Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# matplotlib = this is data visualisation library\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Job Title</th>\n",
       "      <th>Salary Estimate</th>\n",
       "      <th>Job Description</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Size</th>\n",
       "      <th>Founded</th>\n",
       "      <th>Type of ownership</th>\n",
       "      <th>Industry</th>\n",
       "      <th>...</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>min_salary</th>\n",
       "      <th>max_salary</th>\n",
       "      <th>average-salary</th>\n",
       "      <th>currency</th>\n",
       "      <th>company_txt</th>\n",
       "      <th>age</th>\n",
       "      <th>python_yn</th>\n",
       "      <th>data</th>\n",
       "      <th>analysis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>₹417K-₹1,000K (Glassdoor Est.)</td>\n",
       "      <td>If you are a data junkie who would like to wra...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>BlueJeans\\r\\n3.1</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>201 to 500 Employees</td>\n",
       "      <td>2009</td>\n",
       "      <td>Company - Private</td>\n",
       "      <td>Enterprise Software &amp; Network Solutions</td>\n",
       "      <td>...</td>\n",
       "      <td>Unknown / Non-Applicable</td>\n",
       "      <td>417</td>\n",
       "      <td>1000</td>\n",
       "      <td>708.5</td>\n",
       "      <td>LAKh</td>\n",
       "      <td>BlueJeans</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>₹698K-₹960K (Glassdoor Est.)</td>\n",
       "      <td>Relocation Assistance Offered Within Country\\r...</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Colgate-Palmolive\\r\\n4.2</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10000+ Employees</td>\n",
       "      <td>1806</td>\n",
       "      <td>Company - Public</td>\n",
       "      <td>Consumer Products Manufacturing</td>\n",
       "      <td>...</td>\n",
       "      <td>$10+ billion (USD)</td>\n",
       "      <td>698</td>\n",
       "      <td>960</td>\n",
       "      <td>829.0</td>\n",
       "      <td>LAKh</td>\n",
       "      <td>Colgate-Palmolive</td>\n",
       "      <td>214</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Data Scientist - Technology Development</td>\n",
       "      <td>₹350K-₹679K (Glassdoor Est.)</td>\n",
       "      <td>Relocation Level: Domestic\\r\\n\\r\\nHiring Manag...</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Micron Technology\\r\\n3.6</td>\n",
       "      <td>Hyderābād</td>\n",
       "      <td>10000+ Employees</td>\n",
       "      <td>1978</td>\n",
       "      <td>Company - Public</td>\n",
       "      <td>Computer Hardware &amp; Software</td>\n",
       "      <td>...</td>\n",
       "      <td>$10+ billion (USD)</td>\n",
       "      <td>350</td>\n",
       "      <td>679</td>\n",
       "      <td>514.5</td>\n",
       "      <td>LAKh</td>\n",
       "      <td>Micron Technology</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Senior / Data Science Engineer</td>\n",
       "      <td>₹490K-₹1,094K (Glassdoor Est.)</td>\n",
       "      <td>Gainsight™ is a venture-backed, fast-growing t...</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Gainsight\\r\\n4.2</td>\n",
       "      <td>Hyderābād</td>\n",
       "      <td>501 to 1000 Employees</td>\n",
       "      <td>2009</td>\n",
       "      <td>Company - Private</td>\n",
       "      <td>Enterprise Software &amp; Network Solutions</td>\n",
       "      <td>...</td>\n",
       "      <td>Unknown / Non-Applicable</td>\n",
       "      <td>490</td>\n",
       "      <td>1094</td>\n",
       "      <td>792.0</td>\n",
       "      <td>LAKh</td>\n",
       "      <td>Gainsight</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Business Analyst - Data Science</td>\n",
       "      <td>₹479K-₹700K (Glassdoor Est.)</td>\n",
       "      <td>With a startup spirit and 90,000+ curious and ...</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Genpact\\r\\n3.8</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>10000+ Employees</td>\n",
       "      <td>1997</td>\n",
       "      <td>Company - Public</td>\n",
       "      <td>IT Services</td>\n",
       "      <td>...</td>\n",
       "      <td>$2 to $5 billion (USD)</td>\n",
       "      <td>479</td>\n",
       "      <td>700</td>\n",
       "      <td>589.5</td>\n",
       "      <td>LAKh</td>\n",
       "      <td>Genpact</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Job Title                 Salary Estimate  \\\n",
       "0                           Data Scientist  ₹417K-₹1,000K (Glassdoor Est.)   \n",
       "1                           Data Scientist    ₹698K-₹960K (Glassdoor Est.)   \n",
       "2  Data Scientist - Technology Development    ₹350K-₹679K (Glassdoor Est.)   \n",
       "3           Senior / Data Science Engineer  ₹490K-₹1,094K (Glassdoor Est.)   \n",
       "4          Business Analyst - Data Science    ₹479K-₹700K (Glassdoor Est.)   \n",
       "\n",
       "                                     Job Description  Rating  \\\n",
       "0  If you are a data junkie who would like to wra...     3.1   \n",
       "1  Relocation Assistance Offered Within Country\\r...     4.2   \n",
       "2  Relocation Level: Domestic\\r\\n\\r\\nHiring Manag...     3.6   \n",
       "3  Gainsight™ is a venture-backed, fast-growing t...     4.2   \n",
       "4  With a startup spirit and 90,000+ curious and ...     3.8   \n",
       "\n",
       "               Company Name   Location                   Size  Founded  \\\n",
       "0          BlueJeans\\r\\n3.1  Bangalore   201 to 500 Employees     2009   \n",
       "1  Colgate-Palmolive\\r\\n4.2     Mumbai       10000+ Employees     1806   \n",
       "2  Micron Technology\\r\\n3.6  Hyderābād       10000+ Employees     1978   \n",
       "3          Gainsight\\r\\n4.2  Hyderābād  501 to 1000 Employees     2009   \n",
       "4            Genpact\\r\\n3.8  Bangalore       10000+ Employees     1997   \n",
       "\n",
       "   Type of ownership                                 Industry  ...  \\\n",
       "0  Company - Private  Enterprise Software & Network Solutions  ...   \n",
       "1   Company - Public          Consumer Products Manufacturing  ...   \n",
       "2   Company - Public             Computer Hardware & Software  ...   \n",
       "3  Company - Private  Enterprise Software & Network Solutions  ...   \n",
       "4   Company - Public                              IT Services  ...   \n",
       "\n",
       "                    Revenue min_salary  max_salary  average-salary  currency  \\\n",
       "0  Unknown / Non-Applicable        417        1000           708.5      LAKh   \n",
       "1        $10+ billion (USD)        698         960           829.0      LAKh   \n",
       "2        $10+ billion (USD)        350         679           514.5      LAKh   \n",
       "3  Unknown / Non-Applicable        490        1094           792.0      LAKh   \n",
       "4    $2 to $5 billion (USD)        479         700           589.5      LAKh   \n",
       "\n",
       "         company_txt  age  python_yn  data  analysis  \n",
       "0          BlueJeans   11          0     0         0  \n",
       "1  Colgate-Palmolive  214          1     0         1  \n",
       "2  Micron Technology   42          1     0         1  \n",
       "3          Gainsight   11          1     0         1  \n",
       "4            Genpact   23          1     0         1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('GL_sal_data_cleaned.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}