What is Consistent Scaling?

Consistent scaling involves applying one scaling factor per data point (e.g., per row) across several columns. This method is particularly useful in business analytics where

• You want to simulate changes in performance while preserving ratios (e.g., sales distribution across weeks).
• You want to anonymize sensitive values without disrupting trends or relationships.
• You want to test models under different hypothetical scenarios.

How to Use It: An Example in Python

Let’s walk through an example using Python and the pandas library. Suppose we have a dataset of monthly income reports containing values such as sales, commissions, and deductions:

Step 1: Load and Clean the Data

for reading more about it, check the link.

Step 2: Select Columns to Scale

columns_to_scale = df.columns

Step 3: Create Consistent Scaling Factors

import numpy as np

# Set a seed for reproducibility
np.random.seed(42)

‘’’ np.random.seed(42) ensures reproducibility.

Without it, you can’t guarantee that your analysis, charts, or results will be the same next time you run the notebook.

Here is nothing special about 42 technically — it’s just a number’’’

# Generate a random scale factor between 0.85 and 1.15 for each row
scaling_factors = np.random.uniform(0.85, 1.15, size=(df.shape[0],))

‘’’

np.random.uniform(0.85, 1.15, …)

This is a NumPy function that generates random float numbers.

It chooses them uniformly between 0.85 and 1.15.

That means each number will be:

≥ 0.85 (can reduce the value by up to 15%)

≤ 1.15 (can increase the value by up to 15%)

size=(df.shape[0],)

This means: “Create one random number per row in the DataFrame.”

df.shape[0] is the number of rows in your dataset.

The result is an array of random multipliers like:

[0.94, 1.11, 0.89, 1.03, …] — same length as the dataset.

‘’’

Step 5: Apply the Scaling

# Multiply each numeric column by the corresponding row’s scaling factor
df[columns_to_scale] = df[columns_to_scale].multiply (scaling_factors, axis=0)

Step 6: Round the Result

df[columns_to_scale] = df[columns_to_scale].round(2)

Why Use This Method?

• Preserves Proportions: Useful when relative values matter more than absolute values.
• Flexible Simulation: You can test optimistic or pessimistic performance by adjusting the scale range.
• Privacy-Friendly: Scales down real values for privacy while keeping trends.

Conclusion

The consistent scaling method is a powerful and intuitive tool for simulating scenarios, anonymizing data, or preparing input for machine learning. By using a single scale factor per row, analysts can maintain the internal structure of their data while experimenting with different magnitudes, making it ideal for sensitivity analysis, forecasting, or teaching environments.