slots provide a special mechanism to reduce the size of objects. It is especially useful if you need to allocate thousands of objects that would otherwise take lots of memory space. It is not very common but you may find it useful someday. Note, however, that it has some side effects (e.g. pickle may not work) and that Python 3 introduced memory optimisation on objects (sse http://www.python.org/dev/peps/pep-0412/) so slots may not be needed anymore ?

Print('Welcome to the Slot Machine Simulator: You'll start with $50. You'll be asked if you want to play. Answer with yes/no. You can also use y/n: No case sensitivity in your answer. For example you can answer with YEs, yEs, Y, nO, N. To win you must get one of the following combinations: BAR tBAR tBAR t tpays t$250: BELL tBELL tBELL/BAR.

The main idea is as follows. As you may know every object in Python contains a dynamic dictionary that allows adding attributes. You can see the slots as the static version that does not allow additional attributes.

• You have a bunch of constants related to what the slot reel is showing - these can be better described as an Enum class Reel(Enum): CHERRY = 1 LEMON = 2 ORANGE = 3 PLUM = 4 BELL = 5 BAR = 6 SEVEN = 7.
• In contrast, a class instance with slots declared to be (no data) is only 16 bytes, and 56 total bytes with one item in slots, 64 with two. For 64 bit Python, I illustrate the memory consumption in bytes in Python 2.7 and 3.6, for slots and dict (no slots defined) for each point where the dict grows in 3.6 (except for 0, 1, and 2.
• The Python is semi automatic, and will fire at a maximum rate of 625 RPM, equal to all other pistols bar the ASP and the fully automatic CZ-75. Attempting to shoot faster than 625 RPM will initiate the firecap and reduce RPM. The Python is considered below average on the accuracy front.

Contents

• slots
  • Quick Example

Here is the slots syntax uing the __slot__ keyword:

The traditional version would be as follows:

This means that for every instance you’ll have an instance of a dict. Now, for some people this might seem way too much space for just a couple of attributes.

Unfortunately there is a side effect to slots. They change the behavior of the objects that have slots in a way that can be abused by control freaks and static typing weenies. This is bad, because the control freaks should be abusing the metaclasses and the static typing weenies should be abusing decorators, since in Python, there should be only one obvious way of doing something.

Making CPython smart enough to handle saving space without __slots__ is a major undertaking, which is probably why it is not on the list of changes for P3k (yet).

I’d like to see some elaboration on the “static typing”/decorator point, sans pejoratives. Quoting absent third parties is unhelpful. __slots__ doesn’t address the same issues as static typing. For example, in C++, it is not the declaration of a member variable is being restricted, it is the assignment of an unintended type (and compiler enforced) to that variable. I’m not condoning the use of __slots__, just interested in the conversation. Thanks! – hiwaylon Nov 28 ‘11 at 17:541

Each python object has a __dict__ atttribute which is a dictionary containing all other attributes. e.g. when you type self.attr python is actually doing self.__dict__[‘attr’]. As you can imagine using a dictionary to store attribute takes some extra space & time for accessing it.

However, when you use __slots__, any object created for that class won’t have a __dict__ attribute. Instead, all attribute access is done directly via pointers.

So if want a C style structure rather than a full fledged class you can use __slots__ for compacting size of the objects & reducing attribute access time. A good example is a Point class containing attributes x & y. If you are going to have a lot of points, you can try using __slots__ in order to conserve some memory.

No, an instance of a class with __slots__ defined is not like a C-style structure. There is a class-level dictionary mapping attribute names to indexes, otherwise the following would not be possible: class A(object): __slots__= “value”,nna=A(); setattr(a, ‘value’, 1) I really think this answer should be clarified (I can do that if you want). Also, I’m not certain that instance.__hidden_attributes[instance.__class__[attrname]] is faster than instance.__dict__[attrname]. – tzot Oct 15 ‘11 at 13:56up vote 4 down vote

Slots are very useful for library calls to eliminate the “named method dispatch” when making function calls. This is mentioned in the SWIG documentation. For high performance libraries that want to reduce function overhead for commonly called functions using slots is much faster.

Now this may not be directly related to the OPs question. It is related more to building extensions than it does to using the slots syntax on an object. But it does help complete the picture for the usage of slots and some of the reasoning behind them.

By default, instances of both old and new-style classes have a dictionary for attribute storage. This wastes space for objects having very few instance variables. The space consumption can become acute when creating large numbers of instances.

The default can be overridden by defining __slots__ in a new-style class definition. The __slots__ declaration takes a sequence of instance variables and reserves just enough space in each instance to hold a value for each variable. Space is saved because __dict__ is not created for each instance.

This class variable can be assigned a string, iterable, or sequence of strings with variable names used by instances. If defined in a new-style class, __slots__ reserves space for the declared variables and prevents the automatic creation of __dict__ and __weakref__ for each instance. New in version 2.2.

Notes on using __slots__

Without a __dict__ variable, instances cannot be assigned new variables not listed in the __slots__ definition. Attempts to assign to an unlisted variable name raises AttributeError. If dynamic assignment of new variables is desired, then add ‘__dict__’ to the sequence of strings in the __slots__ declaration. Changed in version 2.3: Previously, adding ‘__dict__’ to the __slots__ declaration would not enable the assignment of new attributes not specifically listed in the sequence of instance variable names.

Without a __weakref__ variable for each instance, classes defining __slots__ do not support weak references to its instances. If weak reference support is needed, then add ‘__weakref__’ to the sequence of strings in the __slots__ declaration. Changed in version 2.3: Previously, adding ‘__weakref__’ to the __slots__ declaration would not enable support for weak references.

__slots__ are implemented at the class level by creating descriptors (3.4.2) for each variable name. As a result, class attributes cannot be used to set default values for instance variables defined by __slots__; otherwise, the class attribute would overwrite the descriptor assignment.

If a class defines a slot also defined in a base class, the instance variable defined by the base class slot is inaccessible (except by retrieving its descriptor directly from the base class). This renders the meaning of the program undefined. In the future, a check may be added to prevent this.

Warning

effects of a __slots__ declaration is limited to the class where it is defined. In other words, subclasses will have a __dict__ (unless they also define __slots__).

__slots__ do not work for classes derived from ``variable-length’’ built-in types such as long, str and tuple.

Any non-string iterable may be assigned to __slots__. Mappings may also be used; however, in the future, special meaning may be assigned to the values corresponding to each key.

For every instance of any class, attributes are stored in a dictionary.

This means that for every instance you’ll have an instance of a dict. Now, for some people this might seem way too much space for just a couple of attributes.

If you have lots and lots and looooots of instances, and you want to save some memory, you can use __slots__. The basic idea is that when you define the __slots__ class attribute, those attributes will get just the enough space, without wasting space.

Here is the previous example using __slots__:

Now, one side effect of these __slots__ thing is that, whenever you define the __slots__ class attribute, your __dict__ attribute for every instance will be gone!. It’s not a surprise because that’s why you should use __slots__ in the first place… to get rid off the __dict__ in every instance, to save some memory remember?Can’t bind attributes to the instance any more…

Another side effect is that, as there is no __dict__, there is no way to add, at runtime, any attributes to your instance:

# This should should work if there is no __slots__ defined...>>> instance.new_attr = 10Traceback (most recent call last):

AttributeError: ‘myClass’ object has no attribute ‘new_attr’>>>Read only attributes?

Another one is that, if there is some kind of collision between the slot and a class attribute, then the class attribute will overwrite the slot and, as there is no __dict__, the class attribute will be read-only.

However if you want to have a __dict__, you can always insert into the __slots__ the ‘__dict__’ value, and all these little side effects will go away

But what if I wanted to add the ‘__dict__’ value into __slots__ at runtime?

sorry dude but, no can do.

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reference: http://mypythonnotes.wordpress.com/2008/09/04/__slots__/:wq

Python Lab: Wheel of Fortune
In this lab, you will write a program that plays the game “Wheel of Fortune”. First, your program asks the user to enter an integer number larger than zero (as your spin of the wheel). You need to check whether the input is legitimate. Your wheel has 10 slots (spaces). So your program will generate the awards on the wheel by generating a sequence of 10 random numbers (one for each slot), each number is between 0 and 1000, where the minimum unit is 100. You need to print the sequence and tell how much the player wins based on the spin.
Here’s an example of a run of the program. The output from the program is in blue, and the input (what you type) is in black.
Welcome to Wheel of Fortune!
There are 10 slots in your wheel.
Spin your wheel by entering an integer number: hello
This is not an integer greater than 0.
please enter an integer: 0
This is not an integer greater than 0.
please enter an integer: 22
The wheel is
[100, 800, 700, 200, 400, 400, 600, 700, 0, 0]
You won $700 in the Wheel of Fortune!
press enter to exit:
At the beginning of the spin, the player is at the first slot (the slot with 100). So a spin of 10 will return to the first slot and a spin of 22 will land in the-700-dollar slot.
1. Coding the assignment
Open the python IDLE GUI. Similar to the last lab, you will be using input function to get the user input, and printing out the final “Press enter to exist:” message. There is more than one way to do this lab, so we will not give step-by-step instructions. Some hints and requirements are:

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· Your program should first import the random module. Right after that, you must put in this line of code (in red):
random.seed(1)
· You must check the input is actually an integer greater than 0. It cannot be 0 or any character. You can do this by using a while loop to keep asking the user until he/she enters the correct input.

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· You may use the randrange() function in the random module to generate an integer. How do you use randrange()? You do the following:
random.randrange(11)
This will randomly generate an integer number less than 11. You can multiple this number by 100 to generate the award on each slot on the wheel.
· To generate 10 awards, one for each slot, you may use a for loop. Inside the for loop, you should call randrange to generate a random number and add that to your sequence.

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· You need to use remainder to decide which award the player should get. For example, if the player’s number is 11, it means one round and 1 more slot. So the award is the second number in your sequence.
· You need to write comments for your code (line by line).