Python Tutorial : Exploratory analysis of KPIs

Want to learn more? Take the full course at https://learn.datacamp.com/courses/customer-analytics-ab-testing-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Great work on the exercises! Let us pick up where we left off. We want to determine which conversion rate metric is the most appropriate. Note that most companies will have many KPIs each serving a different purpose, and that here we are only working through one of these cases. To calculate our potential KPIs and measure performance across different groups we will use the `groupby()` and `agg()` pandas methods. This lesson will focus on these methods and the next lesson will more fully explore applying them in practice. We can call the `groupby()` method on a dataFrame to specify groups to aggregate over. Here we will use it on our combined demographics and purchase data dataset. The primary argument is `by` to which we provide a list of dataFrame fields that we want to group on. Here, the potentially relevant fields are "country", "device", "gender", and "age". Let us group by "country" and "device". The next relevant argument is `axis` which specifies whether we are grouping by row or column values. The default value, "0", groups by columns, which is what we will do here, and for the remainder of the course. The other argument of interest is `as_index`. By default, this argument is “True”, which means that the grouped by fields become indices. We want to set this to "False" so that this does not happen. This returns a dataFrameGroupedBy object. The next step is to aggregate over these groups. The easiest way to do this is to call an aggregation method on the dataFrameGroupedBy object. Let’s call `mean` on the `price` value of our dataFrame. The output is the mean amount paid per subscription across all purchasing users. In this case rather than being calculated over the entire set of data, it is calculated over each