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For loop illustration, from i=0 to i=2, resulting in data1=200. A for-loop statement is available in most imperative programming languages. Even ignoring minor differences in syntax, there are many differences in how these statements work and the level of expressiveness they support.
Download QR code; Print/export ... aliased to the loop variable. List literal example: ... in is the only kind of for loop in Python, ...
import sugar let variable = collect (newSeq): for item in @[-9, 1, 42, 0,-1, 9]: item + 1 assert variable == @[-8, 2, 43, 1, 0, 10] The comprehension is implemented as a macro that is expanded at compile time, you can see the expanded code using the expandMacro compiler option:
"PIC S9999", for example, would require a signed variable of four decimal digits precision. If specified as a binary field, this would select a 16-bit signed type on most platforms. If specified as a binary field, this would select a 16-bit signed type on most platforms.
Also, positional parameters as the argv array including argv[1], the $0 shell variable as argv[0], the Count of Indices parameter expansion $#var, the -d and -x operators of a testing syntax regarding directory and executability tests, respectively, the ! negate symbol, a looping construct in the foreach command, the set, echo and exit commands ...
One example of this is Bash, which offers the same grammar and syntax as the Bourne shell, and which also provides a POSIX-compliant mode. [13] As such, most shell scripts written for the Bourne shell can be run in BASH, but the reverse may not be true since BASH has extensions which are not present in the Bourne shell.
In these examples, if N < 1 then the body of loop may execute once (with I having value 1) or not at all, depending on the programming language. In many programming languages, only integers can be reliably used in a count-controlled loop. Floating-point numbers are represented imprecisely due to hardware constraints, so a loop such as
Python 2.5 implements better support for coroutine-like functionality, based on extended generators ; Python 3.3 improves this ability, by supporting delegating to a subgenerator ; Python 3.4 introduces a comprehensive asynchronous I/O framework as standardized in PEP 3156, which includes coroutines that leverage subgenerator delegation