Python for economists

• in this lecture, we look at:
  • motivation to learn python
  • overview of the course
  • typing texts in markdown and \(\LaTeX\)
  • markdown files on github
  • initial assignment
  • first look at jupyter server

• in this course we teach you
  • to combine rigorous economics
  • and how to present it in an understandable way
  • while your results can be easily reproduced
• we do this by teaching you
  • a programming language: Python
  • how to create notebooks where you can explain your code and
  • publish them on-line, e.g. on github

• when you finish this course, you can
  • solve models in Python
  • do simulations in Python
  • create a file or notebook like this
  • go on to the Datascience course next semester

• helps you to present economics in a way that is
  • understandable and
  • reproducible
• gives a good starting point to write your thesis
• check the website and Canvas for relevant information

• indeed, simple things can be done in excel
• e.g. keep track of your grades and calculate the average
• but possibilities are limited: it is not a programming language
• however, the main problem is reproducibility
• if you've done a serious project in excel, look at it again in 3 months time…

• open source is great!
• markdown allows you to create structure in a simple way
• This is illustrated in a screencast: Short introduction to JupyterLab
• examples are:

# this is a heading

## subheading

* first bullet
* second bullet

[link text](actual link, e.g. http://www.etc)

![Alt text for image](/path/to/img.jpg "Optional title")

• look on the web for other syntax like footnotes etc.
• equations you can type in \(\LaTeX\)

• \(\LaTeX\) is great word processing software
• many students write their thesis in \(\LaTeX\)
• here we focus on writing math in \(\LaTeX\)
• you can guess what the following will do:

$x^2$, $\beta$, $\sqrt{9}$, $\frac{1}{2}$, $\bar x$

\begin{equation}

a^2 + b^2 = c^2

\end{equation}

• if you need something, just google; e.g. google "latex phi" or google "latex empty set" etc.
• students write their thesis in \(\LaTeX\) using Overleaf

• you do not need to install anything for the course
• you will be working on the jupyter lab server
• if you also want to install things on your computer, use the anaconda distribution

• github allows you to
  • publish web pages
  • work "in the cloud" with version control
  • collaborate in the cloud
  • have different versions ("branches") of the same project
• version control takes a bit of time to learn
• but once you get it, the benefits are huge!
• for this course learning git is optional
• you can just drag and drop your notebooks on github as we will practice in class
• you need github to submit your assignments (using github classroom)

• you need to finish the datacamp courses and screencasts in time
• otherwise you will be lost during the lectures
• see the Lecture schedule for the planning
• if you want to learn everything on your own, you do not need to attend my lectures
• but if you do attend the lectures, you participate actively
• you do not learn Python by passively typing in answers!
• you have to attend Joyce's career services classes
• Mery's lectures on gender economics, coupled with the Python notebook, give you an idea of what we expect in the final assignment

• in the lectures we will answer questions that you ask us
• in order to prepare these answers, it is useful if you create a github issue
  • create an "issue" where you explain what your question is: what did you try and why did it not work?
    • hence not something like "I do not know the answer to: Question Calculate the total welfare that this planner can achieve. Denote this value max_welfare."
    • but "I tried to calculate total welfare in the following way … but then I get the following error …"
• for us to be able to prepare these answers, submit the issue on the Friday before the week in which we discuss the topic

• follow the Datacamp courses and make notes of what you learn at Datacamp in a notebook on the jupyterlab server
  • in this way it is easy to refer back to useful code snippets that you see on Datacamp
• go through the notebook and try to answer the questions and assignments
• if you get stuck with a problem, watch the corresponding screencast for this part
• if you still cannot figure it out, create an issue on github to ask us
• we will answer your questions in class

• finish the initial assignment (see below) before the deadline
• attend the career services workshops
• Final Assignment
• Deadlines for the final assignment

• follow and finish the steps under Initial Assignment
• type your first Python and markdow at the jupyter lab server and follow along with the screencast: Short introduction to JupyterLab

• assignments can be viewed here: https://github.com/janboone/python_assignments
• assignments 1 and 2 have deadlines before the lectures in the Lecture schedule
• in this lecture, we will discuss the assignment
• then you add to the assignment what you have learned from the assignment and what you got wrong the first time
• we grade both assignments after the deadline of the 2nd assignment
• note that github keeps track of the date on which you submit
• if you get a pass on both assignments, you receive a bonus point for the final assignment
• what we are looking for in assignments 1 and 2 is that you are up-to-date with the course and can google a bit to find (new) python commands

• you can do the assignments on your own or together with 1 other student
• you cannot change team during the semester
• before assignment 1 we will post a link to github classroom with the assignments
• this is the moment where you create your team
• before the deadline you submit your assignment via github classroom
• assignments 1 and 2 you submit two times
• the final assignment you submit only once
• when submitting your first assignment, you fill in the google form (link will be on Canvas)
• see Assignments for details