Basic Python programming

C S 487/519 Applied Machine Learning
Basic Python programming
1 Objective
In this individual homework, you are required to get familiar with programming using basic Python knowledge.
2 Requirements
2.1 Tasks
Write a python program to do the following,
(1) (10 points) Read in the Iris dataset using functions in Pandas package. The Iris dataset (iris.data)
and its description (iris.names.txt) can be downloaded from this page.
(2) (20 points) Calculate and print the number of rows and columns that this dataset contains.
(3) (20 points) Get all the values of the last column and print the distinct values of the last column.
(4) (25 points) When the last column has value “Iris-setosa”, calculate the number of rows, the average
value of the first column, the maximum value of the second column, and the minimum value of the
third column.
(5) (23 points) Draw a scatter plot with the data of the first column and the second column (y axis
represents the second column and x axis represents the first column). Show the points in different
colors and shapes when the last columns values are different.
(6) (2 points) Write a readme file readme.txt with detailed instructions to run your program.
2.2 Other requirements
• Your Python code should be written for Python version 3.5.2 or higher.
• Please write proper comments in your code to help the instructor and teaching assistants to understand it.
• Please properly organize your Python code (e.g., create proper classes, modules).
• You can put your code to Jupyter Notebook or a .py file.
3 Submission instructions
Put all your files (Python code, readme file, report, etc.) to a zip file named hw.zip and upload it to Canvas.
4 Grading criteria
(1) The score allocation has been put beside the questions.
(2) FIVE points will be deducted if files are not submitted in the required format.
(3) If the total points are more than 100. Your grades will be scaled to the range of [0,100].
(4) Please make sure that you test your code thoroughly by considering all possible test cases. Your code
may be tested using more datasets.
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