02. Bar Plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial to get started with bar plot\n",
    "\n",
    "* In this Tutorial, we are going to learn how to use Bar Charts in matplotlib\n",
    "* For Exploring the Usage of Bar Charts, We are going to use the Employee Dataset\n",
    "* At First, We  are going to plot the most simplest Bar Charts for Univariate Variables i.,e Bar Charts for Single Variables\n",
    "* Then, We will plot Bar Charts for Bivariate Variables i.e., Two Variables\n",
    "* Then, we will learn how to plot multiple Variables using Bar Charts in matplotlib\n",
    "* For plotting multiple variables in a single chart, We will use Stacked and Grouped Bar Charts. "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Installation of Matplotlib\n",
    "\n",
    "# !pip install matplotlib\n",
    "\n",
    "\"\"\"\n",
    "To install the matplotlib library used for data visualization, Just remove the # from the above line and run the block,\n",
    "and it willget installed in your jupyter notebook. \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the basic libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# importing matplotlib for data visualization\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 35)\n"
     ]
    }
   ],
   "source": [
    "# lets read the dataset\n",
    "\n",
    "data = pd.read_csv('employee.csv')\n",
    "\n",
    "# lets take only 200 rows as we are only going to visualize it\n",
    "data = data.loc[:199,:]\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department',\n",
       "       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',\n",
       "       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',\n",
       "       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',\n",
       "       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',\n",
       "       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',\n",
       "       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',\n",
       "       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',\n",
       "       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n",
       "       'YearsWithCurrManager'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's check the columns present in the dataset\n",
    "\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate Plots\n",
    "\n",
    "* Where we plot the values of only one variable using a bar plot\n",
    "* Here, We will take a Variable and we will plot the categories against their values.\n",
    "* Univarite Analysis is a process of analyzing an attribute present in the data using plots.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No     170\n",
      "Yes     30\n",
      "Name: Attrition, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Lets try to analyze the values present in Attrition column of the employee dataset\n",
    "\n",
    "# here we are using value_counts() which return the values related to each of the categories present in the attribute\n",
    "x = data['Attrition'].value_counts()\n",
    "\n",
    "# printing the values\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The Value Counts Function returns the values for all the categories present in that attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets use the the values in x to plot \n",
    "\n",
    "plt.rcParams['figure.figsize'] = (5, 3)\n",
    "x.plot(kind = 'bar', color ='green')\n",
    "\n",
    "plt.title('Univariate Analysis on Attrition Column of Employee Dataset', fontsize = 12)\n",
    "plt.xlabel('Attrition')\n",
    "plt.ylabel('Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Using the plot function we plotted these values using a Bar Chart\n",
    "* It is Clearly Visible from the Bar Chart, that Most of the Employees did not attrition out of the Organization\n",
    "* It is Evident now, that using Charts it becomes easy to understand data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# analyzing the attribute Department\n",
    "\n",
    "data['Department'].value_counts().plot(kind = 'bar', color = 'lightblue')\n",
    "plt.ylabel('Values')\n",
    "plt.xlabel('Different Departments')\n",
    "plt.xticks(rotation = 360)\n",
    "plt.title('No. of Employees in each Departments', fontsize =12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The above Bar Plot represents the number of employees present in each of the department\n",
    "* From the Above Graph, It is Clear that most of the Employees are working for Research and Development and only few employees are working in the HR Department.\n",
    "* There is a Huge and visible gap in the number of employees working in different departments of an organization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# analyzing the Education Field\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (8, 4)\n",
    "data['EducationField'].value_counts().plot(kind = 'bar', color = 'orange')\n",
    "plt.ylabel('Values')\n",
    "plt.xlabel('Different Education Fields')\n",
    "plt.xticks(rotation = 360)\n",
    "plt.title('No. of Employees from different Education Fields', fontsize =12)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The Above Chart represents one more example for Univariate Analysis\n",
    "* The Above Chart, Represents the number of employees in different education fields\n",
    "* It is very clear from the graph that most of the Employees are form Life Sciences and Medical Fields."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Plots\n",
    "\n",
    "* Plotting Two Variables in one Plot.\n",
    "* Bivariate Analysis leaves us with more options to visualize the patterns in data.\n",
    "* We can compare two different Variables where the First Variable must be categorical in nature and the Second Variable should be continuous variable.\n",
    "* These Graphs are the most commonly used Bar Graphs.\n",
    "* In this Section, we will be using the Bar Function to plot the Bar plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function bar in module matplotlib.pyplot:\n",
      "\n",
      "bar(x, height, width=0.8, bottom=None, *, align='center', data=None, **kwargs)\n",
      "    Make a bar plot.\n",
      "    \n",
      "    The bars are positioned at *x* with the given *align*\\ment. Their\n",
      "    dimensions are given by *width* and *height*. The vertical baseline\n",
      "    is *bottom* (default 0).\n",
      "    \n",
      "    Each of *x*, *height*, *width*, and *bottom* may either be a scalar\n",
      "    applying to all bars, or it may be a sequence of length N providing a\n",
      "    separate value for each bar.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x : sequence of scalars\n",
      "        The x coordinates of the bars. See also *align* for the\n",
      "        alignment of the bars to the coordinates.\n",
      "    \n",
      "    height : scalar or sequence of scalars\n",
      "        The height(s) of the bars.\n",
      "    \n",
      "    width : scalar or array-like, optional\n",
      "        The width(s) of the bars (default: 0.8).\n",
      "    \n",
      "    bottom : scalar or array-like, optional\n",
      "        The y coordinate(s) of the bars bases (default: 0).\n",
      "    \n",
      "    align : {'center', 'edge'}, optional, default: 'center'\n",
      "        Alignment of the bars to the *x* coordinates:\n",
      "    \n",
      "        - 'center': Center the base on the *x* positions.\n",
      "        - 'edge': Align the left edges of the bars with the *x* positions.\n",
      "    \n",
      "        To align the bars on the right edge pass a negative *width* and\n",
      "        ``align='edge'``.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    container : `.BarContainer`\n",
      "        Container with all the bars and optionally errorbars.\n",
      "    \n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    color : scalar or array-like, optional\n",
      "        The colors of the bar faces.\n",
      "    \n",
      "    edgecolor : scalar or array-like, optional\n",
      "        The colors of the bar edges.\n",
      "    \n",
      "    linewidth : scalar or array-like, optional\n",
      "        Width of the bar edge(s). If 0, don't draw edges.\n",
      "    \n",
      "    tick_label : string or array-like, optional\n",
      "        The tick labels of the bars.\n",
      "        Default: None (Use default numeric labels.)\n",
      "    \n",
      "    xerr, yerr : scalar or array-like of shape(N,) or shape(2,N), optional\n",
      "        If not *None*, add horizontal / vertical errorbars to the bar tips.\n",
      "        The values are +/- sizes relative to the data:\n",
      "    \n",
      "        - scalar: symmetric +/- values for all bars\n",
      "        - shape(N,): symmetric +/- values for each bar\n",
      "        - shape(2,N): Separate - and + values for each bar. First row\n",
      "            contains the lower errors, the second row contains the\n",
      "            upper errors.\n",
      "        - *None*: No errorbar. (Default)\n",
      "    \n",
      "        See :doc:`/gallery/statistics/errorbar_features`\n",
      "        for an example on the usage of ``xerr`` and ``yerr``.\n",
      "    \n",
      "    ecolor : scalar or array-like, optional, default: 'black'\n",
      "        The line color of the errorbars.\n",
      "    \n",
      "    capsize : scalar, optional\n",
      "       The length of the error bar caps in points.\n",
      "       Default: None, which will take the value from\n",
      "       :rc:`errorbar.capsize`.\n",
      "    \n",
      "    error_kw : dict, optional\n",
      "        Dictionary of kwargs to be passed to the `~.Axes.errorbar`\n",
      "        method. Values of *ecolor* or *capsize* defined here take\n",
      "        precedence over the independent kwargs.\n",
      "    \n",
      "    log : bool, optional, default: False\n",
      "        If *True*, set the y-axis to be log scale.\n",
      "    \n",
      "    orientation : {'vertical',  'horizontal'}, optional\n",
      "        *This is for internal use only.* Please use `barh` for\n",
      "        horizontal bar plots. Default: 'vertical'.\n",
      "    \n",
      "    See also\n",
      "    --------\n",
      "    barh: Plot a horizontal bar plot.\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    The optional arguments *color*, *edgecolor*, *linewidth*,\n",
      "    *xerr*, and *yerr* can be either scalars or sequences of\n",
      "    length equal to the number of bars.  This enables you to use\n",
      "    bar as the basis for stacked bar charts, or candlestick plots.\n",
      "    Detail: *xerr* and *yerr* are passed directly to\n",
      "    :meth:`errorbar`, so they can also have shape 2xN for\n",
      "    independent specification of lower and upper errors.\n",
      "    \n",
      "    Other optional kwargs:\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 or None\n",
      "      animated: bool\n",
      "      antialiased or aa: unknown\n",
      "      capstyle: {'butt', 'round', 'projecting'}\n",
      "      clip_box: `.Bbox`\n",
      "      clip_on: bool\n",
      "      clip_path: [(`~matplotlib.path.Path`, `.Transform`) | `.Patch` | None]\n",
      "      color: color\n",
      "      contains: callable\n",
      "      edgecolor or ec: color or None or 'auto'\n",
      "      facecolor or fc: color or None\n",
      "      figure: `.Figure`\n",
      "      fill: bool\n",
      "      gid: str\n",
      "      hatch: {'/', '\\\\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}\n",
      "      in_layout: bool\n",
      "      joinstyle: {'miter', 'round', 'bevel'}\n",
      "      label: object\n",
      "      linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}\n",
      "      linewidth or lw: float or None\n",
      "      path_effects: `.AbstractPathEffect`\n",
      "      picker: None or bool or float or callable\n",
      "      rasterized: bool or None\n",
      "      sketch_params: (scale: float, length: float, randomness: float)\n",
      "      snap: bool or None\n",
      "      transform: `.Transform`\n",
      "      url: str\n",
      "      visible: bool\n",
      "      zorder: float\n",
      "    \n",
      "    .. note::\n",
      "        In addition to the above described arguments, this function can take a\n",
      "        **data** keyword argument. If such a **data** argument is given, the\n",
      "        following arguments are replaced by **data[<arg>]**:\n",
      "    \n",
      "        * All positional and all keyword arguments.\n",
      "    \n",
      "        Objects passed as **data** must support item access (``data[<arg>]``) and\n",
      "        membership test (``<arg> in data``).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# lets check the documentation of Bar Plots in Matplotlib\n",
    "help(plt.bar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets plot Education vs Monthly Rate\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (10, 4)\n",
    "plt.bar(data['EducationField'], data['MonthlyRate'], width = 0.7, align = 'center')\n",
    "plt.title('Average Salary of Employees from Various Departments')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The Above Graph is the first example for Bivariate Analysis, where we are Comparing Different Education Fields of Employees4 in an Organization with their Average Salaries.\n",
    "* The Graph shows that all the Education Fields are having almost similar average salaries.\n",
    "* The HR Department is having least average salary for employees in an organization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets plot Departments vs Monthly Rate\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (8, 4)\n",
    "plt.bar(data['Department'], data['MonthlyRate'], color = 'green', width = 0.55, align = 'center')\n",
    "plt.title('Average Salary of Employees from Various Education Fields')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Here, In the Second Example for Bivariate Analysis using Bar Charts using Bar Function from matplot\n",
    "* Here, The First Variable is the Department\n",
    "* The Second Variable is the Average Salary of Employees in an Organization\n",
    "* We can clearly see that The Human Resource Employee get lesser pay in comparison to Sales and Research and Development Employees."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There are two different types of plots available to visualize Multivariate data using bar Plots\n",
    "* 1. Grouped Bar Plots\n",
    "* 2. Stacked Bar Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We can Plot ```Multi Variate Charts``` for any data using ```Pivot_table()``` function**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grouped Bar Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets plot Departments vs Monthly Rate\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (10, 4)\n",
    "x = data.pivot_table(index = ['Department','EducationField'], values = ['MonthlyRate'], aggfunc = 'mean')\n",
    "x.plot(kind = 'bar', stacked = False)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Using the Pivot Table Function, The Data is not in good shape for best visualization.\n",
    "* We can use ```Crosstab() Function``` for better visualization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can Plot ```Multi Variate Charts``` for any data using ```crosstab()``` function**\n",
    "\n",
    "* We can visualize the data in a very organized manner which have very high story telling value\n",
    "\n",
    "* We have two choices, we can plot them as ```Grouped Charts``` and ```Stacked Charts```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>EducationField</th>\n",
       "      <th>Human Resources</th>\n",
       "      <th>Life Sciences</th>\n",
       "      <th>Marketing</th>\n",
       "      <th>Medical</th>\n",
       "      <th>Other</th>\n",
       "      <th>Technical Degree</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <td>18355.75</td>\n",
       "      <td>8916.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10425.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research &amp; Development</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13771.687500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13053.540984</td>\n",
       "      <td>12780.375</td>\n",
       "      <td>13551.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14644.684211</td>\n",
       "      <td>14645.952381</td>\n",
       "      <td>18956.833333</td>\n",
       "      <td>17519.000</td>\n",
       "      <td>14198.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "EducationField          Human Resources  Life Sciences     Marketing  \\\n",
       "Department                                                             \n",
       "Human Resources                18355.75    8916.000000           NaN   \n",
       "Research & Development              NaN   13771.687500           NaN   \n",
       "Sales                               NaN   14644.684211  14645.952381   \n",
       "\n",
       "EducationField               Medical      Other  Technical Degree  \n",
       "Department                                                         \n",
       "Human Resources         10425.000000        NaN               NaN  \n",
       "Research & Development  13053.540984  12780.375           13551.0  \n",
       "Sales                   18956.833333  17519.000           14198.5  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using crosstab funtion, making a cross tabular using multiple variables.\n",
    "# we have used mean function to aggregate the values of Monthly rate.\n",
    "# for x axis -> index: Department\n",
    "# for y axis -> index: Education Field\n",
    "# values (x X y) -> Monthly Rate, aggreated using mean function\n",
    "\n",
    "x = pd.crosstab(index = data['Department'],\n",
    "                columns = data['EducationField'],\n",
    "                values = data['MonthlyRate'],\n",
    "                aggfunc = 'mean')\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Here, we can see the result which we have received after Using the ```Crosstab function```\n",
    "* Where the ```index``` is ```Department``` attribute.\n",
    "* Where the ```column``` is ```Education Field``` attribute.\n",
    "* Where the ```value``` is ```Monthly Rate``` attribute where aggregation is done using ```Mean Function```. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stacked Bar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets plot a Stacked Bar Chart\n",
    "# we will use x, which we have created using crosstab function\n",
    "\n",
    "# setting the size of the plot\n",
    "plt.rcParams['figure.figsize'] = (8, 4)\n",
    "\n",
    "# keeping the Stacked option to **True**\n",
    "x.plot(kind = 'bar', stacked = True)\n",
    "plt.ylabel('Salary')\n",
    "plt.xticks(rotation = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We can see the Above Stacked Bar plot.\n",
    "* We can that Education Fields are Stacked for all the different Departments.\n",
    "* The Sizes are the average Salaries of the employees.\n",
    "* It is very clear to drive insights from the above graph.\n",
    "* The Employees who took HR in Education are only working in HR Department.\n",
    "* The Employees having Technical Degrees are not working in the HR Department"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grouped Bar Plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets make a Grouped Bar Plot \n",
    "# we will be using the variable x which we have created using the cross tab function\n",
    "\n",
    "# setting the size of the plot\n",
    "plt.rcParams['figure.figsize'] = (8, 4)\n",
    "\n",
    "# keeping the stacked option to **false**\n",
    "x.plot(kind = 'bar', stacked = False)\n",
    "plt.xticks(rotation = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The Above plot is a grouped Plot\n",
    "* This Kind of plots are even more easy to understand\n",
    "* The A"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}