diff --git a/.ipynb_checkpoints/Numpy_Examples_1-checkpoint.ipynb b/.ipynb_checkpoints/Numpy_Examples_1-checkpoint.ipynb
new file mode 100644
index 0000000..b5cd205
--- /dev/null
+++ b/.ipynb_checkpoints/Numpy_Examples_1-checkpoint.ipynb
@@ -0,0 +1,504 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "[View in Colaboratory](https://colab.research.google.com/github/nabhoneel/Assignment-2/blob/nabhoneel/Numpy_Examples_1.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "3pSVAeWfuPcq"
+   },
+   "source": [
+    "# Numpy Examples\n",
+    "\n",
+    "## What is numpy?\n",
+    "\n",
+    "#### Python has built-in:\n",
+    "\n",
+    "-  containers: lists (costless insertion and append), dictionnaries (fast lookup)\n",
+    "-  high-level number objects: integers, floating point\n",
+    "\n",
+    "#### Numpy is:\n",
+    "\n",
+    " - extension package to Python for multidimensional arrays\n",
+    " - closer to hardware (efficiency)\n",
+    " - designed for scientific computation (convenience)\n",
+    "\n",
+    "\n",
+    "#### Import numpy\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ozUi4_X55UHE"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "3-1ghFDF5N2z"
+   },
+   "source": [
+    "### Uncomment Print statement and run each cell to see the output\n",
+    "\n",
+    "#### Create numpy arrays\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "atYpk2ert0b-"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3]\n",
+      "<class 'numpy.ndarray'>\n",
+      "(2, 3)\n",
+      "1 2 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.array([1, 2, 3])   # Create a rank 1 array\n",
+    "print(a)\n",
+    "print(type(a)) #print type of a\n",
+    "\n",
+    "b = np.array([[1,2,3],[4,5,6]])    # Create a rank 2 array\n",
+    "print(b.shape)                     # Prints \"(2, 3)\"\n",
+    "print(b[0, 0], b[0, 1], b[1, 0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "Kro5ZOwXue5n"
+   },
+   "source": [
+    "#### Some basic functions for creating arrays. Print all the defined arrays and see the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "V3rdzgr9uhHS"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a [[0. 0.]\n",
+      " [0. 0.]]\n",
+      "b [[1. 1. 1.]\n",
+      " [1. 1. 1.]\n",
+      " [1. 1. 1.]]\n",
+      "c [[1. 0.]\n",
+      " [0. 1.]]\n",
+      "d [[5 5 5]\n",
+      " [5 5 5]\n",
+      " [5 5 5]]\n",
+      "e [[0.96713436 0.12079029]\n",
+      " [0.68911551 0.5792308 ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.zeros(shape=(2,2))\n",
+    "b = np.ones(shape = (3,3))\n",
+    "c = np.eye(2)\n",
+    "d = np.full(shape=(3,3), fill_value=5)\n",
+    "e = np.random.random((2,2))\n",
+    "\n",
+    "print('a', a)\n",
+    "print('b',b)\n",
+    "print('c',c)\n",
+    "print('d',d)\n",
+    "print('e',e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "8RPW_SutukjF"
+   },
+   "source": [
+    "#### Execute and understand :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "-8JuqYt4upeo"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0. 0.]\n",
+      " [0. 0.]]\n",
+      "[[1. 1. 1.]\n",
+      " [1. 1. 1.]\n",
+      " [1. 1. 1.]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: elementwise == comparison failed; this will raise an error in the future.\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n",
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning: elementwise == comparison failed; this will raise an error in the future.\n",
+      "  \n"
+     ]
+    }
+   ],
+   "source": [
+    "a  ==  np.arange(10)\n",
+    "b  ==  np.linspace(0,10, num=6)\n",
+    "print(a)\n",
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "MRHhbjx4uvYN"
+   },
+   "source": [
+    "#### Array Indexing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "grF5_yUSuxVK"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "77\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
+    "\n",
+    "# Use slicing to pull out the subarray consisting of the first 2 rows\n",
+    "# and columns 1 and 2; b is the following array of shape (2, 2):\n",
+    "# [[2 3]\n",
+    "#  [6 7]]\n",
+    "b = a[:2, 1:3]\n",
+    "\n",
+    "# A slice of an array is a view into the same data, so modifying it\n",
+    "# will modify the original array.\n",
+    "\n",
+    "print(a[0, 1])   # Prints \"2\"\n",
+    "\n",
+    "b[0, 0] = 77     # b[0, 0] is the same piece of data as a[0, 1]\n",
+    "print(a[0, 1])   # Prints \"77\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "s400Gijxu0kO"
+   },
+   "source": [
+    "#### Slicing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "kubpegh2u4zF"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[5 6 7 8] (4,)\n",
+      "[[5 6 7 8]] (1, 4)\n",
+      "[ 2  6 10] (3,)\n",
+      "[[ 2]\n",
+      " [ 6]\n",
+      " [10]] (3, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
+    "\n",
+    "row_r1 = a[1, :]    # Rank 1 view of the second row of a\n",
+    "row_r2 = a[1:2, :]  # Rank 2 view of the second row of a\n",
+    "\n",
+    "print(row_r1, row_r1.shape)  # Prints \"[5 6 7 8] (4,)\"\n",
+    "print(row_r2, row_r2.shape)  # Prints \"[[5 6 7 8]] (1, 4)\"\n",
+    "\n",
+    "col_r1 = a[:, 1]\n",
+    "col_r2 = a[:, 1:2]\n",
+    "\n",
+    "print(col_r1, col_r1.shape)  # Prints \"[ 2  6 10] (3,)\"\n",
+    "print(col_r2, col_r2.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "TmGnCO3AvE8t"
+   },
+   "source": [
+    "#### Aritmetic operations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "YvBw3ImjvGqD"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n",
+      "[4 6]\n",
+      "[3 7]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.array([[1,2],[3,4]])\n",
+    "\n",
+    "print(np.sum(x))  # Compute sum of all elements; prints \"10\"\n",
+    "print(np.sum(x, axis=0))  # Compute sum of each column; prints \"[4 6]\"\n",
+    "print(np.sum(x, axis=1))  # Compute sum of each row; prints \"[3 7]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "uaVY3ZzD4pC2"
+   },
+   "source": [
+    "#### Using Boolean Mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "-PNfOMvh4_Gp"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 1 2 3 4 5 6 7 8 9]\n",
+      "[False  True False  True False  True False  True False  True]\n",
+      "[1 3 5 7 9]\n"
+     ]
+    }
+   ],
+   "source": [
+    "b = np.arange(10)\n",
+    "\n",
+    "print(b)\n",
+    "\n",
+    "mask = b%2!=0 #perform computations on the list \n",
+    "\n",
+    "print(mask)\n",
+    "\n",
+    "print(b[mask]) #applying the mask on the numpy array\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "HbEPBbz-5J9K"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0 -1  2 -1  4 -1  6 -1  8 -1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "modified_b = b\n",
+    "modified_b[mask] = -1\n",
+    "\n",
+    "print(modified_b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "zgSd71EEAHC7"
+   },
+   "source": [
+    "#### Swapping two columns in a 2d numpy array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "-cvqeXd_AGo1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0 1 2]\n",
+      " [3 4 5]\n",
+      " [6 7 8]]\n",
+      "[[1 0 2]\n",
+      " [4 3 5]\n",
+      " [7 6 8]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.arange(9).reshape(3,3)\n",
+    "print(a)\n",
+    "\n",
+    "print(a[:, [1,0,2]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "U7ifiLY3Ayky"
+   },
+   "source": [
+    "#### Swapping two rows in a 2d numpy array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "0FrOURRDAZNP"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0 1 2]\n",
+      " [3 4 5]\n",
+      " [6 7 8]]\n",
+      "[[3 4 5]\n",
+      " [0 1 2]\n",
+      " [6 7 8]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.arange(9).reshape(3,3)\n",
+    "print(a)\n",
+    "\n",
+    "print(a[[1,0,2], :])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "name": "Numpy_Examples 1.ipynb",
+   "provenance": [],
+   "version": "0.3.2"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/List.ipynb b/List.ipynb
new file mode 100644
index 0000000..bc7b6e7
--- /dev/null
+++ b/List.ipynb
@@ -0,0 +1,290 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "List",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "[View in Colaboratory](https://colab.research.google.com/github/nabhoneel/Assignment-2/blob/nabhoneel/List.ipynb)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "up7nFkrYIbui",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "import numpy as np"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "05f1yqOULgfd",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "43184d3e-7abb-471f-e314-1c9c3249308c"
+      },
+      "cell_type": "code",
+      "source": [
+        "dummy_list = [5, 6, 1, 2, 3] # random list\n",
+        "print(dummy_list)"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[5, 6, 1, 2, 3]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KZmpEjFgUTS7",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "2dbd11ec-866f-4102-9392-1d39e0fca80f"
+      },
+      "cell_type": "code",
+      "source": [
+        "print(dummy_list[::-1]) # reversed the random list"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[3, 2, 1, 6, 5]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jLAMyuBiUZW0",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "a1e87b81-8774-4cde-8dd2-16942a94576a"
+      },
+      "cell_type": "code",
+      "source": [
+        "dummy_list_2 = [2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n",
+        "dummy_list = dummy_list + dummy_list_2 # added dummy_list_2 to original dummy_list\n",
+        "print(dummy_list)"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[5, 6, 1, 2, 3, 2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "40h4QgzHUbXi",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "4f7b51d7-6a0b-4d19-8a97-08a8aad5828d"
+      },
+      "cell_type": "code",
+      "source": [
+        "# frequency of each element in dummy_list\n",
+        "dummy_list_keys = list(set(dummy_list))\n",
+        "dummy_dict = {x: dummy_list.count(x) for x in dummy_list_keys}\n",
+        "\n",
+        "# print dummy_dict\n",
+        "print(dummy_dict)"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "{0: 1, 1: 2, 2: 2, 3: 1, 4: 1, 5: 1, 6: 1, 200: 1, 9.45: 1, 12.01: 1, 45.67: 1, 12.02: 1, 16: 1, 90: 1}\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vHqzUbnoVqkj",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 53
+        },
+        "outputId": "5ac41384-2590-4c6d-9419-1dc3258b3fa7"
+      },
+      "cell_type": "code",
+      "source": [
+        "dummy_list.sort() # ascending order sort\n",
+        "print(dummy_list)\n",
+        "\n",
+        "dummy_list.sort(reverse=True) # descending order sort\n",
+        "print(dummy_list)"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[0, 1, 1, 2, 2, 3, 4, 5, 6, 9.45, 12.01, 12.02, 16, 45.67, 90, 200]\n",
+            "[200, 90, 45.67, 16, 12.02, 12.01, 9.45, 6, 5, 4, 3, 2, 2, 1, 1, 0]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fOVj9RrbWM7b",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "c27fcfcb-2468-4660-fc7c-426544e171e7"
+      },
+      "cell_type": "code",
+      "source": [
+        "x = 200\n",
+        "dummy_list.remove(x) # remove first occurrence of 'x' in the list\n",
+        "print(dummy_list)"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[90, 45.67, 16, 12.02, 12.01, 9.45, 6, 5, 4, 3, 2, 2, 1, 1, 0]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "3QgQK_efW1Aj",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "43841b69-1bd5-478e-849e-267651deccbd"
+      },
+      "cell_type": "code",
+      "source": [
+        "del dummy_list[4] # delete element at position 4\n",
+        "print(dummy_list)"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[90, 45.67, 16, 12.02, 5, 4, 3, 2, 2, 1, 1, 0]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kM2R32bxXT0K",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 171
+        },
+        "outputId": "d1ca4b44-a5c3-44c8-a850-1ca1dba6400b"
+      },
+      "cell_type": "code",
+      "source": [
+        "# attempting to delete element at position length + 1\n",
+        "del dummy_list[len(dummy_list) + 1]"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "IndexError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-19-f074f3629e57>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mdel\u001b[0m \u001b[0mdummy_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdummy_list\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;31mIndexError\u001b[0m: list assignment index out of range"
+          ]
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Kh9lTau7XVIM",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "8a374a5b-1b9b-4771-dc6f-52729b403e78"
+      },
+      "cell_type": "code",
+      "source": [
+        "# cleared the list\n",
+        "dummy_list.clear()\n",
+        "print(dummy_list)"
+      ],
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/NumPy_Exercises.ipynb b/NumPy_Exercises.ipynb
new file mode 100644
index 0000000..5acc5d3
--- /dev/null
+++ b/NumPy_Exercises.ipynb
@@ -0,0 +1,327 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "NumPy Exercises ",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "[View in Colaboratory](https://colab.research.google.com/github/nabhoneel/Assignment-2/blob/nabhoneel/NumPy_Exercises.ipynb)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ibRSjDA1YUiF",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "import numpy as np"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "J2uAs0dPZJDP",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 253
+        },
+        "outputId": "3b2439ca-2689-4558-b4bc-7364167ed061"
+      },
+      "cell_type": "code",
+      "source": [
+        "# create a uniform subdivision of the interval -1.3 to 2.5 with 64 subdivisions\n",
+        "np.linspace(-1.3, 2.5, 64)"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([-1.3       , -1.23968254, -1.17936508, -1.11904762, -1.05873016,\n",
+              "       -0.9984127 , -0.93809524, -0.87777778, -0.81746032, -0.75714286,\n",
+              "       -0.6968254 , -0.63650794, -0.57619048, -0.51587302, -0.45555556,\n",
+              "       -0.3952381 , -0.33492063, -0.27460317, -0.21428571, -0.15396825,\n",
+              "       -0.09365079, -0.03333333,  0.02698413,  0.08730159,  0.14761905,\n",
+              "        0.20793651,  0.26825397,  0.32857143,  0.38888889,  0.44920635,\n",
+              "        0.50952381,  0.56984127,  0.63015873,  0.69047619,  0.75079365,\n",
+              "        0.81111111,  0.87142857,  0.93174603,  0.99206349,  1.05238095,\n",
+              "        1.11269841,  1.17301587,  1.23333333,  1.29365079,  1.35396825,\n",
+              "        1.41428571,  1.47460317,  1.53492063,  1.5952381 ,  1.65555556,\n",
+              "        1.71587302,  1.77619048,  1.83650794,  1.8968254 ,  1.95714286,\n",
+              "        2.01746032,  2.07777778,  2.13809524,  2.1984127 ,  2.25873016,\n",
+              "        2.31904762,  2.37936508,  2.43968254,  2.5       ])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "SyeIT1vZaYXe",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 53
+        },
+        "outputId": "5502a6e2-2c3c-4e67-a9b5-6d5a5487ade2"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Generate an array of length 3n filled with the cyclic pattern 1, 2, 3\n",
+        "np.resize(np.array([1, 2, 3]), 30)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1,\n",
+              "       2, 3, 1, 2, 3, 1, 2, 3])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "8jFQNXUOdwTk",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "c5afe067-2e2c-4934-f118-0418ff31d2a6"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Create an array of the first 10 odd integers\n",
+        "np.arange(1, 10 * 2 + 1, 2)"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "uVm8Z_tqeGvI",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "ffd90ce8-5573-4fd1-bb16-c6244eb4314d"
+      },
+      "cell_type": "code",
+      "source": [
+        "# find intersection of a and b\n",
+        "a = np.array([1,2,3,2,3,4,3,4,5,6])\n",
+        "b = np.array([7,2,10,2,7,4,9,4,9,8])\n",
+        "\n",
+        "np.intersect1d(a, b)"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([2, 4])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "mxffY6DgeYfx",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 53
+        },
+        "outputId": "0bbc4ba0-8a90-4157-bb82-bef7e618045c"
+      },
+      "cell_type": "code",
+      "source": [
+        "# reshape 1D array to 2D array of size 2x5\n",
+        "a = np.arange(10)\n",
+        "a.reshape(2, 5)"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[0, 1, 2, 3, 4],\n",
+              "       [5, 6, 7, 8, 9]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "q9sYIsC6ejZi",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "outputId": "8d593125-43c7-46ba-f0cb-0b38aed5edc7"
+      },
+      "cell_type": "code",
+      "source": [
+        "a = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
+        "np.array(a) # list to numpy array\n",
+        "np.array(a).tolist() # numpy array to list"
+      ],
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[1, 2, 3, 4, 5, 6, 7, 8, 9]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 27
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "k9q8AU2hfz17",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 235
+        },
+        "outputId": "7e34c1b3-06bf-4251-be45-8b8c33ddf78b"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Create a 10 x 10 arrays of zeros and then \"frame\" it with a border of ones.\n",
+        "\n",
+        "bordered_array = np.zeros(shape = (10, 10))\n",
+        "bordered_array = np.pad(bordered_array, (1, 1), 'constant', constant_values=(1, 1)) # framing segment\n",
+        "\n",
+        "print(bordered_array)"
+      ],
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+            " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "eJ7rub5Fi0tD",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 162
+        },
+        "outputId": "3b1a7673-e97d-4a09-b726-4fb4d2ee674d"
+      },
+      "cell_type": "code",
+      "source": [
+        "# checkerboard style with slice and stride\n",
+        "\n",
+        "checkerboard = np.ones(shape = (8, 8))\n",
+        "\n",
+        "checkerboard[0::2, 1:8:2] = 0\n",
+        "checkerboard[1::2, 0:8:2] = 0\n",
+        "\n",
+        "print(checkerboard)"
+      ],
+      "execution_count": 88,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[[1. 0. 1. 0. 1. 0. 1. 0.]\n",
+            " [0. 1. 0. 1. 0. 1. 0. 1.]\n",
+            " [1. 0. 1. 0. 1. 0. 1. 0.]\n",
+            " [0. 1. 0. 1. 0. 1. 0. 1.]\n",
+            " [1. 0. 1. 0. 1. 0. 1. 0.]\n",
+            " [0. 1. 0. 1. 0. 1. 0. 1.]\n",
+            " [1. 0. 1. 0. 1. 0. 1. 0.]\n",
+            " [0. 1. 0. 1. 0. 1. 0. 1.]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Numpy_Examples_1.ipynb b/Numpy_Examples_1.ipynb
new file mode 100644
index 0000000..b5cd205
--- /dev/null
+++ b/Numpy_Examples_1.ipynb
@@ -0,0 +1,504 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "[View in Colaboratory](https://colab.research.google.com/github/nabhoneel/Assignment-2/blob/nabhoneel/Numpy_Examples_1.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "3pSVAeWfuPcq"
+   },
+   "source": [
+    "# Numpy Examples\n",
+    "\n",
+    "## What is numpy?\n",
+    "\n",
+    "#### Python has built-in:\n",
+    "\n",
+    "-  containers: lists (costless insertion and append), dictionnaries (fast lookup)\n",
+    "-  high-level number objects: integers, floating point\n",
+    "\n",
+    "#### Numpy is:\n",
+    "\n",
+    " - extension package to Python for multidimensional arrays\n",
+    " - closer to hardware (efficiency)\n",
+    " - designed for scientific computation (convenience)\n",
+    "\n",
+    "\n",
+    "#### Import numpy\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ozUi4_X55UHE"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "3-1ghFDF5N2z"
+   },
+   "source": [
+    "### Uncomment Print statement and run each cell to see the output\n",
+    "\n",
+    "#### Create numpy arrays\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "atYpk2ert0b-"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3]\n",
+      "<class 'numpy.ndarray'>\n",
+      "(2, 3)\n",
+      "1 2 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.array([1, 2, 3])   # Create a rank 1 array\n",
+    "print(a)\n",
+    "print(type(a)) #print type of a\n",
+    "\n",
+    "b = np.array([[1,2,3],[4,5,6]])    # Create a rank 2 array\n",
+    "print(b.shape)                     # Prints \"(2, 3)\"\n",
+    "print(b[0, 0], b[0, 1], b[1, 0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "Kro5ZOwXue5n"
+   },
+   "source": [
+    "#### Some basic functions for creating arrays. Print all the defined arrays and see the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "V3rdzgr9uhHS"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a [[0. 0.]\n",
+      " [0. 0.]]\n",
+      "b [[1. 1. 1.]\n",
+      " [1. 1. 1.]\n",
+      " [1. 1. 1.]]\n",
+      "c [[1. 0.]\n",
+      " [0. 1.]]\n",
+      "d [[5 5 5]\n",
+      " [5 5 5]\n",
+      " [5 5 5]]\n",
+      "e [[0.96713436 0.12079029]\n",
+      " [0.68911551 0.5792308 ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.zeros(shape=(2,2))\n",
+    "b = np.ones(shape = (3,3))\n",
+    "c = np.eye(2)\n",
+    "d = np.full(shape=(3,3), fill_value=5)\n",
+    "e = np.random.random((2,2))\n",
+    "\n",
+    "print('a', a)\n",
+    "print('b',b)\n",
+    "print('c',c)\n",
+    "print('d',d)\n",
+    "print('e',e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "8RPW_SutukjF"
+   },
+   "source": [
+    "#### Execute and understand :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "-8JuqYt4upeo"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0. 0.]\n",
+      " [0. 0.]]\n",
+      "[[1. 1. 1.]\n",
+      " [1. 1. 1.]\n",
+      " [1. 1. 1.]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: elementwise == comparison failed; this will raise an error in the future.\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n",
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning: elementwise == comparison failed; this will raise an error in the future.\n",
+      "  \n"
+     ]
+    }
+   ],
+   "source": [
+    "a  ==  np.arange(10)\n",
+    "b  ==  np.linspace(0,10, num=6)\n",
+    "print(a)\n",
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "MRHhbjx4uvYN"
+   },
+   "source": [
+    "#### Array Indexing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "grF5_yUSuxVK"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "77\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
+    "\n",
+    "# Use slicing to pull out the subarray consisting of the first 2 rows\n",
+    "# and columns 1 and 2; b is the following array of shape (2, 2):\n",
+    "# [[2 3]\n",
+    "#  [6 7]]\n",
+    "b = a[:2, 1:3]\n",
+    "\n",
+    "# A slice of an array is a view into the same data, so modifying it\n",
+    "# will modify the original array.\n",
+    "\n",
+    "print(a[0, 1])   # Prints \"2\"\n",
+    "\n",
+    "b[0, 0] = 77     # b[0, 0] is the same piece of data as a[0, 1]\n",
+    "print(a[0, 1])   # Prints \"77\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "s400Gijxu0kO"
+   },
+   "source": [
+    "#### Slicing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "kubpegh2u4zF"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[5 6 7 8] (4,)\n",
+      "[[5 6 7 8]] (1, 4)\n",
+      "[ 2  6 10] (3,)\n",
+      "[[ 2]\n",
+      " [ 6]\n",
+      " [10]] (3, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
+    "\n",
+    "row_r1 = a[1, :]    # Rank 1 view of the second row of a\n",
+    "row_r2 = a[1:2, :]  # Rank 2 view of the second row of a\n",
+    "\n",
+    "print(row_r1, row_r1.shape)  # Prints \"[5 6 7 8] (4,)\"\n",
+    "print(row_r2, row_r2.shape)  # Prints \"[[5 6 7 8]] (1, 4)\"\n",
+    "\n",
+    "col_r1 = a[:, 1]\n",
+    "col_r2 = a[:, 1:2]\n",
+    "\n",
+    "print(col_r1, col_r1.shape)  # Prints \"[ 2  6 10] (3,)\"\n",
+    "print(col_r2, col_r2.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "TmGnCO3AvE8t"
+   },
+   "source": [
+    "#### Aritmetic operations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "YvBw3ImjvGqD"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n",
+      "[4 6]\n",
+      "[3 7]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.array([[1,2],[3,4]])\n",
+    "\n",
+    "print(np.sum(x))  # Compute sum of all elements; prints \"10\"\n",
+    "print(np.sum(x, axis=0))  # Compute sum of each column; prints \"[4 6]\"\n",
+    "print(np.sum(x, axis=1))  # Compute sum of each row; prints \"[3 7]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "uaVY3ZzD4pC2"
+   },
+   "source": [
+    "#### Using Boolean Mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "-PNfOMvh4_Gp"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 1 2 3 4 5 6 7 8 9]\n",
+      "[False  True False  True False  True False  True False  True]\n",
+      "[1 3 5 7 9]\n"
+     ]
+    }
+   ],
+   "source": [
+    "b = np.arange(10)\n",
+    "\n",
+    "print(b)\n",
+    "\n",
+    "mask = b%2!=0 #perform computations on the list \n",
+    "\n",
+    "print(mask)\n",
+    "\n",
+    "print(b[mask]) #applying the mask on the numpy array\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "HbEPBbz-5J9K"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0 -1  2 -1  4 -1  6 -1  8 -1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "modified_b = b\n",
+    "modified_b[mask] = -1\n",
+    "\n",
+    "print(modified_b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "zgSd71EEAHC7"
+   },
+   "source": [
+    "#### Swapping two columns in a 2d numpy array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "-cvqeXd_AGo1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0 1 2]\n",
+      " [3 4 5]\n",
+      " [6 7 8]]\n",
+      "[[1 0 2]\n",
+      " [4 3 5]\n",
+      " [7 6 8]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.arange(9).reshape(3,3)\n",
+    "print(a)\n",
+    "\n",
+    "print(a[:, [1,0,2]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "U7ifiLY3Ayky"
+   },
+   "source": [
+    "#### Swapping two rows in a 2d numpy array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "0FrOURRDAZNP"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0 1 2]\n",
+      " [3 4 5]\n",
+      " [6 7 8]]\n",
+      "[[3 4 5]\n",
+      " [0 1 2]\n",
+      " [6 7 8]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.arange(9).reshape(3,3)\n",
+    "print(a)\n",
+    "\n",
+    "print(a[[1,0,2], :])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "name": "Numpy_Examples 1.ipynb",
+   "provenance": [],
+   "version": "0.3.2"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}