Missing Value Types and Representation
Explore how to identify and represent various types of missing values in Pandas DataFrames. Understand the use of NaN and the newer <NA> type for integer columns to manage missing data effectively in Python. This lesson helps you handle missing data for accurate data analysis and preprocessing.
We'll cover the following...
We'll cover the following...
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 ...