hoose the correct command to access the element Tamil in the dataframe student_data. student_data[[4]] student_data[[4]][3] student_data[[3]][4] None of the above

tudent_data = data.frame(student_id=c(1:4), student_name=c(‘Ram’,‘Harish’,‘Pradeep’,‘Rajesh’))1 pointChoose the correct command to add a column named student_dept to the dataframe student_data. student_datastudent_dept=c(“Commerce”, “Biology”, “English”, “Tamil”) student_data[“student_dept”]= c(“Commerce”,“Biology”, “English”,“Tamil”) student_dept= student_data[c(“Commerce”,“Biology”,“English”,“Tamil”)] None of the above

Assume that you want to read data to a Dataframe and you have written the following code. And also assume that the data file is in the local directory where you are executing the code. Select the correct line of code that will load the data.import pandas as pydatafile_name = 'my-data.csv'col_name = ['name','age','salary','location','class']dataset = pydata.read_csv(file_name, names = col_name)dataset = pd.read_csv(file_name, names = col_name)dataset = pydata.read_csv(url, names = col_name)pydata.read_csv(file_name, names = col_name)

Consider the dataframe df. How would you access the element in the 2nd row and 1st column?1 pointdf.iloc[1,0]df.iloc[2,1]df.iloc[0,1]

data = pd.DataFrame({ 'name': ['Tatenda', 'Hazel', 'Carlos', 'Tinaye', 'Judah', 'Tawanda', 'Lebo', 'Chenge', 'Solomon', 'Simba'], 'department': ['Data Analyst', 'Data Analyst', 'Actuarial', 'Actuarial', 'Development', 'Development', 'Data Analyst', 'Data Analyst', 'Actuarial', 'Data Analyst'], 'project_count': [8, 10, 20, 30,20 , 15, 20, 10, 20, 11]})data.shape, data.describe()Which of the following statements correctly distinguishes between methods and attributes in the context of data.shape and data.describe()?data.shape is a method that returns the number of rows and columns in the DataFrame, while data.describe() is an attribute that shows a summary of the DataFrame's numeric data.Both data.shape and data.describe() are methods that perform operations on the DataFrame, with data.shape showing dimensions and data.describe() computing summary statistics.data.shape is an attribute that returns a tuple representing the dimensions of the DataFrame, while data.describe() is a method that generates descriptive statistics of the DataFrame's numeric columns.data.describe() is an attribute that returns descriptive statistics, while data.shape is a method that computes the dimensions of the DataFrame.

Consider the following DataFrame Gr and answer any four questions from (i)- (v)   Name             Age0  Rashmi          A11  Harsh            A22  Ganesh         B13  Priya             A14  Vivek            B25  Anita            A26  Karthik         A1  5. Write down the command that will give the following output. Name                Age0  Rashmi            A11  Harsh             A22  Ganesh            B13  Priya             A14  Vivek             B2*1 pointa. print(Gr.iloc[0:5])b. print(Gr[0:5])c. Bothd. None6. The teacher needs to add a column called Percentage with the following data          [92,89,None,95,68,None,93] Help her to identify the correct set of statement/s from the given options :*1 pointa. Gr.column[‘Percentage’]=[92,89,None, 95,68,None ,93]b. Gr[‘ Percentage’]=[92,89,None, 95,68,None ,93]c. Gr.loc[‘Percentage’]= [92,89,None,95,68,None,93]d. Both (b) and (c) are correct7. Which of the following statement/s will  drop the column Grade by name?*1 pointa. Gr.drop[‘Grade’]b. Gr.drop(‘Grade’, axis=1)c. Both a & bd. None of the above8. Which of the following command will display the column labels of the DataFrame?*1 pointa. print(Gr.columns())b. print(Gr.column())c. print(Gr.column)d. print(Gr.Columns)