Why does the large number give me an integer (or at least no decimal points), but the smaller number give me a bunch of decimal points? Is the way I set the precision or declare the variables wrong?
import math
from mpmath import *
mp.prec=1000
x = 5431526412865007456
print mpf((x)/6)
ACTUAL OUTPUT: 905254402144167909.0
WANTED OUTPUT: 905254402144167909.3333333333333333333333(…)
x = 5431526413
print mpf((x)/6.)
OUTPUT: 905254402.16666662693023681640625
Try using mpf(x)/6 or mpf(x)/6.0. The reason your code didn't work is that it did the division using Python's normal rules, then converted it to a arbitrary-precision number, whereas this converts it first so the division is done using arbitrary-precision math.
I'm trying to find an efficient way to do the following in Python:
a = 12345678901234567890123456**12345678
f = open('file', 'w')
f.write(str(a))
f.close()
The calculation of the power takes about 40 minutes while one thread is utilized. Is there a quick and easy way to spread this operation over multiple threads?
As the number is quite huge, I think the string function isn't quite up to the task - it's been going for almost three hours now. I need the number to end up in a text file.
Any ideas on how to better accomplish this?
I would like to give a lavish ;-) answer, but don't have the time now. Elaborating on my comment, the decimal module is what you really want here. It's much faster at computing the power, and very very much faster to convert the result to a decimal string:
>>> import decimal
You need to change its internals so that it avoids floating point, giving it more than enough internal digits to store the final result. We want exact integer arithmetic here, not rounded floating-point. So we fiddle things so decimal uses as much precision as it's capable of using, and tell it to raise the "Inexact" exception if it ever loses information to rounding. Note that you need a 64-bit version of Python for decimal to be capable of using enough precision to hold the exact result in your example:
>>> import decimal
>>> c = decimal.getcontext()
>>> c.prec = decimal.MAX_PREC
>>> c.Emax = decimal.MAX_EMAX
>>> c.Emin = decimal.MIN_EMIN
>>> c.traps[decimal.Inexact] = 1
Now create a Decimal for the base:
>>> base = decimal.Decimal(12345678901234567890123456)
>>> base
Decimal('12345678901234567890123456')
And raise to the power - the exponent will automatically be converted to Decimal, because the base is already Decimal:
>>> x = base ** 12345678
That takes less than a minute on my box! The reasons for that are involved. It's not really because it's working in base 10, but because the person who wrote the decimal module implemented "advanced" algorithms for doing very large multiplications.
Now convert to a string. Because it's already stored in a variant of base 10, converting to a decimal string goes very fast (a few seconds on my box, just because the string has hundreds of millions of digits):
>>> y = str(x)
>>> len(y)
309771765
And, for sanity, let's just look at the last 10, and first 10, digits:
>>> y[-10:]
'6044706816'
>>> y[:10]
'2759594879'
As #StefanPochmann noted in a comment, the last 10 digits can be obtained very quickly with native ints by using modular (3-argument) pow():
>>> pow(int(base), 12345678, 10**10)
6044706816
Which matches the last 10 digits of the string above. For the first 10 digits, we can use decimal again but with much less precision, which will cause it (you'll just to have trust me on this) to use a different approach under the covers:
>>> c.prec = 12
>>> c.traps[decimal.Inexact] = 0 # don't trap on rounding!
>>> base ** 12345678
Decimal('2.75959487945E+309771764')
Rounding that back to 10 digits matches the earlier result, and the exponent is consistent with the length of y too.
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.
What I wanted to do:
Paradox: Suppose Peter Parker were running to catch a bus. To reach it, he’d first need to get halfway there. Before that, he’d need to get a quarter of the way there……before a quarter, an eighth; before an eighth, a 16th; and so on. Since the distance can be halved infinitely, he’d be trying to complete an infinite number of tasks… WHICH WOULD BE LOGICALLY IMPOSSIBLE!
I tried to to resolve this paradox using Python
I have some questions:
How can I get a number to have no limitations with decimals? Python limits the numbers of decimals, I think to 12, How to make that number infinite?
Aparrently there is no way to make the float decimals infinite, the closest I could get was using this
from decimal import Decimal
Is this the correct way of asking the user for an input in numbers?
Code modified
from decimal import Decimal
def infinite_loop():
x = 0;
number = Decimal(raw_input())
while x != number:
x = x + number
number = number/2
print x
infinite_loop()
What you ask is impossible. There are no "infinite precision" floating point values in the real world of finite computing systems. If there were, a single floating point value could consume all of the system's resources. pi * d? Ooops!! pi is infinite. There goes the system!
What you can do, however, is get arbitrary precision decimal values. They're still finite, but you can choose how much precision you want (and are willing to pay for). E.g.:
>>> from decimal import Decimal
>>> x = Decimal('1.' + '0' * 200)
>>> x
Decimal('1.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000')
Now you have 200 digits of precision. Not enough? Go 400. 800. However many you like. As long as that's a finite, practical value.
If you want "infinite" precision (e.g. a decimal number that be extended as far as you have memory for), either use Python's builtin module Decimal or for more heavy computation, mpmath:
import mpmath as mp
mp.mp.dps = 100
print mp.sqrt(mp.mpf(2))
>> 1.414213562373095048801688724209698078569671875376948073176679737990732478462107038850387534327641573
Is there a way to round a python float to x decimals? For example:
>>> x = roundfloat(66.66666666666, 4)
66.6667
>>> x = roundfloat(1.29578293, 6)
1.295783
I've found ways to trim/truncate them (66.666666666 --> 66.6666), but not round (66.666666666 --> 66.6667).
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 52.15 gets parsed and converted to a Python float. The actual number stored is 7339460017730355 * 2**-47, or 52.14999999999999857891452847979962825775146484375.
Internally as the first step of the round operation, 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 of 52.15, we end up rounding down and getting a string 52.1. This explains why we're getting 52.1 as the final output instead of 52.2.
Then in the second step of the round operation, Python turns that string back into a float, getting the closest binary floating-point number to 52.1, which is now 7332423143312589 * 2**-47, or 52.10000000000000142108547152020037174224853515625.
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 52.1 as 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 Decimal type. Doing a decimal round on the Decimal type 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, the decimal module gives you better control of the rounding mode. In Python 3, round does the job directly. In Python 2, you need the quantize method.
>>> 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 round really is what you want: perhaps you're binning floats into bins of size 0.01, 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 the round builtin 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.
Default rounding in python and numpy:
In: [round(i) for i in np.arange(10) + .5]
Out: [0, 2, 2, 4, 4, 6, 6, 8, 8, 10]
I used this to get integer rounding to be applied to a pandas series:
import decimal
and use this line to set the rounding to "half up" a.k.a rounding as taught in school:
decimal.getcontext().rounding = decimal.ROUND_HALF_UP
Finally I made this function to apply it to a pandas series object
def roundint(value):
return value.apply(lambda x: int(decimal.Decimal(x).to_integral_value()))
So now you can do roundint(df.columnname)
And for numbers:
In: [int(decimal.Decimal(i).to_integral_value()) for i in np.arange(10) + .5]
Out: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Credit: kares
The Mark Dickinson answer, although complete, didn't work with the float(52.15) case. After some tests, there is the solution that I'm using:
import decimal
def value_to_decimal(value, decimal_places):
decimal.getcontext().rounding = decimal.ROUND_HALF_UP # define rounding method
return decimal.Decimal(str(float(value))).quantize(decimal.Decimal('1e-{}'.format(decimal_places)))
(The conversion of the 'value' to float and then string is very important, that way, 'value' can be of the type float, decimal, integer or string!)
Hope this helps anyone.
I coded a function (used in Django project for DecimalField) but it can be used in Python project :
This code :
Manage integers digits to avoid too high number
Manage decimals digits to avoid too low number
Manage signed and unsigned numbers
Code with tests :
def convert_decimal_to_right(value, max_digits, decimal_places, signed=True):
integer_digits = max_digits - decimal_places
max_value = float((10**integer_digits)-float(float(1)/float((10**decimal_places))))
if signed:
min_value = max_value*-1
else:
min_value = 0
if value > max_value:
value = max_value
if value < min_value:
value = min_value
return round(value, decimal_places)
value = 12.12345
nb = convert_decimal_to_right(value, 4, 2)
# nb : 12.12
value = 12.126
nb = convert_decimal_to_right(value, 4, 2)
# nb : 12.13
value = 1234.123
nb = convert_decimal_to_right(value, 4, 2)
# nb : 99.99
value = -1234.123
nb = convert_decimal_to_right(value, 4, 2)
# nb : -99.99
value = -1234.123
nb = convert_decimal_to_right(value, 4, 2, signed = False)
# nb : 0
value = 12.123
nb = convert_decimal_to_right(value, 8, 4)
# nb : 12.123
def trim_to_a_point(num, dec_point):
factor = 10**dec_point # number of points to trim
num = num*factor # multiple
num = int(num) # use the trimming of int
num = num/factor #divide by the same factor of 10s you multiplied
return num
#test
a = 14.1234567
trim_to_a_point(a, 5)
output
========
14.12345
multiple by 10^ decimal point you want
truncate with int() method
divide by the same number you multiplied before
done!
Just posted this for educational reasons i think it is correct though :)