Copy_of_FMML_Aug22_M1Lab1_PythonBasics.ipynb

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        "<a href=\"https://colab.research.google.com/github/amodnija/IIITH_assignments/blob/main/Copy_of_FMML_Aug22_M1Lab1_PythonBasics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# Getting Started\n",
        "\n",
        "FMML Module 1, Lab 1 <br>\n",
        " Module Coordinator: Amit Pandey ( amit.pandey@research.iiit.ac.in ) <br>\n",
        " Release date: Aug 2022 <br>\n",
        "\n"
      ],
      "metadata": {
        "id": "TMVRKfbpTAeR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### In this notebook we will be covering the very basics of Python and some basic libraries such as Numpy, Matplotlib and Nltk.\n",
        "#### It is suggested that you go through each line and try some examples."
      ],
      "metadata": {
        "id": "F-XjWYLBTI0f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Section 1 - Python : Basic data types and indexing."
      ],
      "metadata": {
        "id": "wEyEnIhnTdhB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Strings\n",
        "'''\n",
        "A string is a collection of one or more characters put in a single quote,\n",
        " double-quote or triple quote. In python there is no character data type,\n",
        " a character is a string of length one. It is represented by str class.\n",
        "\n",
        "String can have special characters. String can be indexed\n",
        "\n",
        "'''\n",
        "\n",
        "\n",
        "name = 'First Lab'\n",
        "name_extended = name + 'Module 1'\n",
        "last_element_string = name[-1] # -1 in python is index of the last element. \n",
        "## indexing is important for preprocessing of the raw data. "
      ],
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      "execution_count": null,
      "outputs": []
    },
    {
      "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": "5241dfe9-ad74-44f8-86bd-414aef1e1b13"
      },
      "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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        "id": "g7WZtwkpE2tO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(name_list)\n",
        "print(element_2)\n",
        "print(two_dimesional_list)\n",
        "## list can have list, dictionary, string etc."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vblBrQflE9Cs",
        "outputId": "19b44b31-914f-4599-f40c-e0f320f437fc"
      },
      "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"
      ],
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      "execution_count": null,
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        {
          "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-18-38dd6464e9d4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m## tuples are immutable, read the error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\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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    {
      "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"
      ],
      "metadata": {
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        },
        "outputId": "a3fc45c0-fec6-487c-b8fa-40c2ca4013d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Lab1', 1, 2, 3, 5, 6}\n",
            "Lab1\n",
            "{(1, 2), 1, 2, 3, 5, 6}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "set_unique[1] ##it is not indexable"
      ],
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          "height": 166
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        "id": "QgHlNSRPampd",
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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-22-ac8e9a8e8c29>\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;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'set' object is not subscriptable"
          ]
        }
      ]
    },
    {
      "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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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'1': 'A', '2': 'B', 'C': 3, '4': 'New'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Question 0:\n",
        "###### write down 3-5 methods applicable to each data type. (Hint: extend, reverse, etc."
      ],
      "metadata": {
        "id": "hizs_paeceA5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Answer 0: \n",
        "reverse(), count(), sort(), extend()"
      ],
      "metadata": {
        "id": "uGyNfePsnp3L"
      }
    },
    {
      "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": [
        "#### Section 3 - Libraries and Reading data."
      ],
      "metadata": {
        "id": "T9fGzOAKXhtt"
      }
    },
    {
      "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": [
        "#Answer 1:\n",
        "\n",
        "def func(A,B):\n",
        "  ans = B + A[::-1]\n",
        "  return ans\n",
        "\n",
        "print(func(\"hello\",\"world\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QAMcoLixn6Xd",
        "outputId": "63e30381-1ec8-4407-d517-62fa4ebbbcc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "worldolleh\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Answer 2:\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",
        "def max_h(wh):\n",
        "  hrs=[]\n",
        "  for i in wh:\n",
        "    if(i[2]=='F'):\n",
        "      hrs.append([i[0],i[1]])\n",
        "\n",
        "  return (max(hrs)[0],max(hrs)[1]*20) \n",
        "\n",
        "print(\"the female worker that worked the most hours is \" + str(max_h(work_hours)[0]) + \" and she should be paid \" + str(max_h(work_hours)[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FkQZBf-PpM5m",
        "outputId": "9bce2e13-c4cc-4fb2-ab09-da1d404ada03"
      },
      "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": [
        "##### 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": [
        "farr=np.hstack((np.arange(1,6).reshape(5,1),np.arange(1,6).reshape(5,1),np.arange(1,6).reshape(5,1),np.arange(1,6).reshape(5,1),np.arange(1,6).reshape(5,1)))\n",
        "farr\n",
        "narr=np.arange(5,10).reshape(5,1)\n",
        "np.dot(farr,narr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kssumslArCvD",
        "outputId": "5b124f95-d635-498b-b3ed-ff922a67f53f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 35],\n",
              "       [ 70],\n",
              "       [105],\n",
              "       [140],\n",
              "       [175]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "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"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "   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",
              "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",
              "[10 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df.columns), df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iMMaPe3gduKb",
        "outputId": "f6dc3cb8-6c33-48f7-f15c-bb691b593821"
      },
      "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": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "GT1dA9Q3eRQU",
        "outputId": "5514ad00-df53-44e0-beb5-99775e8174de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'longitude,\"latitude\",\"housing_median_age\",\"total_'"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['longitude,\"latitude\",\"housing_median_age\",\"total_'][:5] "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oEZhq9rBe-Hb",
        "outputId": "ddfb73be-f5c4-44ca-8e34-117a4b47562c"
      },
      "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": 38
        }
      ]
    },
    {
      "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": "47fbd865-7d9c-4d54-c9b1-6e3e9db64eb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8b4d6646-be74-40bb-b987-8f46c5498910\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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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              "<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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-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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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "dataframe_from_array.loc[['a','b'],['w','x']]"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "jdIORDyX2h-Z",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   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_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]"
      ],
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        "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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              "       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": 452
        },
        "id": "fqzrScL-GrZU",
        "outputId": "d5de580b-a728-42ae-acf4-a1c38252c27f"
      },
      "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",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "im=plt.imread('/content/drive/MyDrive/WhatsApp Image 2022-09-20 at 11.13.56 PM.jpeg')\n",
        "plt.imshow(im)"
      ],
      "metadata": {
        "id": "mC8rNbeKxiaF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "outputId": "444b3093-143e-44e4-d263-cfc1e4e6dd38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fad87f9caf0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}