The python library pandas allows to load and manipulate large sets of data for analysis.
The following Ipython notebook shows basic manipulations that you can realize using this library for financial purposes.
The notebook is written with a fresh installation of Anaconda with Python 3.4.
You will need to install the following packages with Anaconda’s terminal if they are not already there:
ipython is an excellent and easy way to implement rapidly and nicely some of the techniques we learned during the lecture. The main advantages of Python are the following:
Some of you questioned the difference between “collection” and/or “family” concepts, terms that appeared at different places in the lecture. Even in the math community – myself included – there is often confusion between these two concepts, and both are used with some inconsistencies. However, as long as it is clear what is meant for the writer and the reader, there is actually no real problem. Let me stress the difference between both
The following is by no means ‘the’ introduction on how to write mathematical proofs in English – English is my third tongue, therefore I do not master it very well myself. Just take the following as building blocks so that I can follow your proofs. After a while, you may improve by looking at proof arguments in professional English textbooks – I am not a reference on that topic. That’s also why I recommend you to read only textbooks in English! At the beginning I accept – even if I hate it – the use of logical symbols such as \(\Rightarrow\), \(\Leftrightarrow\), \(\forall\), \(\exists\), etc. in text. Try however to replace them by their English counterparts, it is nicer to read.*