Spotting Anomalies in Data with Python & Pandas

Anomalies in datasets are more than just “odd number sets” they can signal

fraud, errors, or unexpected business insights. Detecting them early is key in fields like finance, operations, QA, and data science.

With Python + Pandas, you can apply simple yet powerful statistical methods:
1. Z-Score / Standard Deviation Method
This flags values that are too far from the mean.
Values with |z| > 2 are considered anomalies (here, 5000).
2. Interquartile Range (IQR) Method
This uses the spread of the middle 50% of data.
Any value outside the IQR boundaries is flagged as an anomaly.
The best part? Both methods are simple, interpretable, and easy to implement in Pandas, perfect for exploratory data analysis or building more advanced anomaly detection pipelines.
Whether you’re cleaning data, validating test results, or monitoring transactions, these techniques will give you a strong foundation in detecting outliers.