "WeatherData.txt" and accomplishes the
following tasks:
Display the average temperature for each month.
Identify and display the hottest day and the coldest
day in the dataset.
Calculate and display the total precipitation for each
season (Spring, Summer, Fall, Winter).
Create a histogram of wind speeds, categorizing
them into ranges (e.g., 0-5 mph, 6-10 mph, 11-15
mph, and so on) and displaying the frequency of
each range.
Store all the unique cities mentioned in the dataset
in a Python set.
Output the number of days with rain and the
number of days with snow in the dataset.
WeatherData.txt:
Date, City, Temperature (F), Precipitation (in), Wind
Speed (mph)
2023-01-01, New York, 32, 0.1, 8
2023-01-01, Los Angeles, 75, 0, 15
2023-02-15, Chicago, 20, 0.5, 12
2023-03-10, Miami, 82, 0, 10
2023-04-05, Seattle, 50, 1.2, 18
2023-05-20, Dallas, 68, 0, 5
2023-06-15, Denver, 90, 0.8, 22
2023-07-03, Boston, 80, 0, 14
2023-08-22, San Francisco, 65, 0.3, 10
2023-09-10, Atlanta, 75, 0, 8
2023-10-30, Phoenix, 95, 0, 25
2023-11-12, Minneapolis, 30, 0.4, 15
2023-12-25, Honolulu, 82, 0, 12
Transcribed Image Text:Imagine you're developing a weather analysis program in Python. Write a program that reads a file named "WeatherData.txt" and accomplishes the following tasks: Display the average temperature for each month. Identify and display the hottest day and the coldest day in the dataset. Calculate and display the total precipitation for each season (Spring, Summer, Fall, Winter). Create a histogram of wind speeds, categorizing them into ranges (e.g., 0-5 mph, 6-10 mph, 11-15 mph, and so on) and displaying the frequency of each range. Store all the unique cities mentioned in the dataset in a Python set. Output the number of days with rain and the number of days with snow in the dataset. WeatherData.txt: Date, City, Temperature (F), Precipitation (in), Wind Speed (mph) 2023-01-01, New York, 32, 0.1, 8 2023-01-01, Los Angeles, 75, 0, 15 2023-02-15, Chicago, 20, 0.5, 12 2023-03-10, Miami, 82, 0, 10 2023-04-05, Seattle, 50, 1.2, 18 2023-05-20, Dallas, 68, 0, 5 2023-06-15, Denver, 90, 0.8, 22 2023-07-03, Boston, 80, 0, 14 2023-08-22, San Francisco, 65, 0.3, 10 2023-09-10, Atlanta, 75, 0, 8 2023-10-30, Phoenix, 95, 0, 25 2023-11-12, Minneapolis, 30, 0.4, 15 2023-12-25, Honolulu, 82, 0, 12
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