project/2_Skill_Demand.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# What are the most demanded skills for the top 3 most popular data roles?\n",
    "### Methodology\n",
    "1. Clean-up skill column\n",
    "2. Calculate skill count based on ```job_title_short```\n",
    "3. Calculate skill percentage\n",
    "4. Plot final findings"
   ],
   "id": "ad37c2d87957b5af"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Importing Libraries, Loading Data and Basic Data Cleanup",
   "id": "f27f1d14e595bd16"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T08:39:16.054882Z",
     "start_time": "2024-09-12T08:39:03.955734Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#Importing Libraries\n",
    "import ast\n",
    "import seaborn as sns\n",
    "import pandas as pd\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'])  # converting job_posted_date to datetime object\n",
    "df['job_skills'] = df['job_skills'].apply(lambda data: ast.literal_eval(data) if pd.notna(data) else data)  # converting job_skills to list"
   ],
   "id": "a78f9c6cf0a3a62d",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T08:39:16.128423Z",
     "start_time": "2024-09-12T08:39:16.059515Z"
    }
   },
   "cell_type": "code",
   "source": "df_US = df[df['job_country'] == 'United States']  # Filter for United States data",
   "id": "e0c592fe77a61d80",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Explode ```job_skills``` Column",
   "id": "c6ab52060dee0efd"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T08:39:16.663828Z",
     "start_time": "2024-09-12T08:39:16.154097Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df_skills = df_US.explode('job_skills')  # Explode job_skills column to break out the list of job skills\n",
    "\n",
    "df_skills[['job_title', 'job_skills']]  # check if list is broken out"
   ],
   "id": "90f791ae3c26ecf8",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                                job_title job_skills\n",
       "0       Senior Clinical Data Engineer / Principal Clin...       None\n",
       "3       LEAD ENGINEER - PRINCIPAL ANALYST - PRINCIPAL ...     python\n",
       "3       LEAD ENGINEER - PRINCIPAL ANALYST - PRINCIPAL ...        c++\n",
       "3       LEAD ENGINEER - PRINCIPAL ANALYST - PRINCIPAL ...       java\n",
       "3       LEAD ENGINEER - PRINCIPAL ANALYST - PRINCIPAL ...     matlab\n",
       "...                                                   ...        ...\n",
       "785692               Data Scientist- Hybrid Work Location          r\n",
       "785703            Data Analyst - CRYPTOGRAPHY - Full-time       None\n",
       "785705          Expert Business Data Analyst - Now Hiring        sql\n",
       "785705          Expert Business Data Analyst - Now Hiring     python\n",
       "785705          Expert Business Data Analyst - Now Hiring    tableau\n",
       "\n",
       "[1073565 rows x 2 columns]"
      ],
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       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Senior Clinical Data Engineer / Principal Clin...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LEAD ENGINEER - PRINCIPAL ANALYST - PRINCIPAL ...</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LEAD ENGINEER - PRINCIPAL ANALYST - PRINCIPAL ...</td>\n",
       "      <td>c++</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LEAD ENGINEER - PRINCIPAL ANALYST - PRINCIPAL ...</td>\n",
       "      <td>java</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LEAD ENGINEER - PRINCIPAL ANALYST - PRINCIPAL ...</td>\n",
       "      <td>matlab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785692</th>\n",
       "      <td>Data Scientist- Hybrid Work Location</td>\n",
       "      <td>r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785703</th>\n",
       "      <td>Data Analyst - CRYPTOGRAPHY - Full-time</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785705</th>\n",
       "      <td>Expert Business Data Analyst - Now Hiring</td>\n",
       "      <td>sql</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785705</th>\n",
       "      <td>Expert Business Data Analyst - Now Hiring</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785705</th>\n",
       "      <td>Expert Business Data Analyst - Now Hiring</td>\n",
       "      <td>tableau</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1073565 rows × 2 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Calculate Skill Count Based on ```job_title_short```",
   "id": "fbba3fb480b4d3cf"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T08:39:16.881266Z",
     "start_time": "2024-09-12T08:39:16.710353Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df_skills_count = df_skills.groupby(['job_skills', 'job_title_short']).size()  # grouping by job_skills and job_title_short columns\n",
    "\n",
    "df_skills_count = df_skills_count.reset_index(name='skill_count')  # converting series to data frame by reset_index method\n",
    "\n",
    "df_skills_count.sort_values(by='skill_count', ascending=False, inplace=True)\n",
    "\n",
    "df_skills_count"
   ],
   "id": "4f9ec4acfce7334c",
   "outputs": [
    {
     "data": {
      "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",
       "1785     vue.js           Business Analyst            1\n",
       "60         arch           Business Analyst            1\n",
       "71        asana  Machine Learning Engineer            1\n",
       "968      no-sql  Machine Learning Engineer            1\n",
       "1861       zoom             Cloud Engineer            1\n",
       "\n",
       "[1870 rows x 3 columns]"
      ],
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       "<style scoped>\n",
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       "<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>1785</th>\n",
       "      <td>vue.js</td>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>arch</td>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>asana</td>\n",
       "      <td>Machine Learning Engineer</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968</th>\n",
       "      <td>no-sql</td>\n",
       "      <td>Machine Learning Engineer</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1861</th>\n",
       "      <td>zoom</td>\n",
       "      <td>Cloud Engineer</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1870 rows × 3 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Getting the Top 3 Roles",
   "id": "101971308fa959f4"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T08:39:16.916057Z",
     "start_time": "2024-09-12T08:39:16.912562Z"
    }
   },
   "cell_type": "code",
   "source": [
    "job_titles = df_skills_count['job_title_short'].unique().tolist()\n",
    "job_titles = sorted(job_titles[:3])\n",
    "job_titles"
   ],
   "id": "d470d02e0ad54d1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Data Analyst', 'Data Engineer', 'Data Scientist']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Plotting",
   "id": "553ad52f8c70edb2"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T08:39:17.520973Z",
     "start_time": "2024-09-12T08:39:17.016879Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig, ax = plt.subplots(len(job_titles), 1)\n",
    "\n",
    "for i, job_title in enumerate(job_titles):\n",
    "    df_plot = df_skills_count[df_skills_count['job_title_short'] == job_title].head(5)\n",
    "    df_plot.plot(kind='barh', x='job_skills', y='skill_count', title=job_title, ax=ax[i])\n",
    "    ax[i].invert_yaxis()\n",
    "    ax[i].set_ylabel('')\n",
    "    ax[i].legend().set_visible(False)\n",
    "\n",
    "fig.suptitle('Counts of Top Skills in Job Postings', fontsize=15)\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    " "
   ],
   "id": "e287347df6251a42",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Converting Skill Counts into Percentage",
   "id": "ff18d6dedc5d6c93"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T08:39:17.564832Z",
     "start_time": "2024-09-12T08:39:17.552472Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df_job_title_count = df_US['job_title_short'].value_counts().reset_index(name='jobs_total')  # Calculate the total jobs postings for data roles\n",
    "df_job_title_count"
   ],
   "id": "f1c140f4295468",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "             job_title_short  jobs_total\n",
       "0               Data Analyst       67816\n",
       "1             Data Scientist       58830\n",
       "2              Data Engineer       35080\n",
       "3      Senior Data Scientist       12946\n",
       "4        Senior Data Analyst       11791\n",
       "5       Senior Data Engineer        9289\n",
       "6           Business Analyst        7382\n",
       "7          Software Engineer        1814\n",
       "8  Machine Learning Engineer         921\n",
       "9             Cloud Engineer         423"
      ],
      "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_title_short</th>\n",
       "      <th>jobs_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>67816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>58830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>35080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Senior Data Scientist</td>\n",
       "      <td>12946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Senior Data Analyst</td>\n",
       "      <td>11791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Senior Data Engineer</td>\n",
       "      <td>9289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>7382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>1814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Machine Learning Engineer</td>\n",
       "      <td>921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Cloud Engineer</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T08:39:42.626235Z",
     "start_time": "2024-09-12T08:39:42.615742Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df_skills_percentage = pd.merge(df_skills_count, df_job_title_count, how='left', on='job_title_short')  # merge skill count with total jobs postings\n",
    "df_skills_percentage"
   ],
   "id": "a1e2f7dc2331fb92",
   "outputs": [
    {
     "data": {
      "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     vue.js           Business Analyst            1        7382\n",
       "1866       arch           Business Analyst            1        7382\n",
       "1867      asana  Machine Learning Engineer            1         921\n",
       "1868     no-sql  Machine Learning Engineer            1         921\n",
       "1869       zoom             Cloud Engineer            1         423\n",
       "\n",
       "[1870 rows x 4 columns]"
      ],
      "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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1865</th>\n",
       "      <td>vue.js</td>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>1</td>\n",
       "      <td>7382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1866</th>\n",
       "      <td>arch</td>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>1</td>\n",
       "      <td>7382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1867</th>\n",
       "      <td>asana</td>\n",
       "      <td>Machine Learning Engineer</td>\n",
       "      <td>1</td>\n",
       "      <td>921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1868</th>\n",
       "      <td>no-sql</td>\n",
       "      <td>Machine Learning Engineer</td>\n",
       "      <td>1</td>\n",
       "      <td>921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1869</th>\n",
       "      <td>zoom</td>\n",
       "      <td>Cloud Engineer</td>\n",
       "      <td>1</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1870 rows × 4 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T08:39:46.831030Z",
     "start_time": "2024-09-12T08:39:46.821955Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df_skills_percentage['skill_percent'] = 100 * (df_skills_percentage['skill_count'] / df_skills_percentage['jobs_total'])  # adding skill_percent column\n",
    "df_skills_percentage"
   ],
   "id": "bba513ce2fdd3dff",
   "outputs": [
    {
     "data": {
      "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     vue.js           Business Analyst            1        7382   \n",
       "1866       arch           Business Analyst            1        7382   \n",
       "1867      asana  Machine Learning Engineer            1         921   \n",
       "1868     no-sql  Machine Learning Engineer            1         921   \n",
       "1869       zoom             Cloud Engineer            1         423   \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       0.013546  \n",
       "1866       0.013546  \n",
       "1867       0.108578  \n",
       "1868       0.108578  \n",
       "1869       0.236407  \n",
       "\n",
       "[1870 rows x 5 columns]"
      ],
      "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>vue.js</td>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>1</td>\n",
       "      <td>7382</td>\n",
       "      <td>0.013546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1866</th>\n",
       "      <td>arch</td>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>1</td>\n",
       "      <td>7382</td>\n",
       "      <td>0.013546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1867</th>\n",
       "      <td>asana</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>no-sql</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>1869</th>\n",
       "      <td>zoom</td>\n",
       "      <td>Cloud Engineer</td>\n",
       "      <td>1</td>\n",
       "      <td>423</td>\n",
       "      <td>0.236407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1870 rows × 5 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Final Plotting",
   "id": "a8b9cb3161afa6eb"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T08:55:43.116210Z",
     "start_time": "2024-09-12T08:55:42.672820Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig, ax = plt.subplots(len(job_titles), 1)\n",
    "\n",
    "for i, job_title in enumerate(job_titles):\n",
    "    df_plot = df_skills_percentage[df_skills_percentage['job_title_short'] == job_title].head(5)\n",
    "    sns.barplot(data=df_plot, x='skill_percent', y='job_skills', ax=ax[i], hue='skill_count', palette='dark:b_r')  # using seaborn for better visualization\n",
    "    ax[i].set_title(job_title)\n",
    "    ax[i].set_ylabel('')\n",
    "    ax[i].set_xlabel('')\n",
    "    ax[i].legend().set_visible(False)\n",
    "    ax[i].set_xlim(0, 80)\n",
    "    \n",
    "    # adding percentage value to the bars\n",
    "    for j, value in enumerate(df_plot['skill_percent']):\n",
    "        ax[i].text(value + 1, j, f'{value:.0f}%', va='center')\n",
    "        \n",
    "    # removing additional skill_percent representation\n",
    "    if i != len(job_titles) - 1:\n",
    "        ax[i].set_xticks([])\n",
    "\n",
    "fig.suptitle('Likelihood of Skills Requested in US Job Postings', fontsize=15)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ],
   "id": "b39d535a1ec12008",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 28
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}