2_Skill_Demand.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e2276333",
   "metadata": {},
   "source": [
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/lukebarousse/Python_Data_Analytics_Course/blob/main/3_Project/2_Skill_Demand.ipynb\">\n",
    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
    "</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Libraries\n",
    "import ast\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from datasets import load_dataset\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Loading Data\n",
    "dataset = load_dataset('lukebarousse/data_jobs')\n",
    "df = dataset['train'].to_pandas()\n",
    "\n",
    "# Data Cleanup\n",
    "df['job_posted_date'] = pd.to_datetime(df['job_posted_date'])\n",
    "df['job_skills'] = df['job_skills'].apply(lambda x: ast.literal_eval(x) if pd.notna(x) else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_US = df[df['job_country'] == 'United States']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>job_skills</th>\n",
       "      <th>job_title_short</th>\n",
       "      <th>skill_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1209</th>\n",
       "      <td>python</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>42379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1521</th>\n",
       "      <td>sql</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>34452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1523</th>\n",
       "      <td>sql</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>30034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>excel</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>27519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>r</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>26022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>clojure</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1738</th>\n",
       "      <td>vb.net</td>\n",
       "      <td>Senior Data Scientist</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>fortran</td>\n",
       "      <td>Machine Learning Engineer</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1116</th>\n",
       "      <td>planner</td>\n",
       "      <td>Cloud Engineer</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>nltk</td>\n",
       "      <td>Senior Data Engineer</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1870 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     job_skills            job_title_short  skill_count\n",
       "1209     python             Data Scientist        42379\n",
       "1521        sql               Data Analyst        34452\n",
       "1523        sql             Data Scientist        30034\n",
       "455       excel               Data Analyst        27519\n",
       "1243          r             Data Scientist        26022\n",
       "...         ...                        ...          ...\n",
       "245     clojure          Software Engineer            1\n",
       "1738     vb.net      Senior Data Scientist            1\n",
       "530     fortran  Machine Learning Engineer            1\n",
       "1116    planner             Cloud Engineer            1\n",
       "960        nltk       Senior Data Engineer            1\n",
       "\n",
       "[1870 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Skills = df_US.explode('job_skills')\n",
    "df_Skills_count = df_Skills.groupby(['job_skills', 'job_title_short']).size()\n",
    "df_Skills_count = df_Skills_count.reset_index(name='skill_count')\n",
    "df_Skills_count.sort_values(by='skill_count',ascending=False,inplace=True)\n",
    "df_Skills_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Data Analyst', 'Data Engineer', 'Data Scientist']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Job_Titles = df_Skills_count['job_title_short'].unique().tolist()\n",
    "top_3_Roles = sorted(Job_Titles[:3])\n",
    "top_3_Roles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(len(top_3_Roles),1)\n",
    "for i , Job in enumerate(top_3_Roles):\n",
    "    df_plot = df_Skills_count[df_Skills_count['job_title_short'] == Job].head(5)\n",
    "    df_plot.plot(kind='barh', x='job_skills', y='skill_count', ax=ax[i], title=Job)\n",
    "    ax[i].invert_yaxis()\n",
    "    ax[i].set_ylabel('')\n",
    "    ax[i].legend().set_visible(False)\n",
    "fig.suptitle('Counts of Top Skills in Job Postings', fontsize=15)\n",
    "fig.tight_layout(h_pad=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>job_skills</th>\n",
       "      <th>job_title_short</th>\n",
       "      <th>skill_count</th>\n",
       "      <th>jobs_total</th>\n",
       "      <th>skill_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>python</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>42379</td>\n",
       "      <td>58830</td>\n",
       "      <td>72.036376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sql</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>34452</td>\n",
       "      <td>67816</td>\n",
       "      <td>50.802171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sql</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>30034</td>\n",
       "      <td>58830</td>\n",
       "      <td>51.052184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>excel</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>27519</td>\n",
       "      <td>67816</td>\n",
       "      <td>40.578919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>r</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>26022</td>\n",
       "      <td>58830</td>\n",
       "      <td>44.232534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1865</th>\n",
       "      <td>clojure</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>1</td>\n",
       "      <td>1814</td>\n",
       "      <td>0.055127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1866</th>\n",
       "      <td>vb.net</td>\n",
       "      <td>Senior Data Scientist</td>\n",
       "      <td>1</td>\n",
       "      <td>12946</td>\n",
       "      <td>0.007724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1867</th>\n",
       "      <td>fortran</td>\n",
       "      <td>Machine Learning Engineer</td>\n",
       "      <td>1</td>\n",
       "      <td>921</td>\n",
       "      <td>0.108578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1868</th>\n",
       "      <td>planner</td>\n",
       "      <td>Cloud Engineer</td>\n",
       "      <td>1</td>\n",
       "      <td>423</td>\n",
       "      <td>0.236407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1869</th>\n",
       "      <td>nltk</td>\n",
       "      <td>Senior Data Engineer</td>\n",
       "      <td>1</td>\n",
       "      <td>9289</td>\n",
       "      <td>0.010765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1870 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     job_skills            job_title_short  skill_count  jobs_total  \\\n",
       "0        python             Data Scientist        42379       58830   \n",
       "1           sql               Data Analyst        34452       67816   \n",
       "2           sql             Data Scientist        30034       58830   \n",
       "3         excel               Data Analyst        27519       67816   \n",
       "4             r             Data Scientist        26022       58830   \n",
       "...         ...                        ...          ...         ...   \n",
       "1865    clojure          Software Engineer            1        1814   \n",
       "1866     vb.net      Senior Data Scientist            1       12946   \n",
       "1867    fortran  Machine Learning Engineer            1         921   \n",
       "1868    planner             Cloud Engineer            1         423   \n",
       "1869       nltk       Senior Data Engineer            1        9289   \n",
       "\n",
       "      skill_percent  \n",
       "0         72.036376  \n",
       "1         50.802171  \n",
       "2         51.052184  \n",
       "3         40.578919  \n",
       "4         44.232534  \n",
       "...             ...  \n",
       "1865       0.055127  \n",
       "1866       0.007724  \n",
       "1867       0.108578  \n",
       "1868       0.236407  \n",
       "1869       0.010765  \n",
       "\n",
       "[1870 rows x 5 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_job_title_count = df_US['job_title_short'].value_counts().reset_index(name='jobs_total')\n",
    "df_skill_perc = pd.merge(df_Skills_count, df_job_title_count, how='left', on='job_title_short')\n",
    "df_skill_perc['skill_percent'] = 100*df_skill_perc['skill_count'] / df_skill_perc['jobs_total']\n",
    "df_skill_perc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(len(top_3_Roles),1)\n",
    "sns.set_theme(style='ticks')\n",
    "for i , Job in enumerate(top_3_Roles):\n",
    "    df_plot = df_skill_perc[df_skill_perc['job_title_short'] == Job].head(5)\n",
    "    sns.barplot(data=df_plot, x='skill_percent', y='job_skills', ax=ax[i], hue='skill_count',palette='dark:b_r')\n",
    "    ax[i].set_title(Job)\n",
    "    ax[i].set_ylabel('')\n",
    "    ax[i].set_xlabel('')\n",
    "    ax[i].set_xlim(0,78)\n",
    "    ax[i].legend().set_visible(False)\n",
    "\n",
    "    for n, v in enumerate(df_plot['skill_percent']):\n",
    "        ax[i].text(v + 1, n, f'{v:.0f}%', va='center')\n",
    "    if i != len(top_3_Roles)-1:\n",
    "        ax[i].set_xticks([])\n",
    "\n",
    "fig.suptitle('Likelihood of Skills Requested in US Job Postings', fontsize=15)\n",
    "fig.tight_layout(h_pad=0.5)\n",
    "plt.show()"
   ]
  }
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