Slots In Python

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If you want to learn how to create an empty list in Python efficiently, then this article is for you.

PyQt5 signals and slots Graphical applications (GUI) are event-driven, unlike console or terminal applications. A users action like clicks a button or selecting an item in a list is called an event. If an event takes place, each PyQt5 widget can emit a signal. PyQt5 signals and slots Graphical applications (GUI) are event-driven, unlike console or terminal applications. A users action like clicks a button or selecting an item in a list is called an event. If an event takes place, each PyQt5 widget can emit a signal. slots Magic ¶ In Python every class can have instance attributes. By default Python uses a dict to store an object’s instance attributes. This is really helpful as it allows setting arbitrary new attributes at runtime. Introduction In Python, each object instance is pre-built with standard functions and attributes. For example, Python uses a dictionary to store the instance attributes of an object. This has many advantages, such as allowing us to add new attributes at run time. However, this convenience comes at a cost. Dictionaries can consume a good deal.

You will learn:

• How to create an empty list using square brackets [].
• How to create an empty list using list().
• Their use cases.
• How efficient they are (one is faster than the other!). We will use the timeit module to compare them.

Let's begin! ✨

🔹 Using Square Brackets

You can create an empty list with an empty pair of square brackets, like this:

💡 Tip: We assign the empty list to a variable to use it later in our program.

For example:

The empty list will have length 0, as you can see right here:

Empty lists are falsy values, which means that they evaluate to False in a boolean context:

Add Elements to an Empty List

You can add elements to an empty list using the methods append() and insert():

• append() adds the element to the end of the list.
• insert() adds the element at the particular index of the list that you choose.

Since lists can be either truthy or falsy values depending on whether they are empty or not when they are evaluated, you can use them in conditionals like this:

The output of this code is:

Because the list was empty, so it evaluates to False.

In general:

• If the list is not empty, it evaluates to True, so the if clause is executed.
• If the list is empty, it evaluates to False, so the else clause is executed.

Example:

In the example below, we create an empty list and assign it to the variable num. Then, using a for loop, we add a sequence of elements (integers) to the list that was initially empty:

We check the value of the variable to see if the items were appended successfully and confirm that the list is not empty anymore:

💡 Tip: We commonly use append() to add the first element to an empty list, but you can also add this element calling the insert() method with index 0:

🔸 Using the list() Constructor

Alternatively, you can create an empty list with the type constructor list(), which creates a new list object.

According to the Python Documentation:

If no argument is given, the constructor creates a new empty list, [].

💡 Tip: This creates a new list object in memory and since we didn't pass any arguments to list(), an empty list will be created.

For example:

This empty list will have length 0, as you can see right here:

And it is a falsy value when it is empty (it evaluates to False in a boolean context):

Example:

This is a fully functional list, so we can add elements to it:

And the result will be a non-empty list, as you can see right here:

🔹 Use Cases

• We typically use list() to create lists from existing iterables such as strings, dictionaries, or tuples.
• You will commonly see square brackets [] being used to create empty lists in Python because this syntax is more concise and faster.

🔸 Efficiency

Wait! I just told you that [] is faster than list()...

But how much faster?

Let's check their time efficiencies using the timeit module.

To use this module in your Python program, you need to import it:

Specifically, we will use the timeit function from this module, which you can call with this syntax:

💡 Tip: The code is repeated several times to reduce time differences that may arise from external factors such as other processes that might be running at that particular moment. This makes the results more reliable for comparison purposes.

🚦 On your marks... get set... ready! Here is the code and output:

First, we import the module.

Then, we start testing each syntax.

Testing []:

Testing list():

💡 Tip: Notice that the code that you want to time has to be surrounded by single quotes ' or double quotes '. The time returned by the timeit function is expressed in seconds.

Compare these results:

• []: 0.0008467000000109692
• list(): 0.002867799999989984

You can see that [] is much faster than list(). There was a difference of approximately 0.002 seconds in this test:

I'm sure that you must be asking this right now: Why is list() less efficient than [] if they do exactly the same thing?

Well... list() is slower because it requires looking up the name of the function, calling it, and then creating the list object in memory. In contrast, [] is like a 'shortcut' that doesn't require so many intermediate steps to create the list in memory.

This time difference will not affect the performance of your program very much but it's nice to know which one is more efficient and how they work behind the scenes.

🔹 In Summary

You can create an empty list using an empty pair of square brackets [] or the type constructor list(), a built-in function that creates an empty list when no arguments are passed.

Square brackets [] are commonly used in Python to create empty lists because it is faster and more concise.

I really hope that you liked my article and found it helpful. Now you can create empty lists in your Python projects. Check out my online courses. Follow me on Twitter. ⭐️

If you want to dive deeper into lists, you may like to read:

Released:

A multi-armed bandit library for Python

Project description

A multi-armed bandit library for Python

Slots is intended to be a basic, very easy-to-use multi-armed bandit library for Python.

Author

Roy Keyes -- roy.coding@gmail

License: MIT

See LICENSE.txt

Introduction

slots is a Python library designed to allow the user to explore and use simple multi-armed bandit (MAB) strategies. The basic concept behind the multi-armed bandit problem is that you are faced with n choices (e.g. slot machines, medicines, or UI/UX designs), each of which results in a 'win' with some unknown probability. Multi-armed bandit strategies are designed to let you quickly determine which choice will yield the highest result over time, while reducing the number of tests (or arm pulls) needed to make this determination. Typically, MAB strategies attempt to strike a balance between 'exploration', testing different arms in order to find the best, and 'exploitation', using the best known choice. There are many variation of this problem, see here for more background.

slots provides a hopefully simple API to allow you to explore, test, and use these strategies. Basic usage looks like this:

Using slots to determine the best of 3 variations on a live website.

Make the first choice randomly, record responses, and input reward 2 was chosen. Run online trial (input most recent result) until test criteria is met.

The response of mab.online_trial() is a dict of the form:

Where:

• If the criterion is met, new_trial = False.
• choice is the current choice of arm to try.
• best is the current best estimate of the highest payout arm.

To test strategies on arms with pre-set probabilities:

To inspect the results and compare the estimated win probabilities versus the true win probabilities:

By default, slots uses the epsilon greedy strategy. Besides epsilon greedy, the softmax, upper confidence bound (UCB1), and Bayesian bandit strategies are also implemented.

Regret analysis

A common metric used to evaluate the relative success of a MAB strategy is 'regret'. This reflects that fraction of payouts (wins) that have been lost by using the sequence of pulls versus the currently best known arm. The current regret value can be calculated by calling the mab.regret() method.

For example, the regret curves for several different MAB strategies can be generated as follows:

API documentation

For documentation on the slots API, see slots-docs.md.

Todo list

• More MAB strategies
• Argument to save regret values after each trial in an array.
• TESTS!

Contributing

I welcome contributions, though the pace of development is highly variable. Please file issues and submit pull requests as makes sense.

The current development environment uses:

• pytest >= 5.3 (5.3.2)
• black >= 19.1 (19.10b0)
• mypy = 0.761

You can pip install these easily by including dev-requirements.txt.

For mypy config, see mypy.ini. For black config, see pyproject.toml.

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