Descriptive Statistics

Before moving on to visualizing the distribution, it makes sense to look at the descriptive statistics of each parameter in the dataset.

Among the key parameters we need are the following:

• Number of observations;
• Average value;
• Standard deviation;
• Median;
• Minimum value;
• Maximum value.

Let's get on with it. We have the results of a controlled experiment for two groups of users.

Preliminary A/A testing showed that the experiment was adequate. Let's display our files:

In this table, we have 4 columns:

'Impression' - the number of views of the product page; 'Click' - the number of transitions to the product page; 'Purchase' - the number of product purchases; 'Earning' - profit from the sale of the product.

Now let's calculate the descriptive statistics and display them on the screen:

We use the .agg() method for the convenience of calculating descriptive statistics. This method is called aggregation. Aggregations are a way of collapsing, summarizing, or grouping data. Also, we use the .concat() method to conveniently display the aggregation results on the screen.

The averages seem pretty close. Or not?