Please help me correct the following code according to the following questions and provided data(): 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

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 2023-01-22,Los Angeles,18,10,0,12,65,18 2023-01-23,Los Angeles,20,12,0,15,62,15 2023-01-24,Los Angeles,22,14,0,18,58,12 2023-01-25,Los Angeles,24,16,0,20,55,10 2023-01-26,Los Angeles,23,15,0,16,50,8 2023-01-27,Los Angeles,21,13,0,14,48,12 2023-01-28,Los Angeles,20,12,0,15,52,15 2023-01-29,Los Angeles,22,14,0,18,55,18 2023-01-30,Los Angeles,25,15,0,20,58,20 2023-01-31,Los Angeles,23,14,0,16,60,22 2023-01-01,London,8,3,5,25,85,80 2023-01-02,London,6,1,7,20,90,85 2023-01-03,London,10,4,3,18,75,75 2023-01-04,London,7,2,2,22,80,70 2023-01-05,London,9,5,6,15,78,78 2023-01-06,London,5,0,8,18,82,82 2023-01-07,London,4,-1,10,20,85,85 2023-01-08,London,7,2,5,16,78,75 2023-01-09,London,8,3,3,14,72,70 2023-01-10,London,6,1,1,12,68,68 2023-01-11,London,9,4,4,18,75,72 2023-01-12,London,11,6,2,20,80,78 2023-01-13,London,10,5,1,22,85,80 2023-01-14,London,8,3,3,15,88,85 2023-01-15,London,6,1,5,12,90,88 2023-01-16,London,5,0,7,16,82,82 2023-01-17,London,9,4,8,20,78,75 2023-01-18,London,11,6,6,18,75,70 2023-01-19,London,10,5,4,14,70,68 2023-01-20,London,8,3,2,12,68,65 2023-01-21,London,7,2,1,15,72,72 2023-01-22,London,10,4,3,18,75,78 2023-01-23,London,12,6,5,20,80,80 2023-01-24,London,11,5,7,22,85,85 2023-01-25,London,9,4,8,16,88,88 2023-01-26,London,8,3,6,14,90,82 2023-01-27,London,7,2,4,18,85,78 2023-01-28,London,10,5,2,20,80,75 2023-01-29,London,12,6,1,16,75,70 2023-01-30,London,11,5,3,14,72,68 2023-01-31,London,9,4,5,18,78,72 2023-01-01,Tokyo,12,5,0.5,10,55,20 2023-01-02,Tokyo,14,7,0.2,12,60,15 2023-01-03,Tokyo,16,8,0,14,58,12 2023-01-04,Tokyo,15,6,0,16,62,18 2023-01-05,Tokyo,13,4,0.1,18,65,25 2023-01-06,Tokyo,10,3,0.2,15,68,22 2023-01-07,Tokyo,9,2,0.3,14,70,20 2023-01-08,Tokyo,11,4,0.1,16,72,18 2023-01-09,Tokyo,13,6,0,18,75,15 2023-01-10,Tokyo,12,5,0,20,78,12 2023-01-11,Tokyo,10,3,0,14,80,10 2023-01-12,Tokyo,8,1,0,12,85,8 2023-01-13,Tokyo,7,0,0,10,88,10 2023-01-14,Tokyo,9,2,0,15,90,12 2023-01-15,Tokyo,11,4,0,18,85,15 2023-01-16,Tokyo,13,6,0.1,20,80,18 2023-01-17,Tokyo,12,5,0.2,16,75,20 2023-01-18,Tokyo,10,3,0.3,14,72,22 2023-01-19,Tokyo,8,1,0.1,18,68,25 2023-01-20,Tokyo,7,0,0.2,20,70,28 2023-01-21,Tokyo,9,2,0,14,75,30 2023-01-22,Tokyo,11,4,0.1,16,78,32 2023-01-23,Tokyo,13,6,0.2,18,80,35 2023-01-24,Tokyo,12,5,0.3,22,82,38 2023-01-25,Tokyo,10,3,0.1,20,85,40 2023-01-26,Tokyo,8,1,0,18,88,42 2023-01-27,Tokyo,7,0,0.2,15,90,45 2023-01-28,Tokyo,9,2,0.3,12,85,48 2023-01-29,Tokyo,11,4,0.1,14,82,50 2023-01-30,Tokyo,13,6,0,16,78,45 2023-01-31,Tokyo,12,5,0.1,18,75,42 2023-01-01,Beijing,-2,-8,1,10,45,5 2023-01-02,Beijing,-3,-10,0.5,12,40,8 2023-01-03,Beijing,1,-5,0,15,50,10 2023-01-04,Beijing,-1,-7,0,14,48,12 2023-01-05,Beijing,-5,-12,0,18,42,15 2023-01-06,Beijing,-3,-9,0.2,10,44,10 2023-01-07,Beijing,-1,-6,0.5,12,38,12 2023-01-08,Beijing,2,-4,0,14,42,8 2023-01-09,Beijing,0,-8,0,16,38,10 2023-01-10,Beijing,3,-3,0.1,18,40,7 2023-01-11,Beijing,1,-7,0,14,36,9 2023-01-12,Beijing,-2,-10,0.5,12,38,12 2023-01-13,Beijing,-1,-6,0,14,40,8 2023-01-14,Beijing,3,-2,0.2,16,36,10 2023-01-15,Beijing,4,0,0.1,18,38,7 2023-01-16,Beijing,2,-4,0,14,36,9 2023-01-17,Beijing,1,-8,0.5,12,40,8 2023-01-18,Beijing,3,-3,0,16,38,10 2023-01-19,Beijing,0,-9,0.1,14,36,9 2023-01-20,Beijing,2,-5,0.2,18,40,7 2023-01-21,Beijing,4,1,0,14,36,9 2023-01-22,Beijing,3,-3,0.5,12,38,8 2023-01-23,Beijing,1,-7,0,14,40,10 2023-01-24,Beijing,-2,-10,0,16,36,9 2023-01-25,Beijing,0,-8,0.2,14,38,10 2023-01-26,Beijing,3,-2,0,16,40,8 2023-01-27,Beijing,4,1,0.1,18,36,10 2023-01-28,Beijing,2,-4,0.2,14,38,7 2023-01-29,Beijing,1,-7,0,16,36,9 2023-01-30,Beijing,3,-3,0.5,12,40,8 2023-01-31,Beijing,0,-9,0,14,38,10 2023-01-01,Sydney,30,20,0,15,40,10 2023-01-02,Sydney,28,18,0,18,45,15 2023-01-03,Sydney,32,22,1,20,38,8 2023-01-04,Sydney,29,19,0.5,16,42,12 2023-01-05,Sydney,31,21,0,14,35,5 2023-01-06,Sydney,33,23,0,16,36,7 2023-01-07,Sydney,30,20,0,14,38,9 2023-01-08,Sydney,29,19,0.2,18,40,8 2023-01-09,Sydney,28,18,0.5,20,37,10 2023-01-10,Sydney,32,22,0,15,42,6 2023-01-11,Sydney,31,21,0,16,39,8 2023-01-12,Sydney,29,19,0,14,41,7 2023-01-13,Sydney,30,20,0,18,38,9 2023-01-14,Sydney,33,23,0,20,35,5 2023-01-15,Sydney,34,24,0,15,37,8 2023-01-16,Sydney,32,22,0.1,16,39,7 2023-01-17,Sydney,31,21,0,14,40,6 2023-01-18,Sydney,29,19,0,18,38,8 2023-01-19,Sydney,28,18,0.3,20,36,7 2023-01-20,Sydney,30,20,0,15,42,5 2023-01-21,Sydney,33,23,0,16,39,8 2023-01-22,Sydney,34,24,0,18,35,6 2023-01-23,Sydney,32,22,0,20,37,7 2023-01-24,Sydney,31,21,0,15,38,9 2023-01-25,Sydney,29,19,0,14,41,6 2023-01-26,Sydney,30,20,0,16,40,8 2023-01-27,Sydney,33,23,0,18,36,5 2023-01-28,Sydney,34,24,0.2,20,38,7 2023-01-29,Sydney,32,22,0,15,39,6 2023-01-30,Sydney,31,21,0,16,37,8 2023-01-31,Sydney,29,19,0.1,18,40,7 2023-01-01,Paris,6,1,3,22,88,75 2023-01-02,Paris,4,-2,5,18,92,80 2023-01-03,Paris,7,2,2,20,80,70 2023-01-04,Paris,5,0,1,24,85,78 2023-01-05,Paris,8,3,4,18,78,68 2023-01-06,Paris,6,1,3,22,85,75 2023-01-07,Paris,4,-2,2,20,90,80 2023-01-08,Paris,7,2,1,18,82,72 2023-01-09,Paris,5,0,3,24,88,78 2023-01-10,Paris,9,4,2,18,75,70 2023-01-11,Paris,8,3,4,20,80,68 2023-01-12,Paris,6,1,2,22,85,72 2023-01-13,Paris,7,2,1,18,82,70 2023-01-14,Paris,5,0,3,24,88,75 2023-01-15,Paris,8,3,2,18,75,68 2023-01-16,Paris,9,4,1,20,78,72 2023-01-17,Paris,6,1,4,22,82,78 2023-01-18,Paris,7,2,2,18,80,70 2023-01-19,Paris,5,0,1,24,85,68 2023-01-20,Paris,8,3,3,18,75,72 2023-01-21,Paris,9,4,2,20,80,68 2023-01-22,Paris,6,1,4,22,85,75 2023-01-23,Paris,7,2,1,18,82,72 2023-01-24,Paris,5,0,3,24,88,70 2023-01-25,Paris,8,3,2,18,75,78 2023-01-26,Paris,9,4,1,20,78,70 2023-01-27,Paris,6,1,4,22,82,72 2023-01-28,Paris,7,2,2,18,80,68 2023-01-29,Paris,5,0,1,24,85,72 2023-01-30,Paris,8,3,3,18,75,68 2023-01-31,Paris,9,4,2,20,80,75 #Group Project #Task 1 import numpy as np data = np.loadtxt('data_temperature.txt', delimiter=',', usecols =[1,2,3,4,5,6,7], dtype='float', skiprows = 1)

#Task 2 #Temperature: def filter_by_city(data, city_name): return data[data[:, 1] == city_name]

def calculate_average(city_data): max_temps = city_data[:, 2].astype(float) min_temps = city_data[:, 3].astype(float) average_temp = (max_temps + min_temps) / 2 result = average_temp.mean() return result

def total_precipitation(city_data): total_precipitation = city_data[:, 4].astype(float).sum() return total_precipitation

def max_wind_speed(city_data): return city_data[:, 5].astype(float).max() def min_wind_speed(city_data): return city_data[:, 5].astype(float).min()

#Task 3 import matplotlib.pyplot as plt

def plot_temperature_over_time(city_data, city_name): dates = city_data[:, 0] max_temps = city_data[:, 2].astype(float) min_temps = city_data[:, 3].astype(float) plt.figure(figsize=(10, 5)) plt.plot(dates, max_temps, label='Max Temperature') plt.plot(dates, min_temps, label='Min Temperature') plt.title(f'Temperature Over Time in {city_name}') plt.xlabel('Date') plt.ylabel('Temperature (C)') plt.legend() plt.xticks(rotation=45) plt.tight_layout() plt.show()

def plot_average_temperatures(city_data_list, city_names): average_temps = [calculate_statistics(city_data)[0] for city_data in city_data_list] plt.figure(figsize=(10, 5)) plt.bar(city_names, average_temps) plt.title('Average Temperature Comparison Among Cities') plt.xlabel('City') plt.ylabel('Average Temperature (C)') plt.show()

def categorize_day(temp): if temp < 0: return 'Cold' elif 0 <= temp <= 15: return 'Moderate' else: return 'Warm'

def hottest_coldest_days(city_data): max_temps = city_data[:, 2].astype(float) min_temps = city_data[:, 3].astype(float)

hottest_day_index = np.argmax(max_temps) coldest_day_index = np.argmin(min_temps)

hottest_day = city_data[hottest_day_index] coldest_day = city_data[coldest_day_index]

return hottest_day, coldest_day # Task 3: Visualization def plot_temperature_over_time(city_data, city_name): dates = city_data[:, 0] max_temps = city_data[:, 2].astype(float) min_temps = city_data[:, 3].astype(float) plt.figure(figsize=(10, 5)) plt.plot(dates, max_temps, label='Max Temperature') plt.plot(dates, min_temps, label='Min Temperature') plt.title(f'Temperature Over Time in {city_name}') plt.xlabel('Date') plt.ylabel('Temperature (C)') plt.legend() plt.xticks(rotation=45) plt.tight_layout() plt.show()

# Task 4: Conditional Statements def categorize_day(temp): if temp < 0: return 'Cold' elif 0 <= temp <= 15: return 'Moderate' else: return 'Warm'

def hottest_coldest_days(city_data): max_temps = city_data[:, 2].astype(float) min_temps = city_data[:, 3].astype(float)

hottest_day_index = np.argmax(max_temps) coldest_day_index = np.argmin(min_temps)

hottest_day = city_data[hottest_day_index] coldest_day = city_data[coldest_day_index]

return hottest_day, coldest_day

# Task 5: Discuss Potential Factors Contributing to Extreme Temperatures # You would analyze associated data points like humidity, cloud cover, etc., here.

# Task 6: Overall Hottest and Coldest Days def overall_hottest_coldest_days(data): max_temps = data[:, 2].astype(float) min_temps = data[:, 3].astype(float)

overall_hottest_index = np.argmax(max_temps) overall_coldest_index = np.argmin(min_temps)

overall_hottest_day = data[overall_hottest_index] overall_coldest_day = data[overall_coldest_index]

return overall_hottest_day, overall_coldest_day