Exploratory Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6f151767",
   "metadata": {},
   "source": [
    "##### Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "17bc074c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_csv(\"data_cleaned.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e94a3892",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>work_year</th>\n",
       "      <th>experience_level</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>job_title</th>\n",
       "      <th>salary_currency</th>\n",
       "      <th>salary_in_usd</th>\n",
       "      <th>employee_residence</th>\n",
       "      <th>remote_ratio</th>\n",
       "      <th>company_location</th>\n",
       "      <th>company_size</th>\n",
       "      <th>senior</th>\n",
       "      <th>roles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>EUR</td>\n",
       "      <td>79833</td>\n",
       "      <td>DE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>DE</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "      <td>DS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Machine Learning Scientist</td>\n",
       "      <td>USD</td>\n",
       "      <td>260000</td>\n",
       "      <td>JP</td>\n",
       "      <td>0.0</td>\n",
       "      <td>JP</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>ML</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Big Data Engineer</td>\n",
       "      <td>GBP</td>\n",
       "      <td>109024</td>\n",
       "      <td>GB</td>\n",
       "      <td>0.5</td>\n",
       "      <td>GB</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>DE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>Product Data Analyst</td>\n",
       "      <td>USD</td>\n",
       "      <td>20000</td>\n",
       "      <td>HN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>HN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>AN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Machine Learning Engineer</td>\n",
       "      <td>USD</td>\n",
       "      <td>150000</td>\n",
       "      <td>US</td>\n",
       "      <td>0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "      <td>ML</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   work_year experience_level employment_type                   job_title  \\\n",
       "0       2020               MI              FT              Data Scientist   \n",
       "1       2020               SE              FT  Machine Learning Scientist   \n",
       "2       2020               SE              FT           Big Data Engineer   \n",
       "3       2020               MI              FT        Product Data Analyst   \n",
       "4       2020               SE              FT   Machine Learning Engineer   \n",
       "\n",
       "  salary_currency  salary_in_usd employee_residence  remote_ratio  \\\n",
       "0             EUR          79833                 DE           0.0   \n",
       "1             USD         260000                 JP           0.0   \n",
       "2             GBP         109024                 GB           0.5   \n",
       "3             USD          20000                 HN           0.0   \n",
       "4             USD         150000                 US           0.5   \n",
       "\n",
       "  company_location company_size  senior roles  \n",
       "0               DE            L       0    DS  \n",
       "1               JP            S       0    ML  \n",
       "2               GB            M       0    DE  \n",
       "3               HN            S       0    AN  \n",
       "4               US            L       0    ML  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "09812b1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['work_year',\n",
       " 'experience_level',\n",
       " 'employment_type',\n",
       " 'job_title',\n",
       " 'salary_currency',\n",
       " 'salary_in_usd',\n",
       " 'employee_residence',\n",
       " 'remote_ratio',\n",
       " 'company_location',\n",
       " 'company_size',\n",
       " 'senior',\n",
       " 'roles']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d5549f02",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>work_year</th>\n",
       "      <th>salary_in_usd</th>\n",
       "      <th>remote_ratio</th>\n",
       "      <th>senior</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>607.000000</td>\n",
       "      <td>607.000000</td>\n",
       "      <td>607.000000</td>\n",
       "      <td>607.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2021.405272</td>\n",
       "      <td>112297.869852</td>\n",
       "      <td>0.709226</td>\n",
       "      <td>0.074135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.692133</td>\n",
       "      <td>70957.259411</td>\n",
       "      <td>0.407091</td>\n",
       "      <td>0.262207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>2859.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2021.000000</td>\n",
       "      <td>62726.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2022.000000</td>\n",
       "      <td>101570.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2022.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2022.000000</td>\n",
       "      <td>600000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         work_year  salary_in_usd  remote_ratio      senior\n",
       "count   607.000000     607.000000    607.000000  607.000000\n",
       "mean   2021.405272  112297.869852      0.709226    0.074135\n",
       "std       0.692133   70957.259411      0.407091    0.262207\n",
       "min    2020.000000    2859.000000      0.000000    0.000000\n",
       "25%    2021.000000   62726.000000      0.500000    0.000000\n",
       "50%    2022.000000  101570.000000      1.000000    0.000000\n",
       "75%    2022.000000  150000.000000      1.000000    0.000000\n",
       "max    2022.000000  600000.000000      1.000000    1.000000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4da3fd9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>work_year</th>\n",
       "      <th>salary_in_usd</th>\n",
       "      <th>remote_ratio</th>\n",
       "      <th>senior</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>work_year</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170493</td>\n",
       "      <td>0.076314</td>\n",
       "      <td>-0.129455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary_in_usd</th>\n",
       "      <td>0.170493</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.132122</td>\n",
       "      <td>0.246784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>remote_ratio</th>\n",
       "      <td>0.076314</td>\n",
       "      <td>0.132122</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>senior</th>\n",
       "      <td>-0.129455</td>\n",
       "      <td>0.246784</td>\n",
       "      <td>0.009041</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "               work_year  salary_in_usd  remote_ratio    senior\n",
       "work_year       1.000000       0.170493      0.076314 -0.129455\n",
       "salary_in_usd   0.170493       1.000000      0.132122  0.246784\n",
       "remote_ratio    0.076314       0.132122      1.000000  0.009041\n",
       "senior         -0.129455       0.246784      0.009041  1.000000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6068c5ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "work_year        0.170493\n",
       "salary_in_usd    1.000000\n",
       "remote_ratio     0.132122\n",
       "senior           0.246784\n",
       "Name: salary_in_usd, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr().salary_in_usd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1ca705d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1648b5a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['work_year', 'experience_level', 'employment_type', 'job_title',\n",
       "       'salary_currency', 'salary_in_usd', 'employee_residence',\n",
       "       'remote_ratio', 'company_location', 'company_size', 'senior', 'roles'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c6f14205",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SE    280\n",
       "MI    213\n",
       "EN     88\n",
       "EX     26\n",
       "Name: experience_level, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['experience_level'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d55f8a95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x2b06b479bb0>,\n",
       "  <matplotlib.patches.Wedge at 0x2b06b3e13d0>,\n",
       "  <matplotlib.patches.Wedge at 0x2b06b3cd3a0>,\n",
       "  <matplotlib.patches.Wedge at 0x2b06b3cd1f0>],\n",
       " [Text(0.1395261931580278, 1.1415044640398166, 'MI'),\n",
       "  Text(-0.7183891320283002, -0.8330168395558553, 'SE'),\n",
       "  Text(0.8236521438616164, -0.7291070880962296, 'EN'),\n",
       "  Text(1.0900556489518896, -0.14757602172465184, 'EX')],\n",
       " [Text(0.07886263091540702, 0.6451981753268528, '46%'),\n",
       "  Text(-0.39184861746998195, -0.454372821575921, '35%'),\n",
       "  Text(0.44926480574269984, -0.3976947753252161, '14%'),\n",
       "  Text(0.5945758085192124, -0.08049601184981008, '4%')])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(df.experience_level.value_counts(), labels = df.experience_level.unique(), autopct =\"%.0f%%\", explode = [0.05,0,0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7576bda4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "experience_level\n",
       "EX    199392.038462\n",
       "SE    138617.292857\n",
       "MI     87996.056338\n",
       "EN     61643.318182\n",
       "Name: salary_in_usd, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('experience_level').salary_in_usd.mean().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dde8fd7c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "da10cfb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FT    588\n",
       "PT     10\n",
       "CT      5\n",
       "FL      4\n",
       "Name: employment_type, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['employment_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9a9a59a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x2b06b0a26a0>,\n",
       "  <matplotlib.patches.Wedge at 0x2b06b0a2f70>,\n",
       "  <matplotlib.patches.Wedge at 0x2b06ae07a90>,\n",
       "  <matplotlib.patches.Wedge at 0x2b06b3c12e0>],\n",
       " [Text(-1.1942026268941568, 0.11781377644017466, 'FT'),\n",
       "  Text(1.0884696758156491, -0.1588513922815151, 'CT'),\n",
       "  Text(1.0975110902171534, -0.07395543827437838, 'PT'),\n",
       "  Text(1.0997642815615354, -0.022771144052073963, 'FL')],\n",
       " [Text(-0.6966181990215914, 0.0687247029234352, '97%'),\n",
       "  Text(0.5937107322630814, -0.0866462139717355, '2%'),\n",
       "  Text(0.5986424128457201, -0.040339329967842744, '1%'),\n",
       "  Text(0.599871426306292, -0.012420624028403979, '1%')])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(df.employment_type.value_counts(), labels = df.employment_type.unique(), autopct =\"%.0f%%\", explode = [.1,0,0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "57b588b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "employment_type\n",
       "CT    184575.000000\n",
       "FT    113468.073129\n",
       "FL     48000.000000\n",
       "PT     33070.500000\n",
       "Name: salary_in_usd, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('employment_type').salary_in_usd.mean().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cdc0187a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "salary_currency\n",
       "USD    137264.788945\n",
       "CHF    122346.000000\n",
       "SGD    104176.500000\n",
       "CAD     97222.888889\n",
       "AUD     87064.000000\n",
       "GBP     81670.977273\n",
       "EUR     65751.473684\n",
       "JPY     60921.333333\n",
       "CNY     40283.500000\n",
       "CLP     40038.000000\n",
       "DKK     37252.500000\n",
       "PLN     36887.666667\n",
       "HUF     35997.000000\n",
       "INR     27765.555556\n",
       "TRY     20096.666667\n",
       "MXN     18185.000000\n",
       "BRL     15904.000000\n",
       "Name: salary_in_usd, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('salary_currency').salary_in_usd.mean().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5c35e858",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USD    398\n",
       "EUR     95\n",
       "GBP     44\n",
       "INR     27\n",
       "CAD     18\n",
       "JPY      3\n",
       "PLN      3\n",
       "TRY      3\n",
       "CNY      2\n",
       "MXN      2\n",
       "HUF      2\n",
       "DKK      2\n",
       "SGD      2\n",
       "BRL      2\n",
       "AUD      2\n",
       "CLP      1\n",
       "CHF      1\n",
       "Name: salary_currency, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['salary_currency'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a27b85e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "US    332\n",
       "GB     44\n",
       "IN     30\n",
       "CA     29\n",
       "DE     25\n",
       "FR     18\n",
       "ES     15\n",
       "GR     13\n",
       "JP      7\n",
       "PT      6\n",
       "BR      6\n",
       "PK      6\n",
       "NL      5\n",
       "PL      4\n",
       "IT      4\n",
       "RU      4\n",
       "AE      3\n",
       "AT      3\n",
       "VN      3\n",
       "TR      3\n",
       "AU      3\n",
       "RO      2\n",
       "BE      2\n",
       "SG      2\n",
       "SI      2\n",
       "DK      2\n",
       "HU      2\n",
       "NG      2\n",
       "MX      2\n",
       "BO      1\n",
       "MY      1\n",
       "TN      1\n",
       "IE      1\n",
       "DZ      1\n",
       "AR      1\n",
       "CZ      1\n",
       "JE      1\n",
       "LU      1\n",
       "PR      1\n",
       "RS      1\n",
       "EE      1\n",
       "CL      1\n",
       "HK      1\n",
       "KE      1\n",
       "MD      1\n",
       "CO      1\n",
       "IR      1\n",
       "CN      1\n",
       "MT      1\n",
       "UA      1\n",
       "IQ      1\n",
       "HN      1\n",
       "BG      1\n",
       "HR      1\n",
       "PH      1\n",
       "NZ      1\n",
       "CH      1\n",
       "Name: employee_residence, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['employee_residence'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4d529361",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    326\n",
       "L    198\n",
       "S     83\n",
       "Name: company_size, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['company_size'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c1e7dd25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x2b06b0e8f10>,\n",
       "  <matplotlib.patches.Wedge at 0x2b06b0c66d0>,\n",
       "  <matplotlib.patches.Wedge at 0x2b06b0c6e20>],\n",
       " [Text(-0.1394256842916048, 1.1918726771596946, 'L'),\n",
       "  Text(-0.33883566866423465, -1.0465134445581008, 'S'),\n",
       "  Text(1.0000569882359693, -0.4581331905465946, 'M')],\n",
       " [Text(-0.08133164917010277, 0.6952590616764885, '54%'),\n",
       "  Text(-0.18481945563503704, -0.5708255152135094, '33%'),\n",
       "  Text(0.5454856299468923, -0.24989083120723338, '14%')])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zfb/GXeH8FuCbaDHFfW1Hx9JTOjqfTuWfWb5fOfnlHz34afp9E9UrP3SvRz44fQ7x3jhWvkVPcw/92/sp3788lZHGQ+SQFqTLCS8Lj6924/JtbcsX9PQ8k84xdryyg8IphZTWjtxLeXpgOltu3kLrY61M+8w0mv/UzIzTZqQzyp7k6MwJTwiPrz7GHQ1bl1b1D6xPx58d74nT8mALM77w0dKV7l3K3J/MZZ/gPvS29FIwxT3z2vTbTWz+7830xzK6WkgH7uONIoQPa2ONCL3Bq0ZXAAUP1DfMK47HU76RUm9zL70tvWy8dCMbfrCBvu19vHfZe/S1D60E4jgOzQ80M+PkGTTf10z1qdVULq2k9c+tqY4zmqcidRGx54TlLwi5s6cu42igCseZfP+Wxo7Pz5m1NW5ZKXsQoGSvEg649oB//nzDDzYw155LQcXQP8f2te1UHFxBfnk+8d64O43k4X6cOX/M5GAfJn1YC/Bn6QDq483uH5i1urG5DcdJes3ezas284/L/0FPUw9vX/Q2bU+1jfr58Z447c+2M+1Y9y3XquOr2HTdJrb+cStTj52abIzx6gXuz9RguyP/Pqe7vEcDepeP0e6ZVP7iZVVTD8OyTPiGngnhSF3kRMkA8v+j7VgvcKd0DDW60zo6Dz9jR0dar+AaRvSQFkwop+sW6QBqzy5u27780F3dKX0P1FC9wH3SIcwopx37G/C6dAy1Z6ubmpfN7O9/UTpHmv05UheJSYcwo5yum6UDqD3Lg7z76xsXlsbjb0lnSaO7pAOAWeW8A4El79X4lTlO+YP1jVPzHUd+S4vUE79KO8icctqxFuAh6RhqbKoHBqpvb9jaheNkfGu8NLvHhENaMKmcrlukA6ixW9TbO+/KltZ3cRzZrRJT67+kAwwyrZwPA/ILSKsx+1xn16Hfju14XjpHiqyL1EWMudhlVjntWD9wu3QMNT7f3x475piuXU9K50iBX0sHGM6scrquAXqkQ6jx+X9bW5bX9vV5eQb9ALh7T59kWZZjWdZtw35eYFlWi2VZKb9eYl457dgmYJV0DDU+7mZJjf8yaSAekc6SpCsjdZGxnDt3Aossyxp8EPWzpGldW/PK6boCkN8vRY1LsUPJQ/UNNQWOs0k6yzg1AavH8fmPAIHEx6cDv095Ikwtpx3bBlwtHUON37R4vGrNlqYBy3G2S2cZh6sjdZHxbLb8B+CrlrsQ2kGkaSkTM8vpuhpolg6hxm9+X98+125t2YTjyG5QNTYtwPXj+QLHcV4H/Liz5sNpyASYXE471oF7eKs8aPmu7oMv3B57STrHGAQjdZGOJL7uAeAq0nRICyaX03U9EJUOoZLzzdiOo4/v6HxSOsco1pH8Pd2rgZ85jpO2C2Bml9N91vMy6RgqeVe1tK6Y19u7VjrHbsSB8yJ1kaRWG3Acp95xnN+kONMI8ish7Inty8Pdhn6RdBSVnF7o/XTt7Dfb8/MXS2cZ5r8jdZFzpEOMxuyZE8COxYGLpWOo5CU2S/IXOc4/pLMktAGXSIfYE/PLCWDHHsLjS5l09zscfmMHB1/fwcLfdnDZX90r95f+pZuDVnWw+PoOjrutk4ad7upyz27q56BVHSy5sYONbe6vtXc7HH97J8Yf7eyGLx6vvLu+scBynG3SWYD/iNRFMrrGZjLMP6wdZPt8uIe3e0tHSYbjOHT2waQii74Bh2U3d/KbE0pYMD2fycXuHiDXvNDDmy1xrj+xlNPWdPGrzxQTbXd4dGM/Vx9fwg/+fzcn71fAcr8JK5omZ11J8RvfnjnjE6neLGkc1gOHR+oiGV1jMxnemDkB7FgMOBMQW+R3IizLYlKRW8K+OPQNuFttDxYToLN3aPvtwnzY1Q9dfQ6F+fBeW5wtO+OeLibAkd09iy5u3f6K0PTfD3zPC8UEL82cg2zf5XjgfGF3BuIOh97Qyca2OOcuKeJXn3Unj0ue6OZ3r/fhK7b4a10Z08vzeLVpgHMe6qa0EG77Qik/fKybn3+qmHnTsmM3xJ9UTX3q3opJyzM87MWRukgow2MmzYvlLACeA5ZIR0lWe7fDF9Z0ce3nSlg0Y6hsv3ymh+5+h59+auQR39Mf9HPf2/2cc1ghl/61h8I8i6uPK6Z6kncOfHbn9FnVz7xRXHxMhoZ7BAgk+9aJBO/97brPfH4N9+kAT6ossVixdwGPbhz5EMQZBxZy91sjf81xHC5/uodLP1nMT5/q4acrijnzoEKuecELd8aN7jZ3s6RM7DRXD5zlpWKCF8sJYMfeBS6UjjEeLZ1x2rvdfxu7+hwef7+f/avyeLd16BT6gQ3urw1362t9BOYVMKXUoqsP8iz31ZUFS6EVQMGD9Q37lcTj76RxmH7gq5G6iAlXicfFe4e1w9m+u4HTpGOMxetbB6i7bxcDcYg7sHJhIT9ZXswX7+piw7Y4eRbsXZnH9YESZk92C9rV5xC4s4vHziyjMN/imQ/6+d7D3RTlw++/WMr8LDn/bCjIb/zcnFlW3LJmpuGP99R55nBeL+dU3MWoZ0tHURPzt+Kit+pqqmuxrFRuYf0wcKLXDmcHefOwdpAdawNOwd3kVHnYIT29B/x8W9vfcZxUvVXmyfPM4bxdTgA7th5YiXtuoTzs1I7Ow8/csTMVN8m3Ayd54S6g0Xi/nAB27BHA6JuY1dj8e1v78iUT2yypC/ctk1dTFElMdpQTwI7dBPxMOoaauP9pal5W09efzNIfvcBpkbrIc6nOJMHbF4R2x/ZdB5wrHUNNzC7L6lpROzvalZe3YIxfMoD7lsmf0pkrk7Jn5hxyPnCTdAg1MaWOU/ZgfWNVvuPUj/FLzs6mYkI2ltOOOcB3cHctUx42Y2Bgxp0NTd04zp42FvphpC6Sdd+Qs6+cMPiAdh1jWMFbmW1Bb98nrm7e9t4omyVdHqmLZOUyqtlZTgA7NoC7dGHWfUfNNcd17Trk7PaPbJbkAD+O1EUulciUCdl3QWh3bN+/Af9JNn8zygHnVVc9+VRZ2QrcTZa/EamLZPWmV7lRTgDbF8BdY7RCOopKjgPOl2bP/Os7RUWhSF3kz9J50i13yglg+w4EHsSjS50oosDJ2DGvbpY0Lrl1mOf+pR6O+7C28pangSW5UkzItXIC2LFm4Fjgtj19qjLGjcBnEhtc5YzcOqz9MNt3Me5+LNaePlWJ2AX8EDv2W+kgEnK7nAC27wTgBmAv6ShqhGeAbyZWvchJuXdY+2F27FFgAXAd7v4ZSlYn8H1geS4XE3TmHMn2HQn8D7BQOkqO+gvwLezY+9JBTKAz53B2bB1wCO7OZt5f3s47duI+j/sZLeYQnTk/ju07APcq4dHSUbLcY8C3sWObpIOYRss5GttnAd8FfglMFk6TbZqAS7Bjq6WDmErLORa2bxYQBL4FlAqn8botwJXADdixbukwJtNyjoftmw5cgLvSQqVsGM/5AAgBN2PHeqTDeIGWMxm2rwL3cPciIB0LIWeT93BPC36HHcuCdeozR8s5EbavGPg68CNgX+E0ptkA/AK4I/FsrRonLWcq2L584Cu456UHCqeRFAeexL3KfVdiRQqVJC1nqtm+I3BXYFgJ1AinyQQHWAusAf6EHdsqnCdraDnTxfblAStwi3oaMFU0T+qtwy3kH7FjW6TDZCMtZya4h71HAScCJwEHyAZK2nrcQt6FHftAOky203JKsH1zgROAw4DFuDfeF0lG2o1+3B3cnsd9OP1ZLWRmaTlNYPsKcQu6GDh42I+ZOhTuAd4EIriFXA+8hB3z7O7h2UDLaTLbV8vQzDoV9xZCX+I1+UM/VjDyQYYeoG03r9ZhH28D3gLewY7pLm2G0XKmiGVZlwBn4O7ZEQfOdhwnmc14kuPeBzwJKAd26qznfVrOFLAsaynwf4EVjuP0WJZVBRQ5jtMgHE15WIF0gCxRA2xzHKcHwHGcnFqISqWHzpwpYFnWJNw34suAx4E1juM8JZtKeZ2uhJACjuN0AIfi7m7WAqyxLOtfRUMpz9OZMw0sy/oSUOc4zknSWZR36cyZApZl7WdZ1rxhv7QY9/lFpZKmF4RSYxJwrWVZlbh31mzEPcRVKml6WKuUofSwVilDaTmVMpSWUylDaTmVMpSWUylDaTmVMpSWUylDaTmVMpSWUylDaTmVMpSWUylDaTmVMpSWUylDaTmVMpSWUylDaTmVMpSWUylDaTmVMpSWUylDaTmVMtT/AlR6NKqV/QwdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(df.company_size.value_counts(), labels = df.company_size.unique(), autopct =\"%.0f%%\", explode = [0.1,0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8bfe71f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company_size\n",
       "L    119242.994949\n",
       "M    116905.466258\n",
       "S     77632.674699\n",
       "Name: salary_in_usd, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('company_size').salary_in_usd.mean().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0630e263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "senior\n",
       "1    174130.577778\n",
       "0    107346.852313\n",
       "Name: salary_in_usd, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('senior').salary_in_usd.mean().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1e8d15f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "roles\n",
       "OT    143292.714286\n",
       "DS    119125.479167\n",
       "DE    118976.598837\n",
       "ML    113026.132353\n",
       "RE    102924.764706\n",
       "AN     98851.718519\n",
       "AI     66135.571429\n",
       "CV     64695.777778\n",
       "Name: salary_in_usd, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('roles').salary_in_usd.mean().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1c3299a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x2b06bc5f490>,\n",
       "  <matplotlib.patches.Wedge at 0x2b06bc5fc40>,\n",
       "  <matplotlib.patches.Wedge at 0x2b06bc6c3a0>,\n",
       "  <matplotlib.patches.Wedge at 0x2b06bc6cac0>,\n",
       "  <matplotlib.patches.Wedge at 0x2b06bc79220>,\n",
       "  <matplotlib.patches.Wedge at 0x2b06bc79970>,\n",
       "  <matplotlib.patches.Wedge at 0x2b06bc870d0>,\n",
       "  <matplotlib.patches.Wedge at 0x2b06bc877f0>],\n",
       " [Text(0.6546949930893298, 1.005671152029212, 'DS'),\n",
       "  Text(-1.0619019707610353, 0.2869916453380293, 'ML'),\n",
       "  Text(-0.2677097068969625, -1.066926198400406, 'DE'),\n",
       "  Text(0.792766234057853, -0.7625756999391796, 'AN'),\n",
       "  Text(1.0420417498646501, -0.35234782749297366, 'RE'),\n",
       "  Text(1.0798923458764296, -0.20936217738049492, 'AI'),\n",
       "  Text(1.0935092190995646, -0.11932136331881409, 'CV'),\n",
       "  Text(1.0992781677464165, -0.03984356806413565, 'OT')],\n",
       " [Text(0.38190541263544237, 0.5866415053503737, '32%'),\n",
       "  Text(-0.5792192567787465, 0.15654089745710686, '28%'),\n",
       "  Text(-0.14602347648925224, -0.5819597445820396, '22%'),\n",
       "  Text(0.4324179458497379, -0.41595038178500704, '11%'),\n",
       "  Text(0.5683864090170818, -0.19218972408707655, '3%'),\n",
       "  Text(0.5890321886598706, -0.11419755129845176, '1%'),\n",
       "  Text(0.5964595740543079, -0.0650843799920804, '1%'),\n",
       "  Text(0.5996062733162272, -0.021732855307710353, '1%')])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(df[\"roles\"].value_counts(), labels = df[\"roles\"].unique(), autopct =\"%.0f%%\", explode = [0.1,0,0,0,0,0,0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "969beaa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "roles  senior\n",
       "DE     1         194256.636364\n",
       "DS     1         181174.285714\n",
       "OT     1         160162.600000\n",
       "AN     1         154434.833333\n",
       "ML     0         113921.303030\n",
       "DE     0         113833.242236\n",
       "DS     0         111505.450292\n",
       "RE     0         102924.764706\n",
       "OT     0         101118.000000\n",
       "AN     0          96266.457364\n",
       "ML     1          83485.500000\n",
       "AI     0          66135.571429\n",
       "CV     0          64695.777778\n",
       "Name: salary_in_usd, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by = ['roles','senior']).salary_in_usd.mean().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f13fb98a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "roles  experience_level\n",
       "DE     EX                  254288.571429\n",
       "OT     EX                  232500.000000\n",
       "DS     EX                  196375.818182\n",
       "AI     MI                  160000.000000\n",
       "OT     SE                  155946.333333\n",
       "DS     SE                  154984.071429\n",
       "DE     SE                  144328.848837\n",
       "ML     SE                  141548.107143\n",
       "AN     EX                  140000.000000\n",
       "RE     MI                  120112.000000\n",
       "AN     SE                  119264.414286\n",
       "ML     MI                   98624.000000\n",
       "CV     MI                   95746.000000\n",
       "RE     SE                   88859.400000\n",
       "DS     MI                   87721.376812\n",
       "RE     EN                   86132.000000\n",
       "DE     MI                   85505.656250\n",
       "ML     EN                   84127.928571\n",
       "       EX                   79039.000000\n",
       "AN     MI                   78704.071429\n",
       "CV     EN                   76721.800000\n",
       "DS     EN                   58590.035714\n",
       "AN     EN                   55050.111111\n",
       "AI     SE                   55000.000000\n",
       "DE     EN                   53287.466667\n",
       "OT     MI                   35105.000000\n",
       "CV     SE                   34302.333333\n",
       "AI     EN                   21987.250000\n",
       "Name: salary_in_usd, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by = ['roles','experience_level']).salary_in_usd.mean().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cf7dcc1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "roles  company_size\n",
       "OT     M               200000.000000\n",
       "       L               154015.000000\n",
       "RE     M               145467.000000\n",
       "DS     M               129193.494624\n",
       "AI     L               127500.000000\n",
       "ML     L               126946.333333\n",
       "DE     M               122180.038095\n",
       "DS     L               121775.802817\n",
       "DE     L               120364.509434\n",
       "ML     M               110147.200000\n",
       "AN     L               107869.062500\n",
       "       M               102767.340659\n",
       "ML     S                99230.789474\n",
       "RE     L                96465.000000\n",
       "DE     S                89696.571429\n",
       "CV     S                83588.750000\n",
       "DS     S                78964.821429\n",
       "AI     M                66000.000000\n",
       "RE     S                53369.000000\n",
       "CV     M                49581.400000\n",
       "AN     S                45112.000000\n",
       "OT     S                32974.000000\n",
       "AI     S                25316.333333\n",
       "Name: salary_in_usd, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by = ['roles','company_size']).salary_in_usd.mean().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d4ccd896",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "senior  company_size\n",
       "1       L               200843.136364\n",
       "        S               149832.100000\n",
       "        M               147615.846154\n",
       "0       M               115629.955272\n",
       "        L               109042.977273\n",
       "        S                67742.342466\n",
       "Name: salary_in_usd, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by = ['senior','company_size']).salary_in_usd.mean().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "79201f7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cypher\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='experience_level', ylabel='salary_in_usd'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(df.experience_level,df.salary_in_usd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3acd5750",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='company_size', ylabel='salary_in_usd'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(df.company_size,df.salary_in_usd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d727b6e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cypher\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='employment_type', ylabel='salary_in_usd'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(df.employment_type,df.salary_in_usd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "820b07a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cypher\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='roles', ylabel='salary_in_usd'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(df.roles,df.salary_in_usd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "2662d55c",
   "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>work_year</th>\n",
       "      <th>experience_level</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>job_title</th>\n",
       "      <th>salary_currency</th>\n",
       "      <th>salary_in_usd</th>\n",
       "      <th>employee_residence</th>\n",
       "      <th>remote_ratio</th>\n",
       "      <th>company_location</th>\n",
       "      <th>company_size</th>\n",
       "      <th>senior</th>\n",
       "      <th>roles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2021</td>\n",
       "      <td>EX</td>\n",
       "      <td>FT</td>\n",
       "      <td>Head of Data</td>\n",
       "      <td>USD</td>\n",
       "      <td>235000</td>\n",
       "      <td>US</td>\n",
       "      <td>1.0</td>\n",
       "      <td>US</td>\n",
       "      <td>L</td>\n",
       "      <td>1</td>\n",
       "      <td>OT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>2021</td>\n",
       "      <td>EX</td>\n",
       "      <td>FT</td>\n",
       "      <td>Head of Data</td>\n",
       "      <td>USD</td>\n",
       "      <td>230000</td>\n",
       "      <td>RU</td>\n",
       "      <td>0.5</td>\n",
       "      <td>RU</td>\n",
       "      <td>L</td>\n",
       "      <td>1</td>\n",
       "      <td>OT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>2021</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Specialist</td>\n",
       "      <td>USD</td>\n",
       "      <td>165000</td>\n",
       "      <td>US</td>\n",
       "      <td>1.0</td>\n",
       "      <td>US</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "      <td>OT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>2021</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Head of Data</td>\n",
       "      <td>EUR</td>\n",
       "      <td>102839</td>\n",
       "      <td>SI</td>\n",
       "      <td>1.0</td>\n",
       "      <td>SI</td>\n",
       "      <td>L</td>\n",
       "      <td>1</td>\n",
       "      <td>OT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>2022</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>NLP Engineer</td>\n",
       "      <td>CNY</td>\n",
       "      <td>37236</td>\n",
       "      <td>US</td>\n",
       "      <td>0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "      <td>OT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>2022</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Head of Data</td>\n",
       "      <td>USD</td>\n",
       "      <td>200000</td>\n",
       "      <td>MY</td>\n",
       "      <td>1.0</td>\n",
       "      <td>US</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>OT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>2022</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>Head of Data</td>\n",
       "      <td>EUR</td>\n",
       "      <td>32974</td>\n",
       "      <td>EE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>EE</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>OT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     work_year experience_level employment_type        job_title  \\\n",
       "74        2021               EX              FT     Head of Data   \n",
       "160       2021               EX              FT     Head of Data   \n",
       "165       2021               SE              FT  Data Specialist   \n",
       "286       2021               SE              FT     Head of Data   \n",
       "455       2022               MI              FT     NLP Engineer   \n",
       "490       2022               SE              FT     Head of Data   \n",
       "501       2022               MI              FT     Head of Data   \n",
       "\n",
       "    salary_currency  salary_in_usd employee_residence  remote_ratio  \\\n",
       "74              USD         235000                 US           1.0   \n",
       "160             USD         230000                 RU           0.5   \n",
       "165             USD         165000                 US           1.0   \n",
       "286             EUR         102839                 SI           1.0   \n",
       "455             CNY          37236                 US           0.5   \n",
       "490             USD         200000                 MY           1.0   \n",
       "501             EUR          32974                 EE           1.0   \n",
       "\n",
       "    company_location company_size  senior roles  \n",
       "74                US            L       1    OT  \n",
       "160               RU            L       1    OT  \n",
       "165               US            L       0    OT  \n",
       "286               SI            L       1    OT  \n",
       "455               US            L       0    OT  \n",
       "490               US            M       1    OT  \n",
       "501               EE            S       1    OT  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.roles == \"OT\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c4104190",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cypher\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='senior', ylabel='salary_in_usd'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(df.senior,df.salary_in_usd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "455fd94b",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}