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    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Copying,-Sorting-and-Reshaping\" data-toc-modified-id=\"Copying,-Sorting-and-Reshaping-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Copying, Sorting and Reshaping</a></span><ul class=\"toc-item\"><li><span><a href=\"#Copy\" data-toc-modified-id=\"Copy-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Copy</a></span></li><li><span><a href=\"#View\" data-toc-modified-id=\"View-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>View</a></span></li><li><span><a href=\"#Sorting\" data-toc-modified-id=\"Sorting-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Sorting</a></span></li><li><span><a href=\"#Flatten:-Flattens-2D-array-to-1D-array\" data-toc-modified-id=\"Flatten:-Flattens-2D-array-to-1D-array-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Flatten: Flattens 2D array to 1D array</a></span></li><li><span><a href=\"#Transpose:-Transposes-array-(rows-become-columns-and-vice-versa)\" data-toc-modified-id=\"Transpose:-Transposes-array-(rows-become-columns-and-vice-versa)-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Transpose: Transposes array (rows become columns and vice versa)</a></span></li><li><span><a href=\"#Reshape:-Reshapes-arr-to-r-rows,-c-columns-without-changing-data\" data-toc-modified-id=\"Reshape:-Reshapes-arr-to-r-rows,-c-columns-without-changing-data-1.6\"><span class=\"toc-item-num\">1.6&nbsp;&nbsp;</span>Reshape: Reshapes arr to <code>r</code> rows, <code>c</code> columns without changing data</a></span></li><li><span><a href=\"#Resize:--Changes-arr-shape-to-rxc-and-fills-new-values-with-0\" data-toc-modified-id=\"Resize:--Changes-arr-shape-to-rxc-and-fills-new-values-with-0-1.7\"><span class=\"toc-item-num\">1.7&nbsp;&nbsp;</span>Resize:  Changes arr shape to <code>rxc</code> and fills new values with 0</a></span></li><li><span><a href=\"#References\" data-toc-modified-id=\"References-1.8\"><span class=\"toc-item-num\">1.8&nbsp;&nbsp;</span>References</a></span></li></ul></li></ul></div>"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Copying, Sorting and Reshaping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import numpy \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Copy\n",
    "- Copies array to new memory\n",
    "- **Syntax:** `np.copy(array)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "# create an array `A1`\n",
    "A1 = np.arange(10)\n",
    "print(A1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "# copy `A1` into A2 \n",
    "A2 = np.copy(A1)\n",
    "print(A2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View\n",
    "- Creates view of array elements with type(dtype)\n",
    "- **Syntax:** `array.view(np.dtype)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0e+00 0.0e+00 0.0e+00 0.0e+00 6.0e-08 0.0e+00 0.0e+00 0.0e+00 1.2e-07\n",
      " 0.0e+00 0.0e+00 0.0e+00 1.8e-07 0.0e+00 0.0e+00 0.0e+00 2.4e-07 0.0e+00\n",
      " 0.0e+00 0.0e+00 3.0e-07 0.0e+00 0.0e+00 0.0e+00 3.6e-07 0.0e+00 0.0e+00\n",
      " 0.0e+00 4.2e-07 0.0e+00 0.0e+00 0.0e+00 4.8e-07 0.0e+00 0.0e+00 0.0e+00\n",
      " 5.4e-07 0.0e+00 0.0e+00 0.0e+00]\n"
     ]
    }
   ],
   "source": [
    "# view of array A2 \n",
    "A3 = A2.view(np.float16)\n",
    "print(A3)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sorting\n",
    "- Returns a sorted copy of an array.\n",
    "- **Syntax:** `array.sort()`\n",
    "    - element-wise sorting(default) \n",
    "    - axis = 0; row\n",
    "    - axis = 1; column\n",
    "![Axis](../img/axis.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 9  2  3  1  5 10]\n"
     ]
    }
   ],
   "source": [
    "# Unsorted array\n",
    "A4 = np.array([9, 2, 3,1, 5, 10])\n",
    "print(A4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  5  9 10]\n"
     ]
    }
   ],
   "source": [
    "# Call sort function\n",
    "A4.sort()\n",
    "print(A4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4 1 3]\n",
      " [9 5 8]]\n"
     ]
    }
   ],
   "source": [
    "# Row and column unsorted\n",
    "A5 = np.array([[4, 1, 3], [9, 5, 8]])\n",
    "print(A5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 3 4]\n",
      " [5 8 9]]\n"
     ]
    }
   ],
   "source": [
    "# Apply sort function on column axis=1\n",
    "A5.sort(axis=1)\n",
    "print(A5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flatten: Flattens 2D array to 1D array\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 1, 3, 9, 5, 8])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2D array\n",
    "A6 = np.array([[4, 1, 3], [9, 5, 8]])\n",
    "# 1D array \n",
    "A6.flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transpose: Transposes array (rows become columns and vice versa)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 1, 3],\n",
       "       [9, 5, 8]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A7 = np.array([[4, 1, 3], [9, 5, 8]])\n",
    "A7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 9],\n",
       "       [1, 5],\n",
       "       [3, 8]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose A7 \n",
    "A7.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshape: Reshapes arr to `r` rows, `c` columns without changing data\n",
    "![Reshape](../img/reshape.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8,  9, 10],\n",
       "       [11, 12, 13]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A8 = np.array([(8,9,10),(11,12,13)])\n",
    "A8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8,  9],\n",
       "       [10, 11],\n",
       "       [12, 13]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reshape --> 3x4\n",
    "A8.reshape(3,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resize:  Changes arr shape to `rxc` and fills new values with 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8,  9, 10],\n",
       "       [11, 12, 13]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A9 = np.array([(8,9,10),(11,12,13)])\n",
    "A9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8,  9],\n",
       "       [10, 11],\n",
       "       [12, 13]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resize \n",
    "A9.resize(3, 2)\n",
    "A9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "- https://numpy.org/\n",
    "- https://www.edureka.co/blog/python-numpy-tutorial/\n",
    "- https://github.com/enthought/Numpy-Tutorial-SciPyConf-2019\n",
    "- [Python Machine Learning Cookbook](https://www.amazon.com/Python-Machine-Learning-Cookbook-Prateek/dp/1786464470)\n",
    "<hr>\n",
    "\n",
    "*This notebook was created by [Jubayer Hossain](https://jhossain.me/) | Copyright &copy; 2020, [Jubayer Hossain](https://jhossain.me/)*"
   ]
  }
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