AI & MACHINE LEARNING PROGRAM • LEVEL 04 — PROBABILITY & STATISTICS

Calculate a Z-score with Python

Learn calculate a z-score with python with a short, executable Python example.

IntermediateZ-scoreStandardization

PROBLEM UNDERSTANDING

Input and expected output

Sample input
No input required
Sample output
1.5

COMPLETE PYTHON PROGRAM

Complete Python implementation

ai-ml-04-z-score.py
Open in compiler
value, mean, standard_deviation = 85, 70, 10
print((value - mean) / standard_deviation)

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

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SELECTED LINE

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EXPECTED OUTPUT FOR THE SAMPLE

1.5
0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

  1. Initialize the sample values used to calculate a z-score.
  2. Apply Z-score and Standardization to compute the required result.
  3. Display the result for calculate a z-score and compare it with the documented sample output.

This example of calculate a z-score computes the result directly from the prepared sample data. It demonstrates Z-score and Standardization and prints a deterministic result that can be checked against the sample output.

EFFICIENCY

Time and space complexity

Time complexity

O(1)

Auxiliary space

O(1)

DEBUGGING CHECKLIST

Common mistakes

Check this

For calculate a z-score, keep the data shape and value types consistent with Z-score.

Check this

Apply Z-score in the same order shown by the algorithm; changing the order can change the result.

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.