FMML-Aug22-M1Lab1_.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# Getting Started\n",
        "\n",
        "FMML Module 1, Lab 1 <br>\n",
        " Module Coordinator: Amit Pandey ( amit.pandey@research.iiit.ac.in ) <br>\n",
        " Release date: Aug 2022 <br>\n",
        "\n"
      ],
      "metadata": {
        "id": "TMVRKfbpTAeR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### In this notebook we will be covering the very basics of Python and some basic libraries such as Numpy, Matplotlib and Nltk.\n",
        "#### It is suggested that you go through each line and try some examples."
      ],
      "metadata": {
        "id": "F-XjWYLBTI0f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Section 1 - Python : Basic data types and indexing."
      ],
      "metadata": {
        "id": "wEyEnIhnTdhB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Strings\n",
        "'''\n",
        "A string is a collection of one or more characters put in a single quote,\n",
        " double-quote or triple quote. In python there is no character data type,\n",
        " a character is a string of length one. It is represented by str class.\n",
        "\n",
        "String can have special characters. String can be indexed\n",
        "\n",
        "'''\n",
        "\n",
        "\n",
        "name = 'First Lab'\n",
        "name_extended = name + 'Module 1'\n",
        "last_element_string = name[-1] # -1 in python is index of the last element.\n",
        "## indexing is important for preprocessing of the raw data."
      ],
      "metadata": {
        "id": "AzguFJmTQIqt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(name ,\"\\n\", name_extended, \"\\n\", last_element_string)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DPokcpuLQO6s",
        "outputId": "7f2a034e-35ec-4037-dd93-71ea7ac118fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First Lab \n",
            " First LabModule 1 \n",
            " b\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## List\n",
        "\n",
        "'''\n",
        "Lists are ordered collection of data, and are very similar to arrays,\n",
        "It is very flexible as the items in a list do not need to be of the same type.\n",
        "'''\n",
        "\n",
        "name_list = ['First Lab', 3 , '1.1' , 'Lab 1'] ## notice elements are of different data type.\n",
        "name_list.extend(['Module 1']) ## adding elements to list (Read about append method as well).\n",
        "element_2 = name_list[1] ## Just like other languages, the index starts from 0.\n",
        "two_dimesional_list = [[1,2],[3,4]] ## practice with multi-dimensional lists and arrays\n",
        "## you would soon be required to handle 4 dimensional data :p :)\n",
        "name_list[2] = '1.111' ##list elements can be changed"
      ],
      "metadata": {
        "id": "g7WZtwkpE2tO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(name_list)\n",
        "print(element_2)\n",
        "print(two_dimesional_list)\n",
        "## list can have list, dictionary, string etc."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vblBrQflE9Cs",
        "outputId": "2e8cf26d-76f0-4dc2-d9b7-37d8c8f8c69b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['First Lab', 3, '1.111', 'Lab 1', 'Module 1']\n",
            "3\n",
            "[[1, 2], [3, 4]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Tuples\n",
        "\n",
        "name_tuple = ('First Lab', 1, (2,3),[1,1,'list having string']) ## A tuple can have a tuple.\n",
        "\n",
        "print(name_tuple[2])\n",
        "print(\"first indexing the last element of the tuple, which is a list and \\n then last element of the list (a string) and then second last element of the string:\")\n",
        "print(name_tuple[-1][-1][-2])\n"
      ],
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      "outputs": [
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          "text": [
            "(2, 3)\n",
            "first indexing the last element of the tuple, which is a list and \n",
            " then last element of the list (a string) and then second last element of the string:\n",
            "n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## tuples are immutable, read the error !\n",
        " #usued when passing parameters etc. and dont want them to be changed\n",
        "name_tuple=list(name_tuple)\n",
        "name_tuple[1] = 2\n",
        "name_tuple\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m92Oab8YRha1",
        "outputId": "1549cb96-2daf-4882-e3f7-d1314dbb3969"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['First Lab', 2, (2, 3), [1, 1, 'list having string']]"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Sets\n",
        "'''a Set is an unordered collection of data types that is iterable, mutable and has no duplicate elements.\n",
        "The order of elements in a set is undefined though it may consist of various elements.\n",
        "The major advantage of using a set, as opposed to a list,\n",
        " is that it has a highly optimized method for checking whether a specific element is contained in the set.\n",
        "'''\n",
        "set_unique = set([1,1,2,3,5,6,'Lab1'])\n",
        "print(set_unique) ##notice it is unordered\n",
        "last_el = set_unique.pop()\n",
        "set_unique.add((1,2))\n",
        "\n",
        "\n",
        "print(last_el)\n",
        "print(set_unique)\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{1, 2, 3, 5, 6, 'Lab1'}\n",
            "1\n",
            "{(1, 2), 2, 3, 5, 6, 'Lab1'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "set_unique=list(set_unique)\n",
        "set_unique[1] ##it is not indexable"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QgHlNSRPampd",
        "outputId": "eb3dd4bb-d3e7-42fd-8ac7-8d20725094bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Dictionary\n",
        "'''\n",
        "Dictionary in Python is an unordered collection of data values, used to store data values like a map,\n",
        " which, unlike other data types which hold only a single value as an element.\n",
        "'''\n",
        "\n",
        "dic = {'1': 'A','2':'B', 'C':3 } ##Observe how key and values can be anything\n",
        "dic['4'] ='New'\n",
        "print(dic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rxhE3hYtZxVb",
        "outputId": "50c3a229-96c5-4f00-cf33-b0329368fa4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'1': 'A', '2': 'B', 'C': 3, '4': 'New'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Question 0:\n",
        "###### write down 3-5 methods applicable to each data type. (Hint: extend, reverse, etc."
      ],
      "metadata": {
        "id": "hizs_paeceA5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Strings"
      ],
      "metadata": {
        "id": "DhlJuZhOAHYQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#string method upper() used to convert string to uppercase\n",
        "s1=\"jagadek\".upper()\n",
        "s1"
      ],
      "metadata": {
        "id": "irV7HAKkAMwB",
        "outputId": "a4234c1d-19a5-4e6b-8486-b6669bc1a395",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'JAGADEK'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#string method lower() used to convert string to lowercase\n",
        "s2=\"JAGADEK\".lower()\n",
        "s2"
      ],
      "metadata": {
        "id": "w_WWed0_APxq",
        "outputId": "8bb18409-3685-42b4-8fcd-50ec52530d05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'jagadek'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#string method swapcase() used to convert lowercase to uppercase and uppercase to lowercase\n",
        "s3=\"JagaDEK MeesALA\".swapcase()\n",
        "s3"
      ],
      "metadata": {
        "id": "wgakL7e6AUMZ",
        "outputId": "c7bdda9d-f3e1-4155-8525-234a1914a456",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'jAGAdek mEESala'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "List Methods"
      ],
      "metadata": {
        "id": "VjxNX6ouAdtZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "name_list = ['First Lab', 3 , '1.1' , 'Lab 1']"
      ],
      "metadata": {
        "id": "beW_JoZnAzBb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#list method reverse() used to reverse\n",
        "name_list.reverse()\n",
        "name_list"
      ],
      "metadata": {
        "id": "Xas2Qhd5Aj_s",
        "outputId": "e882ee72-8e57-4d7d-b6d2-52dd740cdc90",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Lab 1', '1.1', 3, 'First Lab']"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#list method append() used to add single element\n",
        "name_list.append([1,2,3])\n",
        "name_list"
      ],
      "metadata": {
        "id": "Ni_pz5wsA7r_",
        "outputId": "8268ed2d-3896-4828-a268-51e7a0bd958a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Lab 1', '1.1', 3, 'First Lab', [1, 2, 3]]"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#list method insert() used to insert element at particular position by index number\n",
        "name_list.insert(2,'hi')\n",
        "name_list"
      ],
      "metadata": {
        "id": "sDZJKm2BA_23",
        "outputId": "28992999-ce8e-46c1-de3d-88570c0c27d0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Lab 1', '1.1', 'hi', 3, 'First Lab', [1, 2, 3]]"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tuple Methods"
      ],
      "metadata": {
        "id": "sR7F9NX9BDVe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#index() used to give index of particular element\n",
        "elem_index=(3,4,1,5,6,8,6,8,9,8)\n",
        "elem_index.index(3)"
      ],
      "metadata": {
        "id": "bDr6NPm7BG1a",
        "outputId": "abec780f-216f-408b-f5c9-c9d914d8f497",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#count() used to return the count of specific element\n",
        "elem_index.count(7)"
      ],
      "metadata": {
        "id": "n1j4qdYABJ6h",
        "outputId": "654ced26-7fca-45db-bf1a-c5512088584f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Set Methods"
      ],
      "metadata": {
        "id": "tbYXwAI7BSoD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "set1={1,2,3,4,5}\n",
        "set2={4,5,6,7,8}"
      ],
      "metadata": {
        "id": "eRJ-d9fDBTZy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#add() method in set used to a elements\n",
        "set1.add(6)\n",
        "set1"
      ],
      "metadata": {
        "id": "QjUiDuW9BWxg",
        "outputId": "d7d407ef-0292-45f3-a07b-a000ee6f6ba2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{1, 2, 3, 4, 5, 6}"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#intersection() method in set used to see same elememts in two or more sets\n",
        "set4=set1.intersection(set2)\n",
        "set4"
      ],
      "metadata": {
        "id": "s9LIvuzQBZGt",
        "outputId": "937993b9-e80c-4007-ee0e-58a73df033d7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{4, 5, 6}"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#pop() removes random element from a set\n",
        "set4.pop()\n",
        "set4"
      ],
      "metadata": {
        "id": "XKAskFnkBbps",
        "outputId": "5a9e54c5-ad93-449c-b490-e3233e91e0b1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{5, 6}"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dictionary Methods"
      ],
      "metadata": {
        "id": "X2jK5fFmBefZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dictionary={1:'a',2:'b',3:'c'}"
      ],
      "metadata": {
        "id": "d3C3agimBg-Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#keys() method returns all the keys in the dictionary\n",
        "dictionary.keys()"
      ],
      "metadata": {
        "id": "8w-MvsNGBkAd",
        "outputId": "6550dfea-0ae8-439c-8bfd-62aae50c4ba0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys([1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#values() method returns all the values in the dictionary\n",
        "dictionary.values()"
      ],
      "metadata": {
        "id": "gvDrGVt6BnBA",
        "outputId": "9abad4c1-5521-4be2-fb35-beb6893fd4e7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_values(['a', 'b', 'c'])"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#pop() method deletes a element by using key\n",
        "dictionary.pop(1)\n",
        "dictionary"
      ],
      "metadata": {
        "id": "Qwjg8R0cBj9E",
        "outputId": "e1696cf6-0d01-4465-edb0-60f6c8f0a3dd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{2: 'b', 3: 'c'}"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Section 2 - Functions\n",
        "### a group of related statements that performs a specific task."
      ],
      "metadata": {
        "id": "tub_Skp3X1cN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def add_new(a:str, b): ## a and b are the arguments that are passed. to provide data type hint\n",
        "                              # def add_new(x: float, y: float) -> float:\n",
        "  sum = a + b\n",
        "  return sum\n",
        "\n",
        "ans = add_new(1,2) ## intentionally written str, and passed int, to show it doesn't matter. It is just hint\n",
        "print(ans)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K-6Q5T8nX_YF",
        "outputId": "ec219b0f-31b8-47dd-934b-5ed6e2801602"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "asn = add_new(3,5)"
      ],
      "metadata": {
        "id": "aVLU3Bpu0YJV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def check_even_list(num_list):\n",
        "\n",
        "    even_numbers = []\n",
        "\n",
        "    # Go through each number\n",
        "    for number in num_list:\n",
        "        # Once we get a \"hit\" on an even number, we append the even number\n",
        "        if number % 2 == 0:\n",
        "            even_numbers.append(number)\n",
        "        # Don't do anything if its not even\n",
        "        else:\n",
        "            pass\n",
        "    # Notice the indentation! This ensures we run through the entire for loop\n",
        "    return even_numbers"
      ],
      "metadata": {
        "id": "aRa47XCsZLsh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Question 1 :\n",
        "\n",
        "##### Define a function, which takes in two strings A and B. Reverses the first string A, adds it to B, and returns the final string.\n",
        "\n",
        "\n",
        "#### Question 2 :\n",
        "##### Given a list having Names, work_hours, and gender, Write a function to print name of the female worker that worked the most hours. Also how much do should she be paid if the pay is $ 20 per hour.\n",
        "\n",
        "##### work_hours = [('Abby',100 , 'F'),('Billy',400, 'M'),('Cassie',800,'F'), ('Maggi',600,'F'),('Alex',500,'M'),('Raj',225,'M'),('Penny',920,'F'),('Ben',300,'M')]\n",
        "\n",
        "##### Answer : the female worker that worked the most hours is Penny and she should be paid 18400"
      ],
      "metadata": {
        "id": "heD0_u4D01jp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question-1 Answer"
      ],
      "metadata": {
        "id": "LRbh3iE9B0GT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#ANSWER1\n",
        "def combined_string(A,B):\n",
        "  A=A[::-1]\n",
        "  return B+A\n",
        "c=combined_string(\"ih\",'hello')\n",
        "c"
      ],
      "metadata": {
        "id": "mz5CWPJmBzJp",
        "outputId": "d5b968f1-aad4-4c4b-e8b2-85350386e1a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'hellohi'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question-2 Answer"
      ],
      "metadata": {
        "id": "5GhDZo9ACGHB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#ANSWER2\n",
        "def more_work(data):\n",
        "  high=0\n",
        "  for i in range(len(data)):\n",
        "    if data[i][1]>high and data[i][2]=='F':\n",
        "      high=data[i][1]\n",
        "  high_paid=high*20\n",
        "  worker=''\n",
        "  for i in range(len(data)):\n",
        "    if data[i][1]==high and data[i][2]=='F':\n",
        "      worker+=data[i][0]\n",
        "      break\n",
        "  print(f\"the female worker that worked the most hours is {worker} and she should be paid {high_paid}\")\n",
        "more_work([('Abby',100 , 'F'),('Billy',400, 'M'),('Cassie',800,'F'), ('Maggi',600,'F'),('Alex',500,'M'),('Raj',225,'M'),('Penny',920,'F'),('Ben',300,'M')])"
      ],
      "metadata": {
        "id": "kI-Rd_EfB_c0",
        "outputId": "05e50dfd-2570-4f76-a564-3115d96f52ce",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the female worker that worked the most hours is Penny and she should be paid 18400\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Section 3 - Libraries and Reading data."
      ],
      "metadata": {
        "id": "T9fGzOAKXhtt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Numpy - One of the most used libraries - supports for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays."
      ],
      "metadata": {
        "id": "TKVyOsBKYZXa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "_ODaZ44RXkyg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "a = np.array([1,1,2,3,4,5,5,6,1]) ## np.array converts given list to array\n",
        "\n",
        "b = a>1 ## important comparison operation, where frequently used in manipulation and image processing.\n",
        "\n",
        "print(b)\n",
        "print(a[b]) ## [printing only those values in a which are greater than 1]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29vs7Iicavlg",
        "outputId": "5c0661df-e1a7-4b39-f1d6-b88d61f8929f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[False False  True  True  True  True  True  True False]\n",
            "[2 3 4 5 5 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a_range = np.arange(10,19).reshape(3,3) ## create a 3x3 array with values in range 10-19\n",
        "a_range\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MAGJJ1K_jCHN",
        "outputId": "7e15b37d-eb82-413c-c489-439dbfc31f0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10, 11, 12],\n",
              "       [13, 14, 15],\n",
              "       [16, 17, 18]])"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Indexing in arrays works same as that of list\n",
        "\n",
        "a_range[0] # printing all the columns of first row"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BpNpo-eDt9Fr",
        "outputId": "770e609a-d4b8-447e-8532-0cbe8bb7a9c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10, 11, 12])"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a_range[:,2] #printing all the rows of second column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HbDG7M3QuCDQ",
        "outputId": "8c841466-27bf-4542-e558-f8e5e0768cfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([12, 15, 18])"
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iden = np.eye(3) #idnetity matrix of given size\n",
        "iden"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RQ7UV_-fjJw3",
        "outputId": "49202aa7-4eff-41e3-deda-3867761bde89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0.],\n",
              "       [0., 1., 0.],\n",
              "       [0., 0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## adding two matrices\n",
        "summed = a_range + iden\n",
        "summed\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hYJXXvnwjPfE",
        "outputId": "56d9b36c-000e-4ae6-96ef-4df5bbf627d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[11., 11., 12.],\n",
              "       [13., 15., 15.],\n",
              "       [16., 17., 19.]])"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### arrays support normal matrix multiplication that you are used to, point-wise multiplication\n",
        "### and dot product as well.\n",
        "\n",
        "mul = a_range@iden ## normal multiplication\n",
        "mul\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VoyeEptRjYRu",
        "outputId": "98ebb6cf-3d2a-4233-9cd7-03f53a91ff8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10., 11., 12.],\n",
              "       [13., 14., 15.],\n",
              "       [16., 17., 18.]])"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## point wise multiplication\n",
        "p_mul = a_range * iden\n",
        "p_mul"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "58S3a_YmtAe8",
        "outputId": "3bd4100b-b1c9-4310-fca0-a646565d6007"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10.,  0.,  0.],\n",
              "       [ 0., 14.,  0.],\n",
              "       [ 0.,  0., 18.]])"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Transpose of a matrix.\n",
        "\n",
        "mtx_t = mul.T\n",
        "mtx_t\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HC5uJ03ftJHA",
        "outputId": "5a2e6d61-d412-4aa9-d6cd-a71dd0aaa686"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10., 13., 16.],\n",
              "       [11., 14., 17.],\n",
              "       [12., 15., 18.]])"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Here we are changing the values of last row of the transposed matrix.\n",
        "### basically point wise multiplying the values of last row with 1,2 and 3\n",
        "\n",
        "mtx_t[2] = mtx_t[2]*[1,2,3] ## indexing, point wise multiplication and mutation of values\n",
        "mtx_t"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ROFhfs2lutat",
        "outputId": "a479eb73-eb91-4bc5-e554-fa6c49d8e15b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10., 13., 16.],\n",
              "       [11., 14., 17.],\n",
              "       [12., 30., 54.]])"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Just like the greater than 1 (a>1) example we saw earlier.\n",
        "## here we are checking if the elements are divisible by 2 (%), and if they are, then replace by 0.\n",
        "\n",
        "mtx_t[(mtx_t % 2 == 0)] = 0 ## convert even elements of the matrix to zero.\n",
        "mtx_t"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mh80ju2xvAqL",
        "outputId": "cd65fcdf-a6e9-4e67-ae91-b990c824fb0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0., 13.,  0.],\n",
              "       [11.,  0., 17.],\n",
              "       [ 0.,  0.,  0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Question 3 :\n",
        "\n",
        "##### a)Create a 5x5 matrix of the following form,\n",
        "\n",
        "##### [[1,1]\n",
        "#####  [2,2]]\n",
        "\n",
        "#####  i.e. each row is increasing and has repetive elements.\n",
        "\n",
        "######  Hint : you can use hstack, vstack  etc.\n",
        "\n",
        "##### b) find dot product of the matrix with any matrix. (Figure out the size/ shape of the matrix)"
      ],
      "metadata": {
        "id": "uqWUH6Rv4OhT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question-3(a) Answer"
      ],
      "metadata": {
        "id": "YUSccmYcC-rQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#ANSWER3a\n",
        "import numpy as np\n",
        "m=np.zeros((5,5))\n",
        "m+=np.arange(1,6)\n",
        "n=m.T\n",
        "n"
      ],
      "metadata": {
        "id": "KKm6EBH7DCrY",
        "outputId": "3df47c55-d150-4953-fc54-ede48ca10329",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 1., 1., 1., 1.],\n",
              "       [2., 2., 2., 2., 2.],\n",
              "       [3., 3., 3., 3., 3.],\n",
              "       [4., 4., 4., 4., 4.],\n",
              "       [5., 5., 5., 5., 5.]])"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question-3(b) Answer"
      ],
      "metadata": {
        "id": "UWG_TfDODMVo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "j=np.arange(1,26).reshape(5,5)\n",
        "h=np.dot(n,j)\n",
        "h\n",
        "h.size"
      ],
      "metadata": {
        "id": "hJSFzV_nDPfL",
        "outputId": "eedf3b5c-3bd6-409e-9cdb-8d5bee4e5413",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25"
            ]
          },
          "metadata": {},
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Reading Files"
      ],
      "metadata": {
        "id": "GzNoCosKdVa2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## loading from the google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_wCHdrtQwDJe",
        "outputId": "ef082b3c-048b-44c6-f6df-92ce37775099"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open ('/content/sample_data/README.md', 'r') as f:\n",
        "  a = f.readlines()\n",
        "\n",
        "a ## here a is list of elements/strings each splitted at \\n, \\n is also part of the list element.\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1rzXSJobLku",
        "outputId": "0a0cd459-41eb-470b-d73c-2a6d84228e99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['This directory includes a few sample datasets to get you started.\\n',\n",
              " '\\n',\n",
              " '*   `california_housing_data*.csv` is California housing data from the 1990 US\\n',\n",
              " '    Census; more information is available at:\\n',\n",
              " '    https://developers.google.com/machine-learning/crash-course/california-housing-data-description\\n',\n",
              " '\\n',\n",
              " '*   `mnist_*.csv` is a small sample of the\\n',\n",
              " '    [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is\\n',\n",
              " '    described at: http://yann.lecun.com/exdb/mnist/\\n',\n",
              " '\\n',\n",
              " '*   `anscombe.json` contains a copy of\\n',\n",
              " \"    [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it\\n\",\n",
              " '    was originally described in\\n',\n",
              " '\\n',\n",
              " \"    Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American\\n\",\n",
              " '    Statistician. 27 (1): 17-21. JSTOR 2682899.\\n',\n",
              " '\\n',\n",
              " '    and our copy was prepared by the\\n',\n",
              " '    [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).\\n']"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('/content/sample_data/california_housing_test.csv','r')\n",
        "df.head(10) ## pass as argument number of top elements you wish to print. Head is used to have a quick glance and understand the data."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "CgXsQFpobVGk",
        "outputId": "4eddadb2-6c10-4725-c682-b1d7a70a4564"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:3326: FutureWarning: In a future version of pandas all arguments of read_csv except for the argument 'filepath_or_buffer' will be keyword-only\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   longitude,\"latitude\",\"housing_median_age\",\"total_  ooms\",\"total_bed  \\\n",
              "0  -122.050000,37.370000,27.000000,3885.000000,66...               NaN   \n",
              "1  -118.300000,34.260000,43.000000,1510.000000,31...               NaN   \n",
              "2  -117.810000,33.780000,27.000000,3589.000000,50...               NaN   \n",
              "3  -118.360000,33.820000,28.000000,67.000000,15.0...               NaN   \n",
              "4  -119.670000,36.330000,19.000000,1241.000000,24...               NaN   \n",
              "5  -119.560000,36.510000,37.000000,1018.000000,21...               NaN   \n",
              "6  -121.430000,38.630000,43.000000,1009.000000,22...               NaN   \n",
              "7  -120.650000,35.480000,19.000000,2310.000000,47...               NaN   \n",
              "8  -122.840000,38.400000,15.000000,3080.000000,61...               NaN   \n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df.columns), df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iMMaPe3gduKb",
        "outputId": "370455f4-a1e5-404e-e5a3-953e7a017e2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3,\n",
              " Index(['longitude,\"latitude\",\"housing_median_age\",\"total_', 'ooms\",\"total_bed',\n",
              "        'ooms\",\"population\",\"households\",\"median_income\",\"median_house_value\"'],\n",
              "       dtype='object'))"
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns[0]"
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          "height": 36
        },
        "id": "GT1dA9Q3eRQU",
        "outputId": "10c52b89-1916-49b4-dece-f6cecf7e09dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'longitude,\"latitude\",\"housing_median_age\",\"total_'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 178
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df['longitude,\"latitude\",\"housing_median_age\",\"total_'][:5]"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oEZhq9rBe-Hb",
        "outputId": "79d55558-7f4e-4f6e-dffb-117933c20e87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    -122.050000,37.370000,27.000000,3885.000000,66...\n",
              "1    -118.300000,34.260000,43.000000,1510.000000,31...\n",
              "2    -117.810000,33.780000,27.000000,3589.000000,50...\n",
              "3    -118.360000,33.820000,28.000000,67.000000,15.0...\n",
              "4    -119.670000,36.330000,19.000000,1241.000000,24...\n",
              "Name: longitude,\"latitude\",\"housing_median_age\",\"total_, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.rename(columns = {'longitude,\"latitude\",\"housing_median_age\",\"total_':'Detail1'}) ##rename column names as at times it makes it easier for us"
      ],
      "metadata": {
        "id": "-hpbslfWfwy3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "df.head(3)"
      ],
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        },
        "id": "C0gpXPf1hN5Q",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             Detail1  ooms\",\"total_bed  \\\n",
              "0  -122.050000,37.370000,27.000000,3885.000000,66...               NaN   \n",
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              "\n",
              "   ooms\",\"population\",\"households\",\"median_income\",\"median_house_value\"  \n",
              "0                                                NaN                     \n",
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              "2                                                NaN                     "
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              "      <th>ooms\",\"population\",\"households\",\"median_income\",\"median_house_value\"</th>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c037a14c-251f-43e5-a2ed-ee17a3b40a47');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[:5, 0]  ##iloc - index - 0 to 4 rows and first column only."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A0FFUn-ghP1n",
        "outputId": "d960e820-d213-42d5-f988-543144ddea33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    -122.050000,37.370000,27.000000,3885.000000,66...\n",
              "1    -118.300000,34.260000,43.000000,1510.000000,31...\n",
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              "4    -119.670000,36.330000,19.000000,1241.000000,24...\n",
              "Name: Detail1, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib\n",
        "from matplotlib import pyplot as plt"
      ],
      "metadata": {
        "id": "oJ7JrZqWioIO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xpoints = np.array([1, 8])\n",
        "ypoints = np.array([3, 10])\n",
        "\n",
        "plt.plot(xpoints, ypoints)\n",
        "plt.show()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "tzunFbpUxKAP",
        "outputId": "08c402f2-ccce-4187-c59f-8b8ce49b883c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xpoints = np.array([1, 2, 6, 8])\n",
        "ypoints = np.array([3, 8, 1, 10])\n",
        "\n",
        "plt.plot(xpoints, ypoints)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "p7l83i5SxW4k",
        "outputId": "b8823fef-5da2-4e9e-92b9-9c1805c3449d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating a dataframe.\n",
        "#### Task: Study about other methods of creating dataframe (for example: using Pandas Series, Lists etc.)"
      ],
      "metadata": {
        "id": "GT6yXu7l1gtL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "1vR1hTJ51ckl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "values = np.arange(16).reshape(4,4)\n",
        "values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o0W0z3Fr1gOb",
        "outputId": "a02372cb-1f03-46c7-cb69-9ffe0a2131e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  2,  3],\n",
              "       [ 4,  5,  6,  7],\n",
              "       [ 8,  9, 10, 11],\n",
              "       [12, 13, 14, 15]])"
            ]
          },
          "metadata": {},
          "execution_count": 187
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe_from_array = pd.DataFrame(values, index = ['a','b','c','d'], columns=['w','x','y','z'] )\n",
        "dataframe_from_array"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "_p5vKhPN2Bey",
        "outputId": "821b283e-d27e-4766-c25f-c7b9aa1d9a94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    w   x   y   z\n",
              "a   0   1   2   3\n",
              "b   4   5   6   7\n",
              "c   8   9  10  11\n",
              "d  12  13  14  15"
            ],
            "text/html": [
              "\n",
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              "      <td>11</td>\n",
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              "      <th>d</th>\n",
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              "      <td>14</td>\n",
              "      <td>15</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 188
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe_from_array.loc[['a','b'],['w','x']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "jdIORDyX2h-Z",
        "outputId": "5677461c-4015-432c-ab51-4ded6b3f4f1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   w  x\n",
              "a  0  1\n",
              "b  4  5"
            ],
            "text/html": [
              "\n",
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              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-54fe58ba-cf5a-4a27-abc8-72be21b913fe')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-54fe58ba-cf5a-4a27-abc8-72be21b913fe button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-54fe58ba-cf5a-4a27-abc8-72be21b913fe');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe_from_array.iloc[:2,:2] ## it needs position as integer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "iPTlROGv2r7M",
        "outputId": "d8b3573d-baae-4e1d-c307-879a9b919b14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   w  x\n",
              "a  0  1\n",
              "b  4  5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b1d16cd8-85b7-451b-addb-cb6161a56f95\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>w</th>\n",
              "      <th>x</th>\n",
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              "  <tbody>\n",
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              "      <th>a</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1d16cd8-85b7-451b-addb-cb6161a56f95')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b1d16cd8-85b7-451b-addb-cb6161a56f95 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b1d16cd8-85b7-451b-addb-cb6161a56f95');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe_from_array.iloc[1,3] #second row and last column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PI4cRSO526jo",
        "outputId": "17a8456c-c6c7-4222-b447-c33d41b5f129"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe_from_array.iloc[::2,::2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "-BpY8Prl3GQX",
        "outputId": "469ea9ac-92ba-411f-a099-7836bda44f64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   w   y\n",
              "a  0   2\n",
              "c  8  10"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-689bdaee-ca0d-452a-8795-1911ff023d32\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>w</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>a</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
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              "      <th>c</th>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-689bdaee-ca0d-452a-8795-1911ff023d32')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-689bdaee-ca0d-452a-8795-1911ff023d32 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-689bdaee-ca0d-452a-8795-1911ff023d32');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "ys = 200 + np.random.randn(100)\n",
        "x = [x for x in range(len(ys))]\n",
        "\n",
        "plt.plot(x, ys, '-')\n",
        "plt.fill_between(x, ys, 195, where=(ys > 200), facecolor='g', alpha=0.6)\n",
        "\n",
        "plt.title(\"Sample Visualization\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "fqzrScL-GrZU",
        "outputId": "007fc9d6-9281-4204-a2af-ead41a1cecd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reading Files"
      ],
      "metadata": {
        "id": "Ifkfm82YG_JG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## loading from the google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "id": "X0a9SRPBHCDJ",
        "outputId": "7baea0ee-c011-464a-f2d1-f53a0c4f6e41",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open ('/content/sample_data/README.md', 'r') as f:\n",
        "  a = f.readlines()\n",
        "\n",
        "a ## here a is list of elements/strings each splitted at \\n, \\n is also part of the list element."
      ],
      "metadata": {
        "id": "ECdQQMK-HH7R",
        "outputId": "cf91deb1-43e4-43fe-81bd-9e0bf276daf5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['This directory includes a few sample datasets to get you started.\\n',\n",
              " '\\n',\n",
              " '*   `california_housing_data*.csv` is California housing data from the 1990 US\\n',\n",
              " '    Census; more information is available at:\\n',\n",
              " '    https://developers.google.com/machine-learning/crash-course/california-housing-data-description\\n',\n",
              " '\\n',\n",
              " '*   `mnist_*.csv` is a small sample of the\\n',\n",
              " '    [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is\\n',\n",
              " '    described at: http://yann.lecun.com/exdb/mnist/\\n',\n",
              " '\\n',\n",
              " '*   `anscombe.json` contains a copy of\\n',\n",
              " \"    [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it\\n\",\n",
              " '    was originally described in\\n',\n",
              " '\\n',\n",
              " \"    Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American\\n\",\n",
              " '    Statistician. 27 (1): 17-21. JSTOR 2682899.\\n',\n",
              " '\\n',\n",
              " '    and our copy was prepared by the\\n',\n",
              " '    [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).\\n']"
            ]
          },
          "metadata": {},
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Question 3 : Upload an image to your google drive, Use plt.imread to read image from the google drive and then print that image using plt.imshow\n",
        "\n",
        "\n",
        "## Answer 3 :\n",
        "\n",
        "## 1) make sure drive is loaded and then upload a test image onto your drive\n",
        "import matplotlib.pyplot as plt\n",
        "plt.imread('/content/gdrive/MyDrive/50th meet KIET TM Club/20211018_130058.jpg')"
      ],
      "metadata": {
        "id": "mC8rNbeKxiaF",
        "outputId": "673f6d4e-2cdc-46a6-bbab-5da103ab5480",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[214, 221, 231],\n",
              "        [214, 221, 231],\n",
              "        [214, 221, 231],\n",
              "        ...,\n",
              "        [ 26,  17,  12],\n",
              "        [ 27,  18,  13],\n",
              "        [ 28,  19,  14]],\n",
              "\n",
              "       [[214, 221, 231],\n",
              "        [214, 221, 231],\n",
              "        [214, 221, 231],\n",
              "        ...,\n",
              "        [ 28,  19,  14],\n",
              "        [ 28,  19,  14],\n",
              "        [ 28,  19,  14]],\n",
              "\n",
              "       [[214, 221, 231],\n",
              "        [214, 221, 231],\n",
              "        [214, 221, 231],\n",
              "        ...,\n",
              "        [ 29,  20,  15],\n",
              "        [ 28,  19,  14],\n",
              "        [ 27,  18,  13]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[ 61,  80,  34],\n",
              "        [ 55,  74,  29],\n",
              "        [ 47,  66,  21],\n",
              "        ...,\n",
              "        [104, 100,  97],\n",
              "        [102,  98,  95],\n",
              "        [100,  96,  93]],\n",
              "\n",
              "       [[ 65,  84,  38],\n",
              "        [ 58,  77,  31],\n",
              "        [ 52,  71,  26],\n",
              "        ...,\n",
              "        [102,  98,  95],\n",
              "        [100,  96,  93],\n",
              "        [ 99,  95,  92]],\n",
              "\n",
              "       [[ 70,  89,  43],\n",
              "        [ 63,  82,  36],\n",
              "        [ 55,  74,  28],\n",
              "        ...,\n",
              "        [101,  97,  94],\n",
              "        [ 99,  95,  92],\n",
              "        [100,  96,  93]]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 196
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#printing image using plt.imshow()\n",
        "plt.imshow(plt.imread('/content/gdrive/MyDrive/50th meet KIET TM Club/20211018_130058.jpg'))"
      ],
      "metadata": {
        "id": "nPc_Xjh9MuAL",
        "outputId": "0568f557-eb1a-4546-afe3-0597c5cab0ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f1e0327cfd0>"
            ]
          },
          "metadata": {},
          "execution_count": 197
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}