Of course, you cannot represent fractions unless you use fixed-point representation where you regard everything as multiplied by a large power of 10. Computations are performed accurately and there is no loss of digits. That is 2 200, would result in an error message, but in Python you can follow it by: print(myVar)Īnd see: 1606938044258990275541962092341162602522202993782792835301376Īs long as you restrict yourself to integer types you can forget the problems of overflow or generally running out of digits. In most languages and expression such as: myVar = 2**200 The promise of unlimited-precision integers is one that is intoxicating for programmers used to other languages where integer overflow is a constant worry, or should be. If you want faster integers then you can use libraries such as NumPy, which provides fixed-size integers. Although you might expect this method of implementing an unlimited range of integers to be too inefficient, Python makes it work well enough. Instead it supports a “bignum” integer type which can work with arbitrarily large numbers, which simply take as much memory as needed to store the value. Python doesn’t implement integers in this way. For example, a 1-byte integer can only store -127 to +128 and if you try to store anything outside of this range you generate an error or the value is truncated to fit into the range. The idea is that you only need to use the amount of storage appropriate for the range. The majority of programming languages provide several integer types which cover different ranges of values.
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