swied

Nice article. I totally agree about SymPy making math fun. I used Mathematica a lot in college, but stopped once it wasn’t free to me anymore.

Here’s a blog post I made recently showing some fun SymPy calculations. https://www.swied.com/posts/make-a-ringtone-sound/make-a-rin...

What about when the steps don't return a data frame object, like 'groupby'? Then, you will have to think about coming up a name other than 'df'. Save your mental energy for making comments. Here's an example...

    # Per store revenue for high price items   
    df_agg = (
      df1
      .query("unit_price > 400")
      .groupby('store_id','store_name')
      .agg({'revenue':     np.sum, 
            'customer_id': 'nunique'})
    )

A/B testing is a great tool for incrementally improving upon a product design given the correct business strategy. Just don't let the limitations of A/B testing drive how you choose your business strategy. The strategy is ultimately about maximizing total profit, and not necessarily short term profit per user.

So true about the danger of focusing on short term gains. I think that it is a problem of picking the wrong things to optimize and then sampling on the wrong entities. Conversion rate is often the most commonly used target, but, it is short sighted. In the big picture a company is really just trying to maximize profit. Note that you can't use traditional A/B testing tools on a value like total profit. You need an average if you are going to run an A/B test. For conversion rates your average is conversions per user. In this case you sample on the denominator -- the users. What do you sample on if you are testing total profit per quarter? This is the ultimate conundrum for websites who use A/B testing. They don't understand how they might actually get more new/returning users by simply improving the user experience. Instead, they run marketing campaigns to drive growth and then squeeze every dollar out of the customers when they arrive. This is not a good long term strategy. I have reviewed hundreds of A/B tests over the years. The only ones that I think succeeded were the ones that focused on improving the users experience.

Jupyter notebooks are cool, but they are kind of mysterious in terms of how they work. For example, recently the R kernel started crashing whenever I opened a new R notebook. I finally found a note in a local document that said, "If your R kernel keeps dying, you may have to update pcre by executing 'conda update pcre'." I had never heard of pcre, but I updated it and the R kerel started working again. Go figure...

I am really excited about using R Notebooks.