I am using python for optimization purposes. I made a graph using Networkx library with 1100 nodes. The python script includes the following lines. In the next step, some random numbers are generated as follows: I compute the distance between nodes in the graph using the following function. Finally, I defined the variable “shipping_cost” as: Every line of the above
Tag: performance
How to speed up successive pd.apply with successive pd.DataFrame.loc calls?
df has 10,000+ lines, so this code is taking a long time. In addition for each row, I’m doing a df_hist.loc call to get the value. I’m trying to speed up this section of code and then option I’ve found so far is using: But this forces me to use index based selection for row instead of value selection: which
Convert a normal python code to an MPI code
I have this code that I would like to edit and run it as an MPI code. The array in the code mass_array1 is a multi-dimensional array with total ‘iterations’ i*j around 80 million. I mean if I flatten the array into 1 dimensional array, there are 80 million elements. The code takes almost 2 days to run which is
With lowest possible time complexity , subtract every number in a list from the first lower number after it
I have a list of numbers and I want to subtract every number from the smaller value after it but with the lowest possible complexity I have the list [7, 18 ,5 ,5 ,20 ,9 ,14 ,7 ,19] The first lower value after 7 is 5 so it will subtract 7 from 5, the same for 18 the first lower
(JIT) Compilation of Python code with FFI library calls
I’m using Python with a library that uses cppyy to get access to a ton of C++ functions natively in Python. However, the calls to cppyy methods take a lot of time, and looping in Python with a library call means that overhead becomes a serious issue. Here’s an example of what I mean: This code would be really fast
How to skim itertools permutations?
Initial code: Result: Now I want to remove everything starting with “)” or “#” and ends whit “(” or “#” and contenin “(“,”#”,”)” With Now the list is half in size “9k chart from 20k”. ps: Now, how do i remove (‘A’, ‘B’, ‘C’, ‘(‘, ‘)’, ‘#’) this I need to do this operations while the “iterator iterates” so I
Fastest way to split a list into a list of lists based on another list of lists
Say I have a list that contains 5 unique integers in the range of 0 to 9. I also have a list of lists, which is obtained by splitting integers from 0 to 19 into 6 groups: Now I want to split lst based on the reference partitions. For example, if I have I expect the output to be a
Pandas average of previous rows fulfilling condition
I have a huge data-frame (>20m rows) with each row containing a timestamp and a numeric variable X. I want to assign a new column where for each row the value in this new column is the average of X in the previous rows within a specified time window e.g the average of all rows with time stamps no more
Performance comparison: Why is it faster to copy an entire numpy Matrix and then change one column than to just use numpy.column_stack?
I am trying to improve the performance of some Python code. In that code, one column of a matrix (numpy-array) has to be changed temporarily. The given code looks as follows: Now I thought it should be a big improvement to not create a copy of the entire matrix A (in the example used, the matrix is 500×500 with all
How to improve the performance of traversing a large dataset
I want to improve the logic for my task that is defined as following, the tasks is implemented in Python 3 with Django framwork: The source data is onboard to our system, the Job entity defines what involves to fully process a source, Job_instance defines the instance of Job. The requirement is as following: Given the source name, find the