Algorithms&Communication.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, I will be exploring the importance of the knowledge of algorithms and data structure, and communication skills in the recruitment of a web developer. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we import all the required libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to filter all data samples from the dataframe where WebDeveloperType is Full stack Web developer, 'Back-end Web developer' or Front-end Web developer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df['WebDeveloperType'] == 'Full stack Web developer') | \n",
    "       (df['WebDeveloperType'] == 'Back-end Web developer')   | \n",
    "       (df['WebDeveloperType'] == 'Front-end Web developer')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Important               910\n",
       "Somewhat important      888\n",
       "Very important          347\n",
       "Not very important      277\n",
       "Not at all important     48\n",
       "Name: ImportantHiringAlgorithms, dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo = df['ImportantHiringAlgorithms'].value_counts() # Assign value counts for the column ImportantHiringAlgorithms to algo \n",
    "algo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Important               1165\n",
       "Very important           846\n",
       "Somewhat important       399\n",
       "Not very important        52\n",
       "Not at all important      12\n",
       "Name: ImportantHiringCommunication, dtype: int64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comm = df['ImportantHiringCommunication'].value_counts() # Assign value counts for the column ImportantHiringCommunication to algo \n",
    "comm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grouped_bar_chart(algo, comm):\n",
    "    \n",
    "    '''\n",
    "        INPUT: \n",
    "        alog - a pandas series which represents opinions on the importance of algorithms and data structure\n",
    "        comm - a pandas series which represents opinions on the importance of communication skills\n",
    "        \n",
    "        OUTPUT: \n",
    "        Display a grouped bar chart showing both algo and comm\n",
    "    '''    \n",
    "    x = np.arange(len(algo.index))  # the label locations\n",
    "    width = 0.4  # the width of the bars\n",
    "    labels = comm.index\n",
    "\n",
    "    algos = list(algo.values)\n",
    "\n",
    "    algos[1], algos[2] = algos[2], algos[1] # swap elements in algo values to get consistent opinions and values\n",
    "    \n",
    "    comm_plt = plt.bar(x, comm/comm.sum(), width)\n",
    "    algo_plt = plt.bar(x+width, algo/algo.sum(), width)\n",
    "\n",
    "    plt.legend([comm_plt, algo_plt], ['Communication Skills', 'Algorithms & data structure'])\n",
    "    plt.xticks(x+width/2, labels, rotation='vertical') \n",
    "    plt.ylabel('Count')\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be able to see the data more clearly I used a grouped bar chart to display the imporance of algorithms and data structure, and communications skills in the recruitment of Software developers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "Important               0.368421\n",
       "Somewhat important      0.359514\n",
       "Very important          0.140486\n",
       "Not very important      0.112146\n",
       "Not at all important    0.019433\n",
       "Name: ImportantHiringAlgorithms, dtype: float64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_bar_chart(algo, comm)\n",
    "algo/algo.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, '20 to 99 employees', '10,000 or more employees',\n",
       "       '10 to 19 employees', 'Fewer than 10 employees',\n",
       "       '5,000 to 9,999 employees', '100 to 499 employees',\n",
       "       '1,000 to 4,999 employees', '500 to 999 employees', \"I don't know\",\n",
       "       'I prefer not to answer'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df['CompanySize'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I dived in a little bit to see the response to the importance of communication skills in the recruitment process for large companies. The customize bar chart displays this result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12295846943537098"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['LargeCompanies'] = df['CompanySize'].apply(lambda x: 1 if x in ('10,000 or more employees', '5,000 to 9,999 employees') else 0)\n",
    "df['LargeCompanies'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "tc_1 = df[df['LargeCompanies'] == 1]\n",
    "tc_0 = df[df['LargeCompanies'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_fa3e3582_954e_11eb_bb38_58a023061bcerow0_col2 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 54.1%, #5fba7d 54.1%, #5fba7d 77.5%, transparent 77.5%);\n",
       "        }    #T_fa3e3582_954e_11eb_bb38_58a023061bcerow1_col2 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 54.1%, #5fba7d 54.1%, #5fba7d 100.0%, transparent 100.0%);\n",
       "        }    #T_fa3e3582_954e_11eb_bb38_58a023061bcerow2_col2 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,#d65f5f 54.1%, transparent 54.1%);\n",
       "        }    #T_fa3e3582_954e_11eb_bb38_58a023061bcerow3_col2 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 44.7%, #d65f5f 44.7%, #d65f5f 54.1%, transparent 54.1%);\n",
       "        }</style><table id=\"T_fa3e3582_954e_11eb_bb38_58a023061bce\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >tc_1_perc(> 10,000 employees)</th>        <th class=\"col_heading level0 col1\" >tc_0_perc(< 10,000 employees)</th>        <th class=\"col_heading level0 col2\" >DiffLargeCompaniesVals</th>    </tr>    <tr>        <th class=\"index_name level0\" >method</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_fa3e3582_954e_11eb_bb38_58a023061bcelevel0_row0\" class=\"row_heading level0 row0\" >Important</th>\n",
       "                        <td id=\"T_fa3e3582_954e_11eb_bb38_58a023061bcerow0_col0\" class=\"data row0 col0\" >0.489933</td>\n",
       "                        <td id=\"T_fa3e3582_954e_11eb_bb38_58a023061bcerow0_col1\" class=\"data row0 col1\" >0.468290</td>\n",
       "                        <td id=\"T_fa3e3582_954e_11eb_bb38_58a023061bcerow0_col2\" class=\"data row0 col2\" >0.021642</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fa3e3582_954e_11eb_bb38_58a023061bcelevel0_row1\" class=\"row_heading level0 row1\" >Very important</th>\n",
       "                        <td id=\"T_fa3e3582_954e_11eb_bb38_58a023061bcerow1_col0\" class=\"data row1 col0\" >0.379195</td>\n",
       "                        <td id=\"T_fa3e3582_954e_11eb_bb38_58a023061bcerow1_col1\" class=\"data row1 col1\" >0.336857</td>\n",
       "                        <td id=\"T_fa3e3582_954e_11eb_bb38_58a023061bcerow1_col2\" class=\"data row1 col2\" >0.042338</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fa3e3582_954e_11eb_bb38_58a023061bcelevel0_row2\" class=\"row_heading level0 row2\" >Somewhat important</th>\n",
       "                        <td id=\"T_fa3e3582_954e_11eb_bb38_58a023061bcerow2_col0\" class=\"data row2 col0\" >0.117450</td>\n",
       "                        <td id=\"T_fa3e3582_954e_11eb_bb38_58a023061bcerow2_col1\" class=\"data row2 col1\" >0.167279</td>\n",
       "                        <td id=\"T_fa3e3582_954e_11eb_bb38_58a023061bcerow2_col2\" class=\"data row2 col2\" >-0.049830</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fa3e3582_954e_11eb_bb38_58a023061bcelevel0_row3\" class=\"row_heading level0 row3\" >Not very important</th>\n",
       "                        <td id=\"T_fa3e3582_954e_11eb_bb38_58a023061bcerow3_col0\" class=\"data row3 col0\" >0.013423</td>\n",
       "                        <td id=\"T_fa3e3582_954e_11eb_bb38_58a023061bcerow3_col1\" class=\"data row3 col1\" >0.022059</td>\n",
       "                        <td id=\"T_fa3e3582_954e_11eb_bb38_58a023061bcerow3_col2\" class=\"data row3 col2\" >-0.008636</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1d7a7a90848>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tc_1_perc = u.clean_and_plot2(tc_1, column='CousinEducation', rename_col='methodology', possible_vals=possible_vals, title='Large Companies', plot=False)\n",
    "# tc_0_perc = u.clean_and_plot2(tc_0, column='CousinEducation', rename_col='methodology', possible_vals=possible_vals, title='Max of Small Companies', plot=False)\n",
    "\n",
    "tc_1_perc = u.clean_and_plot(tc_1, u.get_unique(df['ImportantHiringCommunication']), 'ImportantHiringCommunication', 'ImportantHiringCommunication', plot=False)\n",
    "tc_0_perc = u.clean_and_plot(tc_0, u.get_unique(df['ImportantHiringCommunication']), 'ImportantHiringCommunication', 'ImportantHiringCommunication', plot=False)\n",
    "\n",
    "tc_comp_df = pd.merge(tc_1_perc, tc_0_perc, left_index=True, right_index=True)\n",
    "tc_comp_df.columns = [\"tc_1_perc(> 10,000 employees)\", \"tc_0_perc(< 10,000 employees)\"]\n",
    "tc_comp_df['DiffLargeCompaniesVals'] = tc_comp_df[\"tc_1_perc(> 10,000 employees)\"] - tc_comp_df[\"tc_0_perc(< 10,000 employees)\"]\n",
    "tc_comp_df.style.bar(subset=['DiffLargeCompaniesVals'], align='mid', color=['#d65f5f', '#5fba7d'])\n",
    "# print(tc_1_perc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Professional developer'], dtype=object)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Professional'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Professionals opinion on the matter can be observed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25314979001399907"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ProfessionDevs'] = df['FormalEducation'].apply(lambda x: 1 if x in (\"Master's degree\", \"Doctoral degree\", \"Professional degree\") else 0)\n",
    "df['ProfessionDevs'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_1 = df[df['ProfessionDevs'] == 1]\n",
    "p_0 = df[df['ProfessionDevs'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_e788b680_953f_11eb_a14f_58a023061bcerow0_col2 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 33.5%, #5fba7d 33.5%, #5fba7d 100.0%, transparent 100.0%);\n",
       "        }    #T_e788b680_953f_11eb_a14f_58a023061bcerow1_col2 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,#d65f5f 33.5%, transparent 33.5%);\n",
       "        }    #T_e788b680_953f_11eb_a14f_58a023061bcerow2_col2 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 13.6%, #d65f5f 13.6%, #d65f5f 33.5%, transparent 33.5%);\n",
       "        }    #T_e788b680_953f_11eb_a14f_58a023061bcerow3_col2 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 31.5%, #d65f5f 31.5%, #d65f5f 33.5%, transparent 33.5%);\n",
       "        }    #T_e788b680_953f_11eb_a14f_58a023061bcerow4_col2 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 22.4%, #d65f5f 22.4%, #d65f5f 33.5%, transparent 33.5%);\n",
       "        }</style><table id=\"T_e788b680_953f_11eb_a14f_58a023061bce\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >p_1_perc()</th>        <th class=\"col_heading level0 col1\" >p_0_perc()</th>        <th class=\"col_heading level0 col2\" >DiffProfessionalVals</th>    </tr>    <tr>        <th class=\"index_name level0\" >method</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_e788b680_953f_11eb_a14f_58a023061bcelevel0_row0\" class=\"row_heading level0 row0\" >Important</th>\n",
       "                        <td id=\"T_e788b680_953f_11eb_a14f_58a023061bcerow0_col0\" class=\"data row0 col0\" >0.408875</td>\n",
       "                        <td id=\"T_e788b680_953f_11eb_a14f_58a023061bcerow0_col1\" class=\"data row0 col1\" >0.354541</td>\n",
       "                        <td id=\"T_e788b680_953f_11eb_a14f_58a023061bcerow0_col2\" class=\"data row0 col2\" >0.054334</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e788b680_953f_11eb_a14f_58a023061bcelevel0_row1\" class=\"row_heading level0 row1\" >Somewhat important</th>\n",
       "                        <td id=\"T_e788b680_953f_11eb_a14f_58a023061bcerow1_col0\" class=\"data row1 col0\" >0.339144</td>\n",
       "                        <td id=\"T_e788b680_953f_11eb_a14f_58a023061bcerow1_col1\" class=\"data row1 col1\" >0.366504</td>\n",
       "                        <td id=\"T_e788b680_953f_11eb_a14f_58a023061bcerow1_col2\" class=\"data row1 col2\" >-0.027359</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e788b680_953f_11eb_a14f_58a023061bcelevel0_row2\" class=\"row_heading level0 row2\" >Very important</th>\n",
       "                        <td id=\"T_e788b680_953f_11eb_a14f_58a023061bcerow2_col0\" class=\"data row2 col0\" >0.128368</td>\n",
       "                        <td id=\"T_e788b680_953f_11eb_a14f_58a023061bcerow2_col1\" class=\"data row2 col1\" >0.144644</td>\n",
       "                        <td id=\"T_e788b680_953f_11eb_a14f_58a023061bcerow2_col2\" class=\"data row2 col2\" >-0.016276</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e788b680_953f_11eb_a14f_58a023061bcelevel0_row3\" class=\"row_heading level0 row3\" >Not very important</th>\n",
       "                        <td id=\"T_e788b680_953f_11eb_a14f_58a023061bcerow3_col0\" class=\"data row3 col0\" >0.110935</td>\n",
       "                        <td id=\"T_e788b680_953f_11eb_a14f_58a023061bcerow3_col1\" class=\"data row3 col1\" >0.112561</td>\n",
       "                        <td id=\"T_e788b680_953f_11eb_a14f_58a023061bcerow3_col2\" class=\"data row3 col2\" >-0.001626</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e788b680_953f_11eb_a14f_58a023061bcelevel0_row4\" class=\"row_heading level0 row4\" >Not at all important</th>\n",
       "                        <td id=\"T_e788b680_953f_11eb_a14f_58a023061bcerow4_col0\" class=\"data row4 col0\" >0.012678</td>\n",
       "                        <td id=\"T_e788b680_953f_11eb_a14f_58a023061bcerow4_col1\" class=\"data row4 col1\" >0.021751</td>\n",
       "                        <td id=\"T_e788b680_953f_11eb_a14f_58a023061bcerow4_col2\" class=\"data row4 col2\" >-0.009073</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1d7a67c6788>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tc_1_perc = u.clean_and_plot2(tc_1, column='CousinEducation', rename_col='methodology', possible_vals=possible_vals, title='Large Companies', plot=False)\n",
    "# tc_0_perc = u.clean_and_plot2(tc_0, column='CousinEducation', rename_col='methodology', possible_vals=possible_vals, title='Max of Small Companies', plot=False)\n",
    "\n",
    "p_1_perc = u.clean_and_plot(p_1, u.get_unique(df['ImportantHiringAlgorithms']), 'ImportantHiringAlgorithms', 'ImportantHiringAlgorithms', plot=False)\n",
    "p_0_perc = u.clean_and_plot(p_0, u.get_unique(df['ImportantHiringAlgorithms']), 'ImportantHiringAlgorithms', 'ImportantHiringAlgorithms', plot=False)\n",
    "\n",
    "p_comp_df = pd.merge(p_1_perc, p_0_perc, left_index=True, right_index=True)\n",
    "p_comp_df.columns = [\"p_1_perc()\", \"p_0_perc()\"]\n",
    "p_comp_df['DiffProfessionalVals'] = p_comp_df[\"p_1_perc()\"] - p_comp_df[\"p_0_perc()\"]\n",
    "p_comp_df.style.bar(subset=['DiffProfessionalVals'], align='mid', color=['#d65f5f', '#5fba7d'])\n",
    "# print(tc_1_perc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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  "language_info": {
   "codemirror_mode": {
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