Seaborn - Box Plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:red\">Seaborn | Part-10: Box Plot</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to another lecture on Seaborn! \n",
    "\n",
    "Today we shall be discussing another important type of plot, i.e. Box Plot. Earlier we've seen plots for linear dataset, and then moved on to focus particularly on Categorical variables. The plot we've decided to cover in this lecture fits the bill very well when we have to show an abstract representation of each distribution of observations, more specifically categorical variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A **Box plot** shows the *spread of all the data points*, and also fetches other intrinsic information like the **median** or where most of the data cluster are, thus reflecting *average trend*. Basically, it is a simple way of representing statistical data on a plot in which a rectangle is drawn to represent the **second and third quartiles**, usually with a *vertical line* inside it, to indicate the *median* value. The **lower and upper quartiles** are shown as *horizontal lines* on either side of this rectangle.\n",
    "\n",
    "Thus it depicts group of numerical data through their *quartiles* in **Descriptive statistics**. To give an elementary level understanding, **Descriptive statistics** is used to describe the basic features of a dataset, providing simple summaries about the sample and related measures, like median. Together with plots like a Box plot, they form the basis of virtually every quantitative analysis of data.\n",
    "\n",
    "I know I'm bringing in Statistics under our hood, but I assure to breeze through all of this. So, as per **Probability Theory**, a **Quartile** is a just a type of **Quantile**, which are actually the **three cut points** dividing a **probability distribution** of a dataset into **four equal-size groups**. These are **contiguous intervals** with equal probabilities, or rephrasing, divides the observations in a *sample* in the same way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a very important concept involved with Box Plot, i.e. **Whisker** and we shall try to understand the purpose of its' association with **Box Plot** BUT before that let us actually get a Box Plot plotted. So let us begin with ifetching our dependancies, that we use in every lecture:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing intrinsic libraries:\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "np.random.seed(44)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set(style=\"darkgrid\", palette=\"icefire\")\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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d8f3f9d5f8>"
      ]
     },
     "execution_count": 17,
     "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": [
    "# Loading built-in Tips dataset:\n",
    "tips = sns.load_dataset(\"tips\")\n",
    "\n",
    "# Plotting basic Box Plot:\n",
    "sns.boxplot(x=\"day\", y=\"total_bill\", data=tips)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:red\">Anatomy of Box Plot:</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot isn't visually something new for us but I am pretty sure that a thought has often popped up in our head as to what are those bars. We got introduced to this term **Whisker** even in our previous lecture of **Strip Plot**, so let us know dive in and get a better insight. There are **three horizontal bars** that we notice. Let us consider the *first box* on our plot, i.e for *Thursday* on *X-axis*. \n",
    "\n",
    "- So, the **first bar** (top-most one), shows the **maximum range variation** at a total bill amount of *30*.\n",
    "- **Second bar** within the box, shows the **median** at a total bill amount of *15*.\n",
    "- And, our **third bar** at the bottom, shows the **minimum range variation** of total bill, amounting somewhere around *8*.\n",
    "\n",
    "Talking about the box in our **Box Plot**, the **bottom spine** is actually the **first quartile**; and **top spine**, at a value of *20* is our **third quartile**. Finally, the **vertical line** connecting these bars are just an indicator of **range of data points**.\n",
    "\n",
    "Well then, what about those **dots beyond our maximum range**? Those are known as **Outliers**. Mathematically, these Outliers are generally *at least 3 times* the *difference between third and first quartile*. Now, you might think that even in our Box plot for Thursday, the dots do not seem to be at a distance thrice the difference. And, you are correct! \n",
    "\n",
    "So, there is another type of **Outlier**, that is known as **Suspected Outliers**. These are generally 1.5 times the difference, and no points for guessing why they're are termed as **\"Suspected\"**. Statistically if required to be stated, you may say that **Outliers** are beyond the **Outer Fence**, and **Suspected Outliers** are beyond the **Inner Fence**. *Inner fence* is nothing but our *first bar* on plot and *Outer Fence* is the distance between **third quartile** (top spine of our box) and **Inner fence**, above *Inner fence*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us quickly plot another Box plot with few variations before we start discussing further on this topic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d8f3e70978>"
      ]
     },
     "execution_count": 15,
     "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.boxplot(x=\"day\", y=\"total_bill\", hue=\"smoker\", data=tips, palette=\"coolwarm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nothing new here as of now in terms of *Optional parameters*, but what we notice differently is that as per `hue` parameter, i.e. `Smoker`, it is helping us visualize data points in *two separate boxes for each day*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us once again visualize multiple Box plots on a single frame with some random data this time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Attempt</th>\n",
       "      <th>Values</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.195128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.243974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.295763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.160567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.077472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Attempt Values     value\n",
       "0        1      1 -0.195128\n",
       "1        1      1 -0.243974\n",
       "2        1      1  1.295763\n",
       "3        1      1  0.160567\n",
       "4        1      1  0.077472"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating regular sample Pandas DataFrames:\n",
    "sample_1 = pd.DataFrame(np.random.randn(5,5), columns=list(range(1,6))).assign(Attempt=1)\n",
    "sample_2 = pd.DataFrame(np.random.randn(5,5), columns=list(range(1,6))).assign(Attempt=2)\n",
    "sample_3 = pd.DataFrame(np.random.randn(5,5), columns=list(range(1,6))).assign(Attempt=3)\n",
    "\n",
    "# Concatenating all created DataFrames:\n",
    "sample_combo = pd.concat([sample_1, sample_2, sample_3])\n",
    "\n",
    "# Melting concatenated DataFrame:\n",
    "melt_df = pd.melt(sample_combo, id_vars=[\"Attempt\"], var_name=[\"Values\"])\n",
    "\n",
    "melt_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d8f7bdf358>"
      ]
     },
     "execution_count": 82,
     "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.boxplot(x=\"Attempt\", y=\"value\", hue=\"Values\", data=melt_df, palette=\"hsv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are three statistical terms that I would like you to research more on your own, and learn about it, if you wish to have a firm grip on **Univariate Data Analysis**. Honestly, these are nothing conceptually difficult or uncommon. So to give you an idea, these are:\n",
    "\n",
    "- **Distribution:** which is just a summary of the frequency of individual values, or range of values for a variable. Simplest distribution would list every value of a variable and the number of factors who had each value. One of the most common ways to describe a single variable is with a **Frequency distribution**.\n",
    "\n",
    "- Next one is **Central Tendency** that includes **Mean**, **Median** and **Mode**. \n",
    " **Mean** being the average of all the values, that we get by adding up all the values and then dividing the sum by number of   values. **Median** being the score found at the *exact middle* of a set of values, which can be computed by listing all scores in numerical order, and then locate the score in the center of the sample. Finally, **Mode** refers to the *most frequently* occurring value in a set of scores.\n",
    "\n",
    "- Finally, we have **Dispersion:** This is one of the most talked about factor statistically as it refers to the spread of values around the **Central Tendency**. There are two common measures of dispersion: **Range** and **Standard Deviation**. You can think of *Range* as the highest value minus the lowest value in a distriution.\n",
    "\n",
    "This is slowly turning out to be more of a Statistics course, instead of a Data Visualization course, so I shall right away get back on track and we shall look into Seaborn official documentation for the optional parameters allowed in a Box Plot. Later on, we shall observe more on variations and few more real-world scenarios:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`seaborn.boxplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, orient=None, color=None, palette=None, saturation=0.75, width=0.8, dodge=True, fliersize=5, linewidth=None, whis=1.5, notch=False, ax=None)`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, *just one variable* on any axis would also be enough, if properly referenced to the dataset, for us to get the figure to be plotted. Let us try this at first, before considering other params:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d8f3f1ed30>"
      ]
     },
     "execution_count": 16,
     "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.boxplot(x=tips[\"total_bill\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Easy, right? You can see what I was talking about. Let us go through other params now, just the ones which are actually new for us. So, we have `saturation` parameter which controls the **proportion of original saturation** to draw colors at. By default it is set to 1 and can be increased or decreased as well like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d8f3f934e0>"
      ]
     },
     "execution_count": 20,
     "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.boxplot(x=tips[\"total_bill\"], saturation=0.35)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And that *fades the original color* by a certain percentage, depending on our input. Then we have `fliersize` that controls size of the markers used to indicate outlier observations. We also have `whis` parameter that represents **proportion of IQR**, past the *low and high quartiles* to extend the plot whiskers. Do not really have to get confused with this abbreviation, as **IQR** just stands for **Inter-Quartile Range**. Lastly, we have `notch`, which by default is set to `False`, and helps to indicate a **Confidence Interval for the median** within a box. Let us plot to comprehend better:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>E</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1333</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>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>C</td>\n",
       "      <td>Second</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</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",
       "5         0       3    male   NaN      0      0   8.4583        Q   Third   \n",
       "6         0       1    male  54.0      0      0  51.8625        S   First   \n",
       "7         0       3    male   2.0      3      1  21.0750        S   Third   \n",
       "8         1       3  female  27.0      0      2  11.1333        S   Third   \n",
       "9         1       2  female  14.0      1      0  30.0708        C  Second   \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  \n",
       "5    man        True  NaN   Queenstown    no   True  \n",
       "6    man        True    E  Southampton    no   True  \n",
       "7  child       False  NaN  Southampton    no  False  \n",
       "8  woman       False  NaN  Southampton   yes  False  \n",
       "9  child       False  NaN    Cherbourg   yes  False  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fetching Titanic dataset and previewing:\n",
    "titanic = sns.load_dataset(\"titanic\")\n",
    "\n",
    "titanic.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d8f5fb3fd0>"
      ]
     },
     "execution_count": 58,
     "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": [
    "titanic[\"town\"] = titanic[\"embark_town\"].isin([\"Southampton\", \"Cherbourg\", \"Queenstown\"])\n",
    "sns.boxplot(x=\"embark_town\", y=\"fare\", hue=\"town\", data=titanic, dodge=False, fliersize=8, whis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Titanic dataset didn't turn out to be a good dataset for my experiment but anyways, what we tried to do here is that: We created a variable `town`to use `hue` parameter without changing box position or it's width. Actually, let us try it on Tips:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d8f601fc88>"
      ]
     },
     "execution_count": 59,
     "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": [
    "# Creating Hue param:\n",
    "tips[\"weekend\"] = tips[\"day\"].isin([\"Sat\", \"Sun\"])\n",
    "\n",
    "sns.boxplot(x=\"day\", y=\"total_bill\", hue=\"weekend\", data=tips, dodge=False, fliersize=8, whis=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although we have seen enough coupling with other type of plots, let us actually see one more coupling of plot before we jump into *mix n match* with some known/unknown data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d8f73c9e10>"
      ]
     },
     "execution_count": 66,
     "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.boxplot(x=\"day\", y=\"total_bill\", data=tips)\n",
    "sns.stripplot(x=\"day\", y=\"total_bill\", data=tips, color=tableau_20[7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d8f7501198>"
      ]
     },
     "execution_count": 69,
     "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.boxplot(x=\"day\", y=\"total_bill\", data=tips)\n",
    "sns.swarmplot(x=\"day\", y=\"total_bill\", data=tips, color=tableau_20[7])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a change, if you don't want Outliers to be displayed, then you just need to adjust `flierprops` parameters. Basically, we should be able to pass any arguments to `seaborn.boxplot` that we can pass to `plt.boxplot`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d8f8091d68>"
      ]
     },
     "execution_count": 89,
     "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.boxplot(x=\"day\", y=\"total_bill\", data=tips, showfliers=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2d8f929fa58>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 571.65x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"sex\", y=\"total_bill\", hue=\"smoker\", col=\"time\", data=tips, kind=\"box\", size=6, aspect=.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Few *important things to remember* about **Box Plot** that I would like you to make a note of would be:\n",
    "\n",
    "- Outliers are expected in normally distributed datasets with more than about 10,000 data-points. Suspected outliers are common in large normally distributed datasets (say more than 100 data-points).\n",
    "- Outliers aren't necessarily *\"bad\"* data-points. Indeed they might be utmost important, most information rich part of our dataset. So under no circumstances should you remove those from the dataset. This is where beginners often make mistake so I would like you to be extra-considerate with Outliers.\n",
    "- One case of particular concern where a Box plot can be deceptive is: When dataset is distributed into \"two lumps/chunks/clusters\" rather than the \"one lump\" cases because in such scenarios, a box plot might not be a good choice. Instead I would suggest choosing a swarm plot instead."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moving on, let us this time consider something new to take care of. Yes! We're going to plot a set of **Prediction Accuracy scores** that we get from a **Machine Learning algorithm**. Let me fetch a small chunk out of it, just to demonstrate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preview of Algorithm:\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "predictions = []\n",
    "for trees in range(1,41):\n",
    "    model = RandomForestClassifier(n_estimators=trees)\n",
    "    score_list = cross_val_score(model, X, Y, cv=10)\n",
    "    predictions.append(score_list)\n",
    "\n",
    "# Declaring Prediction accuracy as 'Score':\n",
    "predictions = {'0':[0.8757764, 0.86335404, 0.75625, 0.85, 0.86875, 0.81875, 0.79375, 0.79245283, 0.8490566, 0.85534591], \n",
    "               '1':[0.89440994, 0.8447205, 0.79375, 0.85, 0.8625, 0.85625, 0.86875, 0.88050314, 0.86792453, 0.8427673], \n",
    "               '2':[0.91304348, 0.9068323, 0.83125, 0.84375, 0.8875, 0.875, 0.825, 0.83647799, 0.83647799, 0.87421384], \n",
    "               '3':[0.86956522, 0.86956522, 0.85, 0.875, 0.88125, 0.86875, 0.8625, 0.8490566, 0.86792453, 0.89308176]}\n",
    "\n",
    "# Creating a Pandas DataFrame out of it:\n",
    "df = pd.DataFrame(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d8f97eb550>"
      ]
     },
     "execution_count": 112,
     "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.boxplot(data=df, palette=\"spring\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With all these variations that we've learnt, we now have a pretty good idea to deal with Box Plot. In the next lecture, we shall be dealing with **Violin plot** that we've already seen couple of times, but this time it would be a detailed discussion about it's scope, variations and few more real-world scenarios.\n",
    "\n",
    "Till then, as always, I would highly recommend to play around with these plots as much as you can and if you have any doubts, feel free to post in the forum.\n",
    "\n",
    "And, I shall meet you in the next lecture where we will discuss *Violin Plot*. 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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}