How to Round Numbers in Python

How to Round Numbers in Python

by David Amos Jul 03, 2023 best-practices intermediate python

With many businesses turning to Python’s powerful data science ecosystem to analyze their data, understanding how to avoid introducing bias into datasets is absolutely vital. If you’ve studied some statistics, then you’re probably familiar with terms like reporting bias, selection bias, and sampling bias. There’s another type of bias that plays an important role when you’re dealing with numeric data: rounding bias.

Understanding how rounding works in Python can help you avoid biasing your dataset. This is an important skill. After all, drawing conclusions from biased data can lead to costly mistakes.

In this tutorial, you’ll learn:

  • Why the way you round numbers is important
  • How to round a number according to various rounding strategies
  • How to implement each strategy 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

Take the Quiz: Test your knowledge with our interactive “Rounding Numbers in Python” quiz. Upon completion you will receive a score so you can track your learning progress over time:

Interactive Quiz

Rounding Numbers in Python

Test your knowledge of rounding numbers in Python.

You won’t get a treatise on numeric precision in computing, although you’ll touch briefly on the subject. Only a familiarity with the fundamentals of Python is necessary, and the math should feel familiar if you’ve had high school algebra.

You’ll start by looking at Python’s built-in rounding mechanism.

Python’s Built-in round() Function

Python has a built-in round() function that takes two numeric arguments, n and ndigits, and returns the number n rounded to ndigits. The ndigits argument defaults to zero, so leaving it out results in a number rounded to an integer. As you’ll see, round() may not work quite as you expect.

The way most people are taught to round a number goes something like this:

  • Round the number n to p decimal places by first shifting the decimal point in n by p places. To do that, multiply n by 10ᵖ (10 raised to the p power) to get a new number, m.

  • Then look at the digit d in the first decimal place of m. If d is less than 5, round m down to the nearest integer. Otherwise, round m up.

  • Finally, shift the decimal point back p places by dividing m by 10ᵖ.

It’s an algorithm! For example, the number 2.5 rounded to the nearest whole number is 3. The number 1.64 rounded to one decimal place is 1.6.

Now open up an interpreter session and round 2.5 to the nearest whole number using Python’s built-in round() function:

>>> round(2.5)


Check out how round() handles the number 1.5:

>>> round(1.5)

So, round() rounds 1.5 up to 2, and 2.5 down to 2!

Before you go raising an issue on the Python bug tracker, rest assured you that round(2.5) is supposed to return 2. There’s a good reason why round() behaves the way it does.

In this tutorial, you’ll learn that there are more ways to round a number than you might expect, each with unique advantages and disadvantages. round() behaves according to a particular rounding strategy—which may or may not be the one you need for a given situation.

You might be wondering, Can the way I round numbers really have that much of an impact? Next up, take a look at just how extreme the effects of rounding can be.

How Much Impact Can Rounding Have?

Suppose you have an incredibly lucky day and find $100 on the ground. Rather than spending all your money at once, you decide to play it smart and invest your money by buying some shares of different stocks.

The value of a stock depends on supply and demand. The more people there are who want to buy a stock, the more value that stock has, and vice versa. In high-volume stock markets, the value of a particular stock can fluctuate on a second-by-second basis.

Now run a little experiment. Pretend the overall value of the stocks that you purchased fluctuates by some small random number each second, say between $0.05 and -$0.05. This fluctuation may not necessarily be a nice value with only two decimal places. For example, the overall value may increase by $0.031286 one second and decrease the next second by $0.028476.

You don’t want to keep track of your value to the fifth or sixth decimal place, so you decide to chop everything off after the third decimal place. In rounding jargon, this is called truncating the number to the third decimal place. There’s some error to be expected here, but by keeping three decimal places, this error couldn’t be substantial. Right?

To run your experiment using Python, you start by writing a truncate() function that truncates a number to three decimal places:

>>> def truncate(n):
...     return int(n * 1000) / 1000

The truncate() function first shifts the decimal point in the number n three places to the right by multiplying n by 1000. You get the integer part of this new number with int(). Finally, you shift the decimal point three places back to the left by dividing n by 1000.

Next, you define the initial parameters of the simulation. You’ll need two variables: one to keep track of the actual value of your stocks after the simulation is complete and one for the value of your stocks after you’ve been truncating to three decimal places at each step.

Start by initializing these variables to 100:

>>> actual_value, truncated_value = 100, 100

Now run the simulation for 1,000,000 seconds (approximately 11.5 days). For each second, generate a random value between -0.05 and 0.05 with the uniform() function in the random module, and then update actual and truncated:

>>> import random
>>> random.seed(100)

>>> for _ in range(1_000_000):
...     delta = random.uniform(-0.05, 0.05)
...     actual_value = actual_value + delta
...     truncated_value = truncate(truncated_value + delta)

>>> actual_value

>>> truncated_value

The meat of the simulation takes place in the for loop, which loops over the range of numbers between 0 and 999,999. The value taken from range() at each step is stored in the variable _, which you use here because you don’t actually need this value inside of the loop.

At each step of the loop, random.uniform() generates a new random number between -0.05 and 0.05, which you assign to the variable delta. You calculate the new value of your investment by adding delta to actual_value, and then you calculate the truncated total by adding delta to truncated_value and then truncating this value with truncate().

As you can see by inspecting the actual_value variable after running the loop, you only lost about $3.55. However, if you’d been looking at truncated_value, you’d have thought that you’d lost almost all of your money!

Ignoring for the moment that round() doesn’t behave quite as you expect, go ahead and try rerunning the simulation. You’ll use round() this time to round to three decimal places at each step, and you’ll seed the simulation again to get the same results as before:

>>> random.seed(100)
>>> actual_value, rounded_value = 100, 100

>>> for _ in range(1_000_000):
...     delta = random.uniform(-0.05, 0.05)
...     actual_value = actual_value + delta
...     rounded_value = round(rounded_value + delta, 3)

>>> actual_value

>>> rounded_value

What a difference!

Shocking as it may seem, this exact error caused quite a stir in the early 1980s when the system designed for recording the value of the Vancouver Stock Exchange truncated the overall index value to three decimal places instead of rounding. Rounding errors have swayed elections and even resulted in the loss of life.

How you round numbers is important, and as a responsible developer and software designer, you need to know what the common issues are and how to deal with them. So it’s time to dive in and investigate what the different rounding methods are and how you can implement each one in pure Python.

Basic but Biased Rounding Strategies

There’s a plethora of rounding strategies, each with advantages and disadvantages. In this section, you’ll learn about some of the simplest techniques and see how they can influence your data. Later, you’ll learn about better methods.


The simplest, albeit crudest, method for rounding a number is to truncate the number to a given number of digits. When you truncate a number, you replace each digit after a given position with 0. Here are some examples:

Value Truncated To Result
12.345 Tens place 10
12.345 Ones place 12
12.345 Tenths place 12.3
12.345 Hundredths place 12.34

You’ve already seen one way to implement this in the truncate() function from the How Much Impact Can Rounding Have? section. In that function, the input number was truncated to three decimal places by:

  • Multiplying the number by 1000 to shift the decimal point three places to the right
  • Taking the integer part of that new number with int()
  • Shifting the decimal place three places back to the left by dividing by 1000

You can generalize this process by replacing 1000 with the number 10ᵖ. That’s 10 raised to the p power, where p is the number of decimal places to truncate to:


def truncate(n, decimals=0):
    multiplier = 10**decimals
    return int(n * multiplier) / multiplier

In this version of truncate(), the second argument defaults to 0 so that if you don’t pass a second argument to the function, then truncate() returns the integer part of whatever number you passed to it.

The truncate() function works well for both positive and negative numbers:

>>> from rounding import truncate

>>> truncate(12.5)

>>> truncate(-5.963, 1)

>>> truncate(1.625, 2)

You can even pass a negative number to decimals to truncate digits to the left of the decimal point:

>>> truncate(125.6, -1)

>>> truncate(-1374.25, -3)

When you truncate a positive number, you’re rounding it down. Likewise, truncating a negative number rounds that number up. In a sense, truncation is a combination of rounding methods depending on the sign of the number that you’re rounding.

Next up, you’ll take a look at each of these rounding methods individually, starting with rounding up.

Rounding Up

The second rounding strategy that you’ll look at is called rounding up. This strategy always rounds a number up to a specified number of digits. The following table summarizes this strategy:

Value Round Up To Result
12.345 Tens place 20
12.345 Ones place 13
12.345 Tenths place 12.4
12.345 Hundredths place 12.35

To implement the rounding up strategy in Python, you’ll use the ceil() function from the math module.

The ceil() function gets its name from the mathematical term ceiling, which describes the nearest integer that’s greater than or equal to a given number.

Every number that’s not an integer lies between two consecutive integers. For example, the number 3.7 lies in the interval between 3 and 4. The ceiling is the greater of the two endpoints of the interval. The lesser of the two endpoints is the floor. Thus, the ceiling of 3.7 is 4, and the floor of 3.7 is 3.

In mathematics, a special function called the ceiling function maps every number to its ceiling. To allow the ceiling function to accept integers, the ceiling of an integer is defined to be the integer itself. So the ceiling of the number 2 is 2.

In Python, math.ceil() implements the ceiling function and always returns the nearest integer that’s greater than or equal to its input:

>>> import math

>>> math.ceil(3.7)

>>> math.ceil(2)

>>> math.ceil(-0.5)

Notice that the ceiling of -0.5 is 0, not -1. This makes sense because 0 is the nearest integer to -0.5 that’s greater than or equal to -0.5.

Now write a function called round_up() that implements the rounding up strategy:


import math

# ...

def round_up(n, decimals=0):
    multiplier = 10**decimals
    return math.ceil(n * multiplier) / multiplier

You may notice that round_up() looks a lot like truncate(). First, you shift the decimal point in n the correct number of places to the right by multiplying n by 10**decimals. You round this new value up to the nearest integer using math.ceil(), and then you shift the decimal point back to the left by dividing by 10**decimals.

This pattern of shifting the decimal point, applying some rounding method to round to an integer, and then shifting the decimal point back will come up over and over again as you investigate more rounding methods. This is, after all, the mental algorithm that humans use to round numbers by hand.

Take a look at how well round_up() works for different inputs:

>>> from rounding import round_up

>>> round_up(1.1)

>>> round_up(1.23, 1)

>>> round_up(1.543, 2)

Just like with truncate(), you can pass a negative value to decimals:

>>> round_up(22.45, -1)

>>> round_up(1352, -2)

When you pass a negative number to decimals, the number in the first argument of round_up() is rounded to the correct number of digits to the left of the decimal point.

Take a guess at what round_up(-1.5) returns:

>>> round_up(-1.5)

Is -1.0 what you expected?

If you examine the logic used in defining round_up()—in particular, the way the math.ceil() function works—then it makes sense that round_up(-1.5) returns -1.0. However, some people naturally expect symmetry around zero when rounding numbers, so that if 1.5 gets rounded up to 2, then -1.5 should get rounded up to -2.

It’s useful to establish some terminology. For this tutorial, you’ll use the terms round up and round down according to the following diagram:

Round up to the right and down to the left
Round up to the right and down to the left. (Image: David Amos)

Rounding up always rounds a number to the right on the number line, and rounding down always rounds a number to the left on the number line.

Rounding Down

The counterpart to rounding up is the rounding down strategy, which always rounds a number down to a specified number of digits. Here are some examples illustrating this strategy:

Value Rounded Down To Result
12.345 Tens place 10
12.345 Ones place 12
12.345 Tenths place 12.3
12.345 Hundredths place 12.34

To implement the rounding down strategy in Python, you can follow the same algorithm that you used for both trunctate() and round_up(). First shift the decimal point, then round to an integer, and finally shift the decimal point back.

In round_up(), you used math.ceil() to round up to the ceiling of the number after shifting the decimal point. For the rounding down strategy, though, you need to round to the floor of the number after shifting the decimal point.

Luckily, the math module has a floor() function that returns the floor of its input:

>>> import math

>>> math.floor(1.2)

>>> math.floor(-0.5)

Here’s the definition of round_down():


# ...

def round_down(n, decimals=0):
    multiplier = 10**decimals
    return math.floor(n * multiplier) / multiplier

That looks just like round_up(), except you’ve replaced math.ceil() with math.floor().

You can test round_down() on a few different values:

>>> from rounding import round_down

>>> round_down(1.5)

>>> round_down(1.37, 1)

>>> round_down(-0.5)

The effects of round_up() and round_down() can be pretty extreme. By rounding the numbers in a large dataset up or down, you could potentially remove a ton of precision and drastically alter computations made from the data.

Before you discuss any more rounding strategies, take a moment to consider how rounding can make your data biased.

Interlude: Rounding Bias

You’ve now seen three rounding methods: truncate(), round_up(), and round_down(). All three of these techniques are rather crude when it comes to preserving a reasonable amount of precision for a given number.

There’s one important difference in truncate() vs round_up() and round_down() that highlights an important aspect of rounding: symmetry around zero.

Recall that round_up() isn’t symmetric around zero. In mathematical terms, a function f(x) is symmetric around zero if, for all values of x, f(x) + f(-x) = 0. For example, round_up(1.5) returns 2, but round_up(-1.5) returns -1. The round_down() function isn’t symmetric around 0, either.

On the other hand, the truncate() function is symmetric around zero. This is because, after shifting the decimal point to the right, truncate() chops off the remaining digits. When the initial value is positive, this amounts to rounding the number down. Negative numbers are rounded up. So, truncate(1.5) returns 1, and truncate(-1.5) returns -1.

The concept of symmetry introduces the notion of rounding bias, which describes how rounding affects numeric data in a dataset.

The rounding up strategy has a round toward positive infinity bias, because it’ll always round the value up in the direction of positive infinity. Likewise, the rounding down strategy has a round toward negative infinity bias.

The truncation strategy exhibits a round toward negative infinity bias on positive values and a round toward positive infinity for negative values. Rounding functions with this behavior are said to have a round toward zero bias, in general.

To see how this works in practice, consider the following list of floats:

>>> numbers = [1.25, -2.67, 0.43, -1.79, 8.19, -4.32]

You can compute the mean of the values in numbers using the statistics.mean() function:

>>> import statistics

>>> statistics.mean(numbers)

Now apply each of truncate(), round_up(), and round_down() in a generator expression to round each number in numbers to one decimal place and calculate the new mean:

>>> from rounding import truncate, round_up, round_down

>>> [truncate(n, 1) for n in numbers]
[1.2, -2.6, 0.4, -1.7, 8.1, -4.3]

>>> statistics.mean(truncate(n, 1) for n in numbers)

>>> statistics.mean(round_up(n, 1) for n in numbers)

>>> statistics.mean(round_down(n, 1) for n in numbers)

After every number in numbers is rounded up, the new mean is about 0.233, which is greater than the actual mean of about 0.182. Rounding down shifts the mean downward to about 0.133. The mean of the truncated values is about 0.183 and is the closest to the actual mean.

This example does not imply that you should always truncate when you need to round individual values while preserving a mean value as closely as possible. The numbers list contains an equal number of positive and negative values. The truncate() function would behave just like round_up() on a list of all positive values, and just like round_down() on a list of all negative values.

What this example does illustrate is the effect that rounding bias has on values computed from data that’s been rounded. You’ll need to keep these effects in mind when drawing conclusions from data that’s been rounded.

Typically, when rounding, you’re interested in rounding to the nearest number with some specified precision, instead of just rounding everything up or down.

For example, if someone asks you to round the numbers 1.23 and 1.28 to one decimal place, then you would probably respond quickly with 1.2 and 1.3. The truncate(), round_up(), and round_down() functions don’t do anything like this.

What about the number 1.25? You probably immediately think to round this to 1.3, but in reality, 1.25 is equidistant from 1.2 and 1.3. In a sense, 1.2 and 1.3 are both the nearest numbers to 1.25 with precision of a single decimal place. The number 1.25 is called a tie with respect to 1.2 and 1.3. In cases like this, you must assign a tiebreaker.

The way that most people learn to break ties is by rounding to the greater of the two possible numbers.

Better Rounding Strategies in Python

In this section, you’ll continue your exploration of rounding strategies. You’ve seen some weaknesses in the methods that you’ve worked with so far. The upcoming techniques are better at dealing with biases, but they’re also a bit more complex to consider and to implement.

Rounding Half Up

The rounding half up strategy rounds every number to the nearest number with the specified precision and breaks ties by rounding up. Here are some examples:

Value Round Half Up To Result
13.825 Tens place 10
13.825 Ones place 14
13.825 Tenths place 13.8
13.825 Hundredths place 13.83

To implement the rounding half up strategy in Python, you start as usual by shifting the decimal point to the right by the desired number of places. At this point, though, you need a way to determine if the digit just after the shifted decimal point is less than or greater than or equal to 5.

One way to do this is to add 0.5 to the shifted value and then round down with math.floor(). This works because:

  • If the digit in the first decimal place of the shifted value is less than 5, then adding 0.5 won’t change the integer part of the shifted value, so the floor is equal to the integer part.

  • If the first digit after the decimal place is greater than or equal to 5, then adding 0.5 will increase the integer part of the shifted value by 1, so the floor is equal to this larger integer.

Here’s what this looks like in Python:


# ...

def round_half_up(n, decimals=0):
    multiplier = 10**decimals
    return math.floor(n * multiplier + 0.5) / multiplier

Notice that round_half_up() looks a lot like round_down(). This might be somewhat counterintuitive, but internally round_half_up() only rounds down. The trick is to add the 0.5 after shifting the decimal point so that the result of rounding down matches the expected value.

Now you can test round_half_up() on a few values to see that it works:

>>> from rounding import round_half_up

>>> round_half_up(1.23, 1)

>>> round_half_up(1.28, 1)

>>> round_half_up(1.25, 1)

Since round_half_up() always breaks ties by rounding to the greater of the two possible values, negative values like -1.5 round to -1, not to -2:

>>> round_half_up(-1.5)

>>> round_half_up(-1.25, 1)

Great! You can now finally get that result that the built-in round() function denied you:

>>> round_half_up(2.5)

Before you get too excited though, check out what happens when you try and round -1.225 to 2 decimal places:

>>> round_half_up(-1.225, 2)

Wait. You just noted how ties get rounded to the greater of the two possible values. -1.225 is smack in the middle of -1.22 and -1.23. Since -1.22 is the greater of these two, round_half_up(-1.225, 2) should return -1.22. But instead, you got -1.23.

Is there a bug in the round_half_up() function?

When round_half_up() rounds -1.225 to two decimal places, the first thing it does is multiply -1.225 by 100. Make sure this works as expected:

>>> -1.225 * 100

Well … that’s wrong! But it does explain why round_half_up(-1.225, 2) returns -1.23. Now continue the round_half_up() algorithm step-by-step, utilizing _ in the REPL to recall the last value output at each step:

>>> _ + 0.5

>>> math.floor(_)

>>> _ / 100

Even though -122.00000000000001 is really close to -122, the nearest integer that’s less than or equal to it is -123. When you shift the decimal point back to the left, the final value is -1.23.

Well, now you know how round_half_up(-1.225, 2) returns -1.23 even though there’s no logical error, but why does Python say that -1.225 * 100 is -122.50000000000001? Is there a bug in Python?

The fact that Python says that -1.225 * 100 is -122.50000000000001 is an artifact of the floating-point representation error. You might be asking yourself, Okay, but is there a way to fix this? A better question to ask yourself is Do I need to fix this?

Floating-point numbers don’t have exact precision, and therefore you should not use them in situations where precision is paramount. For applications where the exact precision is necessary, you can use the Decimal class from Python’s decimal module. You’ll learn more about the Decimal class below.

If you’ve determined that Python’s standard float class is sufficient for your application, then some occasional errors in round_half_up() due to the floating-point representation error shouldn’t be a concern.

Now that you’ve gotten a taste of how machines round numbers in memory, you can continue your journey into rounding strategies by looking at another way to break a tie.

Rounding Half Down

The rounding half down strategy rounds to the nearest number with the desired precision, just like the rounding half up method, except that it breaks ties by rounding to the lesser of the two numbers. Here are some examples:

Value Round Half Down To Result
13.825 Tens place 10
13.825 Ones place 14
13.825 Tenths place 13.8
13.825 Hundredths place 13.82

You can implement the rounding half down strategy in Python by replacing math.floor() in the round_half_up() function with math.ceil() and subtracting 0.5 instead of adding:


# ...

def round_half_down(n, decimals=0):
    multiplier = 10**decimals
    return math.ceil(n * multiplier - 0.5) / multiplier

Now check round_half_down() against a few test cases:

>>> from rounding import round_half_down

>>> round_half_down(1.5)

>>> round_half_down(-1.5)

>>> round_half_down(2.25, 1)

Both round_half_up() and round_half_down() have no bias in general. However, rounding data with lots of ties does introduce a bias. For an extreme example, consider the following list of numbers:

>>> numbers = [-2.15, 1.45, -4.35, 12.75]

Compute the mean of these numbers:

>>> import statistics

>>> statistics.mean(numbers)

Next, compute the mean on the data after rounding to one decimal place with round_half_up() and round_half_down():

>>> statistics.mean(round_half_up(n, 1) for n in numbers)

>>> statistics.mean(round_half_down(n, 1) for n in numbers)

Every number in numbers is a tie with respect to rounding to one decimal place. The round_half_up() function introduces a round toward positive infinity bias, and round_half_down() introduces a round toward negative infinity bias.

The remaining rounding strategies all attempt to mitigate these biases in different ways.

Rounding Half Away From Zero

If you examine round_half_up() and round_half_down() closely, then you’ll notice that neither of these functions is symmetric around zero:

>>> from rounding import round_half_down, round_half_up

>>> round_half_up(1.5)

>>> round_half_up(-1.5)

>>> round_half_down(1.5)

>>> round_half_down(-1.5)

One way to introduce symmetry is to always round a tie away from zero. The following table illustrates how this works:

Value Round Half Away From Zero To Result
15.25 Tens place 20
15.25 Ones place 15
15.25 Tenths place 15.3
-15.25 Tens place -20
-15.25 Ones place -15
-15.25 Tenths place -15.3

To implement the rounding half away from zero strategy on a number n, you start as usual by shifting the decimal point to the right a given number of places. Then you look at the digit d immediately to the right of the decimal place in this new number. At this point, there are four cases to consider:

  1. If n is positive and d >= 5, round up.
  2. If n is positive and d < 5, round down.
  3. If n is negative and d >= 5, round down.
  4. If n is negative and d < 5, round up.

After rounding according to one of the above four rules, you then shift the decimal place back to the left.

Given a number n and a value for decimals, you could implement this in Python by using round_half_up() and round_half_down():

if n >= 0:
    rounded = round_half_up(n, decimals)
    rounded = round_half_down(n, decimals)

That looks pretty good, but there’s actually a simpler way!

If you first take the absolute value of n using Python’s built-in abs() function, then you can just use round_half_up() to round the number. Then all you need to do is give the rounded number the same sign as n. One way to do this is using the math.copysign() function.

math.copysign() takes two numbers a and b and returns a with the sign of b:

>>> import math

>>> math.copysign(1, -2)

Notice that math.copysign() returns a float, even though both of its arguments were integers.

Using abs(), round_half_up(), and math.copysign(), you can implement the rounding half away from zero strategy in just two lines of Python:


# ...

def round_half_away_from_zero(n, decimals=0):
    rounded_abs = round_half_up(abs(n), decimals)
    return math.copysign(rounded_abs, n)

In round_half_away_from_zero(), you round the absolute value of n to decimals decimal places using round_half_up(), and you assign this result to the variable rounded_abs. Then you apply the original sign of n to rounded_abs using math.copysign(), and your function returns this final value with the correct sign.

Checking round_half_away_from_zero() on a few different values shows that the function behaves as expected:

>>> from rounding import round_half_away_from_zero

>>> round_half_away_from_zero(1.5)

>>> round_half_away_from_zero(-1.5)

>>> round_half_away_from_zero(-12.75, 1)

The round_half_away_from_zero() function rounds numbers the way most people tend to round numbers in everyday life. Besides being the most familiar rounding function that you’ve seen so far, round_half_away_from_zero() also eliminates rounding bias well in datasets that have an equal number of positive and negative ties.

Check out how well round_half_away_from_zero() mitigates rounding bias in the example from the previous section:

>>> numbers = [-2.15, 1.45, -4.35, 12.75]

>>> import statistics
>>> statistics.mean(numbers)

>>> statistics.mean(round_half_away_from_zero(n, 1) for n in numbers)

The mean value of the numbers in numbers is preserved exactly when you round each number in numbers to one decimal place with round_half_away_from_zero()!

However, round_half_away_from_zero() will exhibit a rounding bias when you round every number in datasets with only positive ties, only negative ties, or more ties of one sign than the other. It only mitigates bias well if there’s a similar number of positive and negative ties in the dataset.

How do you handle situations where you have a drastically different number of positive and negative ties? The answer to this question brings you full circle to that deceptive function from the beginning of this tutorial: Python’s built-in round() function.

Rounding Half to Even

One way to mitigate rounding bias when rounding values in a dataset is to round ties to the nearest even number at the desired precision. Here are some examples of how to do that:

Value Round Half To Even To Result
15.255 Tens place 20
15.255 Ones place 15
15.255 Tenths place 15.3
15.255 Hundredths place 15.26

The rounding half to even strategy is the strategy that Python’s built-in round() function uses, and it’s the default rounding rule in the IEEE-754 standard. This strategy works under the assumption that there’s an equal probability of rounding a tie in a dataset down or up. In practice, this is usually the case.

Now you know why round(2.5) returns 2. It’s not a mistake. It is a conscious design decision based on solid recommendations.

To prove to yourself that round() really does round to even, try it on a few different values:

>>> round(4.5)

>>> round(3.5)

>>> round(1.75, 1)

>>> round(1.65, 1)

The round() function is nearly free from bias, but it isn’t perfect. For example, rounding bias can still creep in if the majority of the ties in your dataset round up to even instead of rounding down. Strategies that mitigate bias even better than rounding half to even do exist, but they’re somewhat obscure and only necessary in extreme circumstances.

Finally, round() suffers from the same hiccups that you saw in round_half_up(), thanks to floating-point representation error:

>>> # Expected value: 2.68
>>> round(2.675, 2)

You shouldn’t be concerned with these occasional errors if floating-point precision is sufficient for your application.

When precision is paramount, you should use Python’s Decimal class.

The Decimal Class

Python’s decimal module is one of those batteries-included features of the language that you might not be aware of if you’re new to Python. You can find the guiding principle of the decimal module in the documentation:

Decimal “is based on a floating-point model which was designed with people in mind, and necessarily has a paramount guiding principle – computers must provide an arithmetic that works in the same way as the arithmetic that people learn at school.” – excerpt from the decimal arithmetic specification. (Source)

The benefits of the decimal module include:

  • Exact decimal representation: 0.1 is actually 0.1, and 0.1 + 0.1 + 0.1 - 0.3 returns 0, as you’d expect.
  • Preservation of significant digits: When you add 1.20 and 2.50, the result is 3.70, with the trailing zero maintained to indicate significance.
  • User-alterable precision: The default precision of the decimal module is twenty-eight digits, but you can alter this value to match the problem at hand.

But how does rounding work in the decimal module? Start by typing the following into a Python REPL:

>>> import decimal
>>> decimal.getcontext()
    traps=[InvalidOperation, DivisionByZero, Overflow]

decimal.getcontext() returns a Context object representing the default context of the decimal module. The context includes the default precision and the default rounding strategy, among other things.

As you can see in the example above, the default rounding strategy for the decimal module is ROUND_HALF_EVEN. This aligns with the built-in round() function and should be the preferred rounding strategy for most purposes.

Now declare a number using the decimal module’s Decimal class. To do so, create a new Decimal instance by passing a string containing the desired value:

>>> from decimal import Decimal
>>> Decimal("0.1")

You’ve created a Decimal object. You’ll now see how to make calculations with it.

Just for fun, test the assertion that Decimal maintains exact decimal representation:

>>> Decimal("0.1") + Decimal("0.1") + Decimal("0.1")

Ahhh. That’s satisfying, isn’t it?

You can round a Decimal with the .quantize() method or with the round() built-in function:

>>> Decimal("1.65").quantize(Decimal("1.0"))

>>> round(Decimal("1.65"), 1)

The .quantize() method probably looks a little funky, so you’ll break it down. The Decimal("1.0") argument in .quantize() determines the number of decimal places to round the number. Since 1.0 has one decimal place, the number 1.65 rounds to a single decimal place. The default rounding strategy is rounding half to even, so the result is 1.6.

Recall that the round() function, which also uses the rounding half to even strategy, failed to round 2.675 to two decimal places correctly. Instead of giving you 2.68, round(2.675, 2) returns 2.67. Thanks to the decimal module’s exact decimal representation, you won’t have this issue with the Decimal class:

>>> round(Decimal("2.675"), 2)

Another benefit of the decimal module is that it automatically takes care of rounding after performing arithmetic, and it preserves significant digits. To see this in action, change the default precision from twenty-eight digits to two, and then add the numbers 1.23 and 2.32:

>>> decimal.getcontext().prec = 2
>>> Decimal("1.23") + Decimal("2.32")

To change the precision, you call decimal.getcontext() and set the .prec attribute. If setting the attribute on a function call looks odd to you, you can do this because .getcontext() returns a special Context object that represents the current internal context containing the default parameters that the decimal module uses.

The exact value of 1.23 plus 2.32 is 3.55. Since the precision is now two digits, and the rounding strategy is set to the default of rounding half to even, the value 3.55 is automatically rounded to 3.6.

To change the default rounding strategy, you can set the decimal.getcontext().rounding property to any one of several flags. The following table summarizes these flags and which rounding strategy they implement:

Flag Rounding Strategy
decimal.ROUND_DOWN Truncation
decimal.ROUND_CEILING Rounding up
decimal.ROUND_FLOOR Rounding down
decimal.ROUND_UP Rounding away from zero
decimal.ROUND_HALF_UP Rounding half away from zero
decimal.ROUND_HALF_DOWN Rounding half toward zero
decimal.ROUND_HALF_EVEN Rounding half to even
decimal.ROUND_05UP Rounding up and rounding toward zero

The first point to notice is that the decimal module’s naming scheme differs from what you were using earlier in the tutorial. For example, decimal.ROUND_UP implements the rounding away from zero strategy, which actually rounds negative numbers down.

Secondly, some of the rounding strategies mentioned in the table may look unfamiliar since you haven’t explored them here. You’ve already seen how decimal.ROUND_HALF_EVEN works, so you’ll take a look at each of the others in action.

The decimal.ROUND_CEILING strategy works just like the round_up() function that you defined earlier:

>>> decimal.getcontext().rounding = decimal.ROUND_CEILING

>>> round(Decimal("1.32"), 1)

>>> round(Decimal("-1.32"), 1)

Notice that the results of decimal.ROUND_CEILING aren’t symmetric around zero.

The decimal.ROUND_FLOOR strategy works just like your round_down() function:

>>> decimal.getcontext().rounding = decimal.ROUND_FLOOR

>>> round(Decimal("1.32"), 1)

>>> round(Decimal("-1.32"), 1)

Like decimal.ROUND_CEILING, the decimal.ROUND_FLOOR strategy isn’t symmetric around zero.

The decimal.ROUND_DOWN and decimal.ROUND_UP strategies have somewhat deceptive names. Both ROUND_DOWN and ROUND_UP are symmetric around zero:

>>> decimal.getcontext().rounding = decimal.ROUND_DOWN

>>> round(Decimal("1.32"), 1)

>>> round(Decimal("-1.32"), 1)

>>> decimal.getcontext().rounding = decimal.ROUND_UP

>>> round(Decimal("1.32"), 1)

>>> round(Decimal("-1.32"), 1)

The decimal.ROUND_DOWN strategy rounds numbers toward zero, just like the truncate() function. On the other hand, decimal.ROUND_UP rounds everything away from zero. This is a clear break from the terminology that you were using earlier in the article, so keep that in mind when you’re working with the decimal module.

There are three strategies in the decimal module that allow for more nuanced rounding. The decimal.ROUND_HALF_UP method rounds everything to the nearest number and breaks ties by rounding away from zero:

>>> decimal.getcontext().rounding = decimal.ROUND_HALF_UP

>>> round(Decimal("1.35"), 1)

>>> round(Decimal("-1.35"), 1)

Notice that decimal.ROUND_HALF_UP works just like your round_half_away_from_zero() and not like round_half_up().

There’s also a decimal.ROUND_HALF_DOWN strategy that breaks ties by rounding toward zero:

>>> decimal.getcontext().rounding = decimal.ROUND_HALF_DOWN

>>> round(Decimal("1.35"), 1)

>>> round(Decimal("-1.35"), 1)

The final rounding strategy available in the decimal module is very different from anything that you’ve seen so far:

>>> decimal.getcontext().rounding = decimal.ROUND_05UP

>>> round(Decimal("1.38"), 1)

>>> round(Decimal("1.35"), 1)

>>> round(Decimal("-1.35"), 1)

In the above examples, it looks as if decimal.ROUND_05UP rounds everything toward zero. In fact, this is exactly how decimal.ROUND_05UP works, unless the result of rounding ends in a 0 or 5. In that case, the number gets rounded away from zero:

>>> round(Decimal("1.49"), 1)

>>> round(Decimal("1.51"), 1)

In the first example, you first round the number 1.49 toward zero in the second decimal place, producing 1.4. Since 1.4 doesn’t end in a 0 or a 5, you leave it as is. On the other hand, you round 1.51 toward zero in the second decimal place, resulting in the number 1.5. This ends in a 5, so you then round the first decimal place away from zero to 1.6.

In this section, you’ve only focused on the rounding aspects of the decimal module. There are numerous other features that make decimal an excellent choice for applications where the standard floating-point precision is inadequate, such as banking and some problems in scientific computing.

For more information on Decimal, check out the Quick-start Tutorial in the Python docs.

Next, turn your attention to two staples of Python’s scientific computing and data science stacks: NumPy and pandas.

Rounding NumPy Arrays

In the domains of data science and scientific computing, you often store your data as a NumPy array. One of NumPy’s most powerful features is its use of vectorization and broadcasting to apply operations to an entire array at once instead of one element at a time.

You’ll generate some data by creating a 3×4 NumPy array of pseudo-random numbers:

>>> import numpy as np
>>> rng = np.random.default_rng(seed=444)

>>> data = rng.normal(size=(3, 4))
>>> data
array([[-0.69011449, -2.10455459, -0.78789037,  0.93417409],
       [ 0.58204206,  0.7022188 , -0.23768089,  0.1183704 ],
       [-0.41049553, -0.55583871,  0.00684936, -0.50000095]])

First, you create a random number generator and add a seed so that you can easily reproduce the output. Then you create a 3×4 NumPy array of floating-point numbers with .normal(), which produces normally distributed random numbers.

To round all of the values in the data array, you can pass data as the argument to the np.round() function. You set the desired number of decimal places with the decimals keyword argument. The NumPy function uses the round half to even strategy, just like Python’s built-in round() function.

For example, the following code rounds all of the values in data to three decimal places:

>>> np.round(data, decimals=3)
array([[-0.69 , -2.105, -0.788,  0.934],
       [ 0.582,  0.702, -0.238,  0.118],
       [-0.41 , -0.556,  0.007, -0.5  ]])

Alternatively, you can also use the .round() method on the array:

>>> data.round(decimals=3)
array([[-0.69 , -2.105, -0.788,  0.934],
       [ 0.582,  0.702, -0.238,  0.118],
       [-0.41 , -0.556,  0.007, -0.5  ]])

Typically, the .round() method call will be slightly faster than calling the np.round() function, as the latter delegates to the method under the hood.

If you need to round the data in your array to integers, then NumPy offers several options:

The np.ceil() function rounds every value in the array to the nearest integer greater than or equal to the original value:

>>> np.ceil(data)
array([[-0., -2., -0.,  1.],
       [ 1.,  1., -0.,  1.],
       [-0., -0.,  1., -0.]])

Hey, that’s a new number! Negative zero!

Actually, the IEEE-754 standard requires the implementation of both a positive and negative zero. What possible use is there for something like this? Wikipedia knows the answer:

Informally, one may use the notation “−0” for a negative value that was rounded to zero. This notation may be useful when a negative sign is significant; for example, when tabulating Celsius temperatures, where a negative sign means below freezing. (Source)

To round every value down to the nearest integer, use np.floor():

>>> np.floor(data)
array([[-1., -3., -1.,  0.],
       [ 0.,  0., -1.,  0.],
       [-1., -1.,  0., -1.]])

You can also truncate each value to its integer component with np.trunc():

>>> np.trunc(data)
array([[-0., -2., -0.,  0.],
       [ 0.,  0., -0.,  0.],
       [-0., -0.,  0., -0.]])

Finally, to round to the nearest integer using the rounding half to even strategy, use np.rint():

>>> np.rint(data)
array([[-1., -2., -1.,  1.],
       [ 1.,  1., -0.,  0.],
       [-0., -1.,  0., -1.]])

You might have noticed that a lot of the rounding strategies that you studied earlier are missing here. For the vast majority of situations, the .round() method is all you need. If you need to implement another strategy, such as round_half_up(), you can do so with a small modification to your earlier code:


import numpy as np

# ...

def round_half_up(n, decimals=0):
    multiplier = 10**decimals
    return np.floor(n * multiplier + 0.5) / multiplier

Here, you’ve replaced math.floor() with np.floor(). You can do similar modifications to your other rounding functions.

Thanks to NumPy’s vectorized operations, the updated function works just as you expect:

>>> from rounding import round_half_up

>>> round_half_up(data, decimals=2)
array([[-0.69, -2.1 , -0.79,  0.93],
       [ 0.58,  0.7 , -0.24,  0.12],
       [-0.41, -0.56,  0.01, -0.5 ]])

Now that you’re a NumPy rounding master, take a look at Python’s other data science heavyweight: the pandas library.

Rounding pandas Series and DataFrame

The pandas library has become a staple for data scientists and data analysts who work in Python. In the words of Real Python’s own Joe Wyndham:

pandas is a game changer for data science and analytics, particularly if you came to Python because you were searching for something more powerful than Excel and VBA. (Source)

The two main pandas data structures are the DataFrame, which in very loose terms works sort of like an Excel spreadsheet, and the Series, which you can think of as a column in a spreadsheet. You can round both Series and DataFrame objects efficiently using the Series.round() and DataFrame.round() methods:

>>> import numpy as np
>>> import pandas as pd

>>> rng = np.random.default_rng(seed=444)

>>> series = pd.Series(rng.normal(size=4))
>>> series
0   -0.690114
1   -2.104555
2   -0.787890
3    0.934174
dtype: float64

>>> series.round(2)
0   -0.69
1   -2.10
2   -0.79
3    0.93
dtype: float64

>>> df = pd.DataFrame(rng.normal(size=(3, 3)), columns=["A", "B", "C"])
>>> df
          A         B         C
0  0.582042  0.702219 -0.237681
1  0.118370 -0.410496 -0.555839
2  0.006849 -0.500001 -0.141762

>>> df.round(3)
       A      B      C
0  0.582  0.702 -0.238
1  0.118 -0.410 -0.556
2  0.007 -0.500 -0.142

The DataFrame.round() method can also accept a dictionary or a Series to specify a different precision for each column. In the following example, you round the first column of df to one decimal place, the second to two, and the third to three decimal places:

>>> df.round({"A": 1, "B": 2, "C": 3})
     A     B      C
0  0.6  0.70 -0.238
1  0.1 -0.41 -0.556
2  0.0 -0.50 -0.142

If you need more rounding flexibility, you can apply NumPy’s floor(), ceil(), trunc(), and rint() functions to pandas Series and DataFrame objects:

>>> np.floor(df)
     A    B    C
0  0.0  0.0 -1.0
1  0.0 -1.0 -1.0
2  0.0 -1.0 -1.0

>>> np.ceil(df)
     A    B    C
0  1.0  1.0 -0.0
1  1.0 -0.0 -0.0
2  1.0 -0.0 -0.0

>>> np.trunc(df)
     A    B    C
0  0.0  0.0 -0.0
1  0.0 -0.0 -0.0
2  0.0 -0.0 -0.0

>>> np.rint(df)
     A    B    C
0  1.0  1.0 -0.0
1  0.0 -0.0 -1.0
2  0.0 -1.0 -0.0

The modified round_half_up() function from the previous section will also work here:

>>> from rounding import round_half_up

>>> round_half_up(df, decimals=2)
      A     B     C
0  0.58  0.70 -0.24
1  0.12 -0.41 -0.56
2  0.01 -0.50 -0.14

Congratulations, you’re well on your way to rounding mastery! You now know that there are more ways to round a number than there are taco combinations. (Well … maybe not!) You can implement numerous rounding strategies in pure Python, and you’ve sharpened your skills on rounding NumPy arrays and pandas Series and DataFrame objects.

There’s just one more step: knowing when to apply the right strategy.

Applications and Best Practices

The last stretch on your road to rounding virtuosity is understanding when to apply your newfound knowledge. In this section, you’ll learn some best practices to make sure you round your numbers the right way.

Store More and Round Late

When you deal with large sets of data, storage can be an issue. In most relational databases, each column in a table is designed to store a specific data type, and numeric data types are often assigned precision to help conserve memory.

For example, a temperature sensor may report the temperature in a long-running industrial oven every ten seconds, accurate to eight decimal places. You could use the readings from this to detect abnormal fluctuations in temperature that could indicate the failure of a heating element or some other component. So, there might be a Python script running that compares each incoming reading to the last to check for large fluctuations.

The readings from this sensor are also stored in a SQL database so that the daily average temperature inside the oven can be computed each day at midnight. The manufacturer of the heating element inside the oven recommends replacing the component whenever the daily average temperature drops .05 degrees below normal.

For this calculation, you only need three decimal places of precision. But you know from the incident at the Vancouver Stock Exchange that removing too much precision can drastically affect your calculation.

If you have the space available, then you should store the data at full precision. If storage is an issue, then a good rule of thumb is to store at least two or three more decimal places of precision than you need for your calculation.

Finally, when you compute the daily average temperature, you should calculate it to the full precision available and round the final answer.

Obey Local Currency Regulations

When you order a cup of coffee for $2.40 at the coffee shop, the merchant typically adds the required tax. The amount of that tax depends a lot on where you are geographically, but for the sake of argument, say it’s 6%. The tax to be added comes out to $0.144. Should you round this up to $0.15 or down to $0.14? The answer probably depends on the regulations set forth by the local government!

Situations like this can also arise when you’re converting one currency to another. In 1999, the European Commission on Economical and Financial Affairs codified the use of the rounding half away from zero strategy when converting currencies to the Euro, but other currencies may have adopted different regulations.

Another scenario, Swedish rounding, occurs when the minimum unit of currency at the accounting level in a country is smaller than the lowest unit of physical currency. For example, if a cup of coffee costs $2.54 after tax, but there are no one-cent coins in circulation, what do you do? The buyer won’t have the exact amount, and the merchant can’t make exact change.

A country’s government typically determines how to handle situations like this. You can find a list of rounding methods used by various countries on Wikipedia.

If you’re designing software for calculating currencies, then you should always check the local laws and regulations in your users’ locations.

When in Doubt, Round Ties to Even

When you’re rounding numbers in large datasets that are used in complex computations, the primary concern is limiting the growth of the error due to rounding.

Of all the methods that you’ve explored in this article, the rounding half to even strategy minimizes rounding bias the best. Fortunately, Python, NumPy, and pandas all default to this strategy, so by using the built-in rounding functions, you’re already well protected!


Whew! What a journey this has been!

In this article, you’ve learned that:

  • There are various rounding strategies, which you now know how to implement in pure Python.
  • Every rounding strategy inherently introduces a rounding bias, and the rounding half to even strategy mitigates this bias well, most of the time.
  • The way in which computers store floating-point numbers in memory naturally introduces a subtle rounding error, but you learned how to work around this with the decimal module in Python’s standard library.
  • You can round NumPy arrays and pandas Series and DataFrame objects.
  • There are best practices for rounding with real-world data.

Take the Quiz: Test your knowledge with our interactive “Rounding Numbers in Python” quiz. Upon completion you will receive a score so you can track your learning progress over time:

Interactive Quiz

Rounding Numbers in Python

Test your knowledge of rounding numbers in Python.

If you’re interested in learning more and digging into the nitty-gritty details of everything that you’ve covered, then the links below should keep you busy for quite a while.

At the very least, if you’ve enjoyed this tutorial and learned something new from it, pass it on to a friend or team member! Be sure to share your thoughts in the comments. Your fellow programmers would love to hear some of your own rounding-related battle stories!

Happy Pythoning!

Additional Resources

Rounding strategies and bias:

Floating-point and decimal specifications:

Interesting Reads:

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About David Amos

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