How NumPy Helps You Land a DevOps Engineer Job Faster

import numpy as np

# latency_ms.txt: one latency value per line, exported from logs
lat = np.loadtxt("latency_ms.txt")
p50, p95, p99 = np.percentile(lat, [50, 95, 99])
print(f"p50 {p50:.0f} ms | p95 {p95:.0f} ms | p99 {p99:.0f} ms")
print(f"mean {lat.mean():.0f} ms (can hide slow outliers)")

import numpy as np

lat = np.loadtxt("latency_ms.txt")
target_ms = 300
slo = 0.95                      # 95% of requests under 300 ms
compliance = np.mean(lat <= target_ms)
print(f"Compliance: {compliance:.2%} (objective {slo:.0%})")
print("SLO met" if compliance >= slo else "SLO missed")

import numpy as np
from numpy.lib.stride_tricks import sliding_window_view

cpu = np.loadtxt("cpu_percent_per_min.txt")
window = 30
windows = sliding_window_view(cpu[:-1], window)          # previous 30 values
mean = windows.mean(axis=1)
std = windows.std(axis=1) + 1e-9
z = (cpu[window:] - mean) / std
anomalies = np.where(np.abs(z) > 3)[0] + window
print("Anomalous minutes:", anomalies[:20])

import numpy as np

errors = np.loadtxt("errors_per_min.txt")
jumps = np.diff(errors)
threshold = np.percentile(np.abs(jumps), 99)
spike_minutes = np.where(np.abs(jumps) > threshold)[0] + 1
print("Threshold:", threshold, "Spike minutes:", spike_minutes[:20])

import numpy as np

used_gb = np.loadtxt("disk_used_gb_daily.txt")    # one value per day
capacity_gb = 500
days = np.arange(len(used_gb))
slope, intercept = np.polyfit(days, used_gb, 1)
if slope > 0:
    days_until_full = (capacity_gb - used_gb[-1]) / slope
    print(f"Growth {slope:.2f} GB/day, full in about {days_until_full:.0f} days")
else:
    print("No growth trend detected")

import numpy as np

predicted = slope * days + intercept
residuals = used_gb - predicted
r2 = 1 - np.sum(residuals**2) / np.sum((used_gb - used_gb.mean())**2)
print(f"R squared: {r2:.3f}")
# Low R squared means the trend is not linear; check for jumps or seasonality

import numpy as np

# requests.txt: 1 = success, 0 = failure (one line per request, last 30 days)
outcomes = np.loadtxt("requests.txt")
slo = 0.999
allowed_failures = (1 - slo) * outcomes.size
actual_failures = outcomes.size - outcomes.sum()
remaining = 1 - actual_failures / allowed_failures
print(f"Error budget remaining: {remaining:.1%}")

import numpy as np

lat = np.loadtxt("latency_ms.txt")
report = {
    "requests": int(lat.size),
    "p50_ms": float(np.percentile(lat, 50)),
    "p95_ms": float(np.percentile(lat, 95)),
    "p99_ms": float(np.percentile(lat, 99)),
    "share_under_300ms": float(np.mean(lat <= 300)),
}
for k, v in report.items():
    print(f"{k:18} {v:.3f}" if isinstance(v, float) else f"{k:18} {v}")

# Weekly SLO summary every Monday at 09:15
15 9 * * 1 cd /opt/ops-tools && python3 slo_summary.py >> slo.log 2>&1