201905_tailoredpath_transition_api.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data wrangling and functions for [tailoredpath.com/transition.html](https://tailoredpath.com/transition.html)\n",
    "***\n",
    "\n",
    "\n",
    "# Table of contents:\n",
    "\n",
    "1. [Import libraries and load files](#1)\n",
    "2. [Data wrangling](#2)\n",
    "3. [Comparison functions](#3)\n",
    "    1. [Education](#3A)\n",
    "        1. [Functions](#3Aa)\n",
    "        2. [Visualization with Matplotlib](#3Ab)\n",
    "        3. [Prepare data for visualization with Chart.js](#3Ac)\n",
    "    2. [Experience](#3B)\n",
    "        1. [Functions](#3Ba)\n",
    "    3. [Skills](#3C)\n",
    "        1. [Functions](#3Ca)\n",
    "        2. [Visualization with Matplotlib](#3Cb)\n",
    "        3. [Prepare data for visualization with Chart.js](#3Cc)  \n",
    "    4. [Knowledge](#3D)\n",
    "        1. [Functions](#3Da)\n",
    "        2. [Visualization with Matplotlib](#3Db)\n",
    "        3. [Prepare data for visualization with Chart.js](#3Dc)\n",
    "    5. [Estimated transition difficulty](#3E)\n",
    "    5. [Generate & export Json](#3F)\n",
    "    \n",
    "    \n",
    "# Purpose of this notebook:\n",
    "\n",
    "This notebook contains the baseline for the Python script that runs on the **[interactive web app: tailoredpath.com/transition](https://tailoredpath.com/transition.html)**. \n",
    "\n",
    "The app accesses the data via an API built with [Python Flask](http://flask.pocoo.org/). The charts were developped with [Chart.js](https://www.chartjs.org/).\n",
    "\n",
    "This notebook reproduces these visualizations with matplotlib.\n",
    "\n",
    "\n",
    "# Data:\n",
    "\n",
    "The [ONET program](https://www.onetcenter.org/overview.html) is the primary source of occupational information in the US. \n",
    "\n",
    "For each job, ONET rates the importance and level of all skills, knowledge and abilities, along with the required levels of education and experience. This data is available on their [resource center](https://www.onetcenter.org/database.html#individual-files).\n",
    "\n",
    "More specifically, this notebook uses the following datasets:\n",
    "- [Knowledge.xlsx](https://www.onetcenter.org/dictionary/23.3/excel/knowledge.html)\n",
    "- [Skills.xlsx](https://www.onetcenter.org/dictionary/23.3/excel/skills.html)\n",
    "- [Education, Training, and Experience.xlsx](https://www.onetcenter.org/dictionary/23.3/excel/education_training_experience.html)\n",
    "- [Education, Training, and Experience Categories.xlsx](https://www.onetcenter.org/dictionary/23.3/excel/ete_categories.html)\n",
    "- [Job Zones.xlsx](https://www.onetcenter.org/dictionary/23.3/excel/job_zones.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Import libraries and load files <a name=\"1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "\n",
    "import pandas as pd\n",
    "pd.set_option(\"display.max_columns\", 101)\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load files\n",
    "\n",
    "skills = pd.read_excel('../../../../Documents/tailoredpath/databases/ONET/Skills.xlsx')\n",
    "knowledge = pd.read_excel('../../../../Documents/tailoredpath/databases/ONET/Knowledge.xlsx')\n",
    "education_experience = pd.read_excel('../../../../Documents/tailoredpath/databases/ONET/Education, Training, and Experience.xlsx')\n",
    "job_zones = pd.read_excel('../../../../Documents/tailoredpath/databases/ONET/Job Zones.xlsx')\n",
    "e_categories = pd.read_excel('../../../../Documents/tailoredpath/databases/ONET/Education, Training, and Experience Categories.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data wrangling <a name=\"2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter job zones\n",
    "\n",
    "jobzone345 = job_zones[job_zones['Job Zone'].isin([3,4,5])]['O*NET-SOC Code'].tolist()\n",
    "\n",
    "def filter_job_zones(df):\n",
    "    return df[df['O*NET-SOC Code'].isin(jobzone345)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "skills = filter_job_zones(skills)\n",
    "knowledge = filter_job_zones(knowledge)\n",
    "education_experience = filter_job_zones(education_experience)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter columns\n",
    "\n",
    "# Columns to keep for skills & knowledge\n",
    "\n",
    "cols_to_keep = [\n",
    "    'O*NET-SOC Code', \n",
    "    'Title', \n",
    "    'Element Name', \n",
    "    'Scale ID',\n",
    "    'Data Value'\n",
    "]\n",
    "\n",
    "# Columns to keep for education_experience\n",
    "\n",
    "cols_to_keep_ee = [\n",
    "    'O*NET-SOC Code', \n",
    "    'Title', \n",
    "    'Scale ID',\n",
    "    'Category',\n",
    "    'Data Value'\n",
    "]\n",
    "\n",
    "def filter_columns(df, lc):\n",
    "    return df[lc].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "skills = filter_columns(skills, cols_to_keep)\n",
    "knowledge = filter_columns(knowledge, cols_to_keep)\n",
    "education_experience = filter_columns(education_experience, cols_to_keep_ee)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create education DF\n",
    "\n",
    "education = education_experience[education_experience['Scale ID'] == 'RL']\n",
    "education = education[education['Category'].isin([5,6,7,8,9,10,11,12])] #Filtered only Associate, Bachelor, Master and Doctoral degrees and other certificates (7,9,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create experience DF\n",
    "\n",
    "experience = education_experience[education_experience['Scale ID'] == 'RW']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Default jobs to test comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "job1 = \"Executive Secretaries and Executive Administrative Assistants\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "job2 = \"Human Resources Managers\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Comparison functions <a name=\"3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.A. Education <a name=\"3A\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.A.a. Functions <a name=\"3Aa\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_job_ed_stats(job):\n",
    "    job_ed = education[education['Title'] == job]\n",
    "    \n",
    "    job_5 = job_ed.loc[job_ed['Category'] == 5, 'Data Value'].sum() #select value\n",
    "    job_5_to100 = job_5 *100/(job_ed['Data Value'].sum()) #bring to 100 scale in case all values aren't included for this job\n",
    "\n",
    "    job_6 = job_ed.loc[job_ed['Category'].isin([6,7]), 'Data Value'].sum()\n",
    "    job_6_to100 = job_6 *100/(job_ed['Data Value'].sum())\n",
    "\n",
    "    job_8 = job_ed.loc[job_ed['Category'].isin([8,9,10]), 'Data Value'].sum()\n",
    "    job_8_to100 = job_8 *100/(job_ed['Data Value'].sum())\n",
    "\n",
    "    job_11 = job_ed.loc[job_ed['Category'].isin([11,12]), 'Data Value'].sum()\n",
    "    job_11_to100 = job_11 *100/(job_ed['Data Value'].sum())\n",
    "        \n",
    "    return pd.Series([job_5_to100, job_6_to100, job_8_to100, job_11_to100], index=[\"Associate\", \"Bachelor\", \"Master\", \"Doctorate\"])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_ed(job1, job2):\n",
    "    \n",
    "    job1_stats_lst = get_job_ed_stats(job1).tolist()\n",
    "    job2_stats_lst = get_job_ed_stats(job2).tolist()\n",
    "    idx_max_job1 = np.argmax(job1_stats_lst)\n",
    "    idx_max_job2 = np.argmax(job2_stats_lst)\n",
    "    \n",
    "    if idx_max_job2 > idx_max_job1:\n",
    "        level = 2\n",
    "\n",
    "    elif idx_max_job2 == idx_max_job1:\n",
    "        level = 1\n",
    "\n",
    "    else:\n",
    "        level = 0\n",
    "        \n",
    "    return level\n",
    "\n",
    "def ed_info(job):\n",
    "    label_max_job = get_job_ed_stats(job).idxmax().lower()\n",
    "    if label_max_job == 'none':\n",
    "        to_print = \"In general, {} do not hold a college degree.\".format(job)\n",
    "    elif label_max_job.startswith('a'):\n",
    "        to_print = \"In general, {} hold an {} degree.\".format(job, label_max_job)\n",
    "    else:\n",
    "        to_print = \"In general, {} hold a {} degree.\".format(job, label_max_job)\n",
    "    return to_print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2 is higher, 1 is similar, 0 is lower\n",
    "compare_ed(job1, job2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'In general, Human Resources Managers hold a bachelor degree.'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ed_info(job2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.A.b. Visualization with Matplotlib <a name=\"3Ab\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_radar_ed(job1, job2):\n",
    "\n",
    "    # Data\n",
    "    labels = get_job_ed_stats(job1).index\n",
    "    angles=np.linspace(0, 2*np.pi, len(labels), endpoint=False)\n",
    "    angles=np.concatenate((angles,[angles[0]]))\n",
    "    \n",
    "    stats_1 = get_job_ed_stats(job1).tolist()\n",
    "    stats_2 = get_job_ed_stats(job2).tolist()\n",
    "    \n",
    "    stats_1=np.concatenate((stats_1,[stats_1[0]]))\n",
    "    stats_2=np.concatenate((stats_2,[stats_2[0]]))\n",
    "    \n",
    "    # Figure\n",
    "    fig = plt.figure(figsize=(6,6), dpi= 80)\n",
    "    \n",
    "    # Colors\n",
    "    colors=['#81c784', '#42a5f5']\n",
    "    \n",
    "    # Plot\n",
    "    ax = fig.add_subplot(111, polar=True)\n",
    "    ax.plot(angles, stats_1, color=colors[0], linewidth=2)\n",
    "    ax.plot(angles, stats_2, color=colors[1], linewidth=2)\n",
    "    ax.fill(angles, stats_1, color=colors[0], alpha=0.25)\n",
    "    ax.fill(angles, stats_2, color=colors[1], alpha=0.25)\n",
    "    ax.set_thetagrids(angles * 180/np.pi, labels)\n",
    "    ax.set_title(job1)\n",
    "    ax.grid(True)\n",
    "    \n",
    "    # Title\n",
    "    plt.title(\"From {} to {}\".format(job1, job2), fontsize=15)\n",
    "    # Legend\n",
    "    plt.legend({group:col for group, col in zip([job1, job2], colors)})\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_radar_ed(job1, job2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.A.c. Prepare data for visualization with Chart.js <a name=\"3Ac\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### radar_src"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[62.98023125699367, 22.1372622155912, 14.882506527415144, 0.0]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_job_ed_stats(job1).tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### radar_tgt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4.55, 77.27000000000001, 18.18, 0.0]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_job_ed_stats(job2).tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.B. Experience <a name=\"3B\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.B.a. Functions <a name=\"3Ba\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_job_exp_stats(job):\n",
    "    \n",
    "    job_exp = experience[experience['Title'] == job]\n",
    "    \n",
    "    exp_categories = e_categories.loc[e_categories[\"Scale ID\"] == \"RW\", \"Category Description\"]\n",
    "    \n",
    "    return pd.Series(data = job_exp[\"Data Value\"].tolist(), index = exp_categories.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_exp(job1, job2):\n",
    "    \n",
    "    job1_stats_lst = get_job_exp_stats(job1).tolist()\n",
    "    job2_stats_lst = get_job_exp_stats(job2).tolist()\n",
    "    idx_max_job1 = np.argmax(job1_stats_lst)\n",
    "    idx_max_job2 = np.argmax(job2_stats_lst)\n",
    "    \n",
    "    if idx_max_job2 > idx_max_job1:\n",
    "        level = 2\n",
    "\n",
    "    elif idx_max_job2 == idx_max_job1:\n",
    "        level = 1\n",
    "        \n",
    "    else:\n",
    "        level = 0\n",
    "\n",
    "    return level\n",
    "\n",
    "def exp_info(job):\n",
    "    label_max_job = get_job_exp_stats(job).idxmax().lower()\n",
    "    if label_max_job == 'none':\n",
    "        to_print = \"In general, {} do not have any prior experience.\".format(job)\n",
    "    else:\n",
    "        to_print = \"In general, {} have {} of experience.\".format(job, label_max_job)\n",
    "    return to_print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2 is higher, 1 is similar, 0 is lower\n",
    "compare_exp(job1, job2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'In general, Human Resources Managers have over 4 years, up to and including 6 years of experience.'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_info(job2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.C. Skills <a name=\"3C\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.C.a. Functions <a name=\"3Ca\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_skills(job):\n",
    "    return skills[skills[\"Title\"] == job] \n",
    "\n",
    "def get_skills_im(job):\n",
    "    job_skills = get_skills(job)\n",
    "    return job_skills.loc[job_skills[\"Scale ID\"] == \"IM\"].sort_values(by = \"Data Value\", ascending = False).head(10)[\"Element Name\"].tolist()\n",
    "\n",
    "def get_skills_lv(job1, job2):\n",
    "\n",
    "    job1_skills = get_skills(job1)\n",
    "    job2_skills = get_skills(job2)\n",
    "    \n",
    "    skills_im = get_skills_im(job2)\n",
    "    \n",
    "    job1_skills_lv = []\n",
    "    job2_skills_lv = []\n",
    "    gap_skills_lv = []\n",
    "\n",
    "    for s in skills_im:\n",
    "        dv1 = job1_skills.loc[(job1_skills[\"Scale ID\"] == \"LV\") & (job1_skills[\"Element Name\"] == s),\"Data Value\"].iloc[0]\n",
    "        dv2 = job2_skills.loc[(job2_skills[\"Scale ID\"] == \"LV\") & (job2_skills[\"Element Name\"] == s),\"Data Value\"].iloc[0]\n",
    "        job1_skills_lv.append(dv1)\n",
    "        job2_skills_lv.append(dv2)\n",
    "        comp = dv2 - dv1\n",
    "        gap_skills_lv.append(comp)\n",
    "        \n",
    "    return pd.DataFrame(data = {job1: job1_skills_lv, job2: job2_skills_lv, \"Gap\": gap_skills_lv}, index = skills_im, columns = [job1, job2, \"Gap\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.C.b. Visualization with Matplotlib <a name=\"3Cb\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_bar_skills(job1, job2):\n",
    "    \n",
    "    # Data\n",
    "    labels_skills = get_skills_lv(job1, job2).index.tolist()\n",
    "    data_1_skills = get_skills_lv(job1, job2).iloc[:,0].tolist()\n",
    "    data_2_skills = get_skills_lv(job1, job2).iloc[:,1].tolist()\n",
    "    \n",
    "    n_skills = len(labels_skills)\n",
    "    \n",
    "    # Figure\n",
    "    plt.figure(figsize=(8,6), dpi= 80)\n",
    "    \n",
    "    # Colors\n",
    "    colors=['#81c784', '#42a5f5']\n",
    "    \n",
    "    # Plot\n",
    "    y = np.arange(n_skills)\n",
    "    plt.barh(y, width= data_2_skills[::-1], color=colors[0])\n",
    "    plt.barh(y, width= data_1_skills[::-1], color=colors[1])\n",
    "\n",
    "    \n",
    "    # Style\n",
    "    plt.yticks(ticks=range(n_skills), labels=labels_skills)\n",
    "    # Title\n",
    "    plt.title(\"From {} to {}\".format(job1, job2), fontsize=15)\n",
    "    # Legend\n",
    "    plt.legend({group:col for group, col in zip(['Gap', 'Current'], colors)}, loc='upper right')\n",
    "    # Spines \n",
    "    plt.gca().spines[\"top\"].set_alpha(.3)\n",
    "    plt.gca().spines[\"bottom\"].set_alpha(.3)\n",
    "    plt.gca().spines[\"right\"].set_alpha(.3)\n",
    "    plt.gca().spines[\"left\"].set_alpha(.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_bar_skills(job1, job2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.C.c. Prepare data for visualization with Chart.js <a name=\"3Cc\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### bar1_lbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Management of Personnel Resources',\n",
       " 'Active Listening',\n",
       " 'Speaking',\n",
       " 'Social Perceptiveness',\n",
       " 'Critical Thinking',\n",
       " 'Coordination',\n",
       " 'Reading Comprehension',\n",
       " 'Judgment and Decision Making',\n",
       " 'Negotiation',\n",
       " 'Monitoring']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_skills_lv(job1, job2).index.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### bar1_src"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.38, 4.0, 3.88, 3.12, 3.75, 3.0, 4.0, 2.88, 2.62, 3.62]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_skills_lv(job1, job2).iloc[:,0].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### bar1_tgt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.24,\n",
       " 0.3799999999999999,\n",
       " 0.1200000000000001,\n",
       " 1.0,\n",
       " 0.5,\n",
       " 1.12,\n",
       " 0.25,\n",
       " 1.2400000000000002,\n",
       " 1.38,\n",
       " 0.7599999999999998]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_skills_lv(job1, job2).iloc[:,-1].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.D. Knowledge <a name=\"3D\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.D.a. Functions <a name=\"3Da\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_know(job):\n",
    "    return knowledge[knowledge[\"Title\"] == job]  \n",
    "\n",
    "def get_know_im(job):\n",
    "    job_know = get_know(job)\n",
    "    return job_know.loc[job_know[\"Scale ID\"] == \"IM\"].sort_values(by = \"Data Value\", ascending = False).head(10)[\"Element Name\"].tolist()\n",
    "\n",
    "def get_know_lv(job1, job2):\n",
    "\n",
    "    job1_know = get_know(job1)\n",
    "    job2_know = get_know(job2)\n",
    "    \n",
    "    know_im = get_know_im(job2)\n",
    "    \n",
    "    job1_know_lv = []\n",
    "    job2_know_lv = []\n",
    "    gap_know_lv = []\n",
    "\n",
    "    for s in know_im:\n",
    "        dv1 = job1_know.loc[(job1_know[\"Scale ID\"] == \"LV\") & (job1_know[\"Element Name\"] == s),\"Data Value\"].iloc[0]\n",
    "        dv2 = job2_know.loc[(job2_know[\"Scale ID\"] == \"LV\") & (job2_know[\"Element Name\"] == s),\"Data Value\"].iloc[0]\n",
    "        job1_know_lv.append(dv1)\n",
    "        job2_know_lv.append(dv2)\n",
    "        comp = dv2 - dv1\n",
    "        gap_know_lv.append(comp)\n",
    "        \n",
    "    return pd.DataFrame(data = {job1: job1_know_lv, job2: job2_know_lv, \"Gap\": gap_know_lv}, index = know_im, columns = [job1, job2, \"Gap\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.D.b. Visualization with Matplotlib <a name=\"3Db\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_bar_know(job1, job2):\n",
    "    \n",
    "    # Data\n",
    "    labels_know = list(get_know_lv(job1, job2).index)\n",
    "    data_1_know = get_know_lv(job1, job2).iloc[:,0].tolist()\n",
    "    data_2_know = get_know_lv(job1, job2).iloc[:,1].tolist()\n",
    "    \n",
    "    n_know = len(labels_know)\n",
    "    \n",
    "    # Figure\n",
    "    plt.figure(figsize=(6,6), dpi= 80)\n",
    "    \n",
    "    # Colors\n",
    "    colors=['#81c784', '#42a5f5']\n",
    "    \n",
    "    # Plot\n",
    "    y = np.arange(n_know)\n",
    "    plt.barh(y, width= data_2_know[::-1], color=colors[0])\n",
    "    plt.barh(y, width= data_1_know[::-1], color=colors[1])\n",
    "    \n",
    "    # Style\n",
    "    plt.yticks(ticks=range(n_know), labels=labels_know)\n",
    "    # Title\n",
    "    plt.title(\"From {} to {}\".format(job1, job2), fontsize=15)\n",
    "    # Legend\n",
    "    plt.legend({group:col for group, col in zip(['Gap', 'Current'], colors)}, loc='upper right')\n",
    "    # Spines \n",
    "    plt.gca().spines[\"top\"].set_alpha(.3)\n",
    "    plt.gca().spines[\"bottom\"].set_alpha(.3)\n",
    "    plt.gca().spines[\"right\"].set_alpha(.3)\n",
    "    plt.gca().spines[\"left\"].set_alpha(.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_bar_know(job1, job2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.D.c. Prepare data for visualization with Chart.js <a name=\"3Dc\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### bar2_lbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Personnel and Human Resources',\n",
       " 'Administration and Management',\n",
       " 'English Language',\n",
       " 'Customer and Personal Service',\n",
       " 'Law and Government',\n",
       " 'Psychology',\n",
       " 'Education and Training',\n",
       " 'Mathematics',\n",
       " 'Clerical',\n",
       " 'Sociology and Anthropology']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_know_lv(job1, job2).index.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### bar2_src"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.66, 3.28, 3.97, 3.75, 2.07, 1.49, 2.11, 2.78, 6.03, 1.21]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_know_lv(job1, job2).iloc[:,0].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### bar2_tgt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.8899999999999997,\n",
       " 1.5800000000000005,\n",
       " 0.5299999999999998,\n",
       " 1.1100000000000003,\n",
       " 1.98,\n",
       " 2.87,\n",
       " 2.89,\n",
       " 1.4,\n",
       " -1.2700000000000005,\n",
       " 2.29]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_know_lv(job1, job2).iloc[:,-1].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.E. Estimated transition difficulty  <a name=\"3E\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gap(job1, job2):\n",
    "    skills_gap = get_skills_lv(job1, job2).iloc[:,-1].sum()\n",
    "    knowledge_gap = get_know_lv(job1, job2).iloc[:,-1].sum()\n",
    "    total = skills_gap + knowledge_gap\n",
    "    \n",
    "    if total < 10:\n",
    "        difficulty = 0\n",
    "    elif total < 30:\n",
    "        difficulty = 1\n",
    "    else:\n",
    "        difficulty = 2\n",
    "    \n",
    "    return difficulty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0 is low, 1 is medium, 2 is high\n",
    "gap(job1, job2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.F. Generate & export Json  <a name=\"3F\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_json(job1, job2):\n",
    "    result = {\"source_job_name\": job1,\n",
    "              \"target_job_name\" : job2,\n",
    "              \"transition_gen\" : gap(job1, job2),\n",
    "              \"education_level\": compare_ed(job1, job2),\n",
    "              \"education_comp\" : ed_info(job2),\n",
    "              \"radar_src\" : get_job_ed_stats(job1).tolist(),\n",
    "              \"radar_tgt\" : get_job_ed_stats(job2).tolist(),\n",
    "              \"experience_level\" : compare_exp(job1, job2),\n",
    "              \"experience_comp\" : exp_info(job2),\n",
    "              \"bar1_lbl\" : get_skills_lv(job1, job2).index.tolist(),\n",
    "              \"bar1_src\" : get_skills_lv(job1, job2).iloc[:,0].tolist(),\n",
    "              \"bar1_tgt\" : get_skills_lv(job1, job2).iloc[:,-1].tolist(),\n",
    "              \"bar2_lbl\" : get_know_lv(job1, job2).index.tolist(),\n",
    "              \"bar2_src\" : get_know_lv(job1, job2).iloc[:,0].tolist(),\n",
    "              \"bar2_tgt\" : get_know_lv(job1, job2).iloc[:,-1].tolist()\n",
    "             }\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = gen_json(job1, job2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"source_job_name\": \"Executive Secretaries and Executive Administrative Assistants\",\n",
      "    \"target_job_name\": \"Human Resources Managers\",\n",
      "    \"transition_gen\": 1,\n",
      "    \"education_level\": 2,\n",
      "    \"education_comp\": \"In general, Human Resources Managers hold a Bachelor degree.\",\n",
      "    \"radar_src\": [\n",
      "        62.98023125699367,\n",
      "        22.1372622155912,\n",
      "        14.882506527415144,\n",
      "        0.0\n",
      "    ],\n",
      "    \"radar_tgt\": [\n",
      "        4.55,\n",
      "        77.27000000000001,\n",
      "        18.18,\n",
      "        0.0\n",
      "    ],\n",
      "    \"experience_level\": 2,\n",
      "    \"experience_comp\": \"In general, Human Resources Managers have Over 4 years, up to and including 6 years of experience.\",\n",
      "    \"bar1_lbl\": [\n",
      "        \"Management of Personnel Resources\",\n",
      "        \"Active Listening\",\n",
      "        \"Speaking\",\n",
      "        \"Social Perceptiveness\",\n",
      "        \"Critical Thinking\",\n",
      "        \"Coordination\",\n",
      "        \"Reading Comprehension\",\n",
      "        \"Judgment and Decision Making\",\n",
      "        \"Negotiation\",\n",
      "        \"Monitoring\"\n",
      "    ],\n",
      "    \"bar1_src\": [\n",
      "        2.38,\n",
      "        4.0,\n",
      "        3.88,\n",
      "        3.12,\n",
      "        3.75,\n",
      "        3.0,\n",
      "        4.0,\n",
      "        2.88,\n",
      "        2.62,\n",
      "        3.62\n",
      "    ],\n",
      "    \"bar1_tgt\": [\n",
      "        2.24,\n",
      "        0.3799999999999999,\n",
      "        0.1200000000000001,\n",
      "        1.0,\n",
      "        0.5,\n",
      "        1.12,\n",
      "        0.25,\n",
      "        1.2400000000000002,\n",
      "        1.38,\n",
      "        0.7599999999999998\n",
      "    ],\n",
      "    \"bar2_lbl\": [\n",
      "        \"Personnel and Human Resources\",\n",
      "        \"Administration and Management\",\n",
      "        \"English Language\",\n",
      "        \"Customer and Personal Service\",\n",
      "        \"Law and Government\",\n",
      "        \"Psychology\",\n",
      "        \"Education and Training\",\n",
      "        \"Mathematics\",\n",
      "        \"Clerical\",\n",
      "        \"Sociology and Anthropology\"\n",
      "    ],\n",
      "    \"bar2_src\": [\n",
      "        2.66,\n",
      "        3.28,\n",
      "        3.97,\n",
      "        3.75,\n",
      "        2.07,\n",
      "        1.49,\n",
      "        2.11,\n",
      "        2.78,\n",
      "        6.03,\n",
      "        1.21\n",
      "    ],\n",
      "    \"bar2_tgt\": [\n",
      "        2.8899999999999997,\n",
      "        1.5800000000000005,\n",
      "        0.5299999999999998,\n",
      "        1.1100000000000003,\n",
      "        1.98,\n",
      "        2.87,\n",
      "        2.89,\n",
      "        1.4,\n",
      "        -1.2700000000000005,\n",
      "        2.29\n",
      "    ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(json.dumps(result, indent=4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "{\n",
    "  \"source_job_name\": \"Executive Secretaries and Executive Administrative Assistants\",\n",
    "  \"target_job_name\": \"Human Resources Managers\",\n",
    "  \"transition_gen\": 1,\n",
    "  \"education_level\": 2,\n",
    "  \"education_comp\": \"In general, Human Resources Managers hold at least a Bachelor degree.\",\n",
    "  \"radar_src\": [62.98023125699367, 22.1372622155912, 14.882506527415144, 0.0],\n",
    "  \"radar_tgt\": [4.55, 77.27000000000001, 18.18, 0.0],\n",
    "  \"experience_level\": 2,\n",
    "  \"experience_comp\": \"In general, Human Resources Managers have at least Over 4 years, up to and including 6 years of experience.\",\n",
    "  \"bar1_lbl\":[\"Management of Personnel Resources\",\n",
    " \"Active Listening\",\n",
    " \"Speaking\",\n",
    " \"Social Perceptiveness\",\n",
    " \"Critical Thinking\",\n",
    " \"Coordination\",\n",
    " \"Reading Comprehension\",\n",
    " \"Judgment and Decision Making\",\n",
    " \"Negotiation\",\n",
    " \"Monitoring\"],\n",
    "  \"bar1_src\":[2.38, 4.0, 3.88, 3.12, 3.75, 3.0, 4.0, 2.88, 2.62, 3.62],\n",
    "  \"bar1_tgt\": [2.24, 0.3799999999999999, 0.1200000000000001, 1.0, 0.5, 1.12, 0.25, 1.2400000000000002, 1.38, 0.7599999999999998],\n",
    "  \"bar2_lbl\": [\"Personnel and Human Resources\", \"Administration and Management\", \"English Language\", \"Customer and Personal Service\", \"Law and Government\", \"Psychology\", \"Education and Training\", \"Mathematics\", \"Clerical\", \"Sociology and Anthropology\"],\n",
    "  \"bar2_src\":[2.66, 3.28, 3.97, 3.75, 2.07, 1.49, 2.11, 2.78, 6.03, 1.21],\n",
    "  \"bar2_tgt\": [2.8899999999999997, 1.5800000000000005, 0.5299999999999998, 1.1100000000000003, 1.98, 2.87, 2.89, 1.4, -1.2700000000000005, 2.29]\n",
    "}"
   ]
  }
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