Data_Representation.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPXCEiGb6mI8IZI6EpsGwWZ",
      "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/DeepthiTabithaBennet/Python_AppliedStatistics/blob/main/Data_Representation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZvOyO_AHfnS"
      },
      "source": [
        "# Written by Deepthi Tabitha Bennet\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bgYvbkk5Ud5n",
        "outputId": "cdae9180-34bf-43f1-db90-0ddb7db507b5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data = [95, 83, 94, 79, 95, 98, 94, 76, 73, 81, 95, 83, 76, 83, 94, 82, 81, 95, 94, 82]\n",
        "df = pd.Series(data).value_counts()\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "95    4\n",
              "94    4\n",
              "83    3\n",
              "82    2\n",
              "81    2\n",
              "76    2\n",
              "79    1\n",
              "73    1\n",
              "98    1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kHG84JHbX8vK",
        "outputId": "bfef8fc1-35fe-405d-83b8-5d6b1773b775",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        }
      },
      "source": [
        "d = pd.DataFrame(df)\n",
        "d"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
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              "      <th>95</th>\n",
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              "      <th>82</th>\n",
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              "      <th>81</th>\n",
              "      <td>2</td>\n",
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              "      <th>76</th>\n",
              "      <td>2</td>\n",
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              "      <th>79</th>\n",
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              "    <tr>\n",
              "      <th>73</th>\n",
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              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "    0\n",
              "95  4\n",
              "94  4\n",
              "83  3\n",
              "82  2\n",
              "81  2\n",
              "76  2\n",
              "79  1\n",
              "73  1\n",
              "98  1"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_EX0xpu_XPOS",
        "outputId": "91c939e6-3798-4a8d-a6d0-583e731e7179",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        }
      },
      "source": [
        "d['rf'] = d / len(data)\n",
        "d['prf'] = d['rf'] * 100\n",
        "d['cf'] = df.cumsum()\n",
        "d['rcf'] = d['cf'] / df.sum()\n",
        "d['prcf'] = d['rcf'] * 100\n",
        "d"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>rf</th>\n",
              "      <th>prf</th>\n",
              "      <th>cf</th>\n",
              "      <th>rcf</th>\n",
              "      <th>prcf</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>4</td>\n",
              "      <td>0.20</td>\n",
              "      <td>20.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0.20</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>4</td>\n",
              "      <td>0.20</td>\n",
              "      <td>20.0</td>\n",
              "      <td>8</td>\n",
              "      <td>0.40</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>3</td>\n",
              "      <td>0.15</td>\n",
              "      <td>15.0</td>\n",
              "      <td>11</td>\n",
              "      <td>0.55</td>\n",
              "      <td>55.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>2</td>\n",
              "      <td>0.10</td>\n",
              "      <td>10.0</td>\n",
              "      <td>13</td>\n",
              "      <td>0.65</td>\n",
              "      <td>65.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>2</td>\n",
              "      <td>0.10</td>\n",
              "      <td>10.0</td>\n",
              "      <td>15</td>\n",
              "      <td>0.75</td>\n",
              "      <td>75.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>2</td>\n",
              "      <td>0.10</td>\n",
              "      <td>10.0</td>\n",
              "      <td>17</td>\n",
              "      <td>0.85</td>\n",
              "      <td>85.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>1</td>\n",
              "      <td>0.05</td>\n",
              "      <td>5.0</td>\n",
              "      <td>18</td>\n",
              "      <td>0.90</td>\n",
              "      <td>90.0</td>\n",
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              "      <th>73</th>\n",
              "      <td>1</td>\n",
              "      <td>0.05</td>\n",
              "      <td>5.0</td>\n",
              "      <td>19</td>\n",
              "      <td>0.95</td>\n",
              "      <td>95.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>1</td>\n",
              "      <td>0.05</td>\n",
              "      <td>5.0</td>\n",
              "      <td>20</td>\n",
              "      <td>1.00</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    0    rf   prf  cf   rcf   prcf\n",
              "95  4  0.20  20.0   4  0.20   20.0\n",
              "94  4  0.20  20.0   8  0.40   40.0\n",
              "83  3  0.15  15.0  11  0.55   55.0\n",
              "82  2  0.10  10.0  13  0.65   65.0\n",
              "81  2  0.10  10.0  15  0.75   75.0\n",
              "76  2  0.10  10.0  17  0.85   85.0\n",
              "79  1  0.05   5.0  18  0.90   90.0\n",
              "73  1  0.05   5.0  19  0.95   95.0\n",
              "98  1  0.05   5.0  20  1.00  100.0"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WKQfTtXcYbY9",
        "outputId": "67d41dbc-58a3-480e-b98f-3b2d68fd9093",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        }
      },
      "source": [
        "ax = d['rf'].plot(kind='bar', title='Sample', legend=True, fontsize=12)\n",
        "ax.set_xlabel('Marks', fontsize=12)\n",
        "ax.set_ylabel('Relative Frequency', fontsize=12)\n",
        "plt.show()"
      ],
      "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",
      "metadata": {
        "id": "f3WC8onCbH5G",
        "outputId": "0c362314-f0b7-47d6-a1ba-e9ea10a93f23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "ax1 = d['prf'].plot.pie(legend=True, figsize=(10,5))\n",
        "ax1.set_ylabel('Percent Relative Frequency')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_QkiuJmTbVOh",
        "outputId": "64f2fbc4-5377-4b6e-b6e0-33fafa24b3d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        }
      },
      "source": [
        "ax2 = d['prcf'].plot.line()\n",
        "ax2.set_xlabel('Marks', fontsize=12)\n",
        "ax2.set_ylabel('Percent Relative Cumulative Frequency', fontsize=12)\n",
        "plt.show()"
      ],
      "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",
      "metadata": {
        "id": "w0U4tyCkJqSw",
        "outputId": "8058cadc-cf81-4c5c-da6c-15d797cbc480",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "ax3 = d['cf'].plot.hist()\n",
        "ax3.set_xlabel('Marks', fontsize=12)\n",
        "ax3.set_ylabel('Cumulative Frequency', fontsize=12)\n",
        "plt.show()"
      ],
      "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",
      "metadata": {
        "id": "-1svshK3KUTZ",
        "outputId": "8b9c0e52-86dd-4954-dcf2-776a11f54232",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "x = [95, 94, 83, 82, 81, 76, 79, 73, 98]\n",
        "y = [0.20, 0.40, 0.55, 0.65, 0.75, 0.85, 0.90, 0.95, 1.00]\n",
        "plt.xlabel('Marks')\n",
        "plt.ylabel('Relative Cumulative Frequency')\n",
        "plt.scatter(x, y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f27ff249490>"
            ]
          },
          "metadata": {},
          "execution_count": 123
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}