SciPy

Scientific computing tools built on NumPy.

Quick Start

from scipy import stats, optimize
import numpy as np

Statistics for Anomaly Detection

# Z-score anomalies
z_scores = stats.zscore(data)
anomalies = np.abs(z_scores) > 3

# IQR method
q1, q3 = np.percentile(data, [25, 75])
iqr = q3 - q1
lower = q1 - 1.5 * iqr
upper = q3 + 1.5 * iqr
anomalies = (data < lower) | (data > upper)

# Distribution fitting
params = stats.norm.fit(data)
pdf = stats.norm.pdf(x, *params)

Time Series

from scipy.signal import find_peaks, savgol_filter

# Smooth noisy data
smoothed = savgol_filter(data, window_length=11, polyorder=3)

# Find peaks/spikes
peaks, properties = find_peaks(data, height=threshold, distance=min_distance)

Statistical Tests

# Test for normality
stat, p_value = stats.normaltest(data)

# Compare distributions
stat, p = stats.ks_2samp(sample1, sample2)