{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Plotly and Cufflinks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from plotly import __version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.3\n"
     ]
    }
   ],
   "source": [
    "print(__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cufflinks as cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_data_rate_limit`.\n"
     ]
    }
   ],
   "source": [
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some random data \n",
    "df = pd.DataFrame(np.random.randn(800,4), columns='Sabrina Rose Lusi Fiona'.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sabrina</th>\n",
       "      <th>Rose</th>\n",
       "      <th>Lusi</th>\n",
       "      <th>Fiona</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.625979</td>\n",
       "      <td>-1.338837</td>\n",
       "      <td>1.537151</td>\n",
       "      <td>1.333365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.017143</td>\n",
       "      <td>0.043123</td>\n",
       "      <td>-0.950405</td>\n",
       "      <td>-0.035388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.332343</td>\n",
       "      <td>0.067920</td>\n",
       "      <td>0.919870</td>\n",
       "      <td>1.612951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.409426</td>\n",
       "      <td>-0.008151</td>\n",
       "      <td>-0.636676</td>\n",
       "      <td>-0.276871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.434980</td>\n",
       "      <td>-0.642573</td>\n",
       "      <td>-0.093593</td>\n",
       "      <td>-1.935671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sabrina      Rose      Lusi     Fiona\n",
       "0 -0.625979 -1.338837  1.537151  1.333365\n",
       "1 -0.017143  0.043123 -0.950405 -0.035388\n",
       "2 -1.332343  0.067920  0.919870  1.612951\n",
       "3  0.409426 -0.008151 -0.636676 -0.276871\n",
       "4  0.434980 -0.642573 -0.093593 -1.935671"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     "output_type": "display_data"
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   ],
   "source": [
    "# Using Interactive Image from Cufflinks\n",
    "df.iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.iplot(kind='scatter', x='Sabrina', y='Rose', title = 'Friendship Scatter Plot', mode='markers',  size=20)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two of my besties randomly scattered. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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{\"tickfont\": {\"color\": \"#4D5663\"}, \"gridcolor\": \"#E1E5ED\", \"titlefont\": {\"color\": \"#4D5663\"}, \"zerolinecolor\": \"#E1E5ED\", \"showgrid\": true, \"title\": \"\"}, \"titlefont\": {\"color\": \"#4D5663\"}, \"title\": \"Box Plot of Friendship\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot of Friendship\n",
    "df.iplot(kind='box', title='Box Plot of Friendship')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "# Scatter Matrix\n",
    "df.scatter_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Spread type visulization\n",
    "df[['Rose', 'Sabrina']].iplot(kind='spread')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Partner</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rose</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lexie</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fiona</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Partner  Scores\n",
       "0    Rose     200\n",
       "1   Lexie     120\n",
       "2   Fiona     150"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Some more random data\n",
    "df2 = pd.DataFrame({'Partner':['Rose', 'Lexie', 'Fiona'], 'Scores':[200, 120, 150]})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "marker": {
          "color": "rgba(255, 153, 51, 0.6)",
          "line": {
           "color": "rgba(255, 153, 51, 1.0)",
           "width": 1
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         },
         "name": "Scores",
         "orientation": "v",
         "text": "",
         "type": "bar",
         "x": [
          "Rose",
          "Lexie",
          "Fiona"
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         "y": [
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        "paper_bgcolor": "#F5F6F9",
        "plot_bgcolor": "#F5F6F9",
        "title": "Friendship Score",
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   ],
   "source": [
    "# Barplot to show the coolness of my friends\n",
    "df2.iplot(kind='bar', x='Partner', y='Scores', title='Friendship Score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it is apparent that Rose is the boss! Love Fiona -- the best cat friend ever! Lexie is actually just imaginary. "
   ]
  },
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   "cell_type": "code",
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      "4  0.259466  0.272165  0.809376  0.917269\n",
      "5  0.290856  0.739409  0.007345  0.448449\n",
      "6  0.304495  0.026588  0.807316  0.204141\n",
      "7  0.068016  0.935381  0.830328  0.084937\n",
      "8  0.042957  0.934351  0.911425  0.652651\n",
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   "source": [
    "df3 = pd.DataFrame(np.random.rand(10, 4), columns=['Lilo', 'Stitch', 'Moana', 'Maui'])\n",
    "print(df3)\n",
    "df3.iplot(kind='barh',barmode='stack')"
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      ],
      "text/plain": [
       "       Country  underFiveMortality  totalFertilityRate  teenFertility  \\\n",
       "0  Afghanistan               105.0                5.66          107.0   \n",
       "1      Albania                16.6                1.74           11.2   \n",
       "2      Algeria                27.4                2.82            7.0   \n",
       "3    Argentina                14.6                2.22           68.2   \n",
       "4      Armenia                18.0                1.55           28.3   \n",
       "\n",
       "   GDPPerCapita  govtExOnHealthPerCapita  agriPercentGDP  \\\n",
       "0          1637                 4.390408       29.915323   \n",
       "1          9374                94.023613       20.160029   \n",
       "2         12494               138.840923        6.915571   \n",
       "3         15765               405.058875       10.003471   \n",
       "4          6508                54.238760       19.577842   \n",
       "\n",
       "   womenMeanYearsInSchool  improvedDrinkingWaterSourcesInPercentage  \\\n",
       "0                     0.8                                      50.0   \n",
       "1                    10.7                                      95.0   \n",
       "2                     7.1                                      83.0   \n",
       "3                    11.4                                       NaN   \n",
       "4                    11.4                                      98.0   \n",
       "\n",
       "   improvedSanitationFacilitiesInPercentage  lifeExpectancy  totalPopulation  \\\n",
       "0                                      37.0            53.6         31411743   \n",
       "1                                      94.0            77.2          3204284   \n",
       "2                                      95.0            76.0         35468208   \n",
       "3                                       NaN            75.8         40412376   \n",
       "4                                      90.0            73.0          3092072   \n",
       "\n",
       "   DTP3ImmunizedInPercentage  contraceptivePrevalenceInPercentage   CO2  \\\n",
       "0                       66.0                                  NaN  0.08   \n",
       "1                       99.0                                  NaN  0.43   \n",
       "2                       95.0                                  NaN  0.90   \n",
       "3                       94.0                                  NaN  1.24   \n",
       "4                       94.0                                 54.9  0.39   \n",
       "\n",
       "   pneumoniaDeathsInNewborns  \n",
       "0                   6.288837  \n",
       "1                   0.238741  \n",
       "2                   3.021863  \n",
       "3                   0.155241  \n",
       "4                   1.401095  "
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import my mortality rate dataset\n",
    "import pandas as pd\n",
    "study = pd.read_csv('study1.csv')\n",
    "study.head(5)"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Scatter Plots\n",
    "study.iplot(kind='scatter', mode='markers', x='GDPPerCapita', y='lifeExpectancy', xTitle='GDP per Capita', yTitle='Life Expectancy', title='GDPpc VS Life Expectancy 2010')"
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    "study.iplot(kind='bubble', x='GDPPerCapita', y='lifeExpectancy', size='totalPopulation', text='Country', categories='lifeExpectancy', showlegend=False, colorscale='Set1',  xTitle='GDP per Capita', yTitle='Life Expectancy', title='GDPpc VS Life Expectancy 2010')"
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   "cell_type": "markdown",
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    "Interesting to see that life expectancy improves faster when GDP per Capita is lower (steeper positive slope). When GDPpc hits a certain point like China and India, improvement of life expectancy slows down. The colors of the bubbles are based on life expectancy. "
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   "metadata": {},
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   ],
   "source": [
    "study.iplot(kind='bubble', x='totalFertilityRate', y='underFiveMortality', size='totalPopulation', text='Country', categories='lifeExpectancy', showlegend=False, colorscale='Set1',  xTitle='totalFertilityRate', yTitle='underFiveMortality', title='Total Fertility Rate VS Child Mortality Rate 2010')"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting to see that there is a positive correlation between fertility and child mortality. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
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    "Note that in this graph, the bigger the bubble the higer the GDPpc. The big bubble countries have lower percentage of agriculture in their GDP and tend to have lower child mortality rate. On the other hands, the smaller bubble countries with higher percentage of agriculture tend to have higher child mortality rate. "
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    "# Heatmap to study correlations\n",
    "study.corr().iplot(kind='heatmap', colorscale='spectral')"
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