df = pd.DataFrame( { "Name": [ "Braund, Mr. Owen Harris", "Allen, Mr. William Henry", "Bonnell, Miss. Elizabeth", ], "Age": [22, 35, 58], "Sex": ["male", "male", "female"], "Location": ["New York", "California", "Texas"], })

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.

3. import pandas as pddata = pd.DataFrame({'name':['Tatenda','Hazel','Carlos','Tinaye','Judah','Tawanda','Lebo','Chenge','Solomon','Simba'], 'rating':[60,60,98,100,96,96,96,80,94,50]})mask = data.duplicated()filtered_data = data.loc[data.duplicated(keep= False)]Which of the following statements correctly describes the results of mask and filtered_data?mask will be a Series indicating whether each row is a duplicate, including the first occurrence, and filtered_data will contain only rows that are unique.mask will be a Series indicating whether each row is a duplicate, excluding the first occurrence, and filtered_data will be a DataFrame including all rows that are duplicates, including both occurrences of each duplicate.mask will be a DataFrame with boolean values indicating duplicate status, and filtered_data will be a DataFrame containing rows that are unique.mask will be a Series with True for rows that are duplicates including the first occurrence, and filtered_data will be a DataFrame including only the first occurrence of each duplicate.

Based on the dataframe generated in practice 1, use Loop to create a new columnnamed Age_Comparison indicating whether the age of a person is higher, lower or the same asthe average age in the dataset. (You can use Higher/Same/Lower).

Based on the dataframe generated in practice 1, use Loop to create a new columnnamed Age_Comparison indicating whether the age of a person is higher, lower or the same asthe average age in the dataset. (You can use Higher/Same/Lower)

import pandas as pd  import numpy as np    info = pd.DataFrame(np.random.randn(4,2),columns = ['col1','col2'])  for row_index,row in info.iterrows():     print (row_index,row)  Output0 name John degree B.Techscore 90Name: 0, dtype: object1 name Smithdegree B.Comscore 40Name: 1, dtype: object2 name Alexanderdegree M.Comscore 80Name: 2, dtype: object3 name Williamdegree M.Techscore 98Name: 3, dtype: object