6. Sort an Array.ipynb

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   "cell_type": "code",
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   "source": [
    "import numpy as np"
   ]
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  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.random.rand(5,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sort an array along a specified axis"
   ]
  },
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   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "[[0.32367677 0.160538   0.07577069 0.02051216 0.23410999]\n",
      " [0.54927129 0.20910696 0.2196075  0.22756129 0.67417506]\n",
      " [0.63553217 0.56279363 0.62776371 0.32657277 0.79901529]\n",
      " [0.7183594  0.69288395 0.70838123 0.52581954 0.96520237]\n",
      " [0.81701677 0.79908087 0.98800108 0.54773388 0.97694313]]\n"
     ]
    }
   ],
   "source": [
    "# sort along the row and return a copy\n",
    "print(np.sort(arr, axis=0))   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.32367677 0.160538   0.07577069 0.02051216 0.23410999]\n",
      " [0.54927129 0.20910696 0.2196075  0.22756129 0.67417506]\n",
      " [0.63553217 0.56279363 0.62776371 0.32657277 0.79901529]\n",
      " [0.7183594  0.69288395 0.70838123 0.52581954 0.96520237]\n",
      " [0.81701677 0.79908087 0.98800108 0.54773388 0.97694313]]\n"
     ]
    }
   ],
   "source": [
    "# sort along the row in place\n",
    "arr.sort(axis=0)\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.02051216 0.07577069 0.160538   0.23410999 0.32367677]\n",
      " [0.20910696 0.2196075  0.22756129 0.54927129 0.67417506]\n",
      " [0.32657277 0.56279363 0.62776371 0.63553217 0.79901529]\n",
      " [0.52581954 0.69288395 0.70838123 0.7183594  0.96520237]\n",
      " [0.54773388 0.79908087 0.81701677 0.97694313 0.98800108]]\n"
     ]
    }
   ],
   "source": [
    "# sort along the column and return a copy\n",
    "print(np.sort(arr, axis=1))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.02051216 0.07577069 0.160538   0.23410999 0.32367677]\n",
      " [0.20910696 0.2196075  0.22756129 0.54927129 0.67417506]\n",
      " [0.32657277 0.56279363 0.62776371 0.63553217 0.79901529]\n",
      " [0.52581954 0.69288395 0.70838123 0.7183594  0.96520237]\n",
      " [0.54773388 0.79908087 0.81701677 0.97694313 0.98800108]]\n"
     ]
    }
   ],
   "source": [
    "# sort along the column in place\n",
    "arr.sort(axis=1)    \n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### compute the indices that would sort an array along a specified axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.random.rand(5,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 1 2 2 0]\n",
      " [2 2 1 3 2]\n",
      " [0 4 0 1 4]\n",
      " [4 3 3 0 3]\n",
      " [3 0 4 4 1]]\n"
     ]
    }
   ],
   "source": [
    "# along the row\n",
    "print(np.argsort(arr, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4 3 2 0 1]\n",
      " [0 3 1 2 4]\n",
      " [3 2 4 0 1]\n",
      " [3 4 0 1 2]\n",
      " [4 0 1 2 3]]\n"
     ]
    }
   ],
   "source": [
    "# along the column\n",
    "print(np.argsort(arr, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13 12  5 18  4  8  6  7 14  3 10 24  2 11  0 19 20 21  9 15 16 17 22  1\n",
      " 23]\n"
     ]
    }
   ],
   "source": [
    "# if axis=None, return the indices of a flattened array\n",
    "print(np.argsort(arr, axis=None))"
   ]
  }
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