These represent numbers in the range -2147483648 through 2147483647. (The range may be larger on machines with a larger natural word size, but not smaller.)
Therefore, 8 bits can store all integers from -128 to 127. Similarly, if you want to use 16 bits, 32 bits, and 64 bits to store integers, the ranges would be: 16-bits ~ [-215, 215 – 1] = [ -32,768 , 32,767 ] 32-bits ~ [-231, 231 – 1] = [- 2,147,483,648 , 2,147,483,647 ]
The pythonic way Similarly for python, "digit" is in base 2³⁰ which means it will range from 0 to 2³⁰ - 1 = 1073741823 of the decimal system.
Python also supports an integer type bignum , which stores arbitrarily very large numbers. In Python 2.5+, this integer type is called long , which does the same function as bignum , and in Python 3 and above, there is only one int that represents all types of integers irrespective of their size.
Python supports a "bignum" integer type which can work with arbitrarily large numbers. In Python 2.5+, this type is called long
and is separate from the int
type, but the interpreter will automatically use whichever is more appropriate. In Python 3.0+, the int
type has been dropped completely.
That's just an implementation detail, though — as long as you have version 2.5 or better, just perform standard math operations and any number which exceeds the boundaries of 32-bit math will be automatically (and transparently) converted to a bignum.
You can find all the gory details in PEP 0237.
python supports arbitrarily large integers naturally:
example:
>>> 10**1000
10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
You could even get, for example of a huge integer value, fib(4000000).
But still it does not (for now) supports an arbitrarily large float !!
If you need one big, large, float then check up on the decimal Module. There are examples of use on these foruns: OverflowError: (34, 'Result too large')
Another reference: http://docs.python.org/2/library/decimal.html
You can even using the gmpy module if you need a speed-up (which is likely to be of your interest): Handling big numbers in code
Another reference: https://code.google.com/p/gmpy/
You could do this for the fun of it, but other than that it's not a good idea. It would not speed up anything I can think of.
Getting the cards in a hand will be an integer factoring operation which is much more expensive than just accessing an array.
Adding cards would be multiplication, and removing cards division, both of large multi-word numbers, which are more expensive operations than adding or removing elements from lists.
The actual numeric value of a hand will tell you nothing. You will need to factor the primes and follow the Poker rules to compare two hands. h1 < h2 for such hands means nothing.
python supports arbitrarily large integers naturally:
In [1]: 59**3*61**4*2*3*5*7*3*5*7
Out[1]: 62702371781194950
In [2]: _ % 61**4
Out[2]: 0
The python interpreter will handle it for you, you just have to do your operations (+, -, *, /), and it will work as normal.
The int
value is unlimited.
Careful when doing division, by default the quotient is turned into float
, but float
does not support such large numbers. If you get an error message saying float
does not support such large numbers, then it means the quotient is too large to be stored in float
you’ll have to use floor division (//
).
It ignores any decimal that comes after the decimal point, this way, the result will be int
, so you can have a large number result.
>>>10//3
3
>>>10//4
2
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