FINAL_YOURSTUDENTID.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "!pip install numpy pandas pyarrow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from final_test import FinalExamTest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_full_attendees(csv_file):\n",
    "    \"\"\"\n",
    "    >>> find_full_attendees('olympic_medal.csv')\n",
    "    returns a (13, 4) DataFrame\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_gold_winning_teams(csv_file):\n",
    "    \"\"\"\n",
    "    >>> find_gold_winning_teams('olympic_medal.csv')\n",
    "    returns a (10, 4) DataFrame\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_most_confirmed(csv_file):\n",
    "    \"\"\"\n",
    "    >>> find_most_confirmed('covid19_daily_report.csv')\n",
    "    returns a (10, 2) DataFrame\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_daily_increase_tw(csv_file):\n",
    "    \"\"\"\n",
    "    >>> calc_daily_increase_tw('covid19_time_series.csv')\n",
    "    returns a (148, 4) DataFrame\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recall_summary(csv_file):\n",
    "    \"\"\"\n",
    "    >>> recall_summary('kaohsiung_recall_2020.csv')\n",
    "    returns a (38, 2) DataFrame\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rise_and_fall(mayor_csv, president_csv):\n",
    "    \"\"\"\n",
    "    >>> rise_and_fall('mayors_2018.csv', 'presidential_2020.csv')\n",
    "    returns a (38, 4) DataFrame\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_octopus_villages(csv_file):\n",
    "    \"\"\"\n",
    "    >>> find_octopus_villages('presidential_2020.csv')\n",
    "    returns a (10, 5) DataFrame\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "answer_list = [find_full_attendees('olympic_medal.csv'),\n",
    "               find_gold_winning_teams('olympic_medal.csv'),\n",
    "               find_most_confirmed('covid19_daily_report.csv'),\n",
    "               calc_daily_increase_tw('covid19_time_series.csv'),\n",
    "               recall_summary('kaohsiung_recall_2020.csv'),\n",
    "               rise_and_fall('mayors_2018.csv', 'presidential_2020.csv'),\n",
    "               find_octopus_villages('presidential_2020.csv')]\n",
    "fet = FinalExamTest(answer_list)\n",
    "fet.run_tests()"
   ]
  }
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