NumPy Array Searching
In this article, we will explore how NumPy array search works for a specific values or elements and perform various search-related operations.
Numpy array search scans an array and displays the indexes that match a particular value.
Invoke the where() method to find an array.
You can locate the indexes where the value is 20499 as follows:
As a result of the example above, a tuple will be displayed: (array(),) Therefore, index 5 holds the value 20499.
Identify the index of cube_arr that contains the data 27:
The following tuple will be generated as an outcome of the example above: (array(),)
Accordingly, index 2 contains the value 27.
You need to identify the indexes odd_arr array where the numbers are odd:
Search for numbers that are perfectly divisible by four:
Check out the indexes of the even_arr array that have even numbers:
Look for numbers that are completely divisible by the odd number 7:
NumPy Array Search Sorted
The searchsorted() method in Numpy performs a binary search in an array and returns the index where the searched value will be added.
Arrays with sorted items are expected to be retrieved through the searchsorted() method.
Determine the location of the gap_arr array where number 40 should be placed:
Above example explanation: To maintain the sort order, number 40 should be placed on index 3.
According to Numpy array search, the method begins the lookup from the left and displays the first index where 40 is no more larger than the next value.
Where should number 3.7 be positioned in the point_arr array:
Above example explanation: Number 3.7 must be positioned on index 1 to keep the sort order.
Search From the Right Side
We can set side=’right’ to retrieve the rightmost index rather than the leftmost index by default.
Beginning from the right, locate the indexes where the value 50 should be added:
Above example explanation: To maintain the sort order, add 50 on index 5.
Numpy array search begins the query from the right and finds the first index where 50 is no longer less than the next value.
Identify the indexes where 4.5 should be placed starting from the right:
On index 5, insert 4.2 to continue the sequence order.
From right to left, Numpy array search locates the first index where 4.2 is no longer less than 4.3.
An array with multiple values can be utilized to look for more than one value.
The numbers 8 and 10 should be placed at the following indexes:
Following are the locations where 4.1, 4.3, and 6.3 should be added: