Missing Value Types and Representation

Learn how missing values are represented in Pandas.

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The missing values

A row in a DataFrame represents an observation or a data point. A column is a feature or attribute of that observation. In some cases, we don’t have all the feature values of some observations. Let’s say we have a DataFrame that contains information on a bank customer, such as name, age, income, address, and so on. If we don’t have the age information of a customer, it’s considered a missing value.

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