Amod_N_FMML_Aug22_M1Lab1_PythonBasics.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/amodnija/Assignment_1/blob/master/Amod_N_FMML_Aug22_M1Lab1_PythonBasics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Getting Started\n",
        "\n",
        "FMML Module 1, Lab 1 <br>\n",
        " Module Coordinator: Amit Pandey ( amit.pandey@research.iiit.ac.in ) <br>\n",
        " Release date: Aug 2022 <br>\n",
        "\n"
      ],
      "metadata": {
        "id": "TMVRKfbpTAeR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### In this notebook we will be covering the very basics of Python and some basic libraries such as Numpy, Matplotlib and Nltk.\n",
        "#### It is suggested that you go through each line and try some examples."
      ],
      "metadata": {
        "id": "F-XjWYLBTI0f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Section 1 - Python : Basic data types and indexing."
      ],
      "metadata": {
        "id": "wEyEnIhnTdhB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Strings\n",
        "'''\n",
        "A string is a collection of one or more characters put in a single quote,\n",
        " double-quote or triple quote. In python there is no character data type,\n",
        " a character is a string of length one. It is represented by str class.\n",
        "\n",
        "String can have special characters. String can be indexed\n",
        "\n",
        "'''\n",
        "\n",
        "\n",
        "name = 'First Lab'\n",
        "name_extended = name + 'Module 1'\n",
        "last_element_string = name[-1] # -1 in python is index of the last element. \n",
        "## indexing is important for preprocessing of the raw data. "
      ],
      "metadata": {
        "id": "AzguFJmTQIqt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(name ,\"\\n\", name_extended, \"\\n\", last_element_string)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DPokcpuLQO6s",
        "outputId": "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"
      ],
      "metadata": {
        "id": "g7WZtwkpE2tO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(name_list)\n",
        "print(element_2)\n",
        "print(two_dimesional_list)\n",
        "## list can have list, dictionary, string etc."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vblBrQflE9Cs",
        "outputId": "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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x5eOXNK5FtlF",
        "outputId": "285bbce1-d201-426f-ea69-a4c2bfeba7c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "m92Oab8YRha1",
        "outputId": "37950885-a207-4bcf-a915-26ff813f69e1"
      },
      "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-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"
          ]
        }
      ]
    },
    {
      "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": {
        "id": "MfkMO0mlTcep",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "QgHlNSRPampd",
        "outputId": "eaf861ab-4341-4109-b56f-ed45b8dc7522"
      },
      "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-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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rxhE3hYtZxVb",
        "outputId": "3f21c392-6b7d-4d14-dd9d-9a614eed40be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'1': 'A', '2': 'B', 'C': 3, '4': 'New'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Question 0:\n",
        "###### write down 3-5 methods applicable to each data type. (Hint: extend, reverse, etc."
      ],
      "metadata": {
        "id": "hizs_paeceA5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Section 2 - Functions\n",
        "### a group of related statements that performs a specific task."
      ],
      "metadata": {
        "id": "tub_Skp3X1cN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def add_new(a:str, b): ## a and b are the arguments that are passed. to provide data type hint\n",
        "                              # def add_new(x: float, y: float) -> float: \n",
        "  sum = a + b\n",
        "  return sum\n",
        "\n",
        "ans = add_new(1,2) ## intentionally written str, and passed int, to show it doesn't matter. It is just hint\n",
        "print(ans)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K-6Q5T8nX_YF",
        "outputId": "06cbd1d2-4766-4b81-95a3-9e41556d7be0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "asn = add_new()"
      ],
      "metadata": {
        "id": "aVLU3Bpu0YJV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def check_even_list(num_list):\n",
        "    \n",
        "    even_numbers = []\n",
        "    \n",
        "    # Go through each number\n",
        "    for number in num_list:\n",
        "        # Once we get a \"hit\" on an even number, we append the even number\n",
        "        if number % 2 == 0:\n",
        "            even_numbers.append(number)\n",
        "        # Don't do anything if its not even\n",
        "        else:\n",
        "            pass\n",
        "    # Notice the indentation! This ensures we run through the entire for loop    \n",
        "    return even_numbers"
      ],
      "metadata": {
        "id": "aRa47XCsZLsh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Question 1 :\n",
        "\n",
        "##### Define a function, which takes in two strings A and B. Reverses the first string A, adds it to B, and returns the final string.\n",
        "\n",
        "\n",
        "#### Question 2 : \n",
        "##### Given a list having Names, work_hours, and gender, Write a function to print name of the female worker that worked the most hours. Also how much do should she be paid if the pay is $ 20 per hour.\n",
        "\n",
        "##### work_hours = [('Abby',100 , 'F'),('Billy',400, 'M'),('Cassie',800,'F'), ('Maggi',600,'F'),('Alex',500,'M'),('Raj',225,'M'),('Penny',920,'F'),('Ben',300,'M')]\n",
        "\n",
        "##### Answer : the female worker that worked the most hours is Penny and she should be paid 18400"
      ],
      "metadata": {
        "id": "heD0_u4D01jp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Section 3 - Libraries and Reading data."
      ],
      "metadata": {
        "id": "T9fGzOAKXhtt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Numpy - One of the most used libraries - supports for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays."
      ],
      "metadata": {
        "id": "TKVyOsBKYZXa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "_ODaZ44RXkyg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "a = np.array([1,1,2,3,4,5,5,6,1]) ## np.array converts given list to array\n",
        "\n",
        "b = a>1 ## important comparison operation, where frequently used in manipulation and image processing.\n",
        "\n",
        "print(b)\n",
        "print(a[b]) ## [printing only those values in a which are greater than 1]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29vs7Iicavlg",
        "outputId": "33aa6551-b6c4-4806-f1e7-604383c7a30d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[False False  True  True  True  True  True  True False]\n",
            "[2 3 4 5 5 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a_range = np.arange(10,19).reshape(3,3) ## create a 3x3 array with values in range 10-19\n",
        "a_range\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MAGJJ1K_jCHN",
        "outputId": "4561eb1d-5401-48d1-9e19-d5b5490ea5e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10, 11, 12],\n",
              "       [13, 14, 15],\n",
              "       [16, 17, 18]])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Indexing in arrays works same as that of list\n",
        "\n",
        "a_range[0] # printing all the columns of first row"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BpNpo-eDt9Fr",
        "outputId": "69574bc2-5229-4cf8-eafa-5ac70642f838"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10, 11, 12])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a_range[:,2] #printing all the rows of second column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HbDG7M3QuCDQ",
        "outputId": "09459c12-a940-4908-eceb-cb5499d71bfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([12, 15, 18])"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iden = np.eye(3) #idnetity matrix of given size\n",
        "iden"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RQ7UV_-fjJw3",
        "outputId": "784d4b89-bdf1-4bbd-8584-ddef9c6ead92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0.],\n",
              "       [0., 1., 0.],\n",
              "       [0., 0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## adding two matrices\n",
        "summed = a_range + iden\n",
        "summed\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hYJXXvnwjPfE",
        "outputId": "7e7d6bcc-8ecc-4a74-ec1f-87de5c43fbda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[11., 11., 12.],\n",
              "       [13., 15., 15.],\n",
              "       [16., 17., 19.]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### arrays support normal matrix multiplication that you are used to, point-wise multiplication\n",
        "### and dot product as well.\n",
        "\n",
        "mul = a_range@iden ## normal multiplication\n",
        "mul\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VoyeEptRjYRu",
        "outputId": "ba301768-c6e5-49ed-c61f-f39a10df2a97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10., 11., 12.],\n",
              "       [13., 14., 15.],\n",
              "       [16., 17., 18.]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## point wise multiplication\n",
        "p_mul = a_range * iden\n",
        "p_mul"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "58S3a_YmtAe8",
        "outputId": "14ebfbe9-f186-4379-caa7-65e3f89c5977"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10.,  0.,  0.],\n",
              "       [ 0., 14.,  0.],\n",
              "       [ 0.,  0., 18.]])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Transpose of a matrix.\n",
        "\n",
        "mtx_t = mul.T\n",
        "mtx_t\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HC5uJ03ftJHA",
        "outputId": "fb6a9ba4-cd8b-496c-8459-6fd4897f0c87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10., 13., 16.],\n",
              "       [11., 14., 17.],\n",
              "       [12., 15., 18.]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Here we are changing the values of last row of the transposed matrix.\n",
        "### basically point wise multiplying the values of last row with 1,2 and 3\n",
        "\n",
        "mtx_t[2] = mtx_t[2]*[1,2,3] ## indexing, point wise multiplication and mutation of values\n",
        "mtx_t"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ROFhfs2lutat",
        "outputId": "5f0d0e66-2679-4250-c5ca-0c5fdcad7f31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10., 13., 16.],\n",
              "       [11., 14., 17.],\n",
              "       [12., 30., 54.]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Just like the greater than 1 (a>1) example we saw earlier.\n",
        "## here we are checking if the elements are divisible by 2 (%), and if they are, then replace by 0.\n",
        "\n",
        "mtx_t[(mtx_t % 2 == 0)] = 0 ## convert even elements of the matrix to zero.\n",
        "mtx_t"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mh80ju2xvAqL",
        "outputId": "0f134f2e-3ec8-4ca5-dc70-a3f992e4ce15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0., 13.,  0.],\n",
              "       [11.,  0., 17.],\n",
              "       [ 0.,  0.,  0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Question 3 : \n",
        "\n",
        "##### a)Create a 5x5 matrix of the following form, \n",
        "\n",
        "##### [[1,1]\n",
        "#####  [2,2]]\n",
        "\n",
        "#####  i.e. each row is increasing and has repetive elements.\n",
        "\n",
        "######  Hint : you can use hstack, vstack  etc.\n",
        "\n",
        "##### b) find dot product of the matrix with any matrix. (Figure out the size/ shape of the matrix)"
      ],
      "metadata": {
        "id": "uqWUH6Rv4OhT"
      }
    },
    {
      "cell_type": "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": {
            "text/html": [
              "\n",
              "  <div id=\"df-f46c49e0-f0e8-410b-a0bd-02a9706eab43\">\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>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",
              "      <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",
              "    <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",
              "    </tr>\n",
              "    <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-f46c49e0-f0e8-410b-a0bd-02a9706eab43')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f46c49e0-f0e8-410b-a0bd-02a9706eab43 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-f46c49e0-f0e8-410b-a0bd-02a9706eab43');\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": [
              "   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",
              "    }\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>Detail1</th>\n",
              "      <th>ooms\",\"total_bed</th>\n",
              "      <th>ooms\",\"population\",\"households\",\"median_income\",\"median_house_value\"</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-122.050000,37.370000,27.000000,3885.000000,66...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-118.300000,34.260000,43.000000,1510.000000,31...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-117.810000,33.780000,27.000000,3589.000000,50...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b4d6646-be74-40bb-b987-8f46c5498910')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8b4d6646-be74-40bb-b987-8f46c5498910 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8b4d6646-be74-40bb-b987-8f46c5498910');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                             Detail1  ...  ooms\",\"population\",\"households\",\"median_income\",\"median_house_value\"\n",
              "0  -122.050000,37.370000,27.000000,3885.000000,66...  ...                                                NaN                   \n",
              "1  -118.300000,34.260000,43.000000,1510.000000,31...  ...                                                NaN                   \n",
              "2  -117.810000,33.780000,27.000000,3589.000000,50...  ...                                                NaN                   \n",
              "\n",
              "[3 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[:5, 0]  ##iloc - index - 0 to 4 rows and first column only."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A0FFUn-ghP1n",
        "outputId": "161adc0a-69ae-4a53-e7b7-edf91a8c4f4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    -122.050000,37.370000,27.000000,3885.000000,66...\n",
              "1    -118.300000,34.260000,43.000000,1510.000000,31...\n",
              "2    -117.810000,33.780000,27.000000,3589.000000,50...\n",
              "3    -118.360000,33.820000,28.000000,67.000000,15.0...\n",
              "4    -119.670000,36.330000,19.000000,1241.000000,24...\n",
              "Name: Detail1, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib\n",
        "from matplotlib import pyplot as plt"
      ],
      "metadata": {
        "id": "oJ7JrZqWioIO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xpoints = np.array([1, 8])\n",
        "ypoints = np.array([3, 10])\n",
        "\n",
        "plt.plot(xpoints, ypoints)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "tzunFbpUxKAP",
        "outputId": "8c0572dc-8832-4eda-ee70-e438d30b1d0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xpoints = np.array([1, 2, 6, 8])\n",
        "ypoints = np.array([3, 8, 1, 10])\n",
        "\n",
        "plt.plot(xpoints, ypoints)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "p7l83i5SxW4k",
        "outputId": "db30f2a7-289c-4fbf-bd92-43e4c874ae73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating a dataframe.\n",
        "#### Task: Study about other methods of creating dataframe (for example: using Pandas Series, Lists etc.)"
      ],
      "metadata": {
        "id": "GT6yXu7l1gtL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "1vR1hTJ51ckl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "values = np.arange(16).reshape(4,4)\n",
        "values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o0W0z3Fr1gOb",
        "outputId": "9242f716-42cb-4f5d-a30b-54e263054122"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  2,  3],\n",
              "       [ 4,  5,  6,  7],\n",
              "       [ 8,  9, 10, 11],\n",
              "       [12, 13, 14, 15]])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe_from_array = pd.DataFrame(values, index = ['a','b','c','d'], columns=['w','x','y','z'] )\n",
        "dataframe_from_array"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "_p5vKhPN2Bey",
        "outputId": "d10b4d3c-56d8-47b0-de70-c2b6fa98ccde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    w   x   y   z\n",
              "a   0   1   2   3\n",
              "b   4   5   6   7\n",
              "c   8   9  10  11\n",
              "d  12  13  14  15"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4da01efc-0685-400f-adef-668db1c9704d\">\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",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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-4da01efc-0685-400f-adef-668db1c9704d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4da01efc-0685-400f-adef-668db1c9704d 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-4da01efc-0685-400f-adef-668db1c9704d');\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": 5
        }
      ]
    },
    {
      "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": "0e33c053-b6f5-43c3-d917-b35217f81665"
      },
      "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-24b0981e-bf93-4ce2-8a6c-ad572d09e477\">\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-24b0981e-bf93-4ce2-8a6c-ad572d09e477')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-24b0981e-bf93-4ce2-8a6c-ad572d09e477 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-24b0981e-bf93-4ce2-8a6c-ad572d09e477');\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": 6
        }
      ]
    },
    {
      "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]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "-BpY8Prl3GQX",
        "outputId": "173191ad-f956-4477-a440-8eba89bb75f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   w   y\n",
              "a  0   2\n",
              "c  8  10"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-89245783-263e-47f5-bdca-53df2d773e37\">\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-89245783-263e-47f5-bdca-53df2d773e37')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-89245783-263e-47f5-bdca-53df2d773e37 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-89245783-263e-47f5-bdca-53df2d773e37');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "ys = 200 + np.random.randn(100)\n",
        "x = [x for x in range(len(ys))]\n",
        "\n",
        "plt.plot(x, ys, '-')\n",
        "plt.fill_between(x, ys, 195, where=(ys > 200), facecolor='g', alpha=0.6)\n",
        "\n",
        "plt.title(\"Sample Visualization\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "fqzrScL-GrZU",
        "outputId": "b2e971b2-4fca-477b-e121-6a116815f38c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Question 3 : Upload an image to your google drive, Use plt.imread to read image from the google drive and then print that image using plt.imshow\n",
        "\n",
        "\n",
        "## Answer 3 : \n",
        "\n",
        "## 1) make sure drive is loaded and then upload a test image onto your drive\n"
      ],
      "metadata": {
        "id": "mC8rNbeKxiaF"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}