06_Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction and generalities\n",
    "\n",
    "\n",
    "The visualization of the results of an analysis typically tell a lot about the quality of the analysis itself, an example is the claim of [superluminal neutrinos](https://arxiv.org/pdf/1109.4897v1.pdf). \n",
    "\n",
    "Graphics should convey as much information as possible in an intuitive way. There are plenty of bad examples.\n",
    "\n",
    "There are essentially two kinds of plots you'll ever want to make, histograms and scatter plots.\n",
    "\n",
    "### Histograms\n",
    "\n",
    "Histograms represent scalar functions, $f(x): {\\rm I\\!R}^n \\to {\\rm I\\!R}$, where at most $n=2$. They are generally used to display the feature of a data distribution. Histograms are often used for comparison among several distributions, for instance, very importantly, histograms are used to compare measurements with expectations (e.g. simulations). \n",
    "\n",
    "Histograms are \"binned\", i.e. the domain is divided in classes (bins), i.e. for the $i$th bin $x_i<x\\le x_{i+1}$ and the content of the bin (say $h(x)$) is  $h(x)=\\frac{\\int_{x_{i}}^{x_{i+1}} f(x)}{x_{i+1}- x_{i}}$. Bins doesn't need to have the same size, i.e. it could well be that ($x_{i+1}- x_{i}) \\neq (x_{j+1}- x_{j})$ for $i\\neq j$. \n",
    "If the bin size is small, $h(x)\\simeq f(\\frac{x_{i+1}+x_{i}}{2})$.\n",
    "\n",
    "$f(x)$ itself can be normalized ($\\int_{{\\rm I\\!R}^n} f(x) dx =1$) or not.\n",
    "\n",
    "Choosing the number of bins and the bins sizes needs a lot of care. Typically the content of each $i$th bin, $N_i$, should be statistically significant, i.e. the corresponding Poisson uncertainty, $1/\\sqrt{N_i}$, should be small.\n",
    "\n",
    "Often such uncertainty is worth being indicated, especially if the distribution is normalized or if the bin content comes from a simulation (and thus needed to be weighted). \n",
    "\n",
    "\n",
    "### Scatter plots\n",
    "\n",
    "Scatter plots are used to compare two quantities, mainly aiming at displaying possible correlations between them. \n",
    "\n",
    "Scatter plots can be binned or unbinned, the latter case is often prefered untill the number of point isn't too large. In that case the former is preferred, with a color code indicating the \"3rd dimension\", i.e. the relative population of each bin. This can be made into a \"contour plot\", i.e. with smoothened edges and color-coded levels.\n",
    "\n",
    "Display of different population is also possible, marking the data points differently, e.g. with different colors or different markers.\n",
    "\n",
    "Of paramount importance is the condensation of the scatter plots into \"profiles\". The procedure runs as follow: data are binned along the $x$ (if you had to bin on the other variable, just invert the axes), for every bin take the mean and the standard deviation of the corresponding $y$ values, display those as data points and their error.\n",
    "These are also called \"box plots\".\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"profile.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other plots\n",
    "\n",
    "Other kind of plots are there too (e.g. pie-chart), I personally never used them..\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib (pyplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# always useful\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A plot is  a hierarchy of nested Python objects. A Figure object is the outermost container for a matplotlib graphic, which can contain multiple Axes objects. One source of confusion is the name: an Axes actually translates into what we think of as an individual plot or graph (rather than the plural of “axis,” as we might expect).\n",
    "\n",
    "A Figure object is like a container holding one or more Axes (actual plots). Below the Axes in the hierarchy are smaller objects such as tick marks, individual lines, legends, and text boxes. Almost every “element” of a chart is its own manipulable Python object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "Image(url='https://files.realpython.com/media/fig_map.bc8c7cabd823.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, _ = plt.subplots()\n",
    "print (type(fig))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "above we created two variables with plt.subplots(). The first is a top-level Figure object. The second is a “throwaway” variable that we don’t need just yet, denoted with an underscore. Using attribute notation, it is easy to traverse down the figure hierarchy and see the first tick of the y axis of the first Axes object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_tick = fig.axes[0].yaxis.get_major_ticks()[0]\n",
    "print (type(one_tick))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, fig (a Figure class instance) has multiple Axes (a list, for which we take the first element). Each Axes has a yaxis and xaxis, each of which have a collection of “major ticks,” and we grab the first one.\n",
    "\n",
    "Matplotlib documentation presents this as a figure anatomy, rather than an explicit hierarchy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import AutoMinorLocator, MultipleLocator, FuncFormatter\n",
    "\n",
    "\n",
    "np.random.seed(19680801)\n",
    "\n",
    "X = np.linspace(0.5, 3.5, 100)\n",
    "Y1 = 3+np.cos(X)\n",
    "Y2 = 1+np.cos(1+X/0.75)/2\n",
    "Y3 = np.random.uniform(Y1, Y2, len(X))\n",
    "\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "ax = fig.add_subplot(1, 1, 1, aspect=1)\n",
    "\n",
    "\n",
    "def minor_tick(x, pos):\n",
    "    if not x % 1.0:\n",
    "        return \"\"\n",
    "    return \"%.2f\" % x\n",
    "\n",
    "ax.xaxis.set_major_locator(MultipleLocator(1.000))\n",
    "ax.xaxis.set_minor_locator(AutoMinorLocator(4))\n",
    "ax.yaxis.set_major_locator(MultipleLocator(1.000))\n",
    "ax.yaxis.set_minor_locator(AutoMinorLocator(4))\n",
    "ax.xaxis.set_minor_formatter(FuncFormatter(minor_tick))\n",
    "\n",
    "ax.set_xlim(0, 4)\n",
    "ax.set_ylim(0, 4)\n",
    "\n",
    "ax.tick_params(which='major', width=1.0)\n",
    "ax.tick_params(which='major', length=10)\n",
    "ax.tick_params(which='minor', width=1.0, labelsize=10)\n",
    "ax.tick_params(which='minor', length=5, labelsize=10, labelcolor='0.25')\n",
    "\n",
    "ax.grid(linestyle=\"--\", linewidth=0.5, color='.25', zorder=-10)\n",
    "\n",
    "ax.plot(X, Y1, c=(0.25, 0.25, 1.00), lw=2, label=\"Blue signal\", zorder=10)\n",
    "ax.plot(X, Y2, c=(1.00, 0.25, 0.25), lw=2, label=\"Red signal\")\n",
    "ax.plot(X, Y3, linewidth=0,\n",
    "        marker='o', markerfacecolor='w', markeredgecolor='k')\n",
    "\n",
    "ax.set_title(\"Anatomy of a figure\", fontsize=20, verticalalignment='bottom')\n",
    "ax.set_xlabel(\"X axis label\")\n",
    "ax.set_ylabel(\"Y axis label\")\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "\n",
    "def circle(x, y, radius=0.15):\n",
    "    from matplotlib.patches import Circle\n",
    "    from matplotlib.patheffects import withStroke\n",
    "    circle = Circle((x, y), radius, clip_on=False, zorder=10, linewidth=1,\n",
    "                    edgecolor='black', facecolor=(0, 0, 0, .0125),\n",
    "                    path_effects=[withStroke(linewidth=5, foreground='w')])\n",
    "    ax.add_artist(circle)\n",
    "\n",
    "\n",
    "def text(x, y, text):\n",
    "    ax.text(x, y, text, backgroundcolor=\"white\",\n",
    "            ha='center', va='top', weight='bold', color='blue')\n",
    "\n",
    "\n",
    "# Minor tick\n",
    "circle(0.50, -0.10)\n",
    "text(0.50, -0.32, \"Minor tick label\")\n",
    "\n",
    "# Major tick\n",
    "circle(-0.03, 4.00)\n",
    "text(0.03, 3.80, \"Major tick\")\n",
    "\n",
    "# Minor tick\n",
    "circle(0.00, 3.50)\n",
    "text(0.00, 3.30, \"Minor tick\")\n",
    "\n",
    "# Major tick label\n",
    "circle(-0.15, 3.00)\n",
    "text(-0.15, 2.80, \"Major tick label\")\n",
    "\n",
    "# X Label\n",
    "circle(1.80, -0.27)\n",
    "text(1.80, -0.45, \"X axis label\")\n",
    "\n",
    "# Y Label\n",
    "circle(-0.27, 1.80)\n",
    "text(-0.27, 1.6, \"Y axis label\")\n",
    "\n",
    "# Title\n",
    "circle(1.60, 4.13)\n",
    "text(1.60, 3.93, \"Title\")\n",
    "\n",
    "# Blue plot\n",
    "circle(1.75, 2.80)\n",
    "text(1.75, 2.60, \"Line\\n(line plot)\")\n",
    "\n",
    "# Red plot\n",
    "circle(1.20, 0.60)\n",
    "text(1.20, 0.40, \"Line\\n(line plot)\")\n",
    "\n",
    "# Scatter plot\n",
    "circle(3.20, 1.75)\n",
    "text(3.20, 1.55, \"Markers\\n(scatter plot)\")\n",
    "\n",
    "# Grid\n",
    "circle(3.00, 3.00)\n",
    "text(3.00, 2.80, \"Grid\")\n",
    "\n",
    "# Legend\n",
    "circle(3.70, 3.80)\n",
    "text(3.70, 3.60, \"Legend\")\n",
    "\n",
    "# Axes\n",
    "circle(0.5, 0.5)\n",
    "text(0.5, 0.3, \"Axes\")\n",
    "\n",
    "# Figure\n",
    "circle(-0.3, 0.65)\n",
    "text(-0.3, 0.45, \"Figure\")\n",
    "\n",
    "color = 'blue'\n",
    "ax.annotate('Spines', xy=(4.0, 0.35), xycoords='data',\n",
    "            xytext=(3.3, 0.5), textcoords='data',\n",
    "            weight='bold', color=color,\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            connectionstyle=\"arc3\",\n",
    "                            color=color))\n",
    "\n",
    "ax.annotate('', xy=(3.15, 0.0), xycoords='data',\n",
    "            xytext=(3.45, 0.45), textcoords='data',\n",
    "            weight='bold', color=color,\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            connectionstyle=\"arc3\",\n",
    "                            color=color))\n",
    "\n",
    "ax.text(4.0, -0.4, \"Made with http://matplotlib.org\",\n",
    "        fontsize=10, ha=\"right\", color='.5')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stateful Versus Stateless Approaches\n",
    "\n",
    "There is an important difference between the stateful (state-based, state-machine) and stateless (object-oriented, OO) interfaces.\n",
    "\n",
    "Above, we used import matplotlib.pyplot as plt to import the pyplot module from matplotlib and name it plt.\n",
    "\n",
    "Almost all functions from pyplot, such as plt.plot(), are implicitly either referring to an existing current Figure and current Axes, or creating them anew if none exist.\n",
    "\n",
    "We could say that “plt.plot() is a state-machine interface that implicitly tracks the current figure!” In plain words, this means that:\n",
    "\n",
    "* The stateful interface makes its calls with plt.plot() and other top-level pyplot functions. There is only ever one Figure or Axes that you’re manipulating at a given time, and you don’t need to explicitly refer to it.\n",
    "* Modifying the underlying objects directly is the object-oriented approach. We usually do this by calling methods of an Axes object, which is the object that represents a plot itself.\n",
    "\n",
    "This all boils down to the definition of `plt.plot()` as:\n",
    "``` python\n",
    "# matplotlib/pyplot.py\n",
    "def plot(*args, **kwargs):\n",
    "    \"\"\"An abridged version of plt.plot().\"\"\"\n",
    "    ax = plt.gca()\n",
    "    return ax.plot(*args, **kwargs)\n",
    "\n",
    "def gca(**kwargs):\n",
    "    \"\"\"Get the current Axes of the current Figure.\"\"\"\n",
    "    return plt.gcf().gca(**kwargs)\n",
    "```\n",
    "\n",
    "N.B.: the stateless (object-oriented) approach is typically more customizable and comes in handy as graphs become more complex."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A few simple examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.arange(50)\n",
    "rnd = np.random.randint(0, 10, size=(3, rng.size))\n",
    "yrs = 1950 + rng\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.stackplot(yrs, rng + rnd, labels=['Eastasia', 'Eurasia', 'Oceania'])\n",
    "ax.set_title('Combined debt growth over time')\n",
    "ax.legend(loc='upper left')\n",
    "ax.set_ylabel('Total debt')\n",
    "ax.set_xlim(left=yrs[0], right=yrs[-1])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In details, that's what has been done above:\n",
    "\n",
    "* After creating three random time series, one Figure (fig) containing one Axes (a plot, ax) has been defined.\n",
    "\n",
    "* methods of ax have been called directly to create a stacked area chart and to add a legend, title, and y-axis label. Under the object-oriented approach, it’s clear that all of these are attributes of ax.\n",
    "\n",
    "* tight_layout() applies to the Figure object as a whole to clean up whitespace padding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.randint(low=1, high=11, size=50)\n",
    "y = x + np.random.randint(1, 5, size=x.size)\n",
    "data = np.column_stack((x, y))\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2,\n",
    "                               figsize=(8, 4))\n",
    "\n",
    "ax1.scatter(x=x, y=y, marker='o', c='r', edgecolor='b')\n",
    "ax1.set_title('Scatter: $x$ versus $y$')\n",
    "ax1.set_xlabel('$x$')\n",
    "ax1.set_ylabel('$y$')\n",
    "\n",
    "\n",
    "# either this..\n",
    "#ax2.hist(data, bins=np.arange(data.min(), data.max()), label=('x', 'y'))\n",
    "# ..or this\n",
    "ax2.hist(x, bins=np.arange(data.min(), data.max()),label='x', alpha=0.5)\n",
    "ax2.hist(y, bins=np.arange(data.min(), data.max()),label='y', alpha=0.5)\n",
    "\n",
    "ax2.legend(loc=(0.65, 0.8))\n",
    "ax2.set_title('Frequencies of $x$ and $y$')\n",
    "ax2.yaxis.tick_right()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few comments:\n",
    "\n",
    "* As a “1x2” Figure is created, the returned result of plt.subplots(1, 2) is now a Figure object and a NumPy array of Axes objects. Alternatively:\n",
    "\n",
    "``` python\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2,figsize=(8, 4))\n",
    "# ax is now a numpy array of axes's\n",
    "```\n",
    "\n",
    "* We deal with ax1 and ax2 individually, which would be difficult to do with the stateful approach. The final line is a good illustration of the object hierarchy, where we are modifying the yaxis belonging to the second Axes, placing its ticks and ticklabels to the right.\n",
    "\n",
    "* Text inside dollar signs utilizes TeX markup (you can try fancier TeX stuff)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A more advanced example\n",
    "\n",
    "Let's consider some data from the housing market in California"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from io import BytesIO\n",
    "import tarfile\n",
    "from urllib.request import urlopen\n",
    "\n",
    "url = 'http://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.tgz'\n",
    "b = BytesIO(urlopen(url).read())\n",
    "fpath = 'CaliforniaHousing/cal_housing.data'\n",
    "\n",
    "with tarfile.open(mode='r', fileobj=b) as archive:\n",
    "    housing = np.loadtxt(archive.extractfile(fpath), delimiter=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's extract a “response” variable $z$, which is an area’s average home value. $pop$ and $age$ are the area’s population and average house age, respectively:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = housing[:, -1]\n",
    "pop, age = housing[:, [4, 7]].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first create a fancy layout by means of `plt.subplot2grid`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gridsize = (3, 2)\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "\n",
    "ax1 = plt.subplot2grid(gridsize, (0, 0), colspan=2, rowspan=2)\n",
    "scatter = ax1.scatter(x=age, y=pop, c=z, cmap='RdYlGn')\n",
    "plt.colorbar(scatter, ax=ax1, format='$%d')\n",
    "ax1.set_title('Home value as a function of home age & area population', fontsize=14)\n",
    "ax1.set_yscale('log')\n",
    "\n",
    "ax2 = plt.subplot2grid(gridsize, (2, 0))\n",
    "n2, bins2, patches2 = ax2.hist(age, bins='auto')\n",
    "\n",
    "ax3 = plt.subplot2grid(gridsize, (2, 1))\n",
    "n3, bins3, patches3 = ax3.hist(pop, bins='auto', log=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seaborn\n",
    "\n",
    "Seaborn is a wrapper around matplotlib which makes it better, with a better graphics and advanced functionalities, especially with what concerns statistical analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "#sns.set(style=\"darkgrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter plots\n",
    "\n",
    "Let's load some data and start play with it. (Seaborn is nice as it comes with plenty of example datasets you can load as pandas DataFrame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips = sns.load_dataset(\"tips\")\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at some example of scatter plot in seaborn. `sns.replot()` is most commonly used, which refers to `sns.scatterplot()`, also available.\n",
    "\n",
    "Essentialy we want to play with the \"extra dimensions\" it allows to display. Bear in mind that the more dimensions you put in there, the more difficult the plot results to read.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.relplot(x=\"total_bill\", y=\"tip\", hue=\"smoker\", data=tips);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.relplot(x=\"total_bill\", y=\"tip\", hue=\"smoker\", style=\"time\", data=tips);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not only categorical but also quantitative \"3rd dimension\" is of course possible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.relplot(x=\"total_bill\", y=\"tip\", hue=\"size\", data=tips);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.relplot(x=\"total_bill\", y=\"tip\", size=\"size\", sizes=(15, 200), data=tips);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms\n",
    "\n",
    "Seaborn does plenty of things with histograms, not just in terms of graphics, but in terms of statistical analysis too. The `distplot()` method by default plots the kernel density estimate ([KDE](https://en.wikipedia.org/wiki/Kernel_density_estimation))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.randn(100)\n",
    "sns.distplot(x);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with unbinned distribution too\n",
    "sns.distplot(x, kde=False, rug=True);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "x = np.random.gamma(6, size=200)\n",
    "sns.distplot(x, kde=False, fit=stats.gamma);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter plots and projections\n",
    "\n",
    "`sns.jointplot()` allows to visualize at the same time both the (cor-)relation between two quantities and "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "mean, cov = [0, 1], [(1, .5), (.5, 1)]\n",
    "data = np.random.multivariate_normal(mean, cov, 500)\n",
    "df = pd.DataFrame(data, columns=[\"x\", \"y\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.jointplot(x=\"x\", y=\"y\", data=df);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f, ax = plt.subplots(figsize=(6, 6))\n",
    "cmap = sns.cubehelix_palette(as_cmap=True, dark=0, light=1, reverse=True)\n",
    "sns.kdeplot(df.x, df.y, cmap=cmap, n_levels=60, shade=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = sns.jointplot(\"total_bill\", \"tip\", data=tips, kind=\"hex\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = sns.jointplot(\"total_bill\", \"tip\", data=tips, kind=\"reg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = sns.load_dataset(\"iris\")\n",
    "\n",
    "g = sns.jointplot(\"sepal_width\", \"petal_length\", data=iris,kind=\"kde\", space=0, color=\"g\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chasing correlations\n",
    "\n",
    "Seaborn allows you to look for correlations systematically checking pair-wise your dataset's features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.pairplot(iris);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g= sns.PairGrid(iris)\n",
    "g.map_diag(sns.kdeplot)\n",
    "g.map_offdiag(sns.kdeplot, n_levels=6);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot accessibility (Bokeh, Plotly, etc.)\n",
    "\n",
    "More often than not plots needs to be accessible from remote, thus ways of publishing on web servers results extremely useful. In the following an example from Bokeh is shown.\n",
    "Other tools like [Dash](https://plot.ly/products/dash/) are extremely powerful and worth checking out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure, output_file, show\n",
    "\n",
    "# prepare some data\n",
    "x = [0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]\n",
    "y0 = [i**2 for i in x]\n",
    "y1 = [10**i for i in x]\n",
    "y2 = [10**(i**2) for i in x]\n",
    "\n",
    "# output to static HTML file\n",
    "output_file(\"log_lines.html\")\n",
    "\n",
    "# create a new plot\n",
    "p = figure(\n",
    "   tools=\"pan,box_zoom,reset,save\",\n",
    "   y_axis_type=\"log\", y_range=[0.001, 10**11], title=\"log axis example\",\n",
    "   x_axis_label='sections', y_axis_label='particles'\n",
    ")\n",
    "\n",
    "# add some renderers\n",
    "p.line(x, x, legend=\"y=x\")\n",
    "p.circle(x, x, legend=\"y=x\", fill_color=\"white\", size=8)\n",
    "p.line(x, y0, legend=\"y=x^2\", line_width=3)\n",
    "p.line(x, y1, legend=\"y=10^x\", line_color=\"red\")\n",
    "p.circle(x, y1, legend=\"y=10^x\", fill_color=\"red\", line_color=\"red\", size=6)\n",
    "p.line(x, y2, legend=\"y=10^x^2\", line_color=\"orange\", line_dash=\"4 4\")\n",
    "\n",
    "# show the results\n",
    "show(p)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! ls ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In whatever we played with above, try adding:\n",
    "```python\n",
    "with plt.xkcd():\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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