013_Seaborn_Categorical_Bar_Plot_Point_Plot_and_Count_Plot.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "<small><small><i>\n",
    "All the IPython Notebooks in **[Python Seaborn Module](https://github.com/milaan9/12_Python_Seaborn_Module)** lecture series by **[Dr. Milaan Parmar](https://www.linkedin.com/in/milaanparmar/)** are available @ **[GitHub](https://github.com/milaan9)**\n",
    "</i></small></small>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/milaan9/12_Python_Seaborn_Module/blob/main/013_Seaborn_Categorical_Bar_Plot_Point_Plot_and_Count_Plot.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar Plot, Point Plot and Count plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Bar Plot\n",
    "\n",
    "Today we are here to discuss one of the most widely used type of figure in *Research* & *Business* domain. Few refer to it as **Bar Chart**, few call these **Bar Graph**, but we are going to stick with **<span style=\"color:green\">Bar Plot</span>** (well because that is how Seaborn addresses it)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot presents *Categorical data* with **rectangular bars** with *height* (may call it 'length') proportional to the values that they represent, and can be plotted *vertically* or *horizontally*. When plotted vertically, these are often referred to as a **Line Graph**. List of names just doesn't end there, so here comes one more: If this Bar plot is arranged from *highest* to *lowest* incidence of categories being plotted, then it is also referred to as **Pareto Chart**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now try to interpret a **Bar Plot**: So, it is actually a pictorial representation of statistical data in which the *independent variable* attains only the *discrete values*. On the other hand, the *dependent variable* can either be *discrete* or *continuous*, and this is what represents bar of different heights. We shall now segregate the types of Bar Plot that are often found in use:\n",
    "\n",
    "- At first we have **<span style=\"color:red\">Simple Bar Plot</span>** that conveys *discrete* value of an item for each category shown on the corresponding axis by plotting bars of *equal width* but *variable length*. These can be further classified into *Spatial* and *Quantitative*.\n",
    "\n",
    "- Next is **<span style=\"color:red\">Grouped Bar Plot</span>**, where we may have either *Scalar* or *Ordinal Independent variable*. In this plot, we observe the bars for *categorical variable values* kind of very *close* to each other, and hence the name. This type of plot is often used to represent **time** (possibly in *week*, *month* and *year*).\n",
    "\n",
    "- Next variety is **<span style=\"color:red\">Stacked Bar Plot</span>**, where bars represent the *sub-groups*, and are placed on top of each other to form a single column (or say, single bar). Here overall length of the bar gives us the total size of the category, and *different colors* indicate their relative contribution to each sub-group. On the downside, this piling hints no common baseline for individual bar elements; and that has an adverse effect when comparing these subcategories.\n",
    "\n",
    "- Finally, we have **<span style=\"color:red\">Range Bar Plot</span>**, that reflect *variations among the dependent variable* values of our dataset, as *range interval data*. Corresponding bars *begins at first dependent variable* value for that particular bar, rather than starting at a *common zero point*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To conclude the *types of Bar plot*, at the end now I am going to mention another variety, that is **Dot Plot**, or commonly known in Seaborn as **<span style=\"color:red\">Point Plot</span>**. This is one of the simplest ways to represent information pictorially, yet somehow this is a graph that is least used. And before I jump into what Seaborn has in stock to offer, I have a general piece of advice for you: **\"Whenever you find your requirement from the dataset suitable for a Bar Plot, always focus on the number of categories you're trying to compare. Though there is no Thumb rule, I would advise to stick to a Line Plot (one with vertical bars) if you have more than 10 items to compare, or else, it is visually appealing to have a Horizontal Bar Plot instead.\"** But again, this is just a *personal opinion/preference* and you are ofcourse free to choose any type of plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Back to our routine, we are now going to fetch our dependencies and then plot a basic Bar Plot for further discussion elucidating representation of **Central Tendency** and **CI (Confidence Interval)**. In case these terms don't sound familiar, I would recommend revising the *lecture on Box Plot*, where we have covered these topics pretty much in detail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:06.752908Z",
     "start_time": "2021-11-16T08:29:05.181369Z"
    }
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   "outputs": [],
   "source": [
    "# Importing intrinsic libraries:\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "np.random.seed(101)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set(style=\"whitegrid\", palette=\"rainbow\")\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Let us also get tableau colors we defined earlier:\n",
    "tableau_20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),\n",
    "         (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),\n",
    "         (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),\n",
    "         (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),\n",
    "         (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]\n",
    "\n",
    "# Scaling above RGB values to [0, 1] range, which is Matplotlib acceptable format:\n",
    "for i in range(len(tableau_20)):\n",
    "    r, g, b = tableau_20[i]\n",
    "    tableau_20[i] = (r / 255., g / 255., b / 255.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 1: with `Titanic` dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:09.298273Z",
     "start_time": "2021-11-16T08:29:09.244302Z"
    }
   },
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading built-in Tips dataset:\n",
    "titanic = sns.load_dataset(\"titanic\")\n",
    "\n",
    "# Pre-viewing Dataset:\n",
    "titanic.head(5)\n",
    "\n",
    "# Plotting basic Violin Plot:\n",
    "#sns.barplot(x=\"class\", y=\"fare\", data=titanic, palette=\"hsv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*So now I am going to quickly grab a cup of coffee and you guys need to utilize this time in guessing the type of Bar Plot this is. Keep thinking and I will be right back!*\n",
    "\n",
    "And, I a am back! I'm sure by now you would have guessed the type of Bar plot we have in front of us; and you are RIGHT! Indeed, this is a **Simple Bar plot**; or we may even call it a **Line Plot**. On *X-axis*, our plot has the *Class of passengers* travelling on Titanic and on *Y-axis*, we have corresponding *Ticket fare*.\n",
    "\n",
    "There is another type of plot with Seaborn which is very much identical to Bar Plot, and that is a **<span style=\"color:red\">\"Count Plot\"</span>**, so we shall quickly have a look at it as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:10.514996Z",
     "start_time": "2021-11-16T08:29:10.277261Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='class', ylabel='count'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='class', data=titanic, palette=\"Set1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There aren't variations in **Count Plot** as such, hence I decided to give you an insight of plot within this lecture itself. As you may notice, aesthetically both, **Count Plot** and **Bar Plot** are pretty identical. Here, the **Count Plot** actually displays the `number of passengers` travelling on `Titanic` in respectivve `Class`, that is labelled on `X-axis`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could have even set our *Categorical variable* assigned to `Y-axis` to get *Horizontal bars* like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:11.650970Z",
     "start_time": "2021-11-16T08:29:11.412627Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='class'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='class', data=titanic, palette=\"Set1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A **Count Plot** can be thought of as a **Histogram** across a *Categorical* variable, instead of a *Quantitative* variable. Basic options are identical to those for **Bar Plot**, so we may ideally compare *counts across nested variables*. That kind of pulls us back to our discussion on *Bar Plot*; where we are going to look into *optional parameters*.\n",
    "\n",
    "**`seaborn.barplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, estimator=<function mean>, ci=95, n_boot=1000, units=None, orient=None, color=None, palette=None, saturation=0.75, errcolor='.26', errwidth=None, capsize=None, dodge=True, ax=None)`**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, we have **`ci = 95`** representing **Confidence Interval**; and our first new parameter, i.e. **`estimator`** which permits any **Statistical function** to estimate within each categorical bin, and convert **Vector** to **Scalar**. In case these words confuse you, let me quickly give you an overview: \n",
    "\n",
    "Mathematically, any quantity (or value) that we use, can be divided into two categories, either a **Vector** or a **Scalar**:\n",
    "- **Scalars** are quantities that are fully described by a magnitude (or say, numerical value) alone. For example: 5 miles \n",
    "- **Vectors** are quantities that are fully described by both a magnitude and a direction. For example: Just add a direction to 5 miles; as 5 miles North.\n",
    "In case the concept still isn't clear, then I hope you understand that I can't dive into *Statistics* in a *Visualization* course. I highly recommend to pick a high school mathematics book and you would be to good to understand these concepts.\n",
    "\n",
    "Moving on with parameters, for supporting computation of **Confidence Intervals**, we've:\n",
    "- **`n_boot`** to handle number of **bootstrap iterations**.\n",
    "- **`errcolor`** & **`errwidth`** to represent **CI color** and **thickness of error bars** respectively.\n",
    "- **`units`** as an identifier of observation data points, that shall be used if required to perform multilevel bootstrapping.\n",
    "\n",
    "Additionally, last parameter of our interest is **`capsize`** that determines **width of the caps** on error bars.\n",
    "\n",
    "One by one we shall keep advancing with various types of Bar Plot, thus using each parameter that we discussed. So let us add few variations to our existing plot right away:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:12.673958Z",
     "start_time": "2021-11-16T08:29:12.132712Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='class', ylabel='fare'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using Matplotlib to set figure size and Font style for our plot:\n",
    "plt.rcParams['figure.figsize'] = (15.0, 10.0)\n",
    "plt.rcParams['font.family'] = \"Verdana\"\n",
    "\n",
    "# Plotting a set of vertical bars with nested grouping by two variables:\n",
    "sns.barplot(x=\"class\", y=\"fare\", hue=\"sex\", data=titanic, palette=\"rocket\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am sure this one isn't difficult to interpret. Oh by the way the **small grey lines** at the top center that you see are actually the **Confidence Interval**. We may alter it to get something like a **Median**. Let's give it a shot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:13.857662Z",
     "start_time": "2021-11-16T08:29:12.988318Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='embark_town', ylabel='age'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"embark_town\", y=\"age\", data=titanic, hue=\"sex\", palette=\"copper\", estimator=np.median)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now adding a **Standard Deviation** function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:14.410709Z",
     "start_time": "2021-11-16T08:29:14.079212Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='embark_town', ylabel='age'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"embark_town\", y=\"age\", data=titanic, hue=\"sex\", palette=\"coolwarm\", ci=\"sd\", capsize=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretty decent representation of quartiles here, reflecting on *Standard Deviation* of **`age`** across **`embarked town`**, based on **`Gender`**. Let's see what else can Bar Plot do for us:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:15.177618Z",
     "start_time": "2021-11-16T08:29:14.804675Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Taking into account total available values (Setting one person per row):\n",
    "total = float(len(titanic))\n",
    "\n",
    "# Plotting our figure (keeping it simple):\n",
    "ax = sns.countplot(x=\"class\", hue=\"who\", data=titanic, palette=\"Set2\")\n",
    "\n",
    "# Looping over entire Titanic dataset:\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(p.get_x()+p.get_width()/2., height + 3, '{:1.2f}'.format(height/total), ha=\"center\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, instead of a **Bar Plot**, I have used **Count Plot** (remember both are pretty same) to fetch each **`class`** of passenger & then scored in terms of percentage (on top of each bar); Total **`that_particular_class_men`** *divided by* **`Total that_particular_class`** passengers. Grouped with same stats for *Total_women_per_class* divided by *Total per **`class`** passengers*, and lastly for total children (Total First class children / Total First class) on top of their respective bars.\n",
    "\n",
    "Certainly you may try customizing this further to play around with the dataset. My intention was to again show you a way to bring **statistical summary** inside the plot using *Matplotlib* **`plt.bar`**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:18.460046Z",
     "start_time": "2021-11-16T08:29:18.157250Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='class', ylabel='fare'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmAAAAHiCAYAAABP+3CeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcoUlEQVR4nO3df5BV9X3/8dcuy0+3yAqsUGUYRKcknWJiJipRgShDEMKYgM2YTJpqh0ESZKxpTK0hJthYQcgQ+8OmNqlSaye1BcKPUqrjDwaE6hhq0XFpOxPExMqy/gCLLsju3u8fbTahkS8mej932X08Zpy7595z73nvncP63HPO3K2rVCqVAABQTH2tBwAA6GsEGABAYQIMAKAwAQYAUJgAAwAoTIABABTWUOsBflGvvfZGurp8cgYA0HPV19elqemU4z5+0gVYV1dFgAEAJzWnIAEAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgHNe+fS9m69aHs2/fi7UeBQB6lYZaD0DP1dLyTA4ceC0dHUczatQZtR4HAHoNR8A4rqNHO465BQDeGwIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUFhdpVKp1HqIX8QrrxxKV9cvP/KuXT/IwYMH3ruBerFXX30lXV2dqa/vl9NOG17rcXq8U08dlokTP1TrMQDoAerr6zJ8eONxH28oOEuPcPDggbz88v5aj3FS6erq9J4BwHvIKUgAgML63BGwn9XQUEljY1etx+ixXn+9Pl1ddamvr2ToUO/T2zl0qD4dHXW1HgOAk0yfDrDGxq6cd157rcfosXbsGJL29roMHFjxPh3Hzp2Dc+BAv1qPAcBJxilIAIDCBBgAQGECDACgMAEGAFCYAAMAKEyAAQAUJsAAAAoTYAAAhQkwAIDCBBgAQGECDACgMAEGAFBYVQNs3bp1mTVrVmbNmpVly5YlSbZv357Zs2dn+vTpWblyZTU3DwDQI1UtwNrb23Pbbbflvvvuy7p16/LUU0/lkUceyc0335y77rormzZtyrPPPpstW7ZUawQAgB6pagHW2dmZrq6utLe3p6OjIx0dHWlsbMzYsWMzZsyYNDQ0ZPbs2dm8eXO1RgAA6JEaqvXCjY2Nuf7663P55Zdn8ODB+fCHP5z9+/dn5MiR3es0NzentbW1WiMAAPRIVTsCtnv37qxevTqPPvpotm7dmvr6+jz//POpq6vrXqdSqRyzDADQF1QtwLZt25ZJkyZl+PDhGTBgQObMmZMnnngibW1t3eu0tbWlubm5WiMAAPRIVQuwCRMmZPv27XnzzTdTqVTyyCOP5Nxzz82ePXuyd+/edHZ2ZuPGjZk8eXK1RgAA6JGqdg3YxRdfnOeeey5z5sxJ//798xu/8RtZtGhRLrrooixatChHjhzJlClTMmPGjGqNAADQI1UtwJJk/vz5mT9//jH3TZo0KevXr6/mZgEAejSfhA8AUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgHFdDQ+WYWwDgvSHAOK5x497KsGEdGTfurVqPAgC9SkOtB6DnGjGiMyNGdNZ6DADodRwBAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUFhVA+yRRx7JnDlzcvnll+cb3/hGkmT79u2ZPXt2pk+fnpUrV1Zz8wAAPVLVAuxHP/pRvva1r+Wuu+7K+vXr89xzz2XLli25+eabc9ddd2XTpk159tlns2XLlmqNAADQI1UtwB566KHMnDkzo0aNSv/+/bNy5coMHjw4Y8eOzZgxY9LQ0JDZs2dn8+bN1RoBAKBHaqjWC+/duzf9+/fPggUL8tJLL2Xq1Kk555xzMnLkyO51mpub09raWq0RAAB6pKoFWGdnZ5566qncd999GTJkSD7/+c9n0KBBqaur616nUqkcswwA0BdULcBGjBiRSZMm5bTTTkuSTJs2LZs3b06/fv2612lra0tzc3O1RgAA6JGqdg3YRz/60Wzbti2vv/56Ojs7s3Xr1syYMSN79uzJ3r1709nZmY0bN2by5MnVGgEAoEeq2hGwc889N/PmzctnPvOZHD16NBdddFE+/elP56yzzsqiRYty5MiRTJkyJTNmzKjWCAAAPVJdpVKp1HqIX8QrrxxKV9cvP/LWrQ/n5Zf3J0mGDevMeee1v1ej0Qft3Dk4Bw78z2n1ESOac8kll9V4IgB6gvr6ugwf3nj8xwvOAgBABBgAQHECDACgMAEGAFCYAAMAKEyAAQAUJsAAAAoTYAAAhQkwAIDCBBgAQGECDACgMAEGAFCYAAMAKEyAAQAUJsAAAAoTYAAAhQkwAIDCBBgAQGECDACgMAEGAFCYAAMAKEyAAQAUJsAAAAoTYAAAhQkwAIDCBBgAQGECDACgMAEGAFCYAAMAKEyAAQAUJsAAAAp7RwF2+PDh/Pu//3sqlUra29urPRMAQK92wgB7+umnM23atFx77bVpbW3N1KlTs3PnzhKzAQD0SicMsDvuuCP33ntvhg0bllGjRuWOO+7IbbfdVmI2AIBe6YQBdvjw4Zx99tndy1OmTElnZ2dVhwIA6M1OGGANDQ05ePBg6urqkiQ//OEPqz4UAEBv1nCiFRYsWJDPfvazefnll/PFL34xjz/+eG699dYSswEA9EonDLBLLrkk48ePz+OPP56urq4sXLgw48ePLzEbAECvdMIAu/LKK7Nu3bqMHTu2xDwAAL3eCa8BGzx4cPbt21diFgCAPuGER8Da29tz2WWXZdSoURkyZEj3/Rs2bKjqYAAAvdUJA+wrX/lKiTkAAPqMEwbY+eefnwMHDqS9vT2VSiWdnZ154YUXSswGANArnTDA7rzzztx9991Jkn79+uXo0aM5++yznYIEAPglnfAi/HXr1uXRRx/Nxz72sTz44IO5/fbbj/lkfAAAfjEnDLDTTjstzc3NOeuss7J79+584hOfyH/8x3+UmA0AoFd6R3+K6IUXXshZZ52Vp556Kh0dHTly5EiJ2QAAeqXjBti3vvWtJMnkyZPz1a9+NVOnTs2DDz6YqVOn5oILLig1HwBAr3Pci/A3btyYT3/609m8eXNWrVqVt956K6tWrcqrr76a0aNHl5wRAKBXOW6AXXTRRZk6dWqS5CMf+UgqlUrq6uq6b1taWkrNCADQqxz3FOSSJUvS0tKS8847Ly0tLdm9e/cxtwAA/HJOeBH+/fffX2IOAIA+44QBBgDAe0uAAQAUJsAAAAoTYAAAhQkwAIDCBBgAQGECDACgMAEGAFCYAAMAKEyAAQAUJsAAAAoTYAAAhQkwAIDCBBgAQGECDACgMAEGAFCYAAMAKEyAAQAUJsAAAAoTYAAAhQkwAIDCBBgAQGECDACgMAEGAFCYAAMAKEyAAQAUVvUAW7ZsWW666aYkyfbt2zN79uxMnz49K1eurPamAQB6pKoG2I4dO7J27dokyeHDh3PzzTfnrrvuyqZNm/Lss89my5Yt1dw8AECPVLUAO3DgQFauXJkFCxYkSXbt2pWxY8dmzJgxaWhoyOzZs7N58+ZqbR4AoMeqWoDdcsstueGGGzJ06NAkyf79+zNy5Mjux5ubm9Pa2lqtzQMA9FhVCbC///u/z+jRozNp0qTu+7q6ulJXV9e9XKlUjlkGAOgrGqrxops2bUpbW1uuuOKKHDx4MG+++WZefPHF9OvXr3udtra2NDc3V2PzAAA9WlUC7J577un+es2aNXnyySezZMmSTJ8+PXv37s2ZZ56ZjRs3Zu7cudXYPABAj1aVAHs7AwcOzNKlS7No0aIcOXIkU6ZMyYwZM0ptHgCgx6h6gM2ZMydz5sxJkkyaNCnr16+v9iYBAHo0n4QPAFCYAAMAKEyAAQAUJsAAAAoTYAAAhQkwAIDCBBgAQGECDACgMAEGAFCYAAMAKEyAAQAUJsAAAAoTYAAAhQkwAIDCBBgAQGECDACgMAEGAFCYAAMAKEyAAQAUJsAAAAoTYAAAhQkwAIDCBBgAQGECDACgMAEGAFCYAAMAKEyAAQAUJsAAAAoTYAAAhQkwAIDCBBgAQGECDACgMAEGAFCYAAMAKEyAAQAUJsAAAAoTYAAAhQkwAIDCBBgAQGECDACgMAEGAFCYAAMAKEyAAQAUJsAAAAoTYAAAhQkwAIDCBBgAQGECDACgMAEGAFCYAAMAKEyAAQAUJsAAAAoTYAAAhQkwAIDCBBgAQGECDACgMAEGAFCYAAMAKEyAAQAUJsAAAAoTYAAAhQkwAIDCBBgAQGECDACgMAEGAFCYAAMAKEyAAQAUJsAAAAoTYAAAhQkwoE/bt+/FbN36cPbte7HWowB9SEOtBwCopZaWZ3LgwGvp6DiaUaPOqPU4QB/hCBjQpx092nHMLUAJAgwAoDABBgBQmAADAChMgAEAFCbAAAAK8zEU0Avt2vWDHDx4oNZjnBTa29/svt269eEaT9PznXrqsEyc+KFajwEnPQEGvdDBgwfy8sv7az3GSaWrq9N7BhRT1VOQf/qnf5pZs2Zl1qxZueOOO5Ik27dvz+zZszN9+vSsXLmympsHAOiRqnYEbPv27dm2bVvWrl2burq6zJs3Lxs3bsyKFSty3333ZfTo0bn22muzZcuWTJkypVpjQJ/X0FBJY2NXrcfosV5/vT5dXXWpr69k6FDv09s5dKg+HR11tR4DepWqBdjIkSNz0003ZcCAAUmS8ePH5/nnn8/YsWMzZsyYJMns2bOzefNmAQZV1NjYlfPOa6/1GD3Wjh1D0t5el4EDK96n49i5c3AOHOhX6zGgV6naKchzzjknH/jAB5Ikzz//fP7pn/4pdXV1GTlyZPc6zc3NaW1trdYIAAA9UtU/huI///M/8zu/8zv58pe/nDFjxqSu7qeHsSuVyjHLAAB9QVUD7Ac/+EGuvvrq/N7v/V4++clPZtSoUWlra+t+vK2tLc3NzdUcAQCgx6lagL300ktZuHBhVqxYkVmzZiVJzj333OzZsyd79+5NZ2dnNm7cmMmTJ1drBACAHqlqF+F/97vfzZEjR7J06dLu+6666qosXbo0ixYtypEjRzJlypTMmDGjWiMAAPRIVQuwxYsXZ/HixW/72Pr166u1WYBfSEND5ZhbgBL8LUigTxs37q0MG9aRcePeqvUoQB/iTxEBfdqIEZ0ZMaKz1mMAfYwjYAAAhQkwAIDCBBgAQGECDACgMAEGAFCYAAMAKEyAAQAUJsAAAAoTYAAAhQkwAIDCBBgAQGECDACgMAEGAFCYAAMAKEyAAUAvt2/fi9m69eHs2/dirUfhfzXUegAAoLpaWp7JgQOvpaPjaEaNOqPW4xBHwACg1zt6tOOYW2pPgAEAFOYUJAAnpV27fpCDBw/UeoyTQnv7m923W7c+XONper5TTx2WiRM/VNVtCDAATkoHDx7Iyy/vr/UYJ5Wurk7vWQ/hFCQAQGGOgAFw0mtoqKSxsavWY/RYr79en66uutTXVzJ0qPfp7Rw6VJ+Ojrpi2xNgAJz0Ghu7ct557bUeo8fasWNI2tvrMnBgxft0HDt3Ds6BA/2Kbc8pSACAwgQYAPRyDQ2VY26pPQEGAL3cuHFvZdiwjowb91atR+F/uQYMAHq5ESM6M2JEZ63H4Gc4AgYAUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUJgAAwAoTIABABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUFhNAmzDhg2ZOXNmpk+fnvvvv78WIwAA1ExD6Q22trZm5cqVWbNmTQYMGJCrrroqF1xwQc4+++zSo+TQofrs3Dm4+HbpPQ4d6vkHke3nvFv2c/qC0vt58QDbvn17LrzwwgwbNixJ8rGPfSybN2/Odddd946eX19f9662P2TIkDQ2NnYvd3S8q5ejjxs06KdfDxky5F3vn+8V+znvJfs5fcF7vZ+f6PnFA2z//v0ZOXJk93Jzc3N27dr1jp/f1HTKu9r+jBnT39Xz4WRgP6cvsJ9zMit+XLmrqyt1dT+twkqlcswyAEBvVzzARo0alba2tu7ltra2NDc3lx4DAKBmigfYRz7ykezYsSOvvvpq2tvb8+CDD2by5MmlxwAAqJni14CdfvrpueGGG/K5z30uR48ezZVXXpmJEyeWHgMAoGbqKpVKpdZDAAD0JT3/w10AAHoZAQYAUJgAAwAoTIABABQmwAAACiv+MRT0DD/+8Y8zY8aMjB8//pj73//+92fatGm57LLL3tHr/MEf/EGuu+66nHHGGdUYE05o8+bNufvuu9PR0ZFKpZIrrrgi8+bNKzrDmjVr8uSTT2bp0qVFt0vftmTJkuzcuTNHjx7NCy+80P3z/PXXX8+cOXOyaNGiY9Z/+OGH8+yzz+b666///77un/zJnyTJzz2f95YA68Oam5uzbt26d/UaTzzxRBYuXPgeTQS/mNbW1ixbtixr1qxJU1NT3njjjfzWb/1Wxo0b945/iYCT1de+9rUk//ML9ec+97nun+c/Caj/67LLLvPvogcRYBzjpptuyvnnn5/zzz8/8+bNS1NTUwYNGpTf//3fzy233JKOjo4MHDgwt99+ex588MHs378/8+fPz/3335+mpqZaj08f89prr+Xo0aM5fPhwkuSUU07J0qVLM3DgwOzatSu33357Dh8+nKampixZsiRjxoxJS0tLbrnllhw+fDinnnpqVqxYkVGjRuXb3/521q9fn379+uWiiy7KjTfemJdeeinXXXddzjnnnLS0tGT48OG58847M2zYsHz/+9/Pn//5n6exsTFnnHFGhgwZUuN3A35q165dueqqq9La2tp9NOxnj9ReeumlmThxYlpaWvK3f/u3Wbt2bR544IE0NTVl6NChPiC9ANeA9WH79+/PFVdc0f3fd77znWMe37NnT5YvX5577rknq1atyjXXXJM1a9bkU5/6VJ5++unMnz8/zc3Nufvuu8UXNTFhwoRcdtllmTZtWq688sosX748XV1dGT16dBYvXpxvfvObWbt2ba655pp89atfTZJ86Utfyhe+8IVs2LAhM2fOzKpVq7Jly5Y88sgjWb16ddauXZu9e/fme9/7XpJk9+7dueaaa7Jx48YMHTo0GzZsSGtra1asWJH7778/f/d3f5c33nijlm8D/JxXXnklf/3Xf53Vq1fnu9/9bg4dOvRz60yePDn//M//nP/6r//q3vfvueee7Nu3rwYT9z2OgPVhb3cK8qabbur+evjw4TnzzDOTJFOmTMmtt96arVu35tJLL81HP/rRorPC8SxZsiRf+MIXsm3btmzbti2f+tSnMn/+/PzoRz/K5z//+e71Dh06lFdffTVtbW3d++9nPvOZJMmyZcsya9asDB48OEkyd+7cfP/738+UKVMyfPjwvP/970+SnHPOOTl48GD+9V//NR/84AczYsSIJMns2bPzL//yLyW/bfj/uuSSSzJgwICcdtppaWpqysGDB39unXPPPTdJ8uSTT2bKlCk55ZRTkiQzZsxIV1dX0Xn7IgHGcQ0aNKj76xkzZuSDH/xgHn300dx777157LHH8o1vfKOG00Hy2GOP5c0338zMmTMzd+7czJ07Nw888EA2bNiQM888s/sXjM7Ozrz88svp379/6urqup9/5MiR7N+//23/Z9PR0ZEkGThwYPd9dXV1qVQq3bc/0dDgRyk9y8/uk/93f/2Jn+zbb7c/v/XWW9Ufso9zCpJ35Hd/93fzzDPP5Kqrrsr111+f5557LknSr1+/dHZ21ng6+qpBgwblm9/8Zn784x8nSSqVSlpaWvKBD3wgBw8ezFNPPZUkWb16db70pS/lV37lV3L66adn27ZtSZJ169blzjvvzIUXXph//Md/zOHDh9PR0ZHVq1fnwgsvPO52P/ShD+Xpp59Oa2trurq6smnTpup/s1AlkyZNyqOPPpr//u//zpEjR/LQQw/VeqQ+wa9tvCMLFizIV77ylfzZn/1Z+vfvn69//etJkqlTp2b+/Pn5zne+kzFjxtR2SPqcCy+8MNddd10WLFiQo0ePJvmfUy+LFi3KpZdemttuuy1HjhxJY2Njli1bliRZvnx5vv71r2f58uVpamrKHXfckebm5rS0tGTu3Lnp6OjIxRdfnM9+9rPHvRZmxIgRWbx4ca6++uoMHjw4Z599drHvGd5r73vf+/Lbv/3bufLKKzN06ND86q/+aq1H6hPqKm93XBIAgKpxChIAoDABBgBQmAADAChMgAEAFCbAAAAKE2BAr/fEE0/k4x//eK3HAOgmwAAACvNBrECv8w//8A+55557Ul9fn6ampsyZM6f7sT179uTWW2/NG2+8kba2tkyYMCHf+ta3MnDgwPzxH/9xHnroofTv3z9NTU25/fbb09zcfNz7AX5ZAgzoVXbv3p0VK1Zk7dq1GT16dO699958+9vf7v7beA888EA+8YlP5IorrsjRo0czZ86cPPbYY5k4cWJWrVqVHTt2ZMCAAfmrv/qr7Nq1K7/+67/+tvdPmzatxt8pcDITYECvsmPHjlx88cUZPXp0kuTqq6/O+973vvzhH/5hkuTGG2/M448/nr/8y7/M888/n/379+fNN9/M6aefngkTJuSTn/xkJk+enMmTJ2fSpEnp6up62/sB3g0BBvQq/fr1S11dXffy4cOH88Mf/rB7+Ytf/GI6Oztz+eWXZ+rUqXnppZdSqVRSX1+fv/mbv8kzzzyTHTt25I/+6I9yySWX5Mtf/vJx7wf4ZbkIH+hVLrjgguzYsSP79+9Pknzve9/L8uXLux/ftm1bFi5cmJkzZyZJ/u3f/i2dnZ3ZvXt3Pv7xj2f8+PG59tprc/XVV+eZZ5457v0A74YjYECv8mu/9mu58cYbM2/evCTJyJEjs2TJkvzFX/xFkuSGG27IwoULM2TIkDQ2NubDH/5wXnjhhfzmb/5mLr/88sydOzdDhgzJoEGDsnjx4kyYMOFt7wd4N+oqlUql1kMAAPQlTkECABQmwAAAChNgAACFCTAAgMIEGABAYQIMAKAwAQYAUNj/A9j6uhEbaQW7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Few more modifications in Appearance:\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0)\n",
    "sns.set_style(\"dark\")\n",
    "sns.barplot(x=\"class\", y=\"fare\", data=titanic, lw=3.5, facecolor=tableau_20[17], errcolor=\".6\", edgecolor=\".6\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot has a simple representation of data with no major changes in terms of computation but aesthetically, we have an **`errcolor`** determining color of the *quartile* and **`edgecolor`** (that comes from *Matplotlib*) determining *color of edges of the bars*.\n",
    "\n",
    "By now, we have discussed a lot about **Simple Bar Plot** and **Grouped Bar Plot**. Let us now draft a **Stacked Bar Plot**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this experiment, I have built a dummy dataset named **Score Book** that is available for you in your Resource folder. This dataset has *scorecard of 25 students* who had enrolled for *Data Analysis*, *Machine Learning* and *Artificial Intelligence* courses in this fictional university. All the marks obtained by enrolled students in respective tests are scored out of *Total Marks* equalling to **10**. Let us quickly preview this dataset and then try to plot a *Stacked Bar Plot* out of it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:23.101921Z",
     "start_time": "2021-11-16T08:29:23.030279Z"
    }
   },
   "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>Student ID</th>\n",
       "      <th>Data Analysis</th>\n",
       "      <th>Machine Learning</th>\n",
       "      <th>Artificial Intelligence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2.393413</td>\n",
       "      <td>3.324129</td>\n",
       "      <td>0.039631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3.228434</td>\n",
       "      <td>3.109299</td>\n",
       "      <td>5.621415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6.611172</td>\n",
       "      <td>3.603704</td>\n",
       "      <td>4.830770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.553519</td>\n",
       "      <td>5.030114</td>\n",
       "      <td>4.846976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4.151165</td>\n",
       "      <td>6.555816</td>\n",
       "      <td>4.126257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>9.077036</td>\n",
       "      <td>7.478125</td>\n",
       "      <td>3.321004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>7.039631</td>\n",
       "      <td>8.201300</td>\n",
       "      <td>2.667565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>3.021069</td>\n",
       "      <td>8.306264</td>\n",
       "      <td>1.976073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>5.011523</td>\n",
       "      <td>6.622167</td>\n",
       "      <td>1.220087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>2.006191</td>\n",
       "      <td>4.272699</td>\n",
       "      <td>0.656404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Student ID  Data Analysis  Machine Learning  Artificial Intelligence\n",
       "0           1       2.393413          3.324129                 0.039631\n",
       "1           2       3.228434          3.109299                 5.621415\n",
       "2           3       6.611172          3.603704                 4.830770\n",
       "3           4       4.553519          5.030114                 4.846976\n",
       "4           5       4.151165          6.555816                 4.126257\n",
       "5           6       9.077036          7.478125                 3.321004\n",
       "6           7       7.039631          8.201300                 2.667565\n",
       "7           8       3.021069          8.306264                 1.976073\n",
       "8           9       5.011523          6.622167                 1.220087\n",
       "9          10       2.006191          4.272699                 0.656404"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading 'Score Book' Dataset using Pandas:\n",
    "scorebook = pd.read_csv(\"datasets/Score Book.csv\")\n",
    "\n",
    "# Pre-viewing Dataset:\n",
    "scorebook.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:17:34.312382Z",
     "start_time": "2021-11-16T08:17:34.284879Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Importing Dependancy:\n",
    "import matplotlib as mpl\n",
    "\n",
    "# Creating a 'Total' column to contain Total marks obtained by each student:\n",
    "scorebook[\"Total\"] = scorebook.Data_Analysis + scorebook.Machine_Learning + scorebook.Artificial_Intelligence\n",
    "\n",
    "# Plot 1: 'Total' marks obtained (Placing on Top as Background):\n",
    "sns.barplot(x = scorebook.Student_ID, y = scorebook.Total, color = tableau_20[4])\n",
    "\n",
    "# Plot 2: 'Data Analysis' result (Overlaying at Bottom):\n",
    "bottom_plot = sns.barplot(x = scorebook.Student_ID, y = scorebook.Data_Analysis, color = '#0000A3')\n",
    "\n",
    "# Deciding Aesthetics (Look into Matplotlib for customization):\n",
    "topbar = plt.Rectangle((0,0), 1, 1, fc= tableau_20[4], edgecolor = \"None\")\n",
    "bottombar = plt.Rectangle((0,0),0,0, fc= '#0000A3', edgecolor = \"None\")\n",
    "l = plt.legend([bottombar, topbar], [\"Data Analysis\", \"Total Score (DA+ML+AI)\"], loc=1, ncol=1, prop={'size':12})\n",
    "l.draw_frame(False)\n",
    "\n",
    "#Optional code - Make plot look nicer\n",
    "sns.despine()\n",
    "bottom_plot.set_ylabel(\"Marks Obtained\")\n",
    "bottom_plot.set_xlabel(\"Total Number of Students\")\n",
    "\n",
    "#Set fonts to consistent 16pt size\n",
    "for item in ([bottom_plot.xaxis.label, bottom_plot.yaxis.label] +\n",
    "             bottom_plot.get_xticklabels() + bottom_plot.get_yticklabels()):\n",
    "    item.set_fontsize(16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a plot cannot speak for itself, then the plot isn't good enough. By now we know everything about Seaborn Bar plot, and what we're covering is just a couple of variations. So leaving aside the underlying Matplotlib code that I used for customization, if you have any confusion if understanding aesthetics of this plot, do let me know in the forum."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And do remember that this is just one way of doing it, and if we invest more time, we shall be able to do MUCH better so I would leave that as a homework for you guys. Meanwhile, we shall try to obtain the same Stacked Bar Plot but with a little more variation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:16:52.804501Z",
     "start_time": "2021-11-16T08:16:40.262Z"
    }
   },
   "outputs": [],
   "source": [
    "# To make a colormap from a list of Colors:\n",
    "from matplotlib.colors import ListedColormap\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "scorecard = pd.read_csv(\"datasets/Score Book.csv\")\n",
    "scorecard = (scorecard.T)[1:].reset_index()\n",
    "scorecard.rename(index=str, columns={'index': 'Courses'}, inplace=True)\n",
    "\n",
    "ax = scorecard.set_index('Courses')\\\n",
    "  .reindex_axis(scorecard.set_index('Courses').sum().sort_values().index, axis=1)\\\n",
    "  .T.plot(kind='bar', stacked=True,\n",
    "          colormap = ListedColormap(sns.color_palette(\"viridis\", 10)), \n",
    "          figsize = (16,8))\n",
    "\n",
    "plt.rcParams['font.family'] = \"serif\"\n",
    "ax.set_ylabel(\"Marks Obtained\")\n",
    "ax.set_xlabel(\"Student Enrollment ID\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Point Plot\n",
    "\n",
    "With enough on plate for Stacked Bar Plot, let us now try to design another variation, i.e. **Point Plot**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:37.133020Z",
     "start_time": "2021-11-16T08:29:36.621355Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sex', ylabel='survived'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(x=\"sex\", y=\"survived\", hue=\"class\", data=titanic, palette=\"rocket\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, a **Point Plot** just plots the *Point estimate* and *Confidence Interval* and connects points from the same **`hue`** category. As our eyes are pretty good at picking up on differences of slopes, it is easy to see how the main relationship is changing as a *function of a second variable*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:38.669850Z",
     "start_time": "2021-11-16T08:29:38.204634Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(x=\"class\", y=\"survived\", hue=\"sex\", data=titanic, palette={\"male\": \"g\", \"female\": \"r\"},\n",
    "              markers=[\"o\", \"^\"], linestyles=[\"-\", \"--\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now once done with all these variations, let me lastly focus on *getting some labels and random data value on top of each bar* because this is something you might often require in your presentations and I wouldn't like you to miss on it. Let us get started:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:40.118152Z",
     "start_time": "2021-11-16T08:29:39.647365Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating random data:\n",
    "x = ['Apple', 'Peach', 'Mango', 'Guava', 'Pineapple']\n",
    "y = [12, 13, -9, 10.5, 8]\n",
    "\n",
    "# Background Styling:\n",
    "sns.axes_style('white')\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "# Declaring colors and directing Bar Plot:\n",
    "colors = [tableau_20[5] if _y >=0 else tableau_20[10] for _y in y]\n",
    "ax = sns.barplot(x, y, palette=colors)\n",
    "\n",
    "for n, (label, _y) in enumerate(zip(x, y)):\n",
    "    ax.annotate(s='{:.1f}'.format(abs(_y)), xy=(n, _y), ha='center', va='center', xytext=(0,20), \n",
    "                textcoords='offset points', weight='bold')\n",
    "\n",
    "    ax.annotate(s=label, xy=(n, 0), ha='center', va='center', xytext=(0,10), textcoords='offset points')\n",
    "\n",
    "# Axes formatting:\n",
    "ax.set_yticks([])\n",
    "ax.set_xticks([])\n",
    "sns.despine(ax=ax, bottom=True, left=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, completely off-track BUT if you wish to draw a **Stacked Bar plot with Matplotlib**, you may customize and create something like this. Please note that this is just something bonus for you and would have to research on your own if any part of it isn't clear:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:41.393101Z",
     "start_time": "2021-11-16T08:29:41.118517Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Values')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing Dependency:\n",
    "from matplotlib import rc\n",
    "\n",
    "# Ensuring bold Y-axis:\n",
    "rc('font', weight='bold')\n",
    " \n",
    "# Values for each group:\n",
    "bar1 = [12, 28, 1, 8, 22]\n",
    "bar2 = [28, 7, 16, 4, 10]\n",
    "bar3 = [25, 3, 23, 25, 17]\n",
    " \n",
    "# Height of bar1 + bar2:\n",
    "bars = [40, 35, 17, 12, 32]\n",
    " \n",
    "# Position of bars on X-axis:\n",
    "pos = [0,1,2,3,4]\n",
    " \n",
    "# Group Name:\n",
    "fruits = ['Apple','Peach','Mango','Guava','Pineapple']\n",
    " \n",
    "# Create bottom bars:\n",
    "plt.bar(pos, bar1, color=tableau_20[4], edgecolor='white', width=1.5)\n",
    "# Create middle bars on top of the first one:\n",
    "plt.bar(pos, bar2, bottom=bar1, color=tableau_20[14], edgecolor='white', width=1.5)\n",
    "# Create Top bars:\n",
    "plt.bar(pos, bar3, bottom=bars, color='#2d7f5e', edgecolor='white', width=1.5)\n",
    " \n",
    "# Custom X axis:\n",
    "plt.xticks(pos, fruits, fontweight='bold')\n",
    "plt.xlabel(\"Types of Fruits\")\n",
    "plt.ylabel(\"Values\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2: with `Tips` dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:42.389255Z",
     "start_time": "2021-11-16T08:29:42.308430Z"
    },
    "scrolled": false
   },
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the tips data sets\n",
    "\n",
    "tips = sns.load_dataset('tips')\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:42.998469Z",
     "start_time": "2021-11-16T08:29:42.738083Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sex', ylabel='total_bill'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='sex',y='total_bill',data=tips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:43.338427Z",
     "start_time": "2021-11-16T08:29:43.325539Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change the estimator object to your own function, that converts a vector to a scalar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:44.252226Z",
     "start_time": "2021-11-16T08:29:43.949610Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sex', ylabel='total_bill'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='sex',y='total_bill',data=tips,estimator=np.std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:44.999280Z",
     "start_time": "2021-11-16T08:29:44.629951Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day', ylabel='tip'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot\n",
    "sns.barplot(x='day', y='tip', data=tips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:45.696151Z",
     "start_time": "2021-11-16T08:29:45.312430Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day', ylabel='total_bill'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw barplot with days and total_bill\n",
    "sns.barplot(x ='day', y='total_bill', data=tips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:46.553121Z",
     "start_time": "2021-11-16T08:29:46.005068Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day', ylabel='total_bill'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a set of vertical bars with nested grouping by a two variables:\n",
    "sns.barplot(x='day', y ='total_bill', hue = 'sex', data=tips, palette='BuGn_d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:47.434032Z",
     "start_time": "2021-11-16T08:29:46.890261Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day', ylabel='total_bill'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='day', y ='total_bill', hue = 'smoker', data=tips, palette='BuGn_d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:48.086973Z",
     "start_time": "2021-11-16T08:29:47.716883Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='tip', ylabel='day'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a set of horizontal bars:\n",
    "sns.barplot(x='tip', y ='day', data=tips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:48.855048Z",
     "start_time": "2021-11-16T08:29:48.489388Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='total_bill', ylabel='day'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a set of horizontal bars:\n",
    "sns.barplot(x='total_bill', y='day', data=tips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:49.504376Z",
     "start_time": "2021-11-16T08:29:49.121514Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day', ylabel='tip'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Control bar order by passing an explicit order:\n",
    "sns.barplot(x='day', y='tip', data=tips, palette='spring', order=['Fri','Sat','Sun','Thur'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:50.303021Z",
     "start_time": "2021-11-16T08:29:49.800886Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day', ylabel='tip'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use median as the estimate of central tendency:\n",
    "\n",
    "from numpy import median\n",
    "sns.barplot(x='day', y='tip', data=tips, palette='spring', order=['Fri','Sat','Sun','Thur'], estimator=median)\n",
    "\n",
    "# here y axis(tip), we have calculated median."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:50.936470Z",
     "start_time": "2021-11-16T08:29:50.615900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='smoker', ylabel='tip'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x ='smoker', y='tip', data=tips)\n",
    "\n",
    "# black vertical line show the  error bar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:51.750181Z",
     "start_time": "2021-11-16T08:29:51.311225Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day', ylabel='tip'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the standard error of the mean with the error bars:\n",
    "\n",
    "sns.barplot(x=\"day\", y=\"tip\", data=tips, ci=68)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:55.924456Z",
     "start_time": "2021-11-16T08:29:55.709634Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day', ylabel='tip'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show standard deviation of observations instead of a confidence interval:\n",
    "\n",
    "sns.barplot(x=\"day\", y=\"tip\", data=tips, ci=\"sd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:56.600101Z",
     "start_time": "2021-11-16T08:29:56.254394Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day', ylabel='tip'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add “caps” to the error bars:\n",
    "\n",
    "sns.barplot(x=\"day\", y=\"tip\", data=tips, capsize = 0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:57.494206Z",
     "start_time": "2021-11-16T08:29:56.914086Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ce07cc58e0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"day\", y=\"total_bill\", hue='sex', data=tips, capsize=0.2, palette='husl')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:29:58.704015Z",
     "start_time": "2021-11-16T08:29:57.951530Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ce07cb24f0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use hue without changing bar position or width:\n",
    "\n",
    "tips[\"weekend\"] = tips[\"day\"].isin([\"Sat\", \"Sun\"])\n",
    "sns.barplot(x=\"day\", y=\"total_bill\", hue=\"weekend\", data=tips, dodge=False)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:30:01.869108Z",
     "start_time": "2021-11-16T08:30:01.405573Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='size', ylabel='total_bill'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot all bars in a single color:\n",
    "sns.barplot(\"size\", y=\"total_bill\", data=tips, color=\"salmon\", saturation=.5, capsize=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `boxplot()` and `violinplot()`\n",
    "\n",
    "**`boxplot()`** and **`violinplot()`** are used to shown the distribution of categorical data. A box plot (or box-and-whisker plot) shows the distribution of quantitative data in a way that facilitates comparisons between variables or across levels of a categorical variable. The box shows the quartiles of the dataset while the whiskers extend to show the rest of the distribution, except for points that are determined to be **outliers** using a method that is a function of the inter-quartile range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:30:02.614148Z",
     "start_time": "2021-11-16T08:30:02.365592Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day', ylabel='total_bill'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"day\", y=\"total_bill\", data=tips,palette='rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:30:03.778692Z",
     "start_time": "2021-11-16T08:30:03.399321Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Can do entire dataframe with orient='h'\n",
    "sns.boxplot(data=tips,palette='rainbow',orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:30:04.504569Z",
     "start_time": "2021-11-16T08:30:04.122188Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day', ylabel='total_bill'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"day\", y=\"total_bill\", hue=\"smoker\",data=tips, palette=\"coolwarm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Count Plot\n",
    "\n",
    "This is essentially the same as barplot except the estimator is explicitly counting the number of occurrences. Which is why we only pass the x value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-16T08:30:08.879989Z",
     "start_time": "2021-11-16T08:30:08.672597Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sex', ylabel='count'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='sex',data=tips)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
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    "And that brings us to the end of our discussion on **Bar Plot**, **Point Plot** and **Count Plot**. In the next lecture, we shall try to plot last **Categorical Plot** named as **[Factor Plot](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/014_Seaborn_Categorical_Factor_Plot.ipynb)** with Seaborn. Till then keep practicing as much as you can."
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