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There are cases where we still want to use loop variables in a list comprehension. For example, we need to use a loop variable to extract successive overlapping n-grams from a list:. It is quite tricky to get the range of the loop variable right. Since this is a common operation in NLP, NLTK supports it with functions bigrams text and trigrams text , and a general purpose ngrams text, n.
For example, to build an array with m rows and n columns, where each cell is a set, we could use a nested list comprehension:. Observe that the loop variables i and j are not used anywhere in the resulting object, they are just needed for a syntactically correct for statement. Note that it would be incorrect to do this work using multiplication, for reasons concerning object copying that were discussed earlier in this section.
Iteration is an important programming device. It is tempting to adopt idioms from other languages. However, Python offers some elegant and highly readable alternatives, as we have seen. Functions provide an effective way to package and re-use program code, as already explained in 3.
For example, suppose we find that we often want to read text from an HTML file. This involves several steps: Read text from a file. It will return a string, and we can assign this to a variable, e. Each time we want to use this series of steps we only have to call the function. Using functions has the benefit of saving space in our program. More importantly, our choice of name for the function helps make the program readable.
This naming helps to provide some "semantic interpretation" — it helps a reader of our program to see what the program "means". Notice that the above function definition contains a string. The first string inside a function definition is called a docstring.
Not only does it document the purpose of the function to someone reading the code, it is accessible to a programmer who has loaded the code from a file:. We have seen that functions help to make our work reusable and readable. They also help make it reliable. When we re-use code that has already been developed and tested, we can be more confident that it handles a variety of cases correctly.
We also remove the risk that we forget some important step, or introduce a bug. The program that calls our function also has increased reliability. The author of that program is dealing with a shorter program, and its components behave transparently.
To summarize, as its name suggests, a function captures functionality. It is a segment of code that can be given a meaningful name and which performs a well-defined task. Functions allow us to abstract away from the details, to see a bigger picture, and to program more effectively. The rest of this section takes a closer look at functions, exploring the mechanics and discussing ways to make your programs easier to read.
We first define the function to take two parameters, msg and num. Then we call the function and pass it two arguments, monty and 3 ; these arguments fill the "placeholders" provided by the parameters and provide values for the occurrences of msg and num in the function body. A function usually communicates its results back to the calling program via the return statement, as we have just seen.
A Python function is not required to have a return statement. Some functions do their work as a side effect, printing a result, modifying a file, or updating the contents of a parameter to the function such functions are called "procedures" in some other programming languages. Consider the following three sort functions. The third one is dangerous because a programmer could use it without realizing that it had modified its input. The same is true for functions. Python interprets function parameters as values this is known as call-by-value.
We begin by assigning an empty string to w and an empty list to p. After calling the function, w is unchanged, while p is changed:. Notice that w was not changed by the function. Inside the function, the value of word was modified. However, that change did not propagate to w. This parameter passing is identical to the following sequence of assignments:. The function modifies properties , and this change is also reflected in the value of p as we saw. The function also assigned a new value to properties the number 5 ; this did not modify the contents at that memory location, but created a new local variable.
This behavior is just as if we had done the following sequence of assignments:. Remember that you can use the id function and is operator to check your understanding of object identity after each statement. Function definitions create a new, local scope for variables. When you assign to a new variable inside the body of a function, the name is only defined within that function. The name is not visible outside the function, or in other functions.
This behavior means you can choose variable names without being concerned about collisions with names used in your other function definitions.
When you refer to an existing name from within the body of a function, the Python interpreter first tries to resolve the name with respect to the names that are local to the function. If nothing is found, the interpreter checks if it is a global name within the module. Finally, if that does not succeed, the interpreter checks if the name is a Python built-in. This is the so-called LGB rule of name resolution: A function can enable access to a global variable using the global declaration.
However, this practice should be avoided as much as possible. Defining global variables inside a function introduces dependencies on context and limits the portability or reusability of the function.
In general you should use parameters for function inputs and return values for function outputs. Python does not allow us to declare the type of a variable when we write a program, and this permits us to define functions that are flexible about the type of their arguments. However, often we want to write programs for later use by others, and want to program in a defensive style, providing useful warnings when functions have not been invoked correctly.
The author of the following tag function assumed that its argument would always be a string. The author of this function could take some extra steps to ensure that the word parameter of the tag function is a string.
This is a slight improvement, because the function is checking the type of the argument, and trying to return a "special", diagnostic value for the wrong input. However, it is also dangerous because the calling program may not detect that None is intended as a "special" value, and this diagnostic return value may then be propagated to other parts of the program with unpredictable consequences.
This approach also fails if the word is a Unicode string, which has type unicode , not str. If the assert statement fails, it will produce an error that cannot be ignored, since it halts program execution.
Additionally, the error message is easy to interpret. Adding assertions to a program helps you find logical errors, and is a kind of defensive programming.
A more fundamental approach is to document the parameters to each function using docstrings as described later in this section.
Well-structured programs usually make extensive use of functions. When a block of program code grows longer than lines, it is a great help to readability if the code is broken up into one or more functions, each one having a clear purpose.
This is analogous to the way a good essay is divided into paragraphs, each expressing one main idea. Functions provide an important kind of abstraction. They allow us to group multiple actions into a single, complex action, and associate a name with it. Compare this with the way we combine the actions of go and bring back into a single more complex action fetch.
When we use functions, the main program can be written at a higher level of abstraction, making its structure transparent, e. Appropriate use of functions makes programs more readable and maintainable.
It updates the contents of a frequency distribution that is passed in as a parameter, and it also prints a list of the n most frequent words. This function has a number of problems. The function has two side-effects: The function would be easier to understand and to reuse elsewhere if we initialize the FreqDist object inside the function in the same place it is populated , and if we moved the selection and display of results to the calling program.
Given that its task is to identify frequent words, it should probably just return a list, not the whole frequency distribution. If we have done a good job at decomposing our program into functions, then it should be easy to describe the purpose of each function in plain language, and provide this in the docstring at the top of the function definition.
This statement should not explain how the functionality is implemented; in fact it should be possible to re-implement the function using a different method without changing this statement. For the simplest functions, a one-line docstring is usually adequate see 4. You should provide a triple-quoted string containing a complete sentence on a single line. For non-trivial functions, you should still provide a one sentence summary on the first line, since many docstring processing tools index this string.
This should be followed by a blank line, then a more detailed description of the functionality see http: Docstrings can include a doctest block , illustrating the use of the function and the expected output.
Docstrings should document the type of each parameter to the function, and the return type. At a minimum, that can be done in plain text. This format can be automatically converted into richly structured API documentation see http: Illustration of a complete docstring, consisting of a one-line summary, a more detailed explanation, a doctest example, and Sphinx markup specifying the parameters, types, return type, and exceptions.
This section discusses more advanced features, which you may prefer to skip on the first time through this chapter. So far the arguments we have passed into functions have been simple objects like strings, or structured objects like lists.
Python also lets us pass a function as an argument to another function. Now we can abstract out the operation, and apply a different operation on the same data. Notice that parentheses are only used after a function name if we are invoking the function; when we are simply treating the function as an object these are omitted. Python provides us with one more way to define functions as arguments to other functions, so-called lambda expressions.
We can equivalently write the following:. Our next example illustrates passing a function to the sorted function. When we call the latter with a single argument the list to be sorted , it uses the built-in comparison function cmp. However, we can supply our own sort function, e. These functions start by initializing some storage, and iterate over input to build it up, before returning some final object a large structure or aggregated result.
A standard way to do this is to initialize an empty list, accumulate the material, then return the list, as shown in function search1 in 4.
Accumulating Output into a List. The function search2 is a generator. The first time this function is called, it gets as far as the yield statement and pauses.
The calling program gets the first word and does any necessary processing. Once the calling program is ready for another word, execution of the function is continued from where it stopped, until the next time it encounters a yield statement.
This approach is typically more efficient, as the function only generates the data as it is required by the calling program, and does not need to allocate additional memory to store the output cf. In order to force the permutations function to generate all its output, we wrap it with a call to list. The permutations function uses a technique called recursion, discussed below in 4. The ability to generate permutations of a set of words is useful for creating data to test a grammar 8.
Python provides some higher-order functions that are standard features of functional programming languages such as Haskell. We illustrate them here, alongside the equivalent expression using list comprehensions. We use this function as the first parameter of filter , which applies the function to each item in the sequence contained in its second parameter, and only retains the items for which the function returns True.
Another higher-order function is map , which applies a function to every item in a sequence. Here is a simple way to find the average length of a sentence in the news section of the Brown Corpus, followed by an equivalent version with list comprehension calculation:.
We can also provide a lambda expression. The solutions based on list comprehensions are usually more readable than the solutions based on higher-order functions, and we have favored the former approach throughout this book.
When there are a lot of parameters it is easy to get confused about the correct order. Instead we can refer to parameters by name, and even assign them a default value just in case one was not provided by the calling program. Now the parameters can be specified in any order, and can be omitted. These are called keyword arguments. If we mix these two kinds of parameters, then we must ensure that the unnamed parameters precede the named ones. It has to be this way, since unnamed parameters are defined by position.
Dictionaries will be presented in 3. A side-effect of having named arguments is that they permit optionality. Thus we can leave out any arguments where we are happy with the default value: Another common use of optional arguments is to permit a flag. Take care not to use a mutable object as the default value of a parameter. A series of calls to the function will use the same object, sometimes with bizarre results as we will see in the discussion of debugging below. If your program will work with a lot of files, it is a good idea to close any open files once they are no longer required.
Python will close open files automatically if you use the with statement:. Programming is a skill that is acquired over several years of experience with a variety of programming languages and tasks.
Key high-level abilities are algorithm design and its manifestation in structured programming. Key low-level abilities include familiarity with the syntactic constructs of the language, and knowledge of a variety of diagnostic methods for trouble-shooting a program which does not exhibit the expected behavior. This section describes the internal structure of a program module and how to organize a multi-module program.
Then it describes various kinds of error that arise during program development, what you can do to fix them and, better still, to avoid them in the first place.
The purpose of a program module is to bring logically-related definitions and functions together in order to facilitate re-use and abstraction. Python modules are nothing more than individual. For example, if you were working with a particular corpus format, the functions to read and write the format could be kept together. If the format was updated, you would know that only one file needed to be changed. Similarly, a module could contain code for creating and manipulating a particular data structure such as syntax trees, or code for performing a particular processing task such as plotting corpus statistics.
When you start writing Python modules, it helps to have some examples to emulate. This returns the location of the compiled. The file that you will need to open is the corresponding.
Alternatively, you can view the latest version of this module on the web at http: Like every other NLTK module, distance.
Since the code is distributed, it also includes the URL where the code is available, a copyright statement, and license information. Next is the module-level docstring, a triple-quoted multiline string containing information about the module that will be printed when someone types help nltk. After this comes all the import statements required for the module, then any global variables, followed by a series of function definitions that make up most of the module.
Other modules define "classes," the main building block of object-oriented programming, which falls outside the scope of this book. Most NLTK modules also include a demo function which can be used to see examples of the module in use.
Some module variables and functions are only used within the module. These should have names beginning with an underscore, e. If another module imports this one, using the idiom: You can optionally list the externally accessible names of a module using a special built-in variable like this: Some programs bring together a diverse range of tasks, such as loading data from a corpus, performing some analysis tasks on the data, then visualizing it.
We may already have stable modules that take care of loading data and producing visualizations. Our work might involve coding up the analysis task, and just invoking functions from the existing modules. This scenario is depicted in 4.
Structure of a Multi-Module Program: By dividing our work into several modules and using import statements to access functions defined elsewhere, we can keep the individual modules simple and easy to maintain.
This approach will also result in a growing collection of modules, and make it possible for us to build sophisticated systems involving a hierarchy of modules. Designing such systems well is a complex software engineering task, and beyond the scope of this book. Something as trivial as a mis-placed symbol might cause the program to behave very differently. We call these "bugs" because they are tiny in comparison to the damage they can cause.
Sometimes, fixing one bug only reveals another, and we get the distinct impression that the bug is on the move. The only reassurance we have is that bugs are spontaneous and not the fault of the programmer. Flippancy aside, debugging code is hard because there are so many ways for it to be faulty. Our understanding of the input data, the algorithm, or even the programming language, may be at fault. First, the input data may contain some unexpected characters.
For example, WordNet synset names have the form tree. However, this method broke when someone tried to look up the word PhD , which has the synset name ph. Although several people had tested the module before it was released, it was some weeks before someone detected the problem see http: Second, a supplied function might not behave as expected. What looked like a bug in the WordNet interface turned out to be a misunderstanding about WordNet itself: The only "bug" was a misunderstanding of the interface see http: It is easy to make the wrong assumption about the relative scope of two operators.
This is because the percent operator has higher precedence than the comma operator. The fix is to add parentheses in order to force the required scope. As another example, suppose we are defining a function to collect all tokens of a text having a given length.
The function has parameters for the text and the word length, and an extra parameter that allows the initial value of the result to be given as a parameter:. However, it is created just once, at the time the Python interpreter loads the function.
This one list object is used whenever no explicit value is provided to the function. Since most code errors result from the programmer making incorrect assumptions, the first thing to do when you detect a bug is to check your assumptions.
Localize the problem by adding print statements to the program, showing the value of important variables, and showing how far the program has progressed. If the program produced an "exception" — a run-time error — the interpreter will print a stack trace , pinpointing the location of program execution at the time of the error.
If the program depends on input data, try to reduce this to the smallest size while still producing the error. Once you have localized the problem to a particular function, or to a line of code, you need to work out what is going wrong.
It is often helpful to recreate the situation using the interactive command line. Define some variables then copy-paste the offending line of code into the session and see what happens. Check your understanding of the code by reading some documentation, and examining other code samples that purport to do the same thing that you are trying to do. Try explaining your code to someone else, in case they can see where things are going wrong.
Python provides a debugger which allows you to monitor the execution of your program, specify line numbers where execution will stop i.
You can invoke the debugger on your code as follows:. It will present you with a prompt Pdb where you can type instructions to the debugger. Type help to see the full list of commands. Typing step or just s will execute the current line and stop. If the current line calls a function, it will enter the function and stop at the first line. Essay Writing Service Get everything you need right here — essays, research and term papers, courseworks, dissertation and other college papers.
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