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C.   Competency-based/Application-based questions:                            #Communication

                   1.  In a discussion among some class 11 students, they compared DataFrames with Excel and concluded that DataFrames
                      offer no advantages over Excel. Explore and identify potential benefits of using DataFrames that could contribute to
                      this discussion."
                                      th
                   2.  Students of class 11  were unwilling to use Iris dataset for their model testing. How would you ask them to reconsider
                      their choice. Give convincing arguments so that they use this dataset for their AI model.




                                 In Life                                           #Creativity and Innovativeness


                   Find out why Iris dataset in important in real life. Does it have any drawbacks? Also, find out 5 alternative datasets
                   to Iris.







                              Deep Thinking                                      #Problem Solving & Logical Reasoning



                   Find out the difference between NumPy and Pandas – their data compatibility, performance, memory usage, etc.








                                                                                   #Coding & Computational Thinking
                               Lab



                   Write code in Python for the following tasks:
                   1.  Create a NumPy array of 10 zeros.
                   2.  Create a NumPy array with values ranging from 40 to 60.
                   3.  Create a Pandas DataFrame from a dictionary with Name, Age and City as keys. Assume your own values
                      (4 rows).
                   4.  Display ‘Name’ and ‘City’ columns from the DataFrame created in the previous question.
                   5.  Add a new column ‘Marks’ with the values [68, 40, 78, 82] to the DataFrame.
                   6.  Delete the 'Age' column from the DataFrame.
                   7.  Create a simple DataFrame. Display the row labels and columns data types.
                   8.  Use the Iris dataset to do the following:
                      a.  Load the Iris Dataset using Scikit-Learn.
                      b.  Print the first 5 rows of the dataset.
                      c.  Print the names of the features (attributes) in the dataset.
                      d.  Print the target variable names (species) in the dataset.
                   9.  Create a DataFrame from the Iris Dataset with column names. Print the summary statistics of the dataset
                      (mean, min, max, etc.) for each feature.






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