Module1_Lab1_PythonBasics.ipynb

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      "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"
      }
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    {
      "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"
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      "cell_type": "markdown",
      "source": [
        "#### Section 1 - Python : Basic data types and indexing."
      ],
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      "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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    {
      "cell_type": "code",
      "source": [
        "print(name ,\"\\n\", name_extended, \"\\n\", last_element_string)"
      ],
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        "id": "DPokcpuLQO6s",
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          "name": "stdout",
          "text": [
            "First Lab \n",
            " First LabModule 1 \n",
            " b\n"
          ]
        }
      ]
    },
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      "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"
      ],
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    {
      "cell_type": "code",
      "source": [
        "print(name_list)\n",
        "print(element_2)\n",
        "print(two_dimesional_list)\n",
        "## list can have list, dictionary, string etc."
      ],
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          "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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          "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"
          ]
        }
      ]
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      "cell_type": "code",
      "source": [
        "## tuples are immutable, read the error !\n",
        " #usued when passing parameters etc. and dont want them to be changed\n",
        "\n",
        "name_tuple[1] = 2"
      ],
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            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-06fabd7fdbe3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m  \u001b[0;31m#usued when passing parameters etc. and dont want them to be changed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mname_tuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
          ]
        }
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      "source": [
        "##You can convert the tuple into a list, change the list, and convert the list back into a tuple.\n",
        "\n",
        "tuple_to_list = list(name_tuple)\n",
        "tuple_to_list[1] = 2\n",
        "name_tuple = tuple(tuple_to_list)"
      ],
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      "cell_type": "code",
      "source": [
        "print(name_tuple)"
      ],
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        },
        "id": "IN9byp7lvPqT",
        "outputId": "74d1e8e0-0f8b-421f-f879-592e7b16933b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('First Lab', 2, (2, 3), [1, 1, 'list having string'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(name_tuple[1]) #the tuple element with index 1 has been changed from 1 to 2"
      ],
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        "id": "VJp0789yvUTH",
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          "text": [
            "2\n"
          ]
        }
      ]
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      "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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        {
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          "text": [
            "{1, 2, 3, 5, 6, 'Lab1'}\n",
            "1\n",
            "{(1, 2), 2, 3, 5, 6, 'Lab1'}\n"
          ]
        }
      ]
    },
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      "source": [
        "set_unique[1] ##it is not indexable"
      ],
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          "output_type": "error",
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          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-5ad74dbd06c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mset_unique\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m##it is not indexable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'set' object is not subscriptable"
          ]
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      "source": [
        "#Accessing element of a set\n",
        "\n",
        "#Set items cannot be accessed by referring to an index as sets are unordered, the items has no index.\n",
        "#We can ask if key element is present in a set.\n",
        "#we can do this by using the in keyword.\n",
        "\n",
        "print(1 in set_unique)\n",
        "print(2 in set_unique)\n",
        "print((1,2) in set_unique)\n",
        "print('Lab1' in set_unique)\n",
        "print((3,5,6) in set_unique) #Cannot access individual elements together.\n",
        "print(3 in set_unique)\n",
        "print(5 in set_unique)\n",
        "print(6 in set_unique)"
      ],
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            "False\n",
            "True\n",
            "True\n",
            "True\n",
            "False\n",
            "True\n",
            "True\n",
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We can also loop through the set using for loop.\n",
        "\n",
        "#All elements will be printed.\n",
        "\n",
        "print(\"Set elements: \")\n",
        "for i in set_unique:\n",
        "    print(i)\n",
        "\n",
        "#all elements will be in a single line\n",
        "print(\"\\n\\nSet elements: \")\n",
        "for i in set_unique:\n",
        "    print(i, end = \" \")"
      ],
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          "text": [
            "Set elements: \n",
            "(1, 2)\n",
            "2\n",
            "3\n",
            "5\n",
            "6\n",
            "Lab1\n",
            "\n",
            "\n",
            "Set elements: \n",
            "(1, 2) 2 3 5 6 Lab1 "
          ]
        }
      ]
    },
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      "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)"
      ],
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        {
          "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.)"
      ],
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      "source": [
        "## Question 0:\n",
        "\n",
        "## Ans. - \n",
        "\n",
        "## String Methods\n",
        "\n",
        "#1. find() - used to search key element in a string and also it returns the position of where it was found\n",
        "string = \"FMML - Sardaruddin Syed\"\n",
        "key1 = string.find(\"Syed\")\n",
        "key2 = string.find(\"-\")\n",
        "\n",
        "print(key1)\n",
        "\n",
        "print(key2)\n"
      ],
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19\n",
            "5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## String Methods\n",
        "\n",
        "#2. isalpha() - checks whether all characters in the string are alphabets. Returns TRUE or FALSE.\n",
        "st = \"SpaceX\"\n",
        "a = st.isalpha()\n",
        "\n",
        "print(a)\n",
        "\n",
        "\n",
        "stg = \"Novo-2\"\n",
        "b = stg.isalpha()\n",
        "\n",
        "print(b)"
      ],
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        {
          "output_type": "stream",
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          "text": [
            "True\n",
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## String Methods\n",
        "\n",
        "#3. join() - joins all the elements of an iterable into a string. The string contains a SEPARATOR.\n",
        "\n",
        "# Tuple -> String\n",
        "\n",
        "tup = (\"Linear\", \"Algebra\", \"Probability\", \"Statistics\")\n",
        "c = \"_\".join(tup) # Using _ as Separator\n",
        "\n",
        "print(c)\n",
        "\n",
        "d = \"!\".join(tup) # Using ! as Separator\n",
        "\n",
        "print(d)"
      ],
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        },
        "id": "ilLux9leSNi3",
        "outputId": "b473860b-e456-4f2f-ac24-1b3fb9bc0e00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear_Algebra_Probability_Statistics\n",
            "Linear!Algebra!Probability!Statistics\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## List Methods\n",
        "\n",
        "#1. pop() - It removes element at the specified index from the list.\n",
        "li = [9, 8, 'a', 6, 5, 'c', 3, 'h', 1, 0]\n",
        "rmv = li.pop(2)\n",
        "\n",
        "print(li)\n",
        "print(\"\\nPopped Element: \", rmv)\n",
        "print(\"\\nNew List: \", li)"
      ],
      "metadata": {
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        },
        "id": "h8ivlzpRTdYK",
        "outputId": "18fea02d-bad6-4ae1-ba70-943ca5d3ccac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[9, 8, 6, 5, 'c', 3, 'h', 1, 0]\n",
            "\n",
            "Popped Element:  a\n",
            "\n",
            "New List:  [9, 8, 6, 5, 'c', 3, 'h', 1, 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## List Methods\n",
        "\n",
        "#2. reverse() - It reverses the list completely.\n",
        "mylist = ['v', 'b', 5, 7, 2, 3]\n",
        "print(\"Original List: \", mylist)\n",
        "\n",
        "mylist.reverse()\n",
        "\n",
        "print(\"\\nReversed List: \", mylist)"
      ],
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original List:  ['v', 'b', 5, 7, 2, 3]\n",
            "\n",
            "Reversed List:  [3, 2, 7, 5, 'b', 'v']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## List Methods\n",
        "\n",
        "#3. sort() - It sorts the items of a list in Ascending or Descending order.\n",
        "myli = [5, 8, 1, 11, 0, 4, 6]\n",
        "print(\"Original List: \", myli)\n",
        "\n",
        "myli.sort() #Sorts in Ascending Order\n",
        "\n",
        "print(\"\\nSorted List - Ascending Order: \", myli)\n",
        "\n",
        "myli.sort(reverse = True)\n",
        "\n",
        "print(\"\\n\\nSorted List - Descending Oredr: \", myli)"
      ],
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        "id": "NdfIofGJWEUM",
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      },
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original List:  [5, 8, 1, 11, 0, 4, 6]\n",
            "\n",
            "Sorted List - Ascending Order:  [0, 1, 4, 5, 6, 8, 11]\n",
            "\n",
            "\n",
            "Sorted List - Descending Oredr:  [11, 8, 6, 5, 4, 1, 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Tuples Methods - Python has two built-in methods to be used on tuples.\n",
        "\n",
        "#1. count() - It returns the count of a specified element in the tuple.\n",
        "name = ('S', 'a', 'r', 'd', 'a', 'r')\n",
        "cnt = name.count('a')\n",
        "\n",
        "print(\"Count of 'a': \", cnt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "smYWlBwDXq_7",
        "outputId": "c9bd2132-0e7a-48c8-f9de-09b1cb460e90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Count of 'a':  2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Tuples Methods\n",
        "\n",
        "#2. index() - It returns index of a specified element in the tuple.\n",
        "clg = ('i', 'i', 'i', 't')\n",
        "idx = clg.index('t')\n",
        "\n",
        "print(\"Index of 't': \", idx)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QUgaBLZEYpj1",
        "outputId": "82661e58-913f-4def-a060-3bdd501fbd65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index of 't':  3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Sets Methods\n",
        "\n",
        "#1. discard() - Used to remove an element from the set.\n",
        "num = {1, 5, 4, 6, 2, 3, 7, 0, 9}\n",
        "print(\"Original Set: \", num)\n",
        "\n",
        "dis = num.discard(0)\n",
        "\n",
        "print(\"\\nNew Set: \", num)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vYh7kt-JZed3",
        "outputId": "bf7c4486-ecdd-4c29-9c30-46b0def6a20d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Set:  {0, 1, 2, 3, 4, 5, 6, 7, 9}\n",
            "\n",
            "New Set:  {1, 2, 3, 4, 5, 6, 7, 9}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Sets Methods\n",
        "\n",
        "#2. issuperset() - Used to check whether a given set is superset of another set.\n",
        "m = {2, 3, 1, 7, 9, 8, 0, 4, 5, 6}\n",
        "n = {5, 8, 2, 0}\n",
        "p = {9, 5, 0, 4, 3}\n",
        "\n",
        "print(m.issuperset(n))\n",
        "\n",
        "print(\"\\n\", n.issuperset(p))\n",
        "\n",
        "print(\"\\n\", p.issuperset(m))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lgWNI_n0alyj",
        "outputId": "99b38228-37cf-4731-e9a7-83e85b8fdfd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n",
            "\n",
            " False\n",
            "\n",
            " False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#3. difference() - Used to compute difference of two sets. It returns items that are unique to first set.\n",
        "g = {0, 1, 2, 3, 5, 6, 7, 9}\n",
        "h = {2, 4, 6, 9}\n",
        "\n",
        "print(g.difference(h))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5CvIotS1c2Ym",
        "outputId": "501b66f5-befc-4a4a-d27b-b1c0a8f3f10f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0, 1, 3, 5, 7}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Dictionary Methods\n",
        "\n",
        "#1. fromkeys() - It creates dictionary from given sequence of keys and values.\n",
        "alpha = {'d', 'f', 'g', 'h', 'j'}\n",
        "num = {4, 5, 6, 1, 2, 3}\n",
        "\n",
        "dictn = dict.fromkeys(alpha, num)\n",
        "\n",
        "print(dictn)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FAa82DEMdi-H",
        "outputId": "400384fd-ca4a-4258-b125-493599a84f80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'d': {1, 2, 3, 4, 5, 6}, 'g': {1, 2, 3, 4, 5, 6}, 'h': {1, 2, 3, 4, 5, 6}, 'f': {1, 2, 3, 4, 5, 6}, 'j': {1, 2, 3, 4, 5, 6}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Dictionary Methods\n",
        "\n",
        "#2. items() - It returns a view object that displays a list of dictionary's (key,value) tuple pairs.\n",
        "price = {\"Shirt\": 599, \"Jeans\": 799, \"Sweatshirt\": 1199}\n",
        "\n",
        "print(price.items())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9j7h7kSsert6",
        "outputId": "0b1fb2b2-5a65-4f1a-ccea-49787ad70932"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_items([('Shirt', 599), ('Jeans', 799), ('Sweatshirt', 1199)])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Dictionary Methods\n",
        "\n",
        "#3. setdefault() - It inserts key with a value if the key is not present.\n",
        "employee = {\"Name\": \"James\"}\n",
        "\n",
        "age = employee.setdefault(\"Age\", 26) # Key is not in the dictionary. default_value is provided.\n",
        "print(\"Employee = \", employee)\n",
        "print(\"Age = \", age)\n",
        "\n",
        "salary = employee.setdefault(\"Salary\") # Key is not in the dictionary. default_value is NOT provided.\n",
        "print(\"\\n\\nEmployee = \", employee)\n",
        "print(\"Salary = \", salary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mXxP6GSWfVM_",
        "outputId": "e9aad366-719e-44d4-a318-c2193cc68439"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Employee =  {'Name': 'James', 'Age': 26}\n",
            "Age =  26\n",
            "\n",
            "\n",
            "Employee =  {'Name': 'James', 'Age': 26, 'Salary': None}\n",
            "Salary =  None\n"
          ]
        }
      ]
    },
    {
      "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": "06cbd1d2-4766-4b81-95a3-9e41556d7be0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "asn = add_new()"
      ],
      "metadata": {
        "id": "aVLU3Bpu0YJV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def check_even_list(num_list):\n",
        "    \n",
        "    even_numbers = []\n",
        "    \n",
        "    # Go through each number\n",
        "    for number in num_list:\n",
        "        # Once we get a \"hit\" on an even number, we append the even number\n",
        "        if number % 2 == 0:\n",
        "            even_numbers.append(number)\n",
        "        # Don't do anything if its not even\n",
        "        else:\n",
        "            pass\n",
        "    # Notice the indentation! This ensures we run through the entire for loop    \n",
        "    return even_numbers"
      ],
      "metadata": {
        "id": "aRa47XCsZLsh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Question 1 :\n",
        "\n",
        "##### Define a function, which takes in two strings A and B. Reverses the first string A, adds it to B, and returns the final string.\n",
        "\n",
        "\n",
        "#### Question 2 : \n",
        "##### Given a list having Names, work_hours, and gender, Write a function to print name of the female worker that worked the most hours. Also how much do should she be paid if the pay is $ 20 per hour.\n",
        "\n",
        "##### work_hours = [('Abby',100 , 'F'),('Billy',400, 'M'),('Cassie',800,'F'), ('Maggi',600,'F'),('Alex',500,'M'),('Raj',225,'M'),('Penny',920,'F'),('Ben',300,'M')]\n",
        "\n",
        "##### Answer : the female worker that worked the most hours is Penny and she should be paid 18400"
      ],
      "metadata": {
        "id": "heD0_u4D01jp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Question 1: 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",
        "## Answer:\n",
        "\n",
        "def strings(str1, str2):\n",
        "  str1 = str1 [::-1] # Reversing str1\n",
        "  return str2 + str1 # Joining str1 and str2\n",
        "result = strings(\"tsuguA \",\"FMML\")\n",
        "result\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "3-YUz7S1iDkA",
        "outputId": "9013669e-0f14-4e9e-ccc3-f05b03470270"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'FMML August'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Question 2: 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",
        "## 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",
        "## Answers:\n",
        "\n",
        "def worked_most(data):\n",
        "  hours = 0\n",
        "  for i in range(len(data)):\n",
        "    if data[i][1] > hours and data[i][2] == 'F':\n",
        "      hours = data[i][1]\n",
        "  paid_highest = hours*20\n",
        "  worker = ''\n",
        "  for i in range(len(data)):\n",
        "    if data[i][1] == hours and data[i][2] == 'F':\n",
        "      worker += data[i][0]\n",
        "      break\n",
        "  print(\"The female worker that worked the most hours is: \", '\\033[93m' + f\"{worker}\" + '\\033[0m', \"and she should be paid: \", '\\033[93m' + f\"{paid_highest}\" + '\\033[0m')\n",
        "worked_most([('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": "f8h5UbBvja09",
        "outputId": "b808b792-e9bd-4aa5-ebf8-909e8a64ed2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The female worker that worked the most hours is:  \u001b[93mPenny\u001b[0m and she should be paid:  \u001b[93m18400\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Section 3 - Libraries and Reading data."
      ],
      "metadata": {
        "id": "T9fGzOAKXhtt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Numpy - One of the most used libraries - supports for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays."
      ],
      "metadata": {
        "id": "TKVyOsBKYZXa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "_ODaZ44RXkyg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "a = np.array([1,1,2,3,4,5,5,6,1]) ## np.array converts given list to array\n",
        "\n",
        "b = a>1 ## important comparison operation, where frequently used in manipulation and image processing.\n",
        "\n",
        "print(b)\n",
        "print(a[b]) ## [printing only those values in a which are greater than 1]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29vs7Iicavlg",
        "outputId": "33aa6551-b6c4-4806-f1e7-604383c7a30d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[False False  True  True  True  True  True  True False]\n",
            "[2 3 4 5 5 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a_range = np.arange(10,19).reshape(3,3) ## create a 3x3 array with values in range 10-19\n",
        "a_range\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MAGJJ1K_jCHN",
        "outputId": "4561eb1d-5401-48d1-9e19-d5b5490ea5e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10, 11, 12],\n",
              "       [13, 14, 15],\n",
              "       [16, 17, 18]])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "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": "69574bc2-5229-4cf8-eafa-5ac70642f838"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10, 11, 12])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a_range[:,2] #printing all the rows of second column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HbDG7M3QuCDQ",
        "outputId": "09459c12-a940-4908-eceb-cb5499d71bfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([12, 15, 18])"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "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": "784d4b89-bdf1-4bbd-8584-ddef9c6ead92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0.],\n",
              "       [0., 1., 0.],\n",
              "       [0., 0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "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": "7e7d6bcc-8ecc-4a74-ec1f-87de5c43fbda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[11., 11., 12.],\n",
              "       [13., 15., 15.],\n",
              "       [16., 17., 19.]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "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": "ba301768-c6e5-49ed-c61f-f39a10df2a97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10., 11., 12.],\n",
              "       [13., 14., 15.],\n",
              "       [16., 17., 18.]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "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": "14ebfbe9-f186-4379-caa7-65e3f89c5977"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10.,  0.,  0.],\n",
              "       [ 0., 14.,  0.],\n",
              "       [ 0.,  0., 18.]])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "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": "fb6a9ba4-cd8b-496c-8459-6fd4897f0c87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10., 13., 16.],\n",
              "       [11., 14., 17.],\n",
              "       [12., 15., 18.]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "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": "5f0d0e66-2679-4250-c5ca-0c5fdcad7f31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10., 13., 16.],\n",
              "       [11., 14., 17.],\n",
              "       [12., 30., 54.]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "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": "0f134f2e-3ec8-4ca5-dc70-a3f992e4ce15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0., 13.,  0.],\n",
              "       [11.,  0., 17.],\n",
              "       [ 0.,  0.,  0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "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": "code",
      "source": [
        "#Question 3 :\n",
        "#a)Create a 5x5 matrix of the following form,\n",
        "#[[1,1]\n",
        "#[2,2]]\n",
        "#i.e. each row is increasing and has repetive elements.\n",
        "#Hint : you can use hstack, vstack etc.\n",
        "\n",
        "## Answer:\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "a = np.array([[1], [2], [3], [4], [5]])\n",
        "b = np.array([[1], [2], [3], [4], [5]])\n",
        "c = np.array([[1], [2], [3], [4], [5]])\n",
        "d = np.array([[1], [2], [3], [4], [5]])\n",
        "e = np.array([[1], [2], [3], [4], [5]])\n",
        "x = np.hstack((a, b, c, d, e))\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6piFCK5VtYce",
        "outputId": "4fb7f5cb-cb26-46ab-9c58-2ebdbda03888"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 1, 1, 1, 1],\n",
              "       [2, 2, 2, 2, 2],\n",
              "       [3, 3, 3, 3, 3],\n",
              "       [4, 4, 4, 4, 4],\n",
              "       [5, 5, 5, 5, 5]])"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 3 :\n",
        "#b) find dot product of the matrix with any matrix. (Figure out the size/ shape of the matrix)\n",
        "\n",
        "## Answer:\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "a = np.array([[1], [2], [3], [4], [5]])\n",
        "b = np.array([[1], [2], [3], [4], [5]])\n",
        "c = np.array([[1], [2], [3], [4], [5]])\n",
        "d = np.array([[1], [2], [3], [4], [5]])\n",
        "e = np.array([[1], [2], [3], [4], [5]])\n",
        "x = np.hstack((a, b, c, d, e))\n",
        "print(\"Matrix A:\\n\", x)\n",
        "\n",
        "k = np.arange(1,21).reshape(5,4)\n",
        "print(\"\\nMatrix B:\\n\", k)\n",
        "\n",
        "l = np.dot(x,k)\n",
        "\n",
        "print(\"\\nDot Product of A and B:\\n\", l)\n",
        "print(\"\\nSize of Resultant Matrix: \", l.size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2HO2tpLUvH47",
        "outputId": "4472b247-a626-423d-a432-ecaa48f694b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Matrix A:\n",
            " [[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]]\n",
            "\n",
            "Matrix B:\n",
            " [[ 1  2  3  4]\n",
            " [ 5  6  7  8]\n",
            " [ 9 10 11 12]\n",
            " [13 14 15 16]\n",
            " [17 18 19 20]]\n",
            "\n",
            "Dot Product of A and B:\n",
            " [[ 45  50  55  60]\n",
            " [ 90 100 110 120]\n",
            " [135 150 165 180]\n",
            " [180 200 220 240]\n",
            " [225 250 275 300]]\n",
            "\n",
            "Size of Resultant Matrix:  20\n"
          ]
        }
      ]
    },
    {
      "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": "b3fbb284-b8ff-43b7-ca34-49d9c4890f29"
      },
      "execution_count": null,
      "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": "7a2e058e-01bb-482f-f014-153aaa581b7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['This directory includes a few sample datasets to get you started.\\n',\n",
              " '\\n',\n",
              " '*   `california_housing_data*.csv` is California housing data from the 1990 US\\n',\n",
              " '    Census; more information is available at:\\n',\n",
              " '    https://developers.google.com/machine-learning/crash-course/california-housing-data-description\\n',\n",
              " '\\n',\n",
              " '*   `mnist_*.csv` is a small sample of the\\n',\n",
              " '    [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is\\n',\n",
              " '    described at: http://yann.lecun.com/exdb/mnist/\\n',\n",
              " '\\n',\n",
              " '*   `anscombe.json` contains a copy of\\n',\n",
              " \"    [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it\\n\",\n",
              " '    was originally described in\\n',\n",
              " '\\n',\n",
              " \"    Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American\\n\",\n",
              " '    Statistician. 27 (1): 17-21. JSTOR 2682899.\\n',\n",
              " '\\n',\n",
              " '    and our copy was prepared by the\\n',\n",
              " '    [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).\\n']"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "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": 363
        },
        "id": "CgXsQFpobVGk",
        "outputId": "f8026768-aada-4e5d-c660-e1cca88875c5"
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      "execution_count": null,
      "outputs": [
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              "   longitude,\"latitude\",\"housing_median_age\",\"total_  ...  ooms\",\"population\",\"households\",\"median_income\",\"median_house_value\"\n",
              "0  -122.050000,37.370000,27.000000,3885.000000,66...  ...                                                NaN                   \n",
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              "\n",
              "[10 rows x 3 columns]"
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      "execution_count": null,
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          "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'))"
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          "execution_count": 36
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          "metadata": {},
          "execution_count": 37
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      "source": [
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      ],
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        },
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      "execution_count": null,
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              "0    -122.050000,37.370000,27.000000,3885.000000,66...\n",
              "1    -118.300000,34.260000,43.000000,1510.000000,31...\n",
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              "4    -119.670000,36.330000,19.000000,1241.000000,24...\n",
              "Name: longitude,\"latitude\",\"housing_median_age\",\"total_, dtype: object"
            ]
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "df = df.rename(columns = {'longitude,\"latitude\",\"housing_median_age\",\"total_':'Detail1'}) ##rename column names as at times it makes it easier for us"
      ],
      "metadata": {
        "id": "-hpbslfWfwy3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "C0gpXPf1hN5Q",
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              "    }\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>Detail1</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",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-122.050000,37.370000,27.000000,3885.000000,66...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-118.300000,34.260000,43.000000,1510.000000,31...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-117.810000,33.780000,27.000000,3589.000000,50...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b4d6646-be74-40bb-b987-8f46c5498910')\"\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-8b4d6646-be74-40bb-b987-8f46c5498910 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-8b4d6646-be74-40bb-b987-8f46c5498910');\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",
              "  "
            ],
            "text/plain": [
              "                                             Detail1  ...  ooms\",\"population\",\"households\",\"median_income\",\"median_house_value\"\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",
              "[3 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "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": "161adc0a-69ae-4a53-e7b7-edf91a8c4f4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    -122.050000,37.370000,27.000000,3885.000000,66...\n",
              "1    -118.300000,34.260000,43.000000,1510.000000,31...\n",
              "2    -117.810000,33.780000,27.000000,3589.000000,50...\n",
              "3    -118.360000,33.820000,28.000000,67.000000,15.0...\n",
              "4    -119.670000,36.330000,19.000000,1241.000000,24...\n",
              "Name: Detail1, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib\n",
        "from matplotlib import pyplot as plt"
      ],
      "metadata": {
        "id": "oJ7JrZqWioIO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xpoints = np.array([1, 8])\n",
        "ypoints = np.array([3, 10])\n",
        "\n",
        "plt.plot(xpoints, ypoints)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "tzunFbpUxKAP",
        "outputId": "8c0572dc-8832-4eda-ee70-e438d30b1d0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "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": "db30f2a7-289c-4fbf-bd92-43e4c874ae73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating a dataframe.\n",
        "#### Task: Study about other methods of creating dataframe (for example: using Pandas Series, Lists etc.)"
      ],
      "metadata": {
        "id": "GT6yXu7l1gtL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "1vR1hTJ51ckl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "values = np.arange(16).reshape(4,4)\n",
        "values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o0W0z3Fr1gOb",
        "outputId": "9242f716-42cb-4f5d-a30b-54e263054122"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  2,  3],\n",
              "       [ 4,  5,  6,  7],\n",
              "       [ 8,  9, 10, 11],\n",
              "       [12, 13, 14, 15]])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "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": "d10b4d3c-56d8-47b0-de70-c2b6fa98ccde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    w   x   y   z\n",
              "a   0   1   2   3\n",
              "b   4   5   6   7\n",
              "c   8   9  10  11\n",
              "d  12  13  14  15"
            ],
            "text/html": [
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    {
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        "dataframe_from_array.loc[['a','b'],['w','x']]"
      ],
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          "base_uri": "https://localhost:8080/",
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        },
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   w  x\n",
              "a  0  1\n",
              "b  4  5"
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              "                                                     [key], {});\n",
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    {
      "cell_type": "code",
      "source": [
        "dataframe_from_array.iloc['a'] ## it needs position as integer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "iPTlROGv2r7M",
        "outputId": "32f6cfa4-ea28-41de-ecd7-9e2a2af7c564"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-a102e544f01b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataframe_from_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    930\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 931\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    933\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1561\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitem_from_zerodim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1562\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1563\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot index by location index with a non-integer key\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1565\u001b[0m             \u001b[0;31m# validate the location\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Cannot index by location index with a non-integer key"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## DataFrame.iloc\n",
        "\n",
        "## Purely integer-location based indexing for selection by position.\n",
        "\n",
        "## .iloc[] is primarily integer position based (from 0 to (length-1) of the axis), but may also be used with a boolean array."
      ],
      "metadata": {
        "id": "6u_6VTOtyPxO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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": "2ba8878e-166b-44c1-8e62-94789e0d6f51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe_from_array.iloc[::2,::2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "-BpY8Prl3GQX",
        "outputId": "173191ad-f956-4477-a440-8eba89bb75f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   w   y\n",
              "a  0   2\n",
              "c  8  10"
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              "      <td>10</td>\n",
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              "</div>\n",
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              "              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-89245783-263e-47f5-bdca-53df2d773e37 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-89245783-263e-47f5-bdca-53df2d773e37');\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": 11
        }
      ]
    },
    {
      "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": "f3925ab0-055b-416a-bdd4-a0809812736d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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zW8at3DsfBB47Oc3jJ6c9z30+oT33Plu550uNtszEMtkyU9kiqXzZs/ZtM7TfPthrBXdreQPDxYa2DCvVWmnsSmKCewfYd8JaVXAwafnusUizapma596NLtWRWcvrO7GIpONcvsT//NTjK1puWCx7g7u7WibreO7LY8voE2Q7NpAug1zba3Upu09KhouHiUweEeu9Xw0VMya4dwBdwdHbY3WmxcPi9dyLFUQgHLQ6VKE7tsywHdxPTmYX/LtPnp7h3mdG+N6RlVvysFipS6h6PHet3Jfn5JMptJ/A1SptqM/62VRCXZxMZ4ok4tbAQBPcLxBGUwXCwYrjp8fqPfdShXg4iIgQDFSsme4dVu5KKcbnrEC0GOWuSw3Pzqxcpr/o6lAFr3LPLbPnPrcA5T5re+5rdHA/T20xw+KpVBWpXIXeHmvBlvFlEiGtMMG9A4zO5YlGa0EgFhaK5aoTrLLFCj2RIICl4FuM/T03kyNbXHhwmM6WnHnoJycWrtyH7XnkZ6ZXLriXGpS7O6FqvSbpQnVZGsC0LdOOx68ttrW91s8mqXrxMZ0tooC+hB3cjXK/MBhN5YmEawE1HrH+1wEiV6oQCwed+8Ohkm9CVSnFT/zd9/nQ/c8v+BjcXvnxiXSLPf3Rls7Z6YWfGDpFoQ3PHZZHvTuee1u2TBERxUCP9bOxZS4+dHdqPJolFKwwljoPgruIbBOR74jIQRF5VkTeZW9fIyLfFJEj9v+D9vYrRORhESmIyG93+w9YDYzM5oiGa8FVL8Ckk6p525bRBINFX+U+nS0xmSkuyhoZTVnPn4jPcXxyMcHd+v1TUyvXAaoVecLPc3cF9+Woddc5k3YSuDPZEtFwmXjU+7uGiwfdnRoJF4lGiowvU+K/Fe0o9zLwbqXUVcCtwDtF5CrgPcB9Sqk9wH32zwBTwG8Af92F4111KKUYmysQjbiDu7UqoQ7uuWKFeKQW3EPBkm+1zFnbElmMHz9iB/fB5CTjc6UFWztnZzL24xSorFCNbrFSJRwUYuGA87MmV6oQsBd77LZyV0qRKWrPvb2EaihUIm6f1I0tc/Ex5Qru4VDu/LBllFLDSqn99u054BCwBbgDuMfe7R7g9fY+Y0qpx4CL4to0lStTLCtPcNe2TLqJLdNspvsZ2xLRTTELwbJllFOOeWpqYfbKyGyeYKBMpVq7ClhuiuUqkWCAaMh6rbyee4XBhHW72yMIcqUK+vw20YYCm82WCAULzvtuRjpffNSUe4FIJL8qRhAsyHMXkZ3AjcCjwAal1LB91wiwYYGP9XYR2Sci+8bHV678bqmM2q3G3uBuK/e8Du5Vjy0TCpV81bm2YxbTADGayhOLlEjY5ZgnJtq3V9KFMulClf6+ac9xLDelSpVIKEAkZH0s62fL6GqUdtT0UnD3KLRzIpnOFgiFig12nKHzTGeKq7JJTHvu4VCRaLhw3tgyAIhIL/AF4DeVUin3fcp6tRf0iiul7lZK7VVK7V23bt1CfnVVoVVuLNLoueuSuHzR67k3m+muK1UW0+A0MpsnGsnRE7OC+okF1LoP28Fcq/4zK5RULZarhIMBonZwd1fFZEtlBnogIKrrjUz6pBwNBdpuYgqHigQCQiISNLZMl5jKFLn1fffxjWdHV+T5W9mVU5kikVCZQMC6ik/lKiu+yHtbwV1EwliB/dNKqS/am0dFZJN9/yZgrDuHuLoZtbPifp67u1rG47mHShTKquHNd4J7trRgdTI8myUSzhEOlYlFSgsajauTqQN9VnA/u0LlkMWypdxDASEg9cq9SjQE0XCp6567bmDasbaHyfT8SnE2V3LGSvTGQqZapkucnMxQKFc5vUDLsRM8eHSCa//4G01tuslMkUjY+lxGw/aMmRWeYNpOtYwAHwUOKaU+4LrrK8Db7NtvA77c+cNb/WjlHo3U3nTHc7cVXLYuoVqb6e5VeGemrYBcrFQX3MI+OlcgFrWvIqLpBdkyugwyEU8Ti5RWrNa9YNsyIkIkFKhrYipbK1mFC8tmy2xfk6BcVS3nAFWqinSh6rynfbGwsWW6hP6urURO48joHNlihcPD/uujTmUKhEJWDIjYsWClk6rtKPcXA28BXiEiT9r/fhx4P/AqETkC3G7/jIhsFJEzwP8C3isiZ0Qk2aXjX3HGUnkioTLBYE2FR+ttmbpSSGeme539cmYmRzBgBbSZBSyiXShXmMmWnauHnliaYwuodR+2k7HRSJ5YNMPpFbJlSnZCFSAaCjoJVaUU2VKFaBhCoXxbSc6loIPzjrVW4fpEC2tGv4fhkPV+9cVCxpbpEvoKcyXGZet5/cebXBFPpAuE7eCuy6JXetGO0Hw7KKW+D0iTu1/ps/8IsHWJx3XeUF8GCRAQy3vNFKwJgbm64O5MhnQFgVS+RDpfYd1AjvGZBDPZEpv64+0dg2MNWYq7J57h6HiRfF2VTjOGZ/LEIyXbL8ysnOdeqTp+u1u5F8pVlLIGr0XCha7Pl8nUBffJdJFLm6SFZpzgbtsy0ZBZiKVLjDjKfflfX50Ha3ZFPJUpEInr7+H5o9wNLRhJ5YmEG22M3liIdL5MqaKoVFWd527PdHcpEO1zr01agXUhSdWRuqSuTqq2Ww55bjZHJGrtG4/lODeTX5F51DqhClYyUzcxZV3z8CPhYtue+0S64CyUshBqtowO7s2/pHoipFbuyViYdAdtg7FUfsVKU1cbugt7JXIa+vt4fLzxilgpxXSm5HjukbAJ7hcEI7PZBuUOloJLF8rONMP68QPg9Q61zz2UtMshF1Drrj/00brg3q7vfm4mSzRiB/dollJFdd368EMnVMFS7gVbubtHJkfCBTKF6ryVCGems7zs/3yHv/nWkQUfR025W4X1Ey1OJrN1yr1dW+b4RIYPf/d5jvkECzd3ff4p7vr8U20d94XOiGPLrIRytz4Dz/vYnelCmXIVwnZwDwQU0XBpxYeHzWvLGJqjJzFu2+QT3GNh5gplRzl6bRnrQ+DOpuuZLjXl3r7n7pRj2gnVnrgV1Nsd/Ts8m2dorfW7cVvBn57OsT4Za/sYOkGpUnWW2LM8dzu4F3VpIo46ms6U2NjvbzlVq4q7PneAdKHCs+dmF3wc6UIZEdgyYNlirZT7bJ3nrk/qzbj3mWE+8r1j7Ds54zz2H7z2qqb7n5nOEgnNb61dDKxkQlW/z2emc5QrVULBmi52d6dqopGCUe7nM9PZEuUqTZS75bnrmSjxSO2ljoQLxCJFDrjW6DxrJ1P7E9YHYkG2zGyeYKDqJGrDoRLRcKlp8sdNKl8iW6wSi1pXDPFYzjme5abgSqhGQgFHnTu2TLh2ydvqyuLTPzjFw8emiIVLHBtf+KycdKFMIhIiEgow0BNuaQM5S+y5qmWyxQrlSmO109hcnl/71H4OjZ1jz46DxCLleVXoVKZo1mXFElIrm1C1nrNSbfxuON2podpnMhTKrXhC1QT3JVArg2xiy+RrtoxbuYtAf98EjxyfcLadmc7RE8sRDlUJBWRhtkwqTzyaR1xp73isvXJIfakbs5OxMVu5r0RStVipEg7VPHf3yGRrW00dNQu4pyaz/MVXDzI0MMaVOyY4N5tbsO+eKZSdscNrE5GW9cr6fXLbMuDfpXrg9CwKuHr3fnZtPUo8UmauReCuVBUzuZIZRIYVXAtla/bQSiVU1/ZZn6Pjdd8rpzu1TrmPrfAIAhPcl0Dr4B72eO7xiNcBG0xOcm6m4NSYn57OEolkrMAfDy9oBMFoKk847A3G8ViGYxNz8zbgnJvRQd36G0LBCtFwaUUamYrlKlF3QrXs77lD88mQv/OFA5RVkat2P0l/Io9SjV/G+UgXyiSi1sl4bW+05VXCTK5IOFQhELBeZ20r+fnuB07PIKJIJmbtv6fa0p+fzhZRyjqe1dhyv5xo1X7JUIJ0obzsw+1mcyW2rLHeq3rRNOlny4TzTLTRANdNTHBfAroEMdbElkm7bZm6kkTd6v+D47WWf+139/eEF2TLDPskdZOJGUZmi9z54Yd48OhE0w+Zo9yjtWAei2ZXpJHJnVB1K/dcXbUM+Hf/zeZKPHJsih2bjxKP5hnotf62hVoz6ULFUe5DvZGWTVOzuRKRUO29SrYI7k+enqYvMef0RETClZYqVF+dlCrK0617MaIrwvZssAYMtbqaecenH+cTDx7v2HMXyhXypSrr+iuEg5WG0R5TGW+VDFiNTIWyWtGGNhPcl4Bfd6qmN2Yl1rJNgntfYpZwsMJjJ6bIFsvMZMvEY9aHZiAedpZumw+lFKOpYsMJZvum41y56ymeHRnlzR95lF/46KO+FSbndAOTax59NJrh9PTyz3XXg8PATqjWee5Re4HxgCjfgKtPVHody2SP9b48P09FSj2ZQtlR4GsT0dYJ1WyJYKh2LL12B1t9uV61qjhwZoZkYsrZFglVWpb1ua8YLvauV/3eXrbeCu7NkqpKKb59eIzHT8343r8YtNCKR6r0xDMNV4JTmSLBQJVgwN3IaL13K+m7m+C+BEbn8kTDJQKBRlXVGw1TqSqm7Tpod0IVtO8+yaPHJl3WiPV/f7x95T5jL68XjdY1UgUU2zed4MU3fZPd2w/x4NFJ34FLwzM54tGiYysAxKM5zk7nlv2S0l3n7m5i0tUykbD1ukUjJaZ8lPs52+LSzVzhUJXN/bF5yw3rydgJVYC1vRErce6TIAXLOgkFa1/gZp77ickMc/kK/X21oBMJt7Zl3HmFi913H5m1ckq711trGTb7fqTyZfKlqlPO2gl0AjcWVsSjcxwb944gmMoUiYZLnpyXvpLWFTPNPj/dxAT3JTCaauxO1fTanq1WX36dogPJSY6MZXj2nDVkM24H94GeSNvjB2oNTP42SjBQZdfWIyRiOT71yImG+4dn806NuyYezVIoqwUN6Hro+QkOnkvNv2MLihWvLePXxAQQDvnPl9HrwLotpl3rejm2QM99Lu9KqPZayytNZf1fi+lswUmmQi241wftA2esoN7fO+1sm0+5e4K7Ue4M9UZZk7AGNzVT7mP292GxwX1kNs99h7wiqKbcFT3xDGdn8s56v2C9T+GwV6Hrq/l7HjrBnR9+iMv/8Ot85genFnVMi8UE9yUwmsoR9ulOhVpiTZ+5620ZsJKqAP/+xFlrH+25x8NtV8uMtEjqakRg84bj/OD4NEfHvKrj7EzGUbqauFMx077v/u5/O8D7vn6o7f3rqVYVpYrylEIWfZqYAMKhvO8IgpHZHAHx2mS71iU4Np5Z0FVIplh2lvobsoNJs4oZayKky5Zxgrv3/TtwepZQsEJvT+31j4QtL7fURNW5n/Nin1czksqzqT9GMm69vs1KSLUNklnEIvMA//LoSX71k/s874kO7jE7uFcVnsmUE+k8oZD38xiN5BCp8vVnRnhu4iTVquLEAia1dgIT3JfASCrvm0yFmvfqBPdIY3Dv75shEKjywJEJAlJ1AnR/PMxcvr2KgFGnlLF1N9yW9acIiOLTj9bUg1KKkdlCg+qP2bXu7Qb3yXSB4dk8z436T8xrh1LV+jL5KfdcsUI0FCBgX/eGwwVfH/zcbJ41iSABqb1uu+zqinYbSpRSXs/dVu5+wV0pa2Jk2JNQtT33OuX4xOlp+hIznkv3SMj6+5oF7otFuR8bT/Nbn32Sq//313nt3z7A73/paT6377Tn8z8ym2dDMlZ7fZso91FHuS9ulnq6YK3C5c53eDz3mGXxuQP1VKbgqZQBCIfKvOiG7/KyW77BrdffTzhU9awJvByY4L5IqlXFxFyxhS3jVe4xny7DYKBKf+8MlaqiJ1ZwvvgDPdYHuJ1mjXaUu3V/kfVrz/L5x0871SepfJlcqeqxMaCm3M/OtFfrrm2l0VSh7VzBmeks//XsiPOzrozxU+7ZYoUe18kxEi762zKzOYb6vCWnu9ZZHu3zbVbMFMpVShXlsmVs5e5TepktVqy2c1dwj4YChIPiCdjFcpWD51IeS8b6O6z3oVmgmsoUCQf1erzda9yZTBe6ml/JlypOXsnNTLbIu/71CW7/wHf5yoHT9CdPMpI5xuceP8Zdn3+K+39YWyLCUe52cG9WZaTXV1jsyTBvJ/HdYkAXN8QjioTd/X18ovbdmMqUnImQbnGu6b8AACAASURBVHp75pyryFCwcf0GgI8/eJyvPjXcsL0TmOC+AD772Clu/8D9jMzmmcwUqarmQVUHh4l0gVg4QCDgP1hzoM+yZiKRWtKv315puZ1ad728njsh2oxtG08yl6/wn0+dA2qXlrG6ZGw4VCYarrZd635wuOa119s+zfj4gyd457/sd4KKE9xd1TKVqqJcqdrBvRa0I+EC2WLjfJnh2XxDcL/UTsC1OwJZe7UJ+2QylLCU+4SPcp+pGz0AICL0Rr0LdhweSVGqKGcZQ+fvmEe5T2YKbBu0hpd1K6F636FRbvnzb/HKD9zPvzx6yjnx13Pnhx9adHnh737hKX70bx5oCLj/cP/z/MeBs2zffJTbbv4vrr3sCfZe8zAvu+VewkHFg0et70auWGE2V2JDMuZcUTUTPqNL9Nx1w5snuNsWUCyiCIesFZd0rXuhXCFbrDYo93qCQX/lfs9DJ/iGS+R0EhPcF4DlWWd468ce5eiYFSyaBneX5+7nt2u07x53qWet3NtRwY+fnKY/0V7gGkxO0tuT4e4Hnuddn3mCN3zoQQDnUtPNQKLqa8t8/vEzDdUnz55LOX/jc6PtHctoKk+popxkqVbpblsGLCWdr1vJyq9LVSnF8EyedXXBfVMyRiwcaLvWXV/O99oKMRkPEQqIrw2kFV047H2f+mJhTzA+cMZqWurv9ZbnRUL6Csr/fZ5MF9lujx2ut3k6wYmJDO/61ydIxOeYzA3z+196mhe+75s8ftJ7ElJKsf/UDM8sImH+7LlZvvzkOebyZb5qiwqwyl6/uP8suzfluXznQaKR2nsZCFTZtcFK0kPt6nRTf4xgQOiLhpq+ZjooZ4uVRU021f0E3uBeojcaIhiw8lfxWNoph/SbK+NHKODfq5Av1cZcdxoT3BfASCpHPFLi6Ngcv/6Z/cD8yj2VL7cM7gPJKQJScWqzAfrtpZxmmlRoaE5PZXluNM2moamW+2lEYOuGYxwZy/D1Z0+xfugYL7zuAZK9jV/agUSlYYZGrljhtz93gL/79lHP9mfPzfLi3UPEw8G2fXf95dGqtVS2vohuWwYsRZ8tlutsmcZlzGZzJXKlSkNwDwSES4Z62y6H1OpSVzuJCGt7/UcQ6IqmUNB7X/1kyAOnZ4hFig32V82Wae65b+qPEw5Kx5V7tljm7f/8GMVqnhuvfIQXXvddbrnmQVK5itNYp9EdoYsZtftX9/6QSKjM1sEI//rYaWf7/T8cZyJd4PpL/K2/yzbB4ZE5JtK1Lu6N9iC7ZDzcNKHqHo+cXcS454Kvci85V9NgBfdjE9bn/IHnxoE2gntQ+Y7BKJTbW3NhMbSzzN42EfmOiBwUkWdF5F329jUi8k0ROWL/P2hvFxH5WxE5KiJPichNXTnyLvLZx07x/75ztGH78EyeTWszXL37CecyvXlCtRZkYj7JVI2VeLmf7ZuOOdv0B2k+5a5LtjYPTbbcz832Tcd5wbXf56W33MtVlz7NQJ9/s0d/T4UzdbXuej78945MOKooUyhzfCLDNVuS7F7fy5E2lbteHV4HjGLF+uCHXbYMWEoqW/R+AZwuVZdy1+3p9bYM2BUzbZZD6uCecL1/axNRX899poly742GPEr7iVNT9CWmPMlUaG3LVO0eibWJyLyTJheKUor3fOEpnhtNc+2efcRjOUSsK7tgQDWU4erP4UIrdh49Nsl3nxtn55bneP1Na3ni1Aw/HLGC4uf2nWaoN8KlG/0T3Zdvtv5/5NikE7A39lvBva/FOrWjc7XvY7018/nHz/DpR0+2PGa9vOV4XUI16QruiXiG4ZkC/+Of9/G7X3iaZG+KNf3jLR83GKg2Ve6x8Mop9zLwbqXUVcCtwDtF5CrgPcB9Sqk9wH32zwA/Buyx/70d+FDHj7rLfPzBE3zqEe+HQE+lS8RKbF5/hst3PkNfIuWsl1hPLBwgaPvsrZQ7QKInQzBYe+PbtWXuOzzGrnUJ+nranxttfYmnfBuv3AwkqqQL3qmFukJgIl3g0Iil9g+PzKEUXL25nz0bejnSpueulZFOjBV8EqpgKfdcyZtQ1dU97pyAVnf1yh3g0qEEp6eyba1Gn/EL7r0RX89dX5r3RL3qsy8WdgJhKl/i2HiWZG/jSbRVQnUmV6KqYE0i4iz80imeOD3DVw4Ms3v7DxkarAUlEYhFqg3d0foktpATjFKKv7z3MPFoge2bjvPqawYIB4XPPnaaiXSBbx8e46du3EKwSQTathb6oiEePDrJyKz1WdHBPRkL+9oySinGUgU2JKO+x/uZH5zi77/dKNrcaHWtR4uA5e/3x2ufh55YBgV869Awe3Yc5IXXfpdwqPVr46fclVLkV1K5K6WGlVL77dtzwCFgC3AHcI+92z3A6+3bdwCfVBaPAAMisqnjR94lssUyz43OMZLKO0k+sOpqc6UKvTHrQ7VzyzFedMP9nrI7NzqxBvMH93qchGqLWvd0ocyjx6a4/coNC3rsdhlIWB9E93qqJ13lX987YvmhB+156VdvTnLZhr62KmbypYoT/Bzlbr/W0QbPvdJQLROL5oiGFYdcidxzdgOTX3Dfta6Xqmpvvr0OCH2u4D7U66/cfzgyx0Ci6qyspUm6lOW9z4yggKGBRmWnPXc/RTxlP9/a3gi90XBHPXe9yPPmdacb7otHqg3vX2oRyv2+Q2PsPzXDJVsPEwxWGOgJ8eqrN/LFJ87wb/tOU64q7ty7renvBwPwwl1rePj5CUZmcyRjISepnoyHfG2ZVK5MoVxl15CVRK9X7qlcieHZ1itbOZ57urkts37NCDu3HOFHrv8Ou7YebauYIejjuZcqCqVYHZ67iOwEbgQeBTYopXQNzwigo8wWwP2pOWNvq3+st4vIPhHZNz7e+pKmW+RLFU8ABys5WFWgVE0NQq21PRFv33d0gnsLW8aPcDBAIhL0fMn++eETfPaxWo36949MUKxUecUV6xf02O0ykLBeF7fvfmIyy2BPmCs29vG9I9Z79uy5FAM9YTb1x9hjV6Ycmcd3d/uZOmDUV8tEXAnVXLFCPFwLtiKwadC7EMfwbI5gQBhMNL7Wu9ZZKyq147v72zL+nvtzo3Ns6G+8GtBzhQC+8PgZNvYHGiplwApgsXDAV7nr51ubiNIX7axyPzI2RyREQw4AIBauNoiKGSe4t//Z/8qBc8QjRbasr31mf3bvNmayJf7mW0e4ftsAl9lDwJrxokuHODGZZf+pGUe1Q3Plri0Z/X7XK3f9WXOvo1CPf7WMN7iHQmUu33mIRE/7TUnBYGN1ly67XDHlrhGRXuALwG8qpTwZOGUZswtKTSul7lZK7VVK7V23rsnqw13m5+5+hD/40tOebe433l0t4gylii08uC/mzRvoiThfMqUUH7zvKL/3xafZd8JKdt13aJRkLMTNOwYX/Njt0G8rd7f1cXIyw461CW7bM8Rjx6fJFSscHE5x9eYkIuJ8WY+MtQ6iYz7BvVSxPj7uNVTBDu51tgzApoEKh0fmnEaX4dk8G/qijhXmZiG17n62zKaBONlihTGXn1uqVHl+PM2GgcbgrhOqp6eyPHp8ipdeGW3w22v7hpsodyu4O7ZMB5X70bE0G/rxPaZYpNpQgqtFxkLmqI+m8vT25Dyq9iW7h9gyEKdYrnLnzVvnfYwX7V4LwNNnZ9noWizeSqj6BPeUDu5auXvfG31yeupM89W53HXuOt9UH9wXQyigHD/feS77RBJdyeAuImGswP5ppdQX7c2j2m6x/9cdB2cB9/XWVnvbqmI2W+LJ0zP818FRTyfcU2dmncDiXrBCJ+x6FxLc7XLI+sDUDsl42FlqbySVZyJdQAHv+tcnmckW+c4Px3jp5eudYNhpeiKKeDjoOcGdmMiyc20Pt+1ZR7FS5cGjExwemePqzf2AtSxdOxUzXuXuTajWK3ddLVN/9bN5jWXX6DzA8EyeTQNx/OiNhtiQjPrOdf/+kQm+ebA2SyRdV+cOcP1W6+87cLoWFE5MZChVlL9yt4fGffrRU4jAbZdHfY8Lmq+5qtdttWyZzgb358fSbBrwvy8eqTKb9U+oFsvzr12rGU8XiEW835VAQHjrj+ygPx7mJ67fPO9jXL6hj7X2+IeNydprmLRPdvWljton3zVkKXe3LVOuVMnYZbd6zo8fOgDnShUyxYqT81lycPdpYtJ17ytmy4iIAB8FDimlPuC66yvA2+zbbwO+7Nr+Vrtq5lZg1mXfrBqeOG1dJs/mSjx9tvalffrsLC/ZPUQwIJ7ANmzPLemJLsKWWYxyd02G1EHlj153FSOpPG/56A+YSBe5/cruWDJgl00Oxp0u1UK5wrnZHDvWJnjBJWuIhAJ8/KHjFMtVrt6cBKwvbzsVM24/s8GWCXqrZfIla/5K/Wu4cdD6ouhhZcOzOc+lez2b+uO+XuvffvsI7/tabSZOpmCVrrrXyLx6cz/BgPDk6Zq1ctiu+mim3AH+9bFT/MiutQz1NX//+5pYDHrq5WCPpdwXM1umXKk2DHNLF8qcm82zccD/UsJPubttmnbtofFUgXi00cr61dt28fDvvaKtYCki/MillnqvV+5V1Tg/Rtsyl+jg7rpfnxxDAeHA6ZmmNfAFVyAfS+Wd7+BSg3swWG1Q7oVVYMu8GHgL8AoRedL+9+PA+4FXicgR4Hb7Z4CvAceAo8A/Ae/o/GE3kitWeOe/7G97dvf+UzMExApi37NrVWdzJY5PZLhpxyAbk7G64G7Ntggs4CS7NFumNjzs6bMzhALCm16wnd+6fQ9Pn50lIPDSy7prZ20ZjDuvwempHErBzqEeYuEgL7xkjdNBeNWmpPM77VTMjKes8a19rk7OQkOHqvW//nLVX/2sT1YIB4WDwymnkmlzi+C+IRn1De6jqTynprLOSFZrFSZvUjYeCXLFxj6Pcn9udI5gQBhKNlYd6eA+ky3xhpta2w/JJoF7KlOgL2at49oXDS1q/MCXnzzHj//t9zzfiedty6y5creuiNwq0537aeckkytWmCuUiUUag3sgIJ5u4/l40aVDQK3GHWqvb71NNJYq0BcNMdRnqXy3ctcJ2Ju2D5LKl5sO8cqXq2xbY51IxudqxQHJDtgyDZ67HexjK6XclVLfV0qJUuo6pdQN9r+vKaUmlVKvVErtUUrdrpSasvdXSql3KqUuVUpdq5Ta15Ujr+OR45N89alhvvzkufl3BvafnObyjUmu2dzvVH48bXtx123tZ+tg3GPLjMzmWypDPxabUAXvUntPnZnlsg19xMJBfu1lu7ltzxAvu3w9Az2RBT/uQrCUuxXcdaXMzrWWKvpve6wTSywccDxOoK2KmfF0gbWJKP094QbPvb5aRjdy1Qf3UBD2rO/j2XMpprPW+pqb+v1tGYANyZgzd0RjLXSSp1xVzt9prcLU+H7dsG3Ao/h+ODLHzrU9+J23+1x23Guu2dj0mPS+vgnVTNGxJHqjoZbTI5uhZ/64m5J0Z/XGJsE9FrGew/3+zbrq3tuxh5xheT7KfaG84or17Fjbw43bawfszJep+4yNzeVZn4zSY78paZfnrq+OXrzbOln4WTPVqqJYrjojH8bThQ4qd6taxt03oj33FU+ornYesz/AT5xqrEqop1JVPHl6hpt3DHDbniH2n5pmLl9y3vDrtgywdbDHo9zPzebYtNDgHlu8LaOX2lNK8fTZWa6zfd9gQLjnv7+Aj75t74Ifc6FsGehhJlsibTcqQS2433aZ9SW5YmPSk8Rsp2JmfK7Aur6obUl4bZlwnS2jT3D1a9ACXLU5ycFzKWcoVav3Z0Myxmyu5Kk11gs7AM7Sae6JkG5u2DbAXKHszKh5bnSOKzYmG/YDy2oB+LFrNjVcBTTsG22eUNUTKfXxLHReir6CeuyEK7iPpwkHhXX+h05cB/esO7jXbjdr+3czntaTSpce3Df2x/juXS/nStfVoVbR9cF9NFWwr66FRCToVe72cd+8Y5CeSNBzFabRV4/b1tjBfa7gPMfSE6pVlKqN2XA/36oohVzN6A/wk6ea+2maI2NzpAtlbto+yG171lGuKh45NsVTZ2bYubaH/p4wWwfjTq27NRo331IZ+pFYkuceoViucmQszUy2xLV2cAfr0laalV90kC2D1t97djrHycksyVjIabC6fEMflwwleOGuNZ7faadiphbcQ646d/+Eqram/F7DqzcnmUgXnOqHZglVgPX2pbq7OWXMZdPoQVDpfG0VJjc3bLOU4xOnZsgWy5ycyjYt5btkKMHOtT287UU7mh6PpllCdSpTdBam0FeAC/XdtUrfd2Las23n2oRvVRHUlLvbd5/JlpzXr/4Y/uXRU3zh8TOebfo1ji8gP7UQmk2GHE3lneNMREOe4K6Pe6AnzDVb+n2Vu7ZNNiRjhIPisWU6odyt56gFd6Pc2yBfqnDg9CwbkzHmCuV5S/H2n7Te2Ju2D3LTjgF6IkG+d2Scp87Mct1W60u8dTDu1Lqn8tZaqAtV7roRptX4gWboD5OeXXHdlibX0V1kqw7uM1lOTGbYOZRwTioiwtd+4zbuevXlnt9pp2JmfK7Aut4oyVioptybDA5rZstAzet3xjC0eH/W256tuz19xBXc9ZVJulD2jI7QXLqul75oiANnZjg6lkYpuHxjb8N+YDU93X/Xy53PUiv6YmFypUqD5eK2ZZot3deKubzVsLOuL8qpqayTbzg6lnaWqvNDK/eZOuWu1Wx9cP/kwye45+ETnm06Yd4J5e6HXrDDbWfVulOt97m+wkgfdzIW5oZtAzx7LtXQ46Kv4uLhIEO9UcY6GNxDdke4ezKk47mb4N6cp87MUqxU+eWXXALA/nmsmcdPTrMmEWHH2h6ioSC37lrL154eZng279gfW23f7cx0rja4aBltGa2Qv3dkgkgwwOUbWzd8dIOtthI+Yyv3HbYlo4lHvFUlMH/FjFKK8XTNltFfUO25148fqNkyja/hlXaVzvePThAKiGNj+KFb0t3KXXvwfbGQk1Nwr8JU/3ddt62fJ0/X5qPM14TTDk7gdgXNalXVKXfrs7CQ4K5V+xv3WgndfSemKZQrnJzMONaZH45yz3qHsukTfbrOlpnOFhumh46lCvaKWN1R7n0+nvtsrkSxUnVO4vXKXe/bFwtx3dZ+iuWq8z5qako6wLq+KONzBeckt9SEqlbublvQqXM3tkxztCXzhpu3siYRYf/J1sH9iVPT3LR90FGht+0ZcmaHXL+tptzBqnXXNe4LtWWWWgoJ8OjxSa7c1OcEu+VkqDdKJBTg+ESGM9NWjXs7XLahr6ly1wt612wZ72wZvThFtM6W8VPuyViY7Wt6KJSrbEjGmloNABv6bOXuUuv69i0718zruQNcv3WAw8NzTi9E/cluMfituZrKl6hUVS24+5wA5kMH99ffsIV4OMhjJ6Y4MZGlqmoz7v2Ihb0JVWsaZNn5PriPUynFdKbEVKboCaTjcwWGeqO0eDuWhF+1jD5R12yZoFPX7j7uvliI6+0rqifrrBndwBQNBVnXG3VsmUQkuOR+klCg0ZbRt41yb8EPjk+xZ30vaxIRbto+wOMtlPt0psixiQw37ahdMt9mV34EBKdmW8+OPjOdc7pTF5xQjS6tiQmsSze3376cBALCloE4jx6boqpqydT5uHxjL2NzBaZ9VkvSl+zr7eCeLpRRyqpSiAQDzgk3FAwQkNa2DNSsmc0Drd+bgZ4wkWDAY8uMpfIkYyGu3NTHabsc0r04dj03bBugXFX851Pn2LOht+XJpF0cFepSxJOuBiZwee4LVO6RUIBLhhLcuH2AfSennARrK1smGlb26253pdpBfm0iSjQU8BxDplhx7DT3mIqxOcsO6hbhYICeSNCj3PWJWtsyiUioIaHaY19pbh2MszYRaRhDUHBsElu529UyS7VkAEL2YEA/5b6SUyFXNZWqYv/JaV5wiZXYu3H7IMfGM05QUErxD/cfdZbs0s1LN22vte1fui7BloE4e9b3OTW4oWDAqXUfnrEamNYv8AO7cyhBJBRwFlxYCNqWgZXx2zVbBuLOSks7h9pX7oCvetdlctqWqVStBTuK5WrD1Uk0FHRsmWbqRp+MN85zVSUirE9GPbbMSMrqXdixNkG5qjg5laVQrvomVKGWVJ3OljpiyYBV5w5eRaxHD6y1V4Hys27m48hYml1DCULBAHt3ruHguRQHTltruF66rnlwF7H8Za3c9es/0BP22GiA5+TtLhseTxcW/F1ZKPXzZfRIC22/NSZUS87rKCJcu7XfKRXVuBOc6/uiTKYLTGeLS7ZkoElCtctNTO13E6xSDg2nmCuUneCug/YTp2Z4+RXr+dahMf7q3h8C8EsvvoRQUAgGxPHWwXqz/+KnryVUp8R0rXsoIKzvizX4y/NxyVCC5/7sxxb1d7nVwkopd6jZU0DbNoTODzw3OscLd6313OcN7rXAVqxUGoN7OOBqYvL/qF5lB/dWyVSNVevutmWsBJzuaHzG7lRuZsusT8bY3B/j3GyeyzsU3LVyd/vpemhYfbXMQhqZjozNccM267twy85Bqgq+9MQ5tg32zBtMBnoiTlB3JxTrG66ms+7g7lLuqYKnsa0bJOPeY9Hv6/q+mufurnOfy5edKhuwTpz1eaF82avcq8qqolqfXPqJKujYMrVj0lcKkS6NEDnvlbv222/ZaQX367dZreL7T01TrlR5/9cPsWtdgl980U4+9uBx7n7gGFdu6msIFi+9bJ3T4KDRte4jqYU3MC0Va1kvIRYOtEyAdZstdlK1NxpyqjfmY2MyRl8sxA/bUO5gqapSWTl+uyYSDKB7PprZMtdu6ScUECdAt6K+S3XMVu7abtJKzq+JSXOD3UzTqQR37QRXC9xTdbZMTySISPvKPVssc2Y653xubtw+SECsOfytLBlNfzzsXPnq/y3lHvK9woDaeryVqmIyU3SCbLdoUO6pPH2xkJN474021rn3uU7aiWiQbN34glqCM+jYSiensh21ZTzVMmVL0DRbX3mpXBDBfctAnM12EOqJhLhiYx/7T03zucfP8Px4ht/50Sv445+8mo+8dS9rExFeeUV7M9B1rfvJyeyC/falIiIMxMNcvbl/wVcMnWSr3Yq9Y21P27X1IsLlG/p4bqSxYmY8XSBqt9S7E2PFio8tY3uRIs0rCtYnY9z7m7fx0/O0+YOl6rQtU60qxuashR2GeiMkIkFHubdqPNq7Yw0BwdNUsxT8Eqp6lrtW7s6i22167sfGMyhVayjrjYacK5x2gvtAT82WcSv33rpuWu3LR4IBR7lPZYpUqqqrnjtYr5t7pvuoqwwSrPcwV6o4QwHn8mWPvRKPBJ31ezX11TJgnaw6EtybKPdujR6A89yWUUrxg+PT3LbHq7hv2j7IF/ef4chompt3DPKjV1vB/ParNrDvyttRrXucHHSt+6mpbNcWxWjF62/c0rHL/8WyZcDy2dtNpmou29jHV58aRinlOSmMpaxkm4i4/OaSk1B1o3/uCQdbnlh2r2/vNVqfjDJXKJMpWH0L5apiQzKGiLBzKOEo91bB/c23bmfvzkFPIFkK7qsXzUS6SG805HTpAgua6a4Tp3s21AL53h1reOZsqr3gHg87C4rXOjQj9EXDnpyFVu5Xbupzgru+MlvfF+Xw/GujLJpkPOxZOnFsLu/47VCzsrLFstUJnSt5bMVEJEShXKVSVU5ivNYxGmRdb+2170Rwr5VCepuYuuW3w3mu3E9MZplIFxxLRnPTjgEyxQpjcwV+/8ev8AQGEWn7MkjXusPCK2U6wR++7ireeEvz1WqWA92lumOBSeHLN/Qxmyt5ZreDN9lWC2zWCjqRkPeDroPbYmbz+KHLIcfmCg3VFTuHEo5K7WsR3KOhYFvNSe0SCQWsKpQ6u2NNnQW2kJnuR0bThALiCWYvsqcrtuOFW2sJaFumptz76o5hOlskIFbeQydU9cz7biv3ZCxcVy1T8FhB2nbVM90tz921VJ79mXJbMwVXQtV9/J1R7rYt41bu5apzddoNzuvgDnDnzVudsaCam7dbwf7VV23g5h1r/H6tLdzJxOX23FcLm5IxfuUll3DHDQ2LabVEV5PUN4ro0QPgtSRKlSqRes/dvmTtWHBP1mrddRDSas9dwz/fPJhO456xA3quTF1wX8BM9yNjaS4ZSnhqs1911Qb+89dfwjVb5k/O98et46lUFbO5EvFw0JpOWbewyHS2yEBPhO1rEkzbM4hqyr3Lnnvc6m5WSjGZLjCSynu+r4moHh5m7WN57rUgrYO/25rRqjoaDhCPBJ2TfCeVe/34gVjIKHdfLhlK8H/uvL4hmbZ9bQ9/87M38Oc/de2SHl/XusP8ddQXKoGA8N7XXbXgBOJltiVQXw7pDu5JlyXhXwqpbZnOBFunS3Wu4DS9OMrdpXKb1bl3i2Sdlz0+V2hIXvc2WbEJrKTn4ydrw8GeH0t7LBmwrljbCexQK8NN5UrM5ErOz/rqQfvY05kSg/YcJrBmEI25EubdJGmX0eZKFb70xFkqVcXrrqstAKLfw0zBuiosVZQnoVpT7j5153bAdT6nHUmo+neoGltmEbz+xi1L/oDpWneYv47a4GVtb5Sh3qhHuRfLVaazJdb1Wq9pTyRIMCB2KWRjcO+0ctet6WOpPCOz1kx5/RlxC4TlV+61KpRiucqxiXRDHqGvhXL/xweO8YYPPcyH7n+eQtlanWp3i1r2+dDBfCZX8jTxaFtDH4e2j3RwPz2VZXzOmqneqfesGVqFz+ZKfPax09ywbcAjQBKu4K6ratxBWgd3d0VNoVwlILUuaT0XviPK3We2TKFc7droAbiAg3un2DoYRxbRwGSwOlWfcw1xm8x4VZ1TBdIkoao998V0+PqRjIWIhQOOLbM2EXWsC0+yrUUpZDdwNwcdGZujVFFOc5amt0VCVc/F+ct7D/OuzzxJVcHuJSTiB+LWVcNMtshs9MIoQwAAGFhJREFUthbc6weYaVumNocp67ky6yZ6eNgDz41zZCzNz9blpmq9AWWnqsbruVu3cz5KWufo1nU0uFvjuhtsGaPcV47d63vZvqana2uVXshctqGPI6NzzghmXWnh/vJr1VqqVBteY51sWsxsHj9ExFm0wyqdqx3HkL1WaSQY8FSpLAdu5a4rdq6qD+4tEqpnpnPctmeIt/7IDu59dgRgSb0R/U2Ue31lz3S2yJqeCEO9EWJhqxxy2YK7fSwf+d5x4uEgr7tuk+d+R7kXy87xupuYeqKNyj1f9gbbdb2dC+5g2YxeW6batdED0N4aqh8TkTEReca17XoReVhEnhaR/xCRpL09IiIft7cfEJGXde3Il4nfec0VfPpXXrjSh3FecvmGPrLFijN3xF0mp9HJRF/PPdhZW0Y/92gqz6jdwKSxyiF7ll21gze4HzyXoicS5JK60lOdUPVbq+DMdI5ta3r4k5+8mv/1qsu4cfsAu9YtfqiZDmaz2RIzuaLzs3uuvB4aNpiIICJOw1+358potMVyZCzNa6/b5EmWQm2B80yh4iSr/Tz3XF1C1W2T6M7UTq14Fg0FGsYPRFdYuX8CeE3dto8A71FKXQt8CbjL3v6rAPb2VwH/V0TOa8nbHw97SiIN7XPZRm/FjB4a1qjcS3YppL9y75QtA5bvrksh62vVL9vQtyyBqZ7eaM2WOXguxZWbkg3lujow1S8KnSmUmcoUbftQ+I1X7uFL73jxkq4+9ETSmWyRWVdC1d1Nm7WHhg3a920djHNmxrJlul0pA16L5ed8yoXdnvucj+eu5we5J0cWylWPcr/z5m381c9c11CWulhi4aBHuRdKK+y5K6UeAKbqNl8GPGDf/ibwBvv2VcC37d8bA2aA7q8HZ1iVaGtAjyHQyt1d5qcX7ChWGj/oThPTAhZUno8NfTHOzeSYSBc9tgzAe197FR956y0de6526YuFyBQrlCtVDg6nGvx28HrIbvRVUScFiFbqY3MF8qWqjy1TdhqYBu3At3UwzvNjGTLFyrIq913rEty8Y7Dhfj2ywQrujco97ih3ly1Tqng+g+v6orxxb+f6TOqVe6HcXc99sd+aZ4E7gH8H7gT0K3AA+EkR+Yy97Wb7/x/UP4CIvB14O8D27dsXeRiG1UxfLMyWgTjfOTzGWCrPvc+OMNAT9nZexsLM5ef8E6rhzjYxgVUOqb9g9cp9TSLSMZW2EHTQOTicIl0o+wd392RIV0Wjbh5y13gvlVDQGg9x0p4X02/bEu4Jlnpo2Br7vm2DPU5ycjmKD/rjYdb1Rfnll1zi270sIiQi1vCw2kIdrZV7txOc0VDQ08SUL1W7Wue+2OD+S8DfisgfAl8B9AShjwFXAvuAk8BDQMXvAZRSdwN3A+zdu7fNgQCG842rNyf5r4OjPHsuxS2XrOFnbvbOgGmVUNXBvlMJVfAG9HrlvlLoRN8jxyYBuGpTYz16s5nuuu2/k8EdrKTqKXsBE8dz9wR3K2AOJrQtU7tyWA7lHg4GeOT3Xtlypn7CHh42lw8SkJoPD9b8GBFvnXu3bZJYOOAZP1AoV7raobqo4K6UOgy8GkBELgNea28vA7+l9xORh4Dnln6YhvOVP/upa3j7f9vFtVv7fX1g7bkHRJo3MXXUc68Fnk7Nh1kqWrk/cmyKUEC4zGdt1mYz3c9M54iGAk5lR6cY6Alzwi6x1B58PKz7EkrOLPfBnpoto+nEiNx2mG+xlEQ0RLpYJpq3umvrx5D0hINkPXXulY4lT/1wK/dKVVGqqNWn3EVkvVJqzE6Wvhf4sL29BxClVEZEXgWUlVIHO3e4hvON9X2xlgm2vliYqoKqUl1vYoJ65b5agrsVPB87PsXu9b2+J8Fm66iemc6yxU6mdpKBeIRn8lZZplbuIuLMl9Ge+5pEY3Dv9IlmsSQiIbKFMpFgwKmLdxOPhMguY2liNFybIdTtVZigjeBu++cvA4ZE5Azwv4FeEXmnvcsXgY/bt9cD3xCRKnAWeEvHj9hwQeFOci2HctcBPRwUxy9eaZwqlEKZqzf7jwhoto7qmelcV6q5+l0rgbnrvLWNNmMPDdOW0ppEhHg4SKlSddT8SmPZMhUCUqIv2lirnoh6lXt9nXuniYaCTJStk6J71aduMW9wV0q9qcldH/TZ9wRw+RKPyXAR4U5yNU2odmi2DFjedU8kyGBPpGuLJCwU9wmuvnlJ08pzb3dmzEIYcAV095KPumxzKhtkwPUaigjb1sRJ5cqr5nXtjYY4N2MNiPNV7uFgw2yZbnru0XDAsWVq44VXmeduMHSKVso90oUmJrDUuztgrTTuE5xfpQy4SiFdyt1d495p3K+P+/i0co+GSg2v4ZWbks4SgauBRDREplimqhTb1jRe3SSiIW9Cta7OvdPEQkFntsyqUO4GQzdxN6M0KvfO2zIAP3Pz1mWf/NiKdpR7MCD0RIKedVS7UeOu0fNlkrGQJ3GZjFlqOBhotLXe99PX4tNAu2LoRbLLdRMhNT0R71J8XS+FdCl3XTWzop67wdBNPLZMnXJf3xfrytC2d758d0cfb6nEwkEiwQAb+2Oe+Sf11M9070aNu0Z77v116rwvFmauMOerhjvZbNYJ9OtVKFd9X9eeSNBprFNK2XXnXSyFdCl3HeS7OcfovB4NYDj/cSuq+jr3W3YO8v3ffYVnYuOFSjIenneVpN66Baq7VeMONc9dK3jnGKIhp4lpcBVZW370RILkS1XSBe8qTLX7a7aM44F3Wbnn65T7qqtzNxg6RSvlLiJsGbg45uj/1c9cy7Z57JX6me7dqnGHWoVM/URE7blnA+KMHlitaOtNKf8FN3oiQWeZveVIcEZDAUoVRaWqnCBvPHfDBUsiYnUPVlVjcL+YeMUV8y/A3hvzznTvVo071CYhNgZ3awWkSlWtmlLSZrgXXWnmuTvKfRkSnPqxC+VK7fmMLWO4UNELdkBtxK/Bn2QszPBs3hn7260ad6hVyzR67rUguVrq2ZvhDu7+nrtly1SrypXg7Gadu/X5LpSqLhvIrMRkuIDR1kz4Ilbu7fBj127i7EyOb9gLcljBvTu2Vb/jubcI7qvelvEOqKtHV2HlyxXHJumuLaOVe3VZSiHNt8mw4mg/tL4U0uDltdduYtdQgr/79lFnBEC3gnssHOR9P31tw8hbd3Bfk1jdCdVEZH5bBqwFPZYj2Oqyx3ypUrtSMGuoGi5k9BfvYvbc2yEYEN7x8t0cHE7xzw+fBLpT46550wu2s3PIW6nkVsDdHLLVCTy2jG9C1V5HtVhxbJKuzpZxKffCMiRUzbfJsOIkTXBvmztu2MzWwTh/e98RoDtlkK3wKPfzKLi3VO7F8oop9xVdiclg6DZaDRpbZn7CwQDveNluZ2GM5Q7uOvkdEH81vJpIeDx3n+Bu/y3ZYtllk3R3cBjUPPdQQAh18TNvvk2GFcfYMgvjDTdvYVN/rGs17q3QJ+L+eHjeeeorjVOFFQr4doJq5Z4t1jz3blav6MculCv2eOHuLsZu6twNK44T3I1yb4toKMif/9Q1HDyX6kqNeyt0wFztlTJgTX1sdYXhTqg6nnsXlbt+7HzJ8ty7acmACe6GVcDG/jixcKDj0x8vZF5xxYa2Gp86TTBg9SWsdr8dauuo+lky4EqolsrLsniGUe6Gi4437t3KbbuHuv5hN3SG3mho1VfKaBLRkG+NO9TWVHWXQnZ1toyriSnf5fVToQ3PXUQ+JiJjIvKMa9v1IvKwiDwtIv8hIkl7e1hE7rG3HxKR3+vmwRsuDKKhYEPJnWH18vMv3M5P3rB5pQ+jLXqiQd+hYVBbJ8BdCtndBbJrTVOFUrWrFhC0l1D9BPCaum0fAd6jlLoW+BJwl739TiBqb78Z+B8isrMjR2owGFYFv/HKPfzk9edHcL/z5m38RJNj1bZMplimUKog0v3BYaDHD3RfubezzN4DPgH6MuAB+/Y3gW8AfwgoICEiISAOFIFUpw7WYDAYFsKvvezSpvcFA0I0FCBXrKCwgm83E9Ru5Z4vVVaFcvfjWeAO+/adgO5R/jyQAYaBU8BfK6Wm/B5ARN4uIvtEZN/4+PgiD8NgMBgWj16Kr9urMEGtGqxQqtoJ1RX23JvwS8A7RORxoA9LoQO8AKgAm4FLgHeLyC6/B1BK3a2U2quU2rtu3bpFHobBYDAsHr1IdrcXxwYIBIRIMOCMH+jmKkywyGoZpdRh4NUAInIZ8Fr7rp8H7lVKlYAxEXkQ2Asc68CxGgwGQ0dJRINkC5b/vRzVWtFwwBk/sCqVu4ist/8PAO8FPmzfdQp4hX1fArgVOLz0wzQYDIbOE4+EyJaWxwMHqzJMjx/o9smknVLIzwAPA5eLyBkR+WXgTSLyHFbgPgd83N79/wG9IvIs8BjwcaXUU905dIPBYFgaiUiQbKG8LEoarCapQskqvVzxJial1Jua3PVBn33TWAlWg8FgWPX0RILMZEt25cxyKPeAo9y77vF39dENBoNhFWMttVemUK52ve4cLFsmbyv3bnbDggnuBoPhIkYvkr0cHjhYtkwqX3JudxMT3A0Gw0WLXiR7OTxwsJT7bK7k3O4mJrgbDIaLFku5l5fFAwerFFIHd6PcDQaDoUv0RINUFczmSstTLeNS7qt1/IDBYDCc9/SEa6sxLUudezhQW9LPJFQNBoOhO/S4FtFeloSq6wRiSiENBoOhS/S4Vv9aDlvGXW5plLvBYDB0iUSkptyXq4lJYxKqBoPB0CXiy6zc3WrdKHeDwWDoEh7lvix17gHf293ABHeDwXDR4lXuy9GhapS7wWAwdJ1EdPmqV+qfo9uzbExwNxgMFy094eUthYx6SiGNcjcYDIau4LFllmn8gPN8RrkbDAZDd4iEAoSDAiyvchepLZjdLUxwNxgMFzU9dsXMssxzt58jFgoiIl19rnaW2fuYiIyJyDOubdeLyMMi8rSI/IeIJO3tbxaRJ13/qiJyQzf/AIPBYFgKukt1OWbL6OdYjhNJO8/wCeA1dds+ArxHKXUt8CXgLgCl1KeVUjcopW4A3gIcV0o92cHjNRgMho7iBPflsGVcyr3bzBvclVIPAFP/f3t3HyNXVcZx/PvbnX3pdoulsCUIrUDsogjy4kZrQCRgSAFjNYjSkEgCsSGQiGIgNdYQ/E9j0JoYmqZQULAYeRPQSGrF1EREdhGhhQoURYpgF3kzamgLj3/c0zIuM93Z2Xnpnvl9ks3ce86Zuc+Zs31698y9cyYUDwOb0vYG4NwKT10G3Dqt6MzMmmzPtEyrvvK3Vceq9whbgKVp+zxgQYU2nwfWV3sBScsljUoaHR8frzMMM7Pp2XPm3pLvlklJvRXHqje5XwRcKmkMmAPsLK+U9BHgPxGxudKTASJiTUSMRMTI0NBQnWGYmU3P28m9dTcxteLMvTR5k3eKiK3AmQCShoFzJjQ5n32ctZuZ7S8G+kr0lrro6mru1Svw9rx+K77Hpq7kLml+ROyQ1AWsBFaX1XUBnwM+1pgQzcyaZ6CnuyU3MEH5mft+kNwlrQdOAw6WtB24GhiUdFlqcgewruwppwLPRcQzDY7VzKzhznj/fAb76zrPnbK9Z+4t+M9k0h5FxLIqVauqtP8NsHgaMZmZtcySYw9lybGHtuRYpS7RpdacufsOVTOzFpFEX6k100BO7mZmLXTQYC/zBnubfpzWTDSZmRkA67+4mLkDPU0/jpO7mVkLLZg30JLjeFrGzCxDTu5mZhlycjczy5CTu5lZhpzczcwy5ORuZpYhJ3czsww5uZuZZcjJ3cwsQ07uZmYZcnI3M8uQk7uZWYYmTe6SbpC0Q9LmsrLjJT0g6TFJ90g6oKzug6luS6rvb1bwZmZWWS1n7jcCSyaUrQVWRMRxwJ3AlQCSSsDNwCUR8QGK5fl2NSpYMzOrzaTJPSI2AS9PKB4GNqXtDcC5aftM4NGI+FN67j8j4s0GxWpmZjWqd859C7A0bZ8HLEjbw0BIuk/Sw5KuqvYCkpZLGpU0Oj4+XmcYZmZWSb3J/SLgUkljwBxgZyovAacAF6THz0g6o9ILRMSaiBiJiJGhoaE6wzAzs0rqWokpIrZSTMEgaRg4J1VtBzZFxEup7hfAScDG6YdqZma1quvMXdL89NgFrARWp6r7gOMkDaQPVz8OPN6IQM3MrHa1XAq5HngAOFrSdkkXA8skPQlsBf4OrAOIiFeAa4GHgEeAhyPi580K3szMKpt0WiYillWpWlWl/c0Ul0OamVmb+A5VM7MMObmbmWXIyd3MLENO7mZmGXJyNzPLkJO7mVmGnNzNzDLk5G5mliEndzOzDDm5m5llyMndzCxDTu5mZhlycjczy5CTu5lZhpzczcwy5ORuZpYhJ3czswzVsszeDZJ2SNpcVna8pAckPSbpHkkHpPIjJP1X0iPpZ3X1VzYzs2ap5cz9RmDJhLK1wIqIOA64E7iyrG5bRJyQfi5pTJhmZjYVtayhuknSEROKh4FNaXsDcB/wjYZGtp+a1TOLwd7Bfbbp6e6pXN7VM+lzZ5VmTTmmgZ4Bdr+1e8rP25f+Un9DX68Rert7975/faW+im261b23zUDPQNNj6i/17z1eb3dvxTalrlLFcW9FfNWUv5fl6vn9q2Z272x2vrkTgC5VPo8sf/8mxre/6evum/TfbzWNfF9rpYiYvFGR3O+NiGPT/u+Ab0fEXZKuAK6JiDmp3RbgSeB1YGVE/LbKay4HlgMsXLjwQ88+++y0O2Nm1kkkjUXESKW6ej9QvQi4VNIYMAfYmcpfABZGxInAFcCP98zHTxQRayJiJCJGhoaG6gzDzMwqmXRappKI2AqcCSBpGDgnlb8BvJG2xyRto5jCGW1ItGZmVpO6ztwlzU+PXcBKYHXaH5LUnbaPAhYBzzQmVDMzq9WkZ+6S1gOnAQdL2g5cDQxKuiw1uQNYl7ZPBb4paRfwFnBJRLzc8KjNzGyfarlaZlmVqlUV2t4O3D7doMzMbHp8h6qZWYac3M3MMuTkbmaWISd3M7MM1XSHatODkMaB6dyiejDwUoPCmSk6sc/Qmf12nzvHVPv9noioeBfofpHcp0vSaLVbcHPViX2Gzuy3+9w5GtlvT8uYmWXIyd3MLEO5JPc17Q6gDTqxz9CZ/XafO0fD+p3FnLuZmf2/XM7czcysjJO7mVmGZnRyl7RE0p8lPS1pRbvjaQZJCyTdL+lxSVskXZ7K50naIOmp9Hhgu2NtBkndkv4o6d60f6SkB9OY/0TS/rce2zRImivpNklbJT0h6aOdMNaSvpJ+vzdLWi+pP8exlnSDpB2SNpeVVRxfFb6f+v+opJOmcqwZm9zT98b/ADgLOAZYJumY9kbVFLuBr0bEMcBi4LLUzxXAxohYBGxM+zm6HHiibP9bwHcj4r3AK8DFbYmqeVYBv4yI9wHHU/Q967GWdBjwJWAkLeXZDZxPnmN9I7BkQlm18T2LYk2MRRRLkl43lQPN2OQOfBh4OiKeiYidwK3A0jbH1HAR8UJEPJy2/0Xxj/0wir7elJrdBHy6PRE2j6TDKVb5Wpv2BZwO3JaaZNVvSe+iWBPheoCI2BkRr9IBY03x9eOzJJWAAYolO7Mb64jYBExc46La+C4FfhiF3wNzJR1a67FmcnI/DHiubH97KstWWoD8ROBB4JCIeCFVvQgc0qawmul7wFUUC78AHAS8GhG7035uY34kMA6sS1NRayXNJvOxjojnge8Af6NI6q8BY+Q91uWqje+0ctxMTu4dRdIgxUIoX46I18vrorieNatrWiV9EtgREWPtjqWFSsBJwHVpkfl/M2EKJtOxPpDiLPVI4N3AbN45ddERGjm+Mzm5Pw8sKNs/PJVlR1IPRWK/JSLuSMX/2PMnWnrc0a74muRk4FOS/kox5XY6xXz03PSnO+Q35tuB7RHxYNq/jSLZ5z7WnwD+EhHjEbGLYunOk8l7rMtVG99p5biZnNwfAhalT9R7KT6AubvNMTVcmme+HngiIq4tq7obuDBtXwj8rNWxNVNEfC0iDo+IIyjG9tcRcQFwP/DZ1CyrfkfEi8Bzko5ORWcAj5P5WFNMxyyWNJB+3/f0O9uxnqDa+N4NfCFdNbMYeK1s+mZyETFjf4CzgSeBbcDX2x1Pk/p4CsWfaY8Cj6SfsynmnzcCTwG/Aua1O9YmvgenAfem7aOAPwBPAz8F+todX4P7egIwmsb7LuDAThhr4BpgK7AZ+BHQl+NYA+spPlfYRfGX2sXVxhcQxRWB24DHKK4mqvlY/voBM7MMzeRpGTMzq8LJ3cwsQ07uZmYZcnI3M8uQk7uZWYac3M3MMuTkbmaWof8Bey99XyDSgggAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Question 3 : Upload an image to your google drive, Use plt.imread to read image from the google drive and then print that image using plt.imshow\n",
        "\n",
        "\n",
        "## Answer 3 : \n",
        "\n",
        "## 1) make sure drive is loaded and then upload a test image onto your drive\n"
      ],
      "metadata": {
        "id": "mC8rNbeKxiaF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Answer:\n",
        "\n",
        "from google.colab import drive \n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "# Read image\n",
        "plt.imread('/content/gdrive/MyDrive/FMML_Assignments/Sunset.jpg')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oiZ4JqUA0g-3",
        "outputId": "21527589-8351-4c4d-aa70-fdf6b4d9c562"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 54,  30,  88],\n",
              "        [ 54,  30,  88],\n",
              "        [ 54,  30,  88],\n",
              "        ...,\n",
              "        [ 21,   8,  62],\n",
              "        [ 21,   8,  62],\n",
              "        [ 21,   8,  62]],\n",
              "\n",
              "       [[ 54,  30,  88],\n",
              "        [ 54,  30,  88],\n",
              "        [ 54,  30,  88],\n",
              "        ...,\n",
              "        [ 22,   9,  63],\n",
              "        [ 22,   9,  63],\n",
              "        [ 22,   9,  63]],\n",
              "\n",
              "       [[ 54,  30,  88],\n",
              "        [ 54,  30,  88],\n",
              "        [ 54,  30,  88],\n",
              "        ...,\n",
              "        [ 22,   9,  63],\n",
              "        [ 22,   9,  63],\n",
              "        [ 22,   9,  63]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[ 51,  31,  82],\n",
              "        [ 51,  31,  82],\n",
              "        [ 50,  32,  82],\n",
              "        ...,\n",
              "        [243, 172, 188],\n",
              "        [242, 171, 189],\n",
              "        [242, 171, 189]],\n",
              "\n",
              "       [[ 48,  28,  79],\n",
              "        [ 48,  28,  79],\n",
              "        [ 47,  29,  79],\n",
              "        ...,\n",
              "        [232, 160, 180],\n",
              "        [231, 159, 179],\n",
              "        [231, 159, 179]],\n",
              "\n",
              "       [[ 46,  26,  77],\n",
              "        [ 46,  26,  77],\n",
              "        [ 45,  27,  77],\n",
              "        ...,\n",
              "        [224, 152, 174],\n",
              "        [223, 151, 173],\n",
              "        [222, 150, 172]]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print image\n",
        "plt.imshow(plt.imread('/content/gdrive/MyDrive/FMML_Assignments/Sunset.jpg'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "id": "nd1A9YVB4b7G",
        "outputId": "71d5b9cb-f5e4-4d7f-9412-d82c7ddf6900"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fe5d69e7590>"
            ]
          },
          "metadata": {},
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}