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AI & MACHINE LEARNING PROGRAM • LEVEL 05 — DATA PREPARATION

Impute a Missing Value with Python

Learn impute a missing value with python with a short, executable Python example.

IntermediateMissing valuesMean imputation

PROBLEM UNDERSTANDING

Input and expected output

Sample input
No input required
Sample output
[10, 20.0, 20, 30]

COMPLETE PYTHON PROGRAM

Complete Python implementation

ai-ml-05-mean-imputation.py
Open in compiler
values = [10, None, 20, 30]
known = [value for value in values if value is not None]
mean = sum(known) / len(known)
print([mean if value is None else value for value in values])

GUIDED CODE TOUR • NOT LIVE EXECUTION

Study the program line by line

Use the real compiler button above to run and debug with different inputs.

CURRENT STEP

Select Start to walk through the important lines.

SELECTED LINE

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EXPECTED OUTPUT FOR THE SAMPLE

[10, 20.0, 20, 30]
0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

  1. Initialize the sample values used to impute a missing value.
  2. Process the data step by step using Missing values and Mean imputation.
  3. Display the result for impute a missing value and compare it with the documented sample output.

This example of impute a missing value processes the sample values in a controlled iteration. It demonstrates Missing values and Mean imputation and prints a deterministic result that can be checked against the sample output.

EFFICIENCY

Time and space complexity

Time complexity

O(n)

Auxiliary space

O(n)

DEBUGGING CHECKLIST

Common mistakes

Check this

For impute a missing value, keep the data shape and value types consistent with Missing values.

Check this

Keep every dependent statement inside the correct indented Python block.

Check this

Check denominators, numeric ranges and rounding before comparing the calculated value.

Try it yourself

Practice: Run the program with the sample input, predict its output, and then test one boundary case of your own.