PAI session 4

1. Python with AI – I
Session 4

2. Logistics
• Please paste your repl link for this session in the google sheet
• Be prepared to share your screen
• A repl link with questions to all exercises we will do today in the
class will be provided

3. Course Curriculum
Session
Number
What is covered
1 Python basics – conditionals and loops
2 Python packages – pandas dataframes
3 Data structures in python – lists, arrays, dictionaries
4 Create and manipulate datasets in python
5 REST API and Github
6 Connecting two or more AIs in an Application
7 Data Visualization
8 Final Projects

4. Operators in python - Recap
Operator Syntax Description
Sum + Adds two numbers (or) strings
Subtraction - Only numbers
Product * Product of numbers, repetition of strings
Division / Division between numbers
Modulo % Remainder of division between two
numbers
Power ** Power of one number over another

5. Conditionals - Recap
• The if statement
Conditional Description
== Equal
!= Not Equal
> Greater than
< Less than
>= Greater than or equal to
<= Less than or equal to

6. Loops - Recap
• The for loop is of the following form.
• The range indicates the number of times a statement will be
implemented.
• There is a colon value to indicate the start of for loop.
• The code below is executed many times. The number of times it gets
executed depends on the values specified in range.
• Note that the code below the for statement is indented.

7. Lists- Recap
• A list holds ordered collection of items.
• And item can be a string or a number

8. Dictionaries - Recap
• A dictionary consists of two things (a) keys (b) values
• Use strings to represent keys
• Values can be anything

9. Dictionaries - Recap
• Print a value in a dictionary
• Delete a value in a dictionary
• Print all keys of a dictionary
• Add values to a dictionary

10. Functions in python
• Re-usable pieces of code

11. Functions accept parameters
• How would you call this function?

12. Call a function that accepts parameters
• How would you call this function?

13. Functions return values
• How would you call this function?

14. How would you call this function
• How would you call this function?

15. Functions can accept and return multiple
values
• How would you call this function?

16. Functions can accept and return multiple
values
• How would you call this function?

17. Exercise

18. Modules in python
• Use multiple functions written by others

19. Modules in python
• Use multiple functions written by others
• Popular packages: numpy, pandas
• How do you tell python to use these packages?

20. Modules in python
• Use multiple functions written by others
• Popular packages: numpy, pandas
• How do you tell python to use these packages?

21. Numpy package - arrays
• Example of an array

22. Numpy package - arrays
• Example of a 2D array

23. Can all lists be converted to numpy arrays?

24. Why should I use a numpy array?
• Arrays are more convenient – How?

25. Create random numbers

26. Create random numbers
• Generate random integers
• Generate random numbers

27. http://aiclub.world