When we work with variables or start a project, we must understand which type of data we are working with. Using the most efficient data type makes your work easier and faster.
People often get confused at this point so, we are here to provide you with a detailed answer to “which data type is faster in Python” and some other related questions that may be confusing you.
Which data type is faster in python?
That depends on what kind of operation(s) do you need to do over the data structure.
Each data structure is optimized for a set of operations. Some have fast indexed access, some allow you to seek efficiently by a key, some allow for efficient extraction of minimum/maximum…
There’s no meaning in asking “what data structure is optimal” unless you specify the kind of operation you wish to perform.
In the Python language, there are different data types, including None,
which we may also call dictionaries. Out of them, map or dictionary is known as the fastest data type of Python.
The quickest way of processing and looking up data with millions of variables and entries in the dictionaries. They have a built-in mapping feature that helps users with their work
Now, let’s scroll down to see how dictionaries work as a faster data type.
Are dictionaries fast in python?
Finding the answer to which data type is faster in Python, we learned that dictionaries are the ones you should go for.
You don’t have to look through the entire list for what you want because the dictionary will provide you with the value without going through the entire keys. Because of the lookups that the dictionary is so fast, it directly jumps to the key.
Also, a Python dictionary is an implementation of a hash table.
How fast are dictionaries in python?
Its rate is about 6.6x quicker when you search up a value than when you search in a list. That’s why dictionaries are considered to be the fastest data type in Python.
How to find the length of a dictionary in python?
When we discuss length, we know it is a built-in function also called
len(). It allows us to determine how many objects, value key pairs, or key-value pairs we have stored in the dictionary.
- Let’s have an example where we have ‘a’ where it is equal to cat, and the cat also has a name. Type dog and give it a name as well, maybe ‘maggie’. So now, there are two keys and value pairs stored in our dictionary or objects.
- ‘a’ square bracket and then by the key do ‘dog,’ and another square bracket; hit return. It will return the value to you. And in this way you will get the key and the value.
- All-in-all, when you have a sample dictionary in python, you can count its length.
- Len is a building function that is basically a length building function and counts the objects included in a list or dictionary.
- Type len, and then parentheses. Now type ‘a’ for the variable representing our dictionary.
- Close the parenthesis and hit return. We hit two, so there are two objects or two key-value pairs contained in this dictionary.
That’s how we find the length of a dictionary in Python.
- Or we can also do it like another easier way. Len, parenthesis, and curly bracket; now we will create the dictionary within our building function. Just copy the one we had before and type one more to this, like turtle and call it a name.
- Close the dictionary with a curly bracket and the parenthesis. Hit return, and we got three.
- So we got three different value keys here. Isn’t it easier and simpler?
How do I speed up python run time?
You can use multi-processing to speed Python run time. By default, Python runtime is single-threaded for the sake of simplicity. If you have a Python task that is CPU dependent and you want to use hardware threads to speed it up, you can use the multi-processing module to do this.
Multi-processing works by creating a pool of Python interpreters, typically, one interpreter per CPU in your system.
But it’s tunable and allows you to feed jobs to that pool, which are then executed in parallel because launching the process pool takes time. You typically want to do that once at the program, then feed its job as needed.
You need to take a simple benchmark application, and it can show you how multi-processing can be used to speed it up. The benchmark reads a large CSV file taken from Google’s Ngram database about 187 megabytes and tallies some statistics from it.
An i7 3770k processor takes around 11 to 12 seconds to run the benchmark. Multiprocessing usually works one of the two ways.
- The first way is that you take many little jobs that are CPU-dependent and submit them to the process pool.
- The second is to take one big job and find a way to slice it up efficiently into many little jobs.
The benchmark we have here fits into the second category. It is one big job that has to be sliced up. Reading a file line-by-line and gathering statistics about each line, which is then aggregated at the end.
Multi-processing gives us tools to work with both kinds of jobs, and so in this case, we are going to use imap unordered functions.
It takes an inerrable, which reads line by line and parcels it out piece by piece in a round-robin fashion to process it in the pool. Each process runs a defined function on it. In this case, it is a simple statistics gathering function.
When you run multi-processing, it appears like this and speeds up the functions.
Which is faster, list or dictionary?
Regarding faster data types, we mostly prefer dictionaries because they have an
O (1) values lookup while the list has an
O(n) operation. Dictionary has a mapping feature to map keys to various values, while a list is just a collection of values.
Do dictionaries take more space than lists in python?
A comparison is done after getting hundreds of questions like which data type is faster in Python? And the two data types here are dictionary and list.
Python dictionaries take up a lot more space than other data structures. The more the number of values to look up, the more will be the space it covers. For example, a dictionary covers about 4.12x times more space than a list when you need to save 10 million floats.
But when you consider the new updated Python 3.6, they don’t take as much space as before, so the space-tradeoff is no longer now.
What data structure is most efficient?
The answer is Array which is the most preferred, easy to use, and common data structure. It is also used to make and prepare other data types like queues and stacks.
An Array and List are not the same things. Nowadays, a list is used in Python because it stores different kinds of data. However, an array is usually not used in Python. It cannot be used to store different values but the same type of data.
We should conclude that when it comes to which data type is faster in Python, all of the data types have unique properties and much in store. Dictionaries are handy but take more space than lists, which is not fast enough.
Similarly, other data types have other functions. But, when it comes to choosing one, we can and should definitely go with our needs as we all have different needs or requirements.
If still, you have any unanswered questions in your head, just hit us with a comment. We are ready to answer your questions.