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  3. Analyzing Financial Statements in Python

Exercise

Imputing and filling in missing values using averages

When you want to do your analysis, you will likely use your own data. Datasets often have some missing values. In this exercise, you'll practice imputing these missing values. Imputing missing values is important as you do not want missing values to be an obstacle in our analysis.

pandas has been loaded with the alias pd and NumPy has been loaded with the alias np. A pandas DataFrame called dataset has been loaded for you. It has the column "Total Current Liabilities", which has some missing values in it.

Instructions 1/2

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  • Impute the missing values in "Total Current Liabilities" by "company" using the average non-missing value.
  • Impute the missing values in "Total Current Liabilities" by "comp_type" using the average non-missing value.