job-salaries.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3836aeb5",
   "metadata": {},
   "source": [
    "# Job salaries analysis\n",
    "\n",
    "Exploring a dataset about data jobs salaries from 2020 to 2022\n",
    "\n",
    "## Data Dictionary and Metadata\n",
    "\n",
    "Field | Type | ...\n",
    "------|------|-----\n",
    "work_year | int64 | Year of measure\n",
    "experience_level | Category | MI, SE, EN, EX] (1)\n",
    "employment_type | Category | FT, CT, PT, FL (2)\n",
    "job_title | Category | Specific knowledge area: Data Scientist, Machine Learning Scientist, Big Data Engineer, ... \n",
    "salary| int64 | Yearly salary expressed in currency (salary_currency)\n",
    "salary_currency| Category | Currency description (symbol): EUR, USD, GBP, ... (3)\n",
    "salary_in_usd | int64 | Yearly salary expressed in USD (US Dollar)\n",
    "employee_residence | Category | Location employee country (ISO code): DE, JP, GB, HN, US, ...\n",
    "remote_ratio | int64 | This feature indicates the remote relation: 0: Face-to-Face, 50: Hybrid, 100: Full-remote; or 0% remote, 50% remote, 100% remote. \n",
    "company_location | Category | Location company country (ISO code): DE, JP, GB, HN, US, ... \n",
    "company_size| Category | S, M, L (4)\n",
    "\n",
    "\n",
    "(1) Abbreviations of experience level (chatGPT):\n",
    "* MI - Managerial/Intermediate: Positions at a mid-level of experience and responsibility, which may include managerial roles or leadership responsibilities.\n",
    "* SE - Senior/Expert: Positions for individuals with significant experience and expertise in their respective roles.\n",
    "* EN - Entry-Level: Positions suitable for individuals who are at the beginning of their careers or have minimal work experience in the field.\n",
    "* EX - Executive/Executive-level: High-level leadership positions, often at the top of the organizational hierarchy, responsible for making strategic decisions and overseeing the data analysis function.\n",
    "\n",
    "*It's important to note that these abbreviations might not be universally standardized, but they align with common interpretations of experience level categories in the context of data analysis jobs. When applying for jobs or reviewing job descriptions, it's always a good idea to read the full job description to understand the specific requirements and responsibilities associated with each role.*\n",
    "\n",
    "(2) Abbreviations of employment types (chatGPT)\n",
    "\n",
    "* FT - Full-Time: Full-time positions typically involve a standard work schedule of 35-40 hours per week, with benefits and job stability.\n",
    "* CT - Contract: Contract positions involve a specific duration of employment or project-based work. Contractors are usually hired for a fixed period, and their employment ends when the contract expires.\n",
    "* PT - Part-Time: Part-time positions involve working fewer hours per week compared to full-time positions. Part-time employees may receive some benefits, but they are generally prorated based on their work hours.\n",
    "* FL - Freelance: Freelance positions are based on a project-by-project or contract basis. Freelancers work independently and are not employed by a specific company. They may have multiple clients and are responsible for managing their own taxes, benefits, and work arrangements.\n",
    "\n",
    "*These abbreviations are commonly used in job postings to indicate the type of employment being offered for each data analysis role. As with any job application, it's essential to review the full job description and terms of employment to understand the specific details and expectations associated with each position.*\n",
    "\n",
    "(3) Abbreviations of currency descriptions (chatGPT)\n",
    "\n",
    "* EUR - € (Euro) - Eurozone countries\n",
    "* USD - $ (United States Dollar) - United States\n",
    "* GBP - £ (British Pound Sterling) - United Kingdom\n",
    "* HUF - Ft (Hungarian Forint) - Hungary\n",
    "* INR - ₹ (Indian Rupee) - India\n",
    "* JPY - ¥ (Japanese Yen) - Japan\n",
    "* CNY - ¥ (Chinese Yuan Renminbi) - China\n",
    "* MXN - $ (Mexican Peso) - Mexico\n",
    "* DKK - kr (Danish Krone) - Denmark\n",
    "* PLN - zł (Polish Złoty) - Poland\n",
    "* SGD - $ (Singapore Dollar) - Singapore\n",
    "* CLP - $ (Chilean Peso) - Chile\n",
    "* BRL - R$ (Brazilian Real) - Brazil\n",
    "* TRY - ₺ (Turkish Lira) - Turkey\n",
    "* AUD - $ (Australian Dollar) - Australia\n",
    "* CHF - Fr (Swiss Franc) - Switzerland\n",
    "\n",
    "(4) Abbreviations of company size\n",
    "\n",
    "* S - Small\n",
    "* M - Medium\n",
    "* L - Large\n",
    "\n",
    "*Haven't information about number of employees for each case*\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecbfcf28",
   "metadata": {},
   "source": [
    "## Imports and configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d42cbcb2-eb62-4db7-ba8c-77f0f1a88ecc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.10.6 (main, May 29 2023, 11:10:38) [GCC 11.3.0]'"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import sys\n",
    "\n",
    "sys.version"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cde5706a",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1772eb53-2aff-4b94-a054-5f8a0f71b312",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(607, 11)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>work_year</th>\n",
       "      <th>experience_level</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>job_title</th>\n",
       "      <th>salary</th>\n",
       "      <th>salary_currency</th>\n",
       "      <th>salary_in_usd</th>\n",
       "      <th>employee_residence</th>\n",
       "      <th>remote_ratio</th>\n",
       "      <th>company_location</th>\n",
       "      <th>company_size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>70000</td>\n",
       "      <td>EUR</td>\n",
       "      <td>79833</td>\n",
       "      <td>DE</td>\n",
       "      <td>0</td>\n",
       "      <td>DE</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Machine Learning Scientist</td>\n",
       "      <td>260000</td>\n",
       "      <td>USD</td>\n",
       "      <td>260000</td>\n",
       "      <td>JP</td>\n",
       "      <td>0</td>\n",
       "      <td>JP</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Big Data Engineer</td>\n",
       "      <td>85000</td>\n",
       "      <td>GBP</td>\n",
       "      <td>109024</td>\n",
       "      <td>GB</td>\n",
       "      <td>50</td>\n",
       "      <td>GB</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>Product Data Analyst</td>\n",
       "      <td>20000</td>\n",
       "      <td>USD</td>\n",
       "      <td>20000</td>\n",
       "      <td>HN</td>\n",
       "      <td>0</td>\n",
       "      <td>HN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Machine Learning Engineer</td>\n",
       "      <td>150000</td>\n",
       "      <td>USD</td>\n",
       "      <td>150000</td>\n",
       "      <td>US</td>\n",
       "      <td>50</td>\n",
       "      <td>US</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    work_year experience_level employment_type                   job_title  \\\n",
       "id                                                                           \n",
       "0        2020               MI              FT              Data Scientist   \n",
       "1        2020               SE              FT  Machine Learning Scientist   \n",
       "2        2020               SE              FT           Big Data Engineer   \n",
       "3        2020               MI              FT        Product Data Analyst   \n",
       "4        2020               SE              FT   Machine Learning Engineer   \n",
       "\n",
       "    salary salary_currency  salary_in_usd employee_residence  remote_ratio  \\\n",
       "id                                                                           \n",
       "0    70000             EUR          79833                 DE             0   \n",
       "1   260000             USD         260000                 JP             0   \n",
       "2    85000             GBP         109024                 GB            50   \n",
       "3    20000             USD          20000                 HN             0   \n",
       "4   150000             USD         150000                 US            50   \n",
       "\n",
       "   company_location company_size  \n",
       "id                                \n",
       "0                DE            L  \n",
       "1                JP            S  \n",
       "2                GB            M  \n",
       "3                HN            S  \n",
       "4                US            L  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "salaries_df = pd.read_csv('./datasets/job_salaries.zip', compression='zip', index_col=[0])\n",
    "display(salaries_df.shape, salaries_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "cc8df28c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cast to type category\n",
    "category_features = ['experience_level', 'employment_type', 'job_title', 'salary_currency', \n",
    "                     'employee_residence', 'remote_ratio', 'company_location', 'company_size']\n",
    "\n",
    "salaries_df[category_features] = salaries_df[category_features].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1944acfc-858b-4d13-8715-e9a807d5b093",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Means by features\n",
    "features = ['work_year', 'experience_level', 'employment_type', 'company_location', 'company_size']\n",
    "\n",
    "for feature in features:\n",
    "    summary = salaries_df.groupby(feature)['salary'].mean()\n",
    "    fig, ax = plt.subplots(figsize=(12,6))\n",
    "    sns.barplot(x=summary.index, y=summary.values, ax=ax) \\\n",
    "        .set(title=f'Mean salaries by [{feature}]')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3a60e84",
   "metadata": {},
   "source": [
    "## Correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91c24f75",
   "metadata": {},
   "source": [
    "### Between experience and salaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a6b87da4",
   "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>experience_level</th>\n",
       "      <th>salary_in_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>experience_level</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.986067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary_in_usd</th>\n",
       "      <td>0.986067</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  experience_level  salary_in_usd\n",
       "experience_level          1.000000       0.986067\n",
       "salary_in_usd             0.986067       1.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# btw xp and salaries mean\n",
    "salaries_xp = salaries_df.groupby('experience_level')['salary_in_usd'].mean().reset_index()\n",
    "\n",
    "# labeled xp (numerical): in order to stablish correlations, more xp will have greater values?, then:\n",
    "xp_map = {'EN':1., 'EX':4., 'MI':2., 'SE':3.}\n",
    "salaries_xp.experience_level = salaries_xp.experience_level.map(xp_map).astype(float)\n",
    "\n",
    "# correlation\n",
    "display(salaries_xp.corr())\n",
    "\n",
    "# plot\n",
    "sns.scatterplot(x=salaries_xp.experience_level, y=salaries_xp.salary_in_usd)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e7c10813",
   "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>company_size</th>\n",
       "      <th>salary_in_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>company_size</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.889939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary_in_usd</th>\n",
       "      <td>0.889939</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               company_size  salary_in_usd\n",
       "company_size       1.000000       0.889939\n",
       "salary_in_usd      0.889939       1.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# btw company size and salaries mean\n",
    "salaries_company = salaries_df.groupby('company_size')['salary_in_usd'].mean().reset_index()\n",
    "\n",
    "# labeled size (numerical): in order to stablish correlations, more size will have greater values?, then:\n",
    "cpny_size = {'S':1., 'M':2., 'L':3.}\n",
    "salaries_company.company_size = salaries_company.company_size.map(cpny_size).astype(float)\n",
    "\n",
    "# correlation\n",
    "display(salaries_company.corr())\n",
    "\n",
    "# plot\n",
    "sns.scatterplot(x=salaries_company.company_size, y=salaries_company.salary_in_usd)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fdca8a6",
   "metadata": {},
   "source": [
    "### Work in progress ..."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "0eda1227dff56cef3366f7e157bf9b50addc429be250f8d0b3928c5997fa8f19"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}