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  3. Introduction to Data Pipelines

Exercise

Filling missing values with pandas

When building data pipelines, it's inevitable that you'll stumble upon missing data. In some cases, you may want to remove these records from the dataset. But in others, you'll need to impute values for the missing information. In this exercise, you'll practice using pandas to impute missing test scores.

Data from the file "testing_scores.json" has been read into a DataFrame, and is stored in the variable raw_testing_scores. In addition to this, pandas has been loaded as pd.

Instructions 1/3

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  • Print the head of the raw_testing_scores DataFrame, and observe the NaN values.