I feel compelled to provide a counterpoint to Ashwini Chaudhary's answer. Despite appearances, the two-argument form of the round function does not round a Python float to a given number of decimal places, and it's often not the solution you want, even when you think it is. Let me explain...
The ability to round a (Python) float to some number of decimal places is something that's frequently requested, but turns out to be rarely what's actually needed. The beguilingly simple answer round(x, number_of_places) is something of an attractive nuisance: it looks as though it does what you want, but thanks to the fact that Python floats are stored internally in binary, it's doing something rather subtler. Consider the following example:
>>> round(52.15, 1)
52.1
With a naive understanding of what round does, this looks wrong: surely it should be rounding up to 52.2 rather than down to 52.1? To understand why such behaviours can't be relied upon, you need to appreciate that while this looks like a simple decimal-to-decimal operation, it's far from simple.
So here's what's really happening in the example above. (deep breath) We're displaying a decimal representation of the nearest binary floating-point number to the nearest n-digits-after-the-point decimal number to a binary floating-point approximation of a numeric literal written in decimal. So to get from the original numeric literal to the displayed output, the underlying machinery has made four separate conversions between binary and decimal formats, two in each direction. Breaking it down (and with the usual disclaimers about assuming IEEE 754 binary64 format, round-ties-to-even rounding, and IEEE 754 rules):
First the numeric literal
52.15gets parsed and converted to a Python float. The actual number stored is7339460017730355 * 2**-47, or52.14999999999999857891452847979962825775146484375.Internally as the first step of the
roundoperation, Python computes the closest 1-digit-after-the-point decimal string to the stored number. Since that stored number is a touch under the original value of52.15, we end up rounding down and getting a string52.1. This explains why we're getting52.1as the final output instead of52.2.Then in the second step of the
roundoperation, Python turns that string back into a float, getting the closest binary floating-point number to52.1, which is now7332423143312589 * 2**-47, or52.10000000000000142108547152020037174224853515625.Finally, as part of Python's read-eval-print loop (REPL), the floating-point value is displayed (in decimal). That involves converting the binary value back to a decimal string, getting
52.1as the final output.
In Python 2.7 and later, we have the pleasant situation that the two conversions in step 3 and 4 cancel each other out. That's due to Python's choice of repr implementation, which produces the shortest decimal value guaranteed to round correctly to the actual float. One consequence of that choice is that if you start with any (not too large, not too small) decimal literal with 15 or fewer significant digits then the corresponding float will be displayed showing those exact same digits:
>>> x = 15.34509809234
>>> x
15.34509809234
Unfortunately, this furthers the illusion that Python is storing values in decimal. Not so in Python 2.6, though! Here's the original example executed in Python 2.6:
>>> round(52.15, 1)
52.200000000000003
Not only do we round in the opposite direction, getting 52.2 instead of 52.1, but the displayed value doesn't even print as 52.2! This behaviour has caused numerous reports to the Python bug tracker along the lines of "round is broken!". But it's not round that's broken, it's user expectations. (Okay, okay, round is a little bit broken in Python 2.6, in that it doesn't use correct rounding.)
Short version: if you're using two-argument round, and you're expecting predictable behaviour from a binary approximation to a decimal round of a binary approximation to a decimal halfway case, you're asking for trouble.
So enough with the "two-argument round is bad" argument. What should you be using instead? There are a few possibilities, depending on what you're trying to do.
If you're rounding for display purposes, then you don't want a float result at all; you want a string. In that case the answer is to use string formatting:
>>> format(66.66666666666, '.4f') '66.6667' >>> format(1.29578293, '.6f') '1.295783'Even then, one has to be aware of the internal binary representation in order not to be surprised by the behaviour of apparent decimal halfway cases.
>>> format(52.15, '.1f') '52.1'If you're operating in a context where it matters which direction decimal halfway cases are rounded (for example, in some financial contexts), you might want to represent your numbers using the
Decimaltype. Doing a decimal round on theDecimaltype makes a lot more sense than on a binary type (equally, rounding to a fixed number of binary places makes perfect sense on a binary type). Moreover, thedecimalmodule gives you better control of the rounding mode. In Python 3,rounddoes the job directly. In Python 2, you need thequantizemethod.>>> Decimal('66.66666666666').quantize(Decimal('1e-4')) Decimal('66.6667') >>> Decimal('1.29578293').quantize(Decimal('1e-6')) Decimal('1.295783')In rare cases, the two-argument version of
roundreally is what you want: perhaps you're binning floats into bins of size0.01, and you don't particularly care which way border cases go. However, these cases are rare, and it's difficult to justify the existence of the two-argument version of theroundbuiltin based on those cases alone.
I feel compelled to provide a counterpoint to Ashwini Chaudhary's answer. Despite appearances, the two-argument form of the round function does not round a Python float to a given number of decimal places, and it's often not the solution you want, even when you think it is. Let me explain...
The ability to round a (Python) float to some number of decimal places is something that's frequently requested, but turns out to be rarely what's actually needed. The beguilingly simple answer round(x, number_of_places) is something of an attractive nuisance: it looks as though it does what you want, but thanks to the fact that Python floats are stored internally in binary, it's doing something rather subtler. Consider the following example:
>>> round(52.15, 1)
52.1
With a naive understanding of what round does, this looks wrong: surely it should be rounding up to 52.2 rather than down to 52.1? To understand why such behaviours can't be relied upon, you need to appreciate that while this looks like a simple decimal-to-decimal operation, it's far from simple.
So here's what's really happening in the example above. (deep breath) We're displaying a decimal representation of the nearest binary floating-point number to the nearest n-digits-after-the-point decimal number to a binary floating-point approximation of a numeric literal written in decimal. So to get from the original numeric literal to the displayed output, the underlying machinery has made four separate conversions between binary and decimal formats, two in each direction. Breaking it down (and with the usual disclaimers about assuming IEEE 754 binary64 format, round-ties-to-even rounding, and IEEE 754 rules):
First the numeric literal
52.15gets parsed and converted to a Python float. The actual number stored is7339460017730355 * 2**-47, or52.14999999999999857891452847979962825775146484375.Internally as the first step of the
roundoperation, Python computes the closest 1-digit-after-the-point decimal string to the stored number. Since that stored number is a touch under the original value of52.15, we end up rounding down and getting a string52.1. This explains why we're getting52.1as the final output instead of52.2.Then in the second step of the
roundoperation, Python turns that string back into a float, getting the closest binary floating-point number to52.1, which is now7332423143312589 * 2**-47, or52.10000000000000142108547152020037174224853515625.Finally, as part of Python's read-eval-print loop (REPL), the floating-point value is displayed (in decimal). That involves converting the binary value back to a decimal string, getting
52.1as the final output.
In Python 2.7 and later, we have the pleasant situation that the two conversions in step 3 and 4 cancel each other out. That's due to Python's choice of repr implementation, which produces the shortest decimal value guaranteed to round correctly to the actual float. One consequence of that choice is that if you start with any (not too large, not too small) decimal literal with 15 or fewer significant digits then the corresponding float will be displayed showing those exact same digits:
>>> x = 15.34509809234
>>> x
15.34509809234
Unfortunately, this furthers the illusion that Python is storing values in decimal. Not so in Python 2.6, though! Here's the original example executed in Python 2.6:
>>> round(52.15, 1)
52.200000000000003
Not only do we round in the opposite direction, getting 52.2 instead of 52.1, but the displayed value doesn't even print as 52.2! This behaviour has caused numerous reports to the Python bug tracker along the lines of "round is broken!". But it's not round that's broken, it's user expectations. (Okay, okay, round is a little bit broken in Python 2.6, in that it doesn't use correct rounding.)
Short version: if you're using two-argument round, and you're expecting predictable behaviour from a binary approximation to a decimal round of a binary approximation to a decimal halfway case, you're asking for trouble.
So enough with the "two-argument round is bad" argument. What should you be using instead? There are a few possibilities, depending on what you're trying to do.
If you're rounding for display purposes, then you don't want a float result at all; you want a string. In that case the answer is to use string formatting:
>>> format(66.66666666666, '.4f') '66.6667' >>> format(1.29578293, '.6f') '1.295783'Even then, one has to be aware of the internal binary representation in order not to be surprised by the behaviour of apparent decimal halfway cases.
>>> format(52.15, '.1f') '52.1'If you're operating in a context where it matters which direction decimal halfway cases are rounded (for example, in some financial contexts), you might want to represent your numbers using the
Decimaltype. Doing a decimal round on theDecimaltype makes a lot more sense than on a binary type (equally, rounding to a fixed number of binary places makes perfect sense on a binary type). Moreover, thedecimalmodule gives you better control of the rounding mode. In Python 3,rounddoes the job directly. In Python 2, you need thequantizemethod.>>> Decimal('66.66666666666').quantize(Decimal('1e-4')) Decimal('66.6667') >>> Decimal('1.29578293').quantize(Decimal('1e-6')) Decimal('1.295783')In rare cases, the two-argument version of
roundreally is what you want: perhaps you're binning floats into bins of size0.01, and you don't particularly care which way border cases go. However, these cases are rare, and it's difficult to justify the existence of the two-argument version of theroundbuiltin based on those cases alone.
Use the built-in function round():
In [23]: round(66.66666666666,4)
Out[23]: 66.6667
In [24]: round(1.29578293,6)
Out[24]: 1.295783
help on round():
round(number[, ndigits]) -> floating point number
Round a number to a given precision in decimal digits (default 0 digits). This always returns a floating point number. Precision may be negative.
Hello all
Not an expert coder, but I can usually pick things up in Python. However, I found something that stumped me and hoping I can get some help.
I have a pandas data frame. In that df, I have several columns of floats. For each column, each entry is a product of given values, those given values extend to the hundredths place. Once the product is calculated, I round the product to two decimal places.
Finally, for each row, I sum up the values in each column to get a total. That total is rounded to the nearest integer. For the purpose of this project, the rounding rules I want to follow are “round-to-even.”
My understanding is that the round() function in Python defaults to the “round-to-even” rule, which is exactly what I need.
However, I saw that before rounding, one of my totals was 195.50 (after summing up the corresponding products for that row). So the round() function should have rounded this value to 196 according to “round-to-even” rules. But it actually output 195.
When I was doing some digging, I found that sometimes decimals have precision error because the decimal portion can’t be captured in binary notation. And that could be why the round() function inappropriately rounded to 195 instead of 196.
Now, I get the “big picture” of this, but I feel I am missing some critical details my understanding is that integers can always be repped as sums of powers of 2. But not all decimals can be. For example 0.1 is not the sum of powers of 2. In these situations, the decimal portion is basically approximated by a fraction and this approximation is what could lead to 0.1 really being 0.10000000000001 or something similar.
However, my understanding is that decimals that terminate with a 5 are possible to represent in binary. Thus the precision error shouldn’t apply and the round() function should appropriately round.
What am I missing? Any help is greatly appreciated