After thoroughly examining numerous answers on this subject, I have yet to find a satisfactory solution that fits my requirements. Despite realizing that the error may seem trivial, I am unable to resolve it myself. My goal is to extract an element from nu ...
Looking for an improved method to create a 3D plot of a plane with an image using matplotlib? Here is one way: xx, yy = np.meshgrid(np.linspace(0, 1, img_size[1]), np.linspace(0, 1, img_size[0])) zz = np.ones((img_size[0],img_size[1])) ax.plot_surface( ...
How can I organize the data retrieved from an API query into a table with column names and cell values? wea_data = [{'observation_time': '2023-05-09T15:55:00.000000+00:00', 'station': 'KCOF', 'weather_results': {'@id': 'https://api.weathe ...
Is there a way to transpose a vector using numpy? I've been attempting the following: import numpy as np center = np.array([1,2]) center_t = np.transpose(center) Despite my efforts, this method doesn't seem to be working. Are there any other wa ...
I need to manipulate a set of interconnected points. My goal is to move the points/white squares along their designated paths similar to this animation: https://i.stack.imgur.com/naFml.gif Preferably, I would like to achieve this using numpy, but I am u ...
I am working with the code snippet below currSub is a DataFrame containing 2850 elements, from which I extract timestamps numbered 1 to 2850 along with a vector of probabilities of the same length. My objective is to insert the currProb vector into the r ...
Seeking to iterate through each element of "z" using an "if else" condition and return the desired equation, my current implementation is yielding an error. I have experimented with the "z.all" and "z.any" functions, but both are converting "z" into a bool ...
I am currently working with a 3D NumPy array of size (9,9,200) and a 2D array of size (200,200). My goal is to take each channel of shape (9,9,1), multiply it by 200 scalars in a single row, and then average the result to obtain an array of shape (9,9,1). ...
I need to create a Gaussian window with dimensions of m rows and n columns. I have successfully achieved this in one dimension as shown below. from scipy.stats import multivariate_normal multivariate_normal(mean=[1, 5], cov=(2.5)) Now I am looking to ex ...
I am currently utilizing a library called UnbalancedDataset for oversampling purposes. The dimensions of my X_train_features.shape are (30962, 15637) and y_train.shape is (30962,) type(X_train_features) is showing as scipy.sparse.csr.csr_matrix An index ...
Encountering a persistent issue where I am unable to use dtype=np.int8 due to the requirement of precise floats for all arrays. Currently engaged in developing a simple model focusing on the spread of covid-19 infection. Experimenting with multiple numpy a ...
I currently have a DataFrame that includes a column with non-unique values (in this instance, addresses) along with other columns containing related information. df = pd.DataFrame({'address': {0:'11 Star Street', 1:'22 Milky Way&ap ...
I'm currently coding with Numba to JIT compile my Python code. My function takes two arrays of the same length as input, randomly selects a slicing point, and then returns a tuple containing two "Frankenstein" arrays created from parts of the two input s ...
I am working with four arrays, each containing 3 arrays. For example: set1 = [array([1, 0, 0]), array([-1, 0, 0]), array([0, 1, 0]), ...] My goal is to determine the number of combinations of vectors that sum to zero. The current solution involves nested ...
As a beginner with opencv, I am encountering an issue. I have a np.array color and I would like to incorporate it into my image Here is the np.array color that I intend to add turbo_colormap_data = np.array( [[0.18995,0.07176,0.23217], [0.19483,0 ...
Imagine the scenario: def x_squared(x): result = x * x return result def twice_x(x): result = 2 * x return result def x_cubed(x): result = x * x * x return result x_values = np.array([1, 2, 3]) functions = np.array([x_squared, t ...
I am looking to redefine matrix multiplication by having each constant represented as another array, which will be convolved together instead of simply multiplying. Check out the image I created to better illustrate my concept: https://i.stack.imgur.com/p ...
Is there a way to compare the column names of two separate Pandas data frames? Specifically, I am interested in comparing the columns between my train and test data frames. There are some columns missing in the test data frame that I need to identify. ...
Consider the given DataFrame as shown below: import pandas as pd df = pd.DataFrame({'id': [1, 2, 3], 'json_col': [ [{'aa' : 1, 'ab' : 1}, {'aa' : 3, 'ab' : 2, 'ac': 6}], [{'aa' : 1, 'ab' : 2, 'ac': 1}, {'aa' : ...
Specifically: How can I define a function output data type as numpy.ndarray? Is it possible to utilize cimport numpy instead of import numpy in order to create an array without Python overhead? If the line cdef numpy.ndarray array(int start, int end) ...
I have a 3D array consisting of N x N covariance matrices for M channels [M x N x N]. Additionally, I possess a 2D matrix containing scaling factors for each channel at various time points [M x T]. My goal is to generate a 4D array that includes a scaled v ...
Exploring the frequency version of Planck's law has been quite a journey for me. Initially, I attempted to tackle this challenge on my own with the following code snippet: import numpy as np import matplotlib.pyplot as plt import pandas as pd import ...
Let's say we have a function defined as f(x, y, z) = xyz What is the most efficient method to calculate all values for f using three linear input arrays x, y, and z? We could use a nested loop approach like, import numpy as np x = np.linspace(-1., ...
Currently, I am in the process of developing a script to transfer BMP images to Delta Electronics HMI, which is an industrial automation touch-panel. The challenge lies in the fact that HMI has a unique pixel format that resembles 16-bit RGB555, but with s ...
Currently, I am utilizing numpy.histogram2d to create a 2D histogram without specifying a value for the optional bins parameter. In this case, numpy automatically determines the appropriate number of bins needed. From my understanding, it seems that this f ...
import glob import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec import numpy as np dataset_paths = glob.glob("dataset_characters/**/*.jpg") cols=3 rows=3 fig = plt.figure(figsize=(10,8)) plt.rcParams.update({"font.size&q ...
Having recently explored this particular query, I am intrigued by the inner workings of what transpires when invoking np.exp: what precise mathematical or numerical methodology is employed to generate the values within the resulting array? As an illustrati ...
I have a solid grasp on how to assign labels to elements in one input array as shown below: arr_value = np.array([0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 2, 1, 1, 1, 1]) arr_res_1 = np.array([0, 1, 2, 3, 3, 4, 5, 6, 7, 7, 8, 9, 9, 9, 9]) # treat zeros in arr_value ...
Task involves encoding all the text and categorical features, then combining them to create a data matrix. However, encountering an error due to incompatible row dimensions. Progress so far: Encoding categorical feature using Label Encoder from sklearn.p ...
Here's a problem I'm tackling: "Find the maximum price change over any 5-day rolling window, over a 1000-day period." When I say "any 5-day rolling window," I mean it should be of the form "t_i + j", where "i" ranges from 1 to 1000 and "j" range ...
I have come across numerous instances where individuals substitute specific elements in an array with zeroes based on their values. For instance: X = [0.5, 18, -6, 0.3, 1, 0, 0, -1, 10, -0.2, 20] Setting all values < 4 to zero is not what I am looking fo ...
I'm new to python and I have a pandas dataframe with multiple columns representing months. I want to compare these columns across a period of x months and flag any rows that have ever had a value of 2 or more. Here is the code snippet I used to generate m ...
import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import matplotlib.dates as md import datetime as dt import time from zipfile import ZipFile from matplotlib.pyplot import xticks %matplotlib inline ------------ ...
I am trying to create an array, k of dimension N X 1 in MATLAB by using the following code: N = 2^15 dx = 0.1 k = [0:N/2-1 0 -N/2+1:-1]'*2*pi/(N*dx) However, when I attempted to do this in Python, I encountered a problem because I couldn't "flip ...
As a beginner in Python with some experience in Matlab, I am attempting to create a function for the Gaussian kernel with a mean of 0. Here is the code I have written: import numpy as np import math def GaussainKernel(x,sigma): xx = np.array(x) re ...
Given a set of n functions represented by yi(x) = a0 + a1x + a2x^2 + a3x^3, and q queries. For each query, an integer t is provided, with the task being to determine which function yi minimizes yi(t). Assuming we have a list that contains the values of a0 ...
Within my dataset, I have a dataframe that includes the following columns (only showing a portion): START END FREQ VARIABLE '2017-03-26 16:55:00' '2017-10-28 16:55:00' 1234567 x &ap ...
I am working on a Python application for image processing that will be compiled into an EXE file. My tools of choice are tkinter and cx_Freeze. However, I have encountered an error during the process. https://i.stack.imgur.com/ThRs4.jpg Below is the con ...
I am currently working on a project involving a dataframe where I need to perform certain calculations for each row. The process involves creating lists of 100 numbers in step 1, multiplying these lists together in step 2, and then generating a new datafra ...
I am dealing with two lists consisting of zeros and ones. These lists are always the same length, but the values within can vary. Here is an example, showcasing a scenario where I need a solution that can apply to lists of any size with zeros and ones at a ...
Variables: x and y represent arrays of N 2D vectors with sizes (N, 2). Question: Can the dot product be calculated between corresponding vectors in x and y without explicitly listing the elements using list comprehension: [np.dot(x[i], y[i]) for i in ra ...
I am encountering issues while trying to execute the dedupe process. My objective is to utilize this library to eliminate duplicates from an extensive collection of addresses. Below is my code: import collections import logging import optparse from numpy ...
I am struggling to create multiple NumPy arrays based on a list of names. For instance, I have the following list: k=["one","two","three"] I want to generate three arrays with names corresponding to each element in the list. This means: one=np.array() t ...
After much consideration, I have tried my best to outline the issue in the title. The problem at hand is the variability of a numpy array's shape or dimension (which can range from 1 to 3). For instance, in the scenario where the array is of shape [100], ...
My process involves the following steps: from numpy import genfromtxt x = genfromtxt('foo.csv',delimiter=',',usecols=(0,1)) y = genfromtxt('foo.csv',delimiter=',',usecols=(2),dtype=str) After that, I input: x[y= ...
As I progress through my deep learning course on Coursera, I stumbled upon a piece of code on GitHub while working on an assignment. 1. numpyArr[...,c] 2. numpyArr[:,:,:,c] I'm curious to know what distinguishes these two slicing methods? ...
Is it possible to achieve the task of extracting a new dataframe from an existing one, where columns containing the term 'job', any columns with the word 'birth', and specific columns like name, userID, lgID are excluded? If so, what would be the most eff ...
Working with Python and NumPy, I currently have two arrays that look like this: array1 = [1 2 3] array2 = [4 5 6] My goal is to create a new array: array3 = [[1 2 3], [4 5 6]] and add elements to it. For instance, if the new items to append are: array ...
Currently, I am exploring text classification with scikit-learn's TfidfVectorizer and the Nearest Neighbor algorithm. My challenge lies in determining similarity metrics between two datasets, each containing 18000 entries. I am grappling with decidin ...
Currently, I am working on a CSR sparse array where there are many empty elements or cells. This array needs to support both forward and backward indexing. In other words, I should be able to provide two indices and receive the corresponding element (e.g., ...
In my Google Colab drive, I have a list called dir containing the directories of 18900 RGB images with dimensions of 64x64 pixels each. Using the Image module from PIL library, I opened the first image using the code: img = Image.open(dir[0]). Then, I conv ...
I recently came across some code that creates a set of small multiples and it is functioning flawlessly. fig, axes = plt.subplots(6,3, figsize=(21,21)) fig.subplots_adjust(hspace=.3, wspace=.175) for ax, data in zip(axes.ravel(), clean_sets): ax.plot(d ...
Being new to Python, I must apologize if this question seems easy. (it appears simple to me, but I'm having trouble...) After performing an IV sweep on a Keithley SMU 2400, I received a list of numbers in the following order: [v0, c0, t0, v1, c1, t1 ...
Within the context of my program, I've defined a variable h that holds the result of an integration. My goal is to implement a change in coordinates on the output of this integration process. To achieve this, I typically rely on computer algebra software s ...
I retrieved data from an SQLite database stored in a Python list with the following structure: # Here is an example data = [(1, '12345', 1, 0, None), (1, '34567', 1, 1, None)] My goal is to convert this list of tuples into a 2D NumPy a ...
In my Python project, I am looking to generate a unique Journey ID and Journey number. The goal is to increment the ID each time the previous row in the "Purpose" column equals 1, while the Journey number should do the same but within each Respondent ID gr ...
After meticulously reviewing all Stack Overflow questions on this particular topic, I find myself encountering a peculiar issue. The image path is stored in the variable file_names. from skimage import io import numpy as np X = np.array([np.array(io.imre ...
I am working on a loop to calculate the elements of a 3D numpy array. The code looks like this: A = np.zeros((N - 2, N - 2, N - 2)) for i in range(1, N - 1): for j in range(1, N - 1): for k in range(1, N - 1): A[i - 1, j - 1, k - 1] ...
My data includes a mixture of floating numbers and numpy datetime64 values in different rows within a pandas dataframe. df2 = pd.DataFrame( [[np.datetime64('2021-01-01'), np.datetime64('2021-01-01')], [2, 3]], columns=['A', 'B']) After attempting ...
I have a pandas DataFrame that contains a large collection of data (~150k rows), structured with two columns: Id and Features. Each row in the Features column is a 50-position numpy array. My objective is to select a random feature vector from the dataset ...
I'm delving into creating and utilizing dynamic variables in Python for the first time. for i in range(0,len(test_data)): globals()["test_list_{}".format(test_data[i])]=[] globals()["test_calculation_{}".format(test_data[i])]= ...
I'm having some trouble understanding the function of each step in this particular pipeline. Could someone provide a detailed explanation of how this pipeline is functioning? I have a general idea, but more clarity would be greatly appreciated. Wha ...
Is there a more optimal method for computing a sparse boolean matrix I using one or two arrays a,b, where I[i,j]==True if a[i]==b[j]? The current approach is quick but not memory-efficient: I = a[:,None]==b Another option is slower and still memory-ineff ...
After compiling the following function: x = theano.tensor.imatrix('x') y = theano.tensor.fmatrix('y') z = x.dot(y) f = theano.function([x, y], z) I noticed that the output is always float64, even when x is int32 and y is float32. However, when I perform ...
Looking for help with summing an array based on conditions in another array using Python. sum=0 for i in range(grp_num): if lower_bounds[i] > 0: sum = sum + histo1[i] I was thinking the numpy equivalent would be np.where(lower_bounds>0, ...
I'm experimenting with implementing a Sine wave function for navigation purposes. In my main code, I have two functions defined as follows: def stop(): steering = 1024 throttle = 1024 return steering, throttle def case2(): steering = ...
I have a large Spark dataframe containing approximately 1 million rows. I am using pyspark and need to apply the box-cox transformation from the scipy library on each column of the dataframe. However, the box-cox function only accepts 1-d numpy arrays as i ...
Consider the 1D numpy array arrRow provided below. The array consists of 1s and 0s, with a notable characteristic being the presence of exactly two 0-islands. Each island has Edge cells (E) at its right and left ends, while Border cells (B) are located imm ...
I am trying to create a Python script that plots multiple planes parallel to the XZ axis and spaced equidistantly from each other. The number of planes can be specified by the user, so if they input "20", 20 planes will be displayed in a 3D plot. I have ...
My attempts to utilize a custom model function from Sklearn appear to be yielding incorrect distances. I possess two vectors and typically compute their cosine similarity, following it up with 1 - cosine_similarity for usage as a distance metric. Here&apo ...
As I incorporate Numpy into my neural network, I am facing a challenge with implementing a step when updating the weights. This step involves an input rho_deltas (shape: (m,)) and self.node_layers[i-1].val (shape: (n,)), producing an output of self.previo ...
Recently, I utilized Python 3 to develop a program for data calculation. The code I created is outlined below: import pandas as pd import matplotlib.pyplot as plt import numpy as np def data(symbols): dates = pd.date_range('2016/01/01',&apo ...
I've been attempting to merge two 1D arrays using np.concatenate, but it's not producing the desired outcome. Could someone please point out where I might be going wrong? Here is my code: x = np.array([1.13793103, 0.24137931, 0.48275862, 1.24137931, 1.00 ...
How can we solve this particular issue? I have an array with 30 rows containing columns (seq, high, low, status). The objective is to merge similar rows only when they are adjacent to each other in the sequence. The resulting row will have the maximum high ...
I've been attempting to create a numpy series with the type datetime64[ns, UTC], but I'm running into some issues. Here's what I've tried: test = np.array(np.datetime64('2005-01-03 14:30:00.000000000')) test >array('2005-01-03T14:30:00.000000000', dtyp ...