project_argument/argument.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "worldwide-blood",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "understanding-numbers",
   "metadata": {},
   "source": [
    "How is resident teachers' performance imporving during their residency year? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "greater-circular",
   "metadata": {},
   "source": [
    "## Overarching Question: [✏️ PUT YOUR QUESTION HERE ✏️]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "appreciated-testimony",
   "metadata": {},
   "source": [
    "How do resident teachers self-assess their performance as student teachers during the frist semester in comparision to their mentor teachers and clinical coaches? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "permanent-pollution",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "technical-evans",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Matplotlib is building the font cache; this may take a moment.\n"
     ]
    }
   ],
   "source": [
    "#Include any import statements you will need\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "overhead-sigma",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
    "\n",
    "file_name = \"STAR fake data1.csv\"\n",
    "dataset_path = \"data/\" + file_name\n",
    "\n",
    "df = pd.read_csv(dataset_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "776622e3-99fa-4ede-b0ec-4a44b4346994",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()\n",
    "df = df.rename(columns={'Unnamed: 35': 'Q23_2'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cf6c4ac-38d4-413a-bccc-af23fffa654b",
   "metadata": {},
   "source": [
    "## Data Overview\n",
    "Resident teachers' first assessement for their teaching performance. The data is collected from their mentor teachers and their clinical expereince coaches. Their evaluation for the resident performance is submitted via qualtrics. I downloaded from qultrics in CVS format again. This year resident teachers' self-rated score is also submitted. Since there are many information was included and this is each residenct teachers' performance score, I could not use the actual data, so I changed the data with some random numbers and delete some colums to create this fake data to get some practice with Jupyter. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "infinite-instrument",
   "metadata": {},
   "source": [
    "# Methods and Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "basic-canadian",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recognized-positive",
   "metadata": {},
   "source": [
    "## First Research Question: [How similar or different are their rating among mentors, resident teachers (self-rated score), and clinical teachers?]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "graduate-palmer",
   "metadata": {},
   "source": [
    "### Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "endless-variation",
   "metadata": {},
   "source": [
    "*Explain how you will approach this research question below. Consider the following:* \n",
    "  - *Which aspects of the dataset will you use?* \n",
    "  - *How will you reorganize/store the data?* \n",
    "  - *What data science tools/functions will you use and why?* \n",
    "  \n",
    "✏️ *Write your answer below:*\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "portuguese-japan",
   "metadata": {},
   "source": [
    "### Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "negative-highlight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Role', ylabel='Q1'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#######################################################################\n",
    "### 💻 YOUR WORK GOES HERE TO ANSWER THE FIRST RESEARCH QUESTION 💻 \n",
    "### \n",
    "### Your data analysis may include a statistic and/or a data visualization\n",
    "#######################################################################\n",
    "filter_1_4 = df['Q43'].isin([1, 4])\n",
    "mentor_dict = {1: \"Mentor\", 2: \"Clinical Coach\", 3: \"No\", 4: \"Resident Teacher\"}\n",
    "def average_q1(row):\n",
    "    arr = []\n",
    "    for i in range(1, 10):\n",
    "        arr.append(row[f'Q1_{i}'])\n",
    "    return np.mean(arr)\n",
    "df['Q1'] = df.apply(average_q1, axis=1)\n",
    "df['Role'] = df['Q43'].apply(lambda x: mentor_dict[x])\n",
    "sns.boxplot(df.loc[filter_1_4], x='Role', y='Q1', fill=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "victorian-burning",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Role', ylabel='Q47'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter_2_4 = df['Q43'].isin([2, 4])\n",
    "\n",
    "def average_q47(row):\n",
    "    arr = []\n",
    "    for i in range(1, 10):\n",
    "        arr.append(row[f'Q47_{i}'])\n",
    "    return np.mean(arr)\n",
    "df['Q47'] = df.apply(average_q47, axis=1)\n",
    "sns.boxplot(df.loc[filter_2_4], x='Role', y='Q47', fill=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5d613e61-3c0c-4ed0-bac8-d731dfcc7467",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Role', ylabel='Professional Quality'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter_2_4 = df['Q43'].isin([2, 4])\n",
    "\n",
    "def average_q21(row):\n",
    "    arr = []\n",
    "    for i in range(1, 11):\n",
    "        arr.append(row[f'Q21_{i}'])\n",
    "    return np.mean(arr)\n",
    "prof_qual_col = 'Professional Quality'\n",
    "df[prof_qual_col] = df.apply(average_q21, axis=1)\n",
    "sns.boxplot(df.loc[filter_2_4], x='Role', y=prof_qual_col, fill=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "155dac68-9790-42d1-b7db-0618228fd4b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Role', ylabel='Professional Quality'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.boxplot(df.loc[filter_1_4], x='Role', y=prof_qual_col, fill=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a8d4e827-bd28-4f86-8e43-a3d9feb27615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Role', ylabel='Professional Quality'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter_1_2_4 = df['Q43'].isin([1, 2, 4])\n",
    "sns.boxplot(df.loc[filter_1_2_4], x='Role', y=prof_qual_col, fill=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "db430904-5e0d-4846-b8e8-0f84356a7c7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Role', ylabel='Assessment'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NmqRZs2bpvffek6+vr7O9Y8eOSklJcWtxAAAArnI53Ozbt0+dO3cu1h4SEqIzZ864oyYAAIDr5nK4CQ0N1cGDB4u1b9y4UbfeeqtbigIAALheLoebJ554Qs8884z+9a9/yWaz6fjx4/r44481evRoDR06tCxqBAAAKDWXPwr+3//93yosLFTXrl2Vk5Ojzp07y263a/To0frzn/9cFjUCAACUmsvhxmaz6fnnn9eYMWN08OBBnTt3TuHh4apatWpZ1AcAAOASl8PNZX5+fgoPD3dnLQAAADfM5XBz4cIFvfPOO/ryyy918uRJFRYWFpnPx8EBAIAnuRxuhgwZos8//1y/+93vdNddd8lms5VFXQAAANfF5XCzYsUKrVy5Uh07diyLegAAAG6Iyx8Fr1+/voKCgsqiFgAAgBvmcriZOnWqnnvuOR09erQs6gEAALghLp+WioyM1IULF3TrrbcqMDCwyO9LSdK///1vtxUHAADgKpfDzYABA/TTTz9p8uTJqlOnDhcUAwAAr+JyuNm8ebO2bNmiVq1alUU9AAAAN8Tla26aN2+u8+fPl0UtAAAAN8zlcDNlyhSNGjVKycnJOnXqlLKysorcAAAAPMnl01I9evSQJHXt2rVIuzFGNptNBQUF7qkMAADgOrgcbr788suyqAMAAMAtXA430dHRZVEHAACAW7gcblavXq2qVavqnnvukSTNmDFD7733nsLDwzVjxgxVr17d7UUCKJ2cvIueLgEexj4AXEe4GTNmjF599VVJ0q5duzRy5EiNGjVKX375pUaOHKl58+a5vUgApRM5aZ2nSwAAj3M53Bw+fFjh4eGSpMTERPXp00eTJ09WSkqKevXq5fYCAQAAXOFyuPHz81NOTo4kae3atRo4cKAkqUaNGnwUHPCw7eO7KtDP5ac1LCQn7yIjeKjwXD4K3nPPPRo5cqQ6duyorVu3avHixZKk/fv3q0GDBm4vEEDpBfpVJtwAqPBc/hK/6dOnq3Llylq6dKlmzpyp+vXrS5JWrVrl/A4cAAAAT3H5LV7Dhg21YsWKYu3x8fFuKQgAAOBGuDxyk5KSol27djmnly9frtjYWP31r39VXl6eW4sDAABwlcvh5sknn9T+/fslST/88IN+//vfKzAwUEuWLNHYsWPdXiAAAIArXA43+/fvV+vWrSVJS5YsUefOnbVw4UIlJCQoMTHR3fUBAAC4xOVwY4xRYWGhpEsfBb/83TYOh0M///yze6sDAABwkcvhJjIyUpMmTdKCBQu0fv169e7dW9KlL/erU6eO2wsEAABwhcvhZtq0aUpJSdHw4cP1/PPPq2nTppKkpUuXqkOHDm4vEAAAwBUufxQ8IiKiyKelLnv99dfl4+PjlqIAAACul8sjN5J05swZvf/++xo3bpz+/e9/S5L27NmjkydPurU4AAAAV7k8cvPtt9+qa9euqlatmo4cOaInnnhCNWrU0CeffKK0tDR9+OGHZVEnAABAqbg8cjNy5Eg9+uijOnDggPz9/Z3tvXr10oYNG9xaHAAAgKtcDjfbtm3Tk08+Way9fv36ysjIcEtRAAAA18vlcGO325WVlVWsff/+/apVq5ZbigIAALheLoeb3/72t5owYYLy8/MlSTabTWlpaXruuefUr18/txcIAADgCpfDzdSpU3Xu3DnVrl1b58+fV3R0tJo2baqgoCC98sorZVEjAABAqbn8aamQkBAlJSVp06ZN2rlzp86dO6e2bduqW7duZVEfAACAS1wON5d17NhRHTt2lHTpe28AAAC8gcunpV599VUtXrzYOf3ggw+qZs2aql+/vnbu3OnW4gAAAFzlcriZNWuWHA6HJCkpKUlJSUlatWqVevbsqTFjxri9QAAAAFe4fFoqIyPDGW5WrFihBx98UPfdd58aN26su+++2+0FAgAAuMLlkZvq1avr2LFjkqTVq1c7LyQ2xqigoMC91QEAALjI5ZGbBx54QA8//LBuu+02nTp1Sj179pQkffPNN2ratKnbCwQAAHCFy+EmPj5ejRs31rFjx/Taa6+patWqkqT09HQ9/fTTbi8QAADAFS6HG19fX40ePbpY+1/+8he3FAQAAHAjXL7mZv78+frss8+c02PHjlW1atXUoUMHHT161K3FAQAAuMrlcDN58mQFBARIkrZs2aIZM2botdde0y233MLoDQAA8DiXw82xY8ecFw4vW7ZM/fr105/+9CfFxcXpq6++cmldcXFxuvPOOxUUFKTatWsrNjZW+/btu+oyCQkJstlsRW7+/v6u3g0AAGBRLoebqlWr6tSpU5Kkzz//XN27d5ck+fv76/z58y6ta/369Ro2bJi+/vprJSUlKT8/X/fdd5+ys7OvulxwcLDS09OdN06HAQCAy1y+oLh79+56/PHH1aZNG+3fv1+9evWSJO3evVuNGjVyaV2rV68uMp2QkKDatWtrx44d6ty58xWXs9lsCg0NdbV0t8vJu+jpEuBh3rAPGPOfv3Py+K6pis5b9gFjjM7ne0ctnvTLY4Q3HC88LcDXRzabrcy343K4mTFjhsaPH69jx44pMTFRNWvWlCTt2LFDAwYMuKFiMjMzJUk1atS4ar9z586pUaNGKiwsVNu2bTV58mS1aNGixL65ubnKzc11TmdlZd1Qjb8UOWmd29YFXK9fvoBETlrrwUqAS4wx+t2sLdpx9LSnS/EqvGZIkY2qa8lTUWUecFwON9WqVdP06dOd02fPntXf/vY3rVq1Sjt27ND48eOvq5DCwkI9++yz6tixo1q2bHnFfs2aNdMHH3ygiIgIZWZm6o033lCHDh20e/duNWjQoFj/uLg4vfzyy9dVEwCUV5GNqivA18cj2z6fX0CwQYm2Hz2t8/kFCvRzOX645LrXvmHDBs2dO1eJiYmqV6+eHnjgAc2YMeO6Cxk2bJi+++47bdy48ar9oqKiFBUV5Zzu0KGDwsLCNHv2bE2cOLFY/3HjxmnkyJHO6aysLOdvY92o7eO7lvkDBO+Wk3fR4+/Galbx0/bxl34G5dKQr0fL8ZhfPhY8N2/e8P+1bB/fTYF+nglZ8B45eQU3dWTZpWd/RkaGEhISNHfuXGVlZenBBx9Ubm6uli1bpvDw8OsuYvjw4VqxYoU2bNhQ4ujL1fj6+qpNmzY6ePBgifPtdrvsdvt113Y1gX6VK/wBFJ5ns9l0S9Wy2cfLK56b3iPQz4fHAjddqT8t1adPHzVr1kzffvutpk2bpuPHj+udd965oY0bYzR8+HB9+umn+uKLL9SkSROX11FQUKBdu3apbt26N1QLAACwhlLH6VWrVmnEiBEaOnSobrvtNrdsfNiwYVq4cKGWL1+uoKAgZWRkSJJCQkKcXxQ4cOBA1a9fX3FxcZKkCRMmqH379mratKnOnDmj119/XUePHtXjjz/ulpoAAED5VuqRm40bN+rs2bNq166d7r77bk2fPl0///zzDW185syZyszM1L333qu6des6b4sXL3b2SUtLU3p6unP69OnTeuKJJxQWFqZevXopKytLmzdvvqHTYgAAwDpKPXLTvn17tW/fXtOmTdPixYv1wQcfaOTIkSosLFRSUpIcDoeCgoJc2rj55Rd0XEFycnKR6fj4eMXHx7u0HQAAUHG4/A3FVapU0WOPPaaNGzdq165dGjVqlKZMmaLatWvrt7/9bVnUCAAAUGouh5tfatasmV577TX9+OOP+tvf/uaumgAAAK7bDYWby3x8fBQbG6t//OMf7lgdAADAdXNLuAEAAPAWhBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAplT1dQHljzH/+zskr8Fwh8ArsA8DV5eRd9HQJ8AI3ez8g3LjofP5/XswiJ631YCUA4P0iJ63zdAmogDgtBbhBZKPqCvD18XQZAAAxcuOymlX8tH18N0lSgK+PbDYPF+QhOXkXne/Ito/vqkC/ir0rXdoXKujOAFwFxwdIRV8zbgb2OBfZbDbdUtXu6TK8SqBfZQ5eAErE8QGewGkpAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKR4NN3FxcbrzzjsVFBSk2rVrKzY2Vvv27bvmckuWLFHz5s3l7++vO+64QytXrrwJ1QIAgPLAo+Fm/fr1GjZsmL7++mslJSUpPz9f9913n7Kzs6+4zObNmzVgwAANGTJE33zzjWJjYxUbG6vvvvvuJlYOAAC8VWVPbnz16tVFphMSElS7dm3t2LFDnTt3LnGZt956Sz169NCYMWMkSRMnTlRSUpKmT5+uWbNmlXnNAICrM+Y/f+fkFXiwDqPz+Z7bvjcK8PWRzWa76du92fuBR8PNr2VmZkqSatSoccU+W7Zs0ciRI4u0xcTEaNmyZSX2z83NVW5urnM6KyvrxgsFAFzRLwNF5KS1HqwEFZXXXFBcWFioZ599Vh07dlTLli2v2C8jI0N16tQp0lanTh1lZGSU2D8uLk4hISHOm8PhcGvdAACgdCIbVVeAr0+Zb8drRm6GDRum7777Ths3bnTreseNG1dkpCcrK4uAAwBlqGYVP20f303S5dMgnqmD01LFeeq01M3evleEm+HDh2vFihXasGGDGjRocNW+oaGhOnHiRJG2EydOKDQ0tMT+drtddrvdbbUCAK7OZrPplqrecdytYvf1dAnwAI+eljLGaPjw4fr000/1xRdfqEmTJtdcJioqSuvWrSvSlpSUpKioqLIqEwAAlCMeHbkZNmyYFi5cqOXLlysoKMh53UxISIgCAgIkSQMHDlT9+vUVFxcnSXrmmWcUHR2tqVOnqnfv3lq0aJG2b9+uOXPmeOx+AAAA7+HRkZuZM2cqMzNT9957r+rWreu8LV682NknLS1N6enpzukOHTpo4cKFmjNnjlq1aqWlS5dq2bJlV70IGQAAVBweHbkxv/wyhCtITk4u1ta/f3/179+/DCoCAADlndd8FBwAAMAdCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSKnu6ALjOGKPz+QUerSEn72KJf3tKgK+PbDabp8sAAHgBwk05dD6/QOEvrPF0GU6Rk9Z5ugTtmRCjQD92ZwAAp6UAAIDF8Fa3HArw9dGeCTGeLsOrBPj6eLoEAICXINyUQzabjVMwAABcAaelAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApXg03GzYsEF9+vRRvXr1ZLPZtGzZsqv2T05Ols1mK3bLyMi4OQUDAACv59Fwk52drVatWmnGjBkuLbdv3z6lp6c7b7Vr1y6jCgEAQHlT2ZMb79mzp3r27OnycrVr11a1atXcXxCA62KM0fn8Ao/WkJN3scS/PSXA10c2m83TZQAVkkfDzfVq3bq1cnNz1bJlS7300kvq2LHjFfvm5uYqNzfXOZ2VlXUzSgQqlPP5BQp/YY2ny3CKnLTO0yVoz4QYBfqVy0MsUO6VqwuK69atq1mzZikxMVGJiYlyOBy69957lZKScsVl4uLiFBIS4rw5HI6bWDEAALjZbMYY4+kiJMlms+nTTz9VbGysS8tFR0erYcOGWrBgQYnzSxq5cTgcyszMVHBw8I2UDOD/ecNpKW/DaSnAvbKyshQSElKq1+9yP2Z61113aePGjVecb7fbZbfbb2JFQMVjs9k4BQPAa5Sr01IlSU1NVd26dT1dBgAA8BIefat17tw5HTx40Dl9+PBhpaamqkaNGmrYsKHGjRunn376SR9++KEkadq0aWrSpIlatGihCxcu6P3339cXX3yhzz//3FN3AQAAeBmPhpvt27erS5cuzumRI0dKkgYNGqSEhASlp6crLS3NOT8vL0+jRo3STz/9pMDAQEVERGjt2rVF1gEAACo2r7mg+GZx5YIkAADgHVx5/S7319wAAAD8EuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYSoX7Gd/LX8iclZXl4UoAAEBpXX7dLs0PK1S4cHP27FlJksPh8HAlAADAVWfPnlVISMhV+1S435YqLCzU8ePHFRQUJJvN5ulyyrWsrCw5HA4dO3aM3+mCV2CfhDdiv3QPY4zOnj2revXqqVKlq19VU+FGbipVqqQGDRp4ugxLCQ4O5gkLr8I+CW/EfnnjrjVicxkXFAMAAEsh3AAAAEsh3OC62e12vfjii7Lb7Z4uBZDEPgnvxH5581W4C4oBAIC1MXIDAAAshXADAAAshXADAAAshXADwC2OHDkim82m1NTUK/ZJTk6WzWbTmTNnblpd3iAhIUHVqlXzdBkVgs1m07JlyySVbp/8tZdeekmtW7d2Wz3l6bEfPHiwYmNjPV2GWxBuKoDBgwfLZrPpqaeeKjZv2LBhstlsGjx4sNu25+6DA9zj8n5gs9nk6+urJk2aaOzYsbpw4YJb1u9wOJSenq6WLVu6ZX2lVZoXsJdeesl53690g/fLyMjQn//8Z916662y2+1yOBzq06eP1q1bV2L/69knR48efcX1laUvv/xSvXr1Us2aNRUYGKjw8HCNGjVKP/30002vxQoINxWEw+HQokWLdP78eWfbhQsXtHDhQjVs2NCDlV1ZXl6ep0uwnB49eig9PV0//PCD4uPjNXv2bL344otuWbePj49CQ0NVubL3ffH56NGjlZ6e7rw1aNBAEyZMKNJWHuXn53u6hJvmyJEjateunb744gu9/vrr2rVrl1avXq0uXbpo2LBhJS5zPftk1apVVbNmTXeVXSqzZ89Wt27dFBoaqsTERO3Zs0ezZs1SZmampk6delNrsQrCTQXRtm1bORwOffLJJ862Tz75RA0bNlSbNm2cbYWFhYqLi1OTJk0UEBCgVq1aaenSpc75l08rrFu3TpGRkQoMDFSHDh20b98+SZeGYF9++WXt3LnT+Y44ISFBkpSWlqa+ffuqatWqCg4O1oMPPqgTJ0441315xOf9999XkyZN5O/vX8b/lYrHbrcrNDRUDodDsbGx6tatm5KSkpzzr/X4nz59Wo888ohq1aqlgIAA3XbbbZo3b56kkkdQVq5cqdtvv10BAQHq0qWLjhw5UqymjRs3qlOnTgoICJDD4dCIESOUnZ3tnN+4cWNNnjxZjz32mIKCgtSwYUPNmTPHOb9JkyaSpDZt2shms+nee+8tto2qVasqNDTUefPx8VFQUJBzOj8/Xw8++KCqVaumGjVqqG/fvkVq3bZtm7p3765bbrlFISEhio6OVkpKSpFtnDlzRk8++aTq1Kkjf39/tWzZUitWrCjSZ82aNQoLC1PVqlWdQfOX3n//fYWFhcnf31/NmzfXu+++65x3+f+7ePFiRUdHy9/fXx9//HGx+2pVTz/9tGw2m7Zu3ap+/frp9ttvV4sWLTRy5Eh9/fXXJS7z633yWscvqeSR5w8++EAtWrSQ3W5X3bp1NXz4cOe8N998U3fccYeqVKkih8Ohp59+WufOnSv1/frxxx81YsQIjRgxQh988IHuvfdeNW7cWJ07d9b777+vF154wdk3MTHRWUfjxo2LBZ8FCxYoMjLSuW8//PDDOnnyZJE+u3fv1v3336/g4GAFBQWpU6dOOnToUJE+b7zxhurWrauaNWtq2LBh5TNEG1jeoEGDTN++fc2bb75punbt6mzv2rWriY+PN3379jWDBg0yxhgzadIk07x5c7N69Wpz6NAhM2/ePGO3201ycrIxxpgvv/zSSDJ33323SU5ONrt37zadOnUyHTp0MMYYk5OTY0aNGmVatGhh0tPTTXp6usnJyTEFBQWmdevW5p577jHbt283X3/9tWnXrp2Jjo521vPiiy+aKlWqmB49epiUlBSzc+fOm/Y/qggu7weX7dq1y4SGhpq7777b2Xatx3/YsGGmdevWZtu2bebw4cMmKSnJ/OMf/zDGGHP48GEjyXzzzTfGGGPS0tKM3W43I0eONN9//7356KOPTJ06dYwkc/r0aWOMMQcPHjRVqlQx8fHxZv/+/WbTpk2mTZs2ZvDgwc6aGjVqZGrUqGFmzJhhDhw4YOLi4kylSpXM999/b4wxZuvWrUaSWbt2rUlPTzenTp265v+iUaNGJj4+3hhjTF5engkLCzOPPfaY+fbbb82ePXvMww8/bJo1a2Zyc3ONMcasW7fOLFiwwOzdu9fs2bPHDBkyxNSpU8dkZWUZY4wpKCgw7du3Ny1atDCff/65OXTokPnnP/9pVq5caYwxZt68ecbX19d069bNbNu2zezYscOEhYWZhx9+2FnTRx99ZOrWrWsSExPNDz/8YBITE02NGjVMQkJCkf9v48aNnX2OHz9eqse+vDt16pSx2Wxm8uTJ1+wryXz66afGmOL75LWOX8ZcOg61atXKOf3uu+8af39/M23aNLNv3z6zdetW575jjDHx8fHmiy++MIcPHzbr1q0zzZo1M0OHDnXOnzdvngkJCblivW+++aaRdM3Hcvv27aZSpUpmwoQJZt++fWbevHkmICDAzJs3z9ln7ty5ZuXKlebQoUNmy5YtJioqyvTs2dM5/8cffzQ1atQwDzzwgNm2bZvZt2+f+eCDD5zPpUGDBpng4GDz1FNPmb1795p//vOfJjAw0MyZM+eqtXkjwk0FcPlF7eTJk8Zut5sjR46YI0eOGH9/f/O///u/znBz4cIFExgYaDZv3lxk+SFDhpgBAwYYY/5zcFi7dq1z/meffWYkmfPnzxtjih8cjDHm888/Nz4+PiYtLc3Ztnv3biPJbN261bmcr6+vOXnyZFn8Gyq8QYMGGR8fH1OlShVjt9uNJFOpUiWzdOlSY4wp1ePfp08f8+ijj5a4/l+/kIwbN86Eh4cX6fPcc88VCTdDhgwxf/rTn4r0+eqrr0ylSpWc+1OjRo3MH/7wB+f8wsJCU7t2bTNz5swSt1savww3CxYsMM2aNTOFhYXO+bm5uSYgIMCsWbOmxOULCgpMUFCQ+ec//2mMMWbNmjWmUqVKZt++fSX2nzdvnpFkDh486GybMWOGqVOnjnP6N7/5jVm4cGGR5SZOnGiioqKK3M9p06aV+n5axb/+9S8jyXzyySfX7FuacOPK8atevXrm+eefL3WtS5YsMTVr1nROXyvcDB061AQHB19zvQ8//LDp3r17kbYxY8YUe4790rZt24wkc/bsWWPMpedkkyZNTF5eXon9Bw0aZBo1amQuXrzobOvfv7956KGHrlmft/G+k+MoM7Vq1VLv3r2VkJAgY4x69+6tW265xTn/4MGDysnJUffu3Yssl5eXV+TUlSRFREQ4/65bt64k6eTJk1e8fmfv3r1yOBxyOBzOtvDwcFWrVk179+7VnXfeKUlq1KiRatWqdWN3FFfUpUsXzZw5U9nZ2YqPj1flypXVr18/SaV7/IcOHap+/fopJSVF9913n2JjY9WhQ4cSt7V3717dfffdRdqioqKKTO/cuVPffvttkdMrxhgVFhbq8OHDCgsLk1R0f7PZbAoNDS023H69du7cqYMHDyooKKhI+4ULF5zD9SdOnND48eOVnJyskydPqqCgQDk5OUpLS5MkpaamqkGDBrr99tuvuJ3AwED95je/cU7XrVvXeR+ys7N16NAhDRkyRE888YSzz8WLF4v9CnJkZOSN3eFyyLj5i/RLe/w6efKkjh8/rq5du15xXWvXrlVcXJy+//57ZWVl6eLFi7pw4YJycnIUGBh4zVqMMaW6oH3v3r3q27dvkbaOHTtq2rRpKigokI+Pj3bs2KGXXnpJO3fu1OnTp1VYWCjp0iUB4eHhSk1NVadOneTr63vF7bRo0UI+Pj7O6bp162rXrl3XrM/bEG4qmMcee8x5vnjGjBlF5l0+T/zZZ5+pfv36Reb9+jdRfvnkuPzEvPxEuhFVqlS54XXgyqpUqaKmTZtKunQdQatWrTR37lwNGTKkVI9/z549dfToUa1cuVJJSUnq2rWrhg0bpjfeeOO66jl37pyefPJJjRgxoti8X77Q/PpgbLPZ3LK/Xa6hXbt2JV6/cjloDxo0SKdOndJbb72lRo0ayW63KyoqynnRe0BAwDW3U9J9uPyiffl//9577xULhL98oZEq5nPktttuk81m0/fff++W9ZX2+HWtx/XIkSO6//77NXToUL3yyiuqUaOGNm7cqCFDhigvL69U4eb2229XZmam0tPTnUHremRnZysmJkYxMTH6+OOPVatWLaWlpSkmJuaG91N3PdduJi4ormB69OihvLw85efnKyYmpsi88PBw2e12paWlqWnTpkVuvxxxuRY/Pz8VFBQUaQsLC9OxY8d07NgxZ9uePXt05swZhYeH39idwnWpVKmS/vrXv2r8+PE6f/58qR//WrVqadCgQfroo480bdq0Ihf3/lJYWJi2bt1apO3XF362bdtWe/bsKba9pk2bys/Pr1T343K/X+9zpdW2bVsdOHBAtWvXLlbD5VGTTZs2acSIEerVq5fzgs6ff/7ZuY6IiAj9+OOP2r9//3XVUKdOHdWrV08//PBDsRouXzBdkdWoUUMxMTGaMWNGkYvNLyur700KCgpS48aNr/jR8B07dqiwsFBTp05V+/btdfvtt+v48eMubeN3v/ud/Pz89Nprr5U4//J9CwsL06ZNm4rM27Rpk26//Xb5+Pjo+++/16lTpzRlyhR16tRJzZs3Lza6GRERoa+++qp8XiDsIsJNBePj46O9e/dqz549xd4RBgUFafTo0frLX/6i+fPn69ChQ0pJSdE777yj+fPnl3objRs31uHDh5Wamqqff/5Zubm56tatm+644w498sgjSklJ0datWzVw4EBFR0dXyGF2b9G/f3/5+PhoxowZpXr8X3jhBS1fvlwHDx7U7t27tWLFCuepo1976qmndODAAY0ZM0b79u3TwoULnZ+cu+y5557T5s2bNXz4cKWmpurAgQNavnx5kU+jXEvt2rUVEBCg1atX68SJE8rMzHTpf/DII4/olltuUd++ffXVV1/p8OHDSk5O1ogRI/Tjjz9KujRysGDBAu3du1f/+te/9MgjjxR5FxwdHa3OnTurX79+SkpK0uHDh7Vq1SqtXr261HW8/PLLiouL09tvv639+/dr165dmjdvnt58802X7o9VzZgxQwUFBbrrrruUmJioAwcOaO/evXr77beLne50p5deeklTp07V22+/rQMHDjifE5LUtGlT5efn65133tEPP/ygBQsWaNasWS6t3+FwKD4+Xm+99ZaGDBmi9evX6+jRo9q0aZOefPJJTZw4UZI0atQorVu3ThMnTtT+/fs1f/58TZ8+XaNHj5Z0aaTTz8/PWcs//vEP57KXDR8+XFlZWfr973+v7du368CBA1qwYEGRT4tZBeGmAgoODlZwcHCJ8yZOnKj/+Z//UVxcnMLCwtSjRw999tlnLr177Nevn3r06KEuXbqoVq1a+tvf/iabzably5erevXq6ty5s7p166Zbb71VixcvdtfdwnWoXLmyhg8frtdee03Z2dnXfPz9/Pw0btw4RUREqHPnzvLx8dGiRYtKXHfDhg2VmJioZcuWqVWrVpo1a5YmT55cpE9ERITWr1+v/fv3q1OnTmrTpo1eeOEF1atXz6X78Pbbb2v27NmqV69esesSriUwMFAbNmxQw4YN9cADDygsLExDhgzRhQsXnM+TuXPn6vTp02rbtq3++Mc/asSIEapdu3aR9SQmJurOO+/UgAEDFB4errFjx7o0mvT444/r/fff17x583THHXcoOjpaCQkJjNz8v1tvvVUpKSnq0qWLRo0apZYtW6p79+5at26dZs6cWWbbHTRokKZNm6Z3331XLVq00P33368DBw5Iklq1aqU333xTr776qlq2bKmPP/5YcXFxLm/j6aef1ueff66ffvpJ//Vf/6XmzZvr8ccfV3BwsDO8tG3bVn//+9+1aNEitWzZUi+88IImTJjg/ALWWrVqKSEhQUuWLFF4eLimTJlS7HRxzZo19cUXX+jcuXOKjo5Wu3bt9N577131GpzyymbcfaUWAACABzFyAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwA8CykpOTZbPZyuy3hwB4J8INAK81ePBg2Ww22Ww2+fr6qkmTJho7dqwuXLjg6dIAeLHKni4AAK6mR48emjdvnvLz87Vjxw4NGjRINptNr776qqdLA+ClGLkB4NXsdrtCQ0PlcDgUGxurbt26KSkpSZKUm5vr/BFLf39/3XPPPdq2bdtV17dx40Z16tRJAQEBcjgcGjFihLKzs2/GXQFwkxBuAJQb3333nTZv3iw/Pz9J0tixY5WYmKj58+crJSVFTZs2VUxMjP7973+XuPyhQ4fUo0cP9evXT99++60WL16sjRs3avjw4TfzbgAoY/wqOACvNXjwYH300Ufy9/fXxYsXlZubq0qVKunvf/+7evTooerVqyshIUEPP/ywJCk/P1+NGzfWs88+qzFjxig5OVldunTR6dOnVa1aNT3++OPy8fHR7NmzndvYuHGjoqOjlZ2dLX9/f0/dVQBuxDU3ALxaly5dNHPmTGVnZys+Pl6VK1d2jrzk5+erY8eOzr6+vr666667tHfv3hLXtXPnTn377bf6+OOPnW3GGBUWFurw4cMKCwsr8/sDoOwRbgB4tSpVqqhp06aSpA8++ECtWrXS3Llzdeedd7q8rnPnzunJJ5/UiBEjis1r2LDhDdcKwDsQbgCUG5UqVdJf//pXjRw5UgcPHpSfn582bdqkRo0aSbp0Wmrbtm169tlnS1y+bdu22rNnjzMsAbAmLigGUK70799fPj4+mjlzpoYOHaoxY8Zo9erV2rNnj5544gnl5ORoyJAhJS773HPPafPmzRo+fLhSU1N14MABLV++nAuKAYth5AZAuVK5cmUNHz5cr732mg4fPqzCwkL98Y9/1NmzZxUZGak1a9aoevXqJS4bERGh9evX6/nnn1enTp1kjNFvfvMbPfTQQzf5XgAoS3xaCgAAWAqnpQAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKX8H4ZJ/fHLBfeUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = 'Assessment'\n",
    "def average_q23(row):\n",
    "    arr = []\n",
    "    for i in range(1, 5):\n",
    "        arr.append(row[f'Q23_{i}'])\n",
    "    return np.mean(arr)\n",
    "df[col] = df.apply(average_q23, axis=1)\n",
    "sns.boxplot(df.loc[filter_1_2_4], x='Role', y=col, fill=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "719ee657-a80c-4af1-9071-75dd59c024a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Role', ylabel='Pedagogical Content Knowledge'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = 'Pedagogical Content Knowledge'\n",
    "df[col] = None\n",
    "df.loc[df['Q43'] == 1, col] = df.loc[df['Q43'] == 1].apply(average_q1, axis=1)\n",
    "df.loc[df['Q43'] == 2, col] = df.loc[df['Q43'] == 2].apply(average_q47, axis=1)\n",
    "df.loc[df['Q43'] == 4, col] = df.loc[df['Q43'] == 4].apply(average_q47, axis=1)\n",
    "sns.boxplot(df.loc[filter_1_2_4], x='Role', y=col, fill=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collectible-puppy",
   "metadata": {},
   "source": [
    "## Second Research Question: [Among Pedagogical Contnet Knowlege, Professional Quality, and Assessement, which area is showing the strength of resdients? Which areas needs to be imporved?]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "demographic-future",
   "metadata": {},
   "source": [
    "### Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incorporate-roller",
   "metadata": {},
   "source": [
    "Data is still being collected, so it will be hard to finding a meaningful answer for this yet since we have much smller number of clinical coaches' input. Simiple and quick way to do this is finding the mean and compare for each question items. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "juvenile-creation",
   "metadata": {},
   "source": [
    "### Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "pursuant-surrey",
   "metadata": {},
   "outputs": [],
   "source": [
    "#######################################################################\n",
    "### 💻 YOUR WORK GOES HERE TO ANSWER THE SECOND RESEARCH QUESTION 💻 \n",
    "###\n",
    "### Your data analysis may include a statistic and/or a data visualization\n",
    "#######################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "located-night",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "infectious-symbol",
   "metadata": {},
   "source": [
    "# Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "furnished-camping",
   "metadata": {
    "code_folding": []
   },
   "source": [
    "## Considerations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bearing-stadium",
   "metadata": {},
   "source": [
    "Overall, resident teachers' self-rated score is higher than mentors and clinical coaches' scores. Last year's CEC score is much higher than mentor teachers' score, however, this year cohort 6's first semester evlaution shows different patters. Mentors and resident teachers' ranges are greter while clinical teachers' scores does not have a big range. Resdient teachers' also have outlier, and clinical teachers' scores seemed to stay in the much smaller sub-range. Professional Qualties seemed to be residents' strengths."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beneficial-invasion",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "about-raise",
   "metadata": {},
   "source": [
    "This is a fake data, but I will do the same process after addinig more data and colums. I see this is such a powerful tool. I can see this can be much more practical and effective tool compared with excel file that I have been using. "
   ]
  }
 ],
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    "jupytext_version": "1.9.1"
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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