01-Plotly and Cufflinks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
    "___\n",
    "# Plotly and Cufflinks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotly is a library that allows you to create interactive plots that you can use in dashboards or websites (you can save them as html files or static images).\n",
    "\n",
    "## Installation\n",
    "\n",
    "In order for this all to work, you'll need to install plotly and cufflinks to call plots directly off of a pandas dataframe. These libraries are not currently available through **conda** but are available through **pip**. Install the libraries at your command line/terminal using:\n",
    "\n",
    "    pip install plotly\n",
    "    pip install cufflinks\n",
    "\n",
    "** NOTE: Make sure you only have one installation of Python on your computer when you do this, otherwise the installation may not work. **\n",
    "\n",
    "## Imports and Set-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import chart_studio.plotly as py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.8.0\n"
     ]
    }
   ],
   "source": [
    "from plotly import __version__\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "\n",
    "print(__version__) # requires version >= 1.9.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cufflinks as cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For Notebooks\n",
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For offline use\n",
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fake Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(100,4),columns='A B C D'.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.921476</td>\n",
       "      <td>-0.764408</td>\n",
       "      <td>0.679572</td>\n",
       "      <td>-0.614146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.995942</td>\n",
       "      <td>1.490383</td>\n",
       "      <td>-0.646100</td>\n",
       "      <td>-1.019083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.317223</td>\n",
       "      <td>1.775355</td>\n",
       "      <td>-0.872278</td>\n",
       "      <td>-2.111378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.540725</td>\n",
       "      <td>-0.219411</td>\n",
       "      <td>0.844584</td>\n",
       "      <td>2.080530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.642092</td>\n",
       "      <td>1.490906</td>\n",
       "      <td>-0.855280</td>\n",
       "      <td>-0.209715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.921476 -0.764408  0.679572 -0.614146\n",
       "1  0.995942  1.490383 -0.646100 -1.019083\n",
       "2 -1.317223  1.775355 -0.872278 -2.111378\n",
       "3  0.540725 -0.219411  0.844584  2.080530\n",
       "4  0.642092  1.490906 -0.855280 -0.209715"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame({'Category':['A','B','C'],'Values':[32,43,50]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Category</th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Category  Values\n",
       "0        A      32\n",
       "1        B      43\n",
       "2        C      50"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Cufflinks and iplot()\n",
    "\n",
    "* scatter\n",
    "* bar\n",
    "* box\n",
    "* spread\n",
    "* ratio\n",
    "* heatmap\n",
    "* surface\n",
    "* histogram\n",
    "* bubble"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "line": {
          "color": "rgba(255, 153, 51, 1.0)",
          "dash": "solid",
          "shape": "linear",
          "width": 1.3
         },
         "marker": {
          "size": 10,
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "B",
         "text": "",
         "type": "scatter",
         "x": [
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          -0.5064444603327013,
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   ],
   "source": [
    "df.iplot(kind='scatter',x='A',y='B',mode='markers',size=10)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
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        "plotlyServerURL": "https://plot.ly",
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          "line": {
           "color": "rgba(255, 153, 51, 1.0)",
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         "name": "Values",
         "orientation": "v",
         "text": "",
         "type": "bar",
         "x": [
          "A",
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          "C"
         ],
         "y": [
          32,
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       "layout": {
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          "color": "#4D5663"
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        "paper_bgcolor": "#F5F6F9",
        "plot_bgcolor": "#F5F6F9",
        "template": {
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           {
            "error_x": {
             "color": "#2a3f5f"
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            "error_y": {
             "color": "#2a3f5f"
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             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
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            "type": "bar"
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          ],
          "barpolar": [
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              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
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            "type": "barpolar"
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          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
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             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
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            "type": "carpet"
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          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
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            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
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            "colorscale": [
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              0.7777777777777778,
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              0.8888888888888888,
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             [
              1,
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          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
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            "colorscale": [
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              "#0d0887"
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              1,
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            "type": "heatmap"
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          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "df.mean().iplot(kind='bar')"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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    "## 3d Surface"
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   "source": [
    "df3 = pd.DataFrame({'x':[1,2,3,4,5],'y':[10,20,30,20,10],'z':[500,400,300,200,100]})\n",
    "df3.iplot(kind='surface',colorscale='rdylbu')"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
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   "source": [
    "## histogram"
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   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.iplot(kind='bubble',x='A',y='B',size='C')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scatter_matrix()\n",
    "\n",
    "Similar to sns.pairplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "                            \n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.iplot(kind='area')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Great Job!"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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