What link do you see between the easiness of prototyping offered by Python and the effort needed to build a complete application?
Guido: I never meant Python to be a prototyping language. I don’t believe there should be a clear distinction between prototyping and “production” languages. There are situations where the best way to write a prototype would be to write a little throwaway C hack. There are other situations where a prototype can be created using no “programming” at all—for example, using a spreadsheet or a set of find and grep commands.
The earliest intentions I had for Python were simply for it to be a language to be used in cases where C was overkill and shell scripts became too cumbersome. That covers a lot of prototyping, but it also covers a lot of “business logic” (as it’s come to be called these days) that isn’t particularly greedy in computing resources but requires a lot of code to be written. I would say that most Python code is not written as a prototype but simply to get a job done. In most cases Python is fully up to the job, and there is no need to change much in order to arrive at the final application.
A common process is that a simple application gradually acquires more functionality, and ends up growing tenfold in complexity, and there is never a precise cutover point from prototype to final application. For example, the code review application Mondrian that I started at Google has probably grown tenfold in code size since I first released it, and it is still all written in Python. Of course, there are also examples where Python did eventually get replaced by a faster language—for example, the earliest Google crawler/indexer was (largely) written in Python—but those are the exceptions, not the rule.
How does the immediacy of Python affect the design process?
Guido: This is often how I work, and, at least for me, in general it works out well! Sure, I write a lot of code that I throw away, but it’s much less code than I would have written in any other language, and writing code (without even running it) often helps me tremendously in understanding the details of the problem. Thinking about how to rearrange the code so that it solves the problem in an optimal fashion often helps me think about the problem. Of course, this is not to be used as an excuse to avoid using a whiteboard to sketch out a design or architecture or interaction, or other early design techniques. The trick is to use the right tool for the job. Sometimes that’s a pencil and a napkin—other times it’s an Emacs window and a shell prompt.
Do you think that bottom-up program development is more suited to Python?
Guido: I don’t see bottom-up versus top-down as religious opposites like vi versus Emacs. In any software development process, there are times when you work bottom-up, and other times when you work top-down. Top-down probably means you’re dealing with something that needs to be carefully reviewed and designed before you can start coding, while bottom-up probably means that you are building new abstractions on top of existing ones, for example, creating new APIs. I’m not implying that you should start coding APIs without having some kind of design in mind, but often new APIs follow logically from the available lower-level APIs, and the design work happens while you are actually writing code.
Source of Information : Oreilly - Masterminds of Programming
Guido: I never meant Python to be a prototyping language. I don’t believe there should be a clear distinction between prototyping and “production” languages. There are situations where the best way to write a prototype would be to write a little throwaway C hack. There are other situations where a prototype can be created using no “programming” at all—for example, using a spreadsheet or a set of find and grep commands.
The earliest intentions I had for Python were simply for it to be a language to be used in cases where C was overkill and shell scripts became too cumbersome. That covers a lot of prototyping, but it also covers a lot of “business logic” (as it’s come to be called these days) that isn’t particularly greedy in computing resources but requires a lot of code to be written. I would say that most Python code is not written as a prototype but simply to get a job done. In most cases Python is fully up to the job, and there is no need to change much in order to arrive at the final application.
A common process is that a simple application gradually acquires more functionality, and ends up growing tenfold in complexity, and there is never a precise cutover point from prototype to final application. For example, the code review application Mondrian that I started at Google has probably grown tenfold in code size since I first released it, and it is still all written in Python. Of course, there are also examples where Python did eventually get replaced by a faster language—for example, the earliest Google crawler/indexer was (largely) written in Python—but those are the exceptions, not the rule.
How does the immediacy of Python affect the design process?
Guido: This is often how I work, and, at least for me, in general it works out well! Sure, I write a lot of code that I throw away, but it’s much less code than I would have written in any other language, and writing code (without even running it) often helps me tremendously in understanding the details of the problem. Thinking about how to rearrange the code so that it solves the problem in an optimal fashion often helps me think about the problem. Of course, this is not to be used as an excuse to avoid using a whiteboard to sketch out a design or architecture or interaction, or other early design techniques. The trick is to use the right tool for the job. Sometimes that’s a pencil and a napkin—other times it’s an Emacs window and a shell prompt.
Do you think that bottom-up program development is more suited to Python?
Guido: I don’t see bottom-up versus top-down as religious opposites like vi versus Emacs. In any software development process, there are times when you work bottom-up, and other times when you work top-down. Top-down probably means you’re dealing with something that needs to be carefully reviewed and designed before you can start coding, while bottom-up probably means that you are building new abstractions on top of existing ones, for example, creating new APIs. I’m not implying that you should start coding APIs without having some kind of design in mind, but often new APIs follow logically from the available lower-level APIs, and the design work happens while you are actually writing code.
Source of Information : Oreilly - Masterminds of Programming
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