首先，你应该明白，编程或计算机科学一般不是数学，我们经常使用的大多数术语都没有严格的定义。

现在回答你的问题:

什么是解释器(计算机科学)

它按最小的可执行单元翻译源代码，然后执行该单元。

什么是虚拟机

对于JVM来说，虚拟机是一个包含解释器、类加载器、垃圾收集器、线程调度器、JIT编译器和许多其他东西的软件。

正如你所看到的，解释器是JVM的一部分，整个JVM不能被称为解释器，因为它包含许多其他组件。

为什么在谈论python时要用“解释器”这个词

在Java中，编译部分是显式的。 另一方面，Python的编译和解释过程不像Java那样明确，从最终用户的角度来看，解释是用于执行Python程序的唯一机制

他们之间没有真正的区别，人们只是遵循创造者选择的惯例。

for posts that mention that python does not need to generate byte code, I'm not sure that's true. it seems that all callables in Python must have a .__code__.co_code attribute which contains the byte code. I don't see a meaningful reason to call python "not compiled" just because the compiled artifacts may not be saved; and often aren't saved by design in Python, for example all comprehension compile new bytecode for it's input, this is the reason comprehension variable scope is not consistent between compile(mode='exec, ...) and compile compile(mode='single', ...) such as between running a python script and using pdb

首先，你应该明白，编程或计算机科学一般不是数学，我们经常使用的大多数术语都没有严格的定义。

现在回答你的问题:

什么是解释器(计算机科学)

它按最小的可执行单元翻译源代码，然后执行该单元。

什么是虚拟机

对于JVM来说，虚拟机是一个包含解释器、类加载器、垃圾收集器、线程调度器、JIT编译器和许多其他东西的软件。

正如你所看到的，解释器是JVM的一部分，整个JVM不能被称为解释器，因为它包含许多其他组件。

为什么在谈论python时要用“解释器”这个词

在Java中，编译部分是显式的。 另一方面，Python的编译和解释过程不像Java那样明确，从最终用户的角度来看，解释是用于执行Python程序的唯一机制

Python可以解释代码，而无需将其编译为字节码。Java不能。

Python是一种解释型语言，而不是编译型语言，尽管由于字节码编译器的存在，两者的区别可能很模糊。这意味着源文件可以直接运行，而无需显式地创建一个可执行文件，然后再运行。

(来自文档)。

在java中，每个文件都必须编译为.class文件，然后在JVM上运行。相反，python会通过主脚本导入这些文件，以帮助加快后续使用这些文件的速度。

然而，在典型的情况下，大多数python(至少是CPython)代码运行在模拟的堆栈机器中，它与JVM的指令几乎相同，因此没有太大的区别。

然而，这种区别的真正原因是，从一开始，java就把自己打上了“可移植的、可执行的字节码”的标签，而python则把自己打上了带有REPL的动态解释语言的标签。名字贴!

为了深入回答“为什么是Java虚拟机，而不是Python解释器?”这个问题，让我们尝试回到编译理论领域，作为讨论的起点。

程序编译的典型过程包括以下步骤:

Lexical analysis. Splits program text into meaningful "words" called tokens (as part of the process all comments, spaces, new-lines etc. are removed, because they do not affect program behavior). The result is an ordered stream of tokens. Syntax analysis. Builds the so-called Abstract Syntax Tree (AST) from the stream of tokens. AST establish relations between tokens and, as a consequence, defines an order of evaluation of the program. Semantic analysis. Verifies semantical correctness of the AST using information about types and a set of semantical rules of the programming language. (For example, a = b + c is a correct statement from the syntaxis point of view, but completely incorrect from the semantic point of view if a was declared as a constant object) Intermediate code generation. Serializes AST into the linearly ordered stream of machine independent "primitive" operations. In fact, code generator traverses AST and logs the order of evaluation steps. As a result, from the tree-like representation of the program, we achieve much more simple list-like representation in which order of program evaluation is preserved. Machine code generation. The program in the form of machine independent "primitive" bytecode is translated into machine code of particular processor architecture.

好的。现在我们来定义这些术语。

Interpreter, in the classical meaning of that word, assumes execution based on the program evaluation based on AST produced directly from the program text. In that case, a program is distributed in the form of source code and the interpreter is fed by program text, frequently in a dynamic way (statement-by-statement or line-by-line). For each input statement, interpreter builds its AST and immediately evaluates it changing the "state" of the program. This is a typical behavior demonstrated by scripting languages. Consider for example Bash, Windows CMD etc. Conceptually, Python takes this way too.

If we replace the AST-based execution step on the generation of intermediate machine-independent binary bytecode step in the interpreter we will split the entire process of program execution into two separate phases: compilation and execution. In that case what previously was an interpreter will become a bytecode compiler, which will transform the program from the form of the text into some binary form. Then the program is distributed in that binary form, but not in the form of source code. On the user machine, that bytecode is fed into a new entity -- virtual machine, which in fact interpret that bytecode. Due to this, virtual machines are also called bytecode interpreter. But put your attention here! A classical interpreter is a text interpreter, but a virtual machine is a binary interpreter! This is an approach taken by Java and C#.

最后，如果我们将机器代码生成添加到字节码编译器中，我们就实现了我们所说的经典编译器。经典编译器将程序源代码转换为特定处理器的机器码。然后，该机器代码可以直接在目标处理器上执行，而不需要任何额外的中介(不需要任何类型的解释器，无论是文本解释器还是二进制解释器)。

现在让我们回到最初的问题，考虑Java和Python。

Java was initially designed to have as few implementation dependencies as possible. Its design is based on the principle "write once, run anywhere" (WORA). To implement it, Java was initially designed as a programming language that compiles into machine-independent binary bytecode, which then can be executed on all platforms that support Java without the need for its recompilation. You can think about Java like about WORA-based C++. Actually, Java is closer to C++ than to the scripting languages like Python. But in contrast to C++, Java was designed to be compiled into binary bytecode which then is executed in the environment of the virtual machine, while C++ was designed to be compiled in machine code and then directly executed by the target processor.

Python was initially designed as a kind of scripting programing language which interprets scripts (programs in the form of the text written in accordance with the programming language rules). Due to this, Python has initially supported a dynamic interpretation of one-line commands or statements, as the Bash or Windows CMD do. For the same reason, initial implementations of Python had not any kind of bytecode compilers and virtual machines for execution of such bytecode inside, but from the start Python had required interpreter which is capable to understand and evaluate Python program text.

因此，在历史上，Java开发人员倾向于谈论Java虚拟机(因为最初，Java已经作为Java字节码编译器和字节码解释器——JVM的包出现)，而Python开发人员倾向于谈论Python解释器(因为最初Python没有任何虚拟机，是一种经典的文本解释器，直接执行程序文本，而不需要任何形式的编译或转换为任何形式的二进制代码)。

Currently, Python also has the virtual machine under the hood and can compile and interpret Python bytecode. And that fact makes an additional investment into the confusion "Why Java Virtual Machine, but Python interpreter?", because it seems that implementations of both languages contain virtual machines. But! Even in the current moment interpretation of program text is a primary way of Python programs execution. Python implementations exploit virtual machines under the hood exclusively as an optimization technique. Interpretation of binary bytecode in the virtual machine is much more efficient than a direct interpretation of the original program text. At the same time, the presence of the virtual machine in the Python is absolutely transparent for both Python language designers and Python programs developers. The same language can be implemented in interpreters with and without the virtual machine. In the same way, the same programs can be executed in interpreters with and without the virtual machine, and that programs will demonstrate exactly the same behavior and produce equally the same output from the equal input. The only observable difference will be the speed of program execution and the amount of memory consumed by the interpreter. Thus, the virtual machine in Python is not an unavoidable part of the language design, but just an optional extension of the major Python interpreter.

Java can be considered in a similar way. Java under the hood has a JIT compiler and can selectively compile methods of Java class into machine code of the target platform and then directly execute it. But! Java still uses bytecode interpretation as a primary way of Java program execution. Like Python implementations which exploit virtual machines under the hood exclusively as an optimization technique, the Java virtual machines use Just-In-Time compilers exclusively for optimization purposes. Similarly, just because of the fact that direct execution of the machine code at least ten times faster than the interpretation of Java bytecode. And like in the case of Python, the presence of JIT compiler under the hood of JVM is absolutely transparent for both Java language designers and Java program developers. The same Java programming language can be implemented by JVM with and without JIT compiler. And in the same way, the same programs can be executed in JVMs with and without JIT inside, and the same programs will demonstrate exactly the same behavior and produce equally the same output from the equal input on both JVMs (with and without JIT). And like in the case of Python, the only observable difference between them, will be in the speed of execution and in the amount of memory consumed by JVM. And finally, like in the case of Python, JIT in Java also is not an unavoidable part of the language design, but just an optional extension of the major JVM implementations.

From the point of view of design and implementation of virtual machines of Java and Python, they differ significantly, while (attention!) both still stay virtual machines. JVM is an example of a low-level virtual machine with simple basic operations and high instruction dispatch cost. Python in its turn is a high-level virtual machine, for which instructions demonstrate complex behavior, and instruction dispatch cost is not so significant. Java operates with very low abstraction level. JVM operates on the small well-defined set of primitive types and has very tight correspondence (typically one to one) between bytecode instructions and native machine code instructions. In contrary, Python virtual machine operates at high abstraction level, it operates with complex data types (objects) and supports ad-hoc polymorphism, while bytecode instructions expose complex behavior, which can be represented by a series of multiple native machine code instructions. For example, Python supports unbounded range mathematics. Thus Python VM is forced to exploit long arithmetics for potentially big integers for which result of the operation can overflow the machine word. Hence, one bytecode instruction for arithmetics in Python can expose into the function call inside Python VM, while in JVM arithmetic operation will expose into simple operation expressed by one or few native machine instructions.

因此，我们可以得出以下结论。Java虚拟机但Python解释器是因为:

The term of virtual machine assumes binary bytecode interpretation, while the term interpreter assumes program text interpretation. Historically, Java was designed and implemented for binary bytecode interpretation and Python was initially designed and implemented for program text interpretation. Thus, the term "Java Virtual Machine" is historical and well established in the Java community. And similarly, the term "Python Interpreter" is historical and well established in the Python community. Peoples tend to prolong the tradition and use the same terms that were used long before. Finally, currently, for Java, binary bytecode interpretation is a primary way of programs execution, while JIT-compilation is just an optional and transparent optimization. And for Python, currently, program text interpretation is a primary way of Python programs execution, while compilation into Python VM bytecode is just an optional and transparent optimization.

Therefore, both Java and Python have virtual machines are binary bytecode interpreters, which can lead to confusion such as "Why Java Virtual Machine, but Python interpreter?". The key point here is that for Python, a virtual machine is not a primary or necessary means of program execution; it is just an optional extension of the classical text interpreter. On the other hand, a virtual machine is a core and unavoidable part of Java program execution ecosystem. Static or dynamic typing choice for the programming language design affects mainly the virtual machine abstraction level only, but does not dictate whether or not a virtual machine is needed. Languages using both typing systems can be designed to be compiled, interpreted, or executed within the environment of virtual machine, depending on their desired execution model.