SQL_Bonus.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlalchemy\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib import style\n",
    "style.use('seaborn')\n",
    "\n",
    "from sqlalchemy.ext.automap import automap_base\n",
    "from sqlalchemy.orm import Session\n",
    "from sqlalchemy import create_engine, inspect\n",
    "from sqlalchemy import Column, Integer, String, Float, or_\n",
    "\n",
    "from sqlalchemy import func\n",
    "\n",
    "pd.options.display.float_format = \"{:,.3f}\".format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create connection engine\n",
    "engine = create_engine('postgresql://postgres:Quant202!@localhost:5432/Employees_Challenge')\n",
    "connection = engine.connect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Base = automap_base()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Base.prepare(engine, reflect = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "inspector = inspect(engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Departments', 'Titles', 'Employees', 'Salaries', 'Dept_Emp', 'dept_manager']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inspector.get_table_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Departments = Base.classes.Departments\n",
    "Titles = Base.classes.Titles\n",
    "Employees = Base.classes.Employees\n",
    "Salaries = Base.classes.Salaries\n",
    "Dept_Emp = Base.classes.Dept_Emp\n",
    "Dept_Manager = Base.classes.dept_manager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "session = Session(bind=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "salaries=session.query(Salaries.emp_no,Salaries.salary).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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       " ...]"
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     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(salaries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Employee_Salaries_df= pd.DataFrame(salaries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</table>\n",
       "<p>300024 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "        emp_no  salary\n",
       "0        10001   60117\n",
       "1        10002   65828\n",
       "2        10003   40006\n",
       "3        10004   40054\n",
       "4        10005   78228\n",
       "...        ...     ...\n",
       "300019  499995   40000\n",
       "300020  499996   58058\n",
       "300021  499997   49597\n",
       "300022  499998   40000\n",
       "300023  499999   63707\n",
       "\n",
       "[300024 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "Employee_Salaries_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_departments = session.query(Dept_Emp.emp_no,Dept_Emp.dept_no).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "Employee_Departments_df= pd.DataFrame(emp_departments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "      <th>emp_no</th>\n",
       "      <th>dept_no</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
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       "      <th>1</th>\n",
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       "      <th>4</th>\n",
       "      <td>10005</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>331598</th>\n",
       "      <td>499995</td>\n",
       "      <td>d004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331599</th>\n",
       "      <td>499996</td>\n",
       "      <td>d004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331600</th>\n",
       "      <td>499997</td>\n",
       "      <td>d005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331601</th>\n",
       "      <td>499998</td>\n",
       "      <td>d002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331602</th>\n",
       "      <td>499999</td>\n",
       "      <td>d004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>331603 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        emp_no dept_no\n",
       "0        10001    d005\n",
       "1        10002    d007\n",
       "2        10003    d004\n",
       "3        10004    d004\n",
       "4        10005    d003\n",
       "...        ...     ...\n",
       "331598  499995    d004\n",
       "331599  499996    d004\n",
       "331600  499997    d005\n",
       "331601  499998    d002\n",
       "331602  499999    d004\n",
       "\n",
       "[331603 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Employee_Departments_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "Employee_Departments_df=Employee_Departments_df.drop_duplicates(subset=['emp_no'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>331600</th>\n",
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       "      <th>331602</th>\n",
       "      <td>499999</td>\n",
       "      <td>d004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300024 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        emp_no dept_no\n",
       "0        10001    d005\n",
       "1        10002    d007\n",
       "2        10003    d004\n",
       "3        10004    d004\n",
       "4        10005    d003\n",
       "...        ...     ...\n",
       "331598  499995    d004\n",
       "331599  499996    d004\n",
       "331600  499997    d005\n",
       "331601  499998    d002\n",
       "331602  499999    d004\n",
       "\n",
       "[300024 rows x 2 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Employee_Departments_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_df = pd.merge(Employee_Salaries_df, Employee_Departments_df, how = \"left\", on = \"emp_no\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>emp_no</th>\n",
       "      <th>salary</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
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       "      <td>40000</td>\n",
       "      <td>d004</td>\n",
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       "    <tr>\n",
       "      <th>300020</th>\n",
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       "      <td>58058</td>\n",
       "      <td>d004</td>\n",
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       "      <td>d005</td>\n",
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       "    <tr>\n",
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       "      <td>40000</td>\n",
       "      <td>d002</td>\n",
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       "    <tr>\n",
       "      <th>300023</th>\n",
       "      <td>499999</td>\n",
       "      <td>63707</td>\n",
       "      <td>d004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300024 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        emp_no  salary dept_no\n",
       "0        10001   60117    d005\n",
       "1        10002   65828    d007\n",
       "2        10003   40006    d004\n",
       "3        10004   40054    d004\n",
       "4        10005   78228    d003\n",
       "...        ...     ...     ...\n",
       "300019  499995   40000    d004\n",
       "300020  499996   58058    d004\n",
       "300021  499997   49597    d005\n",
       "300022  499998   40000    d002\n",
       "300023  499999   63707    d004\n",
       "\n",
       "[300024 rows x 3 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "departments_names = session.query(Departments.dept_no,Departments.dept_name).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "dpt_name_df = pd.DataFrame(departments_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>dept_no</th>\n",
       "      <th>dept_name</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d001</td>\n",
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       "      <td>Production</td>\n",
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       "      <th>4</th>\n",
       "      <td>d005</td>\n",
       "      <td>Development</td>\n",
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       "      <th>5</th>\n",
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       "      <td>Quality Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>d007</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>d008</td>\n",
       "      <td>Research</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>d009</td>\n",
       "      <td>Customer Service</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "  dept_no           dept_name\n",
       "0    d001           Marketing\n",
       "1    d002             Finance\n",
       "2    d003     Human Resources\n",
       "3    d004          Production\n",
       "4    d005         Development\n",
       "5    d006  Quality Management\n",
       "6    d007               Sales\n",
       "7    d008            Research\n",
       "8    d009    Customer Service"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpt_name_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_df = pd.merge(master_df, dpt_name_df, how = \"left\", on = \"dept_no\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emp_no</th>\n",
       "      <th>salary</th>\n",
       "      <th>dept_no</th>\n",
       "      <th>dept_name</th>\n",
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       "      <td>10001</td>\n",
       "      <td>60117</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002</td>\n",
       "      <td>65828</td>\n",
       "      <td>d007</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>40006</td>\n",
       "      <td>d004</td>\n",
       "      <td>Production</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>40054</td>\n",
       "      <td>d004</td>\n",
       "      <td>Production</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10005</td>\n",
       "      <td>78228</td>\n",
       "      <td>d003</td>\n",
       "      <td>Human Resources</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300019</th>\n",
       "      <td>499995</td>\n",
       "      <td>40000</td>\n",
       "      <td>d004</td>\n",
       "      <td>Production</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300020</th>\n",
       "      <td>499996</td>\n",
       "      <td>58058</td>\n",
       "      <td>d004</td>\n",
       "      <td>Production</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300021</th>\n",
       "      <td>499997</td>\n",
       "      <td>49597</td>\n",
       "      <td>d005</td>\n",
       "      <td>Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300022</th>\n",
       "      <td>499998</td>\n",
       "      <td>40000</td>\n",
       "      <td>d002</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300023</th>\n",
       "      <td>499999</td>\n",
       "      <td>63707</td>\n",
       "      <td>d004</td>\n",
       "      <td>Production</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300024 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        emp_no  salary dept_no        dept_name\n",
       "0        10001   60117    d005      Development\n",
       "1        10002   65828    d007            Sales\n",
       "2        10003   40006    d004       Production\n",
       "3        10004   40054    d004       Production\n",
       "4        10005   78228    d003  Human Resources\n",
       "...        ...     ...     ...              ...\n",
       "300019  499995   40000    d004       Production\n",
       "300020  499996   58058    d004       Production\n",
       "300021  499997   49597    d005      Development\n",
       "300022  499998   40000    d002          Finance\n",
       "300023  499999   63707    d004       Production\n",
       "\n",
       "[300024 rows x 4 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Frequency Distribution of Salaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = [40000, 50000, 60000,70000,80000,90000,100000,110000,120000,130000]\n",
    "\n",
    "fig5 = plt.figure(figsize =(12,8))\n",
    "values = master_df.salary\n",
    "plt.title(\"Frequency Distribution of Employee Salaries\", fontsize = 18, fontweight ='bold', color='darkred')\n",
    "plt.xlabel(\"Salary Ranges\", fontsize = 15, fontweight ='bold')\n",
    "plt.ylabel(\"Salary ($))\", fontsize = 15, fontweight ='bold')\n",
    "\n",
    "plt.hist(values, bins=bins, edgecolor = 'b', facecolor = 'yellow')\n",
    "plt.xticks(fontsize = 12, color = 'k', rotation = 0)\n",
    "plt.yticks(fontsize = 12, color = 'k', rotation = 0)\n",
    "\n",
    "plt.grid(color='k', alpha = 0.15)\n",
    "\n",
    "plt.tight_layout(pad=0.8, w_pad=1.4, h_pad=1.0)\n",
    "\n",
    "plt.savefig(\"Salaries_Histogram.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Boxplot of salaries by department"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "red_square = dict(markerfacecolor = 'r', marker ='s')\n",
    "\n",
    "master_df.boxplot(\"salary\", by=\"dept_name\", figsize = (20,10), notch = True, flierprops = red_square)\n",
    "\n",
    "plt.title(\"Salaries by Department\",fontdict ={\"fontsize\":24, \"color\": 'darkred', \"weight\":'bold'})\n",
    "plt.suptitle(\"\")\n",
    "plt.xlabel(\"Department\", fontdict={\"fontsize\":22})\n",
    "plt.ylabel(\"Salaries\", fontdict={\"fontsize\":22})\n",
    "plt.tight_layout(pad = 2.0, w_pad = 1.5, h_pad = 2.0)\n",
    "\n",
    "plt.xticks(fontsize = 16, color = 'k', rotation = 0)\n",
    "plt.yticks(fontsize = 16, color = 'k', rotation = 0)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Analysis using Pandas csv Read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "salaries_df = pd.read_csv(\"salaries.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "salaries = salaries_df['salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   300,024.000\n",
       "mean     52,970.732\n",
       "std      14,301.478\n",
       "min      40,000.000\n",
       "25%      40,000.000\n",
       "50%      48,681.000\n",
       "75%      61,758.000\n",
       "max     129,492.000\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salaries.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax1 = plt.subplots(figsize = (10,8))\n",
    "ax1.set_title('Employee Salaries',fontdict ={\"fontsize\":24, \"color\": 'blue', \"weight\":'bold'})\n",
    "ax1.set_ylabel('Salary',fontdict={\"fontsize\":20, \"color\":'blue',\"weight\":'bold'})\n",
    "ax1.set_xlabel('All Employees',fontdict={\"fontsize\":20, \"color\":'blue',\"weight\":'bold'})\n",
    "ax1.boxplot(salaries)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = [40000, 50000, 60000,70000,80000,90000,100000,110000,120000,130000]\n",
    "\n",
    "fig3 = plt.figure(figsize =(12,8))\n",
    "\n",
    "plt.title(\"Frequency Distribution of Employee Salaries\", fontsize = 18, fontweight ='bold', color='darkred')\n",
    "plt.xlabel(\"Salary Ranges\", fontsize = 15, fontweight ='bold')\n",
    "plt.ylabel(\"Salary ($))\", fontsize = 15, fontweight ='bold')\n",
    "\n",
    "plt.hist(salaries, bins=bins, edgecolor = 'b', facecolor = 'lightblue')\n",
    "plt.xticks(fontsize = 12, color = 'k', rotation = 0)\n",
    "plt.yticks(fontsize = 12, color = 'k', rotation = 0)\n",
    "\n",
    "plt.grid(color='k', alpha = 0.15)\n",
    "\n",
    "plt.tight_layout(pad=0.8, w_pad=1.4, h_pad=1.0)\n",
    "\n",
    "plt.savefig(\"Salaries_Histogram.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Analyzing all Salaries\n",
    "employees_df = pd.read_csv(\"employees.csv\")\n",
    "departments_df = pd.read_csv(\"departments.csv\")\n",
    "dpt_employees_df = pd.read_csv(\"dept_emp.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = pd.merge(employees_df, salaries_df, how = \"left\", on = \"emp_no\")\n",
    "all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Male vs Female Average Salary\n",
    "Average_Salary_By_Gender = all_data.groupby('sex').salary.mean()\n",
    "Average_Salary_By_Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data.boxplot(\"salary\", by=\"sex\", figsize = (20,10))\n",
    "\n",
    "plt.title(\"Employee Salaries by Gender\",fontdict ={\"fontsize\":24})\n",
    "plt.suptitle(\"\")\n",
    "plt.xlabel(\"Gender\", fontdict={\"fontsize\":22})\n",
    "plt.ylabel(\"Salary in dollars\", fontdict={\"fontsize\":22})\n",
    "plt.tight_layout(pad = 2.0, w_pad = 1.5, h_pad = 2.0)\n",
    "plt.xticks(fontsize = 20, color = 'k', rotation = 0)\n",
    "plt.yticks(fontsize = 20, color = 'k', rotation = 0)\n",
    "\n",
    "plt.savefig(\"Salaries_by_Gender.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:PythonData] *",
   "language": "python",
   "name": "conda-env-PythonData-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}