Ok, since other people are giving awful answers, I'm going to step in.
- You shouldn't do this.
- You shouldn't do this.
- An integer and an array of characters are ultimately the same thing: bytes. You can access the values in the same way.
- Most number representations cap out at 8 bytes (64-bits). You're looking at 8 MB, or 1 million times the largest integer representation. You shouldn't do this. Really.
- You shouldn't do this. Your number will just be a custom, gigantic number type that would be identical under the hood.
- If you really want to do this, despite all the reasons above, here's how...
Code
def lshift(a, b):
# bitwise left shift 8
return (a << (8 * b))
def string_to_int(data):
sum_ = 0
r = range(len(data)-1, -1, -1)
for a, b in zip(bytearray(data), r):
sum_ += lshift(a, b)
return sum_;
DONT DO THIS
Explanation
Characters are essentially bytes: they can be encoded in different ways, but ultimately you can treat them within a given encoding as a sequence of bytes. In order to convert them to a number, we can shift them left 8-bits for their position in the sequence, creating a unique number. r
, the range value, is the position in reverse order: the 4th element needs to go left 24 bytes (3*8), etc.
After getting the range and converting our data to 8-bit integers, we can then transform the data and take the sum, giving us our unique identifier. It will be identical byte-wise (or in reverse byte-order) of the original number, but just "as a number". This is entirely futile. Don't do it.
Performance
Any performance is going to be outweighed by the fact that you're creating an identical object for no valid reason, but this solution is decently performant.
1,000 elements takes ~486 microseconds, 10,000 elements takes ~20.5 ms, while 100,000 elements takes about 1.5 seconds. It would work, but you shouldn't do it. This means it's scaled as O(n**2), which is likely due to memory overhead of reallocating the data each time the integer size gets larger. This might take ~4 hours to process all 8e6 elements (14365 seconds, calculated fitting the lower-order data to ax**2+bx+c
). Remember, this is all to get the identical byte representation as the original data.
Futility
Remember, there are ~1e78 to 1e82 atoms in the entire universe, on current estimates. This is ~2^275. Your value will be able to represent 2^71426504, or about 260,000 times as many bits as you need to represent every atom in the universe. You don't need such a number. You never will.