A_Data Cleaning For ML Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "14132e39-1455-4d8b-9363-274c12b8b823",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bd06c89f-f4e8-403c-823f-b479c58f2b0e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/qq/qm2zshms6rl57mb9s95rh3xc0000gn/T/ipykernel_18501/1876141887.py:1: DtypeWarning: Columns (33,34,35,36,37,38,39) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv('random subset of data files/responses.csv')\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('random subset of data files/responses.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e7431e93-9bf9-4320-a0ab-1e8f1dd8b4a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/qq/qm2zshms6rl57mb9s95rh3xc0000gn/T/ipykernel_18501/3800257269.py:1: DtypeWarning: Columns (32,33,34,35,36,37,38,39) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df_full = pd.read_csv('Data files/responses.csv')\n"
     ]
    }
   ],
   "source": [
    "df_full = pd.read_csv('Data files/responses.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9c752d7c-1042-40b8-8c6c-c49a65c0b7f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63410960"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e003dc2e-6c19-47e4-8705-bd4ebf5e10e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_0_10000 = df_full.iloc[0:10000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77ea22dc-65ce-419c-a0d5-1296aa01a4bc",
   "metadata": {},
   "source": [
    "### Create df with chapter_number, student_id, num_passed DF ###\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e90cb8a4-6ee8-4272-8713-cc6f0fdf6bcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for a specific chapter and section and student\n",
    "code = df_full[df_full['item_type'] == 'code']\n",
    "\n",
    "code_1_5 = code\n",
    "#code_1_5 = code[ (code['chapter_number'] == 1.0) & (code['section_number'] == 5.0) ]\n",
    "\n",
    "s_c_1_5 = code_1_5\n",
    "#s_c_1_5 = code_1_5[ (code_1_5['student_id'] == '83df1b22-c83a-4412-8e27-1c113bd17907') | (code_1_5['student_id'] == '36ada86d-1a6d-4f4a-8d11-3b0ccebf4c11')]\n",
    "#s_c_1_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "86c2ee17-4aab-4d58-bdfa-49fed3ad8fb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create df with only columns needed\n",
    "cleaned_SC15 = s_c_1_5[['student_id', 'chapter_number', 'item_id', 'response', 'prompt', 'points_possible', 'points_earned', 'dt_submitted', 'attempt']]\n",
    "#cleaned_SC15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7e7f4ef6-9db0-4389-a811-80f695e4675f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# does number of CQs passed predict success on EOCs?\n",
    "numPassed = cleaned_SC15.groupby('item_id')['points_earned'].any(1.0).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8927fd57-c487-4c7f-a887-0e4c4b37e5e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 student_id  chapter_number  num_passed\n",
      "0      001824fb-a2fd-431d-aef6-7a1250d97a62             1.0          24\n",
      "1      001824fb-a2fd-431d-aef6-7a1250d97a62             2.0          26\n",
      "2      001824fb-a2fd-431d-aef6-7a1250d97a62             3.0          29\n",
      "3      001824fb-a2fd-431d-aef6-7a1250d97a62             4.0          19\n",
      "4      001824fb-a2fd-431d-aef6-7a1250d97a62             5.0          12\n",
      "...                                     ...             ...         ...\n",
      "15927  ff6ec9fe-de1d-4b45-8136-59465d9c85ab            12.0          14\n",
      "15928  ff6ec9fe-de1d-4b45-8136-59465d9c85ab            13.0          11\n",
      "15929  ff6ec9fe-de1d-4b45-8136-59465d9c85ab            14.0          16\n",
      "15930  ff6ec9fe-de1d-4b45-8136-59465d9c85ab            15.0           6\n",
      "15931  ff6ec9fe-de1d-4b45-8136-59465d9c85ab            16.0           8\n",
      "\n",
      "[15932 rows x 3 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/qq/qm2zshms6rl57mb9s95rh3xc0000gn/T/ipykernel_18501/562977077.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cleaned_SC15['passed'] = cleaned_SC15.groupby('item_id')['points_earned'].transform(lambda x: (x == 1.0).any())\n"
     ]
    }
   ],
   "source": [
    "#dictionary {userid: numPassed, \n",
    "\n",
    "# Assuming 'cleaned_SC15' is your DataFrame and it includes a 'user_id' column.\n",
    "\n",
    "# First, create a column that flags if the points_earned is 1.0 for any row within each item_id group\n",
    "cleaned_SC15['passed'] = cleaned_SC15.groupby('item_id')['points_earned'].transform(lambda x: (x == 1.0).any())\n",
    "cleaned_SC15\n",
    "\n",
    "num_passed_by_user_chapter = (\n",
    "    cleaned_SC15[cleaned_SC15['passed']]\n",
    "    .groupby(['student_id', 'chapter_number'])['item_id']\n",
    "    .nunique()\n",
    "    .reset_index(name='num_passed')\n",
    ")\n",
    "print(num_passed_by_user_chapter)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dfe3c88-09ab-46b4-b589-25fd0394b8e4",
   "metadata": {},
   "source": [
    "### Pull EOC Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "86528a47-7fbf-478c-ae56-2b951eeb46c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoints = pd.read_csv('random subset of data files/checkpoints.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2e366cf5-30d7-44fb-bdb0-7efd9c718c31",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoints_full = pd.read_csv('Data files/checkpoints.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7ea985aa-9e26-4c80-b3c7-28c2c85cd8dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoints_full.size\n",
    "checkpoints_0_10000 = checkpoints_full.iloc[0:10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bcea7793-7420-4b83-b724-d9bdcdf2fe81",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/qq/qm2zshms6rl57mb9s95rh3xc0000gn/T/ipykernel_18501/237249873.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  f_cp_needed['chapter_number'] = f_cp_needed['chapter_number'].astype(float)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chapter_number</th>\n",
       "      <th>student_id</th>\n",
       "      <th>EOC</th>\n",
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       "      <td>2.0</td>\n",
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       "    <tr>\n",
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       "      <th>6</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>0.436620</td>\n",
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       "    <tr>\n",
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       "      <td>6.0</td>\n",
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       "      <td>0.600000</td>\n",
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       "      <th>9</th>\n",
       "      <td>8.0</td>\n",
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       "      <td>0.524390</td>\n",
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       "      <td>16.0</td>\n",
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       "      <td>ff6ec9fe-de1d-4b45-8136-59465d9c85ab</td>\n",
       "      <td>0.897436</td>\n",
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       "      <th>79089</th>\n",
       "      <td>14.0</td>\n",
       "      <td>ff6ec9fe-de1d-4b45-8136-59465d9c85ab</td>\n",
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       "      <td>0.272727</td>\n",
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       "<p>16348 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "       chapter_number                            student_id       EOC\n",
       "3                 2.0  040852c0-3b9c-4fc6-8229-d7ba8264e9ce  0.514563\n",
       "4                 3.0  040852c0-3b9c-4fc6-8229-d7ba8264e9ce  0.642857\n",
       "6                 5.0  040852c0-3b9c-4fc6-8229-d7ba8264e9ce  0.436620\n",
       "7                 6.0  040852c0-3b9c-4fc6-8229-d7ba8264e9ce  0.600000\n",
       "9                 8.0  040852c0-3b9c-4fc6-8229-d7ba8264e9ce  0.524390\n",
       "...               ...                                   ...       ...\n",
       "79087            16.0  ff55bab4-812b-405b-9856-8bedf9fb11f9  1.000000\n",
       "79088             1.0  ff6ec9fe-de1d-4b45-8136-59465d9c85ab  0.897436\n",
       "79089            14.0  ff6ec9fe-de1d-4b45-8136-59465d9c85ab  0.517857\n",
       "79090            15.0  ff6ec9fe-de1d-4b45-8136-59465d9c85ab  0.272727\n",
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       "\n",
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     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we now have [student_id : num_codechunks_passed]\n",
    "#create [student_id : score_checkpoint]\n",
    "f_cp = checkpoints_full.dropna(subset=['EOC'])\n",
    "f_cp_needed = f_cp[['chapter_number', 'student_id', 'EOC']]\n",
    "f_cp_needed['chapter_number'] = f_cp_needed['chapter_number'].astype(float)\n",
    "\n",
    "f_cp_needed = f_cp_needed.drop_duplicates()\n",
    "f_cp_needed\n",
    "#f_cp_needed[ f_cp_needed['student_id' == '03fefe56-a52f-49fe-9cd8-b183e9badc58'] ] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2784e8c0-11ef-42c3-b189-fe9aad43bd76",
   "metadata": {},
   "source": [
    "### Merge Data (Num_Passed and EOC) ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "058fe2a5-04a0-4a6e-80e7-1dfa9827c7cd",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>student_id</th>\n",
       "      <th>chapter_number</th>\n",
       "      <th>num_passed</th>\n",
       "      <th>EOC</th>\n",
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      ],
      "text/plain": [
       "                                 student_id  chapter_number  num_passed  \\\n",
       "0      001824fb-a2fd-431d-aef6-7a1250d97a62             1.0          24   \n",
       "1      001824fb-a2fd-431d-aef6-7a1250d97a62             2.0          26   \n",
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       "15986  ff6ec9fe-de1d-4b45-8136-59465d9c85ab            16.0           8   \n",
       "\n",
       "            EOC  \n",
       "0      0.921053  \n",
       "1      0.839080  \n",
       "2      0.769231  \n",
       "3      0.743590  \n",
       "4      0.777778  \n",
       "...         ...  \n",
       "15982  0.531646  \n",
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       "15984  0.517857  \n",
       "15985  0.272727  \n",
       "15986  0.272727  \n",
       "\n",
       "[15987 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge datasets f_cp_needed and num_passed_by_user_chapter\n",
    "merged_df_NP_EOC = pd.merge(num_passed_by_user_chapter, f_cp_needed, on=['student_id', 'chapter_number'], how='inner')\n",
    "merged_df_NP_EOC "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92e36f6b-d241-42fd-aca4-5ac4f63b548e",
   "metadata": {},
   "source": [
    "### Load in data (engaged) ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e2938b25-506c-462e-9354-280a0f05a708",
   "metadata": {},
   "outputs": [],
   "source": [
    "page_views = pd.read_csv('random subset of data files/page_views.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c7b9053b-d139-4b68-96a7-1e0da37ff851",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/qq/qm2zshms6rl57mb9s95rh3xc0000gn/T/ipykernel_18501/2926529880.py:1: DtypeWarning: Columns (10) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  page_views_full = pd.read_csv('Data files/page_views.csv')\n"
     ]
    }
   ],
   "source": [
    "page_views_full = pd.read_csv('Data files/page_views.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ba14a517-6f5f-43be-9ca3-8edcda99f1e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pv_imp = page_views_full[['student_id', 'chapter_number', 'section_number', 'engaged']]\n",
    "#print(pv_imp[  (pv_imp['student_id'] == \"988aaef5-55d8-4efd-a2aa-8a17e687965e\") & (pv_imp['chapter_number'] == 1.0) & (pv_imp['section_number'] == 3.0)  ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8526bfb8-d158-4998-ac71-0f6a2b9895b5",
   "metadata": {},
   "outputs": [
    {
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       "<p>16363 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "                                 student_id  chapter_number     engaged\n",
       "0      001824fb-a2fd-431d-aef6-7a1250d97a62             1.0   5663330.0\n",
       "1      001824fb-a2fd-431d-aef6-7a1250d97a62             2.0  30902762.0\n",
       "2      001824fb-a2fd-431d-aef6-7a1250d97a62             3.0  34127489.0\n",
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       "\n",
       "[16363 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_pv_imp = pv_imp.groupby(['student_id', 'chapter_number'])['engaged'].sum().reset_index()\n",
    "grouped_pv_imp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86035dea-2c98-4281-996c-f9ff3fa2cb14",
   "metadata": {},
   "source": [
    "### Merge num_passed_EOC with engaged ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d70eeb76-d78b-4b0c-96c8-eb51b79fbc2a",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                                 student_id  chapter_number  num_passed  \\\n",
       "0      001824fb-a2fd-431d-aef6-7a1250d97a62             1.0          24   \n",
       "1      001824fb-a2fd-431d-aef6-7a1250d97a62             2.0          26   \n",
       "2      001824fb-a2fd-431d-aef6-7a1250d97a62             3.0          29   \n",
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       "...                                     ...             ...         ...   \n",
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       "15986  ff6ec9fe-de1d-4b45-8136-59465d9c85ab            16.0           8   \n",
       "\n",
       "            EOC     engaged  \n",
       "0      0.921053   5663330.0  \n",
       "1      0.839080  30902762.0  \n",
       "2      0.769231  34127489.0  \n",
       "3      0.743590  18987025.0  \n",
       "4      0.777778  15971898.0  \n",
       "...         ...         ...  \n",
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       "\n",
       "[15987 rows x 5 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_NP_EOC_Engaged = pd.merge(merged_df_NP_EOC, grouped_pv_imp, on=['student_id', 'chapter_number'], how='inner')\n",
    "merged_df_NP_EOC_Engaged"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a9b2a27-b424-4ccd-8de5-a60cc680cb07",
   "metadata": {},
   "source": [
    "### Import length ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "570931a4-99c8-42d7-9779-7aec222fb5a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>student_id</th>\n",
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       "      <th>length</th>\n",
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       "      <td>ff6ec9fe-de1d-4b45-8136-59465d9c85ab</td>\n",
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       "      <td>12.0</td>\n",
       "      <td>37</td>\n",
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       "<p>9233 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "                                student_id  chapter_number  length\n",
       "0     001824fb-a2fd-431d-aef6-7a1250d97a62             1.0     131\n",
       "1     001824fb-a2fd-431d-aef6-7a1250d97a62             2.0      91\n",
       "2     001824fb-a2fd-431d-aef6-7a1250d97a62             3.0      65\n",
       "3     001824fb-a2fd-431d-aef6-7a1250d97a62             5.0      71\n",
       "4     001824fb-a2fd-431d-aef6-7a1250d97a62             6.0      73\n",
       "...                                    ...             ...     ...\n",
       "9228  ff6ec9fe-de1d-4b45-8136-59465d9c85ab             6.0      56\n",
       "9229  ff6ec9fe-de1d-4b45-8136-59465d9c85ab             9.0      91\n",
       "9230  ff6ec9fe-de1d-4b45-8136-59465d9c85ab            10.0      40\n",
       "9231  ff6ec9fe-de1d-4b45-8136-59465d9c85ab            11.0      54\n",
       "9232  ff6ec9fe-de1d-4b45-8136-59465d9c85ab            12.0      37\n",
       "\n",
       "[9233 rows x 3 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "length_summaries = pd.read_csv('random subset of data files/length_series.csv')\n",
    "length_summaries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "414d99a7-21e5-4407-b7a8-58dde8ca1834",
   "metadata": {},
   "source": [
    "### Merge numPassed_EOC_engaged with length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "160d8946-336a-4eca-b413-980674394b3b",
   "metadata": {},
   "outputs": [
    {
     "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>student_id</th>\n",
       "      <th>chapter_number</th>\n",
       "      <th>num_passed</th>\n",
       "      <th>EOC</th>\n",
       "      <th>engaged</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>001824fb-a2fd-431d-aef6-7a1250d97a62</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>5663330.0</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>001824fb-a2fd-431d-aef6-7a1250d97a62</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26</td>\n",
       "      <td>0.839080</td>\n",
       "      <td>30902762.0</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>001824fb-a2fd-431d-aef6-7a1250d97a62</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>34127489.0</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>001824fb-a2fd-431d-aef6-7a1250d97a62</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>15971898.0</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>001824fb-a2fd-431d-aef6-7a1250d97a62</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.697917</td>\n",
       "      <td>17611781.0</td>\n",
       "      <td>73</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9234</th>\n",
       "      <td>ff6ec9fe-de1d-4b45-8136-59465d9c85ab</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>16650582.0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9235</th>\n",
       "      <td>ff6ec9fe-de1d-4b45-8136-59465d9c85ab</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.611650</td>\n",
       "      <td>14206097.0</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9236</th>\n",
       "      <td>ff6ec9fe-de1d-4b45-8136-59465d9c85ab</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.469697</td>\n",
       "      <td>8793743.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9237</th>\n",
       "      <td>ff6ec9fe-de1d-4b45-8136-59465d9c85ab</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.518519</td>\n",
       "      <td>10646661.0</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9238</th>\n",
       "      <td>ff6ec9fe-de1d-4b45-8136-59465d9c85ab</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.531646</td>\n",
       "      <td>7870316.0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9239 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                student_id  chapter_number  num_passed  \\\n",
       "0     001824fb-a2fd-431d-aef6-7a1250d97a62             1.0          24   \n",
       "1     001824fb-a2fd-431d-aef6-7a1250d97a62             2.0          26   \n",
       "2     001824fb-a2fd-431d-aef6-7a1250d97a62             3.0          29   \n",
       "3     001824fb-a2fd-431d-aef6-7a1250d97a62             5.0          12   \n",
       "4     001824fb-a2fd-431d-aef6-7a1250d97a62             6.0          19   \n",
       "...                                    ...             ...         ...   \n",
       "9234  ff6ec9fe-de1d-4b45-8136-59465d9c85ab             6.0          19   \n",
       "9235  ff6ec9fe-de1d-4b45-8136-59465d9c85ab             9.0          13   \n",
       "9236  ff6ec9fe-de1d-4b45-8136-59465d9c85ab            10.0          12   \n",
       "9237  ff6ec9fe-de1d-4b45-8136-59465d9c85ab            11.0          16   \n",
       "9238  ff6ec9fe-de1d-4b45-8136-59465d9c85ab            12.0          14   \n",
       "\n",
       "           EOC     engaged  length  \n",
       "0     0.921053   5663330.0     131  \n",
       "1     0.839080  30902762.0      91  \n",
       "2     0.769231  34127489.0      65  \n",
       "3     0.777778  15971898.0      71  \n",
       "4     0.697917  17611781.0      73  \n",
       "...        ...         ...     ...  \n",
       "9234  0.500000  16650582.0      56  \n",
       "9235  0.611650  14206097.0      91  \n",
       "9236  0.469697   8793743.0      40  \n",
       "9237  0.518519  10646661.0      54  \n",
       "9238  0.531646   7870316.0      37  \n",
       "\n",
       "[9239 rows x 6 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_NP_EOC_Engaged_Length = pd.merge(merged_df_NP_EOC_Engaged, length_summaries, on=['student_id', 'chapter_number'], how='inner')\n",
    "merged_df_NP_EOC_Engaged_Length"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb884e49-939d-4f85-be56-650303e21e0b",
   "metadata": {},
   "source": [
    "### Create File Path for CSV save ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e9f8c162-6bde-40e8-8588-e3962bf42496",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "subfolder = 'random subset of data files'\n",
    "#filename = 'NP_ENG_EOC.csv'\n",
    "filename = 'NP_ENG_EOC_FULL.csv'\n",
    "filepath = os.path.join(subfolder, filename)\n",
    "merged_df_NP_EOC_Engaged_Length.to_csv(filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83bbe437-bf00-4f89-a2a8-5bc1dd28a04c",
   "metadata": {},
   "source": [
    "### Create Data Visualization of EOC vs num_code_chunks_passed For all Students and All Chapters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cf9d0ead-617f-407b-9403-849e94c0333f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, we'll create the plot using seaborn\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.lineplot(data=merged_df_NP_EOC_Engaged_Length, x='num_passed', y='EOC', marker='o')\n",
    "\n",
    "plt.title('EOC Scores by Chapter Number for Each Student')\n",
    "plt.xlabel('Number of Coding Chunks Passed')\n",
    "plt.ylabel('EOC Score')\n",
    "plt.legend(title='Student ID', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "\n",
    "# Show plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
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