Rolling Mean Python Tutorial: Fast Moving Averages

1. The Naive Approach: Python For-Loops

The simplest way to calculate a moving average is to iterate through the array using a for loop, slicing sub-arrays, and computing their mean:

def manual_rolling_mean(arr, window):
    result = []
    for i in range(len(arr) - window + 1):
        window_slice = arr[i : i + window]
        result.append(sum(window_slice) / window)
    return result

# Slow: executes a Python loop for each element
# Takes ~500ms for 1,000,000 items

🔴 Problem: Python loop iteration is slow. If your dataset contains millions of sensor readings or financial quotes, this function will freeze your application.

2. The Heavy Approach: Pandas Series

Most developers default to Pandas for rolling statistics:

🔴 Problem: Pandas requires a 35 MB+ installation package. If you are deploying to a serverless lambda function or building a lightweight command-line script, importing Pandas adds unnecessary cold-start latency and increases package size constraints.

3. The Optimized Approach: rollit Vectorized Strides

rollit bridges the gap by providing C-speed vectorized reductions with zero heavy dependencies. It does this by creating a 2D memory view (strides) of your 1D array without copying the underlying numbers:

import numpy as np
import rollit

data = np.array([10.0, 12.0, 15.0, 13.0, 16.0, 18.0, 20.0])

# Compute vectorized rolling mean at C-speed
ma = rollit.mean(data, window=3)

print("Moving Average Output:", ma)
# Output: [12.33, 13.33, 14.67, 15.67, 18.00]

🟢 Benefit: Execution completes in under 5 milliseconds on 1,000,000 items while adding only 50 KB to your project size.

Performance Summary (1M Elements)