Develop Reference

Is there any way to do computations with fixed number of digits after decimal point?

For some application I need to my computations using 3 digits of precision after decimal. I found the following method:
getcontext().prec = 100
THREE_PLACES = Decimal('0.001')
x = Decimal('2.41').quantize(THREE_PLACES)
y = (x * x).quantize(THREE_PLACES)
but This makes the code very ugly because I need to write quantize(THREE_PLACES) for all my operations which are actually a lot. And also the numbers I am getting with may be very big so that getcontext().prec = 100 may not work for all of them. Any better alternates to this one ? I also found the solution using f string's but that makes the code even uglier.
Edit: I have to explain that getcontext().prec sets the number of significat digits not digits after decimal and because I may have big enough numbers I set it to a big number. See here.

Understanding sys.float_info for maximum floats in python

I'm trying to understand the information given by sys.float_info to understand what the maximum floats in Python are. On my computer, this gives me the following:
>>> import sys
>>> sys.float_info
sys.float_info(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.220446049250313e-16, radix=2, rounds=1)
The docs give the following example (https://docs.python.org/3/library/sys.html#sys.float_info):
>>> import sys
>>> sys.float_info.dig
15
>>> s = '3.14159265358979' # decimal string with 15 significant digits
>>> format(float(s), '.15g') # convert to float and back -> same value
'3.14159265358979'
>>> s = '9876543211234567' # 16 significant digits is too many!
>>> format(float(s), '.16g') # conversion changes value
'9876543211234568'
However, the following example works just fine for me, even though it also has 16 significant digits (?):
>>> s = '.9876543211234567'
>>> format(float(s), '.16g')
'0.9876543211234567'
Also, sys.float_info.min yields 2.2250738585072014e-308 which is is obviously a lot smaller and also has 17 significant digits, if I'm correct? How does that work when sys.float_info.dig = 15? Am I confusing something here?
How does sys.float_info.dig = 15 relate to the attributes sys.float_info.mant_dig and sys.float_info.radix? As far as I understand, if I were to represent some decimal number as a base-2 number (since sys.float_info.radix = 2), does sys.float_info.mant_dig then give me the maximum integer of the mantissa?
Sorry if I'm confusing some things, maybe I'm not firm enough the mathematical basics here. Any help is much appreciated!

However, the following example works just fine for me, even though it also has 16 significant digits (?):
The documentation says “If X, then Y,” where X is:
A decimal numeral with up to 15 significant digits is converted to your Python implementation’s float and back to the nearest decimal numeral with at most 15 significant digits.
and Y is:
The number represented by the original numeral equals the resulting numeral.
That says only what happens if X is true. It does not say what happens if X is false. You have given us a situation where Y is true (you got back the original number) and complained that X is false. That is consistent with the documentation.
Also, sys.float_info.min yields 2.2250738585072014e-308 which is is obviously a lot smaller and also has 17 significant digits, if I'm correct? How does that work when sys.float_info.dig = 15? Am I confusing something here?
Every floating-point datum other than a NaN (Not a Number) represents one number exactly. It is exactly that number, regardless of how many decimal digits are required to express that number. sys_float_info.dig tells you a property about how fine the floating-point representation is. It tells you the floating-point representation is so fine, meaning that the numbers it does represent are so close together, that they can be used to distinguish between 15-digit decimal numerals. That just tells you how close the representable numbers are to each other. It does not tell you how many decimal digits it takes to exactly express any of those numbers.
In fact, the smallest positive number representable in your Python implementation’s floating-point format is 2−1074, which is 4.940656458412465441765687928682213723650598026143247644255856825006755072702087518652998363616359923797965646954457177309266567103559397963987747960107818781263007131903114045278458171678489821036887186360569987307230500063874091535649843873124733972731696151400317153853980741262385655911710266585566867681870395603106249319452715914924553293054565444011274801297099995419319894090804165633245247571478690147267801593552386115501348035264934720193790268107107491703332226844753335720832431936092382893458368060106011506169809753078342277318329247904982524730776375927247874656084778203734469699533647017972677717585125660551199131504891101451037862738167250955837389733598993664809941164205702637090279242767544565229087538682506419718265533447265625•10−324.

You can learn more about the specifics of floats for your runtime environment, by checking the value of sys.float_info. This will also tell you what's the largest and smallest number that can be represented with them.

How to check if a float value is within a certain range and has a given number of decimal digits?

How to check if a float value is within a range (0.50,150.00) and has 2 decimal digits?
For example, 15.22366 should be false (too many decimal digits). But 15.22 should be true.
I tried something like:
data= input()
if data in range(0.50,150.00):
return True

Is that you are looking for?
def check(value):
if 0.50 <= value <= 150 and round(value,2)==value:
return True
return False
Given your comment:
i input 15.22366 it is going to return true; that is why i specified the range; it should accept 15.22
Simply said, floating point values are imprecise. Many values don't have a precise representation. Say for example 1.40. It might be displayed "as it":
>>> f = 1.40
>>> print f
1.4
But this is an illusion. Python has rounded that value in order to nicely display it. The real value as referenced by the variable f is quite different:
>>> from decimal import Decimal
>>> Decimal(f)
Decimal('1.399999999999999911182158029987476766109466552734375')
According to your rule of having only 2 decimals, should f reference a valid value or not?
The easiest way to fix that issue is probably to use round(...,2) as I suggested in the code above. But this in only an heuristic -- only able to reject "largely wrong" values. See my point here:
>>> for v in [ 1.40,
... 1.405,
... 1.399999999999999911182158029987476766109466552734375,
... 1.39999999999999991118,
... 1.3999999999999991118]:
... print check(v), v
...
True 1.4
False 1.405
True 1.4
True 1.4
False 1.4
Notice how the last few results might seems surprising at first. I hope my above explanations put some light on this.
As a final advice, for your needs as I guess them from your question, you should definitively consider using "decimal arithmetic". Python provides the decimal module for that purpose.

float is the wrong data type to use for your case, Use Decimal instead.
Check python docs for issues and limitations. To quote from there (I've generalised the text in Italics)
Floating-point numbers are represented in computer hardware as base 2 (binary) fractions.
no matter how many base 2 digits you’re willing to use, some decimal value (like 0.1) cannot be represented exactly as a base 2 fraction.
Stop at any finite number of bits, and you get an approximation
On a typical machine running Python, there are 53 bits of precision available for a Python float, so the value stored internally when you enter a decimal number is the binary fraction which is close to, but not exactly equal to it.
The documentation for the built-in round() function says that it rounds to the nearest value, rounding ties away from zero.
And finally, it recommends
If you’re in a situation where you care which way your decimal halfway-cases are rounded, you should consider using the decimal module.
And this will hold for your case as well, as you are looking for a precision of 2 digits after decimal points, which float just can't guarantee.
EDIT Note: The answer below corresponds to original question related to random float generation
Seeing that you need 2 digits of sure shot precision, I would suggest generating integer random numbers in range [50, 15000] and dividing them by 100 to convert them to float yourself.
import random
random.randint(50, 15000)/100.0

Why don't you just use round?
round(random.uniform(0.5, 150.0), 2)

Probably what you want to do is not to change the value itself. As said by Cyber in the comment, even if your round a floating point number, it will always store the same precision. If you need to change the way it is printed:
n = random.uniform(0.5, 150)
print '%.2f' % n # 58.03

The easiest way is to first convert the decimal to string and split with '.' and check if the length of the character. If it is >2 then pass on. i.e. Convert use input number to check if it is in a given range.
a=15.22366
if len(str(a).split('.')[1])>2:
if 0.50 <= value <= 150:
<do your stuff>>

Is there a more readable or Pythonic way to format a Decimal to 2 places?

What the heck is going on with the syntax to fix a Decimal to two places?
>>> from decimal import Decimal
>>> num = Decimal('1.0')
>>> num.quantize(Decimal(10) ** -2) # seriously?!
Decimal('1.00')
Is there a better way that doesn't look so esoteric at a glance? 'Quantizing a decimal' sounds like technobabble from an episode of Star Trek!

Use string formatting:
>>> from decimal import Decimal
>>> num = Decimal('1.0')
>>> format(num, '.2f')
'1.00'
The format() function applies string formatting to values. Decimal() objects can be formatted like floating point values.
You can also use this to interpolate the formatted decimal value is a larger string:
>>> 'Value of num: {:.2f}'.format(num)
'Value of num: 1.00'
See the format string syntax documentation.
Unless you know exactly what you are doing, expanding the number of significant digits through quantisation is not the way to go; quantisation is the privy of accountancy packages and normally has the aim to round results to fewer significant digits instead.

Quantize is used to set the number of places that are actually held internally within the value, before it is converted to a string. As Martijn points out this is usually done to reduce the number of digits via rounding, but it works just as well going the other way. By specifying the target as a decimal number rather than a number of places, you can make two values match without knowing specifically how many places are in them.
It looks a little less esoteric if you use a decimal value directly instead of trying to calculate it:
num.quantize(Decimal('0.01'))
You can set up some constants to hide the complexity:
places = [Decimal('0.1') ** n for n in range(16)]
num.quantize(places[2])

Truncation in python

How can we truncate (not round) the cube root of a given number after the 10th decimal place in python?
For Example:
If number is 8 the required output is 2.0000000000 and for 33076161 it is 321.0000000000

Scale - truncate - unscale:
n = 10.0
cube_root = 1e-10 * int(1e10 * n**(1.0/3.0))

You should only do such truncations (unless you have a serious reason otherwise) while printing out results. There is no exact binary representation in floating point format, for a whole host of everyday decimal values:
print 33076161**(1.0/3.0)
A calculator gives you a different answer than Python gives you. Even Windows calculator does a passable job on cuberoot(33076161), whereas the answer given by python will be minutely incorrect unless you use rounding.
So, the question you ask is fundamentally unanswerable since it assumes capabilities that do not exist in floating point math.
Wrong Answer #1: This actually rounds instead of truncating, but for the cases you specified, it provides the correct output, probably due to rounding compensating for the inherent floating point precision problem you will hit in case #2:
print "%3.10f" % 10**(1.0/3.0)
Wrong Answer #2: But you could truncate (as a string) an 11-digit rounded value, which, as has been pointed out to me, would fail for values very near rollover, and in other strange ways, so DON'T do this:
print ("%3.11f" % 10**(1.0/3.0))[:-1]
Reasonably Close Answer #3: I wrote a little function that is for display only:
import math
def str_truncate(f,d):
s = f*(10.0**(d))
str = `math.trunc(s)`.rstrip('L')
n = len(str)-d
w = str[0:n]
if w=='':
w='0'
ad =str[n:d+n]
return w+'.'+ad
d = 8**(1.0/3.0)
t=str_truncate(d,10)
print 'case 1',t
d = 33076161**(1.0/3.0)
t=str_truncate(d,10)
print 'case 2',t
d = 10000**(1.0/3.0)
t=str_truncate(d,10)
print 'case 3',t
d = 0.1**(1.0/3.0)
t=str_truncate(d,10)
print 'case 4',t
Note that Python fails to perform exactly as per your expectations in case #2 due to your friendly neighborhood floating point precision being non-infinite.
You should maybe know about this document too:
What Every Computer Scientist Should Know About Floating Point
And you might be interested to know that Python has add-ons that provide arbitary precision features that will allow you to calculate the cube root of something to any number of decimals you might want. Using packages like mpmath, you can free yourself from the accuracy limitations of conventional floating point math, but at a considerable cost in performance (speed).
It is interesting to me that the built-in decimal unit does not solve this problem, since 1/3 is a rational (repeating) but non-terminating number in decimal, thus it can't be accurately represented either in decimal notation, nor floating point:
import decimal
third = decimal.Decimal(1)/decimal.Decimal(3)
print decimal.Decimal(33076161)**third # cuberoot using decimal
output:
320.9999999999999999999999998
Update: Sven provided this cool use of Logs which works for this particular case, it outputs the desired 321 value, instead of 320.99999...: Nifty. I love Log(). However this works for 321 cubed, but fails in the case of 320 cubed:
exp(log(33076161)/3)
It seems that fractions doesn't solve this problem, but I wish it did:
import fractions
third = fractions.Fraction(1,3)
def cuberoot(n):
return n ** third
print '%.14f'%cuberoot(33076161)

num = 17**(1.0/3.0)
num = int(num * 100000000000)/100000000000.0
print "%.10f" % num

What about this code .. I have created it for my personal use. although it is so simple, it is working well.
def truncation_machine(original,edge):
'''
Function of the function :) :
it performs truncation operation on long decimal numbers.
Input:
a) the number that needs to undergo truncation.
b) the no. of decimals that we want to KEEP.
Output:
A clean truncated number.
Example: original=1.123456789
edge=4
output=1.1234
'''
import math
g=original*(10**edge)
h=math.trunc(g)
T=h/(10**edge)
print('The original number ('+str(original)+') underwent a '+str(edge)+'-digit truncation to be in the form: '+str(T))
return T