Sorting an array by the nth column using NumPy is a common task in data analysis and scientific computing. It involves rearranging the rows of the array based on the values in a specific column. The nth column refers to the column at index n-1, where n is the position of the column in the array.
NumPy provides a lot of different convenient functionalities, which are listed below:
1. Using “np.lexsort()”:
The code creates a 2D NumPy array called “arr”. It then sorts the array based on the second column (index 1) using np.lexsort(). The resulting sorted array is stored in “sorted_arr”, and it is printed to the console. The column to sort by is defined using the variable “n”.
- You can also use
np.lexsort()and sort the array by multiple columns. The example below creates a 2D NumPy array called “arr”. It then sorts the array based on the second column (index 1), and then by the first column (index 0) usingnp.lexsort(). The resulting sorted array is stored in “sorted_arr”, and it is printed to the console.
Example:
2. Using “np.argsort()” with descending order:
The code creates a 2D NumPy array called “arr”. It then sorts the array based on the second column (index 1) in descending order using np.argsort(-arr[:,1]). The resulting sorted array is stored in “sorted_arr”, and it is printed to the console. Note that np.argsort() sorts the array in ascending order by default. If you want to sort in descending order, you can use negative indexing.