Replace Excel with Python for Finance Professionals

1. Introduction and Setup

   • Demonstrated small bits of Python code.

   • Introduced the Pandas library and its significance.

   • Explained the importing process of a dataset (Our World in Data COVID codebook) into a CSV file using the read_csv() function.

2. Object-Oriented Programming (OOP) in Python

   • Everything in Python is essentially an object.

     • Individual elements are objects.

     • Sets or groups of elements (e.g., data in a column) are also objects.

     • Collections, like column labels, are objects.

     • Function inputs can also be objects.

   • Briefly touched upon functions, mentioning how they can accept objects as parameters.

3. Python Syntax and Notation

   • Highlighted that Python is case-sensitive.

     • Example: The case difference between variables affects code execution.

   • Discussed dot notation:

     • Used for accessing elements or functions from libraries or objects.

     • Demonstrated with the pd.read_csv() function and accessing column values.

   • Introduced the index operator:

     • Explained its use with square brackets to access specific elements within objects.

     • Differentiated between list notation and the index operator.

   • Briefly touched on the concept of indexes in data structures.

4. Accessing Documentation in Pandas

   • Stressed the importance of referring to the Pandas documentation.

   • Demonstrated two methods to access documentation:

     1. Using the Shift + Tab shortcut in Jupyter notebooks.

     2. Searching for specific functions or topics online, ideally on the official pandas documentation website.

   • Differentiated between parameters and arguments in function calls.

   • Emphasized the usefulness of documentation for understanding functions, parameters, and more.

In this lecture, students will delve deeper into Python by understanding:

1. Attributes:

   • Attributes give information about an object.

   • Attributes are accessed using dot notation.

   • Examples on how to retrieving the size of a dataframe and how to accessing column labels using attributes.

   • An attribute is distinguished from a function by its plain text right after an object, without parentheses.

2. Functions:

   • Functions perform predefined tasks on objects.

   • They are often used for routine tasks and are invoked with parentheses.

   • Examples of functions provided include the read_csv() function to import data and the head() function to display the initial rows of a dataframe.

   • Functions can take arguments, which determine their specific behaviour.

3. Method Chaining: allows for performing multiple operations successively.

4. Parameter vs. Argument:

   • A parameter is a variable in the definition of a function.

   • An argument is the actual value that gets passed into the function.

   • Some functions have default arguments, which means they assume a specific value unless provided otherwise by the user.

5. Importance of Documentation:

   • For further guidance, consulting function documentation will be very useful to determine the list of parameters and their default values.