src/notebooks/student_submissions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib\n",
    "\n",
    "# Load LMS helper module\n",
    "# Instead of loading from a published package, we're going to hack the \n",
    "# module search path to find the package on the local file system.\n",
    "import sys\n",
    "from edfi_lms_file_utils import file_reader\n",
    "\n",
    "# removes decimal places from float datatype\n",
    "pd.options.display.float_format = '{:,.0f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the directory path for the output files from google classroom extractor\n",
    "#csv_path = os.path.join(\"..\", \"google-classroom-extractor\", \"data\", \"ed-fi-udm-lms\")\n",
    "\n",
    "# set the directory path for the output files from sample output\n",
    "csv_path = os.path.join(\"..\", \"..\", \"docs\", \"sample-out\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pull user data into a dataframe\n",
    "users_df = file_reader.get_all_users(csv_path)\n",
    "# pull section data into a dataframe\n",
    "sections_df = file_reader.get_all_sections(csv_path)\n",
    "\n",
    "# pull assignment data into a dataframe\n",
    "assignments_df = file_reader.get_all_assignments(csv_path, sections_df)\n",
    "\n",
    "# pull submission data into a dataframe\n",
    "submissions_df = file_reader.get_all_submissions(csv_path, assignments_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge user and submission data for google classroom\n",
    "#user_submissions_df = pd.DataFrame()\n",
    "#user_submissions_df = pd.merge(users_df, submissions_df, how='inner', left_on=('SourceSystemIdentifier', 'SourceSystem'), right_on=('LMSUserIdentifier', 'SourceSystem'))\n",
    "\n",
    "# merge user and submission data for schoology\n",
    "left_key = 'SourceSystemIdentifier'\n",
    "right_key = 'LMSUserSourceSystemIdentifier'\n",
    "user_submissions_df = pd.DataFrame()\n",
    "user_submissions_df = pd.merge(users_df, submissions_df, how='inner', left_on=left_key, right_on=right_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add user columns to submission count dataframe\n",
    "submission_counts_df = pd.DataFrame()\n",
    "submission_counts_df = user_submissions_df[[\"Name\",\"EmailAddress\"]]\n",
    "\n",
    "# drop duplicate rows\n",
    "submission_counts_df = submission_counts_df.drop_duplicates()\n",
    "\n",
    "# rename column headings\n",
    "submission_counts_df = submission_counts_df.rename({\"Name\":\"Student Name\",\"EmailAddress\":\"Email Address\"}, axis='columns')\n",
    "\n",
    "# set email address as the index\n",
    "submission_counts_df = submission_counts_df.set_index('Email Address')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pull unique submission status values to a list for google classroom\n",
    "#submission_status = user_submissions_df['Status'].unique().tolist()\n",
    "\n",
    "# pull unique submission status values to a list\n",
    "submission_status = list(user_submissions_df['SubmissionStatus'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add missing submissions count column for google classroom\n",
    "#submission_counts_df[\"Missing\"]=user_submissions_df.groupby('EmailAddress').AssignmentIdentifier.count()-user_submissions_df.groupby('EmailAddress').Status.count()\n",
    "\n",
    "# add missing submissions count column for schoology\n",
    "submission_counts_df[\"Missing\"]=user_submissions_df.groupby('EmailAddress').AssignmentSourceSystemIdentifier.count()-user_submissions_df.groupby('EmailAddress').SubmissionStatus.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loop through each submission status value and add a column with a count for each student for google classroom\n",
    "#for index in range(len(submission_status)):\n",
    "#    submission_counts_df[submission_status[index]]=user_submissions_df[user_submissions_df.Status == submission_status[index]].groupby('EmailAddress').Status.count()\n",
    "\n",
    "# loop through each submission status value and add a column with a count for each student for schoology\n",
    "for index in range(len(submission_status)):\n",
    "    submission_counts_df[submission_status[index]]=user_submissions_df[user_submissions_df.SubmissionStatus == submission_status[index]].groupby('EmailAddress').SubmissionStatus.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace NaN count values with 0\n",
    "submission_counts_df.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add total submissions count column for google classroom\n",
    "#submission_counts_df[\"Total Submissions\"]=user_submissions_df.groupby('EmailAddress').Status.count()\n",
    "\n",
    "# add total submissions count column for schoology\n",
    "submission_counts_df[\"Total Submissions\"]=user_submissions_df.groupby('EmailAddress').SubmissionStatus.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# format column headings to camel case\n",
    "submission_counts_df.columns = submission_counts_df.columns.str.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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 Name</th>\n",
       "      <th>Missing</th>\n",
       "      <th>Returned</th>\n",
       "      <th>Late</th>\n",
       "      <th>Total Submissions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Email Address</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Roland.Phillips@studentgps.org</th>\n",
       "      <td>Roland Phillips</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Micheal.Turner@studentgps.org</th>\n",
       "      <td>Micheal Turner</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Student Name  Missing  Returned  Late  \\\n",
       "Email Address                                                              \n",
       "Roland.Phillips@studentgps.org  Roland Phillips        0         2     0   \n",
       "Micheal.Turner@studentgps.org    Micheal Turner        0         0     2   \n",
       "\n",
       "                                Total Submissions  \n",
       "Email Address                                      \n",
       "Roland.Phillips@studentgps.org                  2  \n",
       "Micheal.Turner@studentgps.org                   2  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# output dataframe\n",
    "submission_counts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Submission Counts by Status')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set color palette for stacked bar chart\n",
    "colors = [\"#bc4749\",\"#03045E\",\"#0077B6\",\"#00B4D8\"]\n",
    "\n",
    "# define bar plot input and format\n",
    "submission_counts_plot = submission_counts_df.iloc[:,1:len(submission_status)+2].plot.bar(stacked=True, color=colors, figsize=(10,7), rot=80)\n",
    "\n",
    "# set title and axis labels\n",
    "submission_counts_plot.set_title(\"Total Assignment Submissions per Student\")\n",
    "submission_counts_plot.set_xlabel(\"Student Email\")\n",
    "submission_counts_plot.set_ylabel(\"Submission Counts by Status\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}