WebDeveloperJobSatisfactionSalary.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import defaultdict\n",
    "import Utils as u\n",
    "%matplotlib inline\n",
    "\n",
    "# Data gathering\n",
    "df = pd.read_csv('./survey_results_public.csv')\n",
    "schema = pd.read_csv('./survey_results_schema.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WebDeveloperType\n",
       "Back-end Web developer      6.880654\n",
       "Front-end Web developer     7.118280\n",
       "Full stack Web developer    6.969640\n",
       "Name: JobSatisfaction, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp = df\n",
    "\n",
    "# Group by WebDeveloperType, get the mean based on JobSatisfaction\n",
    "df_temp.groupby('WebDeveloperType').mean()['JobSatisfaction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Utilize the get_unique function to find all true unique values and display a bar plot.\n",
    "method_vals = u.get_unique(df['WebDeveloperType'])\n",
    "methodology = u.clean_and_plot(df, method_vals, 'WebDeveloperType', 'web developer', title='Web Developer Types')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Front-end Web developer Salary is $ 48772.55816583559\n",
      "Average Full stack Web developer Salary is $ 58237.962737874615\n",
      "Average Back-end Web developer is $ 52855.462148115046\n"
     ]
    }
   ],
   "source": [
    "# Get the mean of each WebDeveloperType\n",
    "\n",
    "fe_web_dev = df[(df['WebDeveloperType'] == 'Front-end Web developer') & (df['Salary'].isnull() == False)]['Salary'].mean()\n",
    "fs_web_dev = df[(df['WebDeveloperType'] == 'Full stack Web developer') & (df['Salary'].isnull() == False)]['Salary'].mean()\n",
    "be_web_dev = df[(df['WebDeveloperType'] == 'Back-end Web developer') & (df['Salary'].isnull() == False)]['Salary'].mean()\n",
    "\n",
    "print('Average Front-end Web developer Salary is $',fe_web_dev)\n",
    "print('Average Full stack Web developer Salary is $',fs_web_dev)\n",
    "print('Average Back-end Web developer is $',be_web_dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Create a Pandas Series that can be used to easliy get a bar plot.\n",
    "df_dev = pd.Series({'Front-end Web developer': fe_web_dev, 'Full stack Web developer': fs_web_dev, 'Back-end Web developer': be_web_dev})\n",
    "\n",
    "df_dev.plot(kind=\"bar\");\n",
    "plt.title('Web developers average salary');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can also see a bar chart of the most used IDE by web developers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter all data samples from the dataframe where WebDeveloperType is Full stack Web developer, \n",
    "# Back-end Web developer, or Front-end Web developer.\n",
    "\n",
    "gf = df[(df['WebDeveloperType'] == 'Full stack Web developer') | \n",
    "       (df['WebDeveloperType'] == 'Back-end Web developer')   | \n",
    "       (df['WebDeveloperType'] == 'Front-end Web developer')]\n",
    "\n",
    "# df['WebDevIDE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Utilize the get_unique function to find all true unique values and display a bar plot.\n",
    "devIde_vals = u.get_unique(gf['IDE'])\n",
    "methodology = u.clean_and_plot(gf, devIde_vals, 'IDE', 'programming languages', title='DeveloperType')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}