How to find maximum negative and minimum positive number in a numpy array?
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I am given an array containing both positive and negative numbers.
import numpy as np arr = np.array([-10.2, -5.3, -2.1, 0, 1.2, 3.4])
I would like to find the index corresponding to the maximum negative number and to a minimum positive number. In the above, my expected outcome is
4. Is there any
numpy trick to achieve this? I have found a solution in this link, but I would like to know how this could be done through
Finding index of largest negative and smallest positive element in array
Replace non negative values with
-inf, then use
argmax to find the largest negative:
np.where(arr < 0, arr, -np.inf).argmax() # 2
Similarly replace non positive values with
inf then use
argmin to find the smallest positive:
np.where(arr > 0, arr, np.inf).argmin() # 4
numpy.amin — NumPy v1.19 Manual, Return the minimum of an array or minimum along an axis. See also. amax. The maximum value of an array along a given axis, propagating� Python Maximum Value of Numpy Array. Given a numpy array, you can find the maximum value of all the elements in the array. To get the maximum value of a Numpy Array, you can use numpy function numpy.max() function. Syntax. The syntax of max() function as given below. max_value = numpy.max(arr) Pass the numpy array as argument to numpy.max
If your array is always sorted (as in the provided example), then:
# input Out: array([-10.2, -5.3, -2.1, 0. , 1.2, 3.4]) # obtain a `signed` boolean mask In : sign_mask = np.sign(arr) # compute derivative and obtain index for max_negative element In : max_neg_idx = np.where(np.diff(sign_mask, append=1) == 1) # add +2 to that to get index for min_positive element In : min_pos_idx = max_neg_idx + 2
Find the smallest positive number missing from an unsorted array , You are given an unsorted array with both positive and negative elements. You have to find the smallest positive number missing from the array in O(n) have created a list full of 0's with size of the max() value of our given array. (index value + 1) should be our answer since index in python starts from 0. NumPy: Find the maximum and minimum value of a given flattened array Last update on February 26 2020 08:09:24 (UTC/GMT +8 hours) NumPy Statistics: Exercise-1 with Solution
I was facing a similar type of problem and this was my approach:
arr=np.array([-10.2, -5.3, -2.1, 0, 1.2, 3.4]) #Filtering for all the negative values and getting the min of them print('Threshold for Min val',np.where(arr==[max(arr[arr<0])])) #Filtering for all the positive values and getting the max of them print('Threshold for Max val',np.where(val==[min(arr[arr>0])]))
Smallest number that never becomes negative when processed , Given an array of size n your goal is to find a number such that when the 16 When processed with 4, it becomes 28 We always get a positive number. find the maximum element in the array and test against each number starting from 1 Python. filter_none. edit close. play_arrow. link brightness_4 code� Given a list of integers, e.g.: lst = [-5, -1, -13, -11, 4, 8, 16, 32] is there a Pythonic way of retrieving the largest negative number in the list (e.g. -1) and the smallest positive number (e.
numpy.nanmax — NumPy v1.9 Manual, Array containing numbers whose maximum is desired. See also. nanmin: The minimum value of an array along a given axis, ignoring any NaNs. amax: The� In this article we will discuss how to find the minimum or smallest value in a Numpy array and it’s indices using numpy.amin(). numpy.amin() Python’s numpy module provides a function to get the minimum value from a Numpy array i.e.
Algorithm to find maximum and minimum number in an array, Given an array of integers , find out maximum & minimum number in array corresponding to the maximum negative number and to a minimum positive number. So, the first algorithm could be to find the Python examples to find the largest� Find max value in complete 2D numpy array. To find maximum value from complete 2D numpy array we will not pass axis in numpy.amax() i.e. # Get the maximum value from complete 2D numpy array maxValue = numpy.amax(arr2D) It will return the maximum value from complete 2D numpy arrays i.e. in all rows and columns. 17
numpy.nan_to_num — NumPy v1.13 Manual, Returns an array or scalar replacing Not a Number (NaN) with zero, (positive) is replaced by the largest (smallest or most negative) floating point value that fits in the See also. isinf: Shows which elements are positive or negative infinity. Given numpy array, the task is to replace negative value with zero in numpy array. = np.maximum(ini_array1, zero_array) N positive integers such that prime
- Is it guaranteed that the input is sorted?
- No, it is not guaranteed.
- Or replace with
nanargmin. But that's not much different than using