Seaborn - Bar Plot & Count Plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:red\">Seaborn | Part-12: Bar Plot</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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": 117,
   "metadata": {},
   "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": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23537b35a58>"
      ]
     },
     "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23537c81780>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W8EfAwohYbxXfZxbwPprDqh8XEa8HjgKWGhNNFQZF6q89gYsz85nMfJLm0OCbZual8OLhwf+D5o9frcwPMvPZzHy4vf7acgRcrUUMitRfz9EcN2ncW2mOjzTRNJrj6o2NX9b+MaiJ/mfC6RevJ00lBkXqr+uAD0fEjIjYELgYGIuIA+DFo7nOpPkTtI8A27S326+H772c4TzAq6YogyL1UWZeRnM48FuAm2n+fvcuwFERcQfNnyQ+IDOfpflrgnPaF9t3BR5cxbdfBrw+Ii7o1/zS/4fv8pIklXAPRZJUwqBIkkoYFElSCYMiSSphUCRJJQyKJKmEQZEklfhfSuExrWbaD58AAAAASUVORK5CYII=\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": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23537cd8780>"
      ]
     },
     "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x235382afa90>"
      ]
     },
     "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23537fb5630>"
      ]
     },
     "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": {},
   "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": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2353f606668>"
      ]
     },
     "execution_count": 119,
     "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": [
    "# 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": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading 'Score Book' Dataset using Pandas:\n",
    "scorebook = pd.read_csv(\"D:/Sunindu Data Inc/Seaborn/8.1_Bar Plot & Count Plot/Score Book.csv\")\n",
    "\n",
    "# Pre-viewing Dataset:\n",
    "scorebook.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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 Score\", \"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": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Student Enrollment ID')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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(\"D:/Sunindu Data Inc/Seaborn/8.1_Bar Plot & Count Plot/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": [
    "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": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2353b122f60>"
      ]
     },
     "execution_count": 123,
     "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": 124,
   "metadata": {},
   "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": 152,
   "metadata": {},
   "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": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Values')"
      ]
     },
     "execution_count": 156,
     "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": [
    "And that brings us to the end of our discussion on Bar Plots and also pretty much covers our discussion on Categorical Data Plotting. In teh next lecture, we shall try to plot <span style=\"color:red\">\"Time Series data\"</span> with Seaborn. Till then keep practicing as much as you can using resurces from portals like [Kaggle](https://www.kaggle.com/), [UC Repository](https://archive.ics.uci.edu/ml/datasets.html), etc. In fact, in the notebook I shall attach a link to a [Resource where you shall find almost everything that can help you with datasets and much more](https://medium.com/machine-learning-bootcamp/curated-list-of-100-data-science-resources-for-practitioners-3c0510ed47a3). Specially if you've keen interest in *Data Science* or *Machine Learning*, that would be worth bookmarking for future use.\n",
    "\n",
    "I shall take your leave now and will see you in the next lecture. Till then, **Happy Visualizing**!"
   ]
  }
 ],
 "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": "3.6.4"
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 },
 "nbformat": 4,
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}