Employee.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting psycopg2\n",
      "  Downloading psycopg2-2.8.6-cp36-cp36m-win_amd64.whl (1.1 MB)\n",
      "Installing collected packages: psycopg2\n",
      "Successfully installed psycopg2-2.8.6\n"
     ]
    }
   ],
   "source": [
    "!pip install psycopg2 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import psycopg2\n",
    "from config import username, password\n",
    "from sqlalchemy import create_engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#assign variables \n",
    "username = username\n",
    "password = password\n",
    "\n",
    "#database_path = 'EmployeeSQL/Table.sql'\n",
    "engine = create_engine(f'postgresql://{username}:{password}@localhost:5432/EmployeeSQL')\n",
    "conn = engine.connect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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>emp_no</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>60117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002</td>\n",
       "      <td>65828.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>40006.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>40054.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10005</td>\n",
       "      <td>78228.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   emp_no   salary\n",
       "0   10001  60117.0\n",
       "1   10002  65828.0\n",
       "2   10003  40006.0\n",
       "3   10004  40054.0\n",
       "4   10005  78228.0"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#query records from the database\n",
    "salary_data = pd.read_sql(\"select * from salaries\", conn)\n",
    "salary_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the histogram\n",
    "fig=plt.figure(figsize=[20,10])\n",
    "plt.hist(salary_data['salary'], 10, histtype = 'bar', facecolor= 'green')\n",
    "plt.grid(axis='y', alpha=0.75)\n",
    "plt.xlabel('Salary',fontsize=15)\n",
    "plt.ylabel('Employee Frequency',fontsize=15)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.title('Salary Distribution Histogram',fontsize=15)\n",
    "plt.show()\n",
    "fig.savefig('Graphs/Histogram.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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>emp_no</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>sex</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>473302</td>\n",
       "      <td>s0001</td>\n",
       "      <td>1953-07-25</td>\n",
       "      <td>Hideyuki</td>\n",
       "      <td>Zallocco</td>\n",
       "      <td>M</td>\n",
       "      <td>1990-04-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>475053</td>\n",
       "      <td>e0002</td>\n",
       "      <td>1954-11-18</td>\n",
       "      <td>Byong</td>\n",
       "      <td>Delgrande</td>\n",
       "      <td>F</td>\n",
       "      <td>1991-09-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57444</td>\n",
       "      <td>e0002</td>\n",
       "      <td>1958-01-30</td>\n",
       "      <td>Berry</td>\n",
       "      <td>Babb</td>\n",
       "      <td>F</td>\n",
       "      <td>1992-03-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>421786</td>\n",
       "      <td>s0001</td>\n",
       "      <td>1957-09-28</td>\n",
       "      <td>Xiong</td>\n",
       "      <td>Verhoeff</td>\n",
       "      <td>M</td>\n",
       "      <td>1987-11-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>282238</td>\n",
       "      <td>e0003</td>\n",
       "      <td>1952-10-28</td>\n",
       "      <td>Abdelkader</td>\n",
       "      <td>Baumann</td>\n",
       "      <td>F</td>\n",
       "      <td>1991-01-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   emp_no emp_title  birth_date  first_name  last_name sex   hire_date\n",
       "0  473302     s0001  1953-07-25    Hideyuki   Zallocco   M  1990-04-28\n",
       "1  475053     e0002  1954-11-18       Byong  Delgrande   F  1991-09-07\n",
       "2   57444     e0002  1958-01-30       Berry       Babb   F  1992-03-21\n",
       "3  421786     s0001  1957-09-28       Xiong   Verhoeff   M  1987-11-26\n",
       "4  282238     e0003  1952-10-28  Abdelkader    Baumann   F  1991-01-18"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the employee data\n",
    "employee_data =pd.read_sql(\"select * from employees\", conn)\n",
    "employee_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>emp_title</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s0001</td>\n",
       "      <td>Staff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s0002</td>\n",
       "      <td>Senior Staff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e0001</td>\n",
       "      <td>Assistant Engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e0002</td>\n",
       "      <td>Engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e0003</td>\n",
       "      <td>Senior Engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>e0004</td>\n",
       "      <td>Technique Leader</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>m0001</td>\n",
       "      <td>Manager</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  emp_title               title\n",
       "0     s0001               Staff\n",
       "1     s0002        Senior Staff\n",
       "2     e0001  Assistant Engineer\n",
       "3     e0002            Engineer\n",
       "4     e0003     Senior Engineer\n",
       "5     e0004    Technique Leader\n",
       "6     m0001             Manager"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the title data\n",
    "title_data =pd.read_sql(\"select * from titles\", conn)\n",
    "#rename the column to match with the column in employee data\n",
    "title_data.rename(columns = {'title_id':'emp_title'}, inplace = True)\n",
    "title_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "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>emp_title</th>\n",
       "      <th>title</th>\n",
       "      <th>emp_no</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>sex</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s0001</td>\n",
       "      <td>Staff</td>\n",
       "      <td>473302</td>\n",
       "      <td>1953-07-25</td>\n",
       "      <td>Hideyuki</td>\n",
       "      <td>Zallocco</td>\n",
       "      <td>M</td>\n",
       "      <td>1990-04-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s0001</td>\n",
       "      <td>Staff</td>\n",
       "      <td>421786</td>\n",
       "      <td>1957-09-28</td>\n",
       "      <td>Xiong</td>\n",
       "      <td>Verhoeff</td>\n",
       "      <td>M</td>\n",
       "      <td>1987-11-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s0001</td>\n",
       "      <td>Staff</td>\n",
       "      <td>273487</td>\n",
       "      <td>1957-04-14</td>\n",
       "      <td>Christoph</td>\n",
       "      <td>Parfitt</td>\n",
       "      <td>M</td>\n",
       "      <td>1991-06-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s0001</td>\n",
       "      <td>Staff</td>\n",
       "      <td>246449</td>\n",
       "      <td>1958-03-23</td>\n",
       "      <td>Subbu</td>\n",
       "      <td>Bultermann</td>\n",
       "      <td>F</td>\n",
       "      <td>1988-03-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s0001</td>\n",
       "      <td>Staff</td>\n",
       "      <td>48085</td>\n",
       "      <td>1964-01-19</td>\n",
       "      <td>Venkatesan</td>\n",
       "      <td>Gilg</td>\n",
       "      <td>M</td>\n",
       "      <td>1993-06-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  emp_title  title  emp_no  birth_date  first_name   last_name sex   hire_date\n",
       "0     s0001  Staff  473302  1953-07-25    Hideyuki    Zallocco   M  1990-04-28\n",
       "1     s0001  Staff  421786  1957-09-28       Xiong    Verhoeff   M  1987-11-26\n",
       "2     s0001  Staff  273487  1957-04-14   Christoph     Parfitt   M  1991-06-28\n",
       "3     s0001  Staff  246449  1958-03-23       Subbu  Bultermann   F  1988-03-25\n",
       "4     s0001  Staff   48085  1964-01-19  Venkatesan        Gilg   M  1993-06-28"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge the title and employee to get the emp_no\n",
    "title_emp = pd.merge(title_data, employee_data, on='emp_title', how='left' )\n",
    "title_emp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "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>emp_no</th>\n",
       "      <th>salary</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>title</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>sex</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>60117.0</td>\n",
       "      <td>e0003</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>1953-09-02</td>\n",
       "      <td>Georgi</td>\n",
       "      <td>Facello</td>\n",
       "      <td>M</td>\n",
       "      <td>1986-06-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002</td>\n",
       "      <td>65828.0</td>\n",
       "      <td>s0001</td>\n",
       "      <td>Staff</td>\n",
       "      <td>1964-06-02</td>\n",
       "      <td>Bezalel</td>\n",
       "      <td>Simmel</td>\n",
       "      <td>F</td>\n",
       "      <td>1985-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>40006.0</td>\n",
       "      <td>e0003</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>1959-12-03</td>\n",
       "      <td>Parto</td>\n",
       "      <td>Bamford</td>\n",
       "      <td>M</td>\n",
       "      <td>1986-08-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>40054.0</td>\n",
       "      <td>e0003</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>1954-05-01</td>\n",
       "      <td>Chirstian</td>\n",
       "      <td>Koblick</td>\n",
       "      <td>M</td>\n",
       "      <td>1986-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10005</td>\n",
       "      <td>78228.0</td>\n",
       "      <td>s0001</td>\n",
       "      <td>Staff</td>\n",
       "      <td>1955-01-21</td>\n",
       "      <td>Kyoichi</td>\n",
       "      <td>Maliniak</td>\n",
       "      <td>M</td>\n",
       "      <td>1989-09-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   emp_no   salary emp_title            title  birth_date first_name  \\\n",
       "0   10001  60117.0     e0003  Senior Engineer  1953-09-02     Georgi   \n",
       "1   10002  65828.0     s0001            Staff  1964-06-02    Bezalel   \n",
       "2   10003  40006.0     e0003  Senior Engineer  1959-12-03      Parto   \n",
       "3   10004  40054.0     e0003  Senior Engineer  1954-05-01  Chirstian   \n",
       "4   10005  78228.0     s0001            Staff  1955-01-21    Kyoichi   \n",
       "\n",
       "  last_name sex   hire_date  \n",
       "0   Facello   M  1986-06-26  \n",
       "1    Simmel   F  1985-11-21  \n",
       "2   Bamford   M  1986-08-28  \n",
       "3   Koblick   M  1986-12-01  \n",
       "4  Maliniak   M  1989-09-12  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge title_emp with salary data\n",
    "salary_title = pd.merge(salary_data, title_emp, on = 'emp_no', how = 'left')\n",
    "salary_title.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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>title</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Assistant Engineer</td>\n",
       "      <td>48564.434447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Engineer</td>\n",
       "      <td>48535.336511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Manager</td>\n",
       "      <td>51531.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>48506.799871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Senior Staff</td>\n",
       "      <td>58550.172704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Staff</td>\n",
       "      <td>58465.382850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Technique Leader</td>\n",
       "      <td>48582.896092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                title        salary\n",
       "0  Assistant Engineer  48564.434447\n",
       "1            Engineer  48535.336511\n",
       "2             Manager  51531.041667\n",
       "3     Senior Engineer  48506.799871\n",
       "4        Senior Staff  58550.172704\n",
       "5               Staff  58465.382850\n",
       "6    Technique Leader  48582.896092"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the average salary by title\n",
    "average_salary = salary_title.groupby('title')['salary'].mean().reset_index()\n",
    "average_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the bar graph\n",
    "fig2=plt.figure(figsize=[20,10])\n",
    "plt.bar(range(len(average_salary)), average_salary['salary'], color = 'green')\n",
    "plt.xlabel('Titles',fontsize=15)\n",
    "plt.xticks(range(len(average_salary)), average_salary['title'],fontsize=15)\n",
    "plt.ylabel('Average salary', fontsize = 15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.title('Average Salary by Title Bar Garph',fontsize=15)\n",
    "plt.show()\n",
    "fig2.savefig('Graphs/Bargraph.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:PythonName2] *",
   "language": "python",
   "name": "conda-env-PythonName2-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
}