![]() This example will assume numpy has been imported by import numpy as np.įrom our data set let’s create a subset that lists incomes: incomes = You look through the data and you, being the creative data scientist that you are, wonder if there’s a relationship between if the customer churned and the customer’s annual income. If a customer has churned, then that just means they have cancelled membership or is no longer active with a product. ![]() You’re working for your new company, AwesomeCo, and your boss gives you a data set of the last one thousand people who have subscribed to the company’s product and whether that customer has churned or not. Let’s understand the importance of finding outliers by using a fictitious, but practical example. Let’s look at a quick example of how this can happen. ![]() Why does finding outliers matter? The outliers may affect how you get insights from your data and can lead to incorrect results. But, when plotting the data, it is easier recognize outliers.Īn outlier is a value in your data that is either extremely high or low in comparison with the other data. Even sorting or filtering the data may not show anything out of the ordinary. If you have a data set that has a million rows, it will be tedious to analyze all that information line by line. Visualizing data can help in the process of identifying patterns and anomalies that would otherwise be challenging to spot in raw data. But before we dive into the implementation, let’s review the benefits of visualizing data. In this post, we will do the same, but instead of interpreting the raw data we will use visualizations to help us determine patterns in the data. In our last post we interpreted a data set with pandas to gain some insights from it.
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