data-salaries-in-north-america.ipynb

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    "<a id=\"0\"></a>\n",
    "# <p style=\"padding:10px;background-color:#bc4749;margin:0;color:#e9c46a;font-family:Bank Gothic MD BT;font-size:140%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">NORTH AMERICAN SALARY TRENDS IN DATA RELATED CAREERS</p>\n",
    "\n",
    "<p style=\"text-align:justify; \">\n",
    "In today's rapidly changing digital landscape, the importance of data-related jobs has become paramount. As businesses and organizations grapple with an ever-increasing volume of information, professionals adept at extracting insights from data play a pivotal role in shaping strategic decisions. Whether it's analyzing consumer behavior, optimizing operations, or predicting market trends, data-related roles such as data scientists, analysts, and engineers contribute significantly to driving innovation and fostering a competitive edge. These professionals not only transform raw data into actionable insights but also bridge the gap between unstructured information and informed decision-making. \n",
    "\n",
    "As we delve into this project, we will explore the dynamic realm of data-related jobs and their current salaries and popularity within the United States.\n",
    "</p> \n",
    "\n",
    "<a id='top'></a>\n",
    "<div class=\"list-group\" id=\"list-tab\" role=\"tablist\">\n",
    "<p style=\"padding:10px;margin:0;color:#121212;font-family:newtimeroman;font-size:130%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:700\"> Table of Contents </p>   \n",
    "  \n",
    "    \n",
    "|No  | Contents                                                        |No  | Contents                        |\n",
    "|:---| :---                                                            |:---| :----                           |\n",
    "|0   | [<font color=\"#bc4749\">Introduction </font>](#0)                |4  | [<font color=\"#bc4749\"> Recent 2 YR Trends</font>](#4)      | \n",
    "|1   | [<font color=\"#bc4749\">Data Collection & Cleaning </font>](#1)  |5  | [<font color=\"#bc4749\"> Summary of Findings</font>](#5)     |  \n",
    "|2   | [<font color=\"#bc4749\">Data Analysis </font>](#2)               |6  | [<font color=\"#bc4749\"> Interactive Summary</font>](#6)     | \n",
    "|3   | [<font color=\"#bc4749\">Statistical Analysis </font>](#3)        |7  | [<font color=\"#bc4749\"> Thank You</font>](#7)               |         \n"
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   "source": [
    "<a id=\"1\"></a>\n",
    "# <p style=\"padding:10px;background-color:#bc4749;margin:0;color:#e9c46a;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">DATA COLLECTION AND CLEANING</p>"
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   "source": [
    "# Import required python packages\n",
    "%matplotlib inline \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "import seaborn as sns\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interactive\n",
    "import plotly.express as px\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# print('Matplotlib version: ', mpl.__version__)\n",
    "# print(plt.style.available)\n",
    "mpl.style.use(['tableau-colorblind10'])\n",
    "sns.set_theme(style=\"ticks\", palette=\"colorblind\")"
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    "# Create a messagebox template\n",
    "def msgbox(text):\n",
    "    # Find the maximum line length in the text\n",
    "    max_line_length = max(len(line) for line in text.split(\"\\n\")) + 8\n",
    "\n",
    "    # Create the top border of the box\n",
    "    top_border = \"╔\" + \"═\" * (max_line_length + 2) + \"╗\"\n",
    "\n",
    "    # Create the bottom border of the box\n",
    "    bottom_border = \"╚\" + \"═\" * (max_line_length + 2) + \"╝\"\n",
    "\n",
    "    # Create the lines of text inside the box\n",
    "    text_lines = text.split(\"\\n\")\n",
    "    formatted_lines = [\"║ \" + line.center(max_line_length) + \" ║\" for line in text_lines]\n",
    "\n",
    "    # Combine all the components to create the boxed text\n",
    "    boxed_text = \"\\n\".join([top_border] + formatted_lines + [bottom_border])\n",
    "    \n",
    "    # show the output\n",
    "    print(boxed_text)"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9355 entries, 0 to 9354\n",
      "Data columns (total 12 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   work_year           9355 non-null   object\n",
      " 1   job_title           9355 non-null   object\n",
      " 2   job_category        9355 non-null   object\n",
      " 3   salary_currency     9355 non-null   object\n",
      " 4   salary              9355 non-null   object\n",
      " 5   salary_in_usd       9355 non-null   object\n",
      " 6   employee_residence  9355 non-null   object\n",
      " 7   experience_level    9355 non-null   object\n",
      " 8   employment_type     9355 non-null   object\n",
      " 9   work_setting        9355 non-null   object\n",
      " 10  company_location    9355 non-null   object\n",
      " 11  company_size        9355 non-null   object\n",
      "dtypes: object(12)\n",
      "memory usage: 877.2+ KB\n"
     ]
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>work_year</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_category</th>\n",
       "      <th>salary_currency</th>\n",
       "      <th>salary</th>\n",
       "      <th>salary_in_usd</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>2023</td>\n",
       "      <td>Applied Scientist</td>\n",
       "      <td>Data Science and Research</td>\n",
       "      <td>USD</td>\n",
       "      <td>136000</td>\n",
       "      <td>136000</td>\n",
       "      <td>United States</td>\n",
       "      <td>Senior</td>\n",
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       "      <td>In-person</td>\n",
       "      <td>United States</td>\n",
       "      <td>L</td>\n",
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       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>2023</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Data Analysis</td>\n",
       "      <td>USD</td>\n",
       "      <td>104000</td>\n",
       "      <td>104000</td>\n",
       "      <td>United States</td>\n",
       "      <td>Mid-level</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>In-person</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>9251</th>\n",
       "      <td>2021</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Data Science and Research</td>\n",
       "      <td>EUR</td>\n",
       "      <td>65720</td>\n",
       "      <td>77684</td>\n",
       "      <td>France</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>France</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8860</th>\n",
       "      <td>2022</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Data Science and Research</td>\n",
       "      <td>USD</td>\n",
       "      <td>260000</td>\n",
       "      <td>260000</td>\n",
       "      <td>United States</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Remote</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5573</th>\n",
       "      <td>2023</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Data Science and Research</td>\n",
       "      <td>USD</td>\n",
       "      <td>160000</td>\n",
       "      <td>160000</td>\n",
       "      <td>United States</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Remote</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5949</th>\n",
       "      <td>2023</td>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Data Engineering</td>\n",
       "      <td>USD</td>\n",
       "      <td>65000</td>\n",
       "      <td>65000</td>\n",
       "      <td>United States</td>\n",
       "      <td>Entry-level</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>In-person</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7864</th>\n",
       "      <td>2022</td>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Data Engineering</td>\n",
       "      <td>USD</td>\n",
       "      <td>120000</td>\n",
       "      <td>120000</td>\n",
       "      <td>United States</td>\n",
       "      <td>Mid-level</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>In-person</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>2023</td>\n",
       "      <td>Machine Learning Engineer</td>\n",
       "      <td>Machine Learning and AI</td>\n",
       "      <td>USD</td>\n",
       "      <td>100000</td>\n",
       "      <td>100000</td>\n",
       "      <td>United States</td>\n",
       "      <td>Mid-level</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>In-person</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7895</th>\n",
       "      <td>2022</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Data Science and Research</td>\n",
       "      <td>USD</td>\n",
       "      <td>129300</td>\n",
       "      <td>129300</td>\n",
       "      <td>United States</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>In-person</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7373</th>\n",
       "      <td>2023</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Data Science and Research</td>\n",
       "      <td>USD</td>\n",
       "      <td>129300</td>\n",
       "      <td>129300</td>\n",
       "      <td>United States</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>In-person</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
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      "text/plain": [
       "     work_year                  job_title               job_category  \\\n",
       "861       2023          Applied Scientist  Data Science and Research   \n",
       "859       2023               Data Analyst              Data Analysis   \n",
       "9251      2021             Data Scientist  Data Science and Research   \n",
       "8860      2022             Data Scientist  Data Science and Research   \n",
       "5573      2023             Data Scientist  Data Science and Research   \n",
       "5949      2023              Data Engineer           Data Engineering   \n",
       "7864      2022              Data Engineer           Data Engineering   \n",
       "194       2023  Machine Learning Engineer    Machine Learning and AI   \n",
       "7895      2022             Data Scientist  Data Science and Research   \n",
       "7373      2023             Data Scientist  Data Science and Research   \n",
       "\n",
       "     salary_currency  salary salary_in_usd employee_residence  \\\n",
       "861              USD  136000        136000      United States   \n",
       "859              USD  104000        104000      United States   \n",
       "9251             EUR   65720         77684             France   \n",
       "8860             USD  260000        260000      United States   \n",
       "5573             USD  160000        160000      United States   \n",
       "5949             USD   65000         65000      United States   \n",
       "7864             USD  120000        120000      United States   \n",
       "194              USD  100000        100000      United States   \n",
       "7895             USD  129300        129300      United States   \n",
       "7373             USD  129300        129300      United States   \n",
       "\n",
       "     experience_level employment_type work_setting company_location  \\\n",
       "861            Senior       Full-time    In-person    United States   \n",
       "859         Mid-level       Full-time    In-person    United States   \n",
       "9251           Senior       Full-time       Hybrid           France   \n",
       "8860           Senior       Full-time       Remote    United States   \n",
       "5573           Senior       Full-time       Remote    United States   \n",
       "5949      Entry-level       Full-time    In-person    United States   \n",
       "7864        Mid-level       Full-time    In-person    United States   \n",
       "194         Mid-level       Full-time    In-person    United States   \n",
       "7895           Senior       Full-time    In-person    United States   \n",
       "7373           Senior       Full-time    In-person    United States   \n",
       "\n",
       "     company_size  \n",
       "861             L  \n",
       "859             M  \n",
       "9251            M  \n",
       "8860            M  \n",
       "5573            M  \n",
       "5949            M  \n",
       "7864            M  \n",
       "194             M  \n",
       "7895            M  \n",
       "7373            M  "
      ]
     },
     "execution_count": 3,
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   ],
   "source": [
    "# Import jobs data\n",
    "df = pd.read_csv(\"/kaggle/input/jobs-in-data/jobs_in_data.csv\")\n",
    "df = pd.DataFrame(df, dtype=object)\n",
    "df.info()\n",
    "df.sample(10)"
   ]
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     "text": [
      "╔══════════════════════════════════════╗\n",
      "║     UNIQUE RECORDS IN DATA TABLE     ║\n",
      "╚══════════════════════════════════════╝\n",
      "----------------------------------------------------------------------------\n",
      "WORK_YEAR (distinct: 4)\n",
      "[2023 2022 2020 2021]\n",
      "----------------------------------------------------------------------------\n",
      "JOB_TITLE (distinct: 125)\n",
      "['Data DevOps Engineer' 'Data Architect' 'Data Scientist'\n",
      " 'Machine Learning Researcher' 'Data Engineer' 'Machine Learning Engineer'\n",
      " 'Data Analyst' 'Analytics Engineer' 'Applied Scientist' 'BI Developer'\n",
      " 'Business Intelligence Engineer' 'Research Scientist' 'Research Analyst'\n",
      " 'Research Engineer' 'Data Science Engineer' 'Data Quality Analyst'\n",
      " 'Data Product Manager' 'Machine Learning Scientist' 'AI Engineer'\n",
      " 'MLOps Engineer' 'Deep Learning Engineer' 'Data Modeler'\n",
      " 'Data Product Owner' 'Data Science Consultant'\n",
      " 'Business Intelligence Analyst' 'AI Developer' 'Data Manager'\n",
      " 'ML Engineer' 'Data Science Director' 'Head of Data' 'BI Analyst'\n",
      " 'Data Management Analyst' 'Machine Learning Modeler' 'Data Specialist'\n",
      " 'BI Data Analyst' 'Data Integration Engineer'\n",
      " 'Business Intelligence Manager' 'Data Integration Specialist'\n",
      " 'Data Science Practitioner' 'Business Intelligence Developer'\n",
      " 'AI Research Engineer' 'Data Lead' 'Data Management Specialist'\n",
      " 'AI Architect' 'Data Science Manager' 'Data Strategist'\n",
      " 'Business Intelligence Specialist'\n",
      " 'Machine Learning Infrastructure Engineer' 'Data Quality Engineer'\n",
      " 'Director of Data Science' 'Business Data Analyst' 'Decision Scientist'\n",
      " 'Financial Data Analyst' 'Data Strategy Manager'\n",
      " 'Computer Vision Engineer' 'Data Visualization Specialist'\n",
      " 'Insight Analyst' 'Data Visualization Engineer' 'ETL Developer'\n",
      " 'Data Analytics Manager' 'Azure Data Engineer' 'Data Developer'\n",
      " 'Principal Data Scientist' 'Data Science Lead' 'Staff Data Analyst'\n",
      " 'Data Infrastructure Engineer' 'Machine Learning Software Engineer'\n",
      " 'Machine Learning Operations Engineer' 'AI Scientist'\n",
      " 'Head of Machine Learning' 'Applied Data Scientist' 'AI Programmer'\n",
      " 'Data Operations Analyst' 'Applied Machine Learning Scientist'\n",
      " 'Data Analytics Lead' 'Data Operations Engineer'\n",
      " 'Machine Learning Manager' 'Lead Data Scientist'\n",
      " 'Principal Machine Learning Engineer' 'Principal Data Engineer'\n",
      " 'Power BI Developer' 'Head of Data Science'\n",
      " 'Staff Machine Learning Engineer' 'Staff Data Scientist'\n",
      " 'Consultant Data Engineer' 'Machine Learning Specialist'\n",
      " 'Business Intelligence Data Analyst' 'Data Operations Manager'\n",
      " 'Lead Machine Learning Engineer' 'Managing Director Data Science'\n",
      " 'Data Modeller' 'Finance Data Analyst' 'Software Data Engineer'\n",
      " 'Machine Learning Research Engineer' 'Compliance Data Analyst'\n",
      " 'Data Operations Specialist' 'Cloud Data Engineer'\n",
      " 'Analytics Engineering Manager' 'NLP Engineer' 'AWS Data Architect'\n",
      " 'Product Data Analyst' 'Machine Learning Developer'\n",
      " 'Data Visualization Analyst' 'Autonomous Vehicle Technician'\n",
      " 'Sales Data Analyst' 'Applied Machine Learning Engineer' 'ETL Engineer'\n",
      " 'Big Data Engineer' 'Lead Data Analyst' 'BI Data Engineer'\n",
      " 'Cloud Database Engineer' 'Deep Learning Researcher'\n",
      " 'Data Analytics Specialist' 'Big Data Architect'\n",
      " 'Computer Vision Software Engineer' 'Marketing Data Engineer'\n",
      " 'Manager Data Management' 'Data Analytics Consultant'\n",
      " 'Data Science Tech Lead' 'Data Scientist Lead' 'Marketing Data Analyst'\n",
      " 'Data Analytics Engineer' 'Cloud Data Architect' 'Lead Data Engineer'\n",
      " 'Principal Data Analyst']\n",
      "----------------------------------------------------------------------------\n",
      "JOB_CATEGORY (distinct: 10)\n",
      "['Data Engineering' 'Data Architecture and Modeling'\n",
      " 'Data Science and Research' 'Machine Learning and AI' 'Data Analysis'\n",
      " 'Leadership and Management' 'BI and Visualization'\n",
      " 'Data Quality and Operations' 'Data Management and Strategy'\n",
      " 'Cloud and Database']\n",
      "----------------------------------------------------------------------------\n",
      "SALARY_CURRENCY (distinct: 11)\n",
      "['EUR' 'USD' 'GBP' 'CAD' 'AUD' 'PLN' 'BRL' 'TRY' 'CHF' 'SGD' 'DKK']\n",
      "----------------------------------------------------------------------------\n",
      "SALARY (distinct: 1,507)\n",
      "[88000 186000 81800 ... 138350 423000 412000]\n",
      "----------------------------------------------------------------------------\n",
      "SALARY_IN_USD (distinct: 1,786)\n",
      "[95012 186000 81800 ... 423000 28369 412000]\n",
      "----------------------------------------------------------------------------\n",
      "EMPLOYEE_RESIDENCE (distinct: 83)\n",
      "['Germany' 'United States' 'United Kingdom' 'Canada' 'Spain' 'Ireland'\n",
      " 'South Africa' 'Poland' 'France' 'Czech Republic' 'Netherlands'\n",
      " 'Pakistan' 'Ukraine' 'Lithuania' 'Portugal' 'Australia' 'Uganda'\n",
      " 'Colombia' 'Italy' 'Slovenia' 'Romania' 'Greece' 'India' 'Latvia'\n",
      " 'Mauritius' 'Armenia' 'Croatia' 'Thailand' 'South Korea' 'Estonia'\n",
      " 'Turkey' 'Philippines' 'Brazil' 'Qatar' 'Russia' 'Kenya' 'Tunisia'\n",
      " 'Ghana' 'Belgium' 'Switzerland' 'Andorra' 'Ecuador' 'Peru' 'Mexico'\n",
      " 'Moldova' 'Nigeria' 'Saudi Arabia' 'Argentina' 'Egypt' 'Uzbekistan'\n",
      " 'Georgia' 'Central African Republic' 'Finland' 'Austria' 'Singapore'\n",
      " 'Sweden' 'Kuwait' 'Cyprus' 'Bosnia and Herzegovina' 'Iran'\n",
      " 'American Samoa' 'China' 'Costa Rica' 'Chile' 'Puerto Rico' 'Denmark'\n",
      " 'Bolivia' 'Dominican Republic' 'Indonesia' 'United Arab Emirates'\n",
      " 'Malaysia' 'Japan' 'Honduras' 'Algeria' 'Vietnam' 'Iraq' 'Bulgaria'\n",
      " 'Jersey' 'Serbia' 'New Zealand' 'Hong Kong' 'Luxembourg' 'Malta']\n",
      "----------------------------------------------------------------------------\n",
      "EXPERIENCE_LEVEL (distinct: 4)\n",
      "['Mid-level' 'Senior' 'Executive' 'Entry-level']\n",
      "----------------------------------------------------------------------------\n",
      "EMPLOYMENT_TYPE (distinct: 4)\n",
      "['Full-time' 'Part-time' 'Contract' 'Freelance']\n",
      "----------------------------------------------------------------------------\n",
      "WORK_SETTING (distinct: 3)\n",
      "['Hybrid' 'In-person' 'Remote']\n",
      "----------------------------------------------------------------------------\n",
      "COMPANY_LOCATION (distinct: 70)\n",
      "['Germany' 'United States' 'United Kingdom' 'Canada' 'Spain' 'Ireland'\n",
      " 'South Africa' 'Poland' 'France' 'Netherlands' 'Luxembourg' 'Lithuania'\n",
      " 'Portugal' 'Gibraltar' 'Australia' 'Colombia' 'Ukraine' 'Slovenia'\n",
      " 'Romania' 'Greece' 'India' 'Latvia' 'Mauritius' 'Russia' 'Italy'\n",
      " 'South Korea' 'Estonia' 'Czech Republic' 'Brazil' 'Qatar' 'Kenya'\n",
      " 'Denmark' 'Ghana' 'Sweden' 'Turkey' 'Switzerland' 'Andorra' 'Ecuador'\n",
      " 'Mexico' 'Israel' 'Nigeria' 'Saudi Arabia' 'Argentina' 'Japan'\n",
      " 'Central African Republic' 'Finland' 'Singapore' 'Croatia' 'Armenia'\n",
      " 'Bosnia and Herzegovina' 'Pakistan' 'Iran' 'Bahamas' 'Austria'\n",
      " 'Puerto Rico' 'American Samoa' 'Thailand' 'Philippines' 'Belgium' 'Egypt'\n",
      " 'Indonesia' 'United Arab Emirates' 'Malaysia' 'Honduras' 'Algeria' 'Iraq'\n",
      " 'China' 'New Zealand' 'Moldova' 'Malta']\n",
      "----------------------------------------------------------------------------\n",
      "COMPANY_SIZE (distinct: 3)\n",
      "['L' 'M' 'S']\n"
     ]
    }
   ],
   "source": [
    "# Looking at all unique data in the table\n",
    "msgbox('UNIQUE RECORDS IN DATA TABLE')\n",
    "for i in df.columns:\n",
    "    print('--'* 38)\n",
    "    print(f\"{i.upper()} (distinct: {'{:,}'.format(df[i].nunique())})\")\n",
    "    print(df[i].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "97c5f8f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-08T18:00:31.230353Z",
     "iopub.status.busy": "2024-04-08T18:00:31.229043Z",
     "iopub.status.idle": "2024-04-08T18:00:31.348386Z",
     "shell.execute_reply": "2024-04-08T18:00:31.347165Z"
    },
    "papermill": {
     "duration": 0.137099,
     "end_time": "2024-04-08T18:00:31.351500",
     "exception": false,
     "start_time": "2024-04-08T18:00:31.214401",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╔═══════════════════════════════════════════════════════════════════════════════╗\n",
      "║     86.37% OF THE DATA WAS OBTAINED FROM US BASED COMPANIES AND EMPLOYEES     ║\n",
      "╚═══════════════════════════════════════════════════════════════════════════════╝\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "}\n",
       "</style>\n",
       "<table id=\"T_ef437\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_ef437_level0_col0\" class=\"col_heading level0 col0\" >company_location</th>\n",
       "      <th id=\"T_ef437_level0_col1\" class=\"col_heading level0 col1\" >employee_residence</th>\n",
       "      <th id=\"T_ef437_level0_col2\" class=\"col_heading level0 col2\" >count</th>\n",
       "      <th id=\"T_ef437_level0_col3\" class=\"col_heading level0 col3\" >percentage_dataset_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_ef437_row0_col0\" class=\"data row0 col0\" >Australia</td>\n",
       "      <td id=\"T_ef437_row0_col1\" class=\"data row0 col1\" >United States</td>\n",
       "      <td id=\"T_ef437_row0_col2\" class=\"data row0 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row0_col3\" class=\"data row0 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_ef437_row1_col0\" class=\"data row1 col0\" >Canada</td>\n",
       "      <td id=\"T_ef437_row1_col1\" class=\"data row1 col1\" >United States</td>\n",
       "      <td id=\"T_ef437_row1_col2\" class=\"data row1 col2\" >2</td>\n",
       "      <td id=\"T_ef437_row1_col3\" class=\"data row1 col3\" >0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_ef437_row2_col0\" class=\"data row2 col0\" >France</td>\n",
       "      <td id=\"T_ef437_row2_col1\" class=\"data row2 col1\" >United States</td>\n",
       "      <td id=\"T_ef437_row2_col2\" class=\"data row2 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row2_col3\" class=\"data row2 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_ef437_row3_col0\" class=\"data row3 col0\" >Germany</td>\n",
       "      <td id=\"T_ef437_row3_col1\" class=\"data row3 col1\" >United States</td>\n",
       "      <td id=\"T_ef437_row3_col2\" class=\"data row3 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row3_col3\" class=\"data row3 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_ef437_row4_col0\" class=\"data row4 col0\" >Japan</td>\n",
       "      <td id=\"T_ef437_row4_col1\" class=\"data row4 col1\" >United States</td>\n",
       "      <td id=\"T_ef437_row4_col2\" class=\"data row4 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row4_col3\" class=\"data row4 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_ef437_row5_col0\" class=\"data row5 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row5_col1\" class=\"data row5 col1\" >Vietnam</td>\n",
       "      <td id=\"T_ef437_row5_col2\" class=\"data row5 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row5_col3\" class=\"data row5 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_ef437_row6_col0\" class=\"data row6 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row6_col1\" class=\"data row6 col1\" >Uzbekistan</td>\n",
       "      <td id=\"T_ef437_row6_col2\" class=\"data row6 col2\" >2</td>\n",
       "      <td id=\"T_ef437_row6_col3\" class=\"data row6 col3\" >0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_ef437_row7_col0\" class=\"data row7 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row7_col1\" class=\"data row7 col1\" >United States</td>\n",
       "      <td id=\"T_ef437_row7_col2\" class=\"data row7 col2\" >8080</td>\n",
       "      <td id=\"T_ef437_row7_col3\" class=\"data row7 col3\" >86.370000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_ef437_row8_col0\" class=\"data row8 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row8_col1\" class=\"data row8 col1\" >Uganda</td>\n",
       "      <td id=\"T_ef437_row8_col2\" class=\"data row8 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row8_col3\" class=\"data row8 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_ef437_row9_col0\" class=\"data row9 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row9_col1\" class=\"data row9 col1\" >Turkey</td>\n",
       "      <td id=\"T_ef437_row9_col2\" class=\"data row9 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row9_col3\" class=\"data row9 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_ef437_row10_col0\" class=\"data row10 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row10_col1\" class=\"data row10 col1\" >Tunisia</td>\n",
       "      <td id=\"T_ef437_row10_col2\" class=\"data row10 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row10_col3\" class=\"data row10 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_ef437_row11_col0\" class=\"data row11 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row11_col1\" class=\"data row11 col1\" >Thailand</td>\n",
       "      <td id=\"T_ef437_row11_col2\" class=\"data row11 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row11_col3\" class=\"data row11 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_ef437_row12_col0\" class=\"data row12 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row12_col1\" class=\"data row12 col1\" >Spain</td>\n",
       "      <td id=\"T_ef437_row12_col2\" class=\"data row12 col2\" >3</td>\n",
       "      <td id=\"T_ef437_row12_col3\" class=\"data row12 col3\" >0.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_ef437_row13_col0\" class=\"data row13 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row13_col1\" class=\"data row13 col1\" >Russia</td>\n",
       "      <td id=\"T_ef437_row13_col2\" class=\"data row13 col2\" >2</td>\n",
       "      <td id=\"T_ef437_row13_col3\" class=\"data row13 col3\" >0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_ef437_row14_col0\" class=\"data row14 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row14_col1\" class=\"data row14 col1\" >Romania</td>\n",
       "      <td id=\"T_ef437_row14_col2\" class=\"data row14 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row14_col3\" class=\"data row14 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_ef437_row15_col0\" class=\"data row15 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row15_col1\" class=\"data row15 col1\" >Puerto Rico</td>\n",
       "      <td id=\"T_ef437_row15_col2\" class=\"data row15 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row15_col3\" class=\"data row15 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_ef437_row16_col0\" class=\"data row16 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row16_col1\" class=\"data row16 col1\" >Portugal</td>\n",
       "      <td id=\"T_ef437_row16_col2\" class=\"data row16 col2\" >2</td>\n",
       "      <td id=\"T_ef437_row16_col3\" class=\"data row16 col3\" >0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_ef437_row17_col0\" class=\"data row17 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row17_col1\" class=\"data row17 col1\" >Philippines</td>\n",
       "      <td id=\"T_ef437_row17_col2\" class=\"data row17 col2\" >2</td>\n",
       "      <td id=\"T_ef437_row17_col3\" class=\"data row17 col3\" >0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_ef437_row18_col0\" class=\"data row18 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row18_col1\" class=\"data row18 col1\" >Nigeria</td>\n",
       "      <td id=\"T_ef437_row18_col2\" class=\"data row18 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row18_col3\" class=\"data row18 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_ef437_row19_col0\" class=\"data row19 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row19_col1\" class=\"data row19 col1\" >Malaysia</td>\n",
       "      <td id=\"T_ef437_row19_col2\" class=\"data row19 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row19_col3\" class=\"data row19 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_ef437_row20_col0\" class=\"data row20 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row20_col1\" class=\"data row20 col1\" >Kuwait</td>\n",
       "      <td id=\"T_ef437_row20_col2\" class=\"data row20 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row20_col3\" class=\"data row20 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_ef437_row21_col0\" class=\"data row21 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row21_col1\" class=\"data row21 col1\" >Italy</td>\n",
       "      <td id=\"T_ef437_row21_col2\" class=\"data row21 col2\" >2</td>\n",
       "      <td id=\"T_ef437_row21_col3\" class=\"data row21 col3\" >0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "      <td id=\"T_ef437_row22_col0\" class=\"data row22 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row22_col1\" class=\"data row22 col1\" >India</td>\n",
       "      <td id=\"T_ef437_row22_col2\" class=\"data row22 col2\" >5</td>\n",
       "      <td id=\"T_ef437_row22_col3\" class=\"data row22 col3\" >0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "      <td id=\"T_ef437_row23_col0\" class=\"data row23 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row23_col1\" class=\"data row23 col1\" >Greece</td>\n",
       "      <td id=\"T_ef437_row23_col2\" class=\"data row23 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row23_col3\" class=\"data row23 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "      <td id=\"T_ef437_row24_col0\" class=\"data row24 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row24_col1\" class=\"data row24 col1\" >Ghana</td>\n",
       "      <td id=\"T_ef437_row24_col2\" class=\"data row24 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row24_col3\" class=\"data row24 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "      <td id=\"T_ef437_row25_col0\" class=\"data row25 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row25_col1\" class=\"data row25 col1\" >Germany</td>\n",
       "      <td id=\"T_ef437_row25_col2\" class=\"data row25 col2\" >3</td>\n",
       "      <td id=\"T_ef437_row25_col3\" class=\"data row25 col3\" >0.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "      <td id=\"T_ef437_row26_col0\" class=\"data row26 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row26_col1\" class=\"data row26 col1\" >France</td>\n",
       "      <td id=\"T_ef437_row26_col2\" class=\"data row26 col2\" >2</td>\n",
       "      <td id=\"T_ef437_row26_col3\" class=\"data row26 col3\" >0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "      <td id=\"T_ef437_row27_col0\" class=\"data row27 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row27_col1\" class=\"data row27 col1\" >Egypt</td>\n",
       "      <td id=\"T_ef437_row27_col2\" class=\"data row27 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row27_col3\" class=\"data row27 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "      <td id=\"T_ef437_row28_col0\" class=\"data row28 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row28_col1\" class=\"data row28 col1\" >Costa Rica</td>\n",
       "      <td id=\"T_ef437_row28_col2\" class=\"data row28 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row28_col3\" class=\"data row28 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "      <td id=\"T_ef437_row29_col0\" class=\"data row29 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row29_col1\" class=\"data row29 col1\" >China</td>\n",
       "      <td id=\"T_ef437_row29_col2\" class=\"data row29 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row29_col3\" class=\"data row29 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "      <td id=\"T_ef437_row30_col0\" class=\"data row30 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row30_col1\" class=\"data row30 col1\" >Chile</td>\n",
       "      <td id=\"T_ef437_row30_col2\" class=\"data row30 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row30_col3\" class=\"data row30 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "      <td id=\"T_ef437_row31_col0\" class=\"data row31 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row31_col1\" class=\"data row31 col1\" >Canada</td>\n",
       "      <td id=\"T_ef437_row31_col2\" class=\"data row31 col2\" >3</td>\n",
       "      <td id=\"T_ef437_row31_col3\" class=\"data row31 col3\" >0.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "      <td id=\"T_ef437_row32_col0\" class=\"data row32 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row32_col1\" class=\"data row32 col1\" >Bulgaria</td>\n",
       "      <td id=\"T_ef437_row32_col2\" class=\"data row32 col2\" >1</td>\n",
       "      <td id=\"T_ef437_row32_col3\" class=\"data row32 col3\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "      <td id=\"T_ef437_row33_col0\" class=\"data row33 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row33_col1\" class=\"data row33 col1\" >Brazil</td>\n",
       "      <td id=\"T_ef437_row33_col2\" class=\"data row33 col2\" >2</td>\n",
       "      <td id=\"T_ef437_row33_col3\" class=\"data row33 col3\" >0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "      <td id=\"T_ef437_row34_col0\" class=\"data row34 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row34_col1\" class=\"data row34 col1\" >Bolivia</td>\n",
       "      <td id=\"T_ef437_row34_col2\" class=\"data row34 col2\" >2</td>\n",
       "      <td id=\"T_ef437_row34_col3\" class=\"data row34 col3\" >0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "      <td id=\"T_ef437_row35_col0\" class=\"data row35 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row35_col1\" class=\"data row35 col1\" >Belgium</td>\n",
       "      <td id=\"T_ef437_row35_col2\" class=\"data row35 col2\" >2</td>\n",
       "      <td id=\"T_ef437_row35_col3\" class=\"data row35 col3\" >0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ef437_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "      <td id=\"T_ef437_row36_col0\" class=\"data row36 col0\" >United States</td>\n",
       "      <td id=\"T_ef437_row36_col1\" class=\"data row36 col1\" >Argentina</td>\n",
       "      <td id=\"T_ef437_row36_col2\" class=\"data row36 col2\" >3</td>\n",
       "      <td id=\"T_ef437_row36_col3\" class=\"data row36 col3\" >0.030000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7e22540c5810>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Collecting the number of records obtained from each location and their weight on the overall data\n",
    "df_loc_counts = pd.DataFrame(df[['company_location', 'employee_residence']].value_counts())\n",
    "df_loc_counts['percentage_dataset_total'] = round(((df_loc_counts['count']/df_loc_counts.values.sum())*100), 2)\n",
    "\n",
    "# Exploring United States related data in the dataset\n",
    "req = 'United States'\n",
    "us_counts = df_loc_counts.reset_index().query(\"company_location==@req or employee_residence==@req\"\n",
    "                ).sort_values(by=['company_location', 'employee_residence'], ascending=[True, False]\n",
    "                ).reset_index(drop=True)\n",
    "def highlight(x):\n",
    "    if x.percentage_dataset_total > 50:\n",
    "        return['background-color:mistyrose']*4\n",
    "    else:\n",
    "        return['background-color:default']*4\n",
    "\n",
    "msgbox(f\"{df_loc_counts['percentage_dataset_total'].values[0]}% of the data was obtained from US based companies and employees\".upper())\n",
    "us_counts.style.apply(highlight, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b83a1cc0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-08T18:00:31.381747Z",
     "iopub.status.busy": "2024-04-08T18:00:31.381098Z",
     "iopub.status.idle": "2024-04-08T18:00:31.429652Z",
     "shell.execute_reply": "2024-04-08T18:00:31.428377Z"
    },
    "papermill": {
     "duration": 0.066705,
     "end_time": "2024-04-08T18:00:31.432230",
     "exception": false,
     "start_time": "2024-04-08T18:00:31.365525",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 8080 entries, 1 to 9354\n",
      "Data columns (total 7 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   year              8080 non-null   object\n",
      " 1   job_title         8080 non-null   object\n",
      " 2   job_category      8080 non-null   object\n",
      " 3   salary            8080 non-null   int64 \n",
      " 4   experience_level  8080 non-null   object\n",
      " 5   employment_type   8080 non-null   object\n",
      " 6   work_setting      8080 non-null   object\n",
      "dtypes: int64(1), object(6)\n",
      "memory usage: 505.0+ KB\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>year</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_category</th>\n",
       "      <th>salary</th>\n",
       "      <th>experience_level</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>work_setting</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023</td>\n",
       "      <td>Data Architect</td>\n",
       "      <td>Data Architecture and Modeling</td>\n",
       "      <td>186000</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>In-person</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023</td>\n",
       "      <td>Data Architect</td>\n",
       "      <td>Data Architecture and Modeling</td>\n",
       "      <td>81800</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>In-person</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Data Science and Research</td>\n",
       "      <td>212000</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>In-person</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Data Science and Research</td>\n",
       "      <td>93300</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>In-person</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2023</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Data Science and Research</td>\n",
       "      <td>130000</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Remote</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9350</th>\n",
       "      <td>2021</td>\n",
       "      <td>Data Specialist</td>\n",
       "      <td>Data Management and Strategy</td>\n",
       "      <td>165000</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Remote</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9351</th>\n",
       "      <td>2020</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Data Science and Research</td>\n",
       "      <td>412000</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Remote</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9352</th>\n",
       "      <td>2021</td>\n",
       "      <td>Principal Data Scientist</td>\n",
       "      <td>Data Science and Research</td>\n",
       "      <td>151000</td>\n",
       "      <td>Mid-level</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Remote</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9353</th>\n",
       "      <td>2020</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Data Science and Research</td>\n",
       "      <td>105000</td>\n",
       "      <td>Entry-level</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Remote</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9354</th>\n",
       "      <td>2020</td>\n",
       "      <td>Business Data Analyst</td>\n",
       "      <td>Data Analysis</td>\n",
       "      <td>100000</td>\n",
       "      <td>Entry-level</td>\n",
       "      <td>Contract</td>\n",
       "      <td>Remote</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8080 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year                 job_title                    job_category  salary  \\\n",
       "1     2023            Data Architect  Data Architecture and Modeling  186000   \n",
       "2     2023            Data Architect  Data Architecture and Modeling   81800   \n",
       "3     2023            Data Scientist       Data Science and Research  212000   \n",
       "4     2023            Data Scientist       Data Science and Research   93300   \n",
       "5     2023            Data Scientist       Data Science and Research  130000   \n",
       "...    ...                       ...                             ...     ...   \n",
       "9350  2021           Data Specialist    Data Management and Strategy  165000   \n",
       "9351  2020            Data Scientist       Data Science and Research  412000   \n",
       "9352  2021  Principal Data Scientist       Data Science and Research  151000   \n",
       "9353  2020            Data Scientist       Data Science and Research  105000   \n",
       "9354  2020     Business Data Analyst                   Data Analysis  100000   \n",
       "\n",
       "     experience_level employment_type work_setting  \n",
       "1              Senior       Full-time    In-person  \n",
       "2              Senior       Full-time    In-person  \n",
       "3              Senior       Full-time    In-person  \n",
       "4              Senior       Full-time    In-person  \n",
       "5              Senior       Full-time       Remote  \n",
       "...               ...             ...          ...  \n",
       "9350           Senior       Full-time       Remote  \n",
       "9351           Senior       Full-time       Remote  \n",
       "9352        Mid-level       Full-time       Remote  \n",
       "9353      Entry-level       Full-time       Remote  \n",
       "9354      Entry-level        Contract       Remote  \n",
       "\n",
       "[8080 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# filter company and employee locations to United States only\n",
    "US_data = df[df['company_location'] == req]\n",
    "US_data = US_data[US_data['employee_residence'] == req]\n",
    "\n",
    "#remove 'company_size' and other repetitive columns\n",
    "US_data.drop(columns={'company_size','salary_currency','salary_in_usd'\n",
    "                      , 'employee_residence', 'company_location'}, inplace=True)\n",
    "\n",
    "# changed name of column for clarity, year - the year in which the data was collected\n",
    "US_data.rename(columns={'work_year':'year'}, inplace=True)\n",
    "\n",
    "# changed the data type of salary column from object to integer\n",
    "US_data['salary'] = US_data['salary'].astype(int)\n",
    "\n",
    "US_data.info()\n",
    "display(US_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8311c46a",
   "metadata": {
    "papermill": {
     "duration": 0.015057,
     "end_time": "2024-04-08T18:00:31.461997",
     "exception": false,
     "start_time": "2024-04-08T18:00:31.446940",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id=\"2\"></a>\n",
    "# <p style=\"padding:10px;background-color:#bc4749;margin:0;color:#e9c46a;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">EXPLORATORY DATA ANALYSIS</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d3766db4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-08T18:00:31.496716Z",
     "iopub.status.busy": "2024-04-08T18:00:31.496221Z",
     "iopub.status.idle": "2024-04-08T18:00:31.514244Z",
     "shell.execute_reply": "2024-04-08T18:00:31.513046Z"
    },
    "papermill": {
     "duration": 0.039146,
     "end_time": "2024-04-08T18:00:31.517195",
     "exception": false,
     "start_time": "2024-04-08T18:00:31.478049",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;4mDistinct Records in Dataset\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "year                   4\n",
       "job_title            101\n",
       "job_category          10\n",
       "salary              1360\n",
       "experience_level       4\n",
       "employment_type        3\n",
       "work_setting           3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting a count of the distinct records in each column\n",
    "print(f\"\\033[1;4mDistinct Records in Dataset\\033[0m\")\n",
    "US_data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7a2ec072",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-08T18:00:31.552683Z",
     "iopub.status.busy": "2024-04-08T18:00:31.551913Z",
     "iopub.status.idle": "2024-04-08T18:00:32.171081Z",
     "shell.execute_reply": "2024-04-08T18:00:32.169830Z"
    },
    "papermill": {
     "duration": 0.639857,
     "end_time": "2024-04-08T18:00:32.173714",
     "exception": false,
     "start_time": "2024-04-08T18:00:31.533857",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Collecting the number of records in each available year\n",
    "US_rec_counts = pd.DataFrame(US_data['year'].value_counts())\n",
    "US_rec_counts[\"% of dataset\"] = round((US_rec_counts['count']/US_rec_counts.values.sum())*100,2)\n",
    "\n",
    "#calculate the % of data obtained in most recent years\n",
    "k=round(US_rec_counts[\"% of dataset\"].values[0] + US_rec_counts[\"% of dataset\"].values[1], 2)\n",
    "\n",
    "plt.figure()\n",
    "plt.subplot(1,2,1)\n",
    "US_rec_counts[\"% of dataset\"].plot(marker=\"o\", ylim=(0,100))\n",
    "plt.ylabel(\"Total Data Collected\".upper(), loc='bottom')\n",
    "plt.gca().yaxis.set_major_formatter(PercentFormatter())\n",
    "plt.xlabel(\"year\".upper(), loc='left')\n",
    "\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.axis('off')\n",
    "plt.table(cellText=US_rec_counts.reset_index().values,\n",
    "          colLabels=US_rec_counts.reset_index().columns,\n",
    "          cellLoc='center', rowLoc='center',\n",
    "          loc='center', edges='open')\n",
    "\n",
    "plt.title(f\"{k}% of the data was obtained in the past 2 years\", loc='right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0310fcbd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-08T18:00:32.207938Z",
     "iopub.status.busy": "2024-04-08T18:00:32.207133Z",
     "iopub.status.idle": "2024-04-08T18:00:32.235436Z",
     "shell.execute_reply": "2024-04-08T18:00:32.234369Z"
    },
    "papermill": {
     "duration": 0.048126,
     "end_time": "2024-04-08T18:00:32.238104",
     "exception": false,
     "start_time": "2024-04-08T18:00:32.189978",
     "status": "completed"
    },
    "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>job_category</th>\n",
       "      <th>BI and Visualization</th>\n",
       "      <th>Cloud and Database</th>\n",
       "      <th>Data Analysis</th>\n",
       "      <th>Data Architecture and Modeling</th>\n",
       "      <th>Data Engineering</th>\n",
       "      <th>Data Management and Strategy</th>\n",
       "      <th>Data Quality and Operations</th>\n",
       "      <th>Data Science and Research</th>\n",
       "      <th>Leadership and Management</th>\n",
       "      <th>Machine Learning and AI</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</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>2020</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>239</td>\n",
       "      <td>44</td>\n",
       "      <td>417</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>406</td>\n",
       "      <td>65</td>\n",
       "      <td>124</td>\n",
       "      <td>1323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>284</td>\n",
       "      <td>3</td>\n",
       "      <td>975</td>\n",
       "      <td>189</td>\n",
       "      <td>1526</td>\n",
       "      <td>44</td>\n",
       "      <td>38</td>\n",
       "      <td>2179</td>\n",
       "      <td>369</td>\n",
       "      <td>1035</td>\n",
       "      <td>6642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "job_category  BI and Visualization  Cloud and Database  Data Analysis  \\\n",
       "year                                                                    \n",
       "2020                             0                   0              8   \n",
       "2021                             0                   0             16   \n",
       "2022                             4                   2            239   \n",
       "2023                           284                   3            975   \n",
       "\n",
       "job_category  Data Architecture and Modeling  Data Engineering  \\\n",
       "year                                                             \n",
       "2020                                       0                 4   \n",
       "2021                                       4                22   \n",
       "2022                                      44               417   \n",
       "2023                                     189              1526   \n",
       "\n",
       "job_category  Data Management and Strategy  Data Quality and Operations  \\\n",
       "year                                                                      \n",
       "2020                                     0                            0   \n",
       "2021                                     1                            0   \n",
       "2022                                    10                           12   \n",
       "2023                                    44                           38   \n",
       "\n",
       "job_category  Data Science and Research  Leadership and Management  \\\n",
       "year                                                                 \n",
       "2020                                 13                          0   \n",
       "2021                                 27                          5   \n",
       "2022                                406                         65   \n",
       "2023                               2179                        369   \n",
       "\n",
       "job_category  Machine Learning and AI  Total  \n",
       "year                                          \n",
       "2020                                3     28  \n",
       "2021                               12     87  \n",
       "2022                              124   1323  \n",
       "2023                             1035   6642  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Collecting count of job categories surveyed by year\n",
    "jcat2 = US_data.groupby(['year'])['job_category'].value_counts().unstack().fillna(0).astype(int)\n",
    "jcat2['Total'] = jcat2.sum(axis=1)\n",
    "jcat2"
   ]
  },
  {
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   "id": "dda9db50",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_category</th>\n",
       "      <th>BI and Visualization</th>\n",
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       "    <tr>\n",
       "      <th>Remote</th>\n",
       "      <td>0</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">2021</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Remote</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
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       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2022</th>\n",
       "      <th>Hybrid</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>In-person</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>10</td>\n",
       "      <td>222</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>164</td>\n",
       "      <td>24</td>\n",
       "      <td>53</td>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Remote</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>34</td>\n",
       "      <td>195</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>237</td>\n",
       "      <td>40</td>\n",
       "      <td>68</td>\n",
       "      <td>711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2023</th>\n",
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       "    <tr>\n",
       "      <th>In-person</th>\n",
       "      <td>210</td>\n",
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       "      <td>552</td>\n",
       "      <td>102</td>\n",
       "      <td>1079</td>\n",
       "      <td>26</td>\n",
       "      <td>28</td>\n",
       "      <td>1508</td>\n",
       "      <td>268</td>\n",
       "      <td>780</td>\n",
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       "      <td>74</td>\n",
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       "      <td>669</td>\n",
       "      <td>101</td>\n",
       "      <td>253</td>\n",
       "      <td>2081</td>\n",
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      ],
      "text/plain": [
       "job_category       BI and Visualization  Cloud and Database  Data Analysis  \\\n",
       "year work_setting                                                            \n",
       "2020 Hybrid                           0                   0              0   \n",
       "     In-person                        0                   0              2   \n",
       "     Remote                           0                   0              6   \n",
       "2021 Hybrid                           0                   0              1   \n",
       "     In-person                        0                   0              2   \n",
       "     Remote                           0                   0             13   \n",
       "2022 Hybrid                           1                   0              4   \n",
       "     In-person                        0                   0            115   \n",
       "     Remote                           3                   2            120   \n",
       "2023 Hybrid                           0                   0              4   \n",
       "     In-person                      210                   0            552   \n",
       "     Remote                          74                   3            419   \n",
       "\n",
       "job_category       Data Architecture and Modeling  Data Engineering  \\\n",
       "year work_setting                                                     \n",
       "2020 Hybrid                                     0                 0   \n",
       "     In-person                                  0                 0   \n",
       "     Remote                                     0                 4   \n",
       "2021 Hybrid                                     1                 0   \n",
       "     In-person                                  0                 5   \n",
       "     Remote                                     3                17   \n",
       "2022 Hybrid                                     0                 0   \n",
       "     In-person                                 10               222   \n",
       "     Remote                                    34               195   \n",
       "2023 Hybrid                                     0                 0   \n",
       "     In-person                                102              1079   \n",
       "     Remote                                    87               447   \n",
       "\n",
       "job_category       Data Management and Strategy  Data Quality and Operations  \\\n",
       "year work_setting                                                              \n",
       "2020 Hybrid                                   0                            0   \n",
       "     In-person                                0                            0   \n",
       "     Remote                                   0                            0   \n",
       "2021 Hybrid                                   0                            0   \n",
       "     In-person                                0                            0   \n",
       "     Remote                                   1                            0   \n",
       "2022 Hybrid                                   0                            0   \n",
       "     In-person                                6                            4   \n",
       "     Remote                                   4                            8   \n",
       "2023 Hybrid                                   0                            0   \n",
       "     In-person                               26                           28   \n",
       "     Remote                                  18                           10   \n",
       "\n",
       "job_category       Data Science and Research  Leadership and Management  \\\n",
       "year work_setting                                                         \n",
       "2020 Hybrid                                2                          0   \n",
       "     In-person                             1                          0   \n",
       "     Remote                               10                          0   \n",
       "2021 Hybrid                                5                          0   \n",
       "     In-person                             7                          1   \n",
       "     Remote                               15                          4   \n",
       "2022 Hybrid                                5                          1   \n",
       "     In-person                           164                         24   \n",
       "     Remote                              237                         40   \n",
       "2023 Hybrid                                2                          0   \n",
       "     In-person                          1508                        268   \n",
       "     Remote                              669                        101   \n",
       "\n",
       "job_category       Machine Learning and AI  Total  \n",
       "year work_setting                                  \n",
       "2020 Hybrid                              2      4  \n",
       "     In-person                           0      3  \n",
       "     Remote                              1     21  \n",
       "2021 Hybrid                              5     12  \n",
       "     In-person                           0     15  \n",
       "     Remote                              7     60  \n",
       "2022 Hybrid                              3     14  \n",
       "     In-person                          53    598  \n",
       "     Remote                             68    711  \n",
       "2023 Hybrid                              2      8  \n",
       "     In-person                         780   4553  \n",
       "     Remote                            253   2081  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Collecting count of job categories surveyed by year and work setting\n",
    "jcat2_1 = US_data.groupby(['year', 'work_setting'])['job_category'].value_counts().unstack().fillna(0).astype(int)\n",
    "jcat2_1['Total']=jcat2_1.sum(axis=1)\n",
    "\n",
    "jcat2_1"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
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       "      <th>Executive</th>\n",
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       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">2021</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">Hybrid</th>\n",
       "      <th>Entry-level</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mid-level</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">In-person</th>\n",
       "      <th>Entry-level</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executive</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mid-level</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Remote</th>\n",
       "      <th>Entry-level</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executive</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mid-level</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">2022</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">Hybrid</th>\n",
       "      <th>Entry-level</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mid-level</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">In-person</th>\n",
       "      <th>Entry-level</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executive</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mid-level</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>10</td>\n",
       "      <td>180</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>138</td>\n",
       "      <td>14</td>\n",
       "      <td>37</td>\n",
       "      <td>456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Remote</th>\n",
       "      <th>Entry-level</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executive</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mid-level</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>90</td>\n",
       "      <td>34</td>\n",
       "      <td>158</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>26</td>\n",
       "      <td>49</td>\n",
       "      <td>568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">2023</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Hybrid</th>\n",
       "      <th>Entry-level</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mid-level</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">In-person</th>\n",
       "      <th>Entry-level</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executive</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mid-level</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>149</td>\n",
       "      <td>14</td>\n",
       "      <td>207</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>223</td>\n",
       "      <td>78</td>\n",
       "      <td>94</td>\n",
       "      <td>815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior</th>\n",
       "      <td>166</td>\n",
       "      <td>0</td>\n",
       "      <td>352</td>\n",
       "      <td>86</td>\n",
       "      <td>776</td>\n",
       "      <td>14</td>\n",
       "      <td>22</td>\n",
       "      <td>1188</td>\n",
       "      <td>160</td>\n",
       "      <td>674</td>\n",
       "      <td>3438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Remote</th>\n",
       "      <th>Entry-level</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executive</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mid-level</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>9</td>\n",
       "      <td>69</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>79</td>\n",
       "      <td>15</td>\n",
       "      <td>32</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior</th>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>304</td>\n",
       "      <td>78</td>\n",
       "      <td>337</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>554</td>\n",
       "      <td>73</td>\n",
       "      <td>213</td>\n",
       "      <td>1632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "job_category                        BI and Visualization  Cloud and Database  \\\n",
       "year work_setting experience_level                                             \n",
       "2020 Hybrid       Entry-level                          0                   0   \n",
       "                  Senior                               0                   0   \n",
       "     In-person    Mid-level                            0                   0   \n",
       "     Remote       Entry-level                          0                   0   \n",
       "                  Executive                            0                   0   \n",
       "                  Mid-level                            0                   0   \n",
       "                  Senior                               0                   0   \n",
       "2021 Hybrid       Entry-level                          0                   0   \n",
       "                  Mid-level                            0                   0   \n",
       "                  Senior                               0                   0   \n",
       "     In-person    Entry-level                          0                   0   \n",
       "                  Executive                            0                   0   \n",
       "                  Mid-level                            0                   0   \n",
       "                  Senior                               0                   0   \n",
       "     Remote       Entry-level                          0                   0   \n",
       "                  Executive                            0                   0   \n",
       "                  Mid-level                            0                   0   \n",
       "                  Senior                               0                   0   \n",
       "2022 Hybrid       Entry-level                          1                   0   \n",
       "                  Mid-level                            0                   0   \n",
       "                  Senior                               0                   0   \n",
       "     In-person    Entry-level                          0                   0   \n",
       "                  Executive                            0                   0   \n",
       "                  Mid-level                            0                   0   \n",
       "                  Senior                               0                   0   \n",
       "     Remote       Entry-level                          0                   0   \n",
       "                  Executive                            0                   0   \n",
       "                  Mid-level                            0                   0   \n",
       "                  Senior                               3                   2   \n",
       "2023 Hybrid       Entry-level                          0                   0   \n",
       "                  Mid-level                            0                   0   \n",
       "     In-person    Entry-level                          6                   0   \n",
       "                  Executive                            6                   0   \n",
       "                  Mid-level                           32                   0   \n",
       "                  Senior                             166                   0   \n",
       "     Remote       Entry-level                          0                   0   \n",
       "                  Executive                            0                   0   \n",
       "                  Mid-level                           14                   0   \n",
       "                  Senior                              60                   3   \n",
       "\n",
       "job_category                        Data Analysis  \\\n",
       "year work_setting experience_level                  \n",
       "2020 Hybrid       Entry-level                   0   \n",
       "                  Senior                        0   \n",
       "     In-person    Mid-level                     2   \n",
       "     Remote       Entry-level                   3   \n",
       "                  Executive                     0   \n",
       "                  Mid-level                     3   \n",
       "                  Senior                        0   \n",
       "2021 Hybrid       Entry-level                   1   \n",
       "                  Mid-level                     0   \n",
       "                  Senior                        0   \n",
       "     In-person    Entry-level                   0   \n",
       "                  Executive                     0   \n",
       "                  Mid-level                     2   \n",
       "                  Senior                        0   \n",
       "     Remote       Entry-level                   4   \n",
       "                  Executive                     0   \n",
       "                  Mid-level                     5   \n",
       "                  Senior                        4   \n",
       "2022 Hybrid       Entry-level                   4   \n",
       "                  Mid-level                     0   \n",
       "                  Senior                        0   \n",
       "     In-person    Entry-level                   2   \n",
       "                  Executive                     0   \n",
       "                  Mid-level                    44   \n",
       "                  Senior                       69   \n",
       "     Remote       Entry-level                   7   \n",
       "                  Executive                     2   \n",
       "                  Mid-level                    21   \n",
       "                  Senior                       90   \n",
       "2023 Hybrid       Entry-level                   3   \n",
       "                  Mid-level                     1   \n",
       "     In-person    Entry-level                  45   \n",
       "                  Executive                     6   \n",
       "                  Mid-level                   149   \n",
       "                  Senior                      352   \n",
       "     Remote       Entry-level                  31   \n",
       "                  Executive                     6   \n",
       "                  Mid-level                    78   \n",
       "                  Senior                      304   \n",
       "\n",
       "job_category                        Data Architecture and Modeling  \\\n",
       "year work_setting experience_level                                   \n",
       "2020 Hybrid       Entry-level                                    0   \n",
       "                  Senior                                         0   \n",
       "     In-person    Mid-level                                      0   \n",
       "     Remote       Entry-level                                    0   \n",
       "                  Executive                                      0   \n",
       "                  Mid-level                                      0   \n",
       "                  Senior                                         0   \n",
       "2021 Hybrid       Entry-level                                    0   \n",
       "                  Mid-level                                      0   \n",
       "                  Senior                                         1   \n",
       "     In-person    Entry-level                                    0   \n",
       "                  Executive                                      0   \n",
       "                  Mid-level                                      0   \n",
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       "                  Mid-level                                      3   \n",
       "                  Senior                                         0   \n",
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       "                  Mid-level                                      0   \n",
       "                  Senior                                         0   \n",
       "     In-person    Entry-level                                    0   \n",
       "                  Executive                                      0   \n",
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       "\n",
       "job_category                        Data Engineering  \\\n",
       "year work_setting experience_level                     \n",
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       "     In-person    Entry-level                     14   \n",
       "                  Executive                        2   \n",
       "                  Mid-level                       26   \n",
       "                  Senior                         180   \n",
       "     Remote       Entry-level                      5   \n",
       "                  Executive                       12   \n",
       "                  Mid-level                       20   \n",
       "                  Senior                         158   \n",
       "2023 Hybrid       Entry-level                      0   \n",
       "                  Mid-level                        0   \n",
       "     In-person    Entry-level                     40   \n",
       "                  Executive                       56   \n",
       "                  Mid-level                      207   \n",
       "                  Senior                         776   \n",
       "     Remote       Entry-level                     11   \n",
       "                  Executive                       30   \n",
       "                  Mid-level                       69   \n",
       "                  Senior                         337   \n",
       "\n",
       "job_category                        Data Management and Strategy  \\\n",
       "year work_setting experience_level                                 \n",
       "2020 Hybrid       Entry-level                                  0   \n",
       "                  Senior                                       0   \n",
       "     In-person    Mid-level                                    0   \n",
       "     Remote       Entry-level                                  0   \n",
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       "                  Mid-level                                    0   \n",
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       "                  Mid-level                                    0   \n",
       "                  Senior                                       0   \n",
       "     In-person    Entry-level                                  0   \n",
       "                  Executive                                    0   \n",
       "                  Mid-level                                    0   \n",
       "                  Senior                                       0   \n",
       "     Remote       Entry-level                                  0   \n",
       "                  Executive                                    0   \n",
       "                  Mid-level                                    0   \n",
       "                  Senior                                       1   \n",
       "2022 Hybrid       Entry-level                                  0   \n",
       "                  Mid-level                                    0   \n",
       "                  Senior                                       0   \n",
       "     In-person    Entry-level                                  0   \n",
       "                  Executive                                    0   \n",
       "                  Mid-level                                    2   \n",
       "                  Senior                                       4   \n",
       "     Remote       Entry-level                                  0   \n",
       "                  Executive                                    0   \n",
       "                  Mid-level                                    0   \n",
       "                  Senior                                       4   \n",
       "2023 Hybrid       Entry-level                                  0   \n",
       "                  Mid-level                                    0   \n",
       "     In-person    Entry-level                                  0   \n",
       "                  Executive                                    0   \n",
       "                  Mid-level                                   12   \n",
       "                  Senior                                      14   \n",
       "     Remote       Entry-level                                  8   \n",
       "                  Executive                                    0   \n",
       "                  Mid-level                                    6   \n",
       "                  Senior                                       4   \n",
       "\n",
       "job_category                        Data Quality and Operations  \\\n",
       "year work_setting experience_level                                \n",
       "2020 Hybrid       Entry-level                                 0   \n",
       "                  Senior                                      0   \n",
       "     In-person    Mid-level                                   0   \n",
       "     Remote       Entry-level                                 0   \n",
       "                  Executive                                   0   \n",
       "                  Mid-level                                   0   \n",
       "                  Senior                                      0   \n",
       "2021 Hybrid       Entry-level                                 0   \n",
       "                  Mid-level                                   0   \n",
       "                  Senior                                      0   \n",
       "     In-person    Entry-level                                 0   \n",
       "                  Executive                                   0   \n",
       "                  Mid-level                                   0   \n",
       "                  Senior                                      0   \n",
       "     Remote       Entry-level                                 0   \n",
       "                  Executive                                   0   \n",
       "                  Mid-level                                   0   \n",
       "                  Senior                                      0   \n",
       "2022 Hybrid       Entry-level                                 0   \n",
       "                  Mid-level                                   0   \n",
       "                  Senior                                      0   \n",
       "     In-person    Entry-level                                 0   \n",
       "                  Executive                                   0   \n",
       "                  Mid-level                                   0   \n",
       "                  Senior                                      4   \n",
       "     Remote       Entry-level                                 0   \n",
       "                  Executive                                   0   \n",
       "                  Mid-level                                   6   \n",
       "                  Senior                                      2   \n",
       "2023 Hybrid       Entry-level                                 0   \n",
       "                  Mid-level                                   0   \n",
       "     In-person    Entry-level                                 0   \n",
       "                  Executive                                   0   \n",
       "                  Mid-level                                   6   \n",
       "                  Senior                                     22   \n",
       "     Remote       Entry-level                                 0   \n",
       "                  Executive                                   0   \n",
       "                  Mid-level                                   4   \n",
       "                  Senior                                      6   \n",
       "\n",
       "job_category                        Data Science and Research  \\\n",
       "year work_setting experience_level                              \n",
       "2020 Hybrid       Entry-level                               0   \n",
       "                  Senior                                    2   \n",
       "     In-person    Mid-level                                 1   \n",
       "     Remote       Entry-level                               1   \n",
       "                  Executive                                 2   \n",
       "                  Mid-level                                 4   \n",
       "                  Senior                                    3   \n",
       "2021 Hybrid       Entry-level                               1   \n",
       "                  Mid-level                                 4   \n",
       "                  Senior                                    0   \n",
       "     In-person    Entry-level                               1   \n",
       "                  Executive                                 1   \n",
       "                  Mid-level                                 2   \n",
       "                  Senior                                    3   \n",
       "     Remote       Entry-level                               4   \n",
       "                  Executive                                 1   \n",
       "                  Mid-level                                 5   \n",
       "                  Senior                                    5   \n",
       "2022 Hybrid       Entry-level                               0   \n",
       "                  Mid-level                                 2   \n",
       "                  Senior                                    3   \n",
       "     In-person    Entry-level                               8   \n",
       "                  Executive                                 4   \n",
       "                  Mid-level                                14   \n",
       "                  Senior                                  138   \n",
       "     Remote       Entry-level                               7   \n",
       "                  Executive                                 4   \n",
       "                  Mid-level                                26   \n",
       "                  Senior                                  200   \n",
       "2023 Hybrid       Entry-level                               2   \n",
       "                  Mid-level                                 0   \n",
       "     In-person    Entry-level                              63   \n",
       "                  Executive                                34   \n",
       "                  Mid-level                               223   \n",
       "                  Senior                                 1188   \n",
       "     Remote       Entry-level                              20   \n",
       "                  Executive                                16   \n",
       "                  Mid-level                                79   \n",
       "                  Senior                                  554   \n",
       "\n",
       "job_category                        Leadership and Management  \\\n",
       "year work_setting experience_level                              \n",
       "2020 Hybrid       Entry-level                               0   \n",
       "                  Senior                                    0   \n",
       "     In-person    Mid-level                                 0   \n",
       "     Remote       Entry-level                               0   \n",
       "                  Executive                                 0   \n",
       "                  Mid-level                                 0   \n",
       "                  Senior                                    0   \n",
       "2021 Hybrid       Entry-level                               0   \n",
       "                  Mid-level                                 0   \n",
       "                  Senior                                    0   \n",
       "     In-person    Entry-level                               0   \n",
       "                  Executive                                 0   \n",
       "                  Mid-level                                 0   \n",
       "                  Senior                                    1   \n",
       "     Remote       Entry-level                               0   \n",
       "                  Executive                                 1   \n",
       "                  Mid-level                                 1   \n",
       "                  Senior                                    2   \n",
       "2022 Hybrid       Entry-level                               1   \n",
       "                  Mid-level                                 0   \n",
       "                  Senior                                    0   \n",
       "     In-person    Entry-level                               0   \n",
       "                  Executive                                 2   \n",
       "                  Mid-level                                 8   \n",
       "                  Senior                                   14   \n",
       "     Remote       Entry-level                               2   \n",
       "                  Executive                                 6   \n",
       "                  Mid-level                                 6   \n",
       "                  Senior                                   26   \n",
       "2023 Hybrid       Entry-level                               0   \n",
       "                  Mid-level                                 0   \n",
       "     In-person    Entry-level                               2   \n",
       "                  Executive                                28   \n",
       "                  Mid-level                                78   \n",
       "                  Senior                                  160   \n",
       "     Remote       Entry-level                               2   \n",
       "                  Executive                                11   \n",
       "                  Mid-level                                15   \n",
       "                  Senior                                   73   \n",
       "\n",
       "job_category                        Machine Learning and AI  Total  \n",
       "year work_setting experience_level                                  \n",
       "2020 Hybrid       Entry-level                             1      1  \n",
       "                  Senior                                  1      3  \n",
       "     In-person    Mid-level                               0      3  \n",
       "     Remote       Entry-level                             1      6  \n",
       "                  Executive                               0      2  \n",
       "                  Mid-level                               0      9  \n",
       "                  Senior                                  0      4  \n",
       "2021 Hybrid       Entry-level                             1      3  \n",
       "                  Mid-level                               1      5  \n",
       "                  Senior                                  3      4  \n",
       "     In-person    Entry-level                             0      1  \n",
       "                  Executive                               0      1  \n",
       "                  Mid-level                               0      6  \n",
       "                  Senior                                  0      7  \n",
       "     Remote       Entry-level                             3     13  \n",
       "                  Executive                               0      2  \n",
       "                  Mid-level                               1     22  \n",
       "                  Senior                                  3     23  \n",
       "2022 Hybrid       Entry-level                             1      7  \n",
       "                  Mid-level                               2      4  \n",
       "                  Senior                                  0      3  \n",
       "     In-person    Entry-level                             5     29  \n",
       "                  Executive                               0      8  \n",
       "                  Mid-level                              11    105  \n",
       "                  Senior                                 37    456  \n",
       "     Remote       Entry-level                             2     23  \n",
       "                  Executive                               3     27  \n",
       "                  Mid-level                              14     93  \n",
       "                  Senior                                 49    568  \n",
       "2023 Hybrid       Entry-level                             0      5  \n",
       "                  Mid-level                               2      3  \n",
       "     In-person    Entry-level                            10    166  \n",
       "                  Executive                               2    134  \n",
       "                  Mid-level                              94    815  \n",
       "                  Senior                                674   3438  \n",
       "     Remote       Entry-level                             1     73  \n",
       "                  Executive                               7     70  \n",
       "                  Mid-level                              32    306  \n",
       "                  Senior                                213   1632  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Collecting count of job categories surveyed by year, work setting and experience level\n",
    "jcat2_2 = US_data.groupby(['year', 'work_setting', 'experience_level'])['job_category'\n",
    "                        ].value_counts().unstack().fillna(0).astype(int)\n",
    "jcat2_2['Total']=jcat2_2.sum(axis=1)\n",
    "\n",
    "jcat2_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "802d2687",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2024-04-08T18:00:32.425072Z",
     "iopub.status.busy": "2024-04-08T18:00:32.423923Z",
     "iopub.status.idle": "2024-04-08T18:00:34.807487Z",
     "shell.execute_reply": "2024-04-08T18:00:34.806182Z"
    },
    "papermill": {
     "duration": 2.406052,
     "end_time": "2024-04-08T18:00:34.810448",
     "exception": false,
     "start_time": "2024-04-08T18:00:32.404396",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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   ],
   "source": [
    "# Flattened Jcat2_2 table to extract records\n",
    "flattened_table = pd.DataFrame(jcat2_2.to_records())\n",
    "flattened_table.columns = ['year', 'work_setting', 'experience_level'] + list(jcat2_2.columns)\n",
    "flattened_table['year'] = flattened_table['year'].astype(object)\n",
    "\n",
    "# Create chart\n",
    "fig = px.sunburst(flattened_table, path=['year', 'work_setting', 'experience_level']\n",
    "                  , color='year', values=jcat2_2['Total']\n",
    "                  , color_discrete_map={2020:'176d9c', 2021:'c38820', 2022:'158b6a', 2023:'b4600b'}\n",
    "                  , title='Sunburst Chart Showing Count of United States Jobs Surveyed by Year, Work Setting and Experience Level')\n",
    "fig.update_layout(title={'y':.9,'x':0.5,'xanchor':'center','yanchor':'top'})\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf72f6c9",
   "metadata": {
    "papermill": {
     "duration": 0.018765,
     "end_time": "2024-04-08T18:00:34.848499",
     "exception": false,
     "start_time": "2024-04-08T18:00:34.829734",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id=\"3\"></a>\n",
    "# <p style=\"padding:10px;background-color:#bc4749;margin:0;color:#e9c46a;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">STATISTICAL ANALYSIS</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "07845d58",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2024-04-08T18:00:34.888945Z",
     "iopub.status.busy": "2024-04-08T18:00:34.888539Z",
     "iopub.status.idle": "2024-04-08T18:00:34.893809Z",
     "shell.execute_reply": "2024-04-08T18:00:34.892661Z"
    },
    "papermill": {
     "duration": 0.028731,
     "end_time": "2024-04-08T18:00:34.896419",
     "exception": false,
     "start_time": "2024-04-08T18:00:34.867688",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define global variable to change the order of 'experience level' column\n",
    "exp_order='Entry-level', 'Mid-level', 'Senior', 'Executive'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0dac25d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-08T18:00:34.937068Z",
     "iopub.status.busy": "2024-04-08T18:00:34.936619Z",
     "iopub.status.idle": "2024-04-08T18:00:37.082583Z",
     "shell.execute_reply": "2024-04-08T18:00:37.081322Z"
    },
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     "duration": 2.170639,
     "end_time": "2024-04-08T18:00:37.086393",
     "exception": false,
     "start_time": "2024-04-08T18:00:34.915754",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Getting statistical averages for each year's data\n",
    "# Encode data for correlation\n",
    "label_encoder = LabelEncoder()\n",
    "US_data[\"experience\"] = label_encoder.fit_transform(US_data['experience_level'])\n",
    "US_data[\"work setting\"] = label_encoder.fit_transform(US_data['work_setting'])\n",
    "US_data[\"emp. type\"] = label_encoder.fit_transform(US_data['employment_type'])\n",
    "\n",
    "# overall average\n",
    "corr_matrix = US_data[['salary', \"experience\", \"work setting\", \"emp. type\"]].corr()\n",
    "plt.title('Overall Salary Correlation in Data')\n",
    "sns.heatmap(corr_matrix, annot = True, cmap ='copper')\n",
    "plt.show()\n",
    "\n",
    "# Averages by year\n",
    "plt.figure(figsize=(15,15))\n",
    "\n",
    "plt.subplot(221)\n",
    "year1 = US_data[US_data['year']==US_data['year'].min()]\n",
    "corr_matrix1 = year1[['salary', \"experience\", \"work setting\", \"emp. type\"]].corr()\n",
    "plt.title('Salary Correlation in ' + str(int(year1['year'].unique())))\n",
    "sns.heatmap(corr_matrix1, annot = True, cmap ='copper')\n",
    "\n",
    "plt.subplot(222)\n",
    "year2 = US_data[US_data['year']==US_data['year'].min()+1]\n",
    "corr_matrix2 = year2[['salary', \"experience\", \"work setting\", \"emp. type\"]].corr()\n",
    "plt.title('Salary Correlation in ' + str(int(year2['year'].unique())))\n",
    "sns.heatmap(corr_matrix2, annot = True, cmap ='copper')\n",
    "\n",
    "plt.subplot(223)\n",
    "year3 = US_data[US_data['year']==US_data['year'].min()+2]\n",
    "corr_matrix3 = year3[['salary', \"experience\", \"work setting\", \"emp. type\"]].corr()\n",
    "plt.title('Salary Correlation in ' + str(int(year3['year'].unique())))\n",
    "sns.heatmap(corr_matrix3, annot = True, cmap ='copper')\n",
    "\n",
    "plt.subplot(224)\n",
    "year4 = US_data[US_data['year']==US_data['year'].min()+3]\n",
    "corr_matrix4 = year4[['salary', \"experience\", \"work setting\", \"emp. type\"]].corr()\n",
    "plt.title('Salary Correlation in ' + str(int(year4['year'].unique())))\n",
    "sns.heatmap(corr_matrix4, annot = True, cmap ='copper')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aaf47f18",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-08T18:00:37.139875Z",
     "iopub.status.busy": "2024-04-08T18:00:37.138609Z",
     "iopub.status.idle": "2024-04-08T18:00:37.214393Z",
     "shell.execute_reply": "2024-04-08T18:00:37.212990Z"
    },
    "papermill": {
     "duration": 0.104299,
     "end_time": "2024-04-08T18:00:37.217490",
     "exception": false,
     "start_time": "2024-04-08T18:00:37.113191",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╔══════════════════════════════════════╗\n",
      "║     OVERALL STATISTICAL AVERAGES     ║\n",
      "╚══════════════════════════════════════╝\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>year</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_category</th>\n",
       "      <th>salary</th>\n",
       "      <th>experience_level</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>work_setting</th>\n",
       "      <th>experience</th>\n",
       "      <th>work setting</th>\n",
       "      <th>emp. type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8080.0</td>\n",
       "      <td>8080</td>\n",
       "      <td>8080</td>\n",
       "      <td>8080.000000</td>\n",
       "      <td>8080</td>\n",
       "      <td>8080</td>\n",
       "      <td>8080</td>\n",
       "      <td>8080.000000</td>\n",
       "      <td>8080.000000</td>\n",
       "      <td>8080.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>4.0</td>\n",
       "      <td>101</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2023.0</td>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Data Science and Research</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>In-person</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>6642.0</td>\n",
       "      <td>1933</td>\n",
       "      <td>2625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6138</td>\n",
       "      <td>8067</td>\n",
       "      <td>5169</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>158598.940223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.648515</td>\n",
       "      <td>1.350866</td>\n",
       "      <td>0.999134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59228.911481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.728853</td>\n",
       "      <td>0.487026</td>\n",
       "      <td>0.040104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>117875.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>450000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          year      job_title               job_category         salary  \\\n",
       "count   8080.0           8080                       8080    8080.000000   \n",
       "unique     4.0            101                         10            NaN   \n",
       "top     2023.0  Data Engineer  Data Science and Research            NaN   \n",
       "freq    6642.0           1933                       2625            NaN   \n",
       "mean       NaN            NaN                        NaN  158598.940223   \n",
       "std        NaN            NaN                        NaN   59228.911481   \n",
       "min        NaN            NaN                        NaN   24000.000000   \n",
       "25%        NaN            NaN                        NaN  117875.000000   \n",
       "50%        NaN            NaN                        NaN  150000.000000   \n",
       "75%        NaN            NaN                        NaN  192000.000000   \n",
       "max        NaN            NaN                        NaN  450000.000000   \n",
       "\n",
       "       experience_level employment_type work_setting   experience  \\\n",
       "count              8080            8080         8080  8080.000000   \n",
       "unique                4               3            3          NaN   \n",
       "top              Senior       Full-time    In-person          NaN   \n",
       "freq               6138            8067         5169          NaN   \n",
       "mean                NaN             NaN          NaN     2.648515   \n",
       "std                 NaN             NaN          NaN     0.728853   \n",
       "min                 NaN             NaN          NaN     0.000000   \n",
       "25%                 NaN             NaN          NaN     3.000000   \n",
       "50%                 NaN             NaN          NaN     3.000000   \n",
       "75%                 NaN             NaN          NaN     3.000000   \n",
       "max                 NaN             NaN          NaN     3.000000   \n",
       "\n",
       "        work setting    emp. type  \n",
       "count    8080.000000  8080.000000  \n",
       "unique           NaN          NaN  \n",
       "top              NaN          NaN  \n",
       "freq             NaN          NaN  \n",
       "mean        1.350866     0.999134  \n",
       "std         0.487026     0.040104  \n",
       "min         0.000000     0.000000  \n",
       "25%         1.000000     1.000000  \n",
       "50%         1.000000     1.000000  \n",
       "75%         2.000000     1.000000  \n",
       "max         2.000000     2.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╔═════════════════════════════╗\n",
      "║     OVERALL CORRELATION     ║\n",
      "╚═════════════════════════════╝\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "      <th>experience</th>\n",
       "      <th>work setting</th>\n",
       "      <th>emp. type</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.209958</td>\n",
       "      <td>-0.045917</td>\n",
       "      <td>-0.003590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experience</th>\n",
       "      <td>0.209958</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.027708</td>\n",
       "      <td>0.006520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>work setting</th>\n",
       "      <td>-0.045917</td>\n",
       "      <td>0.027708</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.028796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp. type</th>\n",
       "      <td>-0.003590</td>\n",
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       "      <td>-0.028796</td>\n",
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      ],
      "text/plain": [
       "                salary  experience  work setting  emp. type\n",
       "salary        1.000000    0.209958     -0.045917  -0.003590\n",
       "experience    0.209958    1.000000      0.027708   0.006520\n",
       "work setting -0.045917    0.027708      1.000000  -0.028796\n",
       "emp. type    -0.003590    0.006520     -0.028796   1.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical averages for total dataset\n",
    "stats = US_data.describe(include='all')\n",
    "msgbox(f'overall statistical averages'.upper())\n",
    "display(stats)\n",
    "msgbox(f'overall correlation'.upper())\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e0e54a9b",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╔═══════════════════════════════════╗\n",
      "║     2020 STATISTICAL AVERAGES     ║\n",
      "╚═══════════════════════════════════╝\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_category</th>\n",
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       "    <tr>\n",
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       "      <td>2020.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Full-time</td>\n",
       "      <td>Remote</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>28.0</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>160555.357143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.678571</td>\n",
       "      <td>1.607143</td>\n",
       "      <td>0.964286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99650.709740</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.123934</td>\n",
       "      <td>0.737327</td>\n",
       "      <td>0.188982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.750000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>188500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.250000</td>\n",
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       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>450000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          year       job_title               job_category         salary  \\\n",
       "count     28.0              28                         28      28.000000   \n",
       "unique     1.0              15                          4            NaN   \n",
       "top     2020.0  Data Scientist  Data Science and Research            NaN   \n",
       "freq      28.0               6                         13            NaN   \n",
       "mean       NaN             NaN                        NaN  160555.357143   \n",
       "std        NaN             NaN                        NaN   99650.709740   \n",
       "min        NaN             NaN                        NaN   70000.000000   \n",
       "25%        NaN             NaN                        NaN   99500.000000   \n",
       "50%        NaN             NaN                        NaN  119000.000000   \n",
       "75%        NaN             NaN                        NaN  188500.000000   \n",
       "max        NaN             NaN                        NaN  450000.000000   \n",
       "\n",
       "       experience_level employment_type work_setting  experience  \\\n",
       "count                28              28           28   28.000000   \n",
       "unique                4               2            3         NaN   \n",
       "top           Mid-level       Full-time       Remote         NaN   \n",
       "freq                 12              27           21         NaN   \n",
       "mean                NaN             NaN          NaN    1.678571   \n",
       "std                 NaN             NaN          NaN    1.123934   \n",
       "min                 NaN             NaN          NaN    0.000000   \n",
       "25%                 NaN             NaN          NaN    0.750000   \n",
       "50%                 NaN             NaN          NaN    2.000000   \n",
       "75%                 NaN             NaN          NaN    2.250000   \n",
       "max                 NaN             NaN          NaN    3.000000   \n",
       "\n",
       "        work setting  emp. type  \n",
       "count      28.000000  28.000000  \n",
       "unique           NaN        NaN  \n",
       "top              NaN        NaN  \n",
       "freq             NaN        NaN  \n",
       "mean        1.607143   0.964286  \n",
       "std         0.737327   0.188982  \n",
       "min         0.000000   0.000000  \n",
       "25%         1.750000   1.000000  \n",
       "50%         2.000000   1.000000  \n",
       "75%         2.000000   1.000000  \n",
       "max         2.000000   1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╔══════════════════════════╗\n",
      "║     2020 CORRELATION     ║\n",
      "╚══════════════════════════╝\n"
     ]
    },
    {
     "data": {
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       "                salary  experience  work setting  emp. type\n",
       "salary        1.000000    0.192524     -0.123444   0.119093\n",
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      ]
     },
     "execution_count": 16,
     "metadata": {},
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    }
   ],
   "source": [
    "# statistical averages for first year of data collected\n",
    "y1_stats = year1.describe(include='all')\n",
    "msgbox(f'{int(y1_stats[\"year\"][2])} statistical averages'.upper())\n",
    "display(y1_stats)\n",
    "msgbox(f'{int(y1_stats[\"year\"][2])} correlation'.upper())\n",
    "corr_matrix1"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╔═══════════════════════════════════╗\n",
      "║     2021 STATISTICAL AVERAGES     ║\n",
      "╚═══════════════════════════════════╝\n"
     ]
    },
    {
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       "      <td>87.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1.0</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2021.0</td>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Data Science and Research</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Remote</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>87.0</td>\n",
       "      <td>19</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34</td>\n",
       "      <td>84</td>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147206.034483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.965517</td>\n",
       "      <td>1.551724</td>\n",
       "      <td>0.965517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69393.337087</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.104413</td>\n",
       "      <td>0.727510</td>\n",
       "      <td>0.183523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96575.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>182500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>423000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          year      job_title               job_category         salary  \\\n",
       "count     87.0             87                         87      87.000000   \n",
       "unique     1.0             28                          7            NaN   \n",
       "top     2021.0  Data Engineer  Data Science and Research            NaN   \n",
       "freq      87.0             19                         27            NaN   \n",
       "mean       NaN            NaN                        NaN  147206.034483   \n",
       "std        NaN            NaN                        NaN   69393.337087   \n",
       "min        NaN            NaN                        NaN   50000.000000   \n",
       "25%        NaN            NaN                        NaN   96575.000000   \n",
       "50%        NaN            NaN                        NaN  140000.000000   \n",
       "75%        NaN            NaN                        NaN  182500.000000   \n",
       "max        NaN            NaN                        NaN  423000.000000   \n",
       "\n",
       "       experience_level employment_type work_setting  experience  \\\n",
       "count                87              87           87   87.000000   \n",
       "unique                4               2            3         NaN   \n",
       "top              Senior       Full-time       Remote         NaN   \n",
       "freq                 34              84           60         NaN   \n",
       "mean                NaN             NaN          NaN    1.965517   \n",
       "std                 NaN             NaN          NaN    1.104413   \n",
       "min                 NaN             NaN          NaN    0.000000   \n",
       "25%                 NaN             NaN          NaN    2.000000   \n",
       "50%                 NaN             NaN          NaN    2.000000   \n",
       "75%                 NaN             NaN          NaN    3.000000   \n",
       "max                 NaN             NaN          NaN    3.000000   \n",
       "\n",
       "        work setting  emp. type  \n",
       "count      87.000000  87.000000  \n",
       "unique           NaN        NaN  \n",
       "top              NaN        NaN  \n",
       "freq             NaN        NaN  \n",
       "mean        1.551724   0.965517  \n",
       "std         0.727510   0.183523  \n",
       "min         0.000000   0.000000  \n",
       "25%         1.000000   1.000000  \n",
       "50%         2.000000   1.000000  \n",
       "75%         2.000000   1.000000  \n",
       "max         2.000000   1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╔══════════════════════════╗\n",
      "║     2021 CORRELATION     ║\n",
      "╚══════════════════════════╝\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "      <th></th>\n",
       "      <th>salary</th>\n",
       "      <th>experience</th>\n",
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       "      <th>emp. type</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.372423</td>\n",
       "      <td>-0.035906</td>\n",
       "      <td>-0.319002</td>\n",
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       "      <th>experience</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                salary  experience  work setting  emp. type\n",
       "salary        1.000000    0.372423     -0.035906  -0.319002\n",
       "experience    0.372423    1.000000     -0.019462  -0.005935\n",
       "work setting -0.035906   -0.019462      1.000000  -0.117122\n",
       "emp. type    -0.319002   -0.005935     -0.117122   1.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical averages for second year of data collected\n",
    "y2_stats = year2.describe(include='all')\n",
    "msgbox(f'{int(y2_stats[\"year\"][2])} statistical averages'.upper())\n",
    "display(y2_stats)\n",
    "msgbox(f'{int(y2_stats[\"year\"][2])} correlation'.upper())\n",
    "corr_matrix2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5d335f5a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-08T18:00:37.582232Z",
     "iopub.status.busy": "2024-04-08T18:00:37.581760Z",
     "iopub.status.idle": "2024-04-08T18:00:37.639686Z",
     "shell.execute_reply": "2024-04-08T18:00:37.638347Z"
    },
    "papermill": {
     "duration": 0.172936,
     "end_time": "2024-04-08T18:00:37.642461",
     "exception": false,
     "start_time": "2024-04-08T18:00:37.469525",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╔═══════════════════════════════════╗\n",
      "║     2022 STATISTICAL AVERAGES     ║\n",
      "╚═══════════════════════════════════╝\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>year</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_category</th>\n",
       "      <th>salary</th>\n",
       "      <th>experience_level</th>\n",
       "      <th>employment_type</th>\n",
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       "      <th>experience</th>\n",
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       "      <th>emp. type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1323.0</td>\n",
       "      <td>1323</td>\n",
       "      <td>1323</td>\n",
       "      <td>1323.000000</td>\n",
       "      <td>1323</td>\n",
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       "      <td>1323.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2022.0</td>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Data Engineering</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Remote</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1323.0</td>\n",
       "      <td>409</td>\n",
       "      <td>417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1027</td>\n",
       "      <td>1321</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>148294.352230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.660620</td>\n",
       "      <td>1.526833</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50922.812594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.738307</td>\n",
       "      <td>0.520239</td>\n",
       "      <td>0.038895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114450.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>max</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          year      job_title      job_category         salary  \\\n",
       "count   1323.0           1323              1323    1323.000000   \n",
       "unique     1.0             47                10            NaN   \n",
       "top     2022.0  Data Engineer  Data Engineering            NaN   \n",
       "freq    1323.0            409               417            NaN   \n",
       "mean       NaN            NaN               NaN  148294.352230   \n",
       "std        NaN            NaN               NaN   50922.812594   \n",
       "min        NaN            NaN               NaN   24000.000000   \n",
       "25%        NaN            NaN               NaN  114450.000000   \n",
       "50%        NaN            NaN               NaN  142127.000000   \n",
       "75%        NaN            NaN               NaN  180000.000000   \n",
       "max        NaN            NaN               NaN  405000.000000   \n",
       "\n",
       "       experience_level employment_type work_setting   experience  \\\n",
       "count              1323            1323         1323  1323.000000   \n",
       "unique                4               3            3          NaN   \n",
       "top              Senior       Full-time       Remote          NaN   \n",
       "freq               1027            1321          711          NaN   \n",
       "mean                NaN             NaN          NaN     2.660620   \n",
       "std                 NaN             NaN          NaN     0.738307   \n",
       "min                 NaN             NaN          NaN     0.000000   \n",
       "25%                 NaN             NaN          NaN     3.000000   \n",
       "50%                 NaN             NaN          NaN     3.000000   \n",
       "75%                 NaN             NaN          NaN     3.000000   \n",
       "max                 NaN             NaN          NaN     3.000000   \n",
       "\n",
       "        work setting    emp. type  \n",
       "count    1323.000000  1323.000000  \n",
       "unique           NaN          NaN  \n",
       "top              NaN          NaN  \n",
       "freq             NaN          NaN  \n",
       "mean        1.526833     1.000000  \n",
       "std         0.520239     0.038895  \n",
       "min         0.000000     0.000000  \n",
       "25%         1.000000     1.000000  \n",
       "50%         2.000000     1.000000  \n",
       "75%         2.000000     1.000000  \n",
       "max         2.000000     2.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╔══════════════════════════╗\n",
      "║     2022 CORRELATION     ║\n",
      "╚══════════════════════════╝\n"
     ]
    },
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       "      <td>0.059631</td>\n",
       "      <td>0.089701</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-5.008090e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp. type</th>\n",
       "      <td>-0.021265</td>\n",
       "      <td>-0.079023</td>\n",
       "      <td>-5.008090e-15</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                salary  experience  work setting     emp. type\n",
       "salary        1.000000    0.172163  5.963068e-02 -2.126455e-02\n",
       "experience    0.172163    1.000000  8.970115e-02 -7.902302e-02\n",
       "work setting  0.059631    0.089701  1.000000e+00 -5.008090e-15\n",
       "emp. type    -0.021265   -0.079023 -5.008090e-15  1.000000e+00"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical averages for third year of data collected\n",
    "y3_stats = year3.describe(include='all')\n",
    "msgbox(f'{int(y3_stats[\"year\"][2])} statistical averages'.upper())\n",
    "display(y3_stats)\n",
    "msgbox(f'{int(y3_stats[\"year\"][2])} correlation'.upper())\n",
    "corr_matrix3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c3ddbc30",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-08T18:00:37.702427Z",
     "iopub.status.busy": "2024-04-08T18:00:37.701926Z",
     "iopub.status.idle": "2024-04-08T18:00:37.764660Z",
     "shell.execute_reply": "2024-04-08T18:00:37.763390Z"
    },
    "papermill": {
     "duration": 0.095632,
     "end_time": "2024-04-08T18:00:37.768366",
     "exception": false,
     "start_time": "2024-04-08T18:00:37.672734",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╔═══════════════════════════════════╗\n",
      "║     2023 STATISTICAL AVERAGES     ║\n",
      "╚═══════════════════════════════════╝\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\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>year</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_category</th>\n",
       "      <th>salary</th>\n",
       "      <th>experience_level</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>work_setting</th>\n",
       "      <th>experience</th>\n",
       "      <th>work setting</th>\n",
       "      <th>emp. type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6642.0</td>\n",
       "      <td>6642</td>\n",
       "      <td>6642</td>\n",
       "      <td>6642.000000</td>\n",
       "      <td>6642</td>\n",
       "      <td>6642</td>\n",
       "      <td>6642</td>\n",
       "      <td>6642.000000</td>\n",
       "      <td>6642.000000</td>\n",
       "      <td>6642.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1.0</td>\n",
       "      <td>79</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2023.0</td>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Data Science and Research</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>In-person</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>6642.0</td>\n",
       "      <td>1502</td>\n",
       "      <td>2179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5070</td>\n",
       "      <td>6635</td>\n",
       "      <td>4553</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>160792.462210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.659139</td>\n",
       "      <td>1.312105</td>\n",
       "      <td>0.999548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60175.222882</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.711754</td>\n",
       "      <td>0.465980</td>\n",
       "      <td>0.032463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>151600.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>405000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          year      job_title               job_category         salary  \\\n",
       "count   6642.0           6642                       6642    6642.000000   \n",
       "unique     1.0             79                         10            NaN   \n",
       "top     2023.0  Data Engineer  Data Science and Research            NaN   \n",
       "freq    6642.0           1502                       2179            NaN   \n",
       "mean       NaN            NaN                        NaN  160792.462210   \n",
       "std        NaN            NaN                        NaN   60175.222882   \n",
       "min        NaN            NaN                        NaN   24000.000000   \n",
       "25%        NaN            NaN                        NaN  120000.000000   \n",
       "50%        NaN            NaN                        NaN  151600.000000   \n",
       "75%        NaN            NaN                        NaN  198200.000000   \n",
       "max        NaN            NaN                        NaN  405000.000000   \n",
       "\n",
       "       experience_level employment_type work_setting   experience  \\\n",
       "count              6642            6642         6642  6642.000000   \n",
       "unique                4               3            3          NaN   \n",
       "top              Senior       Full-time    In-person          NaN   \n",
       "freq               5070            6635         4553          NaN   \n",
       "mean                NaN             NaN          NaN     2.659139   \n",
       "std                 NaN             NaN          NaN     0.711754   \n",
       "min                 NaN             NaN          NaN     0.000000   \n",
       "25%                 NaN             NaN          NaN     3.000000   \n",
       "50%                 NaN             NaN          NaN     3.000000   \n",
       "75%                 NaN             NaN          NaN     3.000000   \n",
       "max                 NaN             NaN          NaN     3.000000   \n",
       "\n",
       "        work setting    emp. type  \n",
       "count    6642.000000  6642.000000  \n",
       "unique           NaN          NaN  \n",
       "top              NaN          NaN  \n",
       "freq             NaN          NaN  \n",
       "mean        1.312105     0.999548  \n",
       "std         0.465980     0.032463  \n",
       "min         0.000000     0.000000  \n",
       "25%         1.000000     1.000000  \n",
       "50%         1.000000     1.000000  \n",
       "75%         2.000000     1.000000  \n",
       "max         2.000000     2.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╔══════════════════════════╗\n",
      "║     2023 CORRELATION     ║\n",
      "╚══════════════════════════╝\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>salary</th>\n",
       "      <th>experience</th>\n",
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       "      <th>emp. type</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.214525</td>\n",
       "      <td>-0.049619</td>\n",
       "      <td>0.019269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experience</th>\n",
       "      <td>0.214525</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.027062</td>\n",
       "      <td>-0.000147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>work setting</th>\n",
       "      <td>-0.049619</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>-0.020542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp. type</th>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                salary  experience  work setting  emp. type\n",
       "salary        1.000000    0.214525     -0.049619   0.019269\n",
       "experience    0.214525    1.000000      0.027062  -0.000147\n",
       "work setting -0.049619    0.027062      1.000000  -0.020542\n",
       "emp. type     0.019269   -0.000147     -0.020542   1.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical averages for fourth year of data collected\n",
    "y4_stats = year4.describe(include='all')\n",
    "msgbox(f'{int(y4_stats[\"year\"][2])} statistical averages'.upper())\n",
    "display(y4_stats)\n",
    "msgbox(f'{int(y4_stats[\"year\"][2])} correlation'.upper())\n",
    "corr_matrix4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "66d655b3",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fetching the median salary by job category and work setting\n",
    "jcat = US_data.groupby(['job_category', 'work_setting'])['salary'].median().unstack().fillna(\n",
    "    0).sort_index(ascending=False)\n",
    "\n",
    "# creating horizantal bar chart\n",
    "jcat.plot.barh(title='Median United States Salary across Data-Job Categories'.title(), legend=True)\n",
    "plt.legend(title='Work Setting', bbox_to_anchor=(1.05, 1), loc='upper left', reverse=True)\n",
    "plt.grid(axis='x', linestyle='--', alpha=0.4)\n",
    "plt.xlabel('MEDIAN SALARY')\n",
    "plt.ylabel('JOB CATEGORY')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "133f6633",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-08T18:00:38.522871Z",
     "iopub.status.busy": "2024-04-08T18:00:38.522418Z",
     "iopub.status.idle": "2024-04-08T18:00:39.709800Z",
     "shell.execute_reply": "2024-04-08T18:00:39.708534Z"
    },
    "papermill": {
     "duration": 1.223143,
     "end_time": "2024-04-08T18:00:39.713007",
     "exception": false,
     "start_time": "2024-04-08T18:00:38.489864",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qbFdTXRUKhVbavn37BC8vL+F///ufOm3jxo066/TXX38JnTt3Fj755BON9N9//13o0qWLOv2XX34RvLy8hPT09Brfky6rV68WvLy8BF9fX2HatGnCJ598Ivz0009a21VUVGgcc0EQhMLCQiEoKEhYuHChRvqDx0PXccjKyhK8vLyEvXv3qtP27NkjeHl5CePHjxcqKio0tl+1apXQtWtXoaioSJ1269YtoUuXLjqPfVWq/Y4YMUIoKytTpycmJgpeXl7C4cOHBUEQBKVSKfTp00eYN2+exuvlcrng7e0t5OTk1FhOVFSU4OXlpfMRERGhVZ+IiAihsrJSnT5u3DjB29tbWLJkiTqtoqJC6NOnj8Z36+rVq4KXl5fg4+Mj3LhxQ51+7tw5wcvLS4iLi1Onqb63Kr/++qvg5eUlvPnmmxp1j4+PF7y8vITMzEy9j4WXl5fQtWtXjc/vjh07BC8vL6FXr17CnTt31OmrVq3S+KxXVlYKAwYM0DoWCoVCePbZZwWZTKb1Xh78vM2aNUvw9/fXSPP19RWioqIEfaWnpwteXl7C999/r0577bXXhODgYI3v/s8//yx4eXkJe/bsUadNnDhR8PLyEpKTk9Vp9+7dE4YNGyYEBgaqP3tfffWV0KlTJ41zgCAIwvbt2wUvLy/hhx9+0LveZF3YnrI9ZXtqmPZUEP5tb77++mvh7NmzQufOnYW3335bYxvVdy4yMlIjPTY2VvDy8hJ+/fVXQRDq/jkRhH/P+du3b9eq/8SJEzW+w/qc91XflStXrqjTVG331q1b1WkLFiwQOnXqJJw/f16rfFV7mpCQIPj6+grZ2dka+e+//77QuXNn4dq1a1qvffB9DBo0qNr84uJioUePHsKiRYs00vPy8oSnn35aI33cuHHCiBEjNLZT9VtUn119+gSqz2Jt5x+i6rAtZlvMtrjmtvizzz7T+Nuq3uPff/8t/Pnnn4KXl5fwxx9/CIIgCMeOHRO8vLyElJQUQRAEYdOmTeq2WaWsrEwYN26c4Ovrq/59qPpd2717d63vouo7l56eLhQXFwsTJ04UAgIChF9++aXGegvC/XaoZ8+egpeXl/D8888LS5YsEVJTUzWOtyDo1+5U97kWhOp/e+pqq/r166f1nbl165bQtWtXIT4+Xp22fPlywdvbW+P93r59W/D392f7R1aFUxhRnY0cOVIjSqqaJ+/q1at676ugoADff/89wsLCUFxcjPz8fOTn5+P27dsIDg7G5cuX1dOJZGRkwNfXVyMy3apVK/UCUbUZPXo0Nm7ciICAAJw5cwYff/wxXnzxRQwYMKDG4XJVNWvWTP3/e/fuIT8/H0899RQA4Oeff6719f/9739RWVmJsLAw9XvNz8/HI488gscffxwnT54EAPWd7cePH9eYyqUu5s6di1WrVqFz5844fvw4PvzwQ4wcORIjRozQGDooFovVf8fKykoUFBSgoqICXbt21Tm9S3XHoby8HLdv34anpyecnJx0vnbs2LFacwUOGzYMZWVlOHDggDotLS0NFRUVGDp0aJ3e67hx4zTmOxw/fjyaNGmCjIwMAICdnR2GDBmCo0ePori4WL1dSkoK/Pz84OHhUWsZTZs2hVwu13q88cYbWtuOHj0aIpFI/dzHxweCIGgs6isWi9G1a1ed35f+/ftr3P3u4+ODp556Sv1+dFHlyWQyjfSIiAiNfH2PRWBgINzd3dXPVZ/zAQMGaIy8UH0fVe/n119/xeXLlzFkyBDcvn1b/RkvKSlBYGAg/ve//2kNzVbd2aLSo0cPFBQUaNTTkIYNG4bc3Fz19w24f3dQs2bNMGDAAI1tmzRpgnHjxqmfOzg4YNy4cbh165b6O3/gwAF06NAB7du31/he9+zZEwA0yiGqiu3pfWxP72N7+m9dgoODsWLFCixYsAAeHh547bXXdO7zxRdf1Hg+ceJEAFBPD1TXz4mKg4MDRo4cWet70fe8HxQUpHH3YqdOndC8eXP1d72yshKHDx9Gv3790K1bN63yVH2LAwcO4Omnn4aTk5NGuUFBQVAqlfjf//5Xa91rcuLECRQVFWHQoEEa+7ezs8NTTz2l8b7CwsLw888/IycnR52Wnp4OBwcH9aKT9ekTEJka2+L72BbfZ8ttsWodBNXn48yZM3B1dcVjjz2G9u3bw9nZWSOv6mu+/fZbtGnTBoMHD1bvz97eHpMmTUJJSYlW+zNgwAD1KJUH3blzB1OnTsWlS5ewdevWGkeHqDzyyCP4+uuv8cILL6CoqAg7duzA66+/jsDAQCQkJKinBzZXu9OxY0eN9SNatWoFqVSqcR75v//7P/j6+mq8X2dn5zp/54ksBacwojp77LHHNJ6rhn4/OCStLnJyciAIAlavXo3Vq1fr3ObWrVtwdXXFtWvX1B2aqqRSaZ3L6927N3r37g2FQoGff/4ZaWlp2LFjByIjI5Genl7tUHWVgoICrFu3DmlpaVqLNtU0f6PK5cuXIQiC1kVKlSZN7n8VPTw8IJPJIJfLkZqaih49euDZZ5/F0KFDqx3iWdXgwYMxePBgFBcX49y5c/jyyy+xb98+REZGYt++feqhsXv37kVycjKys7M15lKseuFYl9LSUqxfvx5ffvklbt68qTGfv67joGt/HTp0QLdu3ZCamooxY8YAuH8R19fXF48//nit7xGA1nYPPfQQ2rRpozEn7/Dhw5GYmIjDhw9j+PDhuHTpEn7++WcsW7asTmWIxWIEBQXVadsHvxuqv5Wbm5tWetX5LKt7PwDQrl07pKenV1vm33//DTs7O61hlW3atIGTk1O9j8WDdVZ1/B8cIq16j6rv/+XLlwHcn6OzOnfu3FFPbQBUf04pLCzUmibKEHr16oU2bdogJSUFgYGBqKysxL59+xAaGqpVnouLi9aUGe3atQNw/9j7+vriypUruHjxonqO2QdZwgJvZJnYnrI9ZXuqW1xcHPr374/CwkLs2LFD4+JOTfX29PSEnZ2deh7hun5OVFxdXes0lYC+5/0H21QAGtM55Ofno7i4WD3NQU3l/v7779WWq2uKKH2o2vAHp6JQqdpGPv/884iPj0daWhoiIyMhCAIOHDiAPn36qLerT5+AyNTYFrMtbixtsZeXF5ycnDSCBKr1JUQiEXx9fXHmzBmMHTsWZ86cgZubm/r78ffff+Pxxx+HnZ3mvccdOnQAAFy7dk0jvaZjHhcXh7KyMuzdu7fWdq8qFxcXLFu2DLGxsbh8+TKOHz+OxMRErFmzBi4uLhgzZozZ2p3q2vmqv/lVvx0fVN+peYnMhQGERkjV0OpabA4AFAqFzvkUH2w0VKo2tHWliv5GRESgd+/eOrcxxglVIpGgR48e6NGjB1q2bIl169bh22+/rXU+vXnz5iErKwtTp05F586d4ejoiMrKSkybNq1O77+yshIikQiJiYlady0A0LhQGR0djREjRuDIkSP47rvvsGLFCqxfvx67du2qdp7LBzVv3hy9evVCr169YG9vj7179+LcuXPw9/fH119/jejoaPTv3x9Tp05F69atIRaLsX79+lrvuFm+fLl6HkFfX188/PDDEIlEmD9/vs7jUN1cnsOHD8fbb7+NGzduoKysDGfPnsWSJUvq9N7qqmPHjnjyySeRkpKC4cOHIyUlBfb29ggLCzNoOUD1343q0g2p6siH6uhzLHR9PmtKV/3dVf8uWLCg2rtJHrwgb8hzSl2IxWIMGTIEu3btQmxsLM6cOYPc3Nw63x30oMrKSnh5eWHhwoU68+v6fSXrxfaU7SnbU8M6efKkeuHgCxcu1GnuaEC7LdTncwKg2kDFg/Q979fWdtZVZWUlevXqpbXugIoqwF1fqvq8++67aNOmjVZ+1ffh6uqKHj16ID09HZGRkTh79iyuXbumMaKkPn0CovpiW8y2mG1xzezs7ODr64usrCwIgoAzZ85gxowZ6nw/Pz/s2bNHvTaCajRZfdTUnoaGhiItLQ0bNmzAu+++q/dvZZFIBKlUCqlUir59+2LAgAFISUnBmDFjzNbuVNfOE9kiBhAaIVU0OTs7WytiqlAocOPGDfTq1csgZVV3cVM1zM7e3r7Wu9Iee+wxXLlyRSs9Ozu7QXXr2rUrgPuLDwHV17WwsBCZmZmYM2cOZs+erU5XRbmrqm4fnp6eEAQB7u7udbq7xNvbG97e3pg5cybOnDmD8ePHY/v27Zg/f36tr31Q165dsXfvXvX7PHjwIDw8PLBu3TqN+q5Zs6bWfR08eBDDhw9HdHS0Ou3evXt1ulOlqvDwcMTHx2Pfvn0oLS3V+0LElStX1NMFAMDdu3eRl5eHPn36aGw3fPhwxMfHIzc3F/v27UPfvn0t8m43XZ/vy5cvo23bttW+pm3btqisrMSVK1fUd4AAwD///IOioiKt1xr7WKi+082bN6/znabmMGzYMCQnJ+Po0aP49ttv0apVKwQHB2ttl5ubi5KSEo2Opuo7rzq2np6e+O233xAYGFinQA7ZHran97E9ZXtqCLm5uVixYgWCg4Nhb2+PlStXIjg4WGdbeOXKFY0pG65cuYLKykr1nY/6fk7qytDn/VatWqF58+b4448/ai23pKTEaO2r6li2bt26TmWEhYVh2bJluHTpEtLS0iCRSDQW9rSWPgHZBrbF97EtZltck6effhrffvstjhw5glu3bqlHIAD3Awgffvghvv32W5SWlmrktW3bFr///jsqKys1LvhfunQJgPZInpr0798fwcHBiI6OxkMPPVTnkYy6eHh4wMnJSf050KfdMfXvtrZt2+r8zledCpDIGnANhEYoMDAQ9vb22L59u9Y8cDt37kRFRYVWY1VfEokEgPZQ0NatW8Pf3x87d+5Ebm6u1uuqDsUOCQnB2bNncf78eY381NTUOtUhMzNTZ7pqTkFVp6e6ulYXVd68ebNWmmofD3Y6BgwYALFYjHXr1mndzSAIAm7fvg0AKC4uRkVFhUa+l5cX7Ozs1Hfk6aJQKJCVlaUzTzUfsOp9qt5P1XqcO3cOZ8+erXb/KrqOxdatW6FUKmt9bVWtWrVC7969kZKSgtTUVAQHB1c7V6IuO3fu1Bieun37dp2f28GDB0MkEuHtt9/G1atX632nubEdPnxYPS8qAJw/fx7nzp2r8XsYEhICQPtzKJfLNfJVjH0sunbtCk9PTyQnJ+Pu3bta+fWdXsHR0bFeQ8mr06lTJ3h7e+OLL77AoUOHMGjQIK2pLACgoqICO3fuVD8vKyvDzp070apVKzz55JMA7l9AuXnzJnbt2qX1+tLSUpSUlBis3mSZ2J7ex/aU7akhLF68GJWVlXj77bfx1ltvoUmTJnjzzTd13gX6+eefazz/7LPPAEBd77p+TvRl6PO+nZ0d+vfvj2PHjuHHH3/UylfVPSwsDFlZWfi///s/rW2Kioq0Puv66t27N5o3b47169drfB5UHmzDBw4cCLFYjP379+PAgQPo27evRsDdWH0CIl3YFt/HtphtcU1Uaxps3LgREolE4y59Hx8fNGnSBBs3btTYFrjfrubl5SEtLU2dVlFRga1bt8LR0RHPPPNMnesA3A+CLFq0CDt27MB7771X6/bnzp3T2baeP38eBQUF6s+BPu1OdZ9rwPC/PQEgODgYZ8+exa+//qpOKygoqPN3nshScARCI9S6dWvMmjULH330EV588UU8++yzkEgkyMrKwr59+xAcHIxnn33WIGU1a9YMHTt2RHp6Otq1awdnZ2c88cQT8PLywtKlSzFhwgQMGTIEY8eOhYeHB/755x+cPXsWN27cQEpKCgBg2rRp+PrrrzFt2jRMnjwZEokEu3btwmOPPYbff/+91jrMnDkT7u7u6NevHzw8PKBQKHDixAkcO3YM3bp1U98xVVNdn3nmGWzcuBHl5eVwdXXFd999p55ntyrVhcUPP/wQ4eHhsLe3R79+/eDp6Yl58+Zh1apV+Pvvv9G/f3889NBD+Ouvv3D48GGMHTsWU6dOxffff4+33noLzz//PNq1awelUomvv/4aYrEYAwcOrPY9KhQKvPDCC/D19UXv3r3x6KOP4s6dOzh8+DBOnz6N/v37o0uXLgCAvn374tChQ5g1axb69u2Lv/76Czt27EDHjh1r/eHbt29ffP3112jevDk6duyIs2fP4sSJE3B2dq717/Cg4cOHY+7cuQCAV199Va/XlpeX46WXXkJYWBiys7Oxbds2PP300wgNDdXYTtWZO3DgAJycnNC3b986l1FRUYGvv/5aZ95zzz1n0CGQnp6eGD9+PMaPH4+ysjJs2bIFzs7O1U5VANy/ED5ixAjs3LkTRUVFeOaZZ/Djjz9i79696N+/v8ZdLEDDjkVd2NnZYcWKFZg+fToGDx6MkSNHwtXVFTdv3sTJkyfRvHlzfPrpp3rv98knn0RmZibkcjlcXFzg7u6uc95YfQwfPhwrV64EgGo73i4uLkhMTMTff/+Ndu3aIS0tDb/++iuWL1+uXuRs2LBhSE9Px9KlS3Hy5El0794dSqUSly5dwoEDB7Bx40adi2KS7WB7yvaU7Wnt6tKe7tmzB9988w3i4+PVU1osWrQI//nPf7Bt2zatRZP/+usvREZGonfv3jh79ixSUlIwePBgdOrUCQDq/DnRlzHO+6+99hq+++47TJo0CWPHjkWHDh2Ql5eHAwcOYNu2bXBycsLUqVNx9OhRREZGYsSIEXjyySehUChw4cIFHDx4EEeOHKn1YlV+fj4+/vhjrXR3d3cMHToUsbGxWLBgAUaOHInw8HC0atUK165dQ0ZGBrp3764xHUfr1q0REBAAuVyOu3fvIjw8XGOfxuoTEOnCtphtMdvi2vn4+MDe3h5ZWVnw9/fXuIFKIpHA29sbWVlZcHJygpeXlzpv3Lhx2LlzJ6Kjo/Hzzz+jbdu2OHjwIM6cOYOYmJh6rVs3ceJEFBcX48MPP8TDDz+MyMjIarf9+uuvkZqaiv79+6Nr166wt7fHxYsXsWfPHjRt2lT9Wn3aneo+146Ojkb57Tlt2jSkpKRAJpNh4sSJcHR0xO7du+Hm5oaCggKOZCerwQBCI/XKK6+gbdu2+Pzzz/Hxxx+joqIC7u7umDNnDl5++WWDzt2+YsUKLF++HO+88w7Ky8sxe/ZseHl5oWPHjtizZw/WrVuHvXv3oqCgAK1atUKXLl0wa9Ys9etdXFywZcsWrFixAhs2bICzszNeeOEFuLi44M0336xT+UeOHEF6ejpyc3MhCAI8PDwQGRmJ6dOnazSe1dV11apVWL58ObZt2wZBENCrVy8kJiZqzXHp4+ODV199FTt27MD//d//obKyEkeOHIGjoyNefvlltGvXDps2bUJCQgKA+3Pl9urVS92p9fb2RnBwMI4dO4abN2+qG/PExESdC++oODk5YcWKFfjmm2/w5ZdfIi8vD2KxGFKpFAsWLMCkSZPU244cORL//PMPdu7ciePHj6Njx4547733cODAAZw6darGY/nmm2/Czs4OqampuHfvHrp37w65XF7jhe7q9OvXDy1atEBlZaVW56g2S5YsQWpqKtasWYPy8nIMGjQIixYt0tn4Dhs2DMeOHUNYWFidFkhUKSsrw4IFC3Tmqf6mhjJ8+HDY2dlh8+bNuHXrFnx8fLB48WK4uLjU+LoVK1bA3d0de/fuxeHDh/HII49gxowZGsORq6rvsairgIAA7Ny5Ex9//DE+++wzlJSUoE2bNvDx8cG4cePqtc/o6GgsWbIEH330EUpLSzFixIgGd+KGDBmC999/Hx4eHvDx8dG5TYsWLRAfH48VK1Zg165deOSRR7BkyRKMHTtWvY2dnR0SEhKwadMmfP311/jvf/8LiUQCd3d3TJo0yaDTZpDlYnvK9pTtac1qa0+LiorwzjvvoF+/fhrzdg8dOhSHDh3C+++/jz59+mhMWfTRRx9h9erVWLVqFZo0aYKJEydqlVGXz4m+jHHed3V1xa5du7B69WqkpqaiuLgYrq6u6NOnj3ouaYlEgq1bt2L9+vU4cOAAvvrqKzRv3hzt2rXDnDlz6rQY6a1bt3Qu7hoYGIihQ4diyJAhcHFxwYYNG5CUlISysjL1egcjR47Uel14eDhOnDiBhx56SGvUI2CcPgFRddgWsy1mW1yzpk2bomvXrsjKytKYokile/fu+Pnnn+Hr66vxfWnWrBm2bt2K999/H3v37kVxcTGkUineeecdnW1DXUVGRuLOnTvqIMKDNwqojBs3Ds2aNcP333+Po0ePori4GC1btkSvXr0wY8YMdSAJqHu7U9Pn2hi/Pd3c3NTf+fXr16NVq1Z48cUXIZFIsGLFimrX1iCyNCLBWKtVEhHVoKKiAr1790a/fv0QFxdntHIOHz6MWbNm4fPPP0ePHj2MVk59/PXXXwgNDcWCBQvqdSekviz5WJhSfn4+evfujZkzZ2r8oFOZNGkSbt++jX379pmhdkRE+mlM7enatWuxbt06ZGZm6jU9BBERkTE1praYDOPtt9/Gzp07kZWVxcWYySpwDQQiMovDhw8jPz8fw4cPN2o5u3fvhoeHh8Zcjo0Vj8V9e/fuhVKpxLBhw8xdFSKiBmN7SkREZF5si6kmpaWlGs9v376NlJQUPP300wwekNXgFEZEZFLnzp3D77//jo8//hhdunSBv7+/UcrZv38/fv/9d3zzzTd48803G/XcgjwW92VmZuLixYv49NNP0b9/f7i7u5u7SkRE9cb2lIiIyLzYFlNdjBs3Dv7+/ujQoQP++ecf7NmzB8XFxZg5c6a5q0ZUZwwgEJFJbd++HSkpKejUqRPi4+ONVs5rr70GR0dHjB49GhMmTDBaOdaAx+K+jz/+GFlZWfDz88PixYvNXR0iogZhe0pERGRebIupLkJCQnDw4EHs2rULIpEIXbp0wdtvv41nnnnG3FUjqjOugUBERERERERERERERFq4BgIREREREREREREREWlhAIGIiIiIiIiIiIiIiLRwDQQL16NHD5SVlaFNmzbmrgoREZlJXl4eHBwccPr0aXNXxaqwDSUiIrah9cM2lIiI2IaSCgMIFu7evXtQKpXmrgYREZlRRUUFuGSR/tiGEhER29D6YRtKRERsQ0mFAQQL5+LiAgA4cuSImWtCRETmEhoaau4qWCW2oURExDa0ftiGEhER21BS4RoIRERERERERERERESkhQEEIiIiIiIiIiIiIiLSwgACERERERERERERERFpYQCBiIiIiIiIiIiIiIi0MIBARERERERERERERERampi7AkRERERERERERERESqUS5eXl5q6GzbO3t4dYLK7TtgwgEBEREREREREREZHZCIKAGzduoKCgwNxVaTScnZ3x6KOPQiQS1bgdAwhEREREREREREREZDaq4IGLiwscHR1rvahN9ScIAkpKSpCbmwsAcHNzq3F7BhCIiIiIiIiIiIiIyCyUSqU6eNC6dWtzV6dRkEgkAIDc3Fy4uLjUOJ0RF1EmIiIiIiIiIiIiIrNQrXng6Oho5po0LqrjXduaEwwgEBEREREREREREZFZcdoi06rr8WYAgZCRkYHw8HBkZGSYuypEREREOrG/QkREREREZHoMIDRyCoUCcXFxuH79OuLi4qBQKMxdJSIiIiIN7K8QERERERGZBwMIjVxycjLy8vIAAHl5eZDL5WauEREREZEm9leIiIiIiMgYpk2bhmeeeQb//POPVt6dO3cQHByMMWPGoLKy0gy1swwMIDRiOTk5kMvlEAQBACAIAuRyOXJycsxcMyIiIqL72F8hIrJugiBAoVBoPUpKSlBSUqIzT6FQqM/7RERExrR06VKUl5fjnXfe0cr74IMPcPv2bSxfvhx2do33MnoTc1eAzEMQBMTHx1ebnpCQwIVLiIiIyKzYXyEism6CIEAmk+HcuXN6v9bX1xfJyck8zxMRkVF5eHhg1qxZeP/99zFixAgEBwcDAM6fP48dO3YgIiICnTp1Mmod7t27B3t7e4sNUlhmrcjosrOzkZmZCaVSqZGuVCqRmZmJ7OxsM9WMiIiI6D72V4iIrB8DAEREZOlkMhm8vb2xbNky3Lt3D0qlErGxsXjssccwe/ZsXLx4EXPnzoW/vz+6deuGkSNH4siRIxr7KCgowMqVKzFkyBD4+fmhe/fumDZtGn777TeN7U6ePAlvb2/s378fH374IXr37o2nnnoKxcXFpnzLeuEIhEZKKpUiMDAQp06d0vhRLhaLERAQAKlUasbaEREREbG/QkRk7UQiEZKTk1FaWqqRrlAoEBoaCgA4cuQIJBKJ1mubNWvG4AMREZlEkyZNsHz5crzwwgv4+OOP0apVK/z888/YuHEj/vrrL4wfPx6urq6YPn06HB0dkZ6ejlmzZmHt2rV47rnnAABXr17F4cOH8fzzz8Pd3R3//PMPdu7ciYkTJ2L//v1wdXXVKPPjjz+Gvb09pk6dirKyMtjb25vjrdcJAwiNlEgkQnR0NEaOHKkznR01IiIiMjf2V4iIrJ9IJNIZIFCRSCQ15hMREZnCU089hQkTJiApKQn29vYYPHgwevfujZdeeglubm7Ys2cPHBwcAAATJkzA+PHj8f7776sDCN7e3jh48KDGNETDhg1DWFgYvvjiC8yaNUujvHv37mHPnj1o1qyZ6d5kPXEKo0bM09MTMplM/eNbJBJBJpPBw8PDzDUjIiIiuo/9FSIiIiIiMoX58+fD2dkZdnZ2WLhwIQoKCvD9998jLCwMxcXFyM/PR35+Pm7fvo3g4GBcvnwZN2/eBAA4ODiogwdKpRK3b9+Go6MjpFIpfvnlF62yhg8fbhXBA4AjEBq9iIgIpKSkIDc3Fy4uLpDJZOauEhEREZEG9leIiIiIiMjYmjdvDqlUitu3b+ORRx7B+fPnIQgCVq9ejdWrV+t8za1bt+Dq6orKykps2bIF27Ztw19//aUxBauzs7PW69zd3Y31NgyOAYRGTiKRICYmBitXrkRUVBSHjhIREZHFYX+FiIiIiIhMrbKyEsD9G5p69+6tcxtPT08AwKefforVq1dj1KhRePXVV9GiRQvY2dkhLi4OgiBovc5aRh8ADCAQgJCQEISEhJi7GkRERETVYn+FiIiIiIhMSTVtqr29PYKCgmrc9uDBgwgICEBcXJxGelFREVq2bGm0OpoC10AgIiIiIiIiIiIiIqqidevW8Pf3x86dO5Gbm6uVn5+fr/6/WCzWGmmQnp6uXiPBmnEEAhERERERERERERHRA5YuXYoJEyZgyJAhGDt2LDw8PPDPP//g7NmzuHHjBlJSUgAAffv2RUJCAhYuXAg/Pz9cuHABqamp6lEM1owBBCIiIiIiIiIiIiKiB3Ts2BF79uzBunXrsHfvXhQUFKBVq1bo0qULZs2apd4uMjISCoUCqampSEtLQ5cuXbB+/XqsWrXKjLU3DAYQiIiIiIiIiIiIiKjR27p1q1aah4cHVq5cWePrHBwcEBUVhaioqBr3FxAQgN9//73hFTUhroFARERERERERERERERaGEAgIiIiIiIiIiIiIiItDCAQEREREREREREREZEWBhCIiIiIiIiIiIiIiEgLAwhERERERERERERERKSFAQQiIiIiIiIiIiIiItLCAAKZTEZGBsLDw5GRkWHuqhAREVEjxj4JERERUePC/h9R/TGAQCahUCgQFxeH69evIy4uDgqFwtxVIiIiokaIfRIiIiKixoX9P6KGYQCBTCI5ORl5eXkAgLy8PMjlcjPXiIiIiBoj9kmIiIiIGhf2/4gahgEEMrqcnBzI5XIIggAAEAQBcrkcOTk5Zq4ZERERNSbskxARERE1Luz/ETUcAwhkVIIgID4+vtp01QmciIiIyJjYJyEiIiJqXNj/IzKMJuauANm27OxsZGZmaqUrlUpkZmYiOzsb7du3N0PNiIiIqDFhn4SIiIiocWH/zzYolUpUVlaapWw7OzuIxWK9XpOeno6UlBT8/PPPKCoqwuOPP45JkyZh1KhREIlE6u12796NjRs34tq1a5BKpZg/fz769eunzj9//jy2b9+O06dPIzc3F66urhg4cCBeeeUVODo6apR55swZrFy5Er/++itat26N8ePHY/r06RrlNQQDCGRUUqkUgYGBOHXqFJRKpTpdLBYjICAAUqnUjLUjIiKixoJ9EiIiosYpIyMDK1euRFRUFEJCQsxdHTIh9v+sn1KpRP+Bz6Pg1j9mKd+59SM4fPCAXkGETZs2oW3btoiOjkbLli1x4sQJLF68GDdu3MDs2bMBAPv378fixYsRGRmJnj17Ii0tDbNnz8bnn38OX19fAPcDEVeuXMG0adPQrl07/Pnnn1izZg3OnTuHLVu2qMu7cuUKpk6dil69emHevHn4/fff8f7770MsFmPq1KkGOQ4MIJBRiUQiREdHY+TIkTrTDRUJIyIiIqoJ+yRERESNj0KhQFxcHHJzcxEXFwd/f39IJBJzV4tMhP0/61dZWYmCW//getAMQGTimfiFSuDEelRWVuoVQPjkk0/QqlUr9fPAwEAUFBRALpdj5syZsLOzw5o1azBo0CDMmzcPANCzZ09cuHABCQkJSExMBABMnz5dYz8BAQFwcnLCG2+8gZ9++gldu3YFACQlJaFly5b44IMP4ODggMDAQOTn5+PTTz/FpEmT4ODg0OBDwTUQyOg8PT0hk8nUJ2aRSASZTAYPDw8z14yIiIgaE/ZJiIiIGpfk5GTk5eUBAPLy8iCXy81cIzI19v9shMgOsBOb9lHPgEXVi/4qnTt3RnFxMUpKSnD16lVcvnwZYWFhGtuEh4cjMzMTZWVl1e6nS5cuAIDc3Fx12rfffovQ0FCNQEF4eDiKioqQlZVVr/fwIAYQCBkZGQgPD0dGRobRyoiIiECbNm0AAC4uLpDJZEYri4jMTxAEKBQKnY+SkhKUlJTozOMiVkRkbOyT1J8p+oxkHLra5eraY7bFRGRKxmxbcnJyIJfL1ec1QRAgl8uRk5Nj8LLIsrH/R+b2ww8/wNXVFc2bN8elS5cAQGsKrQ4dOqC8vBxXr16tcT8A1Gt3lJSU4Pr161prebRv3x4ikUhdVkNxCqNGzlTD+SQSCWJiYtTzDnLIIJHtEgQBMpkM586d0/u1vr6+SE5O5lBSIjIa9knqh1NAWC9922W2xURkKsZsWwRBQHx8fLXpCQkJPM81Iuz/kTmdPn0aaWlpiIqKAgAUFhYCAJycnDS2Uz1X5T8oPz8fa9euRWhoKNq1awcAuHPnjs59OTg4QCKRVLsvfXEEQiNnyuF8ISEhSEtL46JFRI0AO+NEZMnYJ9Efp4CwbmyXicgSGbNtyc7ORmZmpsbCucD9BVkzMzORnZ1tsLLIOrD/R+Zw48YNzJ8/HwEBAZg8eXK991NeXo7XXnsNABAbG2ug2tUdRyA0YtUN5xs8eDA8PT3NXDsislYikQjJyckoLS3VylMoFAgNDQUAHDlyROvOj2bNmvEiBxGRhWGf0brpapdrao/ZFhORKRi7bZFKpQgMDMSpU6c0gghisRgBAQFaU4cQERlaUVERpk+fDmdnZ6xduxZ2dvfv42/RogWA+6MHVFNrqbavmq8iCAJiYmJw/vx5bNu2DS4uLuq8hx9+WL2vqsrKyqBQKLT2VV8cgdBI1Tacj3OfElFDiEQiSCQSnQ8VXXm8YEFEZFnYZ7QNutplFbbFRGRqpmhbRCIRoqOjq03nuY6IjKm0tBQzZszAnTt3sHHjRvWFfuDf9QseXJ/g0qVLsLe311rge+XKlUhPT0dCQgI6deqkkefo6Ag3NzetfWVnZ0MQBK21EeqLAYRGisP5iIiIiKg27DMSEZGhmapt8fT0hEwmUwcLRCIRZDKZ1sU5IiJDqqiowLx583Dp0iVs3LgRrq6uGvkeHh5o164dDhw4oJGelpaGwMBAODg4qNM2bNiATZs2IT4+HoGBgTrL69OnD44cOYLy8nKNfTk5OcHPz88g74kBhEZKNZxPLBZrpIvFYgQFBVn1cL6MjAyEh4cjIyPDJsohIiKixsPS+hfW0me0tONGRGQstnC+M2XbEhERoZ4ixMXFBTKZzGD7fpAt/G2IqOGWLVuGY8eOITIyEsXFxTh79qz6UVZWBgCYM2cO9u3bhzVr1uDkyZNYunQpzp8/j5kzZ6r3k5qailWrVmHIkCFwd3fX2E9+fr56u6lTpyI/Px+vv/46MjMzsXnzZiQlJSEyMlIjGNEQDCA0UrY6nE+hUCAuLg7Xr19HXFwcFAqFVZdDREREjYcl9i+soc9oiceNiMgYbOV8Z8q2RSKRICYmBm5ubli4cKHWGmyGYit/GyKLJVQClUrTPoTKelX1u+++AwDEx8dj3LhxGo/c3FwAwODBg7F8+XLs27cPU6dOxZkzZ7Bu3TqNEQOq/aSkpGjt55tvvlFv9/jjjyMpKQk3btzAyy+/jOTkZMydOxcRERH1PNjauIhyI6YazpeUlARBEGxiOF9ycjLy8vIAAHl5eZDL5RrRO2srh4iIiBoPS+1fWHqf0VKPGxGRodnS+c6UbUtISAhCQkIMvt+qbOlvQ2RJ7Ozs4Nz6EeDEerOU79z6EfXix3V19OjROm03ZswYjBkzptr8+Ph4nevF6NK9e3fs2rWrTtvWh8WOQLh79y769OkDb29v/Pjjjxp5u3fvxsCBA9GtWzcMHToUx44d03r9nTt3EBMTA39/f/j5+WHu3LnqKE9VZ86cwbhx4+Dj44N+/fphw4YNWgv2CIKADRs2oG/fvvDx8cG4ceNw9uxZrX3dvHkTc+bMgZ+fH/z9/fHmm2+iuLi4YQfCyEw5nM/YcnJyIJfL1X8/QRAgl8uRk5NjleUQEdUX21Ai62Pp/QtL7TNa+nEj68M2lCyVLZ7vLLVt0Zct/m2ILIVYLMbhgwdw6tQpszwOHzygNd1aY2SxAYSPP/5Ya0EdANi/fz8WL16MsLAwJCYmwtfXF7Nnz9bqSM2bNw/fffcdYmNj8f777yM7OxvTp09HRUWFepsrV65g6tSpaNOmDdavX48pU6ZgzZo1SE5O1thXYmIi1qxZg5deegnr169HmzZtEBERgatXr6q3KS8vx7Rp03D58mWsWrUKsbGxOH78OF5//XXDHhgDM9VwPmMTBEFnVE6V/mBn3NLLISJqCLahRNbFGvoXlthntIbjRtaHbShZIls931li26IvW/3bEFkSsVgMe3t7szwYPLjPIqcwunjxIrZt24aoqCgsXbpUI2/NmjUYNGgQ5s2bBwDo2bMnLly4gISEBCQmJgIAsrKycPz4cSQlJSE4OBjA/UV6wsPDcejQIYSHhwMAkpKS0LJlS3zwwQdwcHBAYGAg8vPz8emnn2LSpElwcHDAvXv3sH79ekREROCll14CADz99NN4/vnnkZSUhNjYWADAwYMH8ccffyAtLQ3t27cHADg5OWHq1Kk4f/48fHx8jHzU6s8Uw/mMLTs7G5mZmVrpSqUSmZmZyM7OVv9drKEcIqL6YhtKZH2spX9haX1GazluZD3YhpKlsuXznaW1Lfqy5b8NEZGKRY5AWLFiBV544QVIpVKN9KtXr+Ly5csICwvTSA8PD0dmZqZ6Jetvv/0WTk5O6NWrl3qb9u3bo3Pnzvj222/Vad9++y1CQ0M1VqQODw9HUVERsrKyANwfWlpcXKxRpoODA5577jmtfXl7e2s0DL169YKzszMyMjIacjioDqRSKQIDA7Uig2KxGEFBQVqfJUsvh4iovtiGElkf9i/qh8eNDI1tKFkqnu8sF/82RNQYWFwA4cCBA7hw4QJmzZqllXfp0iUA0DoBd+jQAeXl5eqhnJcuXYJUKoVIJNLYrn379up9lJSU4Pr161qR4Pbt20MkEqm3U/374HYdOnTAtWvXUFpaqt7uwW1EIhGkUql6H2Q8IpEI0dHR1aY/+Fmw9HKIiOqDbSiRdWL/on543MiQ2IaSJeP5znLxb0NEjYFFTWGkUCgQHx+P+fPno3nz5lr5hYWFAO4PyaxK9VyVX1RUhIcffljr9S1atMBPP/0E4P7iVrr25eDgAIlEorEvBwcHNG3aVKtMQRBQWFiIZs2a1Vimal/VCQ0NrTbv+vXrcHNzq/H1dJ+npydkMhmSkpIgCAJEIhFkMhk8PDysshwiIn2wDdXGNpSsCfsX9cPjRobANlQb21DLw/Od5eLfhohsnUWNQPjkk0/QunVrjBo1ytxVISsVERGBNm3aAABcXFwgk8msuhwiorpiG0pk/di/qB8eN2ootqFkLXi+s1z82xCRLbOYEQh///03kpOTkZCQoL4ro6SkRP3v3bt30aJFCwD379pQnZiB+3dnAFDnOzk54caNG1plFBYWqrdR3aWhKkulrKwMCoVCY19lZWW4d++ext0fRUVFEIlEGtsVFxfrLLO2OzeOHDlSbV5Nd4WQNolEgpiYGKxcuRJRUVGQSCRGK2fo0KGQy+UYMmSI0cohIqoLtqG6sQ0la2OqfoytMfRxy8jIUO/Lmhf2pLphG6ob21DLxN+hlottOBHZMosJIPz1118oLy/Hyy+/rJU3efJkPPXUU1i1ahUA7XkeL126BHt7e/XwsPbt2yMzM1M9dEwlOzsbXl5eAABHR0e4ublpzQuZnZ0NQRDU+1f9m52djU6dOmmU+dhjj6FZs2bq7S5cuKCxL0EQkJ2drbGIFhlfSEiI0X/sKRQKpKSkQKlUIiUlBREREewgEJHZsA0lsh2m6MfYIkMdN4VCgbi4OOTm5iIuLg7+/v7s49k4tqFkTfg71LKxDSciW2UxUxh17twZW7Zs0XgsXLgQALBs2TIsXboUHh4eaNeuHQ4cOKDx2rS0NAQGBsLBwQEA0KdPHxQWFiIzM1O9TXZ2Nn755Rf06dNHndanTx8cOXIE5eXlGvtycnKCn58fAKB79+5o3rw50tPT1duUl5fj0KFDWvv67bffcPnyZXVaZmYmCgoK2IDYoOTkZOTl5QEA8vLyIJfLzVwjImrM2IYSERkG+3iND9tQsiY8RxERWb709HS88sor6NOnD3x9fTFs2DB88cUXEARBY7vdu3dj4MCB6NatG4YOHYpjx45p5J8/fx4LFy7Ec889h6eeegoDBgzAqlWr1CMlVX788UcsXLgQYWFh6NSpE2bMmGHw92QxIxCcnJwQEBCgM+/JJ5/Ek08+CQCYM2cO3njjDXh6eiIgIABpaWk4f/48PvvsM/X2fn5+CA4ORkxMDKKiotC0aVN8+OGH8Pb2xoABA9TbTZ06FampqXj99dcxfvx4XLhwAUlJSZg/f766E9i0aVPMmDEDa9euRatWreDl5YXt27ejoKAAU6dOVe9r4MCBWL9+PebMmYPXXnsNCoUC7777Lvr27QsfHx9jHDIyk5ycHMjlcvUXXxAEyOVyDB48GJ6enmauHRE1RmxDiYgajn28xoltKFkLnqOIqLFSKpWorKw0S9l2dnYQi8V6vWbTpk1o27YtoqOj0bJlS5w4cQKLFy/GjRs3MHv2bADA/v37sXjxYkRGRqJnz55IS0vD7Nmz8fnnn8PX1xfA/UDElStXMG3aNLRr1w5//vkn1qxZg3PnzmHLli3q8s6cOYPTp0/Dx8cH9+7dM9h7r8piAgh1NXjwYCgUCiQmJmLDhg2QSqVYt26d+k4NlY8++gjvvPMOlixZgoqKCgQHB2PRokVo0uTft/z4448jKSkJ8fHxePnll9GqVSvMnTsXERERGvuaPn06BEFAcnIy8vPz0blzZyQlJamHqgKAvb09Nm7ciBUrVuC1115DkyZN8NxzzyEmJsa4B4RMShAExMfHV5uekJCgMVyZiMiSsA0lItKNfTyqDdtQMieeo4iosVIqlQgb+Bzybt02S/ltWrdE+sH/6hVE+OSTT9CqVSv188DAQBQUFEAul2PmzJmws7PDmjVrMGjQIMybNw8A0LNnT1y4cAEJCQlITEwEcL8fUHU/AQEBcHJywhtvvIGffvoJXbt2BQBMmjQJU6ZMUf/fGETCg+MnyKKoFq+qaYErMp1Lly5h1KhR1ebv2bNHY15UItKkUCgQFBQEADhx4gTnbK0jtgX1w+NGRHXV2Pp4jak9ZltQP8Y+bo3pM2gIje0cRUSWwZRtaGlpKbKzsyGVStXr/AD3p+/z9/fHqtEFEJt4In5lJfD6F844deoU7O3tG7Svbdu2YdmyZfjhhx9w+/Zt9O/fHwkJCejfv796my1btuDdd9/FmTNn1CMSH3Tx4kWEh4fjk08+wbPPPquVP2nSJDg6OmL9+vV1qld1x/1BFrMGApE1kEqlCAwM1Io8isViBAUFQSqVmqlmRERERFRf7OMRkSXjOYqqysjIQHh4ODIyMsxdFSKTEduZ52EoP/zwA1xdXdG8eXNcunQJALTO3R06dEB5eTmuXr1a434AmDxozAACkR5EIhGio6O1Fj4RBAHR0dEcNkpERERkhdjHIyJLxnMUqSgUCsTFxeH69euIi4uDQqEwd5WIqBanT59GWlqaeqrCwsJCAPfXYapK9VyV/6D8/HysXbsWoaGhaNeunfEqrAMDCER68vT01Fq8pbKyUmMuUiIiIiKyLuzjEZEl8/T01BlA4DmqcUlOTkZeXh4AIC8vD3K53Mw1IqKa3LhxA/Pnz0dAQAAmT55c7/2Ul5fjtddeAwDExsYaqHZ1xwACkZ7eeustvdKJiIiIyPKxj0dEluyjjz7SGUD46KOPzFMhMrmcnBzI5XL150AQBMjlcuTk5Ji5ZkSkS1FREaZPnw5nZ2esXbsWdnb3L8O3aNECAHDnzh2t7avmqwiCgJiYGJw/fx6JiYlwcXExQe01MYBANseY8wGWlZVh7969OvP27t2LsrIyg5dJRERERDVraP+PfTwismTl5eXYvHmzzrzNmzejvLzcxDUiXYx5LUIQBMTHx1eb/mBwiYjMq7S0FDNmzMCdO3ewceNGPPzww+o81foFqrUQVC5dugR7e3utkWUrV65Eeno6EhIS0KlTJ+NXXgcGEMimGHs+wEWLFjUon4iIiIgMyxD9P/bxiMiSrVy5skH5ZHzGvhaRnZ2NzMxMKJVKjXSlUonMzExkZ2cbtDwiqr+KigrMmzcPly5dwsaNG+Hq6qqR7+HhgXbt2uHAgQMa6WlpaQgMDISDg4M6bcOGDdi0aRPi4+MRGBhokvrrwgAC2RRjzwc4derUBuUTERERkWEZov/HPh4RWbKxY8c2KJ+Mz9jXIqRSKQIDAyEWizXSxWIxgoKCIJVKDVoeEdXfsmXLcOzYMURGRqK4uBhnz55VP1SjWufMmYN9+/ZhzZo1OHnyJJYuXYrz589j5syZ6v2kpqZi1apVGDJkCNzd3TX2k5+fr94uPz8fBw4cwIEDB5Cfn4+8vDz1c0MFM5sYZC9EFqC6+QAHDx4MT09Pg5Th5eUFFxcX5ObmauW5urrCy8vLIOUQERERUe0M1f9jH4+ILNkTTzwBNzc3XL9+XSvvsccewxNPPGGGWpGKKa5FiEQiREdHY+TIkTrTRSKRQcohslTKSusp87vvvgMAndOOHTlyBO7u7hg8eDAUCgUSExOxYcMGSKVSrFu3Dn5+flr7SUlJQUpKisZ+3nnnHfX54I8//sCrr76qka96riqvoRhAIJtQ23yACQkJBmlQRSIREhMTMWzYMK28xMRENtpEREREJmLI/h/7eERkyUQiET799FOd56hPP/2U5ygzMtW1CADw9PTEpEmTsGnTJnXapEmTtOZLJ/PJyMjAypUrERUVhZCQEHNXxybY2dmhTeuWeP0L85TfpnVL9eLHdXX06NE6bTdmzBiMGTOm2vz4+Hid55cHBQQE4Pfff69z/eqDAQSyCar5AB9UdT5A1SIlDeXp6YkOHTrg4sWL6rSOHTuy0SYiIiIyIUP3/zw9PfHEE0/gjz/+UKd5eXmxj0dEFsHT0xPDhw/HV199pU4bMWIEz1FmZsprEcD9udVrek7mo1oHIzc3F3FxcfD394dEIjF3tayeWCxG+sH/orLSDEMQcD+A8eDUYY0R10Agm6CaD1AXQ88HmJOTo3Ol9JycHIOVQUREREQ1M3T/j308IrJ0c+fO1Xg+Z84cM9WEVFRt0YOjDEQikVGuRXz++ecaaZ9//jnbKQth7HUwGjOxWAx7e3uzPBg8uI8BBLIJIpEIYWFhOvPCwsIMNmRQEATExsaq5zZUqays1JlORERERMZhyP6faqqJB+9uUyqViI+PZx/PiARBgEKh0HiUlJSgpKREI41/AyJg27ZtGs+3b99uppqQikgkwpQpU7TOUYIgYPLkybwW0UhUtw4GgztkKxhAIJtQWVmJVatW6cx7//33DTbU6dKlS8jKytKZl5WVpXXXGhEREREZhyH7f6opKHRdAFJNQUGGJwgCZDIZgoKCNB69evVCr169NNIiIiJ4gYwaNdUFyqp4gdL8BEHA5s2bdY5A2Lx5s8HOW7wWYblqWweDbRfZAgYQyCYcP34chYWFOvMKCwtx/Phxg5RT24mfDQMRERGRaRiy/9euXTu0aNFCZ56zszPatWtXnypSHXDxV6LaqS5EKpVKjfSKigpeoDQzBqBJ9Rl48PtZdR0MImvHRZTJJgQHB6NFixY6f0Q6OzsjODjYIOXU9gOHP4CIiIiITMOQ/b/Lly9XG4woKCjA5cuXDboIJt0nEomQnJyM0tJSdZpCoUBoaCgA4MiRI+oFKJs1a8a+NjVa1S3UC8AoC/VS3anWQDh16pTGBWSxWIyAgACDrYHQvn17+Pr64uzZs1p53bt359/fjEz1GSAyJ45AIJtgZ2eHlStX6sxbuXIl7OwM81Gv7cTPhoGIiIjINAzZ/6tthAFHIBiPSCSCRCLReKhUTWPwgBoznqMsl0gkQnR0dLXphjp3iUQiLFu2TOdUSbGxsTxHmpGpPgNE5sQAAtmMgIAA+Pr6aqT5+fnB39/fYGX89NNPDconIiIiIsMxVP+PfTwismQ8R1k2T09PyGQy9YVikUgEmUwGDw8Pg5czZcoUjbSXXnrJ4OWQ/kz1GSAyFwYQyKZ88MEH6rvN7Ozsql1Yr77eeOONBuUTERERkWEZov/HPh4RWTKeoyxfREQE2rRpAwBwcXGBTCYzSjkvv/yyes0eZ2dnTJ8+3SjlkP5M9RkgMgcGEMimtGzZEhERERCLxYiIiEDLli0Nuv8vv/yyQflEREREZFiG6P+xj0dEloznKMsnkUgQExMDNzc3LFy4UGM6NkOXs2zZMri5uSE2NtZo5ZD+TPUZIDIHLqJMNmfWrFmYNWuWUfbdvHlzuLm54fr161p5jz32GJo3b26UcomIiIioeg3t/7GPR0SWjOco6xASEoKQkBCbKYf0x7+NcSiVSlRWVpqlbDs7O4jFYr1ek56ejpSUFPz8888oKirC448/jkmTJmHUqFEaa2Ls3r0bGzduxLVr1yCVSjF//nz069dPnX/+/Hls374dp0+fRm5uLlxdXTFw4EC88sorcHR0VG+3Y8cOHDp0CL///jsUCgU6duyIl19+Gf3792/4Afj/GEAg0oMgCGjXrp3Ojtvjjz8OQRC4QA4RERGRlWEfj4gsGc9RRNRYKZVK9B84EAW3bpmlfOfWrXH44EG9ggibNm1C27ZtER0djZYtW+LEiRNYvHgxbty4gdmzZwMA9u/fj8WLFyMyMhI9e/ZEWloaZs+ejc8//1y9vld6ejquXLmCadOmoV27dvjzzz+xZs0anDt3Dlu2bFGX9+mnnyI4OBjjx4+Ho6MjDhw4gFmzZiE+Ph4jRowwyHFgAIFID9nZ2cjMzNSZl5mZiezsbLRv397EtSIiIiKihmAfj4gsGc9RRNRYVVZWouDWLVwbFwDBzrSBUlGlAOw8icrKSr0CCJ988glatWqlfh4YGIiCggLI5XLMnDkTdnZ2WLNmDQYNGoR58+YBAHr27IkLFy4gISEBiYmJAIDp06dr7CcgIABOTk5444038NNPP6Fr164A7k9jV3W7Xr164e+//0ZycrLBAghcA4FID1KpFIGBgTrzgoKCIJVKTVwjIiIiImoo9vGIyJLxHEVEjZ1gJwLs7Ez6qG/AourFfJXOnTujuLgYJSUluHr1Ki5fvoywsDCNbcLDw5GZmYmysrJq99OlSxcAQG5ubq3lVd2moRhAINKDSCRCdHS0zrzo6GgOGyUiIiKyQuzjEZEl4zmKiMi6/fDDD3B1dUXz5s1x6dIlANAK/nbo0AHl5eW4evVqjfsBUOuosx9++MGgI9MYQCDS08KFC3WmV9ehIyIiIiLLxz4eEVkynqOIiKzT6dOnkZaWhoiICABAYWEhAMDJyUljO9VzVf6D8vPzsXbtWoSGhqJdu3bVlpeamoqsrCxMnTrVALW/jwEEIj2UlJTgl19+0Zn3yy+/oKSkxMQ1IiIiIqKGYh+PiCwZz1FUVUZGBsLDw5GRkWHuqhBRLW7cuIH58+cjICAAkydPrvd+ysvL8dprrwEAYmNjq93ut99+w9KlSzFy5Ej079+/3uU9iAEEIj1MnDixQflEREREZHnYxyMiS8ZzFKkoFArExcXh+vXriIuLg0KhMHeViKgaRUVFmD59OpydnbF27VrY2d2/DN+iRQsAwJ07d7S2r5qvIggCYmJicP78eSQmJsLFxUVneX///TemT58OHx8fvPXWWwZ9LwwgEOnhs88+a1A+EREREVke9vGIyJLxHEUqycnJyMvLAwDk5eVBLpebuUZEpEtpaSlmzJiBO3fuYOPGjXj44YfVeaq1CVRrIahcunQJ9vb28PDw0EhfuXIl0tPTkZCQgE6dOuksLz8/H1OnTkXr1q2xbt062NvbG/T9MIBApAdHR0d07NhRZ94TTzwBR0dHE9eIiIiIiBqKfTwismSOjo7o0qWLzryuXbvyHNVI5OTkQC6XQxAEAPfvSpbL5cjJyTFzzYioqoqKCsybNw+XLl3Cxo0b4erqqpHv4eGBdu3a4cCBAxrpaWlpCAwMhIODgzptw4YN2LRpE+Lj4xEYGKizvLt372L69OkoLy/Hhg0b0Lx5c4O/JwYQyOYYcz5AQRCqjeI1adJE3ZATERERkek0tP8nCALatGmjM++RRx5hH4+IzG7u3Lk602fPnm3imlB1jH0tIj4+vtp0tlNElmPZsmU4duwYIiMjUVxcjLNnz6ofZWVlAIA5c+Zg3759WLNmDU6ePImlS5fi/PnzmDlzpno/qampWLVqFYYMGQJ3d3eN/eTn56u3mzNnDn777TfMmTMH165d09jOUJoYbE9EFkA1H2Bubi7i4uLg7+8PiURisP1fvHgRv/76q868X3/9FRcvXqz27jUiIiIiMjxD9P+ys7ORmZmpMy8zMxPZ2dnq4eZERKZWWVmJqKgonXlRUVE4evSoem5tMg9jX4uorp1SKpVsp6hREFUKEFBp8jLr47vvvgMAnUG/I0eOwN3dHYMHD4ZCoUBiYiI2bNgAqVSKdevWwc/PT2s/KSkpSElJ0djPO++8g5EjR2psp6ud+P333+v1Hh7EAALZFF3zAVaN3jXU33//XWs+AwhEREREpmOI/p9UKkVgYKDOizNBQUGQSqUGqSsRUX0cP34chYWFOvMKCwtx/Phx9OnTx8S1oqqMfS1C1U6dOnUKSqVSnS4WixEQEMB2imyWnZ0dnFu3BnaeNEv5zq1b6x2gPXr0aJ22GzNmDMaMGVNtfnx8vM4gxIMMFSSoCQMIZDOqmw9w8ODB8PT0NEgZvXv3RtOmTXHv3j2tvKZNm6J3794GKYeIiIiIameo/p9IJIK/v7/OAMIzzzwDkUhksDoTEekrODgYEokECoVCK08ikSA4ONgMtSIVU1yLEIlEiI6OVt9x/GA62ymyVWKxGIcPHkRlpWlHH6jY2dlBLBabpWxLwjFuZBNMNR+gIAgoLy/XmVdeXs55B4mIiIhMxJD9P6VSiYSEBJ15CQkJGnd7EhHpYuz573XdxAYA9+7d4+9QMzLl2gSenp6QyWTqYIFIJIJMJoOHh4fByiCyRGKxGPb29mZ5MHhwHwMIZBNU8wE++OOu6nyAhrBnz55qo56VlZXYs2ePQcohIiIiopoZsv+3Z88eVFRU6MyrqKhgH4+IaqSa//769euIi4vTOVKgIfg71HKZ6lqESkREBNq0aQMAcHFxgUwmM+j+iYh0YQCBbIJqPsAHh+2JRCKDzls7atQoNGmie+avJk2aYNSoUQYph4iIiIhqZsj+H/t4RNQQuua/NySeoyyXqi168C5lsVhslDV0JBIJYmJi4ObmhoULFxp0oWYiouowgEA2QSQSYcqUKVrDAwVBwOTJkw02H6BYLMasWbN05s2ZM4dDm4iIiIhMxJD9P/bxiKi+qpv/Picnx2Bl8BxluVRrEFSXboy1CUJCQpCWloaQkBCD75uISBcGEMgmCIKAzZs367wDbfPmzQZdA+HUqVM6877//nvOPUlEREQNYsw5tG2NIft/7OMRUX2Yci0+nqMsl6nXJmBfgYhMjQEEsgmqeQd13YFmyHkHVeXoYoz5DYksmSAIUCgUej9U9H0dfxgRka0z9hzatsaQ/T/28YioPkw1/z3PUZbPVGsTsK9AROagexI9Iiujmnfw1KlTGp03sViMgIAAg807+Pjjj6NJkyY6F9lr0qQJHn/8cYOUQ2QNSktLERQUVO/Xh4aG6rX9iRMnOMcnEdk0XXNoz5w508y1slyG7P+xj0dE9cHfoaSiWptg5cqViIqKMtrvFvYViMgcOAKBbIKp5h387rvvdHbaAKCiogLfffedQcohIiKixsUUc2jbGkP2/9jHI6L64O9QqsrYaxOwr0BE5sIRCGQzVPMOJiUlQRAEo8w7WNvd1g25G5vImsUNL4RDk7pNMaSaaaIuv6fKKkSI+apFA2pGRGT5aptDOyEhwSiLMNoCQ/X/evXqVePdvb169TJUlYnIxpjidyjPUcS+AhGZE0cgkE0x9ryDJ06caFA+ka1yaCKgaRPU6dHM/v6jLtvWNShBRGTNTDWHtq0yRP/vypUrNd7de+XKlQbVkYhsm7F/h/IcRewrUGOmVCpRXl5ulseD37m6SE9PxyuvvII+ffrA19cXw4YNwxdffKG1btfu3bsxcOBAdOvWDUOHDsWxY8c08s+fP4+FCxfiueeew1NPPYUBAwZg1apVKCkp0dhu7969GDt2LPz9/dGtWzcMHDgQCQkJKCsr0/9gV4MjEMimGHveweDgYDz00EO4e/euVt5DDz2E4OBgg5ZHREREts9Uc2jbKkP0/6RSKfz8/JCVlaWV5+fnx78BEdXI2L9DpVIpunTpgl9++UUrr0uXLjxHNQLsK1BjpVQqETagP/LyC8xSfptWzkg/dBhisbjOr9m0aRPatm2L6OhotGzZEidOnMDixYtx48YNzJ49GwCwf/9+LF68GJGRkejZsyfS0tIwe/ZsfP755/D19QVwPxBx5coVTJs2De3atcOff/6JNWvW4Ny5c9iyZYu6vMLCQvTu3Rsvv/wymjdvjvPnz2PdunW4ceMGli9fbpDjwAAC2ZyQkBCjzTlIREREZGiqubJHjhypM51TEtTOEP2/B+8KIyLShzF/hwqCgKtXr+rMu3r1qnrqJLJd7CtQY1VZWYm8/AKseCoPYhN/zJUCsOjc/TroE0D45JNP0KpVK/XzwMBAFBQUqBc9t7Ozw5o1azBo0CDMmzcPANCzZ09cuHABCQkJSExMBABMnz5dYz8BAQFwcnLCG2+8gZ9++gldu3YFALz00ksa5ffs2RN3797Fpk2bEBsbq1fdq8MpjIj0cPz4cZ2jDwDg7t27OH78uIlrRERERLZANYe26gKAMebQpuplZ2fj7NmzOvOysrI4NQQRmdXx48dx584dnXl37tzh79BGgn0FaszEIvM86qPqRX+Vzp07o7i4GCUlJbh69SouX76MsLAwjW3Cw8ORmZmpnnpI1366dOkCAMjNza2xDs7OzqioqEBlZWX93sQDGEAg0gMXUSYiIiJjMfYc2lS9du3aoUWLFjrznJ2d0a5dO9NWqAEEQYBCoaj1oVKXbTk6g8i8+DuUVCZMmKBxTh4/frwZa0NEdfXDDz/A1dUVzZs3x6VLlwBAa+qxDh06oLy8vNoRZ6r9AED79u218ioqKqBQKHD69Gls3rwZ48ePh729vUHqzymMiPRQl0WU+/TpY6LaEBERkS0x9hzaVL3Lly+jsLBQZ15BQQEuX76s84eaJSotLdXrYmJoaGit25w4cYKfRyIz4u9QUklOTlb/XxAEyOVyvPbaa2asERHV5vTp00hLS0NUVBQAqPucTk5OGtupnlfXJ83Pz8fatWsRGhqqdXNLRUUFnnzySfXzESNGICYmxlBvgSMQiPTx1FNPNSifiIiIqCYhISFIS0vjek4m9sgjjzQon4jImPg7lAAgJycHn332mUba1q1bkZOTY6YaEVFtbty4gfnz5yMgIACTJ0+u937Ky8vVwcLY2Fit/CZNmuCLL77A559/joULF+LYsWNYuHBhvcvT2r/B9kRWKyMjQ32nG3+s1mz48OG15h87dsw0lSEiIiIig6hLH+/o0aOmqYwBxQ0vhEMT3dMPqWbAqG7dzbIKEWK+0j2tExGZli3/DuX1iLoRBAHR0dE686Kjo/H5559zIWUiC1NUVITp06fD2dkZa9euhZ3d/fv4VdNm3rlzRz19qWr7qvkqgiAgJiYG58+fx7Zt2+Di4qKzvG7dugEAevToAXd3d8yaNQsTJ05UpzcERyA0cgqFAnFxcbh+/Tri4uI05kO1VhkZGQgPD0dGRobB9928efMG5RMRERGR5Xn88ccblG+pHJoIaNoEOh/N7O8/qsuvLvBARKZX9QJTffItla1djzDmtYiLFy/i119/1Zn366+/4uLFiwYvk4jqr7S0FDNmzMCdO3ewceNGPPzww+o81bSYqrUQVC5dugR7e3uthdFXrlyJ9PR0JCQkoFOnTnUqv2vXrgBgsBFKDCA0csnJycjLywMA5OXlQS6Xm7lGDWPsDoifn1+D8omIiIjI8rz++usNyiciMqYhQ4Y0KN9S2dL1CGNfi/j7778blE9EplNRUYF58+bh0qVL2LhxI1xdXTXyPTw80K5dOxw4cEAjPS0tDYGBgXBwcFCnbdiwAZs2bUJ8fDwCAwPrXAfVYssPBiPqiwGERiwnJwdyuRzC/x+/rFqAx5rnzzN2B6S2SF9dI4FEREREZDl+/vnnBuUbgyAIUCgUGo+SkhKUlJRopKn68kRku9zd3RuUb4ls7XqEsa9FtG3btkH5RGQ6y5Ytw7FjxxAZGYni4mKcPXtW/SgrKwMAzJkzB/v27cOaNWtw8uRJLF26FOfPn8fMmTPV+0lNTcWqVaswZMgQuLu7a+wnPz9fvd2LL76ITZs24dtvv8Xx48eRkJCAJUuWoHfv3vDx8THIe+IaCI2UIAiIj4+vNj0hIcHq5s+rrgMyePBgeHp6GqSMsWPH4r333qsxn4iIiIisy6hRo3T2javmm5IgCJDJZDh37lyt2/r6+iI5OdmkfXdBEFBaWqrxHIBWHZo1a2Z1vymILFFISAjEYjGUSqVWnlgstrq1A2zteoQprkW0b98eDz30EO7evauV99BDD6mnRCGyVUoz3C9R3zK/++47ANB5njty5Ajc3d0xePBgKBQKJCYmYsOGDZBKpVi3bp3GzCaq/aSkpCAlJUVjP++88w5GjhwJ4P50Rbt27cK1a9fQpEkTuLu7Y86cOZgwYUL93oAODCA0UtnZ2cjMzNRKVyqVyMzMRHZ2tlU1QKbqgNQ2r+DFixfh7e3d4HKIiIiIyHQssY9nqRfPLD24QWSLKioqdAYPgPu/4SsqKjSmvLB0tnQ9wlTXIi5fvqwzeAAAd+/exeXLl63mmBHpw87ODm1aOWNR7d0Oo2jTylm9+HFdHT16tE7bjRkzBmPGjKk2Pz4+vsYbXFQWLlyIhQsX1rl+9cEAQiMllUoRGBiIU6dOaXRExGIxAgICIJVKzVg7/ZmqA5KQkFBr/po1axpcDhERERGZjqX18UQiEZKTkzXu8lcoFAgNDQVw/+41iUQCwDx3+TMgQGRa//nPf2rNX716tYlq03C2dD3CVNciVMfs+++/15i6TiQSITAw0KqOGZE+xGIx0g8dRmVlpVnKt7Ozg1gsNkvZloQBhEZKJBIhOjpaPdzlwXRr+1Fgqg7I1atXG5RPRERERJbHEvt4IpFIHSR4kEQiqTbP2B4MblQX2AA4hRGRodS2LoC1rRtgS9cjTHUtQnVshg0bppEuCILVHTMifYnFYl7ENzMuotyIeXp6QiaTqRsakUgEmUxmsBW6TUnVmFaXbqjG9PPPP29QPhERERFZHvbx9KMKbjwYyKiaJpFIeEGLyEBs8RxlK9cjTHUtoiZVRyQQERkDAwiNXEREBNq0aQMAcHFxgUwmM3ON6s8UHRCJRFLt3JIODg5muxOMiIiIqDHLyMhAeHg4MjIy6vV6c/bxBEGAQqGo00OlrtsRkW1wdHREly5ddOZ17doVjo6OJq6RYdjK9QhTXItQjTTQJTo6mkEEIjIqTmHUyEkkEsTExGDlypWIioqy+gvgEyZMQHJyMgRBgEgkwvjx4w26/4sXL6KsrExnXllZGS5evIiOHTsatEwiIiIiqp5CoUBcXBxyc3MRFxcHf39/vfu05uzjlZaWIigoSK/XqKYMIiLbJQiCxjooGzdu1Hmu2LBhgzpwaG3ThtnS9QhTXIv49ddfdeb9+uuvvBZBREbFEQiEkJAQpKWlISQkxNxVabBt27apF1aprKzE9u3bG7zPqneFXbx4scZtL168CIVCweg/ERERkYkkJycjLy8PAJCXlwe5XK73Pv76668G5RMRGZIgCJDJZAgKCtJ46FI1PyIiwup+i9rK9QhjXIuo6u+//25QPpG1sLZzmLWr6/HmCASyGTk5OVo/GOVyOQYPHgxPT8967VPVcTt37lydtlcNKfT19UVycrJV3f1BREREZG1U/T/Vjx9BEOrV/6uoqGhQvqHcDIiAILavfgPVj7xq+pgiZTlcTyYboWZEZGr8LWk9jHEt4kHBwcEQi8UaCzWriMViBAcHG6QcInOxt7/f/ykpKbHq0UjWpqSkBMC/x786DCCQTRAEAfHx8dWmJyQk1LsDxo4bERERkeUxZP/v4MGDteb379+/XvXUhyC2rzmAQESNgkgkQnJyssYURsD9KdtUU5gdOXJE6yKbtU1hZAuMeS2iqitXrugMHgCAUqnElStX0L59+waXQ2QuYrEYzs7OyM3NBXB/7Reez4xHEASUlJQgNzcXzs7OEIvFNW7PAALZhOzsbGRmZmqlK5VKZGZmIjs7u16N6YMdt/Ly8hqHVmZkZMDe3p4dNyIiIiIjM2T/7+2338bhw4drzDcFkbLcrK8nIsshEolqvAtXIpHwLl0LYKxrEQ+SSqUIDAzUWVZQUBCkUmmDyyAyt0cffRQA1EEEMj5nZ2f1ca8JAwhkE4zZmFbtuEkkEkyZMgWbN2/W2k4mk8HJyane5RARERFR3an6f6dOndK4K1MsFiMgIKBO/b+qi5QOHToUKSkpWtsMGzYMSqVSvTCmoVWde5bTDxERWRdTXdgXiUSIjo7GsGHDtPKio6N5AyPZBJFIBDc3N7i4uKC8nDdFGJu9vX2tIw9UGEAgmyASiRAWFqaz0Q4LCzNoYzpv3jxs3bpVvUAScP+H6ty5cw1WBhERERHVTHUxZeTIkTrTa+v/1XWtq6+//hpff/0117giIiItprwW8eWXX+pM37NnD+bNm2ewcojMTSwW1/nCNpkGAwhkEyorK7Fq1Sqdee+//z7Cw8NhZ2dnsPKSk5Px0ksvqZ9/9tlnBts3EREREdWNp6cnZDIZkpKS1CMEZDIZPDw86vR6SwgGVK1DrYso17YvLqJMRGRSproWUV5ernMmBADYvHkzZs2aVesiqERE9cUAAtmE48ePo7CwUGdeYWEhjh8/jj59+hisPC8vL/X/27Vrh06dOhls30RERERUdxEREUhJSUFubi5cXFwgk8nq9Dpdi5RWXaD0kUce0ZjSyBRrXHERZSIi62KqaxHvvvturflvvvlmg8shItLFcLdkE5lRcHAwWrRooTPP2dkZwcHBRit727ZtRts3EREREdVMIpEgJiYGbm5uWLhwoV6LiqrWuqr6UElJSdFIN8VoBZGyvOZHRdn9Rw3bEBGR6ZjqWsSCBQsalE9E1BAWNQIhIyMDiYmJ+PPPP1FcXAxXV1f0798fs2fPxsMPP6ze7ujRo/joo4+QnZ2Nxx57DC+//DJGjRqlsa+ysjJ8+OGHSElJwd27d+Hn54fFixejffv2GttdvHgRK1asQFZWFh566CEMGzYM8+bNg4ODg8Z2u3fvxsaNG3Ht2jVIpVLMnz8f/fr109jmzp07eOedd3D48GGUl5ejd+/eWLRoEVxcXAx8pOhBdnZ2WLlyJSIjI7XyVq5cadDpi4iILBHbUCJqzEJCQhASEmLuajQYpx8yD7ahRFRfproWUVsQ2xKm5CMi22VRV1ULCgrg4+ODZcuWISkpCTKZDF999RVeffVV9TanT5/G7Nmz4evri8TERISFheHNN9/EgQMHNPa1YsUK7N69G/Pnz8fatWtRVlaGl156CXfu3FFvU1hYiClTpqC8vBxr167F/PnzsWvXLsTHx2vsa//+/Vi8eDHCwsKQmJgIX19fzJ49G2fPntXYbt68efjuu+8QGxuL999/H9nZ2Zg+fToqKioMf7BIS0BAAHx9fTXS/Pz84O/vb54KERGZENtQItvQr18/+Pn5aV0gpJrxuFFDsA0lQxg5ciT8/Py0FnYn22eKaxGffvppg/KJiBrCokYgDBs2TON5QEAAHBwcsHjxYty8eROurq745JNP4OPjg7feegsA0LNnT1y9ehVr1qzB888/DwC4ceMGvvjiCyxduhSjR48GAHTr1g39+vXDjh07MH36dADAjh07cPfuXaxbtw7Ozs4AAKVSiWXLlmHGjBlwdXUFAKxZswaDBg1Sr2rfs2dPXLhwAQkJCUhMTAQAZGVl4fjx40hKSlIPUZNKpQgPD8ehQ4cQHh5uvANHapMmTdLoUE+cONF8lSEiMiG2oUTW79SpUygoKABw/4LmqVOneCNEHVj7cWvWrBlOnDhR63ZV12c4cuRItVM1Vd2O6oZtKDXUb7/9huzsbABAdnY2fvvtN66T18gsWbJEI3i0ePFig+6/c+fODconImoIixqBoIuqQ1VeXo6ysjKcPHlS3UFTCQ8Px8WLF/HXX38BuL+ITWVlpcZ2zs7O6NWrF7799lt12rfffovAwEB1GQAQFhaGyspKfPfddwCAq1ev4vLlywgLC9MqMzMzE2VlZep9OTk5oVevXupt2rdvj86dO2uUScb1+uuv1/iciKgxYRtKZF1mzJhR43PSzdqPm651GKp7qNR1O6o/tqGkj8mTJ9f4nGyfKriosnz5coPuv127dg3KJyJqCIsMICiVSty7dw8///wzEhIS8Oyzz8Ld3R05OTkoLy/Xmj+yQ4cOAIBLly6p/23durXWQjYdOnRQb6Pa7sF9OTk5oU2bNhr7Au7fxfHgvsrLy3H16lX1dlKpVGveufbt22uUaU6CIEChUGg9SkpKUFJSojNPoVBAEARzV71Oqls0iIsJEVFjwjaUyDqxH1M/tnzcdPXdVayxr24N2IZSfXz00UcoL9dcwLy8vBwfffSReSpkgaq7FlHb9QhrOb+dPHlSa2qxrKwsnDx5skH7rXrc3Nzcqh3V0rlzZ7i5uVnVMSMi62JRUxip9OvXDzdv3gQA9O7dG6tWrQJwf65I4H7nqirVc1V+UVGRxmJXVbdTbaPa7sF9AUCLFi3U2zW0zBYtWuCnn36q8f3WNMT4+vXrcHNzq/H1dSEIAmQyGc6dO6f3a319fZGcnGzRi/Lcu3cP//3vf3Xm/fe//8W9e/fQtGlTE9eKiMj02Ib+y1BtKJGxsR9TP7Z83Grru1c991lDX91asA39F9vQuikvL8fmzZt15m3evBmzZs2Cvb29iWtlWWz9WkRlZSWioqJ05kVFReHo0aP1WkhZn+P266+/qkchWcMxIyLrY5EjEDZs2IAdO3ZgxYoVuHTpEiIjI6FUKs1dLatnyw3Ia6+91qB8IiJbwTaUyPqwH1M/tn7cbLnvbqnYhpK+3n333QblNxa2fD47fvy4RoCwqsLCQhw/frze+7bl40ZE1sUiRyCohmX5+fmhW7duGDZsGP773/+iY8eOAIA7d+5obF9UVAQA6qGiTk5OKC4u1tpvUVGRxnBSJycnrX0B90/yqu1U/965cwdt2rSpscwbN27UuK/qHDlypNo8Qy2AJhKJkJycjNLSUo30uizG1qxZM4tvuFatWoXAwMAa84mIGgO2of/iIqJkLT744AP07NmzxnzSZsvHrbq+u2pqiqp9c2voq1sLtqH/YhtaNwsWLMAXX3xRY35jV935DKj9eoQ1nN969eqFJk2aoKKiQiuvSZMmGuuT6KO643b79m0MGjQIAODo6Ih9+/ahWbNm6nxrOGZEZH0scgRCVd7e3rC3t0dOTg48PT1hb2+vNZej6rlqHsn27dvjn3/+0YoCPzjXpK55Ie/cuYO8vDyNfVUto+q+7O3t4eHhod4uOztba7657OxsrfktzaW6BdpUqluEzRoan2vXrjUon4jIFrENJbIOTZs2xXPPPaczb+DAgVY7DY+x2fpx09V3d3R0hKOjo9X11a0R21CqC3t7e0yZMkVnnkwma/TTF6nUtFi8irVei7hy5YrO4AEAVFRU4MqVK/Xet67j1rJlS3V+bGwsWrZsaXXHjIisj8UHEM6dO4fy8nK4u7vDwcEBAQEBOHjwoMY2aWlp6NChA9zd3QEAwcHBsLOzw6FDh9TbqIaO9enTR53Wp08fnDhxQn0XBwAcOHAAdnZ26iixh4cH2rVrhwMHDmiVGRgYCAcHB/W+CgsLkZmZqd4mOzsbv/zyi0aZZBxSqbTaEQhBQUFai48RETUGbEOJrEd101zEx8ebuCbWhceNjIVtKNXVvHnztAIF9vb2mDt3rplqRKZkzmsRwcHBRts3EVFVFjWF0ezZs9G1a1d4e3ujWbNm+O2335CUlARvb2/0798fAPDKK69g8uTJiI2NRVhYGE6ePIl9+/bhww8/VO/n0UcfxejRo/Huu+/Czs4Orq6uWL9+PR5++GG88MIL6u1eeOEFbN26FbNmzcKMGTNw8+ZNvPvuu3jhhRfg6uqq3m7OnDl444034OnpiYCAAKSlpeH8+fP47LPP1Nv4+fkhODgYMTExiIqKQtOmTfHhhx/C29sbAwYMMMHRa9xEIhGio6MxfPhwjbtvVOmMwhORrWMbSmT91q9fjxkzZmg8p9rxuFFDsQ2lhtqyZQvGjx+v8ZwaB9U1hxEjRqCyslKdLhaLeS2CiGyGRQUQfHx8kJaWhg0bNkAQBLRt2xZjxozB1KlT1XdY9OjRA2vXrsVHH32EL774Ao899hhWrFiBsLAwjX0tWrQIDz30EFatWoW7d++ie/fukMvlePjhh9XbtGjRAps3b8by5csxa9YsPPTQQxg9ejTmz5+vsa/BgwdDoVAgMTERGzZsgFQqxbp16+Dn56ex3UcffYR33nkHS5YsQUVFBYKDg7Fo0SI0aWJRh9lmeXp6YurUqdi4caM6bdq0aerhvUREtoxtKJH18/f3h7OzMwoKCuDs7Ax/f39zV8kq8LhRQ7ENpYbq1KkTpFIpsrOzIZVK1etpUOPg6emJiIgIjWsRERERvBZBRDZDJDw4WSJZlKoLChmDQqFAUFAQAODEiRM6F1G2JgqFAsOHD0dubi5cXV2xd+9eo7wnWztuRPVR9Xvw/ugCNDXCb9R7FcAbXzgDaNzfNWO3BbaKx42I9GULfTxDtc91bYMNfcwMvT+2BfXD36GWydaOmy29H16LIFvENpRULH4NBCJ9SCQSxMTEwM3NDQsXLmRjSkRERFYlIyMD4eHhyMjIMHdViIiIqI54LYKIbBnHNJLNCQkJQUhIiLmrQURERKQXhUKBuLg45ObmIi4uDv7+/rwAQUREZCV4LYKIbBVHIBARERERWYDk5GTk5eUBAPLy8iCXy81cIyIiIiIiauwYQCAiIiIiAJw+x5xycnIgl8uhWp5MEATI5XLk5OSYuWZERERERNSYMYBAREREROrpc65fv464uDgoFApzV6nREAQB8fHx1aarggpERERERESmxgACEREREXH6HDPKzs5GZmYmlEqlRrpSqURmZiays7PNVDMiIiIiImrsGEAgIiIiauQ4fY55SaVSBAYGQiQSaaSLRCIEBQVBKpWaqWZERERERNTYMYBARERE1Ihx+hzzE4lEmDJlitaxFgQBkydP1gosEBERERERmQoDCERERESNGKfPMT9BELB582adIxA2b97MIA4REREREZkNAwhEREREjZhq+hyxWKyRLhaLOX2OiaiCOLpGIDCIQ0RERERE5sQAAhEREVEjJhKJEB0dXW06p88xPgZxiIiIiIjIUjUxdwXI9giCgNLSUp3pAKq9ENGsWTNepCAiIjIDT09PyGQyJCUlQRAEiEQiyGQyeHh4mLtqjYIqWDNs2DCN9MrKSpsI4ujqG1bXL2R/kIjqi79DiYiIjIMBBDIoQRAgk8lw7tw5vV/r6+uL5OTkOnfe6tNBZOeQiIhIt4iICKSkpCA3NxcuLi6QyWTmrlKjcv36da00QRBw7do1qw7k6Ns3rEt/sK4BCfb7iEynut9m1VEoFDr/X1cPfr9N+TuUiMEqImpsGEAggzNFg1jfDiI7h0RERLpJJBLExMRg5cqViIqKgkQiMXeVjCYjI0P9PkNCQsxdHVRWViIqKkpnXlRUFI4ePQo7O+udedSQ/S59+oDs9xGZTmlpKYKCgur12tDQUL1fc+LECa12it91MgUGq4ioMWIAgQxKJBIhOTlZKxqvUCjUHcMjR47ovCihbzSejS4REZFhhYSEWMQFdWNSKBSIi4tDbm4u4uLi4O/vb/ZgyfHjx1FYWKgzr7CwEMePH0efPn1MXCvD0NU3rKlfWJf+IPuARPQgU/4OJeLnhYgaGwYQyOBEIlGNP8QlEkmDf6jXt4PIziEREVHjlpycjLy8PABAXl4e5HI5Zs6cadY6BQcHo0WLFjqDCM7OzggODjZDrQynpr6hvv1CfQIS7PcRmcfNgAgIYvvaN/z/072gjt9TkbIcrieTq883we9QIgariKgx0iuA8N5772Hu3Llo2rSpsepDVGfsIBJZjnsV1rVfImqccnJyIJfL1XMUC4IAuVyOwYMHw9PT02z1srOzw0svvYTVq1dr5b300ktWPX2RMRgyIEFEhieI7esWQCAyMFOtTcBrEUTU2OgVQEhOTsahQ4ewdOlSq78TioiIGkbVEQeAN79yNml5RET6EgQB8fHx1aYnJCSY7a7AyspKbNq0SWeeXC7HpEmTGEQgIiKqAdcmICIyHr1+iWzfvh2Ojo6YPn06Xn/9ddy6dctY9SIiIiIiMpjs7GxkZmZCqVRqpCuVSmRmZiI7O9tMNavbGghERERUMwYAiIiMQ68RCL6+vvjyyy+xefNmrFu3DsePH8cbb7yBMWPGGKt+RERkoap20N8eXoCmRlhV517Fv6Mb+IOAiBpCKpUiMDAQp06d0ggiiMViBAQEQCqVmq1utr4GAhERkbFxbQIiIuPR+3KPWCxGREQEwsLCsGLFCixZsgRffvklnnzySZ3bL1q0qMGVJCIiy9a0CYwSQCAiMhSRSITo6GiMHDlSZ7o5LxzY2dlh5cqViIyM1MpbuXIlpy8iIiKqA65NQERkHPW+3OPm5oYXX3wRp0+fRlZWFrKysrS2EYlEDCAQERERkUXw9PSETCZDUlISBEGASCSCTCaDh4dHg/abkZGBlStXIioqCiEhIfXaR0BAAHx8fHD+/Hl12lNPPQV/f/8G1Y2IiIiIiKgh6hVAyM/PxzvvvIN9+/ahU6dO2LhxI7p162bouhERERERGVRERARSUlKQm5sLFxcXyGSyBu1PoVAgLi4Oubm5iIuLg7+/f73vbmzfvr1GAKF9+/YNqhsREREREVFD6T0eevfu3QgLC8Phw4fxn//8B1988QWDB0RERERkFSQSCWJiYuDm5oaFCxc2eCqD5ORk5OXlAQDy8vIgl8vrtZ+cnBx89dVXGml79+5FTk5Og+pHRESGJQgCFAqF1qOkpAQlJSU68wRBMHe1iYiI6k2vEQgTJkxAVlYWevfujaVLl6Jt27bGqhcRERERkVGEhITUe6qhqnJyciCXy9UXhgRBgFwux+DBg+Hp6Vnn/QiCgNdff11n3uuvv45du3ZxcUciIgsgCAJkMhnOnTun1+t8fX2RnJzMczkREVklvUYg5OTkYNWqVdiwYUOdggfFxcX1rhgRERERkaUSBAHx8fHVputzt+mff/6JP//8U+88IiIyPQYBiIiosdFrBEJ6ejoefvjhWre7desWNm/ejO3bt+N///tfvStHRERERGSJsrOzkZmZqZWuVCqRmZmJ7OzsWtcwEAQBpaWlOvdTVWZmJjp27MiLVkREZiYSiZCcnIzS0lKNdIVCgdDQUADAkSNHtKbHa9asGc/hRERktfQKIDz88MO4desWvvrqK+Tk5KBFixYYMGAAunbtCgC4efMmPvnkE+zduxf37t2Dv7+/USpNRERERIaXkZGBlStXIioqyiBT/NgyqVSKwMBAnDp1CkqlUp0uFosREBAAqVRa4+v1mQbjww8/xLFjxzj9BRGRBRCJRDWunyORSBq8vg4REZEl0SuAcPHiRUycOBEFBQXqYdkbN27Ee++9B5FIhDfffBNlZWUYMGAApk6dqg4sEBEREZFlUygUWLBgAcrKyrBgwQJ88803vABSA5FIhOjoaIwcOVJnel0u9DMYQERERERElk6vNRBWr16NkpISLF26FPv27cOnn34KDw8PxMXFITo6Gn369MGBAwfw4YcfMnhARERENiMjIwPh4eHIyMgwd1WMZs2aNSgrKwMAlJWVYe3atWaukeXz9PSETCZTBwJEIhFkMhk8PDxqfa1qGowTJ07gxIkTWLx4sc7tFi9ejBMnTnD0ARERERERmYVeAYTTp09j/PjxeOGFF9CxY0f07dsXixYtwq1btzBo0CCsXr26Tj+YiIiIiKyFQqFAXFwcrl+/jri4OCgUCnNXyeBycnKwY8cOjbTt27cjJyfHTDWyHhEREWjTpg0AwMXFBTKZrM6vVU2DIZFIMHLkSLRu3Vojv02bNhg5ciQkEgmDB0REREREZBZ6BRAKCgrg7e2tkdapUycAQP/+/Q1XKyIiIiILkZycjLy8PABAXl4e5HK5mWtkWIIg4PXXX9eZ9/rrr6unrSTdJBIJYmJi4ObmhoULFzZo2qfExESN55s3b25o9YiIiIiIiBpErzUQKisr0aSJ5ktUzx0dHQ1XKyIiIiILkJOTA7lcrr6ILggC5HI5Bg8eDE9PTzPXzjD+/PNP/PnnnzXmPfHEEyaulXUJCQkxyKLTjz76qPr/oaGhcHNza/A+qXG7V2Ge1xIREVUlCAJKS0urzQN0rw3VrFkzvUZhmqocosZGrwACAPz0009o2rSp+vndu3chEonwww8/4M6dO1rbDxgwoGE1JCIiIjIDQRAQHx9fbXpCQoJN/NA4c+ZMrfkMIJje8uXLzV0FslJVRw29+ZWzwfdJRESkD0EQIJPJcO7cOb1f6+vrW+d1oExVDlFjpHcAYfPmzTqHU69bt04rTSQS4ddff61fzYiIiIjMKDs7G5mZmVrpSqUSmZmZyM7ORvv27c1QM8MaPXo03nvvPSiVSq08sViM0aNHm6FWRERERGQrTHVhngEAIuPQK4CwZcsWY9WDiIiIyKJIpVIEBgbi1KlTGhfXxWIxAgICIJVKzVg7wxGLxVi6dCmWLFmilbds2TKIxWIz1IqI6qvqxZO3hxegqd63jN13r+LfEQy8IENERPUlEomQnJysc2ohhUKB0NBQAMCRI0e01pLSZ2ohU5VD1Bjp1Z309/c3Vj2IiIiILIpIJEJ0dDRGjhypM92WfmQMGTIE69atQ25urjrN1dUVgwYNMmOtrEdGRgZWrlyJqKgog6yFQGQoTZug3gEEIiIiQxGJRFoX7R8kkUhq3cZSyiFqbOyMsdO8vDxs2rQJo0aNMsbuiYiIiEzC09MTMplMHSwQiUSQyWTw8PAwc80Mb9OmTRrP5XK5eSpiZRQKBZYuXYrr169j6dKlUCgU5q4SERERERGRwRgsgHD37l3s3bsXERER6Nu3L+Lj41FUVGSo3RMRERGZRUREBNq0aQMAcHFxgUwmM3ONjMPZ2RkODg4AAAcHBzg7O5u3QlZiw4YNKCwsBAAUFhYiMTHRzDUi+ldZhQj3KqDzUVp+/1FdflmF7YyyIiIiIqL6a9CA1oqKCmRkZCA1NRXffPMN7t27By8vL8ydOxfPPvssnnjiCUPVk4iIiMgsJBIJYmJi1FPU2OqQ5+TkZJSXlwMAysvLIZfLMXPmTDPXyrLl5ORg8+bNGmmbN2/G8OHD4enpaaZaEf0r5qsW5q4CEREREVm5eo1AOH36NJYsWYJevXph1qxZuHr1KsaPHw9BEDBr1izMmDGDwQMiIiKyGSEhIUhLS7PZ+e1zcnIgl8shCAIAQBAEyOVy5OTkmLlmlksQBMTGxqqPmUplZaXOdCIiIiIiImuk1wiEVatWYf/+/bh27Ro6dOiAKVOmYNCgQXj88cfVPzyJiIiIyHoIgoD4+Phq0xMSEmxqwWhDuXTpErKysnTmZWVl4dKlS+jQoYOJa0UENGvWDCdOnKhxG4VCgdDQUADAkSNHah1Z1axZswbXSxAElJaW1rpd1XVE6rKmSLNmzXiOIiIiIjIivQIIiYmJcHd3x5YtW+Dv72+sOhERERGRiWRnZyMzM1MrXalUIjMzE9nZ2Wjfvr0ZakZE9SESifSaak0ikZhkarbS0lIEBQXp9RpVkKMmJ06csNmp5YiIiIgsgV5TGA0cOBB5eXmYOnUqIiMjsW/fPpSUlBirbkRERERkZFKpFIGBgRCLxRrpYrEYQUFBkEqlZqqZZWvfvj18fX115nXv3p1BFyIiIiIisgl6jUBYvXo1iouLcfDgQezbtw8LFixA06ZN0bdvX/j5+XHoKBEREZGVEYlEiI6OxsiRI3Wms3+nm0gkwrJlyzB8+HCN9Q5EIhFiY2N53MiiPDh9UE3TBJliSqAlXf+Bg13164SovlLVVaOsUoS3fnrECDUjIiIiogfpFUAAgObNm2PUqFEYNWoUcnNzsW/fPuzbtw/p6ekAgG3btuHevXsICQmBk5OTwStMRERERIbl6ekJmUyGpKQkCIIAkUgEmUwGDw8Pc1fNonl6emLKlCnYtGmTOu2ll17icSOLIggCZDIZzp07pzP/wWmCfH19kZycbNQggoOdAAdx7dtVj4uUExEREZmKXlMYPcjFxQURERH48ssvsX//fsyYMQNXr17Ff/7zHwQFBWHSpEmGqicRERERGVFERATatGkD4H4fTyaTmblG1uHll1+Go6MjAOChhx7C9OnTzVwjIm0cEUNERERE9aX3CITqdOjQAfPnz8f8+fNx+vRppKam4uDBg4baPREREREZkUQiQUxMDFauXImoqCguSqoHe3t7AECTJgbrWhMZjEgkQnJyssYURgDUU289GFyoOoXRg1Mf6VLTdEjVbUdERERE1sMov3J69OgBd3d3DB061Bi7JyIiIiIjCAkJQUhIiLmrYVWSk5NRVFQEACgqKoJcLsfMmTPNXCsiTSKRqF5BwdLSUgQFBdV5+wenQyIiIiIi69egKYxqsnfvXkycONFYuyciIiIiMqucnBzI5XL1ndyCIEAulyMnJ8fMNSMiIiIiIjIMjrMmIiIiMqGMjAz1NEG82996CYKA+Pj4atMTEhI47zzZlOujekBoUs3Kx/8/iIYaPvN2pWV49OssI9SMiIiIiIzJaCMQiIiIiEiTQqFAXFwcrl+/jri4OM4JbsWys7ORmZkJpVKpka5UKpGZmYns7Gwz1YzIOIQm4uof9k3uP2raprrgAxERERFZNAYQiIiIiEwkOTkZeXl5AIC8vDzI5XIz14jqSyqVIjAwEGKx5kVRsViMoKAgSKVSM9WMiIiIiIjIcBhAICIiIjIBzpdvGwRBgEKhQGlpKebPn6+VLxKJMG/ePNNXjIiIiIiIyAj0WgPh0KFDdd72zz//1LsyRERERLaI8+XbBkEQIJPJcO7cuWq3qaiowNixY+Hr64vk5GT+XYmIiIiIyKrpFUCYO3cuRCKR+s652vAHExEREdG/8+U/qOp8+e3btzdDzUhf7N8SEREREVFjolcAYcuWLcaqBxEREZHNUs2Xf+rUKY1Fd8ViMQICAjhfvpUQiURITk5GaWmpOk2hUCA0NBQAsHz5cvX/mzVrxmADERERERFZPb0CCP7+/saqBxEREZHNEolEiI6OxsiRI3Wmm+NCsyAIGhfCVWmqelXFi+H/EolEkEgkOvNCQ0OrzSMiIiIi0oeu/roqHah+ZCz77mRoegUQ6iovLw/79+9Hamoq9uzZY4wiiIiIiKyKp6cnZDIZkpKSIAgCRCIRZDIZPDw8TF6XuszlXxXn8yciIiIiMh19++tVse9OhmawAMLdu3dx6NAhpKam4uTJk1AqlWb5QUxERERkqSIiIpCSkoLc3Fy4uLhAJpOZrS78QUFEREREZLnYXydL0aAAQkVFBTIyMpCamopvvvkG9+7dg5eXF+bOnYtnn30WTzzxhKHqSURERGT1JBIJYmJisHLlSkRFRZltupva5vI/cuSIRt04DJqIiIiIyHR09deBmvvsKuy7k6HVK4Bw+vRppKSk4ODBgygsLMSTTz6J8ePHQy6XY9asWRgwYICh60lERERkE0JCQhASEmLuatQ4l79EIuFc/kREREREZlRTfx1gn51MR68AwqpVq7B//35cu3YNHTp0wJQpUzBo0CA8/vjjyMnJgVwuN1Y9iYiIiIiIiIiIiIjIhPQKICQmJsLd3R1btmyBv7+/sepERERERERERERERERmZqfPxgMHDkReXh6mTp2KyMhI7Nu3DyUlJcaqGxERERERERERERERmYleIxBWr16N4uJiHDx4EPv27cOCBQvQtGlT9O3bF35+flygg4iIiIiIiIiIiIjIRui9iHLz5s0xatQojBo1Crm5udi3bx/27duH9PR0AMC2bdtw7949hISEwMnJyeAVJiIiIiIiIiIiIiIi49M7gFCVi4sLIiIiEBERgYsXLyIlJQX79u3Df/7zHzRp0gR+fn7YunWroepKREREREREREREREQmotcaCDXp0KED5s+fjyNHjuDzzz/HqFGj8Mcffxhq90REREREREREREREZEIGCyBcvHgRCQkJiI2Nxc8//4z//Oc/OH78uF77SE9PxyuvvII+ffrA19cXw4YNwxdffAFBEDS22717NwYOHIhu3bph6NChOHbsmNa+7ty5g5iYGPj7+8PPzw9z585Fbm6u1nZnzpzBuHHj4OPjg379+mHDhg1a5QmCgA0bNqBv377w8fHBuHHjcPbsWa193bx5E3PmzIGfnx/8/f3x5ptvori4WK9jQEREVB9sQ4mIiOqHbSgRERFR9fQKIHz22WcYOHAg8vPzNdKPHj2K4cOHY+3atdixYwfi4uIwYsQIFBUV6VWZTZs2QSKRIDo6Gp988gn69OmDxYsXIyEhQb3N/v37sXjxYoSFhSExMRG+vr6YPXu2Vkdq3rx5+O677xAbG4v3338f2dnZmD59OioqKtTbXLlyBVOnTkWbNm2wfv16TJkyBWvWrEFycrLGvhITE7FmzRq89NJLWL9+Pdq0aYOIiAhcvXpVvU15eTmmTZuGy5cvY9WqVYiNjcXx48fx+uuv63UMiIiI6oNtKBFR9QRBgEKhqNNDpS7bPnjBl6wT21AiIiKi6um1BsLRo0fh4eGBVq1aqdMqKiqwaNEiiMVivPXWW+jatSu++eYbfPTRR/j0008RExNT5/1/8sknGvsODAxEQUEB5HI5Zs6cCTs7O6xZswaDBg3CvHnzAAA9e/bEhQsXkJCQgMTERABAVlYWjh8/jqSkJAQHBwMApFIpwsPDcejQIYSHhwMAkpKS0LJlS3zwwQdwcHBAYGAg8vPz8emnn2LSpElwcHDAvXv3sH79ekREROCll14CADz99NN4/vnnkZSUhNjYWADAwYMH8ccffyAtLQ3t27cHADg5OWHq1Kk4f/48fHx89DnUREREemEbSkRUvdLSUgQFBen1mtDQ0Fq3OXHiBCQSSX2rRRaCbSgRERFR9fQagfDnn3/C19dXI+3kyZPIz8/HlClTMGLECDzxxBOYPn06nn/+/7V33+FRFfsfxz+bskkQCL0oIOVCECkJIAiEJlWKhaJYAOmCNBGlqAiIUhRFA6ggoSjYAKmRYlRQULxI8XIRRYGrIFUgIZKQtr8/8tvjbnaz2U3dJO/X8/Bozs6ZM9ndzJwz3yldtWvXLo8KY3vTZnXbbbcpLi5O169f1x9//KFTp07p7rvvtkvTrVs3ffvtt0pMTJQk7d69WyVLllSrVq2MNDVr1tRtt92m3bt3G8d2796tDh06yGw22+UVGxurgwcPSkqbWhoXF2d3TbPZrE6dOjnkFRISYty0SVKrVq1UqlQpj98HAAA8RRsKAEDW0IYCAABkzKMZCFevXlWlSpXsjn377bcymUzq1KmT3fHGjRtr586d2S7gDz/8oIoVK6p48eL64YcfJKWN4rBVq1YtJSUl6Y8//lCtWrV04sQJ1ahRQyaTyS5dzZo1deLECUnS9evXdfbsWbsbLWsak8mkEydOqHnz5kb69Olq1aqllStXKiEhQYGBgTpx4oRDGpPJpBo1ahh5AACQl2hDgTQWi0UJCQl2P0ty+J4HBgY6HINn0r/XzqRfJsgdOfnZTKt/SWafjJcesq5KlNHlElNNmnmkXI6UpSAxJafk6/l5jTYUuc2d+jK9rNSftmjnAABZ4VEAoVy5crp06ZLdsf379yswMFB169a1O242m+Xv75+twu3fv19RUVGaNGmSJCkmJkZS2pRMW9afra/HxsaqRIkSDvkFBwfryJEjktI2t3KWl9lsVlBQkF1eZrNZAQEBDte0WCyKiYlRYGCgy2ta88qIq+nRZ8+eVeXKlV2eDwBAerShtKFIY7FYNGjQIB0+fDjTtKGhoYqMjKRzJRs8XSrInWWCpJxdKsjsY5HZNzs5FJ19D2z3eKi8bn8O5ptjWeUK2lDa0LyQlaXVbLlbf9pi2TUAQFZ4FECoX7++Pv30Uz366KMqXry4jh8/rv/85z/q0KGD/Pzsszpx4oTDbAVPnDt3Tk8++aSaN2+uAQMGZDkfIDOM/ABQ2NCGAvZocwG4izYUAADAnkcBhCeeeEJ9+vRRly5d9K9//Uv//e9/ZTKZNHz4cIe0O3fu1J133pmlQsXGxmrYsGEqVaqUIiIi5OOTtlVDcHCwpLRRG+XLl7dLb/t6yZIlde7cOYd8Y2JijDTWURrWESBWiYmJio+Pt8srMTFRN27csBv9ERsbK5PJZJcuLi7O6TUzG7kRHR2d4WtZGVUAzzDyA0BhQhv6D9pQSGnBg8jISGOwQHx8vPHdiI6OtmuPCfDnrJfvi5HZz/lQ88yWCZKkxGSTpm4IzoWSwV22fw9nezeVxS/rUzd8EhJVaePB/88320XLFbSh/6ANzVuZLa1my53601ZRXXYNAJBzPNpEOSQkRCtXrtTtt9+uCxcuqFGjRlqyZInq169vl27fvn0KCgpS165dPS5QQkKCRowYoWvXrundd9+1m45pXdsx/VqOJ06ckL+/v6pWrWqkO3nypN2UW0k6efKkkUexYsVUuXJlh7ys51nTWf978uRJh2vefPPNCgwMNNKlz8tisdhdEwCA3EQbCjhnMpkUFBRk/LOyPRYUFETwIIeZ/SwK8JPTf4H+af8yej3ATxkGH5A/LH6+2f7nzWhDkZ+sS6u588+oI91M725gAgCAjHg0A0FK2xx5yZIlLtM0b95cmzdv9rgwycnJGj9+vE6cOKHVq1erYsWKdq9XrVpV1atX17Zt29SxY0fjeFRUlFq0aCGz2SxJatOmjRYvXqxvv/3WGFl+8uRJHT16VEOHDjXOa9OmjaKjo/X0008b+zVERUWpZMmSCgsLM37f4sWL67PPPjP2eUhKStKOHTvUpk0bu7w2bdqkU6dOqXr16pLSNpi+evWq2rZt6/F7gfzByA8ABRVtKAAAWUMbCgAAkDGPAwi5acaMGfryyy81efJkxcXF6dChQ8Zr9erVk9ls1pgxYzRx4kRVq1ZNzZs3V1RUlH788Ue9//77RtqwsDCFh4dr6tSpmjRpkgICAvT6668rJCREnTt3NtINGTJEmzdv1lNPPaWHHnpIv/zyi5YtW6Ynn3zSuAkMCAjQiBEjFBERoTJlyqhOnTr64IMPdPXqVQ0ZMsTIq0uXLnrnnXc0ZswYTZgwQfHx8Zo3b57atWunhg0b5v6bhxyR/U31XGHkB4DcQxsKAEDW0IYWPO7uQ8fedQAAZJ9XBRD27NkjSZozZ47Da9HR0apSpYp69Oih+Ph4LV26VEuWLFGNGjW0cOFCY6SG1YIFCzR79mxNmzZNycnJCg8P13PPPWe32fOtt96qZcuWac6cORo+fLjKlCmjsWPHavDgwXZ5DRs2TBaLRZGRkbp8+bJuu+02LVu2zJiqKkn+/v569913NWvWLE2YMEF+fn7q1KmTpk6dmpNvEQAATtGGAgCQNbShBU9W9mhg7zoAALLGqwIIX3zxhVvp+vbtq759+7pMU6JECb388st6+eWXXaZr3LixPv74Y5dpTCaTRowYoREjRrhMV7FiRUVERLhMAwBAbqANBZAVFovF2GDanePppR+dm5URvkB+ow0FkB/cbWttMasGQH7wqgACss/TBojGBwCAgsNZO2/drNO2PaZ9hrsSEhKMtdqBvJKYmr/no3B4+b4YtzZb93jvumSTpm4IzkbJAPdktw1mVg2AvEIAoZDJTgNE4wMgqxKTTXJ3nw9PHuLS8gUgpQUKBg0apMOHD2eaNjQ0VJGRkQQRAHgPyz/3CTOPlM/BbNlnrKgy+1kUkCs9GnynsiKvR9MzWAIA8g4BBABAtjFKC8gbPCgjp9H5CgDICXk9mr6wDWacVv+SzD45PyBLkhJTTZp5pFwWSwYABBAKtfPNB8vi6595Qg9bH1NKkirui8xGyQAAgKdMJpMiIyPtRvfFx8cbD9zR0dHGgzSj8lAY2MY2XI1MdWcEK3szeAGbOmla/Ysy+2Q9q8TUf2YxUNcByA15sTy0bTqzj0VmX/fL55m8GSyQV7NQrNfx5H6X2S5A9hBAKMQsvv7uBRAAIAsCAwO1d+9ej87JqLPT3esBRZ3JZMrw7yYoKKhQjcRD3rB9IHZ78ElGeeXwIJPElH/+392RqVlZkhN5z+yjXOwoA5DfPGpPPBjQmJeDGfN6eejCID/2dMiL6xS22S5AVhBAAABkiauOTHfQ2QkA3oXBJwCAnEB7AgCFCwEEAAAAAF7tbO+msvi5GLKeyQhWn4REVdp4MBdKBgAo7DJtg6w8XB66MLdNeTUL5eX7YmT2y/m9IxKTTezzB9gggAAAAADAq1n8fN3rvHFxPgAAWZHdNshVvoVVXs1CMftZFJArPZt5s28EUFAQQIDXyqsNeNhUD0BmMqqPLP8/jCWjTbXYcAsAAAAAABRkBBDgtQrKBjwACjeLxaJBgwbp8OHDHp8bGhqqyMhIgggAAAAAAKBA8snvAgAA4O0IAAAAAAAAgKKIGQjIlqwsE5SVc9zetEhi4yIAOcpkMikyMtJhCaP4+HhjplN0dLSCgoIczmUJIwAAACDn5WZfhKdpAaCwI4CAbMnKMkFZOSe3Ni2y5g0ArphMJqcBAqugoCCXrwMAAKBgsNjsnZpXA+bgubzqiwAAEEAAAAAAAACQJCWm/PP/dFIDcMZisTjMULcelzJeApcZ6iioCCAg216+L0ZmP0um6TxcWUhxCSZN3xKcjZIBAAAAAIDCKLf6IhKTTZq6gb4IOGexWDRo0CAdPnzY43NDQ0MVGRlJEAEFDgEEZJvZz6KAXPgm3XDjRgAAAAAAgNzAXnzeLbf6IiT6IuAaAQAUNQQQAAAAAOS4G8nZOz8xm+cDQHaxFx+A9EwmkyIjIx2WMIqPjzeWMIuOjna6Rx5LGKGgIoAAAAAAIMc9u6FUzmVmYTQoAADwDiaTyWmAwCooKMjl60BB45PfBQAAAAAAAAAAAN6HGQgAAAAActxL913N1trU1xKkGVtKpf3AdH8AAAAgXxBAAAAAAJDjAvyUrQDCDZ5UAAAAgHzHEkYAAAAAAAAAAMABAQQAAAAAAAAAAOCAAAIAAAAAAAAAAHDAyqIAAABALrNYLEpISHCZJj4+3un/ZyQwMFAmNhf2WGJq/p5fUJmSUzJ+0WL5/0QZfx9dng8AAACvRQABAAAAyGUJCQlq2bKl2+k7dOiQaZq9e/cqKCgoO8UqOqwd3JJmHimfg9laMk9USFRetz+/iwAAAIB8QAABAAAAgEwpSa4TZDLKPNPzAQBAkZBb9wTcawD5gwACAAAAkIem1b8ks4/zkeuZrQSTmGrSzCPlcqVcFfdF5kq+XsHmDZ1W/6LM2dgJLjH1n1kMhX0JqcDAQO3du9dlmvj4eGPGTHR0dIazYmzTAQAKH9tZeYX6niKHubPMpS1Pl7xMjyUwkRUEEAAAAIA8ZPaxyOyb1bOLzpI5ucXso2y8/0WLyWTyaJmsoKAgltUCAMADni5zaSsrgXmWwERWEEAAAAAAiih3RphLjDIHAACu2Y5qP998sCy+/jl/jZQkZjcA+YAAAgAAAFBEeTrCXGKUOQAAcM3i658rAYTCztUyl7YyW/IyvdxcAhNFAwEEwEZiasHMGwAAAABQMPEcCngvd/cZyOreBLZps7fMpSssgYnsIYAA2Gz0Y90QL/cvSeUNAEBOc2cTOncf7mzzcrXZnLv5ZWWTOwBAIcZzKGwQRPJeWVmakeUcUdgQQAAAAECh4OkmdDn9cMfDIgAAcBtBJAAFBAEEwGZE4bT6F2X2yZ3LJKb+c1OQ0ShGAAAAAEARwHMoUGCc7d1UFj831hbycHMCU3KKKq/bn42SAXmDAAJgw+yjXFpvDgAA5KWX74uR2c/5KDt3nu3iEkyaviVYkhsPjZlkyMMhAMAVnkOz5kZywcrXAUGkAsPi5+teAAEopAggAAAAoNAx+1kUkI073Rs2wQceGrMmMdmkjDbtcyeIk5T8z4um5JRslSW75yNnJaZm/N2QMv9+pJ0PoKh7dkOpPLlObrUhtvkSRALgzQggAAAAAMhxUzcE51heOTmDg+Wf89/MI+XyuwgA4La8mEVI2wTAmxFAAAAAAAAAQIHy0n1XszXbMCM3kvNudkNhZUpJcj+xB/sGeJQvgBxDAAEFQm5OO2dKOwAAQM4IDAzU3r17XaaJj49Xhw4dJEnR0dEKCgrKNJ3bmxdmwCchUZU2HpTk9r6GyGHufDck978ftvkCKJoC/JQrAYT0stsGZaQwt00V90XmdxEA5CACCPBatlP48mrjQaYNAgAAZJ3JZMq0w9dWUFCQW+mzuw8Fe1jkP0+/G5L73w8gtzCQDVLu7YVE2wSgoCCAAAAAAAAAIAayAVnl7kwzW57OOkt/DoC8QQAB2XYjOXfyTbIZkJFbUwalwj1tEADgOYvFooSEBIdjUtoIWluBgYEOx5B9fAYAAAAFS1Zmmtli1hngvQggINvyYnMhi69PrgUQmDYIALCyWCwaNGiQDh8+7Fb60NBQRUZG0oGdg/gMAAD5ybY5YSAbAAAEEAAAAOzQEZ3/+AwAAN4gt9a+t+YNAEBBQAAB2fbSfVcVkAvfpGsJ0owtpdJ+oCMBAJAHTCaTIiMj7ZbPcbU2a24tn+NsCR9n4uPjnf5/RgrCcj/e8hkAAAAAAAggIAcE+ClXAgg3+HbCS2TUkZfRetwSHVpAQeZq/da8Wps1ISFBLVu29OgcdzaTc7U5nafBCCn36jpv+AwAAAAAAAQQAMAlT9fitmJNbgDeyJ0ggyfp9u7dS2c+AAAAABRiBBAAIBMEAQDkp0w3cPz/2VAZLfdnSk5R5XX7c6FkAAAAAIDCjgACALjgbC1uyfV63BJLGAHIOTm9geO0+pdk9rE4v5brWIQkKTHVpJlHyuVYeYC8lphqkuT8b0DK/O8g7XwAAACgaCCAAACZcLUWt8R63AA8s2vXLs2dO1eTJk1S27Zt8/z6Zh+LzNmKR2Tc8QoUBATAAAAAAPf55HcBAAAAior4+Hi9/PLLOnv2rF5++WW3NysGAAAAACA/MAMBAAAgj0RGRurixYuSpIsXL2r58uUaNWpUPpcKKPwsvv+Mm3K29KBVZksUphcYGJhzhQQAAAC8EAEEAACAPPD7779r+fLlsvz/AusWi0XLly9Xjx49VK1atXwuHfJSYmr+nFuk2Wxo4O7SgyxRCACAd7uRXLDyBQoqAggAAAC5zGKxaM6cORkeX7RoERuvFyEzj5TPkXyswSgAAICiwvb+59kNpfKvIEARQgABAAAgl508eVLffvutw/GUlBR9++23OnnypGrWrJkPJctfFotFCQkJmaaz3SvC1b4R7CkBAACAnGZKTilQ+QI5jQACAAAostzpwHa389oqMDDQYTZBjRo11KJFC33//fdKSfnnQcHX11fNmzdXjRo1PCx54ZCQkKCWLVt6dI51ffq8lN2Hu/TnT6t/UWafDBJnIjH1nxkMzFoBAABFje39z0v3XVVALvRs3ki2n91Qed3+nL8IUIAQQCjETClJBSpfb5CYapLk3nIA1llz7j67p+UNAPAmnnZgu9N5vXfvXod1000mkyZPnqxevXo5PU5HsPexXR0opx8azT6S2TdHs8xx6YNrGQXSnAXMckOmQZxMbswY4QcUPrm1RnliPqx9znMoCruMBu1kNlAns/uMAD/lSgABgD3+zAoZ27XgKu6LzMeSFEwzj5TL7yIAAAqpatWqadCgQVq2bJksFotMJpMGDRqkqlWr5nfRvML55oNl8fXPOEFmHcQpSdz75BCLxaJBgwbp8OHDTl+3DaSFhoYqMjIy14MIjPwDkF6erH2eR3vN8ByKwiyz+worZwN18uo+IzNnezeVxS/nR3+YklO4x0GBQAABAABAmXRg51Dn9eDBg7Vp0yZduHBBFSpU0KBBg7Ja3ELH4uvvOoCQx2w/6uw+NBbEh8P8flAHAACFR0G/r7D4+eZKAAEoKAggFDK2lXKmI/myeo1CNsLP4vvPIsTR0dEOy044Ex8fb0TH3T3HVmBgoGeFBADkurzowA4KCtLUqVM1d+5cTZo0yeP2A/mjqD00mkwmRUZGOiw1YJ3panu/mZtLGAUGBmrv3r2ZpvP0voz7MKBwyK21z68lSDO2lEr7IRc7PXkOLTjcXgbPw/WlisryehndV1g5u7+wyqulEgG4RgChEPO2kXzZ4VHD6mmjnZJq/H9QUJDHN2FZOQcAUHS1bdtWbdu2ze9iAC6ZTKZ8v7/JShm4LwOKjtxa+/xGXvWS2Dyv8hzq3fJiFmFh3wfDG+4rAGQdAQQUCAVt2j8Klow2dHIls82eMsNICgAAAACAxD4YALwbAQQARV5CQoJatmyZ5fOdbfaUmb179zICAwBy0Y3k7J2fmM3zAQBAwZeby0vZngMA3owAAryW2WapYdaEBAAAnnh2Q6mcy8zi3pICAACgcMnN5aXc3evHFn0eAPIDAQR4LdvVXVgTEnllWv1LMvvk3tqTTE0FAAAFne3yj66WdWTJRhR0uboXXyHeQNeUklSg8s0v2d0XgD4PAHmFAAIA2DD7WOxmv+QsRrACQF556b6r2dpc81qCNGNLqbQf6AAFDBaLRYMGDdLhw4cdXku/FEdoaKgiIyMJIqDAYi8+91lsZutV3BeZjyUBAOQ0AggAAAAodAL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",
      "text/plain": [
       "<Figure size 1700x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fetching salary data by statistically significant categories\n",
    "plt.figure(figsize=(17,5))\n",
    "\n",
    "# creating subplot for employment_type to salary\n",
    "plt.subplot(131)\n",
    "sns.boxplot(data=US_data, x='employment_type', y='salary',hue='year')\n",
    "plt.xlabel('EMPLOYMENT TYPE')\n",
    "plt.ylabel('SALARY')\n",
    "plt.legend().remove()\n",
    "plt.title('United States Salary by Employment Type')\n",
    "\n",
    "# creating subplot for experience_level to salary\n",
    "plt.subplot(132)\n",
    "sns.boxplot(data=US_data, x='experience_level', y='salary', hue='year'\n",
    "            , order=exp_order)\n",
    "plt.legend().remove()\n",
    "plt.ylabel(\"\")\n",
    "plt.xlabel('EXPERIENCE LEVEL')\n",
    "plt.title('United States Salary by Experience Level')\n",
    "\n",
    "# creating subplot for work_setting to salary\n",
    "plt.subplot(133)\n",
    "sns.boxplot(data=US_data, x='work_setting', y='salary', hue='year')\n",
    "plt.legend(title='Year', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "plt.ylabel(\"\")\n",
    "plt.xlabel('WORK SETTING')\n",
    "plt.title('United States Salary by Work Setting')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edfd16f1",
   "metadata": {
    "papermill": {
     "duration": 0.032564,
     "end_time": "2024-04-08T18:00:39.778196",
     "exception": false,
     "start_time": "2024-04-08T18:00:39.745632",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id=\"4\"></a>\n",
    "# <p style=\"padding:10px;background-color:#bc4749;margin:0;color:#e9c46a;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\"> Most Recent Salary Data</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2439d378",
   "metadata": {
    "papermill": {
     "duration": 0.031728,
     "end_time": "2024-04-08T18:00:39.841733",
     "exception": false,
     "start_time": "2024-04-08T18:00:39.810005",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> Since experience level stands out as the primary determinant of salary and a majority of respondents identify as full-time employees, our focus shifts to examining the salary breakdown for full-time employees based on experience for the most recent two years.\n",
    "\n",
    "NB. Approximately 99% of the data pertains to the past two years. Restricting our trend analysis to this timeframe ensures more meaningful results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ba0247c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-08T18:00:39.907372Z",
     "iopub.status.busy": "2024-04-08T18:00:39.906948Z",
     "iopub.status.idle": "2024-04-08T18:00:40.738476Z",
     "shell.execute_reply": "2024-04-08T18:00:40.737206Z"
    },
    "papermill": {
     "duration": 0.867925,
     "end_time": "2024-04-08T18:00:40.741407",
     "exception": false,
     "start_time": "2024-04-08T18:00:39.873482",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# tabulating the data for the most recent years\n",
    "this_year = US_data['year'].max()\n",
    "last_year = US_data['year'].max()-1\n",
    "emp_type = 'Full-time'\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(121)\n",
    "\n",
    "# Getting the penultimate year data\n",
    "lyear = US_data.query('year == @last_year and employment_type == @emp_type')\n",
    "sns.boxplot(data=lyear, x='experience_level', y='salary',hue='work_setting', order=exp_order)\n",
    "plt.xlabel('EXPERIENCE LEVEL')\n",
    "plt.ylabel('SALARY')\n",
    "plt.legend().remove()\n",
    "plt.title(str(last_year) + ' North American Salary Trend for Full-Time Employees')\n",
    "\n",
    "plt.subplot(122)\n",
    "# Getting the most recent year data\n",
    "present = US_data.query('year == @this_year and employment_type == @emp_type')\n",
    "sns.boxplot(data=present, x='experience_level', y='salary',hue='work_setting', order=exp_order)\n",
    "plt.xlabel('EXPERIENCE LEVEL')\n",
    "plt.legend(title='Work Setting', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "plt.title(str(this_year) + ' North American Salary Trends for Full-Time Employees')\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "42265c63",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2024-04-08T18:00:40.812842Z",
     "iopub.status.busy": "2024-04-08T18:00:40.812030Z",
     "iopub.status.idle": "2024-04-08T18:00:40.855928Z",
     "shell.execute_reply": "2024-04-08T18:00:40.854713Z"
    },
    "papermill": {
     "duration": 0.083195,
     "end_time": "2024-04-08T18:00:40.858644",
     "exception": false,
     "start_time": "2024-04-08T18:00:40.775449",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╔══════════════════════════════════════════════════════════════════════════╗\n",
      "║     PERCENTAGE CHANGE IN MEDIAN SALARY REPORTED FOR THE PAST 2 YEARS     ║\n",
      "╚══════════════════════════════════════════════════════════════════════════╝\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_f51de_row0_col0, #T_f51de_row0_col1, #T_f51de_row0_col2 {\n",
       "  background-color: mediumseagreen;\n",
       "}\n",
       "#T_f51de_row1_col0, #T_f51de_row1_col1, #T_f51de_row1_col2, #T_f51de_row3_col0, #T_f51de_row3_col1, #T_f51de_row3_col2, #T_f51de_row4_col0, #T_f51de_row4_col1, #T_f51de_row4_col2, #T_f51de_row5_col0, #T_f51de_row5_col1, #T_f51de_row5_col2, #T_f51de_row7_col0, #T_f51de_row7_col1, #T_f51de_row7_col2, #T_f51de_row8_col0, #T_f51de_row8_col1, #T_f51de_row8_col2, #T_f51de_row9_col0, #T_f51de_row9_col1, #T_f51de_row9_col2 {\n",
       "  background-color: default;\n",
       "}\n",
       "#T_f51de_row2_col0, #T_f51de_row2_col1, #T_f51de_row2_col2, #T_f51de_row6_col0, #T_f51de_row6_col1, #T_f51de_row6_col2 {\n",
       "  background-color: tomato;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_f51de\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_f51de_level0_col0\" class=\"col_heading level0 col0\" >salary_lastyear</th>\n",
       "      <th id=\"T_f51de_level0_col1\" class=\"col_heading level0 col1\" >salary_present</th>\n",
       "      <th id=\"T_f51de_level0_col2\" class=\"col_heading level0 col2\" >percentage_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >employment_type</th>\n",
       "      <th class=\"index_name level1\" >work_setting</th>\n",
       "      <th class=\"index_name level2\" >experience_level</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f51de_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"10\">Full-time</th>\n",
       "      <th id=\"T_f51de_level1_row0\" class=\"row_heading level1 row0\" rowspan=\"2\">Hybrid</th>\n",
       "      <th id=\"T_f51de_level2_row0\" class=\"row_heading level2 row0\" >Entry-level</th>\n",
       "      <td id=\"T_f51de_row0_col0\" class=\"data row0 col0\" >76000.000000</td>\n",
       "      <td id=\"T_f51de_row0_col1\" class=\"data row0 col1\" >105000.000000</td>\n",
       "      <td id=\"T_f51de_row0_col2\" class=\"data row0 col2\" >38.157895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f51de_level2_row1\" class=\"row_heading level2 row1\" >Mid-level</th>\n",
       "      <td id=\"T_f51de_row1_col0\" class=\"data row1 col0\" >149000.000000</td>\n",
       "      <td id=\"T_f51de_row1_col1\" class=\"data row1 col1\" >135000.000000</td>\n",
       "      <td id=\"T_f51de_row1_col2\" class=\"data row1 col2\" >-9.395973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f51de_level1_row2\" class=\"row_heading level1 row2\" rowspan=\"4\">In-person</th>\n",
       "      <th id=\"T_f51de_level2_row2\" class=\"row_heading level2 row2\" >Entry-level</th>\n",
       "      <td id=\"T_f51de_row2_col0\" class=\"data row2 col0\" >130000.000000</td>\n",
       "      <td id=\"T_f51de_row2_col1\" class=\"data row2 col1\" >102000.000000</td>\n",
       "      <td id=\"T_f51de_row2_col2\" class=\"data row2 col2\" >-21.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f51de_level2_row3\" class=\"row_heading level2 row3\" >Executive</th>\n",
       "      <td id=\"T_f51de_row3_col0\" class=\"data row3 col0\" >191080.000000</td>\n",
       "      <td id=\"T_f51de_row3_col1\" class=\"data row3 col1\" >180000.000000</td>\n",
       "      <td id=\"T_f51de_row3_col2\" class=\"data row3 col2\" >-5.798618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f51de_level2_row4\" class=\"row_heading level2 row4\" >Mid-level</th>\n",
       "      <td id=\"T_f51de_row4_col0\" class=\"data row4 col0\" >120000.000000</td>\n",
       "      <td id=\"T_f51de_row4_col1\" class=\"data row4 col1\" >128000.000000</td>\n",
       "      <td id=\"T_f51de_row4_col2\" class=\"data row4 col2\" >6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f51de_level2_row5\" class=\"row_heading level2 row5\" >Senior</th>\n",
       "      <td id=\"T_f51de_row5_col0\" class=\"data row5 col0\" >144000.000000</td>\n",
       "      <td id=\"T_f51de_row5_col1\" class=\"data row5 col1\" >160000.000000</td>\n",
       "      <td id=\"T_f51de_row5_col2\" class=\"data row5 col2\" >11.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f51de_level1_row6\" class=\"row_heading level1 row6\" rowspan=\"4\">Remote</th>\n",
       "      <th id=\"T_f51de_level2_row6\" class=\"row_heading level2 row6\" >Entry-level</th>\n",
       "      <td id=\"T_f51de_row6_col0\" class=\"data row6 col0\" >93000.000000</td>\n",
       "      <td id=\"T_f51de_row6_col1\" class=\"data row6 col1\" >75000.000000</td>\n",
       "      <td id=\"T_f51de_row6_col2\" class=\"data row6 col2\" >-19.354839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f51de_level2_row7\" class=\"row_heading level2 row7\" >Executive</th>\n",
       "      <td id=\"T_f51de_row7_col0\" class=\"data row7 col0\" >200000.000000</td>\n",
       "      <td id=\"T_f51de_row7_col1\" class=\"data row7 col1\" >210914.000000</td>\n",
       "      <td id=\"T_f51de_row7_col2\" class=\"data row7 col2\" >5.457000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f51de_level2_row8\" class=\"row_heading level2 row8\" >Mid-level</th>\n",
       "      <td id=\"T_f51de_row8_col0\" class=\"data row8 col0\" >122500.000000</td>\n",
       "      <td id=\"T_f51de_row8_col1\" class=\"data row8 col1\" >115000.000000</td>\n",
       "      <td id=\"T_f51de_row8_col2\" class=\"data row8 col2\" >-6.122449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f51de_level2_row9\" class=\"row_heading level2 row9\" >Senior</th>\n",
       "      <td id=\"T_f51de_row9_col0\" class=\"data row9 col0\" >150000.000000</td>\n",
       "      <td id=\"T_f51de_row9_col1\" class=\"data row9 col1\" >160000.000000</td>\n",
       "      <td id=\"T_f51de_row9_col2\" class=\"data row9 col2\" >6.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7e222707de40>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the past 2 years of data to make % comparisons\n",
    "present_data = present.groupby(['employment_type', 'experience_level', 'work_setting'])['salary'].median()\n",
    "lyear_data = lyear.groupby(['employment_type','experience_level', 'work_setting'])['salary'].median()\n",
    "recent_data = pd.merge(lyear_data, present_data, on=['employment_type','work_setting', 'experience_level']\n",
    "                       , suffixes=('_lastyear', '_present'))\n",
    "\n",
    "# Calculate percentage change for the past 2 years\n",
    "recent_data['percentage_change'] = ((recent_data['salary_present'] - recent_data['salary_lastyear']) \n",
    "                           / recent_data['salary_lastyear']) * 100\n",
    "\n",
    "# table header\n",
    "msgbox('percentage change in median salary reported for the past 2 years'.upper())\n",
    "\n",
    "# highlight cells based on percentage change\n",
    "def highlight(x):\n",
    "    #Average salary increase accoring to payscale is 3 to 13%\n",
    "    if x.percentage_change > 13:\n",
    "        return['background-color:mediumseagreen']*3\n",
    "    elif x.percentage_change <=-13:\n",
    "        return['background-color:tomato']*3\n",
    "    else:\n",
    "        return['background-color:default']*3\n",
    "\n",
    "# display output\n",
    "recent_data.sort_values(by=['work_setting','experience_level','percentage_change']\n",
    "                        , ascending=[True, True, False]).style.apply(highlight, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c2b92a9d",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2024-04-08T18:00:40.930431Z",
     "iopub.status.busy": "2024-04-08T18:00:40.929930Z",
     "iopub.status.idle": "2024-04-08T18:00:41.323483Z",
     "shell.execute_reply": "2024-04-08T18:00:41.321295Z"
    },
    "papermill": {
     "duration": 0.433627,
     "end_time": "2024-04-08T18:00:41.326748",
     "exception": false,
     "start_time": "2024-04-08T18:00:40.893121",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 900x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_data['percentage_change'].unstack().plot.barh(title=\n",
    "    f'Hybrid Entry-Level employees saw the HIGHEST % increase in salary from {str(last_year)} to {str(this_year)} ({round(recent_data[\"percentage_change\"][0],2)}%)'\n",
    "    , figsize=(9,3), xlim=(-40, 40)).axvline(x=0, color='black', linestyle='--', linewidth=1, alpha=0.6)\n",
    "plt.xlabel('PERCENTAGE CHANGE IN SALARY')\n",
    "plt.ylabel('EXPERIENCE LEVEL')\n",
    "plt.legend(title='Work Setting', reverse=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf92ec15",
   "metadata": {
    "papermill": {
     "duration": 0.035202,
     "end_time": "2024-04-08T18:00:41.400637",
     "exception": false,
     "start_time": "2024-04-08T18:00:41.365435",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id=\"5\"></a>\n",
    "# <p style=\"padding:10px;background-color:#bc4749;margin:0;color:#e9c46a;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">SUMMARY OF FINDINGS</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "32ef2dcb",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2024-04-08T18:00:41.473121Z",
     "iopub.status.busy": "2024-04-08T18:00:41.472657Z",
     "iopub.status.idle": "2024-04-08T18:00:41.494631Z",
     "shell.execute_reply": "2024-04-08T18:00:41.492016Z"
    },
    "papermill": {
     "duration": 0.063033,
     "end_time": "2024-04-08T18:00:41.498759",
     "exception": false,
     "start_time": "2024-04-08T18:00:41.435726",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Defining new variables for summary of findings\n",
    "years = sorted(US_data['year'].unique())\n",
    "x = int(US_data['year'].value_counts().describe()[0])\n",
    "UScount = int(stats.iloc[0,0])\n",
    "m = \"{:,.2f}\".format(US_data['salary'].median())\n",
    "medianpaypercentage = \"{:,.2f}\".format((US_data[US_data['salary']==US_data['salary'].median()].count()[0] / UScount)* 100)\n",
    "maxpay = US_data[US_data['salary']==stats.iloc[-1,3]]\n",
    "minpay = US_data[US_data['salary']==stats.iloc[6,3]]\n",
    "sal_cor = corr_matrix['salary'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1d322ef4",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2024-04-08T18:00:41.572931Z",
     "iopub.status.busy": "2024-04-08T18:00:41.571646Z",
     "iopub.status.idle": "2024-04-08T18:00:41.583765Z",
     "shell.execute_reply": "2024-04-08T18:00:41.582752Z"
    },
    "papermill": {
     "duration": 0.052404,
     "end_time": "2024-04-08T18:00:41.586718",
     "exception": false,
     "start_time": "2024-04-08T18:00:41.534314",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t This assessment is a linchpin for understanding current salary trends in North American data related jobs. All employers and employees are based in North America. The dataset spans 4 years (2020 to 2023) and comprises 8,080 records, distributed across the years as follows: 28 in 2020, 87 in 2021, 1,323 in 2022, and 6,642 in 2023. \n",
      "\n",
      "\t\u001b[1;4mKEY FINDINGS:\u001b[0m \n",
      "\t1. The maximum reported salary across all categories over the 4 years is $450,000.00 for a Mid-level Research Scientist while the minimum reported salary stands at $24,000.00 for an Entry-level Data Scientist. The median salary is $150,000.00, representing 2.48% of the total reported jobs.\n",
      "\n",
      "\t2. Salary correlates positively with experience level (0.210, highest reported) and inversely with work location (-0.046, lowest reported). On average, employees with more experience report higher salaries, while the location of work has a minimal effect on reported salary. Full-time, in-person employees are the highest paid, closely followed by full-time remote employees. \"Machine Learning and AI\" claims the title of the highest-paying job category for both in-person and remote data professionals, while \"Data Architecture and Modeling\" proves most lucrative for hybrid employees. \n",
      "\n",
      "\t3. Approximately 99% of the data has been obtained in the most recent period, making it the most significant for analysis. The median salary reported across experience levels for full-time employees has remained relatively stable over the last 2 years across work settings and experience levels. The sharpest spikes, both positive and negative, have been observed in Entry-Level positions, indicating that they have the most variability in pay most recently. Senior-level positions showed an overall positive median salary increase while Mid-level positions showed an overall decrease. Executive-level salary stayed neutral most recently.\n",
      "\n",
      "This analysis offers valuable insights into the current landscape of North American data-related job salaries, shedding light on the impactful interplay between experience levels, employment type and work settings. \n"
     ]
    }
   ],
   "source": [
    "# Assessment findings\n",
    "print(\n",
    "f'\\t This assessment is a linchpin for understanding current salary trends \\\n",
    "in North American data related jobs. All employers and employees are based in North America. \\\n",
    "The dataset spans {x} years ({years[0]} to {years[3]}) and comprises {\"{:,}\".format(UScount)} \\\n",
    "records, distributed across the years as follows: \\\n",
    "{\"{:,}\".format(int(y1_stats.iloc[0,0]))} in {years[0]}, \\\n",
    "{\"{:,}\".format(int(y2_stats.iloc[0,0]))} in {years[1]}, \\\n",
    "{\"{:,}\".format(int(y3_stats.iloc[0,0]))} in {years[2]}, \\\n",
    "and {\"{:,}\".format(int(y4_stats.iloc[0,0]))} in {years[3]}. \\n\\n\\t\\\n",
    "\\033[1;4mKEY FINDINGS:\\033[0m \\n\\t\\\n",
    "1. The maximum reported salary across all categories over the {x} years is \\\n",
    "${\"{:,.2f}\".format(stats.iloc[-1,3])} for a {maxpay.iloc[0,4]} {maxpay.iloc[0,1]} \\\n",
    "while the minimum reported salary stands at ${\"{:,.2f}\".format(stats.iloc[6,3])} \\\n",
    "for an {minpay.iloc[0,4]} {minpay.iloc[0,1]}. The median salary is ${m}, representing \\\n",
    "{medianpaypercentage}% of the total reported jobs.\\n\\n\\t\\\n",
    "2. Salary correlates positively with experience level ({\"{:,.3f}\".format(sal_cor[-2])}, highest reported) and \\\n",
    "inversely with work location ({\"{:,.3f}\".format(sal_cor[0])}, lowest reported). On average, employees with more \\\n",
    "experience report higher salaries, while the location of work has a minimal effect on \\\n",
    "reported salary. Full-time, in-person employees are the highest paid, closely followed by \\\n",
    "full-time remote employees. \"Machine Learning and AI\" claims the title of the \\\n",
    "highest-paying job category for both in-person and remote data professionals, while \\\n",
    "\"Data Architecture and Modeling\" proves most lucrative for hybrid employees. \\n\\n\\t\\\n",
    "3. Approximately {round(k)}% of the data has been obtained in the most recent period, \\\n",
    "making it the most significant for analysis. The median salary reported across \\\n",
    "experience levels for full-time employees has remained relatively stable over \\\n",
    "the last 2 years across work settings and experience levels. The sharpest spikes, \\\n",
    "both positive and negative, have been observed in Entry-Level positions, indicating \\\n",
    "that they have the most variability in pay most recently. \\\n",
    "Senior-level positions showed an overall positive median salary increase \\\n",
    "while Mid-level positions showed an overall decrease. \\\n",
    "Executive-level salary stayed neutral most recently.\\n\\n\\\n",
    "This analysis offers valuable insights into the current landscape of North American \\\n",
    "data-related job salaries, shedding light on the impactful interplay between \\\n",
    "experience levels, employment type and work settings. \\\n",
    "')"
   ]
  },
  {
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   "source": [
    "<a id=\"6\"></a>\n",
    "# <p style=\"padding:10px;background-color:#bc4749;margin:0;color:#e9c46a;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">INTERACTIVE SUMMARY</p>"
   ]
  },
  {
   "cell_type": "markdown",
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     "start_time": "2024-04-08T18:00:41.692548",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "> Delve into the interactive graph showcasing salary details categorized by job title, providing an in-depth view of the roles associated with each job category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9edc1c33",
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     "status": "completed"
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       "interactive(children=(Dropdown(description='Year:', options=(2023, 2022, 2021, 2020), style=DescriptionStyle(d…"
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   "source": [
    "# Interactive full US data exploratory chart tool\n",
    "\n",
    "# Define chart function\n",
    "def update_chart(selected_year, work_setting, experience_level, employment_type):\n",
    "    filtered_data = US_data[\n",
    "        (US_data['year'] == selected_year)\n",
    "        & (US_data['work_setting'] == work_setting) \n",
    "        & (US_data['experience_level'] == experience_level)\n",
    "        & (US_data['employment_type'] == employment_type)] \n",
    "    grouped_filtered_data = filtered_data.groupby(['job_category', 'job_title'])['salary'].median().reset_index()\n",
    "\n",
    "    fig = px.bar(grouped_filtered_data, y='job_title', x='salary', color='job_category', orientation='h',\n",
    "                 title=(f\"Median salary for {experience_level} {employment_type} employees in {selected_year} for {work_setting} data-jobs\".upper()),\n",
    "                 height=800, width=1000, template='simple_white')\n",
    "\n",
    "    for i, (job_title, median_salary) in enumerate(zip(grouped_filtered_data['job_title'], grouped_filtered_data['salary'])):\n",
    "        fig.add_annotation(\n",
    "            go.layout.Annotation(x=median_salary,\n",
    "                                 y=job_title, xref='x', yref='y'\n",
    "                                 , text=f'{median_salary:,.0f}'\n",
    "                                 , showarrow=True, arrowhead=3\n",
    "                                 , ax=-70, ay=0,))\n",
    "    fig.update_layout(\n",
    "            legend=dict(traceorder=\"reversed\",\n",
    "            title_font_family=\"Ariel Bold\",\n",
    "            font=dict(family=\"Courier Bold\",\n",
    "                size=14, color=\"black\"),\n",
    "            bgcolor=\"white\", bordercolor=\"gold\",\n",
    "            borderwidth=2,title=\"Job Category\",\n",
    "            itemdoubleclick='toggleothers'))\n",
    "    fig.update_layout(yaxis_title='JOB TITLE'.upper(), xaxis_title='MEDIAN SALARY'.upper())\n",
    "    fig.show()\n",
    "\n",
    "# Dropdown for employment_type\n",
    "employment_type_dropdown = widgets.Dropdown(\n",
    "    options=US_data['employment_type'].unique()\n",
    "    , value=US_data['employment_type'].unique()[0]\n",
    "    , disabled=False\n",
    "    , description='Employment Type:'\n",
    "    , style={'description_width': 'initial'})   \n",
    "\n",
    "# Dropdown for experience_level\n",
    "experience_level_dropdown = widgets.Dropdown(\n",
    "    options=exp_order, value=exp_order[1]\n",
    "    , disabled=False\n",
    "    , description='Experience Level:'\n",
    "    , style=employment_type_dropdown.style)  \n",
    "\n",
    "# Dropdown for year\n",
    "year_dropdown = widgets.Dropdown(\n",
    "    options=US_rec_counts.index\n",
    "    , value=US_data['year'].unique()[0]\n",
    "    , disabled=False\n",
    "    , description='Year:'\n",
    "    , style=employment_type_dropdown.style)\n",
    "\n",
    "# Dropdown for work_setting\n",
    "work_setting_dropdown = widgets.Dropdown(\n",
    "    options=US_data['work_setting'].unique()\n",
    "    , value=US_data['work_setting'].unique()[1]\n",
    "    , disabled=False\n",
    "    , description='Work Setting:'\n",
    "    , style=employment_type_dropdown.style)\n",
    "\n",
    "# Add dropdowns and interactive chart\n",
    "interactive_chart = interactive(update_chart, selected_year=year_dropdown, work_setting=work_setting_dropdown,\n",
    "                                experience_level=experience_level_dropdown, employment_type=employment_type_dropdown)\n",
    "\n",
    "interactive_chart"
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     "exception": false,
     "start_time": "2024-04-08T18:00:42.490623",
     "status": "completed"
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    "tags": []
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   "source": [
    "<a id=\"7\"></a>\n",
    "# <p style=\"padding:10px;background-color:#bc4749;margin:0;color:#e9c46a;font-family:Segoe Print;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">THANK YOU</p>\n",
    "\n",
    "<p>\n",
    "<h3><font color=\"#bc4749\">If you like this notebook please upvote and leave a comment. Your feedback is greatly appreciated.</font></h3>\n",
    "\n",
    "<h4><b>Author :</b> Sonya Lawrence-Thompson</h4>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2930d20b",
   "metadata": {
    "papermill": {
     "duration": 0.036001,
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     "exception": false,
     "start_time": "2024-04-08T18:00:42.566616",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "👉Lets chat : [Twitter](https://twitter.com/Sonya_Lawrence1)      \n",
    "👉Connect with me : [LinkedIn](https://www.linkedin.com/in/sonya-lt/)      \n",
    "👉Explore my projects : [Github](https://github.com/sonya-7)"
   ]
  }
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