Dealing with NaN
Dealing with NaN
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As mentioned earlier, before we can begin training our learning algorithms with large datasets, we usually need to clean the data first. This means we need to have a method for detecting and correcting errors in our data. While any given dataset can have many types of bad data, such as outliers or incorrect values, the type of bad data we encounter almost always is missing values. As we saw earlier, Pandas assigns NaN values to missing data. In this lesson we will learn how to detect and deal with NaN values.
As mentioned earlier, before we can begin training our learning algorithms with large datasets, we usually need to clean the data first. This means we need to have a method for detecting and correcting errors in our data. While any given dataset can have many types of bad data, such as outliers or incorrect values, the type of bad data we encounter almost always is missing values. As we saw earlier, Pandas assigns NaN values to missing data. In this lesson we will learn how to detect and deal with NaN values.
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