It’s the era of big data, and every day more and more business are trying to leverage their data to make informed decisions. Many businesses are turning to ’s powerful data science ecosystem to analyze their data, as evidenced by Python’s rising popularity in the data science realm.

One thing every data science practitioner must keep in mind is how a dataset may be biased. Drawing conclusions from biased data can lead to costly mistakes.

There are many ways bias can creep into a dataset. If you’ve studied some statistics, you’re probably familiar with terms like reporting bias, selection bias and sampling bias. There is another type of bias that plays an important role when you are dealing with numeric data: rounding bias.

In this article, you will learn:

  • Why the way you round is important
  • How to round a number according to various rounding strategies, and how to implement each method in pure Python
  • How rounding affects data, and which rounding strategy minimizes this effect
  • How to round numbers in NumPy arrays and Pandas DataFrames
  • When to apply different rounding strategies