Question The question here asks to make two lists/arrays in python and fill it with 0s initially and then occupy them with the relevant values. import numpy as np x = [] y = [] for i in range (0,101): x.append(0) y.append(0) xx = np.linspace(1,10,101) print(xx) for a in range (len(y)): for j in xx:… Read More How to replace all elements of a list using a for loop
Let’s that we have geotiff of 0 and 1. import rasterio src = rasterio.open(‘myData.tif’) data = src.read(1) data array([[0, 1, 1, 0], [1, 0, 0, 1], [0, 0, 1, 0], [1, 0, 1, 1]]) I would like to have for each pixel 1 the sum of all adjacent pixels forming a cluster of ones and… Read More Python: how to count the adjacent values with values of 1 in a geotiff array?
Is there any way of swapping min and max value in my NumPy 2D array? import numpy as np n = int(input("How many arrays: ")) m = int(input("How many numbers: ")) rng = np.random.default_rng() arr = rng.random((n, m)) print(arr) >Solution : With following snippet you first determine the minimum and maximum value in your array… Read More Swap min and max value in NumPy array
I would like to optimise the process of initialising python np.zeros() arrays. Right now I just have a list of them like import numpy as np n_elements = 10 a = np.zeros(n_elements) b = np.zeros(n_elements) c = np.zeros(n_elements) d = np.zeros(n_elements) Do you know how I could make this bulk of code shorter? I know… Read More Optimise initialisation of several np.zeros() arrays of fixed size in Python
I wish to create an increased increment of the "type" grouped by the [ID], and [Date] columns. DATA ID Date type AA Q1.21 hi AA Q1.21 hi AA Q1.21 hello AA Q1.21 ok AA Q1.21 ok AA Q1.21 ok AA Q2.21 hello AA Q2.21 hello BB Q1.21 hi BB Q1.21 hi DESIRED ID Date type… Read More Incremental counts based on group within Pandas Dataframe
I am trying to convert one column in my numpy array from strings to ints, however when I tried the methods I found online, it didn’t mutate the column. Here is my code: frequencies = np.array([[‘a’, ‘24273’], [‘b’, ‘11416’], [‘c’, ‘8805’], [‘d’, ‘6020’]]) frequencies[:, 1] = frequencies[:, 1].astype(int) print(frequencies) The array is unchanged when I… Read More Question about converting a list of strings to ints
I’d like to state off the bat that I don’t have a lot of experience with numPy, and deeper explanation would be appreciated(even obvious ones). Here’s my issue: converted_X = X for col in X: curr_data = X[col] i = 0 for pix in curr_data: inv_pix = 255.0 – pix curr_data[i] = inv_pix i+=1 converted_X[col]… Read More How to make a for loop in python, which is called multiple times consecutively, execute faster?
I have the following DataFrame: Fruit Color Apple Orange Pear Peach How can I fill the second column that depends on the ‘Fruit’ column value? For instance, if the fruit is ‘Apple’, then the second column should be ‘Red’, if the fruit is Orange, then the color should be ‘Orange’ and so on. I have… Read More Fill DataFrame Column depends on condition
I have two 3d arrays of shape (N, M, D) and I want to perform an efficient row wise (over N) matrix multiplication such that the resulting array is of shape (N, D, D). An inefficient code sample showing what I try to achieve is given by: N = 100 M = 10 D =… Read More Efficient numpy row-wise matrix multiplication using 3d arrays
I have a 2D array of 5*5 like this: >>> np.random.seed(100) >>> a = np.random.randint(0,100, (5,5)) >>> a array([[ 8, 24, 67, 87, 79], [48, 10, 94, 52, 98], [53, 66, 98, 14, 34], [24, 15, 60, 58, 16], [ 9, 93, 86, 2, 27]]) if I have an initial position, is there any way… Read More Query the value of the four neighbors of an element in a numpy 2D array