the dataset is...

the dataset is ('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')

 

You will be given a COVID-19 dataset read in from python code, and are required to do below tasks: 1. (4.5 marks, 1.5 marks for each) Good structure of Python Jupyter Notebook a. Containing title cells, subtitle cells. b. Python codes are reasonable separated into groups (code cells) with functionalities. c. Containing meaningful comments and sensible variable and function names. 2. (10 marks, 2 marks each)) download csv data with pandas with below code: . import pandas as pd deaths_of = pd.read_csv('https /faith WOODECIA 10/bloh/masterJesse covia_19_outalesso_dovid_19_time series/time senes_covic40 deaths globalnow . Identify and handle missing values in the dataset. . Remove duplicate entries if any. . Convert the date column to a consistent date format (e.g., YYYY- MM-DD) . Save the cleaned dataset to a new CSV file.3. (3 marks) Display first 5 rows of the loaded data (1 mark) and do a short summary about the data (2 marks) 4. (2.5 marks) Calculate the mean and median of the daily cases. 5. (3 marks) Get daily death cases worldwide (hint: summarizing daily death cases over all countries.) 6. (3 marks) Get daily increasement of deaths cases via defining a function (hint: use the death cases of today minus the death cases of yesterday from the data obtained in task 5.) 7. (4 marks) Visualize the data obtained in task 5 with library matplotlib

You will be given a COVID-19 dataset read in from python code, and are required to do below tasks: 1. (4.5 marks, 1.5 marks for each) Good structure of Python Jupyter Notebook a. Containing title cells, subtitle cells. b. Python codes are reasonable separated into groups (code cells) with functionalities. c. Containing meaningful comments and sensible variable and function names. 2. (10 marks, 2 marks each)) download csv data with pandas with below code: import pandas as pd deaths df=pd.read_csv('https:/laitom/essECIO-Date/CAVID- 10/blob/mastor/coe_covio_19_data/esse uvid_19_thme series/time eres covid49_deaths_ globak.cev") Identify and handle missing values in the dataset. Remove duplicate entries if any. Convert the date column to a consistent date format (e.g., YYYY- MM-DD) Save the cleaned dataset to a new CSV file.