FMML_Aug22_M1Lab1_PythonBasics.ipynb

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      "cell_type": "markdown",
      "metadata": {
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        "<a href=\"https://colab.research.google.com/github/harika136/IIIT_IHUB_HYD/blob/main/FMML_Aug22_M1Lab1_PythonBasics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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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. "
      ],
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      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(name ,\"\\n\", name_extended, \"\\n\", last_element_string)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DPokcpuLQO6s",
        "outputId": "82de0af9-b569-4ffe-87c1-725cf2c72a4d"
      },
      "execution_count": 2,
      "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": 3,
      "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": "6e537131-8ca7-45c5-abe6-779572b0bf32"
      },
      "execution_count": 4,
      "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"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m92Oab8YRha1",
        "outputId": "730b83ed-9e43-4933-ae53-e2e186489c47"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['First Lab', 2, (2, 3), [1, 1, 'list having string']]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "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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        "outputId": "10c0199d-11ef-409c-9675-db50078d6f12"
      },
      "execution_count": 7,
      "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": "f544fed1-a5a8-4300-b17e-2ca5381311c1"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "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": "c5a0a3b5-f4fe-42d0-b600-6a22dfb7f67a"
      },
      "execution_count": 9,
      "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": [
        "# **String Methods**"
      ],
      "metadata": {
        "id": "ieoa6ETqfVln"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#string method upper() used to convert string to uppercase\n",
        "s1=\"Reddy Harika\".upper()\n",
        "s1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "2qjt-yZXfi2s",
        "outputId": "ca38c9ec-092b-4dec-dc8f-0bf1ae446a55"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'REDDY HARIKA'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#string method lower() used to convert string to lowercase\n",
        "s2=\"ReddY HArika\".lower()\n",
        "s2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "KjOA8bmZmIW8",
        "outputId": "e7327e81-e94d-415d-93eb-0c917306b100"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'reddy harika'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#string method swapcase() used to convert lowercase to uppercase and uppercase to lowercase\n",
        "s3=\"ReDDy HaRiKa\".swapcase()\n",
        "s3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "QWgMHpywmeXP",
        "outputId": "ae65fc04-2a3d-45aa-fca8-53972859a5ba"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'rEddY hArIkA'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#string method strip() used to remove either end elements\n",
        "s4=\"mgklharikao\".strip(\"mgklo\")\n",
        "s4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Te6aHuponMMw",
        "outputId": "beb638ce-b03f-4e59-bd0a-b8cfc5f57df4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'harika'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#string method count() used to return the count specific character\n",
        "s5=\"Reddy Harika\".count(\"a\")\n",
        "s5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OQrZOw9in1-A",
        "outputId": "34c62d29-be6f-4bc5-e8f1-56ea6f1c654e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **List** **Methods**"
      ],
      "metadata": {
        "id": "afn39k8zWzjd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#list method reverse() used to reverse\n",
        "name_list.reverse()\n",
        "name_list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYrceVHHoa51",
        "outputId": "f94c5e75-27b4-4d97-d8e7-5f7ffdd14b60"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Module 1', 'Lab 1', '1.111', 3, 'First Lab']"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#list method extend() used to add elements\n",
        "name_list.extend([5,7])\n",
        "name_list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9-xjnKaYoqoW",
        "outputId": "7976e160-f0f2-4347-f0c7-e126edc47dfb"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Module 1', 'Lab 1', '1.111', 3, 'First Lab', 5, 7]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#list method append() used to add single element\n",
        "name_list.append([1,2,3])\n",
        "name_list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3kg4uIYxo9dx",
        "outputId": "dbeac3d2-3449-4507-da74-50324cbcc99e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Module 1', 'Lab 1', '1.111', 3, 'First Lab', 5, 7, [1, 2, 3]]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#list method pop() to delete an element\n",
        "name_list.pop(1)\n",
        "name_list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m7H6GhOfpOVm",
        "outputId": "ff09ef7f-92b7-4fe5-a393-b8305dcac0d6"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Module 1', '1.111', 3, 'First Lab', 5, 7, [1, 2, 3]]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#list method copy() used to make copy of a list\n",
        "name_list1=name_list.copy()\n",
        "name_list1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49cY-fwtpZvt",
        "outputId": "ffcd7f29-619f-4aba-fa00-410f85826ac3"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Module 1', '1.111', 3, 'First Lab', 5, 7, [1, 2, 3]]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8N_UNawpsdr",
        "outputId": "54681c49-ecf8-417d-bf9b-65aa86510149"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Module 1', '1.111', 'hi', 3, 'First Lab', 5, 7, [1, 2, 3]]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#list method sort() used to sort a list of numbers from ascending to descending\n",
        "num_list=[8,7,9,0,5,5,8,4]\n",
        "num_list.sort()\n",
        "num_list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mq3CYPRzqFM6",
        "outputId": "2704da81-7e71-42ec-8cc3-4e70cd477ffc"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 4, 5, 5, 7, 8, 8, 9]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Tuple Methods**"
      ],
      "metadata": {
        "id": "-dI-hRHbqlYa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**As tuples are immutable we have basic methods like index(),count()etc**"
      ],
      "metadata": {
        "id": "wyQeRWjkq0kj"
      }
    },
    {
      "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(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tr0-gRp1rEcY",
        "outputId": "10b9bbfd-871a-46e8-e31f-9d1b39038243"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#count() used to return the count of specific element\n",
        "elem_index.count(8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l-XoYV1lrgIb",
        "outputId": "07da904d-5e78-473c-eacb-0fff6b9e1802"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Set Methods**"
      ],
      "metadata": {
        "id": "kEdzARQOrvEu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "set1={1,2,3,4,5}\n",
        "set2={4,5,6,7,8}"
      ],
      "metadata": {
        "id": "s77IcRKHr2u2"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#add() method in set used to a elements\n",
        "set1.add(6)\n",
        "set1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lNUPTqqRsBFG",
        "outputId": "91511eb1-8dba-4463-97c9-77ae79dbca05"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{1, 2, 3, 4, 5, 6}"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#difference() method in set used to see difference between two sets\n",
        "set3=set1.difference(set2)\n",
        "set3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ZI25QkrsN8x",
        "outputId": "6ee567c9-4388-44e1-afc1-ecab91d1bd5b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{1, 2, 3}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#intersection() method in set used to see same elements in two or more sets\n",
        "set4=set1.intersection(set2)\n",
        "set4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hY6kRLFqshxF",
        "outputId": "1b393790-1558-4d31-f6b2-22b0281a0a64"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{4, 5, 6}"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#pop() remove random element from a set\n",
        "set4.pop()\n",
        "set4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tgmjdBOBtZJ6",
        "outputId": "0a718129-02f2-4613-d951-2834fa603653"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{5, 6}"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#union() combines two sets\n",
        "set5=set1.union(set2)\n",
        "set5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yN_vceK2toEJ",
        "outputId": "71f10d3d-4a4a-4cb0-b2e0-f41d9010c741"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{1, 2, 3, 4, 5, 6, 7, 8}"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Dictionary Methods**"
      ],
      "metadata": {
        "id": "fTIbzkiBt8UM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dictionary={1:'a',2:'b',3:'c'}"
      ],
      "metadata": {
        "id": "f9kRKXHGuCWQ"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#keys() method returns all the keys in the dictionary\n",
        "dictionary.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DoXmzby_uYqO",
        "outputId": "891abebd-365f-44c9-c3a2-f5393a71040b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys([1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#values() method returns all the values in the dictionary\n",
        "dictionary.values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BTq7HGkxukH9",
        "outputId": "e4d72f3a-7125-43ab-e2bb-b3b6d74062c5"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_values(['a', 'b', 'c'])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#items() method returns a list of each item in a tuple\n",
        "dictionary.items()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5lXOUWb2u4B-",
        "outputId": "3e4671ba-9c2a-4885-f493-0f6dbce22d3d"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_items([(1, 'a'), (2, 'b'), (3, 'c')])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#pop() method deletes a element by using key\n",
        "dictionary.pop(1)\n",
        "dictionary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-scaa2LgvIlm",
        "outputId": "9bc1941d-02b2-4660-a8ec-45de0ee4cede"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{2: 'b', 3: 'c'}"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "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": "7d0c24ee-6558-4c5b-fbfd-cf67af97d627"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "asn = add_new(3,5)"
      ],
      "metadata": {
        "id": "aVLU3Bpu0YJV"
      },
      "execution_count": 36,
      "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": 37,
      "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": [
        "# **Answer for Question no 1**"
      ],
      "metadata": {
        "id": "Pmm86uc_voqW"
      }
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "4FypUX21vu4R",
        "outputId": "78752134-72f1-41ba-f88f-422309ddcf40"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'hellohi'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Answer for Question no 2**"
      ],
      "metadata": {
        "id": "WZpBdN3KwJrK"
      }
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OeOTedFKwRSK",
        "outputId": "4dc781ff-d7ac-4752-ba4e-8ba5f0edd8c4"
      },
      "execution_count": 39,
      "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": 40,
      "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": "cf712be7-39dc-4098-c62f-377261b222c4"
      },
      "execution_count": 41,
      "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": "9a49e69e-b0d4-4d2f-a2f2-96e867eb4752"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10, 11, 12],\n",
              "       [13, 14, 15],\n",
              "       [16, 17, 18]])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "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": "4cf73e5f-cfbf-4302-fb37-5d3c20f757a3"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10, 11, 12])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a_range[:,2] #printing all the rows of second column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HbDG7M3QuCDQ",
        "outputId": "a2e25ab2-d9c0-4b04-8584-28580876cbc2"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([12, 15, 18])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "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": "7fe7318d-9098-4cb1-e09c-cee15e58dd44"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0.],\n",
              "       [0., 1., 0.],\n",
              "       [0., 0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "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": "731be764-a885-48bb-e8ec-23fb9a409eeb"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[11., 11., 12.],\n",
              "       [13., 15., 15.],\n",
              "       [16., 17., 19.]])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "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": "92af9153-705a-481b-efc4-b0c6b41c0089"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10., 11., 12.],\n",
              "       [13., 14., 15.],\n",
              "       [16., 17., 18.]])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "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": "912920b8-3004-47c8-e292-f2aaa3110da4"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10.,  0.,  0.],\n",
              "       [ 0., 14.,  0.],\n",
              "       [ 0.,  0., 18.]])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "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": "6860245f-221b-498f-8f22-b326d208d0a1"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10., 13., 16.],\n",
              "       [11., 14., 17.],\n",
              "       [12., 15., 18.]])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "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": "84e9dfd9-4da8-4db3-a13b-85006846d301"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10., 13., 16.],\n",
              "       [11., 14., 17.],\n",
              "       [12., 30., 54.]])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "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": "1ca49d95-ef0e-4d91-df35-12f65a408e09"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0., 13.,  0.],\n",
              "       [11.,  0., 17.],\n",
              "       [ 0.,  0.,  0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "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": [
        "# **Answer for Question no 3a**"
      ],
      "metadata": {
        "id": "w6Jr9qwhxelH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#ANSWER3a\n",
        "m=np.zeros((5,5))\n",
        "m+=np.arange(1,6)\n",
        "n=m.T\n",
        "n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PHTpUz41xmEd",
        "outputId": "a8e903ff-ab8e-4e79-e085-e163369b496c"
      },
      "execution_count": 52,
      "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": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Answer for Question no 3b**"
      ],
      "metadata": {
        "id": "JLbeMRZJyMO4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#ANSWER3b\n",
        "j=np.arange(1,26).reshape(5,5)\n",
        "h=np.dot(n,j)\n",
        "h\n",
        "h.size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WtfA7nnwyHsS",
        "outputId": "93b41c82-7ed9-4f18-c110-2ce9e83f2c6f"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "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": "5eabfc8d-6172-494b-da31-151be713ed2e"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\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": "932ca097-eb55-48ea-ad5b-171f2659af0d"
      },
      "execution_count": 55,
      "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": 55
        }
      ]
    },
    {
      "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": "b68ec345-8534-49d4-c9bf-768e2d07f0ad"
      },
      "execution_count": 56,
      "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",
              "9  -118.020000,34.080000,31.000000,2402.000000,63...               NaN   \n",
              "\n",
              "   ooms\",\"population\",\"households\",\"median_income\",\"median_house_value\"  \n",
              "0                                                NaN                     \n",
              "1                                                NaN                     \n",
              "2                                                NaN                     \n",
              "3                                                NaN                     \n",
              "4                                                NaN                     \n",
              "5                                                NaN                     \n",
              "6                                                NaN                     \n",
              "7                                                NaN                     \n",
              "8                                                NaN                     \n",
              "9                                                NaN                     "
            ],
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              "      <th></th>\n",
              "      <th>longitude,\"latitude\",\"housing_median_age\",\"total_</th>\n",
              "      <th>ooms\",\"total_bed</th>\n",
              "      <th>ooms\",\"population\",\"households\",\"median_income\",\"median_house_value\"</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <th>3</th>\n",
              "      <td>-118.360000,33.820000,28.000000,67.000000,15.0...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>NaN</td>\n",
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              "      <td>-119.560000,36.510000,37.000000,1018.000000,21...</td>\n",
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              "      <th>6</th>\n",
              "      <td>-121.430000,38.630000,43.000000,1009.000000,22...</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-120.650000,35.480000,19.000000,2310.000000,47...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-122.840000,38.400000,15.000000,3080.000000,61...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-118.020000,34.080000,31.000000,2402.000000,63...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3676a768-4c63-4449-9b25-a99ec91c7d47')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-3676a768-4c63-4449-9b25-a99ec91c7d47');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df.columns), df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iMMaPe3gduKb",
        "outputId": "42de64e4-9bee-492f-b0c0-6b662834ffb7"
      },
      "execution_count": 57,
      "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": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns[0]"
      ],
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        "colab": {
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          "height": 35
        },
        "id": "GT1dA9Q3eRQU",
        "outputId": "fdf65f88-1de3-4ee1-b3cf-b58203315310"
      },
      "execution_count": 58,
      "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": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['longitude,\"latitude\",\"housing_median_age\",\"total_'][:5] "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oEZhq9rBe-Hb",
        "outputId": "92c182ab-9051-4330-b22d-2d26c04051eb"
      },
      "execution_count": 59,
      "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": 59
        }
      ]
    },
    {
      "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": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(3)"
      ],
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      "execution_count": 61,
      "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",
              "1  -118.300000,34.260000,43.000000,1510.000000,31...               NaN   \n",
              "2  -117.810000,33.780000,27.000000,3589.000000,50...               NaN   \n",
              "\n",
              "   ooms\",\"population\",\"households\",\"median_income\",\"median_house_value\"  \n",
              "0                                                NaN                     \n",
              "1                                                NaN                     \n",
              "2                                                NaN                     "
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-759d9195-327b-4268-af7f-56c0b7544a40')\"\n",
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              "       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-759d9195-327b-4268-af7f-56c0b7544a40 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-759d9195-327b-4268-af7f-56c0b7544a40');\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": 61
        }
      ]
    },
    {
      "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": "7f379bdd-e5c2-4b79-f3b3-5aa640d129e7"
      },
      "execution_count": 62,
      "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: Detail1, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib\n",
        "from matplotlib import pyplot as plt"
      ],
      "metadata": {
        "id": "oJ7JrZqWioIO"
      },
      "execution_count": 63,
      "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()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "tzunFbpUxKAP",
        "outputId": "f740e6a1-dc79-4e5d-a035-b45f22cb7654"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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8u4ZPiWtU4CInYcPOQ8xJSiFzbzEXDu/O3RcOpWNrDZ8Sd6nARY6jrLKGRz7YxLOfZhPVpgXPXJvAOUO7uB1LBFCBixzTmq0HmJecQs6BUq4cG828qYNp21LDpyRwqMBFjlBUXsXCdzP559od9O4Yzj9vHseEfho+JYFHBS7yPR9l7uO25DTyisu5+bQ+/P6cQbQK0zZ4CUwqcBHgwOEK7l2WwdLvdjOoSxuevmY0I3q1czuWyHGpwCWkWWt5a8Nu7nk7g+LyKn579gB+fkZ/wpppG7wEPhW4hKw9hWXMX5LGh5l5DO/VjkUz4hnUtY3bsUROmgpcQo7PZ/nX1zt58J2NVPl8zJ82hBsm9qGptsGLx6jAJaTk7C9hbnIKX2YXML5vRxbOiKN3Rw2fEm9SgUtIqK7x8cLnOfz5/SyaN2nCwkvj+PGYXtoGL56mApegl7m3iDmJKWzYVcjZQzpz3yVxdI1s6XYsEb+pwCVoVVTX8MSqrTy5aguRrZrztytHckF8N626JWiowCUofbvjIHOSUti07zDTR/bgjguG0iEizO1YIo5SgUtQKa2s5s8rN/H859vo2rYlz1+fwOTBGj4lwUkFLkHjiy37mZucyo6CUq4+JZo5UwbTRsOnJIipwMXzCsuqePCdjfzr653EdAznX7NO4ZS+ulGUBD8VuHjayvS9zH8zjf2HK7jl9L787uyBtGyu4VMSGvy9qXE74FkgFrDATGvtGieCiRzP/sMV3P1WOstS9jC4axuevS6B+J4aPiWhxd8V+GPACmvtZcaYMCDcgUwix2St5c3vcrnn7QxKK2r4wzkD+ekZ/WjeVMOnJPTUu8CNMZHAJOB6AGttJVDpTCyR/7X7UBm3L0llVVY+I6Nrh08N6KLhUxK6/FmB9wHygReMMcOB9cBvrLUl33+SMWYWMAsgOjraj9NJqPL5LK9+tYOH3s2kxme584KhXDchRsOnJOT583VnM2AU8JS1diRQAsw98knW2sXW2gRrbUJUVJQfp5NQlJ1/mCsWf8kdb6Yxolc7Vv5uEjNP1eRAEfBvBb4L2GWtXVv340SOUuAi9VFd4+PZz7bxyPubaNGsCYsui+fy0T21DV7ke+pd4NbavcaYncaYQdbaLOAsIMO5aBKqMnYXMTtpA2m5RZw3rAsLLo6lc1sNnxI5kr/vQvkV8GrdO1CygRv8jyShqqK6hsc/2sJTH2+lXXhznrxqFOfHdtWqW+QY/Cpwa+13QIJDWSSErd9eO3xqS95hLh3VgzumDaW9hk+JHJd2YoqrSiqqeXhlFi9+kUP3yFa8eMMYzhjU2e1YIp6gAhfXfLo5n3nJqew6WMa143sze8pgWrfQP0mRk6XPFml0haVV3Lc8gzfW76Jvpwhev2U8Y/t0cDuWiOeowKVRrUjbyx1L0ygoqeTnZ/Tj12cN0PApkXpSgUujyCsu5+630nkndS9Du7XlhevHENsj0u1YIp6mApcGZa0l6ZtcFizLoKyqhlvPG8SsSX01fErEASpwaTC7DpZy25I0Vm/KZ3Tv9jw0I57+nVu7HUskaKjAxXE+n+UfX27noRWZANxz0TCuOaU3TTS/RMRRKnBx1Nb8w8xJTGHd9oNMGhjFA9Nj6dleY+JFGoIKXBxRVeNj8epsHvtwM62aN+Xhy4czY1QPbYMXaUAqcPFbWm4hc5JSSN9dxNS4rtx90TA6t9HwKZGGpgKXeiuvquGvH27m76uzaR8extNXj2JKbDe3Y4mEDBW41MvXOQXMSUwhe38Jl4/uyfxpQ4kMb+52LJGQogKXH+RwRTWLVmTy8prt9GjXipdnjmXSQN1pScQNKnA5aZ9syue25FR2F5Zx/YQYbj1vEBEaPiXiGn32yQkdKq3k3mUZJH+TS7+oCBJ/Op7RvTV8SsRtKnA5rndS93Dn0jQOlVbxyzP788vJ/TV8SiRAqMDlqPKKyrljaRrvpe8jtkdbXpo5lmHdNXxKJJCowOW/WGt5Y/0u7luWQXm1jzlTBnPzaX1opuFTIgHHrwI3xuQAxUANUG2t1f0xPWxnQSnzklP5bMt+xsZ0YOGMOPpGafiUSKByYgV+prV2vwPHEZfU+Cwvr8lh0YosmhhYcEksV42N1vApkQCnSyghbkteMbMTU/hmxyHOGBTF/dPj6NGulduxROQk+FvgFlhpjLHA3621i498gjFmFjALIDo62s/TiVOqanw8/fFW/vbRFsJbNOWRHw/nkhEaPiXiJf4W+KnW2lxjTGfgfWNMprV29fefUFfqiwESEhKsn+cTB6TuKuTWxA1k7i1mWnw37rloGJ1at3A7loj8QH4VuLU2t+77PGPMEmAssPr4v0rcUl5VwyMfbOKZ1dl0at2Cv18zmvOGdXU7lojUU70L3BgTATSx1hbXfXwucK9jycRRa7MPMDc5lW37S7hiTC/mTR1CZCsNnxLxMn9W4F2AJXXXTJsB/7TWrnAklTimuLyKh1Zk8sqXO+jVoRWv3jSOif07uR1LRBxQ7wK31mYDwx3MIg5blZnH7UtS2VNUzo2n9uEP5w4kPExvPBIJFvpsDkIFJZUsWJbBkm9zGdC5NUk/m8Co6PZuxxIRh6nAg4i1lmUpe7j7rXQKy6r49VkD+MWZ/WjRTMOnRIKRCjxI7Csq5/YlaXywcR/xPSN55aZxDOnW1u1YItKAVOAeZ63l31/v5P53NlJZ7eO2qYOZOVHDp0RCgQrcw3YcKGVucgpfbD3AuD4deGhGPDGdItyOJSKNRAXuQTU+ywufb+PhlVk0a9KEB6bHccWYXho+JRJiVOAek7W3mNlJKWzYeYjJgztz//RYukVq+JRIKFKBe0RltY8nP97CE6u20KZlcx67YgQXDe+u4VMiIUwF7gEbdh5idmIKWfuKuWh4d+66cCgdNXxKJOSpwANYWWUNf3k/i+c+20bnNi159toEzh7axe1YIhIgVOABas3WA8xNTmH7gVJ+Mi6auecPpm1LDZ8Skf+jAg8wReVVPPhOJq99tYPeHcP5583jmNBPw6dE5H+pwAPIBxn7uP3NVPKLK5g1qS+/O3sgrcK0DV5Ejk4FHgAOHK7gnrczeGvDbgZ1acPfr0lgRK92bscSkQCnAneRtZa3Nuzm7rfSOVxRze/OHsjPzuhHWDNtgxeRE1OBu2RPYRnzl6TxYWYeI3q1Y9Fl8Qzs0sbtWCLiISrwRubzWV77egcPvpNJtc/H/GlDuGFiH5pqG7yI/EAq8Ea0bX8Jc5NSWLutgAn9OrLw0niiO4a7HUtEPEoF3giqa3w8//k2/rxyE2FNm7Dw0jh+PKaXtsGLiF9U4A1s454i5iSlkLKrkLOHdOG+S2LpGtnS7VgiEgT8LnBjTFNgHZBrrb3A/0jBoaK6hidWbeXJVVuIbNWcx38ykmlx3bTqFhHHOLEC/w2wEdD9u+p8s+MgcxJT2Jx3mOkje3DnBUNpHxHmdiwRCTJ+FbgxpicwDbgf+L0jiTystLKaP6/cxPOfb6Nr25a8cP0Yzhzc2e1YIhKk/F2BPwrMBo75BmZjzCxgFkB0dLSfpwtcn2/Zz9zkFHYWlHH1KdHMmTKYNho+JSINqN4Fboy5AMiz1q43xpxxrOdZaxcDiwESEhJsfc8XqArLqnhg+Ub+vW4nfTpF8O9ZpzCub0e3Y4lICPBnBT4RuMgYMxVoCbQ1xrxirb3amWiBb2X6Xua/mcb+wxXccnrt8KmWzTV8SkQaR70L3Fo7D5gHULcC/2OolHd+cQV3v53O8pQ9DO7ahmevSyC+p4ZPiUjj0vvAfwBrLW9+l8s9b2dQWlHDH88dyC2n96N5Uw2fEpHG50iBW2s/Bj524liBKvdQGbcvSeXjrHxGRdcOn+rfWcOnRMQ9WoGfgM9neXXtdha+m4nPwl0XDuXa8TEaPiUirlOBH0d2/mHmJqXyVU4Bp/bvxIOXxtGrg4ZPiUhgUIEfRXWNj2c+3cYjH2yiZbMmLLosnstH99Q2eBEJKCrwI2TsLmJ20gbScos4b1gXFlwcS+e2Gj4lIoFHBV6nvKqGxz/awtOfbKVdeBhPXTWK8+O6uR1LROSYVODA+u0FzE5MYWt+CTNG9eSOC4bQLlzDp0QksIV0gZdUVPOn97J4aU0O3SNb8dLMsZw+MMrtWCIiJyVkC3z1pnzmJaeSe6iM68b35tYpg2ndImT/OETEg0KusQpLq1iwPIPE9bvoGxXBGz8dz5iYDm7HEhH5wUKqwFek7eGOpekUlFTy8zP68euzBmj4lIh4VkgUeF5xOXctTefdtL0M7daWF64fQ2yPSLdjiYj4JagL3FpL4vpd3Ld8I2VVNdx63iBmTeqr4VMiEhSCtsB3FpRy25JUPt28n4Te7Vk4I57+nVu7HUtExDFBV+A+n+XlNTksei8LgHsuGsY1p/SmiYZPiUiQCaoC35J3mLlJKazbfpBJA6N4YHosPdtr+JSIBKegKPCqGh+LV2fz2AebaRXWlD9fPpxLR/XQ8CkRCWqeL/C03EJmJ6aQsaeIqXFdueeiWKLatHA7lohIg/NsgZdX1fDYh5tZvDqbDhFhPH31KKbEaviUiIQOTxb41zkFzElMIXt/CZeP7sn8aUOJDG/udiwRkUZV7wI3xrQEVgMt6o6TaK29y6lgR3O4oppFKzJ5ec12erZvxT9uHMtpAzR8SkRCkz8r8ApgsrX2sDGmOfCZMeZda+2XDmX7Lx9n5XH7kjR2F5Zxw8QY/njuICI0fEpEQli9G9Baa4HDdT9sXvfNOhHqSPOSU3ntqx3079yaxJ9OYHTv9g1xGhERT/FrCWuMaQqsB/oDT1hr1x7lObOAWQDR0dH1Ok9Mx3B+Nbk/v5zcnxbNNHxKRATA1C6k/TyIMe2AJcCvrLVpx3peQkKCXbdund/nExEJJcaY9dbahCMfd2Sqk7X2ELAKmOLE8URE5MTqXeDGmKi6lTfGmFbAOUCmU8FEROT4/LkG3g14qe46eBPgdWvtMmdiiYjIifjzLpQUYKSDWURE5AfQnQ1ERDxKBS4i4lEqcBERj1KBi4h4lCMbeU76ZMbkA9vr+cs7AfsdjNPQvJRXWRuOl/J6KSt4K6+/WXtba/9ncl+jFrg/jDHrjrYTKVB5Ka+yNhwv5fVSVvBW3obKqksoIiIepQIXEfEoLxX4YrcD/EBeyqusDcdLeb2UFbyVt0GyeuYauIiI/DcvrcBFROR7VOAiIh4V8AVujHneGJNnjDnmjSIChTGmlzFmlTEmwxiTboz5jduZjscY09IY85UxZkNd3nvcznQixpimxphvjTEBP/nSGJNjjEk1xnxnjAnoO5kYY9oZYxKNMZnGmI3GmPFuZzoWY8yguj/T/3wrMsb81u1cx2KM+V3d51eaMea1uhvCO3PsQL8GboyZRO29N1+21sa6ned4jDHdgG7W2m+MMW2ovd3cJdbaDJejHZUxxgAR378xNfCbhroxtROMMb8HEoC21toL3M5zPMaYHCDBWhvwm02MMS8Bn1prnzXGhAHhdTdqCWh146xzgXHW2vpuEmwwxpge1H5eDbXWlhljXgfesda+6MTxA34Fbq1dDRS4neNkWGv3WGu/qfu4GNgI9HA31bHZWo1yY2onGGN6AtOAZ93OEkyMMZHAJOA5AGttpRfKu85ZwNZALO/vaQa0MsY0A8KB3U4dOOAL3KuMMTHUzkv/nxs9B5K6SxLfAXnA+0e7MXUAeRSYDfjcDnKSLLDSGLO+7ubegaoPkA+8UHd56lljTITboU7SFcBrboc4FmttLvAwsAPYAxRaa1c6dXwVeAMwxrQGkoDfWmuL3M5zPNbaGmvtCKAnMNYYE5CXqYwxFwB51tr1bmf5AU611o4Czgd+UXc5MBA1A0YBT1lrRwIlwFx3I51Y3aWei4A33M5yLMaY9sDF1P4n2R2IMMZc7dTxVeAOq7uWnAS8aq1NdjvPyfLAjaknAhfVXVf+FzDZGPOKu5GOr271hbU2D1gCjHU30THtAnZ976uvRGoLPdCdD3xjrd3ndpDjOBvYZq3Nt9ZWAcnABKcOrgJ3UN2Lgs8BG621f3E7z4l46cbU1tp51tqe1toYar9s/sha69hKxmnGmIi6F7KpuxxxLhCQ76Sy1u4FdhpjBsSYLvcAAAC9SURBVNU9dBYQkC+8H+FKAvjySZ0dwCnGmPC6fjiL2tfGHBHwBW6MeQ1YAwwyxuwyxtzodqbjmAhcQ+3q8D9vcZrqdqjj6AasMsakAF9Tew084N+e5xFdgM+MMRuAr4Dl1toVLmc6nl8Br9b9WxgBPOBynuOq+0/xHGpXtAGr7quaROAbIJXaznVsW33Av41QRESOLuBX4CIicnQqcBERj1KBi4h4lApcRMSjVOAiIh6lAhcR8SgVuIiIR/0/5ilyG1WEMiUAAAAASUVORK5CYII=\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": "cd4d642b-0994-442f-9d6b-a98044ac716d"
      },
      "execution_count": 65,
      "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": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "values = np.arange(16).reshape(4,4)\n",
        "values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o0W0z3Fr1gOb",
        "outputId": "7bf138f6-52e1-437b-c6aa-ab416bcca486"
      },
      "execution_count": 67,
      "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": 67
        }
      ]
    },
    {
      "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": "fb1f3411-49ce-4fe5-f3e1-eecb82e09540"
      },
      "execution_count": 68,
      "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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              "      <th>w</th>\n",
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              "      <td>7</td>\n",
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              "      <th>c</th>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9d503428-d7dc-4184-910e-b1888fa01708')\"\n",
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              "\n",
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              "          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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              "          dataTable['output_type'] = 'display_data';\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "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": "d13ff5fb-5bc6-4fe0-b0de-99465aa141dc"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   w  x\n",
              "a  0  1\n",
              "b  4  5"
            ],
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              "\n",
              "  <div id=\"df-fb72da67-3d08-407d-af41-2ebb4861ed60\">\n",
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              "\n",
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              "      <th>a</th>\n",
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              "      <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-fb72da67-3d08-407d-af41-2ebb4861ed60')\"\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",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-fb72da67-3d08-407d-af41-2ebb4861ed60 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-fb72da67-3d08-407d-af41-2ebb4861ed60');\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": 69
        }
      ]
    },
    {
      "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": "d68a929b-0e4a-4d10-8c1e-e9eca8549996"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   w  x\n",
              "a  0  1\n",
              "b  4  5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c2fbb29d-e38a-42eb-b294-aef8d0ee3109\">\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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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <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-c2fbb29d-e38a-42eb-b294-aef8d0ee3109')\"\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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              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\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-c2fbb29d-e38a-42eb-b294-aef8d0ee3109 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-c2fbb29d-e38a-42eb-b294-aef8d0ee3109');\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": 70
        }
      ]
    },
    {
      "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": "492b4ee5-c81f-45ff-d230-4f91d7850506"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe_from_array.iloc[::2,::2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "-BpY8Prl3GQX",
        "outputId": "a156f48d-1a91-4929-8f56-c2c6406f7ef7"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   w   y\n",
              "a  0   2\n",
              "c  8  10"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a4c8c536-de9f-4412-81d3-525630577f57\">\n",
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              "<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",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>w</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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-a4c8c536-de9f-4412-81d3-525630577f57')\"\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-a4c8c536-de9f-4412-81d3-525630577f57 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-a4c8c536-de9f-4412-81d3-525630577f57');\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": 72
        }
      ]
    },
    {
      "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": "fad257ce-5de2-4d2b-f266-61adb1283505"
      },
      "execution_count": 73,
      "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": [
        "\n",
        "## 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",
        "#reading image\n",
        "plt.imread('/content/1.jpg')"
      ],
      "metadata": {
        "id": "mC8rNbeKxiaF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0fccedf3-5c56-49c4-e621-3c24dc4f28a1"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[11, 11, 11],\n",
              "        [11, 11, 11],\n",
              "        [11, 11, 11],\n",
              "        ...,\n",
              "        [14, 15, 17],\n",
              "        [13, 14, 16],\n",
              "        [21, 22, 24]],\n",
              "\n",
              "       [[11, 11, 11],\n",
              "        [11, 11, 11],\n",
              "        [11, 11, 11],\n",
              "        ...,\n",
              "        [14, 15, 17],\n",
              "        [13, 14, 16],\n",
              "        [18, 19, 21]],\n",
              "\n",
              "       [[11, 11, 11],\n",
              "        [11, 11, 11],\n",
              "        [11, 11, 11],\n",
              "        ...,\n",
              "        [13, 14, 16],\n",
              "        [13, 14, 16],\n",
              "        [15, 16, 18]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[11, 11, 11],\n",
              "        [11, 11, 11],\n",
              "        [11, 11, 11],\n",
              "        ...,\n",
              "        [10, 11, 13],\n",
              "        [10, 11, 13],\n",
              "        [10, 11, 13]],\n",
              "\n",
              "       [[11, 11, 11],\n",
              "        [11, 11, 11],\n",
              "        [11, 11, 11],\n",
              "        ...,\n",
              "        [10, 11, 13],\n",
              "        [10, 11, 13],\n",
              "        [10, 11, 13]],\n",
              "\n",
              "       [[11, 11, 11],\n",
              "        [11, 11, 11],\n",
              "        [11, 11, 11],\n",
              "        ...,\n",
              "        [11, 11, 13],\n",
              "        [11, 11, 13],\n",
              "        [11, 11, 13]]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#printing image using plt.imshow()\n",
        "plt.imshow(plt.imread('/content/1.jpg'))"
      ],
      "metadata": {
        "id": "VZG0oe0ASouz",
        "outputId": "d389ba64-5676-4028-bd06-7d36022ba846",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fe6c9c94d50>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}