I’m very new to python and programming in general (This is my first programming language, I started about a month ago). I have a CSV file with data ordered like this (CSV file data at the bottom). There are 31 columns of data. The first column (wavelength) must be read in as the independent variable (x) and for the first
Tag: numpy
What is the more efficient way to create a pairwise 2D array for a 1D numpy array?
Given 2 numPy arrays of length N, I would like to create a pairwise 2D array (N x N) based on a custom function. I would like to create an array C of size NxN. such that Example: I would like to create I know I can do this by nested loop: but looking for a neat and more efficient
Unable to load numpy array into `model.fit`
i’m new to deep learning with Keras, so please inform me if i need to include more data in this post! So currently i have done some image augmentation to my training set for the MNIST dataset i had. So, i referred to this post here and i tried to save my augmented image models into the array. But when
Python: Convert a pandas Series into an array and keep the index
I’m running a k-means algorithm (k=5) to cluster my Data. To check the stability of my algorithm, I first run the algorithm once on my whole dataset and afterwards I run the algorithm multiple times on 2/3 of my dataset (using a different random states for the splits). I use the results to predict the cluster of the remaining 1/3
Initialize high dimensional sparse matrix
I want to initialize 300,000 x 300,0000 sparse matrix using sklearn, but it requires memory as if it was not sparse: it gives the error: which is the same error as if I initialize using numpy: Even when I go to a very low density, it reproduces the error: Is there a more memory-efficient way to create such a sparse
How to create sum of columns in Pandas based on a conditional of multiple columns?
I am trying to sum two columns of the DataFrame to create a third column where the value in the third column is equal to the sum of the positive elements of the other columns. I have tried the below and just receive a column of NaN values DataFrame: Answer You can use df.mask here and fill value less than
Python/Pandas time series correlation on values vs differences
I am familiar with Pandas Series corr function to compute the correlation between two Series, so example: This willl compute the correlation in the VALUES of the two series, but if I’m working with a Time Series, I might want to compute teh correlation on changes (absolute changes or percentage changes and over 1d, 1w, 1m, etc). Some of the
List of binary numbers: How many positions have a one and zero
I have a list of integers, e.g. i=[1,7,3,1,5] which I first transform to a list of the respective binary representations of length L, e.g. b=[“001″,”111″,”011″,”001″,”101”] with L=3. Now I want to compute at how many of the L positions in the binary representation there is a 1 as well as a zero 0. In my example the result would be
How to modify global numpy array safely with multithreading in Python?
I am trying to run my simulations in a threadpool and store my results for each repetition in a global numpy array. However, I get problems while doing that and I am observing a really interesting behavior with the following (, simplified) code (python 3.7): The issue is: I get the correct “Start record & Finish record” outputs, e.g. Start
Filter in opencv/python
I am trying to learn filters in opencv and running this code. But the problem is that when ı run the code it gives me an almost dark image and warns me with “c:/Users/fazil/Desktop/Yeni Metin Belgesi (3).py:19: RuntimeWarning: overflow encountered in ubyte_scalars result[j,i,a]=int((image[j,i,a]+image[j,i-1,a]+image[j,i+1,a]+image[j+1,i,a]+image[j-1,i,a]+image[j+1,i+1,a]+image[j+1,i-1,a]+image[j-1,i-1,a]+image[j-1,i+1,a])/9)”. And if ı comment these out and run code with the lines working with cv2.filter2d method it