Python en:More

In This Guide: 1 Introduction 2 Passing tuples around 3 Special Methods 4 Single Statement Blocks 5 Lambda Forms 6 List Comprehension 7 Receiving Tuples and Lists in Functions 8 exec and eval 9 The assert statement 10 The repr function 11 Summary

Introduction

So far we have covered majority of the various aspects of Python that you will use. In this chapter, we will cover some more aspects that will make our knowledge of Python more well-rounded.

Passing tuples around

Ever wished you could return two different values from a function? You can. All you have to do is use a tuple.

<source lang="python"> >>> def get_error_details(): ... return (2, 'second error details') ... >>> errnum, errstr = get_error_details() >>> errnum 2 >>> errstr 'second error details' </source>

Notice that the usage of a, b = <some expression> interprets the result of the expression as a tuple with two values.

If you want to interpret the results as (a, <everything else>), then you just need to star it just like you would in function parameters:

<source lang="python"> >>> a, *b = [1, 2, 3, 4] >>> a 1 >>> b [2, 3, 4] </source>

This also means the fastest way to swap two variables in Python is:

<source lang="python"> >>> a = 5; b = 8 >>> a, b = b, a >>> a, b (8, 5) </source>

Special Methods

There are certain methods such as the __init__ and __del__ methods which have special significance in classes.

Special methods are used to mimic certain behaviors of built-in types. For example, if you want to use the x[key] indexing operation for your class (just like you use it for lists and tuples), then all you have to do is implement the __getitem__() method and your job is done. If you think about it, this is what Python does for the list class itself!

Some useful special methods are listed in the following table. If you want to know about all the special methods, see the manual.

Some Special Methods

Name	Explanation
__init__(self, ...)	This method is called just before the newly created object is returned for usage.
__del__(self)	Called just before the object is destroyed
__str__(self)	Called when we use the print function or when str() is used.
__lt__(self, other)	Called when the less than operator (<) is used. Similarly, there are special methods for all the operators (+, >, etc.)
__getitem__(self, key)	Called when x[key] indexing operation is used.
__len__(self)	Called when the built-in len() function is used for the sequence object.

Single Statement Blocks

We have seen that each block of statements is set apart from the rest by its own indentation level. Well, there is one caveat. If your block of statements contains only one single statement, then you can specify it on the same line of, say, a conditional statement or looping statement. The following example should make this clear:

<source lang="python"> >>> flag = True >>> if flag: print 'Yes' ... Yes </source>

Notice that the single statement is used in-place and not as a separate block. Although, you can use this for making your program smaller, I strongly recommend avoiding this short-cut method, except for error checking, mainly because it will be much easier to add an extra statement if you are using proper indentation.

Lambda Forms

A lambda statement is used to create new function objects and then return them at runtime.

<source lang="python">

1. !/usr/bin/python
2. Filename: lambda.py

def make_repeater(n):

   return lambda s: s * n

twice = make_repeater(2)

print(twice('word')) print(twice(5)) </source>

Output:

   $ python lambda.py
   wordword
   10

How It Works:

Here, we use a function make_repeater to create new function objects at runtime and return it. A lambda statement is used to create the function object. Essentially, the lambda takes a parameter followed by a single expression only which becomes the body of the function and the value of this expression is returned by the new function. Note that even a print statement cannot be used inside a lambda form, only expressions.

TODO

Can we do a list.sort() by providing a compare function created using lambda?

<source lang="python"> points = [ { 'x' : 2, 'y' : 3 }, { 'x' : 4, 'y' : 1 } ]

1. points.sort(lambda a, b : cmp(a['x'], b['x']))

</source>

List Comprehension

List comprehensions are used to derive a new list from an existing list. Suppose you have a list of numbers and you want to get a corresponding list with all the numbers multiplied by 2 only when the number itself is greater than 2. List comprehensions are ideal for such situations.

<source lang="python">

1. !/usr/bin/python
2. Filename: list_comprehension.py

listone = [2, 3, 4] listtwo = [2*i for i in listone if i > 2] print(listtwo) </source>

Output:

   $ python list_comprehension.py
   [6, 8]

How It Works:

Here, we derive a new list by specifying the manipulation to be done (2*i) when some condition is satisfied (if i > 2). Note that the original list remains unmodified.

The advantage of using list comprehensions is that it reduces the amount of boilerplate code required when we use loops to process each element of a list and store it in a new list.

Receiving Tuples and Lists in Functions

There is a special way of receiving parameters to a function as a tuple or a dictionary using the * or ** prefix respectively. This is useful when taking variable number of arguments in the function.

<source lang="python"> >>> def powersum(power, *args): ... Return the sum of each argument raised to specified power. ... total = 0 ... for i in args: ... total += pow(i, power) ... return total ... >>> powersum(2, 3, 4) 25

>>> powersum(2, 10) 100 </source>

Because we have a * prefix on the args variable, all extra arguments passed to the function are stored in args as a tuple. If a ** prefix had been used instead, the extra parameters would be considered to be key/value pairs of a dictionary.

exec and eval

The exec function is used to execute Python statements which are stored in a string or file, as opposed to written in the program itself. For example, we can generate a string containing Python code at runtime and then execute these statements using the exec statement:

<source lang="python"> >>> exec('print("Hello World")') Hello World </source>

Similarly, the eval function is used to evaluate valid Python expressions which are stored in a string. A simple example is shown below.

<source lang="python"> >>> eval('2*3') 6 </source>

The assert statement

The assert statement is used to assert that something is true. For example, if you are very sure that you will have at least one element in a list you are using and want to check this, and raise an error if it is not true, then assert statement is ideal in this situation. When the assert statement fails, an AssertionError is raised.

<source lang="python"> >>> mylist = ['item'] >>> assert len(mylist) >= 1 >>> mylist.pop() 'item' >>> mylist [] >>> assert len(mylist) >= 1 Traceback (most recent call last):

 File "<stdin>", line 1, in <module>

AssertionError </source>

The assert statement should be used judiciously. Most of the time, it is better to catch exceptions, either handle the problem or display an error message to the user and then quit.

The repr function

The repr function is used to obtain a canonical string representation of the object. The interesting part is that you will have eval(repr(object)) == object most of the time.

<source lang="python"> >>> i = [] >>> i.append('item') >>> repr(i) "['item']" >>> eval(repr(i)) ['item'] >>> eval(repr(i)) == i True </source>

Basically, the repr function is used to obtain a printable representation of the object. You can control what your classes return for the repr function by defining the __repr__ method in your class.

Summary

We have covered some more features of Python in this chapter and yet we haven't covered all the features of Python. However, at this stage, we have covered most of what you are ever going to use in practice. This is sufficient for you to get started with whatever programs you are going to create.

Next, we will discuss how to explore Python further.

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