Python NumPy Searching is a method in python and numpy searching is a library of Python programming language that adds support for large, multi-dimensional arrays and matrices that add support for large, multi-dimensional arrays and matrices. Along with it, it adds a large collection of high-level mathematical functions to operate on the arrays
In this article, we will learn about the techniques we use for Python NumPy searching.
A NumPy array stores similar types of elements in a continuous structure. We have seen so many times that there is a need to look at the maximum and minimum elements of the arrays at a dynamic run time. NumPy provides us with a set of functions that enables us to search for the specific elements having certain conditions applied to them.
How to Search NumPy Arrays
- argmax() function: With this function, it becomes easy to fetch and display the index of the maximum element present in the array structure. By this, the index of the largest elements is a result value from the argmax() function.
- NumPy nanargmax() function: With the nanargmax() function, one can easily deal with the NAN or NULL values present in the array. It does not get treated differently. There will be no effect on the functioning of the search values of the NAN values. The syntax will be:
In the example given, the array elements contain a NULL value passed using the numpy.NaN function. Now we use nanargmax() function to search NumPy arrays and find the maximum value from the array elements without letting the NAN elements affect the search.
import numpy as np x = np.array([[40, 10, 20,np.nan,-1,0,10],[1,2,3,4,np.nan,0,-1]]) y = np.nanargmax(x) print(x) print("Max element's index:", y)
The output will be:
[[40. 10. 20. nan -1. 0. 10.] [ 1. 2. 3. 4. nan 0. -1.]] Max element's index: 0
- NumPy argmin() function : With the argmin() function, we can easily search NumPy arrays and fetch the index of the smallest elements that are present in the array at a border scale. It searches for the smallest value present in the array structure and returns the index of the same value. Therefore, with the index, it becomes easy to get the smallest element present in the array. The syntax will be:
import numpy as np x = np.array([[40, 10, 20,11,-1,0,10],[1,2,3,4,5,0,-1]]) y = np.argmin(x) print(x) print("Min element's index:", y)
The output will be:
[[40 10 20 11 -1 0 10] [ 1 2 3 4 5 0 -1]] Min element's index: 4 >
In the example given below, there are two indexes that cover the lowest element that is [-1]. The argmin() function returns the index of the first occurrence of the smallest elements from the array values.
- NumPy where() function: This function easily searches NumPy arrays for the index values of any element. It matches the condition passed as the parameter of the function.
- NumPy nanargmin() function: This function helps you to search NumPy arrays. It gets easy to find the index of the smallest value present in the array elements. The user doesnt have to worry about the NAN values present in them. The NULL values have a zero effect on the search of the elements.