Da Iqr Outlier Detection — Free Data Analytics Tutorial
Learn Da Iqr Outlier Detection in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Da Iqr Outlier Detection in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
Written & reviewed by the Syllab.in Academic Team (CBSE/NCERT subject experts) · Updated
Da Iqr Outlier Detection in Data Analytics
Interquartile Range (IQR) = Q3 - Q1 (middle 50% of data).
Outlier bounds: Lower = Q1 - 1.5*IQR, Upper = Q3 + 1.5*IQR
Values outside bounds are considered outliers.
More robust than mean±2σ for skewed distributions.
Use describe() to get quartiles quickly.
Da Iqr Outlier Detection — Syntax
# Q1, Q3, IQR: # Q1 = df['col'].quantile(0.25) # Q3 = df['col'].quantile(0.75) # IQR = Q3 - Q1 # Outliers: (df['col'] < Q1 - 1.5*IQR) | (df['col'] > Q3 + 1.5*IQR)
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