FMML_Aug22_M1Lab1_PythonBasics.ipynb

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        "<a href=\"https://colab.research.google.com/github/itsdivya1309/SRISHTI-23/blob/main/FMML_Aug22_M1Lab1_PythonBasics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# Getting Started\n",
        "\n",
        "FMML Module 1, Lab 1 <br>\n",
        " Module Coordinator: Amit Pandey ( amit.pandey@research.iiit.ac.in ) <br>\n",
        " Release date: Aug 2022 <br>\n",
        "\n"
      ],
      "metadata": {
        "id": "TMVRKfbpTAeR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### In this notebook we will be covering the very basics of Python and some basic libraries such as Numpy, Matplotlib and Nltk.\n",
        "#### It is suggested that you go through each line and try some examples."
      ],
      "metadata": {
        "id": "F-XjWYLBTI0f"
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    {
      "cell_type": "markdown",
      "source": [
        "#### Section 1 - Python : Basic data types and indexing."
      ],
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        "id": "wEyEnIhnTdhB"
      }
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    {
      "cell_type": "code",
      "source": [
        "## Strings\n",
        "'''\n",
        "A string is a collection of one or more characters put in a single quote,\n",
        " double-quote or triple quote. In python there is no character data type,\n",
        " a character is a string of length one. It is represented by str class.\n",
        "\n",
        "String can have special characters. String can be indexed\n",
        "\n",
        "'''\n",
        "\n",
        "\n",
        "name = 'First Lab'\n",
        "name_extended = name + 'Module 1'\n",
        "last_element_string = name[-1] # -1 in python is index of the last element. \n",
        "## indexing is important for preprocessing of the raw data. "
      ],
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "print(name ,\"\\n\", name_extended, \"\\n\", last_element_string)"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "DPokcpuLQO6s",
        "outputId": "ab832713-3480-4b89-acf1-bca44570a567"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First Lab \n",
            " First LabModule 1 \n",
            " b\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## List\n",
        "\n",
        "'''\n",
        "Lists are ordered collection of data, and are very similar to arrays, \n",
        "It is very flexible as the items in a list do not need to be of the same type.\n",
        "'''\n",
        "\n",
        "name_list = ['First Lab', 3 , '1.1' , 'Lab 1'] ## notice elements are of different data type.\n",
        "name_list.extend(['Module 1']) ## adding elements to list (Read about append method as well).\n",
        "element_2 = name_list[1] ## Just like other languages, the index starts from 0.\n",
        "two_dimesional_list = [[1,2],[3,4]] ## practice with multi-dimensional lists and arrays\n",
        "## you would soon be required to handle 4 dimensional data :p :)\n",
        "name_list[2] = '1.111' ##list elements can be changed"
      ],
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      "outputs": []
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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."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vblBrQflE9Cs",
        "outputId": "3c3b490a-8e4e-45ea-d006-debb3333a4ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['First Lab', 3, '1.111', 'Lab 1', 'Module 1']\n",
            "3\n",
            "[[1, 2], [3, 4]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Tuples\n",
        "\n",
        "name_tuple = ('First Lab', 1, (2,3),[1,1,'list having string']) ## A tuple can have a tuple.\n",
        " \n",
        "print(name_tuple[2])\n",
        "print(\"first indexing the last element of the tuple, which is a list and \\n then last element of the list (a string) and then second last element of the string:\")\n",
        "print(name_tuple[-1][-1][-2]) \n"
      ],
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          "text": [
            "(2, 3)\n",
            "first indexing the last element of the tuple, which is a list and \n",
            " then last element of the list (a string) and then second last element of the string:\n",
            "n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## tuples are immutable, read the error !\n",
        " #usued when passing parameters etc. and dont want them to be changed\n",
        "\n",
        "# name_tuple[1] = 2     --> TypeError: 'tuple' object does not support item assignment\n",
        "\n",
        "# If we have to change certain element of tuple\n",
        "# We first convert the tuple to a list, change the element and then convert it back to a tuple\n",
        "name_list = list(name_tuple)\n",
        "name_list[1] = 2\n",
        "name_tuple = tuple(name_list)\n",
        "print(name_tuple)"
      ],
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      "outputs": [
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          "name": "stdout",
          "text": [
            "('First Lab', 2, (2, 3), [1, 1, 'list having string'])\n"
          ]
        }
      ]
    },
    {
      "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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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{1, 2, 3, 5, 6, 'Lab1'}\n",
            "1\n",
            "{2, 3, (1, 2), 5, 6, 'Lab1'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##it is not indexable\n",
        "#set_unique[1]    --> TypeError: 'set' object is not subscriptable\n",
        "\n",
        "# We can use for loop or in keyword to excess elements of a set\n",
        "for val in set_unique:\n",
        "  print(val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QgHlNSRPampd",
        "outputId": "9bf093de-5c07-45b0-930c-9c84badcf0a4"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n",
            "3\n",
            "(1, 2)\n",
            "5\n",
            "6\n",
            "Lab1\n"
          ]
        }
      ]
    },
    {
      "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",
        "There are certain sources on web which show that, as of version 3.7 of Python, dictionary is ordered.\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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      "outputs": [
        {
          "output_type": "stream",
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          "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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        "id": "hizs_paeceA5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Answer 0:\n",
        "\n",
        "\n",
        "###### Strings:"
      ],
      "metadata": {
        "id": "nsAQTGSDLPw8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "name = 'SRiSHTI-23 IIIT Hyderabad'\n",
        "\n",
        "# Method1: split()\n",
        "print('Splitting the string: ',name.split(' '))\n",
        "\n",
        "# Method2: upper()\n",
        "print('Capitalize the whole string: ', name.upper())\n",
        "\n",
        "# Method3: format()\n",
        "name += ' Tutorial {}'\n",
        "print('Formatting the string: ',name.format(1))"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "AK9ogujoLO5Q",
        "outputId": "c3b50569-dc7d-439a-afcf-f29936494e00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Splitting the string:  ['SRiSHTI-23', 'IIIT', 'Hyderabad']\n",
            "Capitalize the whole string:  SRISHTI-23 IIIT HYDERABAD\n",
            "Formatting the string:  SRiSHTI-23 IIIT Hyderabad Tutorial 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### List:"
      ],
      "metadata": {
        "id": "u9YkGijJNl6p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lst = ['SRISHTI',23,'IIIT','Hyderabad']\n",
        "\n",
        "# Method1: append()\n",
        "lst.append('Day8')\n",
        "print('Adding an element: ', lst)\n",
        "\n",
        "# Method2: extend()\n",
        "lst1 = ['Tutorial',1]\n",
        "lst.extend(lst1)\n",
        "print('Adding an iterable: ', lst)\n",
        "\n",
        "# Method3: remove()\n",
        "lst.remove(lst[4])\n",
        "print(lst)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xt2qSufZL1rQ",
        "outputId": "93fdc191-9029-4157-8ef1-0d7716c83cd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Adding an element:  ['SRISHTI', 23, 'IIIT', 'Hyderabad', 'Day8']\n",
            "Adding an iterable:  ['SRISHTI', 23, 'IIIT', 'Hyderabad', 'Day8', 'Tutorial', 1]\n",
            "['SRISHTI', 23, 'IIIT', 'Hyderabad', 'Tutorial', 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### Tuples:"
      ],
      "metadata": {
        "id": "cDA_aVFLPs7c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tpl = ('IIIT','Hyderabad',2023)\n",
        "\n",
        "# Method1: count()\n",
        "print('2023 occurs ',tpl.count(2023),' times')\n",
        "\n",
        "# Method2: index()\n",
        "print('Index of 2023 is: ', tpl.index(2023))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f_Ys-SpwPvxV",
        "outputId": "b2a070ba-310a-4c36-8565-2d2f2ae6fb64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023 occurs  1  times\n",
            "Index of 2023 is:  2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### Sets:"
      ],
      "metadata": {
        "id": "3SaPqK5BQqxC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "set1 = {'This','is','a','long','Tutorial'}\n",
        "\n",
        "# Method1: add()\n",
        "set1.add('May')\n",
        "print('Adding an element to the set: ', set1)\n",
        "\n",
        "# Method2: remove()\n",
        "set1.remove('May')\n",
        "print('Removing an element: ', set1)\n",
        "\n",
        "# Method3: intersection()\n",
        "set2 = set(lst)\n",
        "set3 = set1.intersection(set2)\n",
        "print('Common elements of two sets: ',set3)\n",
        "\n",
        "# Method4: diffenerce()\n",
        "set4 = set1.difference(set2)\n",
        "print('Difference between two sets: ', set4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2j8KLGH3QwLP",
        "outputId": "d110e1d0-5a4b-4254-f985-45fc5516f65d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Adding an element to the set:  {'May', 'This', 'Tutorial', 'is', 'long', 'a'}\n",
            "Removing an element:  {'This', 'Tutorial', 'is', 'long', 'a'}\n",
            "Common elements of two sets:  {'Tutorial'}\n",
            "Difference between two sets:  {'a', 'long', 'This', 'is'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### Dictionary"
      ],
      "metadata": {
        "id": "zyNRQt6bSaaK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dict = {'Tutorial': 1,\n",
        "        'IIIT': 'Hyderabad',\n",
        "        'Month': 'May'}\n",
        "\n",
        "# Method1: keys()\n",
        "print('Keys of the dictionary: ', dict.keys())\n",
        "\n",
        "# Method2: values()\n",
        "print('Values of the dictionary: ', dict.values())\n",
        "\n",
        "# Method3: pop()\n",
        "print('Remove the value: ', dict.pop('Month'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IEmHfO3xSg72",
        "outputId": "d76e24da-3d47-4140-86e0-fdb780085502"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keys of the dictionary:  dict_keys(['Tutorial', 'IIIT', 'Month'])\n",
            "Values of the dictionary:  dict_values([1, 'Hyderabad', 'May'])\n",
            "Remove the value:  May\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": "52a029dd-5c64-4746-934b-b6c1317d7eea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# asn = add_new()   --> TypeError: add_new() missing 2 required positional arguments: 'a' and 'b'\n",
        "\n",
        "# We need to provide two arguments\n",
        "asn = add_new('Late','Submission')\n",
        "print(asn)"
      ],
      "metadata": {
        "id": "aVLU3Bpu0YJV",
        "outputId": "12083d5f-15a2-4239-b8de-6fa316304510",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LateSubmission\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def check_even_list(num_list):\n",
        "    \n",
        "    even_numbers = []\n",
        "    \n",
        "    # Go through each number\n",
        "    for number in num_list:\n",
        "        # Once we get a \"hit\" on an even number, we append the even number\n",
        "        if number % 2 == 0:\n",
        "            even_numbers.append(number)\n",
        "        # Don't do anything if its not even\n",
        "        else:\n",
        "            pass\n",
        "    # Notice the indentation! This ensures we run through the entire for loop    \n",
        "    return even_numbers"
      ],
      "metadata": {
        "id": "aRa47XCsZLsh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Question 1 :\n",
        "\n",
        "##### Define a function, which takes in two strings A and B. Reverses the first string A, adds it to B, and returns the final string.\n",
        "\n",
        "\n",
        "#### Question 2 : \n",
        "##### Given a list having Names, work_hours, and gender, Write a function to print name of the female worker that worked the most hours. Also how much do should she be paid if the pay is $ 20 per hour.\n",
        "\n",
        "##### work_hours = [('Abby',100 , 'F'),('Billy',400, 'M'),('Cassie',800,'F'), ('Maggi',600,'F'),('Alex',500,'M'),('Raj',225,'M'),('Penny',920,'F'),('Ben',300,'M')]\n",
        "\n",
        "##### Answer : the female worker that worked the most hours is Penny and she should be paid 18400"
      ],
      "metadata": {
        "id": "heD0_u4D01jp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### Answer 1:"
      ],
      "metadata": {
        "id": "wIIrJuNqWSE3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def new_str(str1,str2):\n",
        "  str1 = str1[::-1]\n",
        "  str3 = str2+str1\n",
        "  return str3\n",
        "ans = new_str('abcd','pqrs')\n",
        "print(ans)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xcVEPP53WVdm",
        "outputId": "aa544a14-6f08-4e78-e0fd-3935b3542531"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pqrsdcba\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### Answer 2:"
      ],
      "metadata": {
        "id": "4je1gwTNX49K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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",
        "max_work = 0\n",
        "employee = 'Name'\n",
        "for i in range(len(work_hours)):\n",
        "  if work_hours[i][2] == 'F':\n",
        "    if work_hours[i][1] > max_work:\n",
        "      max_work = work_hours[i][1]\n",
        "      employee = work_hours[i][0]\n",
        "charge = 20\n",
        "pay = max_work * charge\n",
        "print('the female worker that worked the most hours is', employee,' and she should be paid ', pay)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IwrX09tIX8W7",
        "outputId": "96d58136-0e76-472b-ebf0-f01609ed91c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the female worker that worked the most hours is Penny  and she should be paid  18400\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Section 3 - Libraries and Reading data."
      ],
      "metadata": {
        "id": "T9fGzOAKXhtt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Numpy - One of the most used libraries - supports for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays."
      ],
      "metadata": {
        "id": "TKVyOsBKYZXa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "_ODaZ44RXkyg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "a = np.array([1,1,2,3,4,5,5,6,1]) ## np.array converts given list to array\n",
        "\n",
        "b = a>1 ## important comparison operation, where frequently used in manipulation and image processing.\n",
        "\n",
        "print(b)\n",
        "print(a[b]) ## [printing only those values in a which are greater than 1]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29vs7Iicavlg",
        "outputId": "d1fb9a2c-faef-471e-8da8-dc818bf4a09c"
      },
      "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": "6767cdac-6ef4-4850-eec3-5923fef384bf"
      },
      "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": 69
        }
      ]
    },
    {
      "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": "9fda2bfb-6cdc-4fcd-f2ab-101e1191688a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10, 11, 12])"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a_range[:,2] #printing all the rows of second column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HbDG7M3QuCDQ",
        "outputId": "bc766b71-2b63-47df-a9ce-da32d7e339b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([12, 15, 18])"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "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": "41a24f09-882c-40ec-9cd2-6e3b83ecb702"
      },
      "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": 72
        }
      ]
    },
    {
      "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": "e74da250-6ff1-4435-e207-691ee01196e5"
      },
      "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": 73
        }
      ]
    },
    {
      "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": "35979a96-6316-473d-f725-a8b190241f48"
      },
      "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": 74
        }
      ]
    },
    {
      "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": "5f1bda1f-f4a6-484f-d744-fb427845c232"
      },
      "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": 75
        }
      ]
    },
    {
      "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": "05ce2f5f-3373-45e9-b081-a6ec6d7e408a"
      },
      "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": 76
        }
      ]
    },
    {
      "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": "dc2f50af-af75-42ab-cec8-a22fe994197d"
      },
      "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": 77
        }
      ]
    },
    {
      "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": "d19b65f7-fc0b-4655-e948-7ce2e559e7a6"
      },
      "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": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Question 3 : \n",
        "\n",
        "##### a)Create a 5x5 matrix of the following form, \n",
        "\n",
        "##### [[1,1]\n",
        "#####  [2,2]]\n",
        "\n",
        "#####  i.e. each row is increasing and has repetive elements.\n",
        "\n",
        "######  Hint : you can use hstack, vstack  etc.\n",
        "\n",
        "##### b) find dot product of the matrix with any matrix. (Figure out the size/ shape of the matrix)"
      ],
      "metadata": {
        "id": "uqWUH6Rv4OhT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### Answer 3:"
      ],
      "metadata": {
        "id": "wOUTAij7aDAj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a1 = np.arange(1,6).reshape(5,1)\n",
        "a2 = np.array(a1)\n",
        "A = np.hstack((a1,a2))\n",
        "print(A)\n",
        "\n",
        "I = np.eye(2)\n",
        "print(np.dot(A,I))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gyTEY7ugaFo0",
        "outputId": "58c79928-0ae4-4081-8c6c-c1eba59ac233"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 1]\n",
            " [2 2]\n",
            " [3 3]\n",
            " [4 4]\n",
            " [5 5]]\n",
            "[[1. 1.]\n",
            " [2. 2.]\n",
            " [3. 3.]\n",
            " [4. 4.]\n",
            " [5. 5.]]\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": "7b3f6073-4e11-4ffd-c972-304d39004da6"
      },
      "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": "4787116b-79be-4650-e73e-dad727888f33"
      },
      "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": 92
        }
      ]
    },
    {
      "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": 397
        },
        "id": "CgXsQFpobVGk",
        "outputId": "eb310332-0da6-44b0-9c18-1c087453c580"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-93-ad5bb400a483>:3: FutureWarning: In a future version of pandas all arguments of read_csv except for the argument 'filepath_or_buffer' will be keyword-only.\n",
            "  df = pd.read_csv('/content/sample_data/california_housing_test.csv','r')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   longitude,\"latitude\",\"housing_median_age\",\"total_  ooms\",\"total_bed  \\\n",
              "0  -122.050000,37.370000,27.000000,3885.000000,66...               NaN   \n",
              "1  -118.300000,34.260000,43.000000,1510.000000,31...               NaN   \n",
              "2  -117.810000,33.780000,27.000000,3589.000000,50...               NaN   \n",
              "3  -118.360000,33.820000,28.000000,67.000000,15.0...               NaN   \n",
              "4  -119.670000,36.330000,19.000000,1241.000000,24...               NaN   \n",
              "5  -119.560000,36.510000,37.000000,1018.000000,21...               NaN   \n",
              "6  -121.430000,38.630000,43.000000,1009.000000,22...               NaN   \n",
              "7  -120.650000,35.480000,19.000000,2310.000000,47...               NaN   \n",
              "8  -122.840000,38.400000,15.000000,3080.000000,61...               NaN   \n",
              "9  -118.020000,34.080000,31.000000,2402.000000,63...               NaN   \n",
              "\n",
              "   ooms\",\"population\",\"households\",\"median_income\",\"median_house_value\"  \n",
              "0                                                NaN                     \n",
              "1                                                NaN                     \n",
              "2                                                NaN                     \n",
              "3                                                NaN                     \n",
              "4                                                NaN                     \n",
              "5                                                NaN                     \n",
              "6                                                NaN                     \n",
              "7                                                NaN                     \n",
              "8                                                NaN                     \n",
              "9                                                NaN                     "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6558e799-efe4-4968-bea3-9c93591f63c3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude,\"latitude\",\"housing_median_age\",\"total_</th>\n",
              "      <th>ooms\",\"total_bed</th>\n",
              "      <th>ooms\",\"population\",\"households\",\"median_income\",\"median_house_value\"</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-122.050000,37.370000,27.000000,3885.000000,66...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-118.300000,34.260000,43.000000,1510.000000,31...</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>2</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-118.360000,33.820000,28.000000,67.000000,15.0...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-119.670000,36.330000,19.000000,1241.000000,24...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-119.560000,36.510000,37.000000,1018.000000,21...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-121.430000,38.630000,43.000000,1009.000000,22...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-120.650000,35.480000,19.000000,2310.000000,47...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-122.840000,38.400000,15.000000,3080.000000,61...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-118.020000,34.080000,31.000000,2402.000000,63...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6558e799-efe4-4968-bea3-9c93591f63c3')\"\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",
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              "      \n",
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              "      flex-wrap:wrap;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6558e799-efe4-4968-bea3-9c93591f63c3 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-6558e799-efe4-4968-bea3-9c93591f63c3');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df.columns), df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iMMaPe3gduKb",
        "outputId": "be3ea893-ef01-4e31-d5dd-52fa84ad7393"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3,\n",
              " Index(['longitude,\"latitude\",\"housing_median_age\",\"total_', 'ooms\",\"total_bed',\n",
              "        'ooms\",\"population\",\"households\",\"median_income\",\"median_house_value\"'],\n",
              "       dtype='object'))"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "GT1dA9Q3eRQU",
        "outputId": "86af96e4-ee60-424c-a1de-d994b5c97538"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'longitude,\"latitude\",\"housing_median_age\",\"total_'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['longitude,\"latitude\",\"housing_median_age\",\"total_'][:5] "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oEZhq9rBe-Hb",
        "outputId": "5a0227c6-c30a-45ea-d4b1-770cdeac3379"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    -122.050000,37.370000,27.000000,3885.000000,66...\n",
              "1    -118.300000,34.260000,43.000000,1510.000000,31...\n",
              "2    -117.810000,33.780000,27.000000,3589.000000,50...\n",
              "3    -118.360000,33.820000,28.000000,67.000000,15.0...\n",
              "4    -119.670000,36.330000,19.000000,1241.000000,24...\n",
              "Name: longitude,\"latitude\",\"housing_median_age\",\"total_, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.rename(columns = {'longitude,\"latitude\",\"housing_median_age\",\"total_':'Detail1'}) ##rename column names as at times it makes it easier for us"
      ],
      "metadata": {
        "id": "-hpbslfWfwy3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "C0gpXPf1hN5Q",
        "outputId": "34c1296d-09eb-47ea-a0bc-7dd88c6cea2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             Detail1  ooms\",\"total_bed  \\\n",
              "0  -122.050000,37.370000,27.000000,3885.000000,66...               NaN   \n",
              "1  -118.300000,34.260000,43.000000,1510.000000,31...               NaN   \n",
              "2  -117.810000,33.780000,27.000000,3589.000000,50...               NaN   \n",
              "\n",
              "   ooms\",\"population\",\"households\",\"median_income\",\"median_house_value\"  \n",
              "0                                                NaN                     \n",
              "1                                                NaN                     \n",
              "2                                                NaN                     "
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "      <th>Detail1</th>\n",
              "      <th>ooms\",\"total_bed</th>\n",
              "      <th>ooms\",\"population\",\"households\",\"median_income\",\"median_house_value\"</th>\n",
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              "      <td>NaN</td>\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",
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              "  </svg>\n",
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              "    }\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-be5c8e47-badd-4b9a-9b79-259c7a3c3404 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-be5c8e47-badd-4b9a-9b79-259c7a3c3404');\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": 99
        }
      ]
    },
    {
      "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": "3d26a7d3-4786-4855-dfb3-fee801a7cc86"
      },
      "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": 100
        }
      ]
    },
    {
      "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": 430
        },
        "id": "tzunFbpUxKAP",
        "outputId": "f77bbeee-0f50-4a6a-9fe5-35d715c266b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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": 430
        },
        "id": "p7l83i5SxW4k",
        "outputId": "5a154b9c-9715-4d1b-f835-9330d343e9cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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": "8f8e2a9c-3496-4f6b-9501-e30a739a2e6b"
      },
      "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": 106
        }
      ]
    },
    {
      "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": "ad302390-0537-40e9-83f4-af17e796c8cd"
      },
      "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"
            ],
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
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              "      <td>3</td>\n",
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              "      <th>b</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-59f88d66-dda4-466b-afb8-184df6d21ad4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\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",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-59f88d66-dda4-466b-afb8-184df6d21ad4 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-59f88d66-dda4-466b-afb8-184df6d21ad4');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe_from_array.loc[['a','b'],['w','x']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "jdIORDyX2h-Z",
        "outputId": "f600636d-80b5-47a2-9dbc-484f1fef57c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   w  x\n",
              "a  0  1\n",
              "b  4  5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4d282a45-a468-474a-bd67-bb4cd542687e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>w</th>\n",
              "      <th>x</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d282a45-a468-474a-bd67-bb4cd542687e')\"\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",
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              "    }\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-4d282a45-a468-474a-bd67-bb4cd542687e 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-4d282a45-a468-474a-bd67-bb4cd542687e');\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": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## it needs position as integer\n",
        "#dataframe_from_array.iloc['a']    --> TypeError: Cannot index by location index with a non-integer key\n",
        "\n",
        "print(dataframe_from_array.iloc[[0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iPTlROGv2r7M",
        "outputId": "d13eae0b-6a00-42e9-ead2-41413a5f9bb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   w  x  y  z\n",
            "a  0  1  2  3\n"
          ]
        }
      ]
    },
    {
      "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": "72065c3f-9dee-4084-e6c0-051c1b67454d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe_from_array.iloc[::2,::2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "-BpY8Prl3GQX",
        "outputId": "30d061fb-6cc4-4a33-9600-cc977d18f0af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   w   y\n",
              "a  0   2\n",
              "c  8  10"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b08e1fe2-d1e8-4f38-a2aa-009d3ea16bef\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>w</th>\n",
              "      <th>y</th>\n",
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              "      <th>a</th>\n",
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              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b08e1fe2-d1e8-4f38-a2aa-009d3ea16bef')\"\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",
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              "    <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",
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              "      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-b08e1fe2-d1e8-4f38-a2aa-009d3ea16bef 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-b08e1fe2-d1e8-4f38-a2aa-009d3ea16bef');\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": 119
        }
      ]
    },
    {
      "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": 452
        },
        "id": "fqzrScL-GrZU",
        "outputId": "67835c44-2885-4abe-d4b9-a9a90295c237"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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",
        "\n",
        "pic = plt.imread('/content/gdrive/MyDrive/sofa.jpg')\n",
        "plt.imshow(pic)\n"
      ],
      "metadata": {
        "id": "mC8rNbeKxiaF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "9f3d34e7-7738-4509-8b94-8685229d75b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc3d0375780>"
            ]
          },
          "metadata": {},
          "execution_count": 123
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}