data_clean.ipynb

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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hd-yosqXno_w",
        "outputId": "2d4d45d8-164b-4e1a-8abb-3e9dc87e0fc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Specify the path to the CSV file on Google Drive\n",
        "data_set = \"/content/drive/MyDrive/table_hackathon_data_set_anonym.csv\"\n",
        "\n",
        "# Read the CSV file into a Pandas DataFrame\n",
        "df = pd.read_csv(data_set) # 'df' contains the data from the CSV file on Google Drive"
      ],
      "metadata": {
        "id": "4UzFkt06guJn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data columns into multiple with one piece of information per column\n",
        "\n",
        "import csv\n",
        "import re\n",
        "q1index = 8\n",
        "\n",
        "csvfile = open('/content/drive/MyDrive/table_hackathon_data_set_anonym.csv')\n",
        "outfile = open('/content/drive/MyDrive/data_clean.csv', 'w', newline='')\n",
        "\n",
        "reader = csv.DictReader(csvfile)\n",
        "headers = reader.fieldnames\n",
        "n_questions = (len(headers) - q1index)//3\n",
        "new_base_headers = headers[:q1index]\n",
        "new_base_headers.remove(\"Surname\")\n",
        "new_base_headers.remove(\"First.name\")\n",
        "new_base_headers.remove(\"State\")\n",
        "new_headers = new_base_headers + [f'Q{i}_{tp}' for i in range(1, n_questions+1) for tp in ['seed', 'problem', 'solution', 'mistakes', 'ans', 'validity', 'score', 'prt']]\n",
        "\n",
        "writer = csv.DictWriter(outfile, fieldnames=new_headers)\n",
        "writer.writeheader()\n",
        "\n",
        "for row in reader:\n",
        "    if row['State'] != 'Finished':\n",
        "        continue\n",
        "    new_row = {k : row[k] for k in new_base_headers}\n",
        "    for q in range(n_questions):\n",
        "        i = q+1\n",
        "        qtext = row[f'Question.{i}']\n",
        "        qtlines = [qt for qt in qtext.splitlines() if qt]\n",
        "        mistakes = []\n",
        "        for j, qtline in enumerate(qtlines):\n",
        "            line = re.search(r'<p>(.*)</p>', qtline).group(1)\n",
        "            pieces = line.split(' - ')\n",
        "            assert len(pieces) == 2\n",
        "            description = pieces[0].strip()\n",
        "            expression = pieces[1].strip()\n",
        "            if j == 0:\n",
        "                new_row[f'Q{i}_problem'] = expression\n",
        "            elif j >= 2:\n",
        "                mistakes.append(expression)\n",
        "        new_row[f'Q{i}_mistakes'] = ';'.join(mistakes)\n",
        "        new_row[f'Q{i}_solution'] = re.search(r'ans1: (.*) \\[score\\];', row[f'Right.answer.{i}']).group(1)\n",
        "        res = row[f'Response.{i}']\n",
        "        is_blank = False\n",
        "        parts = res.split('; ')\n",
        "        if len(parts) == 2:\n",
        "            seed, prt = parts\n",
        "            is_blank = True\n",
        "        else:\n",
        "            seed, ans, prt = parts[0], '; '.join(parts[1:-1]), parts[-1]\n",
        "        new_row[f'Q{i}_seed'] = re.search(r'Seed: ([0-9]*)', seed).group(1)\n",
        "        prt = re.search(r'prt1: (.*)', prt).group(1)\n",
        "        new_row[f'Q{i}_prt'] = prt\n",
        "        new_row[f'Q{i}_validity'] = 'blank'\n",
        "        if not is_blank:\n",
        "            new_row[f'Q{i}_ans'] = re.search(r'ans1: (.*) \\[', ans).group(1)\n",
        "            new_row[f'Q{i}_validity'] = re.search(r'ans1: .* \\[(.*)\\]', ans).group(1)\n",
        "        if new_row[f'Q{i}_validity'] == 'score':\n",
        "            score = re.search(r'# = ([0-9.]*) |', prt).group(1)\n",
        "        else:\n",
        "            score = 0\n",
        "        new_row[f'Q{i}_score'] = score\n",
        "    writer.writerow(new_row)\n",
        "outfile.close()\n",
        "csvfile.close()"
      ],
      "metadata": {
        "id": "8P2jqIBNwyX0",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Username', 'Surname', 'First.name', 'State', 'Started.on', 'Completed', 'Time.taken', 'Grade.6.00', 'Question.1', 'Response.1', 'Right.answer.1', 'Question.2', 'Response.2', 'Right.answer.2', 'Question.3', 'Response.3', 'Right.answer.3', 'Question.4', 'Response.4', 'Right.answer.4', 'Question.5', 'Response.5', 'Right.answer.5', 'Question.6', 'Response.6', 'Right.answer.6']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#adding indexing to the dataset\n",
        "df.insert(0,'TestNo', df.index)\n",
        "df['TestNo']=df['TestNo'].apply(lambda x: 'T'+str(x))"
      ],
      "metadata": {
        "id": "dbC3sWAszJKO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#here 0 is not anticipitated and 1 is anticipitated and -1 are the values which don't need the anticipitation (as they are either empty or true or invalid)\n",
        "# 0 = wrong answer and not anticipitated\n",
        "# 1 = wrong answer and anticipitated\n",
        "# -1 = right answer and invalid and empty\n",
        "def isColumnAnticipated(row):\n",
        "  if row[f'Q{i+1}_validity'] == \"valid\":\n",
        "    return \"0\"\n",
        "  elif row[f'Q{i+1}_validity'] == \"score\"  and row[f'Q{i+1}_score'] >= 0 and row[f'Q{i+1}_score'] <1:\n",
        "    if row[f'Q{i+1}_prt']:\n",
        "      for eachCol in row[f'Q{i+1}_prt'].split('|'):\n",
        "        eachCol = eachCol.strip();\n",
        "        if(eachCol[0:3]=='prt' and eachCol[-1] == 'T'):\n",
        "            return \"1\"\n",
        "      return \"0\"\n",
        "  else:\n",
        "    return \"-1\"\n",
        "\n",
        "#looping over all the 6 questions and make a new column of anticipate for each of them.\n",
        "for i in range(6):\n",
        "  dataf = \"q\" + str(i+1)+\"_df\"\n",
        "  dataf = df\n",
        "  dataf[f'Q{i+1}_anticipate'] = dataf.apply(isColumnAnticipated, axis=1)\n",
        "  output_file_path = \"/content/drive/MyDrive/update_data_clean.csv\"  # Replace with your desired output file path\n",
        "  df.to_csv(output_file_path, index=False)\n",
        "df.head()\n"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  TestNo Username      Started.on       Completed        Time.taken  \\\n",
              "0     T0  user004  10-10-22 10:10  10-10-22 10:57   46 mins 30 secs   \n",
              "1     T1  user004  10-10-22 14:16  10-10-22 14:35   18 mins 34 secs   \n",
              "2     T2  user006  10-08-22 14:07   10-09-22 0:53  10 hours 45 mins   \n",
              "3     T3  user006   10-09-22 8:37   10-09-22 9:10   32 mins 59 secs   \n",
              "4     T4  user006   10-09-22 9:15   10-09-22 9:43   28 mins 18 secs   \n",
              "\n",
              "   Grade.6.00  Q1_seed                   Q1_problem       Q1_solution  \\\n",
              "0         5.0   808626   \\(\\int{\\frac{4}{p}} d{a}\\)   4*log(abs(p))+c   \n",
              "1         3.0   490862  \\(\\int{\\frac{10}{y}} d{a}\\)  10*log(abs(y))+c   \n",
              "2         1.0   397885   \\(\\int{\\frac{9}{r}} d{a}\\)   9*log(abs(r))+c   \n",
              "3         3.0   154367  \\(\\int{\\frac{13}{t}} d{a}\\)  13*log(abs(t))+c   \n",
              "4         5.0   342032  \\(\\int{\\frac{15}{t}} d{a}\\)  15*log(abs(t))+c   \n",
              "\n",
              "                                        Q1_mistakes  ...  \\\n",
              "0   \\({4\\cdot \\ln \\left( \\left| p\\right| \\right)}\\)  ...   \n",
              "1  \\({10\\cdot \\ln \\left( \\left| y\\right| \\right)}\\)  ...   \n",
              "2   \\({9\\cdot \\ln \\left( \\left| r\\right| \\right)}\\)  ...   \n",
              "3  \\({13\\cdot \\ln \\left( \\left| t\\right| \\right)}\\)  ...   \n",
              "4  \\({15\\cdot \\ln \\left( \\left| t\\right| \\right)}\\)  ...   \n",
              "\n",
              "                                  Q6_ans Q6_validity  Q6_score  \\\n",
              "0                7/2*(x^2 - 3)^(6/7) + c       score       1.0   \n",
              "1                (x^2 -5)^(2/3)/(2/3) +c       score       1.0   \n",
              "2  (9/2)*(x^2-4)/((2/3)*(x^2-4)^(3/2))+c       score       0.0   \n",
              "3                          7/2*u^(6/7)+c       score       0.0   \n",
              "4                    7/6*(x^2-4)^(6/7)+c       score       1.0   \n",
              "\n",
              "                                              Q6_prt  Q1_anticipate  \\\n",
              "0                     # = 1 | ATInt_true. | prt1-1-T              0   \n",
              "1                     # = 1 | ATInt_true. | prt1-1-T             -1   \n",
              "2       # = 0 | ATInt_generic. | prt1-1-F | prt1-2-F              0   \n",
              "3  # = 0 | ATInt_generic. ATInt_var_SB_notSA. | p...              0   \n",
              "4                     # = 1 | ATInt_true. | prt1-1-T              0   \n",
              "\n",
              "  Q2_anticipate Q3_anticipate Q4_anticipate Q5_anticipate Q6_anticipate  \n",
              "0            -1            -1            -1            -1            -1  \n",
              "1            -1            -1             0             0            -1  \n",
              "2             0            -1             0             0             0  \n",
              "3            -1            -1             0            -1             0  \n",
              "4            -1            -1             0            -1            -1  \n",
              "\n",
              "[5 rows x 60 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TestNo</th>\n",
              "      <th>Username</th>\n",
              "      <th>Started.on</th>\n",
              "      <th>Completed</th>\n",
              "      <th>Time.taken</th>\n",
              "      <th>Grade.6.00</th>\n",
              "      <th>Q1_seed</th>\n",
              "      <th>Q1_problem</th>\n",
              "      <th>Q1_solution</th>\n",
              "      <th>Q1_mistakes</th>\n",
              "      <th>...</th>\n",
              "      <th>Q6_ans</th>\n",
              "      <th>Q6_validity</th>\n",
              "      <th>Q6_score</th>\n",
              "      <th>Q6_prt</th>\n",
              "      <th>Q1_anticipate</th>\n",
              "      <th>Q2_anticipate</th>\n",
              "      <th>Q3_anticipate</th>\n",
              "      <th>Q4_anticipate</th>\n",
              "      <th>Q5_anticipate</th>\n",
              "      <th>Q6_anticipate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>T0</td>\n",
              "      <td>user004</td>\n",
              "      <td>10-10-22 10:10</td>\n",
              "      <td>10-10-22 10:57</td>\n",
              "      <td>46 mins 30 secs</td>\n",
              "      <td>5.0</td>\n",
              "      <td>808626</td>\n",
              "      <td>\\(\\int{\\frac{4}{p}} d{a}\\)</td>\n",
              "      <td>4*log(abs(p))+c</td>\n",
              "      <td>\\({4\\cdot \\ln \\left( \\left| p\\right| \\right)}\\)</td>\n",
              "      <td>...</td>\n",
              "      <td>7/2*(x^2 - 3)^(6/7) + c</td>\n",
              "      <td>score</td>\n",
              "      <td>1.0</td>\n",
              "      <td># = 1 | ATInt_true. | prt1-1-T</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>T1</td>\n",
              "      <td>user004</td>\n",
              "      <td>10-10-22 14:16</td>\n",
              "      <td>10-10-22 14:35</td>\n",
              "      <td>18 mins 34 secs</td>\n",
              "      <td>3.0</td>\n",
              "      <td>490862</td>\n",
              "      <td>\\(\\int{\\frac{10}{y}} d{a}\\)</td>\n",
              "      <td>10*log(abs(y))+c</td>\n",
              "      <td>\\({10\\cdot \\ln \\left( \\left| y\\right| \\right)}\\)</td>\n",
              "      <td>...</td>\n",
              "      <td>(x^2 -5)^(2/3)/(2/3) +c</td>\n",
              "      <td>score</td>\n",
              "      <td>1.0</td>\n",
              "      <td># = 1 | ATInt_true. | prt1-1-T</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>T2</td>\n",
              "      <td>user006</td>\n",
              "      <td>10-08-22 14:07</td>\n",
              "      <td>10-09-22 0:53</td>\n",
              "      <td>10 hours 45 mins</td>\n",
              "      <td>1.0</td>\n",
              "      <td>397885</td>\n",
              "      <td>\\(\\int{\\frac{9}{r}} d{a}\\)</td>\n",
              "      <td>9*log(abs(r))+c</td>\n",
              "      <td>\\({9\\cdot \\ln \\left( \\left| r\\right| \\right)}\\)</td>\n",
              "      <td>...</td>\n",
              "      <td>(9/2)*(x^2-4)/((2/3)*(x^2-4)^(3/2))+c</td>\n",
              "      <td>score</td>\n",
              "      <td>0.0</td>\n",
              "      <td># = 0 | ATInt_generic. | prt1-1-F | prt1-2-F</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>3</th>\n",
              "      <td>T3</td>\n",
              "      <td>user006</td>\n",
              "      <td>10-09-22 8:37</td>\n",
              "      <td>10-09-22 9:10</td>\n",
              "      <td>32 mins 59 secs</td>\n",
              "      <td>3.0</td>\n",
              "      <td>154367</td>\n",
              "      <td>\\(\\int{\\frac{13}{t}} d{a}\\)</td>\n",
              "      <td>13*log(abs(t))+c</td>\n",
              "      <td>\\({13\\cdot \\ln \\left( \\left| t\\right| \\right)}\\)</td>\n",
              "      <td>...</td>\n",
              "      <td>7/2*u^(6/7)+c</td>\n",
              "      <td>score</td>\n",
              "      <td>0.0</td>\n",
              "      <td># = 0 | ATInt_generic. ATInt_var_SB_notSA. | p...</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>T4</td>\n",
              "      <td>user006</td>\n",
              "      <td>10-09-22 9:15</td>\n",
              "      <td>10-09-22 9:43</td>\n",
              "      <td>28 mins 18 secs</td>\n",
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              "      <td>342032</td>\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9b3f3f87-3160-42e4-a2ec-b08fe163b272');\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",
              "\n",
              "\n",
              "<div id=\"df-9ef2c5ef-d5c8-4a9b-83e7-91e37241e6e5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9ef2c5ef-d5c8-4a9b-83e7-91e37241e6e5')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9ef2c5ef-d5c8-4a9b-83e7-91e37241e6e5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Giving Insight about the Dataset\n"
      ],
      "metadata": {
        "id": "r8htGa0cWBPB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "hWmrAOUhzRJs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "data = {'Question 1':62.01456310679612, 'Question 2':49.11003236245955, 'Question 3':63.10679611650486,'Question 4':51.577669902912625,'Question 5':50.36407766990292,'Question 6':35.47734627831715}\n",
        "courses = list(data.keys())\n",
        "values = list(data.values())\n",
        "\n",
        "fig = plt.figure(figsize = (10, 5))\n",
        "\n",
        "# creating the bar plot\n",
        "plt.bar(courses, values,width = 0.4)\n",
        "\n",
        "plt.xlabel(\"Question\")\n",
        "plt.ylabel(\"% of student given right answer\")\n",
        "plt.title(\"Check how students are performing in each question\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "etbhix3mimNH",
        "outputId": "79e80f93-915f-49ff-e8ec-f0bf1745ee3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#bar Graph\n",
        "x2 = user\n",
        "y2 = ans\n",
        "# plotting the line 2 points\n",
        "plt.plot(x2, y2, label = \"line 2\")\n",
        "\n",
        "# naming the x axis\n",
        "plt.xlabel('No of Students')\n",
        "# naming the y axis\n",
        "plt.ylabel('Time in minute')\n",
        "# giving a title to my graph\n",
        "plt.title('Time take by students to solve 6 questions')\n",
        "plt.xticks(rotation=30, ha='right')\n",
        "# show a legend on the plot\n",
        "plt.legend()\n",
        "\n",
        "# function to show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "tYNUT1f-k-Y2",
        "outputId": "94732f0b-e562-4441-9cef-a96ffb6568dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "copydf = df.copy()\n",
        "copydf['Grade'] = copydf['Grade.6.00']\n",
        "var_copydf = copydf.Grade\n",
        "groupGrad = []\n",
        "arrgroupGrad = [0]*7\n",
        "count = 2400\n",
        "for i in range(len(var_copydf)):\n",
        "  #print(int(a[i]))\n",
        "  arrgroupGrad[int(var_copydf[i])] = arrgroupGrad[int(var_copydf[i])] + 1\n",
        "  groupGrad.append(int(var_copydf[i]))\n",
        "\n",
        "gradeValue = []\n",
        "for i in range(7):\n",
        "  arrgroupGrad[i] =  (arrgroupGrad[i]/count)*100\n",
        "  gradeValue.append(i)\n",
        "\n",
        "\n",
        "data = {gradeValue[i]: arrgroupGrad[i] for i in range(len(gradeValue))}\n",
        "courses = list(data.keys())\n",
        "values = list(data.values())\n",
        "\n",
        "fig = plt.figure(figsize = (10, 5))\n",
        "x = np.arange((len(gradeValue)))\n",
        "width = 0.35\n",
        "# creating the bar plot\n",
        "#plt.bar(courses, values,width = 0.4)\n",
        "pps = plt.bar(x-width/2,values,width)\n",
        "for p in pps:\n",
        "  height = p.get_height()\n",
        "  plt.text(p.get_x()+p.get_width()/10,y=height+.10,s =\"{:.2f}\".format(height))\n",
        "\n",
        "plt.xlabel(\"Grades\")\n",
        "plt.ylabel(\"Count (%)\")\n",
        "plt.title(\"Grade scored by students\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "Xej4xrKHxZVj",
        "outputId": "7ba01d15-c056-4ae7-e447-2b29a39225bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yzSv94tu62cC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}