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Project Overview analyze datasets containing weather information for multiple cities, specifically in Paris. The datasets include maximum and minimum temperatures, precipitation, wind speed, humidity, and

Project Overview
analyze datasets containing weather information for multiple cities, specifically in Paris. The datasets include maximum and minimum temperatures, precipitation, wind speed, humidity, and cloud cover. Additionally, the Paris dataset incorporates climate change-related factors, including CO2 levels and rising sea levels. The overar ching goal is to gain insights into the impact of weather patterns and climate change and leverage this knowledge in creating a startup.
Task 1: Data Loading and Inspection
1. Load the provided dataset for Paris and multiple cities.
Task 2: City-wise Analysis
1. For each city, calculate and display the following statistics:
Average temperatures.
Total precipitation.
Maximum and minimum wind speed.
Task 3: Visualization
1. Make a plot showing the temperature over time for each city.
2. Create a bar chart to compare the average temperatures for different cities.
Task 4: Conditional Statements
1. Implement a function that categorizes a day as Cold,Moderate, or
Warm based on the average temperature for a selected city. Apply this
function to every city and display the results.
2. Implement a Python function that identifies a selected citys hottest and
coldest days based on maximum and minimum temperatures.
3. Apply this function to every city in the dataset and display the dates and
temperature details for the hottest and coldest days.
4. Discuss potential factors contributing to extreme temperatures in each
city.
5. Calculate the overall hottest day considering the maximum temperatures
across all ten cities and Identify the date and temperature details of this
hottest day.
6. Do the same for the coldest day
Given the temperature is following:
Date,Location,Max Temperature (C),Min Temperature (C),Precipitation (mm),Wind Speed (km/h),Humidity (%),Cloud Cover (%)
2023-01-01,New York,5,-2,10,20,70,40
2023-01-02,New York,4,-3,8,18,75,30
2023-01-03,New York,8,1,5,15,65,20
2023-01-04,New York,7,0,3,12,80,45
2023-01-05,New York,6,-1,2,10,85,50
2023-01-06,New York,3,-4,12,22,78,35
2023-01-07,New York,2,-5,15,25,82,30
2023-01-08,New York,1,-6,10,18,88,40
2023-01-09,New York,4,-3,8,20,85,38
2023-01-10,New York,6,0,5,15,75,42
2023-01-11,New York,7,1,2,14,70,38
2023-01-12,New York,5,-2,4,18,72,36
2023-01-13,New York,3,-4,6,16,78,40
2023-01-14,New York,2,-5,8,20,80,45
2023-01-15,New York,4,-3,10,22,75,35
2023-01-16,New York,6,0,12,18,68,30
2023-01-17,New York,7,1,15,15,62,28
2023-01-18,New York,5,-2,10,20,70,40
2023-01-19,New York,4,-3,8,18,75,30
2023-01-20,New York,8,1,5,15,65,20
2023-01-21,New York,7,0,3,12,80,45
2023-01-22,New York,6,-1,2,10,85,50
2023-01-23,New York,3,-4,12,22,78,35
2023-01-24,New York,2,-5,15,25,82,30
2023-01-25,New York,1,-6,10,18,88,40
2023-01-26,New York,4,-3,8,20,85,38
2023-01-27,New York,6,0,5,15,75,42
2023-01-28,New York,7,1,2,14,70,38
2023-01-29,New York,5,-2,4,18,72,36
2023-01-30,New York,3,-4,6,16,78,40
2023-01-31,New York,2,-5,8,20,80,45
2023-01-01,Los Angeles,20,12,0,15,60,10
2023-01-02,Los Angeles,22,14,0,18,55,5
2023-01-03,Los Angeles,25,15,0,20,50,8
2023-01-04,Los Angeles,21,13,0,16,58,12
2023-01-05,Los Angeles,19,11,0,14,62,15
2023-01-06,Los Angeles,18,10,0,12,65,18
2023-01-07,Los Angeles,20,12,0,15,68,20
2023-01-08,Los Angeles,22,14,0,18,70,22
2023-01-09,Los Angeles,24,16,0,20,72,25
2023-01-10,Los Angeles,23,15,0,16,75,28
2023-01-11,Los Angeles,21,13,0,14,78,30
2023-01-12,Los Angeles,20,12,0,15,80,32
2023-01-13,Los Angeles,22,14,0,18,82,35
2023-01-14,Los Angeles,25,15,0,20,85,38
2023-01-15,Los Angeles,23,14,0,16,88,40
2023-01-16,Los Angeles,21,13,0,14,90,42
2023-01-17,Los Angeles,20,12,0,15,85,35
2023-01-18,Los Angeles,22,14,0,18,80,30
2023-01-19,Los Angeles,25,15,0,20,75,25
2023-01-20,Los Angeles,21,13,0,16,70,22
2023-01-21,Los Angeles,19,11,0,14,68,20
2023-0

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