Kopie_sešitu_exercie_01.ipynb

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  "metadata": {
    "colab": {
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      "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/KristynaPijackova/Tutorials_NNs_and_signals/blob/main/Kopie_se%C5%A1itu_exercie_01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exercise 1.4.1"
      ],
      "metadata": {
        "id": "z2Jdst0Khkeh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num = [1, 2, 3, 4, 5, 4, 3, 2, 1]\n",
        "\n",
        "for i in num:\n",
        "    print('x'*i)"
      ],
      "metadata": {
        "id": "ypD3tOGDAB3p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3a5e6b9-b8b3-430a-b67d-10f6e8f4ef5a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x\n",
            "xx\n",
            "xxx\n",
            "xxxx\n",
            "xxxxx\n",
            "xxxx\n",
            "xxx\n",
            "xx\n",
            "x\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "print the following pattern (using loops)\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "4mKrJYuxiFmd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exercise 1.4.2"
      ],
      "metadata": {
        "id": "nOAWuH8qp79q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "write a script that will sum all numbers in the following string"
      ],
      "metadata": {
        "id": "Zz2j3lx6qH2K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "input_str = \"n45as29@#8ss6\"\n",
        "nr = re.findall(r'\\d+', input_str)\n",
        "nrs = [int(i) for i in nr]\n",
        "sum(nrs)"
      ],
      "metadata": {
        "id": "15fJxcCsqBH7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2f1c3dc-7713-4fb9-97ef-448d90e964d4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "88"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exercise 1.4.3"
      ],
      "metadata": {
        "id": "NjNQc3AXMPmi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "write script that will convert an arbitary integer to binary number (number will be represented as string with only *0* and *1*). Avoid using *bin()* function or any other pythons default functions"
      ],
      "metadata": {
        "id": "-algCktYMZ4Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nr = 12\n",
        "max_bin = 8\n",
        "x = 0\n",
        "bin_nr = \"\"\n",
        "while nr > 0 and x < max_bin:\n",
        "    help_str = str(int(nr%2))\n",
        "    bin_nr = bin_nr + help_str\n",
        "    nr = nr/2\n",
        "    x += 1\n",
        "\n",
        "bin_nr[::-1]"
      ],
      "metadata": {
        "id": "3jOfppJ6MVlG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "703b617f-b368-4b11-f4dc-60e59ab5e191"
      },
      "execution_count": 260,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'00001100'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 260
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exercise 1.5-1:"
      ],
      "metadata": {
        "id": "pCSSAjAj0ZlY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Fibonacci Sequence is a series of numbers. The next number is found by adding up the two numbers before it. The first two numbers are 0 and 1. For example, 0, 1, 1, 2, 3, 5, 8, 13. The next number in this series above is 8 + 13 = 21"
      ],
      "metadata": {
        "id": "JPgVzgDG1pwt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "Create a function that takes an integer as an input and return a list that contains all fibonaci numbers smaller than input integer\n",
        "\n",
        "example:\n",
        "\n",
        "*print(fibonaci(10))*\n",
        "\n",
        "[0, 1, 1, 2, 3, 5, 8]\n"
      ],
      "metadata": {
        "id": "uEHzKT7k1sfr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def fibonaci(upper_threshold: int) -> list:\n",
        "    ls = [0, 1]\n",
        "    for i in range(0, 100):\n",
        "        if ls[-1] < upper_threshold:\n",
        "            a = ls[-1] + ls[-2]\n",
        "            ls.append(a) \n",
        "    return ls[:-1]"
      ],
      "metadata": {
        "id": "Lu9nP9hG2VzU"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fibonaci(2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_eJrnWb_JUlt",
        "outputId": "26aa595c-62ed-43ba-844f-a88e606d9558"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 1]"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exercise 1.5-2:"
      ],
      "metadata": {
        "id": "eVT1qWQGgXm2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Basic:"
      ],
      "metadata": {
        "id": "2hv2gSRIzI7-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "implement fucntion *display_as_digi* that will print an arbitary integer in segment display style\n",
        "\n",
        "example:\n",
        "\n",
        "*display_as_digi(588)*\n",
        "\n",
        "\n",
        "![digit.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "BN8bKPh6y7Dm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def display_as_digi(number: int) -> None:\n",
        "    seven_seg_repre = {\n",
        "    '0': ('xxx', 'x x', 'x x', 'x x', 'xxx'),\n",
        "    '1': ('  x', '  x', '  x', '  x', '  x'),\n",
        "    '2': ('xxx', '  x', 'xxx', 'x  ', 'xxx'),\n",
        "    '3': ('xxx', '  x', 'xxx', '  x', 'xxx'),\n",
        "    '4': ('x x', 'x x', 'xxx', '  x', '  x'),\n",
        "    '5': ('xxx', 'x  ', 'xxx', '  x', 'xxx'),\n",
        "    '6': ('xxx', 'x  ', 'xxx', 'x x', 'xxx'),\n",
        "    '7': ('xxx', '  x', '  x', '  x', '  x'),\n",
        "    '8': ('xxx', 'x x', 'xxx', 'x x', 'xxx'),\n",
        "    '9': ('xxx', 'x x', 'xxx', '  x', 'xxx'),\n",
        "    }\n",
        "    digits = [seven_seg_repre[digit] for digit in str(number)]\n",
        "    digits = np.array(digits).T\n",
        "    print(digits)\n"
      ],
      "metadata": {
        "id": "3GcRk6OYzKuV"
      },
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display_as_digi(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U78szaTWNBK8",
        "outputId": "a8d67ea9-bf36-40a5-e074-29cb8d2830e4"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['  x' 'xxx']\n",
            " ['  x' 'x x']\n",
            " ['  x' 'x x']\n",
            " ['  x' 'x x']\n",
            " ['  x' 'xxx']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Extension"
      ],
      "metadata": {
        "id": "__gcTtmby5Yu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "extend our sement number printer to be able to print decimal numbers"
      ],
      "metadata": {
        "id": "ek8Z7Agx0H-Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def display_as_digi(number: int) -> None:\n",
        "    seven_seg_repre = {\n",
        "    '0': ('xxx', 'x x', 'x x', 'x x', 'xxx'),\n",
        "    '1': ('  x', '  x', '  x', '  x', '  x'),\n",
        "    '2': ('xxx', '  x', 'xxx', 'x  ', 'xxx'),\n",
        "    '3': ('xxx', '  x', 'xxx', '  x', 'xxx'),\n",
        "    '4': ('x x', 'x x', 'xxx', '  x', '  x'),\n",
        "    '5': ('xxx', 'x  ', 'xxx', '  x', 'xxx'),\n",
        "    '6': ('xxx', 'x  ', 'xxx', 'x x', 'xxx'),\n",
        "    '7': ('xxx', '  x', '  x', '  x', '  x'),\n",
        "    '8': ('xxx', 'x x', 'xxx', 'x x', 'xxx'),\n",
        "    '9': ('xxx', 'x x', 'xxx', '  x', 'xxx'),\n",
        "    '.': ('   ', '   ', '   ', '   ', '  x'),\n",
        "    }\n",
        "    digits = [seven_seg_repre[digit] for digit in str(number)]\n",
        "    digits = np.array(digits).T\n",
        "    print(digits)\n",
        "\n",
        "display_as_digi(20.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nLqgAVutMwfH",
        "outputId": "af064602-a65f-4756-8087-1ff9726bc92b"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['xxx' 'xxx' '   ' 'xxx']\n",
            " ['  x' 'x x' '   ' 'x  ']\n",
            " ['xxx' 'x x' '   ' 'xxx']\n",
            " ['x  ' 'x x' '   ' '  x']\n",
            " ['xxx' 'xxx' '  x' 'xxx']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exercise 2"
      ],
      "metadata": {
        "id": "Tu7yI05aT1y5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1\n"
      ],
      "metadata": {
        "id": "7GaHLE8Zh8aJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Create a numpy array with shape 5x5, that contains number from 25..to 0\n",
        "- create a function that takes an arbitary numpy array as an input and sets all numbers smaller than user defined threshold to 0\n",
        "- test your function on your array\n",
        "- make one implementation using loops and one withnout using loops, compare execution time with *time* library"
      ],
      "metadata": {
        "id": "ZIyURJ6HZ5Rb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def np_array(chosen_nr: int):\n",
        "    a = np.random.randint(0, 25, size=(5,5))\n",
        "    a = np.where(a > chosen_nr, a, 0)\n",
        "    return a\n",
        "\n",
        "## for loop proste bude trvat minimalne 3x tolik :)))"
      ],
      "metadata": {
        "id": "DekVpsLVN8RV"
      },
      "execution_count": 243,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%timeit\n",
        "\n",
        "np_array(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DF3y_uyxS4ly",
        "outputId": "00b0a01f-cdde-4044-bfd9-847c377e0d2b"
      },
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "23.2 µs ± 4.09 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2\n",
        " - lets return to our digi_diplay, create a function that will take an arbitary intiger an an input and display it as an binary image in segment display style.\n",
        "*hint: to show an image use library matplotlib.pyplot, also you will maybe find usefull function np.concatenate*"
      ],
      "metadata": {
        "id": "tl_vFRrwiArx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def show_in_digi(input_integer: int) -> None:\n",
        "  pass"
      ],
      "metadata": {
        "id": "ha5_BgE0mxoY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "    seven_seg_repre = {\n",
        "    '0': ('xxx', 'x x', 'x x', 'x x', 'xxx'),\n",
        "    '1': ('  x', '  x', '  x', '  x', '  x'),\n",
        "    '2': ('xxx', '  x', 'xxx', 'x  ', 'xxx'),\n",
        "    '3': ('xxx', '  x', 'xxx', '  x', 'xxx'),\n",
        "    '4': ('x x', 'x x', 'xxx', '  x', '  x'),\n",
        "    '5': ('xxx', 'x  ', 'xxx', '  x', 'xxx'),\n",
        "    '6': ('xxx', 'x  ', 'xxx', 'x x', 'xxx'),\n",
        "    '7': ('xxx', '  x', '  x', '  x', '  x'),\n",
        "    '8': ('xxx', 'x x', 'xxx', 'x x', 'xxx'),\n",
        "    '9': ('xxx', 'x x', 'xxx', '  x', 'xxx'),\n",
        "    '.': ('   ', '   ', '   ', '   ', '  x'),\n",
        "    }\n",
        "    digits = [seven_seg_repre[digit] for digit in str(10)]\n",
        "    digits = np.array(digits).T\n",
        "    digits = np.concatenate(digits)\n",
        "    print(digits)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vrjBfs7YPanj",
        "outputId": "0518a445-dca0-4246-a26a-e6ee4394755b"
      },
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['  x' 'xxx' '  x' 'x x' '  x' 'x x' '  x' 'x x' '  x' 'xxx']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "show_in_digi(5289) will show:\n"
      ],
      "metadata": {
        "id": "pcOmCLI1muSr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "aHN2vh9dm-S5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exercise 3"
      ],
      "metadata": {
        "id": "6s8LqeO6fuS1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use google colab dataset loaded in the lecture and do following operations:"
      ],
      "metadata": {
        "id": "9MV_c3_3f9V0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- check what *dataset.describe()* does"
      ],
      "metadata": {
        "id": "ZctJOSglgYjc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- display all rows where *total_bedrooms* is bigger than 310"
      ],
      "metadata": {
        "id": "ANvmvaQzpp2k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- drop also first and last row and also "
      ],
      "metadata": {
        "id": "AJnkbsLDpcrh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- save mean of values in *households* columns to variable, using *matplotlib* display number show number of household in graph (x axis would be ID, y axis - numbers of households) as dots with the same colour, display also mean as a single line in the graph with different colour as dots is"
      ],
      "metadata": {
        "id": "jjj3NIPGheCC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " - check if any of the columns contains NaN values, if yes replace the NaN values with arithmetic mean of whole dataset "
      ],
      "metadata": {
        "id": "Ga-8a7TFgFDT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " - display *lat* and *long* into plot, *lat* would be on *x* axis and *long* on *y* axis "
      ],
      "metadata": {
        "id": "L6_0MzSRqYK9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- choose two arbitary columns and normalize all values inside using min-max normalization:"
      ],
      "metadata": {
        "id": "Eba6rEqcixWl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Create a Correlation Matrix from all columns"
      ],
      "metadata": {
        "id": "nZ5hAWKlpVKK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('/content/california_housing_test.csv')\n",
        "df"
      ],
      "metadata": {
        "id": "xt8IQzHifulb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "7cddfb0f-b607-4805-de5f-a5c6ef507b7f"
      },
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0       -122.05     37.37                27.0       3885.0           661.0   \n",
              "1       -118.30     34.26                43.0       1510.0           310.0   \n",
              "2       -117.81     33.78                27.0       3589.0           507.0   \n",
              "3       -118.36     33.82                28.0         67.0            15.0   \n",
              "4       -119.67     36.33                19.0       1241.0           244.0   \n",
              "...         ...       ...                 ...          ...             ...   \n",
              "2995    -119.86     34.42                23.0       1450.0           642.0   \n",
              "2996    -118.14     34.06                27.0       5257.0          1082.0   \n",
              "2997    -119.70     36.30                10.0        956.0           201.0   \n",
              "2998    -117.12     34.10                40.0         96.0            14.0   \n",
              "2999    -119.63     34.42                42.0       1765.0           263.0   \n",
              "\n",
              "      population  households  median_income  median_house_value  \n",
              "0         1537.0       606.0         6.6085            344700.0  \n",
              "1          809.0       277.0         3.5990            176500.0  \n",
              "2         1484.0       495.0         5.7934            270500.0  \n",
              "3           49.0        11.0         6.1359            330000.0  \n",
              "4          850.0       237.0         2.9375             81700.0  \n",
              "...          ...         ...            ...                 ...  \n",
              "2995      1258.0       607.0         1.1790            225000.0  \n",
              "2996      3496.0      1036.0         3.3906            237200.0  \n",
              "2997       693.0       220.0         2.2895             62000.0  \n",
              "2998        46.0        14.0         3.2708            162500.0  \n",
              "2999       753.0       260.0         8.5608            500001.0  \n",
              "\n",
              "[3000 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-796f9ed1-d5d8-4877-becf-feac33050252\">\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-122.05</td>\n",
              "      <td>37.37</td>\n",
              "      <td>27.0</td>\n",
              "      <td>3885.0</td>\n",
              "      <td>661.0</td>\n",
              "      <td>1537.0</td>\n",
              "      <td>606.0</td>\n",
              "      <td>6.6085</td>\n",
              "      <td>344700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-118.30</td>\n",
              "      <td>34.26</td>\n",
              "      <td>43.0</td>\n",
              "      <td>1510.0</td>\n",
              "      <td>310.0</td>\n",
              "      <td>809.0</td>\n",
              "      <td>277.0</td>\n",
              "      <td>3.5990</td>\n",
              "      <td>176500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-117.81</td>\n",
              "      <td>33.78</td>\n",
              "      <td>27.0</td>\n",
              "      <td>3589.0</td>\n",
              "      <td>507.0</td>\n",
              "      <td>1484.0</td>\n",
              "      <td>495.0</td>\n",
              "      <td>5.7934</td>\n",
              "      <td>270500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-118.36</td>\n",
              "      <td>33.82</td>\n",
              "      <td>28.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.1359</td>\n",
              "      <td>330000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-119.67</td>\n",
              "      <td>36.33</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1241.0</td>\n",
              "      <td>244.0</td>\n",
              "      <td>850.0</td>\n",
              "      <td>237.0</td>\n",
              "      <td>2.9375</td>\n",
              "      <td>81700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2995</th>\n",
              "      <td>-119.86</td>\n",
              "      <td>34.42</td>\n",
              "      <td>23.0</td>\n",
              "      <td>1450.0</td>\n",
              "      <td>642.0</td>\n",
              "      <td>1258.0</td>\n",
              "      <td>607.0</td>\n",
              "      <td>1.1790</td>\n",
              "      <td>225000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>-118.14</td>\n",
              "      <td>34.06</td>\n",
              "      <td>27.0</td>\n",
              "      <td>5257.0</td>\n",
              "      <td>1082.0</td>\n",
              "      <td>3496.0</td>\n",
              "      <td>1036.0</td>\n",
              "      <td>3.3906</td>\n",
              "      <td>237200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>-119.70</td>\n",
              "      <td>36.30</td>\n",
              "      <td>10.0</td>\n",
              "      <td>956.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>693.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>2.2895</td>\n",
              "      <td>62000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>-117.12</td>\n",
              "      <td>34.10</td>\n",
              "      <td>40.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3.2708</td>\n",
              "      <td>162500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>-119.63</td>\n",
              "      <td>34.42</td>\n",
              "      <td>42.0</td>\n",
              "      <td>1765.0</td>\n",
              "      <td>263.0</td>\n",
              "      <td>753.0</td>\n",
              "      <td>260.0</td>\n",
              "      <td>8.5608</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3000 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-796f9ed1-d5d8-4877-becf-feac33050252')\"\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",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-796f9ed1-d5d8-4877-becf-feac33050252 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-796f9ed1-d5d8-4877-becf-feac33050252');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "PyGpt-uSQx3V",
        "outputId": "ac98a7c7-ec7a-4ec0-ec37-a3283b752a54"
      },
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         longitude    latitude  housing_median_age   total_rooms  \\\n",
              "count  3000.000000  3000.00000         3000.000000   3000.000000   \n",
              "mean   -119.589200    35.63539           28.845333   2599.578667   \n",
              "std       1.994936     2.12967           12.555396   2155.593332   \n",
              "min    -124.180000    32.56000            1.000000      6.000000   \n",
              "25%    -121.810000    33.93000           18.000000   1401.000000   \n",
              "50%    -118.485000    34.27000           29.000000   2106.000000   \n",
              "75%    -118.020000    37.69000           37.000000   3129.000000   \n",
              "max    -114.490000    41.92000           52.000000  30450.000000   \n",
              "\n",
              "       total_bedrooms    population  households  median_income  \\\n",
              "count     3000.000000   3000.000000  3000.00000    3000.000000   \n",
              "mean       529.950667   1402.798667   489.91200       3.807272   \n",
              "std        415.654368   1030.543012   365.42271       1.854512   \n",
              "min          2.000000      5.000000     2.00000       0.499900   \n",
              "25%        291.000000    780.000000   273.00000       2.544000   \n",
              "50%        437.000000   1155.000000   409.50000       3.487150   \n",
              "75%        636.000000   1742.750000   597.25000       4.656475   \n",
              "max       5419.000000  11935.000000  4930.00000      15.000100   \n",
              "\n",
              "       median_house_value  \n",
              "count          3000.00000  \n",
              "mean         205846.27500  \n",
              "std          113119.68747  \n",
              "min           22500.00000  \n",
              "25%          121200.00000  \n",
              "50%          177650.00000  \n",
              "75%          263975.00000  \n",
              "max          500001.00000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-74c96bab-dd7f-465f-a792-4eac2cdc06d2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.00000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.00000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-119.589200</td>\n",
              "      <td>35.63539</td>\n",
              "      <td>28.845333</td>\n",
              "      <td>2599.578667</td>\n",
              "      <td>529.950667</td>\n",
              "      <td>1402.798667</td>\n",
              "      <td>489.91200</td>\n",
              "      <td>3.807272</td>\n",
              "      <td>205846.27500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.994936</td>\n",
              "      <td>2.12967</td>\n",
              "      <td>12.555396</td>\n",
              "      <td>2155.593332</td>\n",
              "      <td>415.654368</td>\n",
              "      <td>1030.543012</td>\n",
              "      <td>365.42271</td>\n",
              "      <td>1.854512</td>\n",
              "      <td>113119.68747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-124.180000</td>\n",
              "      <td>32.56000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>0.499900</td>\n",
              "      <td>22500.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-121.810000</td>\n",
              "      <td>33.93000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1401.000000</td>\n",
              "      <td>291.000000</td>\n",
              "      <td>780.000000</td>\n",
              "      <td>273.00000</td>\n",
              "      <td>2.544000</td>\n",
              "      <td>121200.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-118.485000</td>\n",
              "      <td>34.27000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>2106.000000</td>\n",
              "      <td>437.000000</td>\n",
              "      <td>1155.000000</td>\n",
              "      <td>409.50000</td>\n",
              "      <td>3.487150</td>\n",
              "      <td>177650.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-118.020000</td>\n",
              "      <td>37.69000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>3129.000000</td>\n",
              "      <td>636.000000</td>\n",
              "      <td>1742.750000</td>\n",
              "      <td>597.25000</td>\n",
              "      <td>4.656475</td>\n",
              "      <td>263975.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-114.490000</td>\n",
              "      <td>41.92000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>30450.000000</td>\n",
              "      <td>5419.000000</td>\n",
              "      <td>11935.000000</td>\n",
              "      <td>4930.00000</td>\n",
              "      <td>15.000100</td>\n",
              "      <td>500001.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-74c96bab-dd7f-465f-a792-4eac2cdc06d2')\"\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",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "df[df['total_bedrooms'] > 310]"
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        "id": "uKfZeH3mRuUF",
        "outputId": "42cfe5f9-b0af-4914-81ac-680771ebd73e"
      },
      "execution_count": 217,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0       -122.05     37.37                27.0       3885.0           661.0   \n",
              "2       -117.81     33.78                27.0       3589.0           507.0   \n",
              "7       -120.65     35.48                19.0       2310.0           471.0   \n",
              "8       -122.84     38.40                15.0       3080.0           617.0   \n",
              "9       -118.02     34.08                31.0       2402.0           632.0   \n",
              "...         ...       ...                 ...          ...             ...   \n",
              "2990    -118.23     34.09                49.0       1638.0           456.0   \n",
              "2991    -117.17     34.28                13.0       4867.0           718.0   \n",
              "2993    -117.91     33.60                37.0       2088.0           510.0   \n",
              "2995    -119.86     34.42                23.0       1450.0           642.0   \n",
              "2996    -118.14     34.06                27.0       5257.0          1082.0   \n",
              "\n",
              "      population  households  median_income  median_house_value  \n",
              "0         1537.0       606.0         6.6085            344700.0  \n",
              "2         1484.0       495.0         5.7934            270500.0  \n",
              "7         1341.0       441.0         3.2250            166900.0  \n",
              "8         1446.0       599.0         3.6696            194400.0  \n",
              "9         2830.0       603.0         2.3333            164200.0  \n",
              "...          ...         ...            ...                 ...  \n",
              "2990      1500.0       430.0         2.6923            150000.0  \n",
              "2991       780.0       250.0         7.1997            253800.0  \n",
              "2993       673.0       390.0         5.1048            500001.0  \n",
              "2995      1258.0       607.0         1.1790            225000.0  \n",
              "2996      3496.0      1036.0         3.3906            237200.0  \n",
              "\n",
              "[2135 rows x 9 columns]"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df.drop(index=df.index[0], axis=0, inplace=True)\n",
        "df.drop(df.tail(1).index,inplace=True)"
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      "metadata": {
        "id": "Akn-bidsR0y9"
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      "execution_count": 218,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "X5TFmwR9SOzT",
        "outputId": "a053e3e7-a270-4191-d4e0-5b6903cb9e5f"
      },
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "1       -118.30     34.26                43.0       1510.0           310.0   \n",
              "2       -117.81     33.78                27.0       3589.0           507.0   \n",
              "3       -118.36     33.82                28.0         67.0            15.0   \n",
              "4       -119.67     36.33                19.0       1241.0           244.0   \n",
              "5       -119.56     36.51                37.0       1018.0           213.0   \n",
              "...         ...       ...                 ...          ...             ...   \n",
              "2994    -117.93     33.86                35.0        931.0           181.0   \n",
              "2995    -119.86     34.42                23.0       1450.0           642.0   \n",
              "2996    -118.14     34.06                27.0       5257.0          1082.0   \n",
              "2997    -119.70     36.30                10.0        956.0           201.0   \n",
              "2998    -117.12     34.10                40.0         96.0            14.0   \n",
              "\n",
              "      population  households  median_income  median_house_value  \n",
              "1          809.0       277.0         3.5990            176500.0  \n",
              "2         1484.0       495.0         5.7934            270500.0  \n",
              "3           49.0        11.0         6.1359            330000.0  \n",
              "4          850.0       237.0         2.9375             81700.0  \n",
              "5          663.0       204.0         1.6635             67000.0  \n",
              "...          ...         ...            ...                 ...  \n",
              "2994       516.0       174.0         5.5867            182500.0  \n",
              "2995      1258.0       607.0         1.1790            225000.0  \n",
              "2996      3496.0      1036.0         3.3906            237200.0  \n",
              "2997       693.0       220.0         2.2895             62000.0  \n",
              "2998        46.0        14.0         3.2708            162500.0  \n",
              "\n",
              "[2998 rows x 9 columns]"
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              "      <th>1</th>\n",
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              "      <td>33.78</td>\n",
              "      <td>27.0</td>\n",
              "      <td>3589.0</td>\n",
              "      <td>507.0</td>\n",
              "      <td>1484.0</td>\n",
              "      <td>495.0</td>\n",
              "      <td>5.7934</td>\n",
              "      <td>270500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-118.36</td>\n",
              "      <td>33.82</td>\n",
              "      <td>28.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.1359</td>\n",
              "      <td>330000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-119.67</td>\n",
              "      <td>36.33</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1241.0</td>\n",
              "      <td>244.0</td>\n",
              "      <td>850.0</td>\n",
              "      <td>237.0</td>\n",
              "      <td>2.9375</td>\n",
              "      <td>81700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-119.56</td>\n",
              "      <td>36.51</td>\n",
              "      <td>37.0</td>\n",
              "      <td>1018.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>663.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>1.6635</td>\n",
              "      <td>67000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2994</th>\n",
              "      <td>-117.93</td>\n",
              "      <td>33.86</td>\n",
              "      <td>35.0</td>\n",
              "      <td>931.0</td>\n",
              "      <td>181.0</td>\n",
              "      <td>516.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>5.5867</td>\n",
              "      <td>182500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2995</th>\n",
              "      <td>-119.86</td>\n",
              "      <td>34.42</td>\n",
              "      <td>23.0</td>\n",
              "      <td>1450.0</td>\n",
              "      <td>642.0</td>\n",
              "      <td>1258.0</td>\n",
              "      <td>607.0</td>\n",
              "      <td>1.1790</td>\n",
              "      <td>225000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>-118.14</td>\n",
              "      <td>34.06</td>\n",
              "      <td>27.0</td>\n",
              "      <td>5257.0</td>\n",
              "      <td>1082.0</td>\n",
              "      <td>3496.0</td>\n",
              "      <td>1036.0</td>\n",
              "      <td>3.3906</td>\n",
              "      <td>237200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>-119.70</td>\n",
              "      <td>36.30</td>\n",
              "      <td>10.0</td>\n",
              "      <td>956.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>693.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>2.2895</td>\n",
              "      <td>62000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>-117.12</td>\n",
              "      <td>34.10</td>\n",
              "      <td>40.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3.2708</td>\n",
              "      <td>162500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2998 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e62ba03d-5d38-485b-9eec-46edb5469066')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </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",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e62ba03d-5d38-485b-9eec-46edb5469066 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-e62ba03d-5d38-485b-9eec-46edb5469066');\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": 222
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "save mean of values in households columns to variable, using matplotlib display number show number of household in graph (x axis would be ID, y axis - numbers of households) as dots with the same colour, display also mean as a single line in the graph with different colour as dots is"
      ],
      "metadata": {
        "id": "mGhVJrHZStCH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mean_households = df['households'].mean()\n",
        "mean_households"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0B6FtVJuTHqQ",
        "outputId": "58003f71-e945-4dcf-9e12-5e5c46ea6d6b"
      },
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "489.9499666444296"
            ]
          },
          "metadata": {},
          "execution_count": 225
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "households = df['households']\n",
        "households"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jRDSFGOWTOUA",
        "outputId": "918eb633-35d4-4c82-a516-d79a33c946bb"
      },
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1        277.0\n",
              "2        495.0\n",
              "3         11.0\n",
              "4        237.0\n",
              "5        204.0\n",
              "         ...  \n",
              "2994     174.0\n",
              "2995     607.0\n",
              "2996    1036.0\n",
              "2997     220.0\n",
              "2998      14.0\n",
              "Name: households, Length: 2998, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(households)\n",
        "plt.axhline(y=mean_households, color='red')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "iWjDYikATr3K",
        "outputId": "198e4f4a-04db-4ae9-b662-5328172f3b3f"
      },
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7f1ab9f2c550>"
            ]
          },
          "metadata": {},
          "execution_count": 231
        },
        {
          "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": [
        "df.isnull().values.any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vFLge2V8Tvdq",
        "outputId": "3e54c9a5-8b8d-4819-d99d-1871920e4fb2"
      },
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df['latitude']\n",
        "y = df['longitude']\n",
        "plt.scatter(x, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "h2rT7qikUb7r",
        "outputId": "3206d139-ca57-4793-dc1a-0a88333b59c4"
      },
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f1ab6c95250>"
            ]
          },
          "metadata": {},
          "execution_count": 242
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "choose two arbitary columns and normalize all values inside using min-max normalization:"
      ],
      "metadata": {
        "id": "qaCEHkAXUiXN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "normalized_df_housing=(df['housing_median_age']-df['housing_median_age'].min())/(df['housing_median_age'].max()-df['housing_median_age'].min())"
      ],
      "metadata": {
        "id": "GyzoYoczU3aN"
      },
      "execution_count": 246,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "normalized_df_housing"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wMPs7j0dVTfa",
        "outputId": "3e695c21-7db6-4765-fa5c-820daa79a6c4"
      },
      "execution_count": 247,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1       0.823529\n",
              "2       0.509804\n",
              "3       0.529412\n",
              "4       0.352941\n",
              "5       0.705882\n",
              "          ...   \n",
              "2994    0.666667\n",
              "2995    0.431373\n",
              "2996    0.509804\n",
              "2997    0.176471\n",
              "2998    0.764706\n",
              "Name: housing_median_age, Length: 2998, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a Correlation Matrix from all columns"
      ],
      "metadata": {
        "id": "crQdoU8DVVCS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corrM = df.corr()\n",
        " \n",
        "corrM"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "Mys0n431Vbfk",
        "outputId": "4329ebb7-c266-43c5-9e92-315d720a146d"
      },
      "execution_count": 248,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    longitude  latitude  housing_median_age  total_rooms  \\\n",
              "longitude            1.000000 -0.925073           -0.064285     0.050125   \n",
              "latitude            -0.925073  1.000000           -0.024913    -0.039878   \n",
              "housing_median_age  -0.064285 -0.024913            1.000000    -0.367785   \n",
              "total_rooms          0.050125 -0.039878           -0.367785     1.000000   \n",
              "total_bedrooms       0.071019 -0.068470           -0.323002     0.937763   \n",
              "population           0.111657 -0.117501           -0.299738     0.838888   \n",
              "households           0.051206 -0.068519           -0.305018     0.914125   \n",
              "median_income       -0.018093 -0.072404           -0.145379     0.221625   \n",
              "median_house_value  -0.050221 -0.138481            0.090703     0.160754   \n",
              "\n",
              "                    total_bedrooms  population  households  median_income  \\\n",
              "longitude                 0.071019    0.111657    0.051206      -0.018093   \n",
              "latitude                 -0.068470   -0.117501   -0.068519      -0.072404   \n",
              "housing_median_age       -0.323002   -0.299738   -0.305018      -0.145379   \n",
              "total_rooms               0.937763    0.838888    0.914125       0.221625   \n",
              "total_bedrooms            1.000000    0.856371    0.970753       0.024454   \n",
              "population                0.856371    1.000000    0.895520       0.032885   \n",
              "households                0.970753    0.895520    1.000000       0.049079   \n",
              "median_income             0.024454    0.032885    0.049079       1.000000   \n",
              "median_house_value        0.082829   -0.000699    0.100739       0.671771   \n",
              "\n",
              "                    median_house_value  \n",
              "longitude                    -0.050221  \n",
              "latitude                     -0.138481  \n",
              "housing_median_age            0.090703  \n",
              "total_rooms                   0.160754  \n",
              "total_bedrooms                0.082829  \n",
              "population                   -0.000699  \n",
              "households                    0.100739  \n",
              "median_income                 0.671771  \n",
              "median_house_value            1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a2830a71-b06c-431b-a73e-11c6f8246f37\">\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</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>longitude</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.925073</td>\n",
              "      <td>-0.064285</td>\n",
              "      <td>0.050125</td>\n",
              "      <td>0.071019</td>\n",
              "      <td>0.111657</td>\n",
              "      <td>0.051206</td>\n",
              "      <td>-0.018093</td>\n",
              "      <td>-0.050221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>latitude</th>\n",
              "      <td>-0.925073</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.024913</td>\n",
              "      <td>-0.039878</td>\n",
              "      <td>-0.068470</td>\n",
              "      <td>-0.117501</td>\n",
              "      <td>-0.068519</td>\n",
              "      <td>-0.072404</td>\n",
              "      <td>-0.138481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>housing_median_age</th>\n",
              "      <td>-0.064285</td>\n",
              "      <td>-0.024913</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.367785</td>\n",
              "      <td>-0.323002</td>\n",
              "      <td>-0.299738</td>\n",
              "      <td>-0.305018</td>\n",
              "      <td>-0.145379</td>\n",
              "      <td>0.090703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_rooms</th>\n",
              "      <td>0.050125</td>\n",
              "      <td>-0.039878</td>\n",
              "      <td>-0.367785</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.937763</td>\n",
              "      <td>0.838888</td>\n",
              "      <td>0.914125</td>\n",
              "      <td>0.221625</td>\n",
              "      <td>0.160754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_bedrooms</th>\n",
              "      <td>0.071019</td>\n",
              "      <td>-0.068470</td>\n",
              "      <td>-0.323002</td>\n",
              "      <td>0.937763</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.856371</td>\n",
              "      <td>0.970753</td>\n",
              "      <td>0.024454</td>\n",
              "      <td>0.082829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>population</th>\n",
              "      <td>0.111657</td>\n",
              "      <td>-0.117501</td>\n",
              "      <td>-0.299738</td>\n",
              "      <td>0.838888</td>\n",
              "      <td>0.856371</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.895520</td>\n",
              "      <td>0.032885</td>\n",
              "      <td>-0.000699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>households</th>\n",
              "      <td>0.051206</td>\n",
              "      <td>-0.068519</td>\n",
              "      <td>-0.305018</td>\n",
              "      <td>0.914125</td>\n",
              "      <td>0.970753</td>\n",
              "      <td>0.895520</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.049079</td>\n",
              "      <td>0.100739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>median_income</th>\n",
              "      <td>-0.018093</td>\n",
              "      <td>-0.072404</td>\n",
              "      <td>-0.145379</td>\n",
              "      <td>0.221625</td>\n",
              "      <td>0.024454</td>\n",
              "      <td>0.032885</td>\n",
              "      <td>0.049079</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.671771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>median_house_value</th>\n",
              "      <td>-0.050221</td>\n",
              "      <td>-0.138481</td>\n",
              "      <td>0.090703</td>\n",
              "      <td>0.160754</td>\n",
              "      <td>0.082829</td>\n",
              "      <td>-0.000699</td>\n",
              "      <td>0.100739</td>\n",
              "      <td>0.671771</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a2830a71-b06c-431b-a73e-11c6f8246f37')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </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-a2830a71-b06c-431b-a73e-11c6f8246f37 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-a2830a71-b06c-431b-a73e-11c6f8246f37');\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": 248
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aaEmNVleVgtU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}