How to sort 2D array (numpy.ndarray) based to the second column in python?
arrays, numpy, python, sorting
Solution
Use `.argsort()` it returns an `numpy.array` of indices that sort the given `numpy.array`. You call it as a function or as a method on your array. For example, suppose you have
import numpy as np
arr = np.array([[-0.30565392, -0.96605562],
[ 0.85331367, -2.62963495],
[ 0.87839643, -0.28283675],
[ 0.72676698, 0.93213482],
[-0.52007354, 0.27752806],
[-0.08701666, 0.22764316],
[-1.78897817, 0.50737573],
[ 0.62260038, -1.96012161],
[-1.98231706, 0.36523876],
[-1.07587382, -2.3022289 ]])
You can now call `.argsort()` on the column you want to sort, and it will give you an array of row indices that sort that particular column which you can pass as an index to your original array.
>>> arr[arr[:, 1].argsort()]
array([[ 0.85331367, -2.62963495],
[-1.07587382, -2.3022289 ],
[ 0.62260038, -1.96012161],
[-0.30565392, -0.96605562],
[ 0.87839643, -0.28283675],
[-0.08701666, 0.22764316],
[-0.52007354, 0.27752806],
[-1.98231706, 0.36523876],
[-1.78897817, 0.50737573],
[ 0.72676698, 0.93213482]])
You can equivalently use `numpy.argsort()`
>>> arr[np.argsort(arr[:, 1])]
array([[ 0.85331367, -2.62963495],
[-1.07587382, -2.3022289 ],
[ 0.62260038, -1.96012161],
[-0.30565392, -0.96605562],
[ 0.87839643, -0.28283675],
[-0.08701666, 0.22764316],
[-0.52007354, 0.27752806],
[-1.98231706, 0.36523876],
[-1.78897817, 0.50737573],
[ 0.72676698, 0.93213482]])
Problem
I'm trying to convert all my codes to Python. I want to sort an array which has two columns so that the sorting must be based on the 2th column in the ascending order. Then I need to sum the first column data (from first line to, for example, 100th line). I used "Data.sort(axis=1)", but it doesn't work. Does anyone have any idea to solve this problem?