I'm wondering if there is a better way of assigning values from a numpy array to a 2D numpy array based on an index array. Python's numpy module provides a function to get the minimum value from a Numpy array i.e. arr1 = np.array ( [1, 1, 1]) arr2 = np.array ( [1, 1, 1]) arr3 = np.array ( [1, 1, 1]) index = np.array ( [1, 2, 1]) values = np.array ( [0, 0, 0]) for idx, arr_x in enumerate (newest): arr_x [index [idx]] = values [idx] Take an array, say, arr [] and an element, say x to which we have to find the nearest value. Find the index of minimum value from the 2D numpy array So here we are going to discuss how to find out the axis and coordinate of the min value in the array. Many of the most popular numerical packages use NumPy as their base library. Take the code as an example. Searching Arrays You can search an array for a certain value, and return the indexes that get a match. import numpy as np # sytnax with all the default arguments ar_unique = np.unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None) # to just get the unique values use default parameters ar_unique = np.unique(ar). So, let's explore the concept well. # Imports import numpy as np # Let's create a 1D numpy array array_1d = np.array( [1,2,3]) # Let's get value at index 2 array_1d[2] 3 You can access an array element by referring to its index number. We import numpy using the alias np We create an array, arr, which contains six unique values We then print the result of passing our array into the np.argmin () function The function returns 2. axis : It's optional and if not provided then it will flattened the passed numpy array and returns . Step 2 - Find the index of the min value. # Get the index of elements with value 15 result = np.where(arr == 15) print('Tuple of arrays returned : ', result) print("Elements with value 15 exists at following indices", result[0], sep='\n') Python3 import numpy as np a = np.array ( [1, 2, 3, 4, 8, 6, 7, 3, 9, 10]) condition is a conditional expression which returns the Numpy array of bool; x,y are two optional arrays i.e either both are passed or not passed; In this article we will discuss about how to get the index of an element in a Numpy array (both 1D & 2D) using this function. Find maximum value: Numpy find max index: To find the maximum value in the array, we can use numpy.amax( ) function . It's an open source Python library that enables a wide range of applications in the fields of science, statistics, and data analytics through its support of fast, parallelized computations on multidimensional arrays of numbers. Live Demo. In Python, if this argument is set=True then the np.unique () function will always return the index of the NumPy array along with the specified axis. Just pass the input array as an argument inside the max () method. max = np.max (array) print ("The maximum value in the array is :",max) Find index of a value in 1D Numpy array In the above numpy array element with value 15 occurs at different places let's find all it's indices i.e. You can use the numpy unique () function to get the unique values of a numpy array. For Example: Input : array = [1,4,7,6,3,9] k = 3. First we fetch value at index 2 in a 1D array then we fetch value at index (1,2) of a 2D array. Syntax: numpy.where (condition [, x, y]) Example 1: Get index positions of a given value Here, we find all the indexes of 3 and the index of the first occurrence of 3, we get an array as output and it shows all the indexes where 3 is present. import numpy arr2D = numpy.array( [ [11, 12, 13], [14, 15, 16], [17, 15, 11], [12, 14, 15]]) result = numpy.where(arr2D == numpy.amin(arr2D)) print('Tuple of arrays returned : ', result) You can access an array element by referring to its index number. We can use the np. import numpy as np nparr = np.array ( [3,6,9,12,15,18,21,24,27,30,9,9,9]) minval = np.amin (nparr). ======Update========= array ([2, 2, 4, 5, 7, 9, 11, 12, 3, 19]) #get index of values greater than 10 np. This represents the index position of the minimum value in the array. y = np.array ( [0,1,1,0,3,0,1,0,1,0]) y array ( [0, 1, 1, 0, 3, 0, 1, 0, 1, 0]) nonzero () (array([6, 7, 9], dtype=int32),) From the output we can see that the . 1. import numpy as np indexes = [1, 5, 7] # index list y = np.array ( [9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]) #array example y [indexes] [2] #3rd (0,1,>>2<<) item of y array (1,5,>>7<<). The indexes in NumPy arrays start with 0, meaning that the first element has index 0, and the second has index 1 etc. How to Find Index of Value in NumPy Array (With Examples) You can use the following methods to find the index position of specific values in a NumPy array: Method 1: Find All Index Positions of Value np.where(x==value) Method 2: Find First Index Position of Value np.where(x==value) [0] [0] Method 3: Find First Index Position of Several Values However, when set values to numpy array using fancy indexing, what python interpreter does is calling __setitem__function. Example: import numpy as npnew_val = np.array ( [65,65,71,86,95,32,65,71,86,55,76,71])b= np.unique (new_val, return_index=True)print (b) Say I have an array np.zeros((4,2)) I have a list of values [4,3,2,1], which I want to assign to the following positions: [(0,0),(1,1),(2,1),(3,0)] How can I do that without using the for loop or flattening the array? In NumPy, we have this flexibility, we can remove values from one array and add them to another array. In below examples we use python like slicing to get values at indices in numpy arrays. Here, we create a Numpy array with some integer values. In this case it is y [7] equal 16. tip 02 This can also be useful. Use the Numpy argmin() function to compute the index of the minimum value in the above array. You can use numpy.ndenumerate for example import numpy as np test_array = np.arange (2, 3, 0.1) for index, value in np.ndenumerate (test_array): print (index [0], value) For more information refer to https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndenumerate.html Share Improve this answer Follow edited Apr 10, 2019 at 18:38 Frank Bryce Pictorial Presentation: Sample Solution:- Python Code:. Example 1: Python3 import numpy as np a1 = np.array ( [11, 10, 22, 30, 33]) print("Array 1 :") print(a1) #1-D array import numpy as np array = np.array ( [19,5,10,1,9,17,50,19,25,50]) print (array) Creation of 1D- Numpy array Finding the Maximum Value To find the max value you have to use the max () method. Python3. Find maximum value & its index in a 2D Numpy Array; numpy.amax() & NaN; Maximum value & its index in a 1D Numpy Array: Numpy.amax: Let's create a 1D numpy array from a list given below and find the maximum values and its index. It returns a new sorted list. The Numpy boolean array is a type of array (collection of values) that can be used to represent logical 'True' or 'False' values stored in an array data structure in the Python programming language. You can see that the minimum value in the above array is 1 which occurs at index 3. Write a NumPy program to get the values and indices of the elements that are bigger than 10 in a given array . numpy.amin(a, axis=None, out=None, keepdims=<no value>, initial= <no value>) Arguments : a : numpy array from which it needs to find the minimum value. import numpy as np nparr = np.array ( [3,6,9,12,15,18,21,24,27,30,9,9,9]) indice = np.where (nparr == np.amax (nparr)) Get the first element from the following array: import numpy as np. Pass the array for which you want the get the unique values as an argument. asarray (my_array> 10). Because NumPy arrays are zero-based indexed, 2 represents the third item in the list. In this line: a[np.array([10,20,30,40,50])] = 1 What python actually does is a.__setitem__(np.array([10,20,30,40,50]), 1) You can convert a numpy array to list and get its index . NumPy Fancy Indexing returns a copy of numpy array instead of a view. where () function To get the indices of max values that returns tuples of the array that contain indices (one for each axis), wherever max value exists. Example. Find index of a value in 1D . I can use fancy index to retrieve the value, but not to assign them. Call the numpy. The indexes in NumPy arrays start with 0, meaning that the first element has index 0, and the second has index 1 etc. Example Get the first element from the following array: import numpy as np arr = np.array ( [1, 2, 3, 4]) print(arr [0]) Try it Yourself Example Approach to Find the nearest value and the index of NumPy Array. We can perform this operation using numpy.put () function and it can be applied to all forms of arrays like 1-D, 2-D, etc. for example: tmp = [1,2,3,4,5] #python list a = numpy.array (tmp) #numpy array i = list (a).index (2) # i will return index of 2, which is 1 this is just what you wanted. NumPy is short for Numerical Python. To do this task we are going to use the np.argmin () function and it will return the index number of minimum value. The following is the syntax: import numpy as np # sytnax with all the default arguments ar_unique = np.unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None) Get unique values and counts in a numpy array . NumPy : Array Object Exercise-31 with Solution. abs (d) function, with d as the difference between the elements of array and x, and store the values in a different array, say difference_array []. where function with amin function to get the indices of min values that returns tuples of the array that contain indices (one for each axis), wherever min value exists. Maximum value & its index in a 1D Numpy Array: Numpy.amax: Let's create a 1D numpy array from a list given below and find the maximum values and its index. We can access indices by using indices [0]. In this section, we will discuss how to get the index number of the minimum value in NumPy array Python. The following code shows how to get all indices in a NumPy array where the value is greater than 10: import numpy as np #create NumPy array my_array = np. Example Find the indexes where the value is 4: import numpy as np arr = np.array ( [1, 2, 3, 4, 5, 4, 4]) x = np.where (arr == 4) print (x) Try it Yourself The np. We can access indices by using indices [0]. 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