# Lecture 2 ## Topic In this lecture we cover more python basics, numpy and pandas. ## Lecture Slides ```{raw} html ``` ```{eval-rst} :download:`Download the slides <_static/bld/pdfs/lecture_2.pdf>` ``` ## Exercises ```{toctree} --- maxdepth: 1 --- bld/notebooks/exercises/exercise_2.ipynb bld/notebooks/solutions/exercise_2.ipynb ``` ## Suggested Homework - Re-do or finish the exercise notebook a few days after class to practice the basics - Go through chapter 2 and 3 of the [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/#2.-Introduction-to-NumPy) and type all the code examples into a jupyter notebook. - Watch the concise introduction to pandas (see below) ## Additional materials ### Python Data Science Handbook The excellent [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/#2.-Introduction-to-NumPy) by Jake VanderPlas has a free online version. The lecture slides on numpy and pandas are based on Chapter 2 and 3 of that book. However, the book is more complete. ### Concise Intro to pandas This video is a very short introduction to pandas that covers topics that will be useful in this class: ### Video Series on Pandas The following video series is completely optional and goes much deeper than what we need. The video titles in the playlist are very informative. I would not suggest to watch all of this from start to finiss but to watch a video if you are struggling with a specific topic in your finl project. ### Numpy tutorial from the scipy conference This is a detailed tutorial on numpy. It goes beyond what we need in the class but is a good starting point if you want to learn more. ### Blogpost on tracebacks I highly recommend to read this [blogpost](https://realpython.com/python-traceback/).