05. Improving the Visibility of Charts.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improving the Visibility of the Charts\n",
    "\n",
    "* It is very important that the charts we make look beautiful to our eyes.\n",
    "* The Charts should be Informative and Impactful, but it seeks attention only when it looks attractive.\n",
    "* It is very important to mention the titles, labels, legends wherever it is required.\n",
    "* There should be appropriate use of colors and color palettes so that charts have more story telling values.\n",
    "* There should be proper shaping and sizing of the text fonts and Charts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets import the basic libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# for data visualizations\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470, 35)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets import the dataset\n",
    "data = pd.read_csv('employee.csv')\n",
    "\n",
    "# lets also check the shape of the dataset\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "   ...  RelationshipSatisfaction StandardHours  StockOptionLevel  \\\n",
       "0  ...                         1            80                 0   \n",
       "1  ...                         4            80                 1   \n",
       "2  ...                         2            80                 0   \n",
       "3  ...                         3            80                 0   \n",
       "4  ...                         4            80                 1   \n",
       "\n",
       "   TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
       "0                  8                      0               1               6   \n",
       "1                 10                      3               3              10   \n",
       "2                  7                      3               3               0   \n",
       "3                  8                      3               3               8   \n",
       "4                  6                      3               3               2   \n",
       "\n",
       "  YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                  4                        0                     5  \n",
       "1                  7                        1                     7  \n",
       "2                  0                        0                     0  \n",
       "3                  7                        3                     0  \n",
       "4                  2                        2                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets check the head of the dataset\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fast',\n",
       " 'fivethirtyeight',\n",
       " 'ggplot',\n",
       " 'grayscale',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn-dark',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-deep',\n",
       " 'seaborn-muted',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-paper',\n",
       " 'seaborn-pastel',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-ticks',\n",
       " 'seaborn-white',\n",
       " 'seaborn-whitegrid',\n",
       " 'seaborn',\n",
       " 'Solarize_Light2',\n",
       " 'tableau-colorblind10',\n",
       " '_classic_test']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets check out the available themes available to apply on the charts\n",
    "\n",
    "plt.style.available"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pie Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Beautifying the plots\n",
    "# pie chart \n",
    "\n",
    "size = data['Attrition'].value_counts()\n",
    "labels = ['Yes', 'No']\n",
    "colors = ['orange', 'yellow']\n",
    "explode = [0, 0.1]\n",
    ", \n",
    "plt.rcParams['figure.figsize'] = (7, 7)\n",
    "plt.style.use('dark_background')\n",
    "plt.pie(size, colors = colors, labels = labels, shadow = True, explode = explode, autopct = '%.2f%%')\n",
    "plt.title('Attrition Analysis', fontsize = 20)\n",
    "plt.axis('off')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qqdbgwYOt+Pj4wLaNGzcG5bz44ouBxwwePNgaM2aMNWbMGGvFihWWZVnWxx9/bEmyxowZE9Jr+vHHH1vsOWvWLCs+Pt5KTk62hg0bZvXu3TvwvA8++GCzvMbGRuvcc8+1JFkxMTHWr371K2vw4MFWTEyMdeaZZ1oFBQWWJOv5558PepzX67W6du1qSbJ69epljRo1yhozZox1//33H/Jr1/ScTbmSrKysLGvo0KGBr1+/fv2s9evXN+vadHy4X5tD2b59uzVkyBBLkhUbG2vl5uZagwYNCjzXueeea+3duzciz3XnnXdakqzTTz/9sMdWVVVZcXFxliSrrKwssH3lypVWWlqaJclKTEwM+n6ePHlyoNemTZuC8urr662rrroq8Lp69uxpnXbaaVZubq6VnJwc2P7dd9+F/HoAAM7AmXAAgHHr1q3T0qVLtXTpUq1du1YNDQ0699xzNXXqVK1Zs0avvfaaunXrFnJefn6+HnjgAZ133nmKjY3VqlWrtHXrVg0ePFizZ8/W6tWr1a9fv6DH/P73v9djjz2mk046SevXr9cnn3yiTz75JOhsZiScccYZ+vTTTzV69GhVVFRo165dGjFihN58883AGeiDuVwu/fWvf9Wtt96q3r1768cff9SePXt011136YMPPmjxzKp04KPcPvjgA1144YXat2+fysrK9MknnwTuoT4Ul8ul+fPn68UXX9QZZ5yh7du369tvv1X//v11++236+uvvw6cabdbRkaGysrKNGvWLJ1wwgn6/vvvtXHjRp166qkqKipSaWlpi5+7Hq7GxkYtWLBA0uHPgktS9+7dAx+JdvACbSeddJKWL1+uK6+8Up07d9bq1auVnJys4uJizZs3r9UV1N1ut1566SW99957uvTSSyVJK1as0NatWzVw4EBNmjRJS5Yssf0+fACAeS7LCvFzQwAAABCyHTt2KD09XWlpadq1a1e06wAA2omj9kx4eXm5IzLtynVSV7tyndTVrly6OivXSV3tyqWrs3Kd1NWO3Oeff16SNHLkyIjmSvx52ZXrpK525dLVWblO6mpXrpO6NonKEF5eXq4777xTd9xxh2677bbAgiRer1f33XefCgsLddttt4V0CV1btba6bHvLtCvXSV3tynVSV7ty6eqsXCd1tSuXrs7KdVLXtuauWrVKTz/9dNDHu1mWpfnz5+uee+6RJN1www0R69iEPy++Bnbl0tVZuU7qaleuk7o2aXmJVRtZlqV58+Zp+vTp6t+/v7Zv364//elPGj58uBYsWKDs7GxNnTpVFRUVmjNnjoqKihQbG2u6JgAAwGHt2LFD119/vW666Sb1799f3bt31w8//KAdO3ZIksaOHatf//rXUW4JAGhPonY5etNCJXv37lVSUpLi4uJUVlYWWPQkKytLqamptp0Nb21hm/aWaVeuk7raleukrnbl0tVZuU7qalcuXZ2V66Subc0dNGiQ7rjjDg0ZMkRer1crVqyQZVk655xz9Morr2jixIk2NOXPy65cJ3W1K5euzsp1Ule7cp3UtUlUFmZbtWqV5s6dq06dOmnPnj267bbbdOyxx+rGG28M+vzNOXPm6JRTTtGYMWNazCkvLw9cJtC5c2ddccUVRvoDAAAAANDktdde0969eyVJubm5ysvLa/VY45ej+/1+vfXWW7r99tuVk5OjiooKPfTQQ3r44YflcrnCysrLy2v24rZt26ZQfq6QnJys2trasJ4vGpl25Tqpq125TupqVy5dnZXrpK525dLVWblO6mpXLl2dleukrnbl0tVZuU7qaldutLu6XC717NkzrBPCxofwDRs2aOfOncrJyZF04LLzbt26aePGjZKkmpoapaSkSJIqKyuVnp4eVr5lWSEN4U3HRppdFxYc7V3tynVSV7ty6eqsXCd1tSuXrs7KdVJXu3Lp6qxcJ3W1K5euzsp1Ule7cp3UVYrCPeHdu3fXzp07tWXLFkkHzlxv27ZNvXv31ogRI7Ro0SJJUkVFhaqrqwPDOgAAAAAATmf8THhaWpomTpyoRx55RDExMbIsS9ddd526deumCRMmqLi4WIWFhXK73Zo8eTIrowMAAAAAOgzjQ7gkjR49WqNHj262PS0tTdOmTYtCIwAAAAAA7Be1jygDAAAAAOBowxAOAAAAAIAhDOEAAAAAABjCEA4AAAAAgCFRWZjNpLptdfJ5fM22e91e+Rv8QdsSMxOV0DPBVDUAAAAAwFGmww/hPo9PJeNLQjo2vzSfIRwAAAAAYBsuRwcAAAAAwBCGcAAAAAAADGEIBwAAAADAkA5/T7hdWlrwraXF3iQWfAMAAAAAHMAQ3kYs+AYAAAAACBeXowMAAAAAYAhDOAAAAAAAhjCEAwAAAABgCEM4AAAAAACGMIQDAAAAAGAIQzgAAAAAAIYwhAMAAAAAYAhDOAAAAAAAhjCEAwAAAABgCEM4AAAAAACGMIQDAAAAAGAIQzgAAAAAAIYwhAMAAAAAYAhDOAAAAAAAhjCEAwAAAABgCEM4AAAAAACGMIQDAAAAAGAIQzgAAAAAAIYwhAMAAAAAYAhDOAAAAAAAhjCEAwAAAABgCEM4AAAAAACGMIQDAAAAAGAIQzgAAAAAAIYwhAMAAAAAYAhDOAAAAAAAhjCEAwAAAABgCEM4AAAAAACGuE0/4Z49ezRjxozA7/fv3y+Px6Nnn31Wfr9fxcXF8ng8iouL08SJE5WTk2O6IgAAAAAAtjA+hHfp0kUPPfRQ4PfvvPOO1qxZo6SkJD3xxBPKzs7W1KlTVVFRoTlz5qioqEixsbGmawIAAAAAEHFRvxx9yZIlOvvssyVJZWVlGjdunCQpKytLqampWrt2bTTrAQAAAAAQMcbPhB/s+++/V21trU455RTV1tbKsiylpKQE9mdkZKiqqqrVx5eXl2vlypWSpLi4OBUUFCg5OTnoGK/bG3KfWHds0PMfil25LYmPjz+ix5vKdFquk7ralUtXZ+U6qatduXR1Vq6TutqVS1dn5Tqpq125dHVWrpO62pXbXrouXLhQ9fX1kqTc3Fzl5eW1emxUh/DFixdrzJgxgcvNXS5XWI/Py8tr9uKahvkm/gZ/yHn+Br9qampCPtaO3JakpKQc0eNNZTot10ld7cqlq7NyndTVrly6OivXSV3tyqWrs3Kd1NWuXLo6K9dJXe3KjXZXl8ulpKQkFRQUhJwdtcvR6+rqVFZWprPOOkuSAmewD36hlZWVSk9Pj0o/AAAAAAAiLWpD+Oeff65+/fqpT58+gW0jRozQokWLJEkVFRWqrq5mdXQAAAAAQIcRtcvRFy9eHFiQrcmECRNUXFyswsJCud1uTZ48mZXRAQAAAAAdRtSG8FmzZjXblpaWpmnTpkWhTftQt61OPo+v2Xav29vsHvTEzEQl9Exoc25LmeHmAgAAAADCE9WF2RDM5/GpZHxJSMfml+aHPCzblQsAAAAACE/UPyccAAAAAICjBUM4AAAAAACGMIQDAAAAAGAI94SjzUJd8I3F3gAAAADgAIZwtFmoC76x2BsAAAAAHMDl6AAAAAAAGMIQDgAAAACAIQzhAAAAAAAYwhAOAAAAAIAhDOEAAAAAABjCEA4AAAAAgCEM4QAAAAAAGMIQDgAAAACAIQzhAAAAAAAYwhAOAAAAAIAhDOEAAAAAABjCEA4AAAAAgCEM4QAAAAAAGMIQDgAAAACAIQzhAAAAAAAYwhAOAAAAAIAhDOEAAAAAABjCEA4AAAAAgCEM4QAAAAAAGMIQDgAAAACAIQzhAAAAAAAYwhAOAAAAAIAhDOEAAAAAABjCEA4AAAAAgCEM4QAAAAAAGMIQDgAAAACAIQzhAAAAAAAYwhAOAAAAAIAhDOEAAAAAABjCEA4AAAAAgCEM4QAAAAAAGMIQDgAAAACAIe5oFwAOVretTj6Pr9l2r9srf4M/aFtiZqISeiaYqgYAAAAARywqQ3h9fb1efPFFrVy5Um63WwMGDFBhYaG8Xq+Ki4vl8XgUFxeniRMnKicnJxoVESU+j08l40tCOja/NJ8hHAAAAICjRGUIX7BggVwulx577DG5XC7t2rUrsD07O1tTp05VRUWF5syZo6KiIsXGxkajJgAAAAAAEWX8nvC6ujotWbJEBQUFcrlckqSuXbtKksrKyjRu3DhJUlZWllJTU7V27VrTFQEAAAAAsIXxM+Eej0fJyckqKSnRqlWrFB8fr8svv1wDBgyQZVlKSUkJHJuRkaGqqqpWs8rLy7Vy5UpJUlxcnAoKCpScnBx0jNftDblbrDs26PkPxY5cJ3UNJ7c9dN21YZd2b90dtK3GVSPLspodm9QrSV0HdA25xy/Fx8eH3CvauXR1Vq6TutqVS1dn5Tqpq125dHVWrpO62pVLV2flOqmrXbntpevChQtVX18vScrNzVVeXl6rxxofwv1+vzwej4455hhNmDBBGzZs0L333qtHHnkkcGY8VHl5ec1eXG1tbdBg9cvFvA7ZrcGvmpqakI+NdK6TuoaT2x66ejd7w7rXPLZb22+BSElJCblXtHPp6qxcJ3W1K5euzsp1Ule7cunqrFwndbUrl67OynVSV7tyo93V5XIpKSlJBQUFIWcbvxw9PT1dLpdLZ5xxhiRpwIAB6tGjh37++WdJCnqhlZWVSk9PN10RAAAAAABbGB/CU1JSNGTIEJWXl0s6MGhv375dvXv31ogRI7Ro0SJJUkVFhaqrq1kdHQAAAADQYURldfSJEyfqySef1IIFCxQTE6Prr79eXbt21YQJE1RcXKzCwkK53W5NnjyZldEBAAAAAB1GVIbwzMxMzZgxo9n2tLQ0TZs2zXwhAAAAAAAMMH45OgAAAAAARyuGcAAAAAAADGEIBwAAAADAEIZwAAAAAAAMicrCbEBHULetTj6Pr9l2r9srf4M/aFtiZqISeiZENRcAAABA9DGEA23k8/hUMr4kpGPzS/NDHpbtygUAAAAQfVyODgAAAACAIQzhAAAAAAAYwhAOAAAAAIAh3BM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\n",
      "text/plain": [
       "<Figure size 1200x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.rcParams['figure.figsize'] = (15, 6)\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "data['Age'].value_counts().plot(kind = 'bar', color = 'purple')\n",
    "plt.title('Distribution of Age', fontsize = 20)\n",
    "plt.xlabel('Different Categories of Age', fontsize = 20)\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function plot in module matplotlib.pyplot:\n",
      "\n",
      "plot(*args, scalex=True, scaley=True, data=None, **kwargs)\n",
      "    Plot y versus x as lines and/or markers.\n",
      "    \n",
      "    Call signatures::\n",
      "    \n",
      "        plot([x], y, [fmt], *, data=None, **kwargs)\n",
      "        plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)\n",
      "    \n",
      "    The coordinates of the points or line nodes are given by *x*, *y*.\n",
      "    \n",
      "    The optional parameter *fmt* is a convenient way for defining basic\n",
      "    formatting like color, marker and linestyle. It's a shortcut string\n",
      "    notation described in the *Notes* section below.\n",
      "    \n",
      "    >>> plot(x, y)        # plot x and y using default line style and color\n",
      "    >>> plot(x, y, 'bo')  # plot x and y using blue circle markers\n",
      "    >>> plot(y)           # plot y using x as index array 0..N-1\n",
      "    >>> plot(y, 'r+')     # ditto, but with red plusses\n",
      "    \n",
      "    You can use `.Line2D` properties as keyword arguments for more\n",
      "    control on the appearance. Line properties and *fmt* can be mixed.\n",
      "    The following two calls yield identical results:\n",
      "    \n",
      "    >>> plot(x, y, 'go--', linewidth=2, markersize=12)\n",
      "    >>> plot(x, y, color='green', marker='o', linestyle='dashed',\n",
      "    ...      linewidth=2, markersize=12)\n",
      "    \n",
      "    When conflicting with *fmt*, keyword arguments take precedence.\n",
      "    \n",
      "    \n",
      "    **Plotting labelled data**\n",
      "    \n",
      "    There's a convenient way for plotting objects with labelled data (i.e.\n",
      "    data that can be accessed by index ``obj['y']``). Instead of giving\n",
      "    the data in *x* and *y*, you can provide the object in the *data*\n",
      "    parameter and just give the labels for *x* and *y*::\n",
      "    \n",
      "    >>> plot('xlabel', 'ylabel', data=obj)\n",
      "    \n",
      "    All indexable objects are supported. This could e.g. be a `dict`, a\n",
      "    `pandas.DataFame` or a structured numpy array.\n",
      "    \n",
      "    \n",
      "    **Plotting multiple sets of data**\n",
      "    \n",
      "    There are various ways to plot multiple sets of data.\n",
      "    \n",
      "    - The most straight forward way is just to call `plot` multiple times.\n",
      "      Example:\n",
      "    \n",
      "      >>> plot(x1, y1, 'bo')\n",
      "      >>> plot(x2, y2, 'go')\n",
      "    \n",
      "    - Alternatively, if your data is already a 2d array, you can pass it\n",
      "      directly to *x*, *y*. A separate data set will be drawn for every\n",
      "      column.\n",
      "    \n",
      "      Example: an array ``a`` where the first column represents the *x*\n",
      "      values and the other columns are the *y* columns::\n",
      "    \n",
      "      >>> plot(a[0], a[1:])\n",
      "    \n",
      "    - The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*\n",
      "      groups::\n",
      "    \n",
      "      >>> plot(x1, y1, 'g^', x2, y2, 'g-')\n",
      "    \n",
      "      In this case, any additional keyword argument applies to all\n",
      "      datasets. Also this syntax cannot be combined with the *data*\n",
      "      parameter.\n",
      "    \n",
      "    By default, each line is assigned a different style specified by a\n",
      "    'style cycle'. The *fmt* and line property parameters are only\n",
      "    necessary if you want explicit deviations from these defaults.\n",
      "    Alternatively, you can also change the style cycle using the\n",
      "    'axes.prop_cycle' rcParam.\n",
      "    \n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x, y : array-like or scalar\n",
      "        The horizontal / vertical coordinates of the data points.\n",
      "        *x* values are optional and default to `range(len(y))`.\n",
      "    \n",
      "        Commonly, these parameters are 1D arrays.\n",
      "    \n",
      "        They can also be scalars, or two-dimensional (in that case, the\n",
      "        columns represent separate data sets).\n",
      "    \n",
      "        These arguments cannot be passed as keywords.\n",
      "    \n",
      "    fmt : str, optional\n",
      "        A format string, e.g. 'ro' for red circles. See the *Notes*\n",
      "        section for a full description of the format strings.\n",
      "    \n",
      "        Format strings are just an abbreviation for quickly setting\n",
      "        basic line properties. All of these and more can also be\n",
      "        controlled by keyword arguments.\n",
      "    \n",
      "        This argument cannot be passed as keyword.\n",
      "    \n",
      "    data : indexable object, optional\n",
      "        An object with labelled data. If given, provide the label names to\n",
      "        plot in *x* and *y*.\n",
      "    \n",
      "        .. note::\n",
      "            Technically there's a slight ambiguity in calls where the\n",
      "            second label is a valid *fmt*. `plot('n', 'o', data=obj)`\n",
      "            could be `plt(x, y)` or `plt(y, fmt)`. In such cases,\n",
      "            the former interpretation is chosen, but a warning is issued.\n",
      "            You may suppress the warning by adding an empty format string\n",
      "            `plot('n', 'o', '', data=obj)`.\n",
      "    \n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    scalex, scaley : bool, optional, default: True\n",
      "        These parameters determined if the view limits are adapted to\n",
      "        the data limits. The values are passed on to `autoscale_view`.\n",
      "    \n",
      "    **kwargs : `.Line2D` properties, optional\n",
      "        *kwargs* are used to specify properties like a line label (for\n",
      "        auto legends), linewidth, antialiasing, marker face color.\n",
      "        Example::\n",
      "    \n",
      "        >>> plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)\n",
      "        >>> plot([1,2,3], [1,4,9], 'rs',  label='line 2')\n",
      "    \n",
      "        If you make multiple lines with one plot command, the kwargs\n",
      "        apply to all those lines.\n",
      "    \n",
      "        Here is a list of available `.Line2D` properties:\n",
      "    \n",
      "      agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array\n",
      "      alpha: float\n",
      "      animated: bool\n",
      "      antialiased or aa: bool\n",
      "      clip_box: `.Bbox`\n",
      "      clip_on: bool\n",
      "      clip_path: [(`~matplotlib.path.Path`, `.Transform`) | `.Patch` | None]\n",
      "      color or c: color\n",
      "      contains: callable\n",
      "      dash_capstyle: {'butt', 'round', 'projecting'}\n",
      "      dash_joinstyle: {'miter', 'round', 'bevel'}\n",
      "      dashes: sequence of floats (on/off ink in points) or (None, None)\n",
      "      drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'\n",
      "      figure: `.Figure`\n",
      "      fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}\n",
      "      gid: str\n",
      "      in_layout: bool\n",
      "      label: object\n",
      "      linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}\n",
      "      linewidth or lw: float\n",
      "      marker: marker style\n",
      "      markeredgecolor or mec: color\n",
      "      markeredgewidth or mew: float\n",
      "      markerfacecolor or mfc: color\n",
      "      markerfacecoloralt or mfcalt: color\n",
      "      markersize or ms: float\n",
      "      markevery: None or int or (int, int) or slice or List[int] or float or (float, float)\n",
      "      path_effects: `.AbstractPathEffect`\n",
      "      picker: float or callable[[Artist, Event], Tuple[bool, dict]]\n",
      "      pickradius: float\n",
      "      rasterized: bool or None\n",
      "      sketch_params: (scale: float, length: float, randomness: float)\n",
      "      snap: bool or None\n",
      "      solid_capstyle: {'butt', 'round', 'projecting'}\n",
      "      solid_joinstyle: {'miter', 'round', 'bevel'}\n",
      "      transform: `matplotlib.transforms.Transform`\n",
      "      url: str\n",
      "      visible: bool\n",
      "      xdata: 1D array\n",
      "      ydata: 1D array\n",
      "      zorder: float\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    lines\n",
      "        A list of `.Line2D` objects representing the plotted data.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    scatter : XY scatter plot with markers of varying size and/or color (\n",
      "        sometimes also called bubble chart).\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    **Format Strings**\n",
      "    \n",
      "    A format string consists of a part for color, marker and line::\n",
      "    \n",
      "        fmt = '[marker][line][color]'\n",
      "    \n",
      "    Each of them is optional. If not provided, the value from the style\n",
      "    cycle is used. Exception: If ``line`` is given, but no ``marker``,\n",
      "    the data will be a line without markers.\n",
      "    \n",
      "    Other combinations such as ``[color][marker][line]`` are also\n",
      "    supported, but note that their parsing may be ambiguous.\n",
      "    \n",
      "    **Markers**\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        description\n",
      "    =============    ===============================\n",
      "    ``'.'``          point marker\n",
      "    ``','``          pixel marker\n",
      "    ``'o'``          circle marker\n",
      "    ``'v'``          triangle_down marker\n",
      "    ``'^'``          triangle_up marker\n",
      "    ``'<'``          triangle_left marker\n",
      "    ``'>'``          triangle_right marker\n",
      "    ``'1'``          tri_down marker\n",
      "    ``'2'``          tri_up marker\n",
      "    ``'3'``          tri_left marker\n",
      "    ``'4'``          tri_right marker\n",
      "    ``'s'``          square marker\n",
      "    ``'p'``          pentagon marker\n",
      "    ``'*'``          star marker\n",
      "    ``'h'``          hexagon1 marker\n",
      "    ``'H'``          hexagon2 marker\n",
      "    ``'+'``          plus marker\n",
      "    ``'x'``          x marker\n",
      "    ``'D'``          diamond marker\n",
      "    ``'d'``          thin_diamond marker\n",
      "    ``'|'``          vline marker\n",
      "    ``'_'``          hline marker\n",
      "    =============    ===============================\n",
      "    \n",
      "    **Line Styles**\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        description\n",
      "    =============    ===============================\n",
      "    ``'-'``          solid line style\n",
      "    ``'--'``         dashed line style\n",
      "    ``'-.'``         dash-dot line style\n",
      "    ``':'``          dotted line style\n",
      "    =============    ===============================\n",
      "    \n",
      "    Example format strings::\n",
      "    \n",
      "        'b'    # blue markers with default shape\n",
      "        'or'   # red circles\n",
      "        '-g'   # green solid line\n",
      "        '--'   # dashed line with default color\n",
      "        '^k:'  # black triangle_up markers connected by a dotted line\n",
      "    \n",
      "    **Colors**\n",
      "    \n",
      "    The supported color abbreviations are the single letter codes\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        color\n",
      "    =============    ===============================\n",
      "    ``'b'``          blue\n",
      "    ``'g'``          green\n",
      "    ``'r'``          red\n",
      "    ``'c'``          cyan\n",
      "    ``'m'``          magenta\n",
      "    ``'y'``          yellow\n",
      "    ``'k'``          black\n",
      "    ``'w'``          white\n",
      "    =============    ===============================\n",
      "    \n",
      "    and the ``'CN'`` colors that index into the default property cycle.\n",
      "    \n",
      "    If the color is the only part of the format string, you can\n",
      "    additionally use any  `matplotlib.colors` spec, e.g. full names\n",
      "    (``'green'``) or hex strings (``'#008000'``).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plt.plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 832x572 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (15, 6)\n",
    "plt.style.use('seaborn-talk')\n",
    "\n",
    "data['EducationField'].value_counts().plot(kind = 'line',\n",
    "                                           linewidth = 2,\n",
    "                                           marker = 'o',\n",
    "                                           markersize = 20,\n",
    "                                           markerfacecolor = 'yellow',\n",
    "                                           markeredgecolor = 'green',\n",
    "                                           color = 'purple')\n",
    "plt.title('Distribution of Age', fontsize = 20)\n",
    "plt.xlabel('Different Categories of Age', fontsize = 20)\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###"
   ]
  }
 ],
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