How do you detect outliers?
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Detecting outliers is important to ensure data quality and improve model performance. Outliers are values that are significantly different from others in a dataset. Here are common methods to detect them:
1. IQR (Interquartile Range) Method
Calculate Q1 (25th percentile) and Q3 (75th percentile).
Compute IQR = Q3 - Q1
Outliers are values below Q1 - 1.5×IQR or above Q3 + 1.5×IQR.
Q1 = df['column'].quantile(0.25)
Q3 = df['column'].quantile(0.75)
IQR = Q3 - Q1
outliers = df[(df['column'] < Q1 - 1.5*IQR) | (df['column'] > Q3 + 1.5*IQR)]
2. Z-Score Method
Measures how many standard deviations a value is from the mean.
Values with z-score > 3 or < -3 are often considered outliers.
from scipy.stats import zscore
df['zscore'] = zscore(df['column'])
outliers = df[df['zscore'].abs() > 3]
3. Visual Methods
Box Plot: Displays outliers as points beyond whiskers.
Scatter Plot: Highlights unusually distant points.
Summary:
Use IQR for skewed data and z-score for normally distributed data. Visualizations like box plots make outlier detection easier and intuitive.
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