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AI & MACHINE LEARNING PROGRAM • LEVEL 15 — ANOMALY DETECTION

Calculate IQR Outlier Bounds with Python

Learn calculate iqr outlier bounds with python with a short, executable Python example.

IntermediateIQROutlier

PROBLEM UNDERSTANDING

Input and expected output

Sample input
No input required
Sample output
-5.0 35.0

COMPLETE PYTHON PROGRAM

Complete Python implementation

ai-ml-15-iqr-bounds.py
Open in compiler
q1, q3 = 10, 20
iqr = q3-q1
print(q1-1.5*iqr, q3+1.5*iqr)

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

-5.0 35.0
0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

  1. Initialize the sample values used to calculate iqr outlier bounds.
  2. Apply IQR and Outlier to compute the required result.
  3. Display the result for calculate iqr outlier bounds and compare it with the documented sample output.

This example of calculate iqr outlier bounds computes the result directly from the prepared sample data. It demonstrates IQR and Outlier 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 iqr outlier bounds, keep the data shape and value types consistent with IQR.

Check this

Apply IQR in the same order shown by the algorithm; changing the order can change the result.

Check this

Verify the final IQR and Outlier result against the sample before trying new data.

Try it yourself

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