How to Round Numbers in Python

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)
2

Gasp!

Check out how round() handles the number 1.5:

>>> round(1.5)
2

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
96.45273913513529

>>> truncated_value
0.239

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
96.45273913513529

>>> rounded_value
96.258

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.

# rounding.py

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

>>> from rounding import truncate

>>> truncate(12.5)
12.0

>>> truncate(-5.963, 1)
-5.9

>>> truncate(1.625, 2)
1.62

>>> truncate(125.6, -1)
120.0

>>> truncate(-1374.25, -3)
-1000.0

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:

Python

>>> import math

>>> math.ceil(3.7)
4

>>> math.ceil(2)
2

>>> math.ceil(-0.5)
0

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:

Python

# rounding.py

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:

Python

>>> from rounding import round_up

>>> round_up(1.1)
2.0

>>> round_up(1.23, 1)
1.3

>>> round_up(1.543, 2)
1.55

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

Python

>>> round_up(22.45, -1)
30.0

>>> round_up(1352, -2)
1400.0

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:

Python

>>> round_up(-1.5)
-1.0

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:

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.

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>>> import math

>>> math.floor(1.2)
1

>>> math.floor(-0.5)
-1

# rounding.py

# ...

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

>>> from rounding import round_down

>>> round_down(1.5)
1.0

>>> round_down(1.37, 1)
1.3

>>> round_down(-0.5)
-1.0

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)
0.18166666666666653

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)
0.1833333333333333

>>> statistics.mean(round_up(n, 1) for n in numbers)
0.23333333333333325

>>> statistics.mean(round_down(n, 1) for n in numbers)
0.13333333333333316

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.py

# ...

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

>>> from rounding import round_half_up

>>> round_half_up(1.23, 1)
1.2

>>> round_half_up(1.28, 1)
1.3

>>> round_half_up(1.25, 1)
1.3

>>> round_half_up(-1.5)
-1.0

>>> round_half_up(-1.25, 1)
-1.2

>>> round_half_up(2.5)
3.0

>>> round_half_up(-1.225, 2)
-1.23

>>> -1.225 * 100
-122.50000000000001

>>> _ + 0.5
-122.00000000000001

>>> math.floor(_)
-123

>>> _ / 100
-1.23

>>> 0.1 + 0.1 + 0.1
0.30000000000000004

# rounding.py

# ...

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

>>> from rounding import round_half_down

>>> round_half_down(1.5)
1.0

>>> round_half_down(-1.5)
-2.0

>>> round_half_down(2.25, 1)
2.2

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

>>> import statistics

>>> statistics.mean(numbers)
1.925

>>> statistics.mean(round_half_up(n, 1) for n in numbers)
1.975

>>> statistics.mean(round_half_down(n, 1) for n in numbers)
1.8749999999999996

>>> from rounding import round_half_down, round_half_up

>>> round_half_up(1.5)
2.0

>>> round_half_up(-1.5)
-1.0

>>> round_half_down(1.5)
1.0

>>> round_half_down(-1.5)
-2.0

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

>>> import math

>>> math.copysign(1, -2)
-1.0

# rounding.py

# ...

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

>>> from rounding import round_half_away_from_zero

>>> round_half_away_from_zero(1.5)
2.0

>>> round_half_away_from_zero(-1.5)
-2.0

>>> round_half_away_from_zero(-12.75, 1)
-12.8

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

>>> import statistics
>>> statistics.mean(numbers)
1.925

>>> statistics.mean(round_half_away_from_zero(n, 1) for n in numbers)
1.925

>>> round(4.5)
4

>>> round(3.5)
4

>>> round(1.75, 1)
1.8

>>> round(1.65, 1)
1.6

>>> # Expected value: 2.68
>>> round(2.675, 2)
2.67

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()
Context(
    prec=28,
    rounding=ROUND_HALF_EVEN,
    Emin=-999999,
    Emax=999999,
    capitals=1,
    clamp=0,
    flags=[],
    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")
Decimal('0.1')

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

>>> Decimal(0.1)
Decimal('0.1000000000000000055511151231257827021181583404541015625')

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

>>> Decimal("0.1") + Decimal("0.1") + Decimal("0.1")
Decimal('0.3')

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"))
Decimal('1.6')

>>> round(Decimal("1.65"), 1)
Decimal('1.6')

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)
Decimal('2.68')

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")
Decimal('3.6')

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:

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)
Decimal('1.4')

>>> round(Decimal("-1.32"), 1)
Decimal('-1.3')

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)
Decimal('1.3')

>>> round(Decimal("-1.32"), 1)
Decimal('-1.4')

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)
Decimal('1.3')

>>> round(Decimal("-1.32"), 1)
Decimal('-1.3')

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

>>> round(Decimal("1.32"), 1)
Decimal('1.4')

>>> round(Decimal("-1.32"), 1)
Decimal('-1.4')

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)
Decimal('1.4')

>>> round(Decimal("-1.35"), 1)
Decimal('-1.4')

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)
Decimal('1.3')

>>> round(Decimal("-1.35"), 1)
Decimal('-1.3')

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)
Decimal('1.3')

>>> round(Decimal("1.35"), 1)
Decimal('1.3')

>>> round(Decimal("-1.35"), 1)
Decimal('-1.3')

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)
Decimal('1.4')

>>> round(Decimal("1.51"), 1)
Decimal('1.6')

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:

• numpy.ceil()
• numpy.floor()
• numpy.trunc()
• numpy.rint()

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:

# rounding.py

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.

Conclusion

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:

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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:

• Rounding, Wikipedia
• Rounding Numbers without Adding a Bias, from ZipCPU

Floating-point and decimal specifications:

• IEEE-754, Wikipedia
• IBM’s General Decimal Arithmetic Specification

Interesting Reads:

• What Every Computer Scientist Should Know About Floating-Point Arithmetic, David Goldberg, ACM Computing Surveys, March 1991
• Floating Point Arithmetic: Issues and Limitations, from python.org
• Why Python’s Integer Division Floors, by Guido van Rossum