Seaborn - Factor Plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:red\">Seaborn | Part-15: Factor Plot:</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome back to another lecture on *Data Visualization with Seaborn*! This lecture is kind of a continuation to **<span style=\"color:red\">FacetGrid</span>** that we had been discussing in previous lecture. Today our major emphasis is once again going to be on plotting *Categorical Data*. \n",
    "\n",
    "Well, you might think that we have already done enough of these in previous section, when we covered visualization methods like **Swarm Plot**, **Strip Plot**, **Box Plot**, **Violin Plot**, **Bar Plot** and **Point Plot**. News for you today is that all the above mentioned plots are generally considered *low-level methods* as they all plot onto a specific pair of *Matplotlib axes*.\n",
    "\n",
    "Today we shall discuss a higher level function, i.e. **<span style=\"color:red\">Factor Plot</span>**, which combines all the *low-level functions* with our **FacetGrid** to apply a *Categorical plot* across a grid of figure panels on [Tidy DataFrame](http://vita.had.co.nz/papers/tidy-data.pdf). I shall attach a link in our notebook for you to better assess *<span style=\"color:green\">Tidy Data</span>* as defined by official page."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now dive little deeper to understand this *high-level* **Factor Plot**; not actually in terms of underlying code but in terms of the conceptual foundation, where it majorly holds relevance to **Factor Analysis**. To give you an overview, [Factor Analysis](https://en.wikipedia.org/wiki/Factor_analysis) is again a *statistical method* that describes *variability* among observed, correlated variables in terms of a potentially lower number of unobserved variables, which are referred to as **Factors**. \n",
    "\n",
    "**Factor Analysis** searches for similar Joint variations in response to an unobserved set of [Latent variables](https://en.wikipedia.org/wiki/Latent_variable). The term **Latent** refers to the fact that even though these variables were not measured directly in a research design, still they are the ultimate goal of that project. Hence, the observed variables are modelled as *Linear combinations of potential factors*, plus *\"error\"* terms. \n",
    "\n",
    "Our Factor analysis aims to find such independent Latent variables and the theory behind these methods is that the *Information* gained about the inter-dependencies between observed variables can be used later to reduce the set of variables in a dataset. In short, we may say that Factor analysis is related to **Principal Component Analysis (PCA)**, though those two are not identical. \n",
    "\n",
    "During visualization, A *Factor plot* simply drafts the same plot generated for different response and factor variables and arranged on a single page. Here, the underlying plot generated can be any *Univariate* or *Bivariate* plot, and *Scatter Plot* serves this purpose quite frequently than others.\n",
    "\n",
    "Let us now get our package dependancies and plot a simple Factor Plot to understand the parameters offered by Seaborn to make our task easier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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=\"ticks\", palette=\"hsv\")\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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>diet</th>\n",
       "      <th>pulse</th>\n",
       "      <th>time</th>\n",
       "      <th>kind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>low fat</td>\n",
       "      <td>85</td>\n",
       "      <td>1 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>low fat</td>\n",
       "      <td>85</td>\n",
       "      <td>15 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>low fat</td>\n",
       "      <td>88</td>\n",
       "      <td>30 min</td>\n",
       "      <td>rest</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>low fat</td>\n",
       "      <td>90</td>\n",
       "      <td>1 min</td>\n",
       "      <td>rest</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>low fat</td>\n",
       "      <td>92</td>\n",
       "      <td>15 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>low fat</td>\n",
       "      <td>93</td>\n",
       "      <td>30 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>low fat</td>\n",
       "      <td>97</td>\n",
       "      <td>1 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>low fat</td>\n",
       "      <td>97</td>\n",
       "      <td>15 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>low fat</td>\n",
       "      <td>94</td>\n",
       "      <td>30 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>low fat</td>\n",
       "      <td>80</td>\n",
       "      <td>1 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  id     diet  pulse    time  kind\n",
       "0           0   1  low fat     85   1 min  rest\n",
       "1           1   1  low fat     85  15 min  rest\n",
       "2           2   1  low fat     88  30 min  rest\n",
       "3           3   2  low fat     90   1 min  rest\n",
       "4           4   2  low fat     92  15 min  rest\n",
       "5           5   2  low fat     93  30 min  rest\n",
       "6           6   3  low fat     97   1 min  rest\n",
       "7           7   3  low fat     97  15 min  rest\n",
       "8           8   3  low fat     94  30 min  rest\n",
       "9           9   4  low fat     80   1 min  rest"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading Built-in Dataset:\n",
    "exercise = sns.load_dataset(\"exercise\")\n",
    "\n",
    "# Pre-viewing Dataset:\n",
    "exercise.head(10)\n",
    "#exercise.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x26904e9ae80>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 501.75x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a basic Factor Plot:\n",
    "sns.factorplot(x=\"time\", y=\"pulse\", hue=\"kind\", data=exercise, size=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks quite informative and we already know how to interpret a **Point Plot** that we have on screen right now. If an individual is at `rest`, his/her `pulse` remains pretty constant at approximately 90, when measured for a `time` interval from 1 minute to half an hour. Even while `walking`, the `pulse` soars high for first `15 minutes`, but then stabilizes around 93, and then remains constant at that `pulse`. But the story is totally different when the individual is `running`, because the pulse then takes a major upwards leap in first `time` segment, and constantly keeps pounding with increase in `time`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now look at the parameters offered by Seaborn to expand our horizon with **Factor Plot**:\n",
    "\n",
    "`seaborn.factorplot(x=None, y=None, hue=None, data=None, row=None, col=None, col_wrap=None, estimator=<function mean>, ci=95, n_boot=1000, units=None, order=None, hue_order=None, row_order=None, col_order=None, kind='point', size=4, aspect=1, orient=None, color=None, palette=None, legend=True, legend_out=True, sharex=True, sharey=True, margin_titles=False, facet_kws=None)`\n",
    "\n",
    "The good news is that Seaborn seems to offer almost all the *optional parameters* that we've covered till now; and the other good news is that there isn't any extra parameter for us to fiddle with. So instead, let us play around with few more *Factor Plots* to visualize the difference. As of now, we just have a **Point Plot** on one facet, so we shall eventually even try to draw subplots to get further acquainted with the syntax and corresponding results. There isn't much we need to do in terms of *inference*, so let us run through few examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x269063c2240>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 573.75x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let us begin by altering the type of plot to a \"BarPlot\" on our FactorPlot:\n",
    "sns.factorplot(x=\"time\", y=\"pulse\", hue=\"kind\", data=exercise, size=7, kind=\"bar\", palette=\"rocket\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you would have guessed by now, the `kind` parameter by default is for **Point Plot**, but just like this, we may modify it to `box`, `violin`, `swarm`, etc.\n",
    "\n",
    "Let us now *facet our plot along variable columns in the same row*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x269065020f0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For a change, here we shall use a \"Box Plot\", instead of a \"Bar Plot\" to visualize the difference:\n",
    "#sns.factorplot(x=\"time\", y=\"pulse\", hue=\"kind\", col=\"diet\", data=exercise, size=6, kind=\"box\", palette=\"rocket\")\n",
    "\n",
    "# Let us pull our Legend inside the plot:\n",
    "sns.factorplot(x=\"time\", y=\"pulse\", hue=\"kind\", col=\"diet\", data=exercise, size=7, kind=\"box\", palette=\"rocket\", legend_out=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That comfortably divides `exercise` datapoints with respect to the *factor* whether they are on `low fat` *or* on `no fat` *diet*. \n",
    "\n",
    "Now let us add in few more *optional parameters* and tweak our presentation, and for this purpose we shall use our *Tips* dataset, so we shall commence by reloading this built-in dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2690084be80>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 773.25x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loading Built-in Tips Dataset:\n",
    "tips = sns.load_dataset(\"tips\")\n",
    "\n",
    "# Let us get all the facets of our grid vertically stacked this time:\n",
    "sns.factorplot(x=\"day\", y=\"total_bill\", hue=\"smoker\", row=\"time\", data=tips, \n",
    "               orient=\"v\", size=4, aspect=2.5, palette=\"bwr\", kind=\"point\", dodge=True, cut=0, bw=.2, margin_titles=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmm! So we have a competent plot here presenting the variations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now try to extemporize few modifications in our **Barplot** that we plotted earlier using `exercise` dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2690d50af60>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1077.75x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let us also assign a variable \"ax\" to it:\n",
    "ax = sns.factorplot(x=\"time\", y=\"pulse\", hue=\"kind\", col=\"diet\", data=exercise, size=7, kind=\"bar\", \n",
    "                    palette=\"rocket\", ci=None)\n",
    "\n",
    "# Let us now customize it by using methods on our FacetGrid:\n",
    "ax.set_axis_labels(\"Time Taken\", \"Pulse Rate\")\n",
    "ax.set_xticklabels([\"1 minute\", \"15 minutes\", \"30 minutes\"])\n",
    "ax.set_titles(\"{col_name} {col_var}\")\n",
    "ax.despine(left=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are pretty familiar with these *Matplotlib customizations* as we have used these earlier as well. Rest is a *Bar Plot* representation as set by our `kind` parameter. And with that, we shall close our discussion for today on **Factor Plot**. In the next lecture, we shall learn to work with another type f grid, i.e **<span style=\"color:red\">Pair Grid</span>**. \n",
    "\n",
    "Meanwhile keep playing around with all that we have learnt till date on sample datasets, and I shall see you in our next lecture on **Grids**. 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
}