Copy_of_ans4.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of ans4.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/prakshalj0512/MAT494/blob/master/Copy_of_ans4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "MRiM3uK2kKnN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set(font_scale=1.5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_IeJP_8mkQUr",
        "colab_type": "code",
        "outputId": "8c1a5b29-5601-4a19-ef06-46785abbc2f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        }
      },
      "cell_type": "code",
      "source": [
        "# Load the raw data\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/rgerkin/PythonDataScienceHandbook/master/notebooks/data/ans4.csv')\n",
        "\n",
        "# Renumber the indices\n",
        "df['id'] = [x%11 for x in range(df.shape[0])]\n",
        "\n",
        "# Set new row names\n",
        "df = df.set_index(['dataset','id'])\n",
        "\n",
        "df.head(15)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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></th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dataset</th>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"11\" valign=\"top\">I</th>\n",
              "      <th>0</th>\n",
              "      <td>10.0</td>\n",
              "      <td>8.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.0</td>\n",
              "      <td>6.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.0</td>\n",
              "      <td>7.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.0</td>\n",
              "      <td>8.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11.0</td>\n",
              "      <td>8.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>14.0</td>\n",
              "      <td>9.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6.0</td>\n",
              "      <td>7.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>4.0</td>\n",
              "      <td>4.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>12.0</td>\n",
              "      <td>10.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>7.0</td>\n",
              "      <td>4.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5.0</td>\n",
              "      <td>5.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">II</th>\n",
              "      <th>0</th>\n",
              "      <td>10.0</td>\n",
              "      <td>9.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.0</td>\n",
              "      <td>8.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.0</td>\n",
              "      <td>8.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.0</td>\n",
              "      <td>8.77</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               x      y\n",
              "dataset id             \n",
              "I       0   10.0   8.04\n",
              "        1    8.0   6.95\n",
              "        2   13.0   7.58\n",
              "        3    9.0   8.81\n",
              "        4   11.0   8.33\n",
              "        5   14.0   9.96\n",
              "        6    6.0   7.24\n",
              "        7    4.0   4.26\n",
              "        8   12.0  10.84\n",
              "        9    7.0   4.82\n",
              "        10   5.0   5.68\n",
              "II      0   10.0   9.14\n",
              "        1    8.0   8.14\n",
              "        2   13.0   8.74\n",
              "        3    9.0   8.77"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "mDi2i2cIkwLs",
        "colab_type": "code",
        "outputId": "557d2818-c871-4d15-a021-f72ba65813b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        }
      },
      "cell_type": "code",
      "source": [
        "df.plot.scatter(x='x',y='y');"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6Ia5rh/0tU+cSADALS+Hgeg1nzoReN5y5wE8CQOdAuMP1dh86HvYaADobwh0GiLrINQB0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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f23d5448b38>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "8KWifia0lEvf",
        "colab_type": "code",
        "outputId": "89eefb15-22d8-4eb1-9da5-bdfb11d33322",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "cell_type": "code",
      "source": [
        "# Enter your group number here:\n",
        "group = 'IV' # Enter I, II, III, or IV\n",
        "\n",
        "df.loc[group]"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>x</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.0</td>\n",
              "      <td>6.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.0</td>\n",
              "      <td>5.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.0</td>\n",
              "      <td>7.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.0</td>\n",
              "      <td>8.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8.0</td>\n",
              "      <td>8.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8.0</td>\n",
              "      <td>7.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>8.0</td>\n",
              "      <td>5.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>19.0</td>\n",
              "      <td>12.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8.0</td>\n",
              "      <td>5.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8.0</td>\n",
              "      <td>7.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>8.0</td>\n",
              "      <td>6.89</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       x      y\n",
              "id             \n",
              "0    8.0   6.58\n",
              "1    8.0   5.76\n",
              "2    8.0   7.71\n",
              "3    8.0   8.84\n",
              "4    8.0   8.47\n",
              "5    8.0   7.04\n",
              "6    8.0   5.25\n",
              "7   19.0  12.50\n",
              "8    8.0   5.56\n",
              "9    8.0   7.91\n",
              "10   8.0   6.89"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "DGu0O_n9lc9d",
        "colab_type": "code",
        "outputId": "e9d88a92-2e89-42c2-a544-99c483dfc1b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "cell_type": "code",
      "source": [
        "df.loc[group].mean().round(2)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "x    9.0\n",
              "y    7.5\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "q3sCvKMTmNtS",
        "colab_type": "code",
        "outputId": "d694314a-13e4-40a8-e195-eede15275a7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "cell_type": "code",
      "source": [
        "print(\"Median\")\n",
        "print(df.loc[group].median().round(2))\n",
        "print(\"\\nStandard Deviation\")\n",
        "print(df.loc[group].std().round(2))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Median\n",
            "x    8.00\n",
            "y    7.04\n",
            "dtype: float64\n",
            "\n",
            "Standard Deviation\n",
            "x    3.32\n",
            "y    2.03\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}