faster alternative to nested for loops python

Traditional methods like for loops cannot process this huge amount of data especially on a slow programming language like Python. Readability is often more important than speed. Lets see a simple example. In many circumstances, although it might seem more legitimate to do things with regular Pythonic expressions, there are times where you just cannot beat a C-based library. I am wondering if anyone knows how I can improve the speed of this? Making statements based on opinion; back them up with references or personal experience. Usage Example 1. Your budget ($1600) is the sacks capacity (C). Short story about swapping bodies as a job; the person who hires the main character misuses his body. List Comprehensions with Multiple For Loops: You can actually incorporate multiple for loops into a list comprehension to iterate over multiple iterables or to create nested loops. Now we can solve the knapsack problem step-by-step. And things are just getting more fun! 16,924 Solution 1. . Nothing changes about this from looping to the apply method: When using the apply() method, it can be called off both the Series and DataFrame type. This includes lambdas. One can easily write the recursive function calculate(i) that produces the ith row of the grid. That is to say, there are certainly some implementations where while loops are doing some very iterative-loopy-things. For loops in this very conventional sense can pretty much be avoided entirely. How do I merge two dictionaries in a single expression in Python? + -+ + + -+ +, Vectorization with Pandas and Numpy arrays. This gives us the solution to the knapsack problem. The for loop has a particular purpose, but also so do some of the options on this list. rev2023.4.21.43403. Second place however, and a close second, was the inline for-loop. Note that the NumPy function does all this in a single call. How about saving the world? Let implement using a for loop to iterate over element of a list and check the status of each application for failures (Status not equal to 200 or 201). When the loops are completed, we have the solution grid and the solution value. If you are familiar with the subject, you can skip this part. And we can perform same inner loop extraction on our create_list function. This way we examine all items from the Nth to the first, and determine which of them have been put into the knapsack. To some of you this might not seem like a lot of time to process 1 million rows. You don't need the second loop to start from the beginning, because you will compare the same keys many times. How a top-ranked engineering school reimagined CS curriculum (Ep. So, are we stuck and is NumPy of no use? Then, instead of generating the whole set of neighbors at once, we generate them one at a time and check for inclusion in the data dictionary. Checking Irreducibility to a Polynomial with Non-constant Degree over Integer. Is it possible to post your name, so that I can credit you in the source code? I was just trying to prove a point for-loops could be eliminated in your code. However, this doesnt the elimination any better. To learn more, see our tips on writing great answers. It tells where to pick from: if an element of condition is evaluated to True, the corresponding element of x is sent to the output, otherwise the element from y is taken. How do I stop the Flickering on Mode 13h? Note that this requires python 3.6 or later. sum(grid[x][y: y + 4]) There are plenty of other ways to use lambda of course, too. THIS IS HARD TO READ. They take arrays as parameters and return arrays as results. The nested list comprehension transposes a 3x3 matrix, i.e., it turns the rows into columns and vice versa. The interpreter takes tens of seconds to calculate the three nested for loops. For a final function that looks like this: An awesome way we could tackle this problem from a bit more of an base implementation perspective is by using itertools. This limit is surely conservative but, when we require a depth of millions, stack overflow is highly likely. This example is very convoluted and hard to digest and will make your colleagues hate you for showing off. The problem is that list comprehension creates a list of values, but we store these values in a NumPy array which is found on the left side of the expression. In our case, the scalar is expanded to an array of the same size as grid[item, :-this_weight] and these two arrays are added together. Computer nerd, Science and Journalism fanatic. Now, use it as below by plugging it into @tdelaney's answer: Thanks for contributing an answer to Stack Overflow! It is dedicated solely to raising the. The above outputs 13260, for the particular grid created in the first line of code. Lambda is more of a component, however, that being said; fortunately, there are applications where we could combine another component from this list with lambda in order to make a working loop that uses lambda to apply different operations. It is already Python's general 'break execution' mechanism. 565), Improving the copy in the close modal and post notices - 2023 edition, New blog post from our CEO Prashanth: Community is the future of AI. It is important to realize that everything you put in a loop gets executed for every loop iteration. What does the "yield" keyword do in Python? Numpy is a library with efficient data structures designed to hold matrix data. How do I loop through or enumerate a JavaScript object? Recursion occurs when the definition of a concept or process depends on a simpler version of itself. The package 'concordexR' is an R implementation of the original concordex Python-based command line tool. Burst: Fixed MethodDecoderException when trying to call CompileFunctionPointer on a nested static method. Since there is no need for the, @BurhanKhalid, OP clarified that it should just be a, Ah, okay. Our mission: to help people learn to code for free. Faster alternative to nested loops? Solution to this problem is to add some precalculations. The innermost sum adds up the numbers in grid[x][y: y + 4], plus the slightly strange initial value sum = 1 shown in the code in the question. What were the poems other than those by Donne in the Melford Hall manuscript? The list of stocks to buy is rather long (80 of 100 items). The answer is no. Also works with mixed dictionaries (mixuture of nested lists and dicts). This should make my program useable. Note that I will treat L* lists as some global variables, which I don't need to pass to every function. Until the knapsacks capacity reaches the weight of the item newly added to the working set (this_weight), we have to ignore this item and set solution values to those of the previous working set. Since the computation of the (i+1)th row depends on the availability of the ith, we need a loop going from 1 to N to compute all the row parameters. This other loop is exactly the loop we are trying to replace. The outer sum adds up the middle values over possible x values. So how do you combine flexibility of Python with the speed of C. This is where packages known as Pandas and Numpy come in. Make Python code 1000x Faster with Numba . Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Instead, I propose you do: How about if you have some internal state in the code block to keep? Design a super class called Staff with details as StaffId, Name, Phone . Further on, we will focus exclusively on the first part of the algorithm as it has O(N*C) time and space complexity. So far, so good. Please share your findings. Therefore, the solution value taken from the array is the second argument of the function, temp. Iterative looping, particularly in single-threaded applications, can cause a lot of serious slowdowns that can certainly cause a lot of issues in a programming language like Python. If you absolutely need to speed up the loop that implements a recursive algorithm, you will have to resort to Cython, or to a JIT-compiled version of Python, or to another language. The next technique we are going to be taking a look at is Lambda. I mentioned optimization. Find centralized, trusted content and collaborate around the technologies you use most. This way you spend $1516 and expect to gain $1873. Suppose the alphabet over which the characters of each key has k distinct values. Refresh the page, check Medium 's site status, or find something interesting to read. Does Python have a ternary conditional operator? The price estimates are the values. The outer loop produces a 2D-array from 1D-arrays whose elements are not known when the loop starts. A simple "For loop" approach. In the example of our function, for example: Then we use a 1-line for-loop to apply our expression across our data: Given that many of us working in Python are Data Scientists, it is likely that many of us work with Pandas. Loop through every list item in the events list (list of dictionaries) and append every value associated with the key from the outer for loop to the list called columnValues. It backtracks the grid to find what items have been taken into the knapsack. What does the power set mean in the construction of Von Neumann universe? And the first loop is quite simple, so let's collapse it into listOfLists = [create_list(l1) for l1 in L1]. tar command with and without --absolute-names option. Each share has a current market price and the one-year price estimate. Or is there a even more expressive way? This article isnt trying to be dictating the way you think about writing code. Note that lambdas are not faster than usual functions doing same thing in same way. Thanks for reading this week's tip! Quite Shocking, huh? Let us quickly get our data into a DataFrame: Now we will write our new function, note that the type changed to pd.DataFrame, and the calls are slightly altered: Now let us use our lambda call. Starting from s(i=N, k=C), we compare s(i, k) with s(i1, k). Does it actually need to be put in three lines like you did it? You are willing to buy no more than one share of each stock. The results shown below is for processing 1,000,000 rows of data. It is this prior availability of the input data that allowed us to substitute the inner loop with either map(), list comprehension, or a NumPy function. Avoid calling functions written in Python in your inner loop. With line 279 accounting for 99.9% of the running time, all the previously noted advantages of numpy become negligible. The code is as follows. This would take ~8 days to finish. And now we assume that, by some magic, we know how to optimally pack each of the sacks from this working set of i items. A nested for loop's map equivalent does the same job as the for loop but in a single line. If you have done any sort of data analysis or machine learning using python, Im pretty sure you have used these packages. This is pretty straightforward (line 8): Then we build an auxiliary array temp (line 9): This code is analogous to, but much faster than: It calculates would-be solution values if the new item were taken into each of the knapsacks that can accommodate this item. As Data science practitioners we always deal with large datasets and often we need to modify one or multiple columns. But we still need a means to iterate through arrays in order to do the calculations. I wish the code is flatter, I hear you. This method applies a function along a specific axis (meaning, either rows or columns) of a DataFrame. This is how we use where() as a substitute of the internal for loop in the first solver or, respectively, the list comprehension of the latest: There are three pieces of code that are interesting: line 8, line 9 and lines 1013 as numbered above. They can be used to iterate over multi-dimensional arrays, which can make the code more readable and easier to understand. Executing an operation that takes 1 microsecond a million times will take 1 second to complete. Vectorization is something we can get with NumPy. To learn more, see our tips on writing great answers. Firstly, I'd spawn the threads in daemon mode (pointing at the model_params function monitoring a queue), then each loop place a copy of the data onto the queue. ), If you want to reduce a sequence into a single value, use reduce. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Moreover, the experiment shows that recursion does not even provide a performance advantage over a NumPy-based solver with the outer for loop. Your task is to pack the knapsack with the most valuable items. Using . In this post we will be looking at just how fast you can process huge datasets using Pandas and Numpy, and how well it performs compared to other commonly used looping methods in Python. Its been a while since I started exploring the amazing language features in Python. iterrows() is the best method to actually loop through a Python Dataframe. It's 133% slower than the list comprehension (104/44.52.337) and 60% slower than the "for loop" (104/65.41.590). Yes, I can hear the roar of the audience chanting NumPy! Can the game be left in an invalid state if all state-based actions are replaced? A place to read and write about all things Python. All you need is to shift your mind and look at the things in a different angle. Nobody on the planet has enough time to learn every module and every call available to them, so weighing the ones that one can learn, and reading articles that overview new options, is certainly a great way to make sure that ones skill-set is diverse enough. But trust me I will shoot him whoever wrote this in my code. I challenge you to avoid writing for-loops in every scenario. What is Wario dropping at the end of Super Mario Land 2 and why? Why are elementwise additions much faster in separate loops than in a combined loop? EDIT: I can not use non-standard python 2.7 modules (numpy, scipy). While this apparently defines an infinite number of instances . Word order in a sentence with two clauses. If you would like to read into this technique a bit more, you may do so here: Lambda is incredibly easy to use, and really should only take a few seconds to learn. The alternative to this is appending or pushing. That format style is only for your readability. We are going to use a method to generate Pandas Dataframes filled with random coordinates of 10000, 100000 and 100000 rows to see the efficiency of these methods. The middle sum adds up those values for the 17 possible y values. Lets take a look at applying lambda to our function. In this example, we are dealing with multiple layers of code. This is the insight I needed! We can optimize loops by vectorizing operations. Mafor 7743 Credit To: stackoverflow.com A minor scale definition: am I missing something? The problem has many practical applications. For your reference, the investment (the solution weight) is 999930 ($9999.30) and the expected return (the solution value) is 1219475 ($12194.75). Why is it shorter than a normal address? Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. However, there are few cases that cannot be vectorized in obvious ways. A Medium publication sharing concepts, ideas and codes. You should be using the sum function. for every key, comparison is made only with keys that appear later than this key in the keys list. English version of Russian proverb "The hedgehogs got pricked, cried, but continued to eat the cactus", Word order in a sentence with two clauses. This led to curOuter starting from the beginning again.. Note that, by the way of doing this, we have built the grid of NxC solution values. The itertools module is included in the Python standard library, and is an awesome tool that I would recommend the use of all the time. The original title was Never Write For-Loops Again but I think it misled people to think that for-loops are bad. As we are interested in first failure occurrence break statement is used to exit the for loop. Plot a one variable function with different values for parameters? The other way to avoid the outer for loop is to use the recursion. now it looks more readable, and should work a bit faster. It will then look like this: This is nice, but comprehensions are faster than loop with appends (here you can find a nice article on the topic). This will help you visualize what is happening. The second part (lines 917) is a single for loop of N iterations. names = ["Ann", "Sofie", "Jack"] By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. 678 20 : 33. Use built-in functions and tools. Nested loops mean loops inside a loop. The time taken using this method is just 6.8 seconds,. An implied loop in map () is faster than an explicit for loop; a while loop with an explicit loop counter is even slower. They make it very convenient to deal with huge datasets. Can you make a dict that will have L4 elements for keys and l3 indices for value (you won't to iterate through L3 then), How to speed up nested for loops in Python, docs.python.org/2/extending/extending.html. Your home for data science. Derived from a need to search for keys in a nested dictionary; too much time was spent on building yet another full class for nested dictionaries, but it suited our needs. They key to optimizing loops is to minimize what they do. The entire outer loop can then be replaced with calculate(N). If you sign up using my link, Ill earn a small commission with no extra cost to you. That will help each iteration run faster, but that's still 6 million items. How about more complex logic? If you find the following explanations too abstract, here is an annotated illustration of the solution to a very small knapsack problem. For example, you seem to never use l1_index, so you can get rid of it. Asking for help, clarification, or responding to other answers. Lets find solution values for all auxiliary knapsacks with this new working set. Yes, it works but it's far uglier: You need to look at the except blocks to understand why they are there if you didn't write the program To learn more, see our tips on writing great answers. Happy programming! In other words, Python came out 500 times slower than Go. Now for our final component, we are going to be writing a normal distribution function, which will standard scale this data. The way that a programmer uses and interacts with their loops is most definitely a significant contributor to how the end result of ones code might reflect. Firstly, a while loop must be broken. You could also try to use built-in list function for finding element in list (l3_index = l3.index(L4[element-1]), ), but I don't know if it will be any faster. This is why we should choose built-in functions over loops. But first, lets take a step back and see whats the intuition behind writing a for-loop: Fortunately, there are already great tools that are built into Python to help you accomplish the goals! Multiprocessing is a little heavier as each spawned mp object is a full copy of Python, and you need to work on heavier data sharing techniques (doable, but faster to thread then mp). Say we want to sum the numbers from 1 to 100000000 (we might never do that but that big number will help me make my point). We need a statically-typed compiled language to ensure the speed of computation. We start with the empty working set (i=0). Imagine we have an array of random exam scores (from 1 to 100) and we want to get the average score of those who failed the exam (score<70). This number is already known to us because, by assumption, we know all solution values for the working set of i items. Alexander Nguyen in Level Up Coding Why I Keep Failing Candidates During Google Interviews Abhishek Verma in Geek Culture Mastering Python Tuples: A Comprehensive Guide to Efficient Coding Help Status Writers Blog Careers Privacy Terms At the beginning, its just a challenge I gave myself to practice using more language features instead of those I learned from other programming language. Now we fetch the next, (i+1)th, item from the collection and add it to the working set. For example, while loop inside the for loop, for loop inside the for loop, etc. No matter how you spin it, 6 million is just a lot of items, as it turns out. How about saving the world? Could you provide the length of each vector? List comprehension Why is processing a sorted array faster than processing an unsorted array? First, the example with basic for loops. Pandas can out-pace any Python code we write, which both demonstrates how awesome Pandas is, and how awesome using C from Python can be. What you need is to know for each element of L4 a corresponding index of L3. No need to run loops anymore a super-fast alternative to loops in Python. Find centralized, trusted content and collaborate around the technologies you use most. tar command with and without --absolute-names option, enjoy another stunning sunset 'over' a glass of assyrtiko. The basic idea is to start from a trivial problem whose solution we know and then add complexity step-by-step. What are the advantages of running a power tool on 240 V vs 120 V? In this case, nothing changes in our knapsack, and the candidate solution value would be the same as s(i, k). Vectorization or similar methods have to be implemented in order to handle this huge load of data more efficiently. And will it be even more quicker if it's only one line? For example, here is a simple for loop that prints a list of names into the console. The regular for loops takes 187 seconds to loop 1,000,000 rows through the calculate distance function. What is scrcpy OTG mode and how does it work? Write a function that accepts a number, N, and a vector of numbers, V. The function will return two vectors which will make up any pairs of numbers in the vector that add together to be N. Do this with nested loops so the the inner loop will search the vector for the number N-V(n) == V(m).
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