01_pdf-data-extraction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d49cffd2-cdf7-41ea-a83e-df7a7294620c",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1f822d0f-724c-4807-9a59-8538229a89f7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "# import PyPDF2\n",
    "import pdfplumber\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7353e195-38d8-4481-8157-c755ab0fca0e",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Function to extract text from PDFs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8173983a-12f3-4822-9aa7-b45318c9b22a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define a function to extract information from a PDF\n",
    "def extract_information(pdf_path):\n",
    "    with pdfplumber.open(pdf_path) as pdf:\n",
    "        resume_text = \"\"\n",
    "        for page in pdf.pages:\n",
    "            resume_text = \" \".join([resume_text, page.extract_text()])\n",
    "    resume_text = resume_text.strip()\n",
    "    return resume_text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d40dfae8-f8b9-4751-859a-04b17eaf50cc",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Defining Regex to extract Skills & Education from extracted resume text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ba743403-abf0-4790-b288-6d23ee111fe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to extract Skills, and Education\n",
    "def extract_details(resume_text):\n",
    "    # Define regular expressions to extract Skills & Education\n",
    "    skills_pattern = r'Skills\\n([\\s\\S]*?)(?=\\n[A-Z]|$)' \n",
    "    education_pattern = r'Education\\n([\\s\\S]*?)(?=\\n[A-Z][a-z]*\\n|$)'\n",
    "    \n",
    "    # Get Skills & Education\n",
    "    skills_match = re.findall(skills_pattern, resume_text, re.DOTALL)\n",
    "    education_match = re.findall(education_pattern, resume_text, re.DOTALL)\n",
    "    \n",
    "    # Skills & Education\n",
    "    if len(skills_match)!=0:\n",
    "        skills = skills_match[0]\n",
    "    else:\n",
    "        skills_pattern = r'skills\\n((?:.*)*)' \n",
    "        skills_match = re.findall(skills_pattern, resume_text, re.DOTALL)\n",
    "        if len(skills_match)!=0:\n",
    "            skills = skills_match[0]\n",
    "        else:\n",
    "            skills = None\n",
    "            \n",
    "    if len(education_match)!=0:\n",
    "        education = education_match[0]\n",
    "    else:\n",
    "        education = None\n",
    "    \n",
    "    return {\n",
    "        'Skills': skills,\n",
    "        'Education': education\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88729de5-131f-4b18-92aa-28b849defa3e",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Extracting CVs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f4aadfa6-7005-446b-8544-ce980691602a",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PDF Extraction Done!\n",
      "CPU times: total: 25min 45s\n",
      "Wall time: 26min 29s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "data_folder = 'data'\n",
    "resume_data = []\n",
    "\n",
    "# Iterate through sub-folders and PDF files\n",
    "for category_folder in os.listdir(data_folder):\n",
    "    category_path = os.path.join(data_folder, category_folder)\n",
    "    if os.path.isdir(category_path):\n",
    "        for pdf_file in os.listdir(category_path):\n",
    "            if pdf_file.endswith('.pdf'):\n",
    "                pdf_path = os.path.join(category_path, pdf_file)\n",
    "                # print(pdf_path)\n",
    "                text = extract_information(pdf_path)\n",
    "                details = extract_details(text)\n",
    "                \n",
    "                # Adding Category & ID\n",
    "                details['ID'] = pdf_file.replace('.pdf', '')\n",
    "                details['Category'] = category_folder\n",
    "                \n",
    "                # print(f'File: [{pdf_path}]')\n",
    "                # print(details, end='\\n\\n')\n",
    "                resume_data.append(details)\n",
    "                \n",
    "print('PDF Extraction Done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0b464d07-4c1c-482e-963d-bee2017e0f6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "resume_df = pd.DataFrame(resume_data)\n",
    "resume_df.to_csv('./pdf_extracted_skills_education.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "370c4203-c525-4cac-aad1-b843161f8d96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2484, 4)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resume_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31613917-f873-47dd-9dc8-4174125ddfaf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c3348774-c0f9-431d-901b-4a131016c0c0",
   "metadata": {},
   "source": [
    "### Analyzing Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7d30658a-fdfb-43cf-8f27-9604b76430c0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Skills        91\n",
       "Education    590\n",
       "ID             0\n",
       "Category       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null values\n",
    "df.isna().sum()\n",
    "\n",
    "# It seems that Education was not extracted properly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2ebd1e3a-ded6-4f84-a3c6-5263d4fbf9d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Skills Education        ID                Category\n",
      "145     NaN       NaN  17021141                ADVOCATE\n",
      "367     NaN       NaN  31225895                 APPAREL\n",
      "407     NaN       NaN  14248724                    ARTS\n",
      "802     NaN       NaN  12632728    BUSINESS-DEVELOPMENT\n",
      "1047    NaN       NaN  14849103            CONSTRUCTION\n",
      "1270    NaN       NaN  14014749                DESIGNER\n",
      "1357    NaN       NaN  90066849                DESIGNER\n",
      "1492    NaN       NaN  20566550             ENGINEERING\n",
      "1723    NaN       NaN  17576030                 FITNESS\n",
      "1738    NaN       NaN  21178545                 FITNESS\n",
      "1802    NaN       NaN  77266989                 FITNESS\n",
      "1829    NaN       NaN  15499825              HEALTHCARE\n",
      "2078    NaN       NaN  20237244  INFORMATION-TECHNOLOGY\n",
      "2116    NaN       NaN  29051656  INFORMATION-TECHNOLOGY\n",
      "2290    NaN       NaN  16280971                   SALES\n"
     ]
    }
   ],
   "source": [
    "# We have 15 Resumes where Skills & Education were not extracted\n",
    "# So, let's remove them\n",
    "\n",
    "print(df[(df.Skills.isna() & df.Education.isna())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f3f6b14a-0932-4478-b7a1-ee5fca64209c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2469, 4)\n"
     ]
    },
    {
     "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>Skills</th>\n",
       "      <th>Education</th>\n",
       "      <th>ID</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Accounting; General Accounting; Accounts Payab...</td>\n",
       "      <td>Northern Maine Community College 1994 Associat...</td>\n",
       "      <td>10554236</td>\n",
       "      <td>ACCOUNTANT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>accounting, accounts payable, Accounts Receiva...</td>\n",
       "      <td>Bachelor of Science : Accounting , May 2010 Un...</td>\n",
       "      <td>10674770</td>\n",
       "      <td>ACCOUNTANT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>accounts payables, accounts receivables, Accou...</td>\n",
       "      <td>Computer Applications Specialist Certificate P...</td>\n",
       "      <td>11163645</td>\n",
       "      <td>ACCOUNTANT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>accounting, balance sheet, budgets, client, cl...</td>\n",
       "      <td>EMORY UNIVERSITY, Goizueta Business School 5 2...</td>\n",
       "      <td>11759079</td>\n",
       "      <td>ACCOUNTANT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aderant/CMS Financial reporting</td>\n",
       "      <td>Bachelor of Business Administration : Accounti...</td>\n",
       "      <td>12065211</td>\n",
       "      <td>ACCOUNTANT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Skills  \\\n",
       "0  Accounting; General Accounting; Accounts Payab...   \n",
       "1  accounting, accounts payable, Accounts Receiva...   \n",
       "2  accounts payables, accounts receivables, Accou...   \n",
       "3  accounting, balance sheet, budgets, client, cl...   \n",
       "4                    Aderant/CMS Financial reporting   \n",
       "\n",
       "                                           Education        ID    Category  \n",
       "0  Northern Maine Community College 1994 Associat...  10554236  ACCOUNTANT  \n",
       "1  Bachelor of Science : Accounting , May 2010 Un...  10674770  ACCOUNTANT  \n",
       "2  Computer Applications Specialist Certificate P...  11163645  ACCOUNTANT  \n",
       "3  EMORY UNIVERSITY, Goizueta Business School 5 2...  11759079  ACCOUNTANT  \n",
       "4  Bachelor of Business Administration : Accounti...  12065211  ACCOUNTANT  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We are left with 2469 resumes after removing those 15 resumes with null data in both of them\n",
    "\n",
    "print(df[~(df['Skills'].isna() & df['Education'].isna())].shape)\n",
    "\n",
    "cv_df = df[~(df['Skills'].isna() & df['Education'].isna())].reset_index(drop=True)\n",
    "cv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bd4850ee-243f-40bb-bda6-a4328c9a9908",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Skills        76\n",
       "Education    575\n",
       "ID             0\n",
       "Category       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# New number of null values in Skills & Education Section\n",
    "cv_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4b215337-ae9a-4d8e-a290-9bc4bbb8092e",
   "metadata": {
    "tags": []
   },
   "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>Skills</th>\n",
       "      <th>Education</th>\n",
       "      <th>ID</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Bachelor of Science , Accounting Strayer Unive...</td>\n",
       "      <td>12442909</td>\n",
       "      <td>ACCOUNTANT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>University of Phoenix Masters of Business Admi...</td>\n",
       "      <td>15363277</td>\n",
       "      <td>ACCOUNTANT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Masters , Accounting University of Florida ï¼​...</td>\n",
       "      <td>21794875</td>\n",
       "      <td>ACCOUNTANT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>NaN</td>\n",
       "      <td>High School Diploma : Business Management/Acco...</td>\n",
       "      <td>27637576</td>\n",
       "      <td>ACCOUNTANT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Bachelor of Arts : Economics City College of N...</td>\n",
       "      <td>82649935</td>\n",
       "      <td>ACCOUNTANT</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>2242</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Master of Journalism : PR/Integrated Communica...</td>\n",
       "      <td>59346975</td>\n",
       "      <td>PUBLIC-RELATIONS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2245</th>\n",
       "      <td>NaN</td>\n",
       "      <td>B.A : Marketing June 2005 Berkeley College Mar...</td>\n",
       "      <td>75329822</td>\n",
       "      <td>PUBLIC-RELATIONS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2387</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Plans.\\nApplied progressive teaching principle...</td>\n",
       "      <td>17481570</td>\n",
       "      <td>TEACHER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2417</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Ph.D. : Philosophy in Education , 2019\\nNorthc...</td>\n",
       "      <td>27126818</td>\n",
       "      <td>TEACHER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2452</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Ph.D , Counseling Psychology 2012 Walden Unive...</td>\n",
       "      <td>58665241</td>\n",
       "      <td>TEACHER</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Skills                                          Education        ID  \\\n",
       "7       NaN  Bachelor of Science , Accounting Strayer Unive...  12442909   \n",
       "23      NaN  University of Phoenix Masters of Business Admi...  15363277   \n",
       "46      NaN  Masters , Accounting University of Florida ï¼​...  21794875   \n",
       "76      NaN  High School Diploma : Business Management/Acco...  27637576   \n",
       "115     NaN  Bachelor of Arts : Economics City College of N...  82649935   \n",
       "...     ...                                                ...       ...   \n",
       "2242    NaN  Master of Journalism : PR/Integrated Communica...  59346975   \n",
       "2245    NaN  B.A : Marketing June 2005 Berkeley College Mar...  75329822   \n",
       "2387    NaN  Plans.\\nApplied progressive teaching principle...  17481570   \n",
       "2417    NaN  Ph.D. : Philosophy in Education , 2019\\nNorthc...  27126818   \n",
       "2452    NaN  Ph.D , Counseling Psychology 2012 Walden Unive...  58665241   \n",
       "\n",
       "              Category  \n",
       "7           ACCOUNTANT  \n",
       "23          ACCOUNTANT  \n",
       "46          ACCOUNTANT  \n",
       "76          ACCOUNTANT  \n",
       "115         ACCOUNTANT  \n",
       "...                ...  \n",
       "2242  PUBLIC-RELATIONS  \n",
       "2245  PUBLIC-RELATIONS  \n",
       "2387           TEACHER  \n",
       "2417           TEACHER  \n",
       "2452           TEACHER  \n",
       "\n",
       "[76 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null values in Skills Section\n",
    "cv_df[cv_df.Skills.isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b5b41e30-bc4b-4d4d-a946-49dafe207994",
   "metadata": {
    "tags": []
   },
   "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>Skills</th>\n",
       "      <th>Education</th>\n",
       "      <th>ID</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Budget forecasting expertise General ledger ac...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13130984</td>\n",
       "      <td>ACCOUNTANT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Full Accounting cycle Auditing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13491889</td>\n",
       "      <td>ACCOUNTANT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>account reconciliations, Accounting, accruals,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14491649</td>\n",
       "      <td>ACCOUNTANT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Accounting, billing, Change Management, contra...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17407184</td>\n",
       "      <td>ACCOUNTANT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Analytical reasoning Effective time management</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18132924</td>\n",
       "      <td>ACCOUNTANT</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>2442</th>\n",
       "      <td>Anatomy, Arts, EMR software, front office, inj...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37402097</td>\n",
       "      <td>TEACHER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2446</th>\n",
       "      <td>Behavior modeling Troubleshooting</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45987048</td>\n",
       "      <td>TEACHER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2453</th>\n",
       "      <td>Computer literate, drivers, listening, natural...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58708773</td>\n",
       "      <td>TEACHER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2458</th>\n",
       "      <td>â€¢ Well versed with the standards of hygiene ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69005326</td>\n",
       "      <td>TEACHER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2468</th>\n",
       "      <td>Superb communication both verbal and written.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99244405</td>\n",
       "      <td>TEACHER</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>575 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Skills Education        ID  \\\n",
       "11    Budget forecasting expertise General ledger ac...       NaN  13130984   \n",
       "13                       Full Accounting cycle Auditing       NaN  13491889   \n",
       "20    account reconciliations, Accounting, accruals,...       NaN  14491649   \n",
       "29    Accounting, billing, Change Management, contra...       NaN  17407184   \n",
       "31       Analytical reasoning Effective time management       NaN  18132924   \n",
       "...                                                 ...       ...       ...   \n",
       "2442  Anatomy, Arts, EMR software, front office, inj...       NaN  37402097   \n",
       "2446                  Behavior modeling Troubleshooting       NaN  45987048   \n",
       "2453  Computer literate, drivers, listening, natural...       NaN  58708773   \n",
       "2458  â€¢ Well versed with the standards of hygiene ...       NaN  69005326   \n",
       "2468      Superb communication both verbal and written.       NaN  99244405   \n",
       "\n",
       "        Category  \n",
       "11    ACCOUNTANT  \n",
       "13    ACCOUNTANT  \n",
       "20    ACCOUNTANT  \n",
       "29    ACCOUNTANT  \n",
       "31    ACCOUNTANT  \n",
       "...          ...  \n",
       "2442     TEACHER  \n",
       "2446     TEACHER  \n",
       "2453     TEACHER  \n",
       "2458     TEACHER  \n",
       "2468     TEACHER  \n",
       "\n",
       "[575 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null values in Education Section\n",
    "cv_df[cv_df.Education.isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c41827c-6c44-412a-a905-13170bfc3ce7",
   "metadata": {},
   "source": [
    "### Distribution of Different Resumes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2f59603b-f3ff-4754-aad8-5207afffd8d8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category\n",
       "BUSINESS-DEVELOPMENT      119\n",
       "ACCOUNTANT                118\n",
       "INFORMATION-TECHNOLOGY    118\n",
       "FINANCE                   118\n",
       "CHEF                      118\n",
       "ENGINEERING               117\n",
       "AVIATION                  117\n",
       "ADVOCATE                  117\n",
       "SALES                     115\n",
       "CONSULTANT                115\n",
       "BANKING                   115\n",
       "FITNESS                   114\n",
       "HEALTHCARE                114\n",
       "CONSTRUCTION              111\n",
       "PUBLIC-RELATIONS          111\n",
       "HR                        110\n",
       "DESIGNER                  105\n",
       "ARTS                      102\n",
       "TEACHER                   102\n",
       "DIGITAL-MEDIA              96\n",
       "APPAREL                    96\n",
       "AGRICULTURE                63\n",
       "AUTOMOBILE                 36\n",
       "BPO                        22\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_df.Category.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a774fe34-5085-4571-ab0e-6f1eacba44bc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can see here the distribution of different CV categories\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "cv_df.Category.value_counts().plot(kind='barh')\n",
    "\n",
    "for index, value in enumerate(cv_df.Category.value_counts().values):\n",
    "    plt.text(value, index, str(value))\n",
    "    \n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "695760da-bc0a-4d78-b7c5-2ab6a9de4c8f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Skills        76\n",
       "Education    575\n",
       "ID             0\n",
       "Category       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccd1c447-0771-4d53-8d32-3bd08df04ff7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}