data analysis/Data Viz - Intro.ipynb

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    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Seaborn\" data-toc-modified-id=\"Seaborn-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Seaborn</a></span><ul class=\"toc-item\"><li><span><a href=\"#Notebook-Modes\" data-toc-modified-id=\"Notebook-Modes-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Notebook Modes</a></span></li><li><span><a href=\"#Dataset-Distribution\" data-toc-modified-id=\"Dataset-Distribution-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Dataset Distribution</a></span></li><li><span><a href=\"#Categorical-Data\" data-toc-modified-id=\"Categorical-Data-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Categorical Data</a></span></li><li><span><a href=\"#Time-Series-Data\" data-toc-modified-id=\"Time-Series-Data-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Time-Series Data</a></span></li></ul></li><li><span><a href=\"#Pandas-Plotting\" data-toc-modified-id=\"Pandas-Plotting-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Pandas Plotting</a></span></li><li><span><a href=\"#Plotly\" data-toc-modified-id=\"Plotly-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Plotly</a></span><ul class=\"toc-item\"><li><span><a href=\"#Transfer-Matplotlib-Figure-to-Plotly\" data-toc-modified-id=\"Transfer-Matplotlib-Figure-to-Plotly-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Transfer Matplotlib Figure to Plotly</a></span></li></ul></li><li><span><a href=\"#Animation\" data-toc-modified-id=\"Animation-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Animation</a></span></li><li><span><a href=\"#Jupyter-Magic\" data-toc-modified-id=\"Jupyter-Magic-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Jupyter Magic</a></span></li></ul></div>"
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   "cell_type": "code",
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   "source": [
    "# Name a more iconic trio... I'll wait…\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sample array from normal distribution\n",
    "x = np.random.normal(size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot histogram with Matplotlib\n",
    "_ = plt.hist(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seaborn"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# import and set Seaborn style\n",
    "# notice how this will update Matplotlib style too\n",
    "import seaborn as sns\n",
    "sns.set(style=\"darkgrid\")\n",
    "sns.set_context(\"talk\", font_scale=1)"
   ]
  },
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   "metadata": {},
   "source": [
    "## Notebook Modes\n",
    "Use `matplotlib` magic to switch between `notebook` and `inline` mode.\n",
    "`tk` for new external plotting windows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# interactive figure controls\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.distplot(np.random.normal(size=100))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# default to static image\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot histogram with seaborn\n",
    "sns.distplot(np.random.normal(size=100), kde=False) # kde, rug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# boxplot, violinplot\n",
    "sns.violinplot(np.random.normal(size=100))"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset Distribution"
   ]
  },
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   "cell_type": "code",
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   "outputs": [],
   "source": [
    "# load iris dataset\n",
    "iris_df = sns.load_dataset('iris')\n",
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# exaple pairplot for iris dataset\n",
    "# notice we define what column to use for hue variations \n",
    "sns.pairplot(iris_df, hue='species')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load titanic dataset\n",
    "titanic_df = sns.load_dataset('titanic')\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# exaple factorplot/catplot for titanic dataset\n",
    "sns.catplot(data=titanic_df, x='alive', col='deck', row='sex', \n",
    "            kind='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# exaple factorplot/catplot for titanic dataset (bar, violin, swarm, box)\n",
    "sns.catplot(data=titanic_df, y='age', col='deck', kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time-Series Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dummy time-series dataframe\n",
    "df = pd.DataFrame(dict(time=pd.date_range(\"2017-1-1\", periods=500),\n",
    "                       value=np.random.randn(500).cumsum()))\n",
    "# plot with seaborn\n",
    "g = sns.relplot(x=\"time\", y=\"value\", kind=\"line\", data=df)\n",
    "g.fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# direct plot from Pandas df\n",
    "iris_df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute and plot average attribute value for each species\n",
    "iris_df.groupby('species').agg(np.mean).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# install plotly and cufflinks in current virtual-env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install plotly\n",
    "pip install cufflinks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries and set offline mode\n",
    "from plotly.offline import init_notebook_mode, plot, iplot\n",
    "import cufflinks as cf\n",
    "init_notebook_mode(connected=True)\n",
    "cf.go_offline(connected=True)\n",
    "#enable_mpl_offline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot data via plotly\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "data = [go.Histogram(x=x)]\n",
    "iplot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot Pandas data via plotly\n",
    "iris_df.iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export cufflink plot\n",
    "fig = iris_df.iplot(asFigure=True)\n",
    "plot(fig, filename=\"iris.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transfer Matplotlib Figure to Plotly"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import plotly.tools as tls\n",
    "\n",
    "# create figure and plot in matplot\n",
    "mpl_fig = plt.figure()\n",
    "ax = mpl_fig.add_subplot(111)\n",
    "ax.hist(np.random.normal(size=100))\n",
    "\n",
    "# convert and plot in plotly\n",
    "plotly_fig = tls.mpl_to_plotly(ax.figure)\n",
    "iplot(plotly_fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Animation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import animation\n",
    "\n",
    "%matplotlib notebook\n",
    "\n",
    "fig, ax = plt.subplots(dpi=100, figsize=(5, 4))\n",
    "ax.set_xlim(0, 2)\n",
    "ax.set_ylim(-2, 2)\n",
    "\n",
    "line, = ax.plot([], [], lw=2)\n",
    "#epoch_text = ax.text(0, 0, \"Epoch 0\")\n",
    "\n",
    "def animate(i, line):\n",
    "    x = np.linspace(0, 2, 1000)\n",
    "    y = np.sin(2 * np.pi * (x - 0.01 * i))\n",
    "    #epoch_text.set_text(\"Epoch {}\".format(i, cost))\n",
    "    line.set_data(x, y)\n",
    "    return line\n",
    "\n",
    "ani = animation.FuncAnimation(fig, animate, 100, interval=10, repeat=True,\n",
    "                             fargs=[line])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jupyter Magic"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "%timeit -r 1 x+1"
   ]
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   "cell_type": "code",
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   "source": [
    "%%html\n",
    "<h1>Table</h1>\n",
    "<table style=\"width:100%\">\n",
    "  <tr>\n",
    "    <th>Firstname</th>\n",
    "    <th>Lastname</th> \n",
    "    <th>Age</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>Jill</td>\n",
    "    <td>Smith</td> \n",
    "    <td>50</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>Eve</td>\n",
    "    <td>Jackson</td> \n",
    "    <td>94</td>\n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# Debug\n",
    "import pdb\n",
    "\n",
    "counter = 0\n",
    "for i in range(10):\n",
    "    foo = 2\n",
    "    pdb.set_trace()\n",
    "    counter += i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# Debug in Python 3.7\n",
    "b = 0\n",
    "for i in range(10):\n",
    "    a = 2\n",
    "    breakpoint()\n",
    "    b += i"
   ]
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    "fig, ax = plt.subplots(figsize=(6, 3))\n",
    "ax.set_title(\"Should I use piecharts?\")\n",
    "ax.pie([70, 30], labels=['No', 'Also no, but in red'], colors=['Blue', 'Red'])\n",
    "plt.show()"
   ]
  },
  {
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