I am trying to write a function to round a floating point number up to n decimal places. The function can take one or two arguments. If there is only one argument the number should be rounded to two decimal places.
This is where I have gotten so far:
def roundno(num,point=2):
import math
x=1*(math.pow(10,-point))
round=0
while (num>x):
while(num>0):
round+=num/10
num=num/10
round*=10
round+=num/10
num=num/10
round*=0.1
return round
I am getting infinity as the output, every time... Where did I go wrong?
I can't see how your algorithm is supposed to round numbers. I guess a similar strategy could work, but you'd need a subtraction in there somewhere...
One way to do this would be to convert the argument to a string, adjust the number of digits after the decimal point, and then convert the string back to a float, but I suspect that your teacher would not like that solution. :)
Here's a simple way to do rounding arithmetically:
def roundno(num, point=2):
scale = 10.0 ** point
return int(num * scale) / scale
data = [123, 12.34, 1.234, 9.8765, 98.76543]
for n in data:
print n, roundno(n), roundno(n, 3)
output
123 123.0 123.0
12.34 12.34 12.34
1.234 1.23 1.234
9.8765 9.87 9.876
98.76543 98.76 98.765
This simply drops unwanted digits, but it's not hard to modify it to round up or off (your question isn't clear on exactly what type of rounding you want).
Note that this function doesn't check the point argument. It really should check that it's a non-negative integer and raise ValueError with an appropriate error message otherwise.
Related
When I convert a float to decimal.Decimal in Python and afterwards call Decimal.shift it may give me completely wrong and unexpected results depending on the float. Why is this the case?
Converting 123.5 and shifting it:
from decimal import Decimal
a = Decimal(123.5)
print(a.shift(1)) # Gives expected result
The code above prints the expected result of 1235.0.
If I instead convert and shift 123.4:
from decimal import Decimal
a = Decimal(123.4)
print(a.shift(1)) # Gives UNexpected result
it gives me 3.418860808014869689941406250E-18 (approx. 0) which is completely unexpected and wrong.
Why is this the case?
Note:
I understand the floating-point imprecision because of the representation of floats in memory. However, I can't explain why this should give me such a completely wrong result.
Edit:
Yes, in general it would be best to not convert the floats to decimals but convert strings to decimals instead. However, this is not the point of my question. I want to understand why the shifting after float conversion gives such a completely wrong result. So if I print(Decimal(123.4)) it gives 123.40000000000000568434188608080148696899414062 so after shifting I would expect it to be 1234.0000000000000568434188608080148696899414062 and not nearly zero.
You need to change the Decimal constructor input to use strings instead of floats.
a = Decimal('123.5')
print(a.shift(1))
a = Decimal('123.4')
print(a.shift(1))
or
a = Decimal(str(123.5))
print(a.shift(1))
a = Decimal(str(123.4))
print(a.shift(1))
The output will be as expected.
>>> 1235.0
>>> 1234.0
Decimal instances can be constructed from integers, strings, floats, or tuples. Construction from an integer or a float performs an exact conversion of the value of that integer or float.
For floats, Decimal calls Decimal.from_float()
Note that Decimal.from_float(0.1) is not the same as Decimal('0.1'). Since 0.1 is not exactly representable in binary floating point, the value is stored as the nearest representable value which is 0x1.999999999999ap-4. The exact equivalent of the value in decimal is 0.1000000000000000055511151231257827021181583404541015625.
Internally, the Python decimal library converts a float into two integers representing the numerator and denominator of a fraction that yields the float.
n, d = abs(123.4).as_integer_ratio()
It then calculates the bit length of the denominator, which is the number of bits required to represent the number in binary.
k = d.bit_length() - 1
And then from there the bit length k is used to record the coefficient of the decimal number by multiplying the numerator * 5 to the power of the bit length of the denominator.
coeff = str(n*5**k)
The resulting values are used to create a new Decimal object with constructor arguments of sign, coefficient, and exponent using this values.
For the float 123.5 these values are
>>> 1 1235 -1
and for the float 123.4 these values are
1 123400000000000005684341886080801486968994140625 -45
So far, nothing is amiss.
However when you call shift, the Decimal library has to calculate how much to pad the number with zeroes based on the shift you've specified. To do this internally it takes the precision subtracted by length of the coefficient.
amount_to_pad = context.prec - len(coeff)
The default precision is only 28 and with a float like 123.4 the coefficient becomes much longer than the default precision as noted above. This creates a negative amount to pad with zeroes and makes the number very tiny as you noted.
A way around this is to increase the precision to the length of the exponent + the length of the number you started with (45 + 4).
from decimal import Decimal, getcontext
getcontext().prec = 49
a = Decimal(123.4)
print(a)
print(a.shift(1))
>>> 123.400000000000005684341886080801486968994140625
>>> 1234.000000000000056843418860808014869689941406250
The documentation for shift hints that the precision is important for this calculation:
The second operand must be an integer in the range -precision through precision.
However it does not explain this caveat for floats that don't play nice with memory limitations.
I would expect this to raise some kind of error and prompt you to change your input or increase the precision, but at least you know!
#MarkDickinson noted in a comment above that you can view this Python bug tracker for more information: https://bugs.python.org/issue7233
Basically, I have a list of float numbers with too many decimals. So when I created a second list with two decimals, Python rounded them. I used the following:
g1= ["%.2f" % i for i in g]
Where g1 is the new list with two decimals, but rounded, and g is the list with float numbers.
How can I make one without rounding them?
I'm a newbie, btw. Thanks!
So, you want to truncate the numbers at the second digit?
Beware that rounding might be the better and more accurate solution anyway.
If you want to truncate the numbers, there are a couple of ways - one of them is to multiply the number by 10 elevated to the number of desired decimal places (100 for 2 places), apply "math.floor", and divide the total back by the same number.
However, as internal floating point arithmetic is not base 10, you'd risk getting more decimal places on the division to scale down.
Another way is to create a string with 3 digits after the "." and drop the last one - that'd be rounding proof.
And again, keep in mind that this converts the numbers to strings - what should be done for presentation purposes only. Also, "%" formatting is quite an old way to format parameters in a string. In modern Python, f-strings are the preferred way:
g1 = [f"{number:.03f}"[:-1] for number in g]
Another, more correct way, is, of course, treat numbers as numbers, and not play tricks on adding or removing digits on it. As noted in the comments, the method above would work for numbers like "1.227", that would be kept as "1.22", but not for "2.99999", which would be rounded to "3.000" and then truncated to "3.00".
Python has the decimal modules, which allows for arbitrary precision of decimal numbers - which includes less precision, if needed, and control of the way Python does the rounding - including rounding towards zero, instead of the nearest number.
Just set the decimal context to the decimal.ROUND_DOWN strategy, and then convert your numbers using either the round built-in (the exact number of digits is guaranteed, unlike using round with floating point numbers), or just do the rounding as part of the string formatting anyway. You can also convert your floats do Decimals in the same step:
from decimals import Decimal as D, getcontext, ROUND_DOWN
getcontext().rounding = ROUND_DOWN
g1 = [f"{D(number):.02f}" for number in g]
Again - by doing this, you could as well keep your numbers as Decimal objects, and still be able to perform math operations on them:
g2 = [round(D(number, 2)) for number in g]
Here is my solution where we don't even need to convert the number's to string to get the desired output:
def format_till_2_decimal(num):
return int(num*100)/100.0
g = [-5.427926, -12.222018, 7.214379, -16.771845, -6.1441464, 10.1383295, 14.740516, 5.9209185, -9.740783, -10.098338]
formatted_g = [format_till_2_decimal(num) for num in g]
print(formatted_g)
Hope this solution helps!!
Here might be the answer you are looking for:
g = [-5.427926, -12.222018, 7.214379, -16.771845, -6.1441464, 10.1383295, 14.740516, 5.9209185, -9.740783, -10.098338]
def trunc(number, ndigits=2):
parts = str(number).split('.') # divides number into 2 parts. for ex: -5, and 4427926
truncated_number = '.'.join([parts[0], parts[1][:ndigits]]) # We keep this first part, while taking only 2 digits from the second part. Then we concat it together to get '-5.44'
return round(float(truncated_number), 2) # This should return a float number, but to make sure it is roundded to 2 decimals.
g1 = [trunc(i) for i in g]
print(g1)
[-5.42, -12.22, 7.21, -16.77, -6.14, 10.13, 14.74, 5.92, -9.74, -10.09]
Hope this helps.
Actually if David's answer is what you are looking for, it can be done simply as following:
g = [-5.427926, -12.222018, 7.214379, -16.771845, -6.1441464, 10.1383295, 14.740516, 5.9209185, -9.740783, -10.098338]
g1 = [("%.3f" % i)[:-1] for i in g]
Just take 3 decimals, and remove the last chars from the result strings. (You may convert the result to float if you like)
I was investigating different rounding method using Python built-in solution and some other external libraries such SymPy and while doing so I stumbled upon some cases that I need help with understanding the reason behind it.
Ex-1:
print(round(1.0065,3))
output:
1.006
In the first case, using the Python built-in rounding function the output was 1.006 instead of 1.007 and I can understand that this is not a mistake as Python rounds to the nearest even and that's known as Bankers rounding.
And this is why I from the beginning started searching for another way to control the rounding behaviour. With a quick search, I've found decimal.Decimal module which can easily handle decimal values and efficiently round is using quantize() as in this example:
from decimal import Decimal, getcontext, ROUND_HALF_UP
context= getcontext()
context.rounding='ROUND_HALF_UP'
print(Decimal('1.0065').quantize(Decimal('.001')))
output:1.007
This is a very good solution but the only problem is it is not easy to be hardcoded in long math expressions as I'll need to convert every number to string then after using decimal I will pass it the precession as in the form of "0.001" instead of writing '3' directly as in the case of built-in round.
While searching for another solution I found that SymPy, which I already use a lot in my scripts, offers some very powerful functions that might help but when I tried it the output was not as I expected.
Ex-1 using SymPy sympify():
print(sympify(1.0065).evalf(3))
output: 1.01
Ex-2 using SymPy N (normalize):
print(N(1.0065,3))
output: 1.01
Af first the output was a little bit weird but after investigating I realized that N and sympify already performing round right but rounding to significant figures, not to decimal places.
And here the questions come:
As I can use with Decimal objects getcontext().rounding='ROUND_HALF_UP' to change the rounding behaviour, is there a way to change the N and sympify rounding behaviour to decimal places instead of significant figures?
Instead of re-implementing decimal rounding in SymPy, perhaps use decimal to do the rounding, but hide the calculation in a utility function:
import sympy as sym
import decimal
from decimal import Decimal as D
def dround(d, ndigits, rounding=decimal.ROUND_HALF_UP):
result = D(str(d)).quantize(D('0.1')**ndigits, rounding=rounding)
# result = sym.sympify(result) # if you want a SymPy Float
return result
for x in [0.0065, 1.0065, 10.0065, 100.0065]:
print(dround(x, 3))
prints
0.007
1.007
10.007
100.007
The n of evalf gives the first n significant digits of x (measured from the left). If you use x.round(3) it will round x to the nth digit from the decimal point and can be positive (right of decimal pt) or negative (left of decimal pt).
>>> for x in '0.0065, 1.0065, 10.0065, 100.0065'.split(', '):
... print S(x).round(3)
0.006
1.006
10.007
100.007
>>> int(S(12345).round(-2))
12300
First of all, N and evalf are essentially the same thing; N(x, n) amounts to sympify(x).evalf(n). In your case, since x is a Python float, it's easier to use N because it sympifies the input.
To get three digits after decimal dot, use N(x, 3 + log(x, 10) + 1). The adjustment log(x, 10) + 1 is 0 when x is between 0.1 and 1; in this case the number of significant digits is the same as the number of digits after the decimal dot. If x is larger, we get more significant digits.
Example:
for x in [0.0065, 1.0065, 10.0065, 100.0065]:
print(N(x, 3 + log(x, 10) + 1))
prints
0.006
1.007
10.007
100.007
The transition from 6 to 7 is curious, but not entirely surprising. These numbers are not exactly represented in binary system, so the truncation to nearest double-precision float may be a factor here. I've made a few additional observation on this effect on my blog.
I've been searching around for hours and I can't find a simple way of accomplishing the following.
Value 1 = 0.00531
Value 2 = 0.051959
Value 3 = 0.0067123
I want to increment each value by its smallest decimal point (however, the number must maintain the exact number of decimal points as it started with and the number of decimals varies with each value, hence my trouble).
Value 1 should be: 0.00532
Value 2 should be: 0.051960
Value 3 should be: 0.0067124
Does anyone know of a simple way of accomplishing the above in a function that can still handle any number of decimals?
Thanks.
Have you looked at the standard module decimal?
It circumvents the floating point behaviour.
Just to illustrate what can be done.
import decimal
my_number = '0.00531'
mnd = decimal.Decimal(my_number)
print(mnd)
mnt = mnd.as_tuple()
print(mnt)
mnt_digit_new = mnt.digits[:-1] + (mnt.digits[-1]+1,)
dec_incr = decimal.DecimalTuple(mnt.sign, mnt_digit_new, mnt.exponent)
print(dec_incr)
incremented = decimal.Decimal(dec_incr)
print(incremented)
prints
0.00531
DecimalTuple(sign=0, digits=(5, 3, 1), exponent=-5)
DecimalTuple(sign=0, digits=(5, 3, 2), exponent=-5)
0.00532
or a full version (after edit also carries any digit, so it also works on '0.199')...
from decimal import Decimal, getcontext
def add_one_at_last_digit(input_string):
dec = Decimal(input_string)
getcontext().prec = len(dec.as_tuple().digits)
return dec.next_plus()
for i in ('0.00531', '0.051959', '0.0067123', '1', '0.05199'):
print(add_one_at_last_digit(i))
that prints
0.00532
0.051960
0.0067124
2
0.05200
As the other commenters have noted: You should not operate with floats because a given number 0.1234 is converted into an internal representation and you cannot further process it the way you want. This is deliberately vaguely formulated. Floating points is a subject for itself. This article explains the topic very well and is a good primer on the topic.
That said, what you could do instead is to have the input as strings (e.g. do not convert it to float when reading from input). Then you could do this:
from decimal import Decimal
def add_one(v):
after_comma = Decimal(v).as_tuple()[-1]*-1
add = Decimal(1) / Decimal(10**after_comma)
return Decimal(v) + add
if __name__ == '__main__':
print(add_one("0.00531"))
print(add_one("0.051959"))
print(add_one("0.0067123"))
print(add_one("1"))
This prints
0.00532
0.051960
0.0067124
2
Update:
If you need to operate on floats, you could try to use a fuzzy logic to come to a close presentation. decimal offers a normalize function which lets you downgrade the precision of the decimal representation so that it matches the original number:
from decimal import Decimal, Context
def add_one_float(v):
v_normalized = Decimal(v).normalize(Context(prec=16))
after_comma = v_normalized.as_tuple()[-1]*-1
add = Decimal(1) / Decimal(10**after_comma)
return Decimal(v_normalized) + add
But please note that the precision of 16 is purely experimental, you need to play with it to see if it yields the desired results. If you need correct results, you cannot take this path.
I am working on a project where it has to take user inputs and do calculations.
What I am aiming for is the print to appear as
Inform the customer they saved 0.71 today
Not
Inform the customer they saved 0.7105000000000001 today
Is there something I can put into the same line of code with the print function to have it be rounded? Or do I have to modify each variable.
I can post my code if requested.
You can use the builtin round() function and float formatting:
>>> print "{0:0.2f}".format(round(x, 2))
0.71
Some Notes:
{0.2f} will format a float to 2 decimal places.
round(x, 2) will round up to 2 decimal places.
Side Note: round() is really necessary IHMO if you want to "round" the number before "display". It really depends on what you're doing!
round() is return the floating point value number rounded to ndigits digits after the decimal point. which takes as first argument the number and the second argument is the precision
no = 0.7105000000000001
print round(no, 2)
second solution:
print "%.2f" % 0.7105000000000001
use decimal instead of round()
from decimal import *
print(round(8.494,2)) # 8.49
print(round(8.495,2)) # 8.49
print(round(8.496,2)) # 8.5
print(Decimal('8.494').quantize(Decimal('.01'), rounding=ROUND_HALF_UP)) #8.49
print(Decimal('8.495').quantize(Decimal('.01'), rounding=ROUND_HALF_UP)) #8.50
print(Decimal('8.496').quantize(Decimal('.01'), rounding=ROUND_HALF_UP)) #8.50