B2022 - PYTHON FOR ECONOMISTS

Learning outcomes

This course provides theory and tools for using Python as a programming language in economic research. It (i) introduces students to the basic logic and syntax of Python (e.g. object-oriented programming) and (ii) emphasizes how to use it to perform Data Science-related tasks (working with relational dataset, big data, data visualization, among others).

Course contents

Basic Python

1. Using the Python Interpreter
2. An Informal Introduction to Python
   
3. Control Flow Tools
   
4. Data Structures
5. Modules
6. Input and Output
7. Errors and Exceptions
8. Classes
9. Brief Tour of the Standard Library
10. Virtual Environments and Packages
    
    

NumPy

1. Array objects
   The N-dimensional array (ndarray)
   Scalars
   Data type objects (dtype)
   Indexing routines
   Iterating Over Arrays
   Standard array subclasses
   Masked arrays
   The array interface protocol
   Datetimes and Timedeltas
   

1. Constants
2. Routines
   Array creation routines
   Array manipulation routines
   Binary operations
   String operations
   Mathematical functions with automatic domain
   Floating point error handling
   Functional programming
   NumPy-specific help functions
   Input and output
   Linear algebra (numpy.linalg)
   Logic functions
   Masked array operations
   Mathematical functions
   Miscellaneous routines
   Random sampling (numpy.random)
   Set routines
   Sorting, searching, and counting
   Statistics

SciPy

Introduction
Special functions (scipy.special)
Integration (scipy.integrate)
Optimization (scipy.optimize)
Interpolation (scipy.interpolate)
Linear Algebra (scipy.linalg)
Statistics (scipy.stats)


pandas

1. basics
   Object creation
   Viewing data
   Selection
   Missing data
   Operations
   Merge
   Grouping
   Reshaping
   Time series
   Categoricals
   Plotting
   Getting data in/out
   Gotchas
2. Intro to data structures
3. Essential basic functionality
4. IO tools (text, CSV, HDF5, …)
   
5. Indexing and selecting data
   
6. MultiIndex / advanced indexing
   
7. Merge, join, concatenate and compare
   
8. Reshaping and pivot tables
   
9. Working with text data
   
10. Working with missing data
    
11. Categorical data
    
12. Computational tools
    
13. Group by: split-apply-combine

Elements of matplotlib

Readings/Bibliography

https://docs.python.org/3/tutorial/

https://numpy.org/doc/stable/reference/index.html

https://docs.scipy.org/doc/scipy/tutorial/index.html

https://pandas.pydata.org/docs/getting_started/tutorials.html

https://matplotlib.org/stable/tutorials/introductory/index.html

Teaching methods

In presence.

Teacher explaining concepts and discussing exercises

Assessment methods

Through the course there will be 2 or 3 home assignment that students are expected to do and return to the tutor for grading.

Home assigned are tailored to occupy the student from 1 to 2 hours.

Final exam will be a written exam consisting in a challenging programming task.

Th final grade will be weighted equal between home assignment and the final exam.

Writing a computer program entails, among other three aspects I want to emphasize: correctness, clean programming style and performance. Correctness will absorb 60% of the value, coding style will weight 30%, the remaining 10% is taken up by performance

Teaching tools

Most will be frontal lectures were the teacher explains concepts and discusses examples.

Python code will be showcased both as standalone scripts, developed within an IDE but most of all examples will be presented via the Notebook.

The most important tool will be the large number of exercises that will be proposed and will challenge the student.

Office hours

See the website of Pietro Rossi