2017-11-02_Lighting_Data_Vizualization.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Quick Tour of the Python Visualization Landscape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import healthcareai\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "diabetes = pd.read_csv('diabetes.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One Line Data Profiling\n",
    "\n",
    "- Using the amazing [pandas_profiling](https://github.com/JosPolfliet/pandas-profiling) package.\n",
    "- Install: `pip install pandas_profiling`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas_profiling\n",
    "\n",
    "pandas_profiling.ProfileReport(diabetes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Real Data Demo\n",
    "\n",
    "[Boston](profile%20reports/2017-10-28_commercial_data_profile.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas - Exploring Relationships"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scatter = pd.plotting.scatter_matrix(diabetes, figsize=(20, 20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seaborn: Statistical Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()\n",
    "\n",
    "iris = sns.load_dataset(\"iris\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variable Relationships\n",
    "\n",
    "#### PairPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.pairplot(iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### JointPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.jointplot(\n",
    "    data=iris,\n",
    "    x='sepal_width',\n",
    "    y='sepal_length',\n",
    "    size=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.jointplot(\n",
    "    data=iris,\n",
    "    x='sepal_width',\n",
    "    y='sepal_length',\n",
    "    size=10,\n",
    "    kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "g = sns.lmplot(\n",
    "    data=iris,\n",
    "    x=\"sepal_length\",\n",
    "    y=\"sepal_width\",\n",
    "    hue=\"species\",\n",
    "    truncate=True,\n",
    "    size=7)\n",
    "\n",
    "g.set_axis_labels(\"Sepal length (mm)\", \"Sepal width (mm)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "box = sns.boxplot(\n",
    "    data=diabetes,\n",
    "    x='MaritalStatusCD',\n",
    "    y='SystolicBPNBR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "violin = sns.violinplot(\n",
    "    data=diabetes,\n",
    "    x='MaritalStatusCD',\n",
    "    y='A1CNBR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Factors Across Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "g = sns.factorplot(x=\"time\", y=\"pulse\", hue=\"kind\", col=\"diet\", data=sns.load_dataset(\"exercise\"),\n",
    "                   capsize=.2, palette=\"YlGnBu_d\", size=6, aspect=.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Yellowbrick\n",
    "\n",
    "Yellowbrick is a suite of visual diagnostic tools called “Visualizers” that extend the Scikit-Learn API to allow human steering of the model selection process.\n",
    "\n",
    "- The delightful [yellowbrick](http://www.scikit-yb.org/) library.\n",
    "- Install: `pip install yellowbrick`\n",
    "- Conforms to familiar scikit-learn API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Relationships"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('bikeshare.csv')\n",
    "X = data[[\n",
    "    \"season\", \"month\", \"hour\", \"holiday\", \"weekday\", \"workingday\",\n",
    "    \"weather\", \"temp\", \"feelslike\", \"humidity\", \"windspeed\"\n",
    "]]\n",
    "y = data[\"riders\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from yellowbrick.features import Rank2D, ParallelCoordinates\n",
    "\n",
    "visualizer = Rank2D(algorithm=\"pearson\")\n",
    "visualizer.fit_transform(X)\n",
    "visualizer.poof()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parallel Coordinate Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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