Online_grad_persistence_EDA.ipynb

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   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import psycopg2\n",
    "import pandas\n",
    "import pandas.io.sql as psql\n",
    "import configparser\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetchDBCredentials(dbcred_file):\n",
    "    conf = configparser.ConfigParser()\n",
    "    conf.read(dbcred_file)\n",
    "    host = conf.get('database_creds','host')\n",
    "    port = conf.get('database_creds','port')\n",
    "    user = conf.get('database_creds','user')\n",
    "    database = conf.get('database_creds','database')\n",
    "    password = conf.get('database_creds','password')\n",
    "    conn_str = \"\"\"dbname='{database}' user='{user}' host='{host}' port='{port}' password='{password}'\"\"\".format(\n",
    "    database=database,\n",
    "    host=host,\n",
    "    port=port,\n",
    "    user=user,\n",
    "    password=password)\n",
    "    return conn_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpdb_cred_file = os.path.join(os.path.expanduser('C:\\\\Users\\\\jkommera\\\\'), '.dslab_user.cred.txt')\n",
    "conn_gpdb = psycopg2.connect(fetchDBCredentials(gpdb_cred_file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=psql.read_sql(\"\"\"SELECT * FROM jkommera.online_persistence_for_charts_details;\"\"\",conn_gpdb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "iso_code_data=pd.read_csv('countries_codes_and_coordinates.csv') # country iso 3 codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Student persistence over terms enrolled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "terms_temp_df=df.copy()\n",
    "terms_temp_df=terms_temp_df[terms_temp_df['num_sems_enrolled'].notna()]\n",
    "terms_temp_df.loc[terms_temp_df['num_sems_enrolled'] >=12, 'num_sems_enrolled'] = '>12'\n",
    "terms_temp_df['num_sems_enrolled']=terms_temp_df['num_sems_enrolled'].astype(str) ## not working"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "terms_enrolled_df = terms_temp_df.groupby('num_sems_enrolled')['persistence_term_1_desc'].value_counts(normalize=True)\n",
    "terms_enrolled_df = terms_enrolled_df.mul(100).round(2).rename('Percentage').reset_index()\n",
    "terms_enrolled_df.columns=['Terms Enrolled','next_term_enrollement','percentage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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