{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotly.offline import init_notebook_mode, plot, iplot\n",
    "import plotly.graph_objs as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><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 type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "marker": {
          "color": "red",
          "size": 10,
          "symbol": 104
         },
         "mode": "markers+lines",
         "name": "1st Trace",
         "text": [
          "one",
          "two",
          "three"
         ],
         "type": "scatter",
         "uid": "4b5288c3-88ae-4ae1-a6a2-a8fb2685c936",
         "x": [
          1,
          2,
          3
         ],
         "y": [
          7,
          5,
          9
         ]
        }
       ],
       "layout": {
        "autosize": true,
        "title": "First Plot",
        "xaxis": {
         "autorange": true,
         "range": [
          0.8697212255037261,
          3.130278774496274
         ],
         "title": "x1",
         "type": "linear"
        },
        "yaxis": {
         "autorange": true,
         "range": [
          4.657266811279826,
          9.342733188720173
         ],
         "title": "x2",
         "type": "linear"
        }
       }
      },
      "image/png": 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",
      "text/html": [
       "<div id=\"d7a5539b-c50f-4251-b5dc-289ae735ba27\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"d7a5539b-c50f-4251-b5dc-289ae735ba27\", [{\"marker\": {\"color\": \"red\", \"size\": 10, \"symbol\": 104}, \"mode\": \"markers+lines\", \"name\": \"1st Trace\", \"text\": [\"one\", \"two\", \"three\"], \"x\": [1, 2, 3], \"y\": [7, 5, 9], \"type\": \"scatter\", \"uid\": \"4b5288c3-88ae-4ae1-a6a2-a8fb2685c936\"}], {\"title\": \"First Plot\", \"xaxis\": {\"title\": \"x1\"}, \"yaxis\": {\"title\": \"x2\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"d7a5539b-c50f-4251-b5dc-289ae735ba27\"));});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"d7a5539b-c50f-4251-b5dc-289ae735ba27\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"d7a5539b-c50f-4251-b5dc-289ae735ba27\", [{\"marker\": {\"color\": \"red\", \"size\": 10, \"symbol\": 104}, \"mode\": \"markers+lines\", \"name\": \"1st Trace\", \"text\": [\"one\", \"two\", \"three\"], \"x\": [1, 2, 3], \"y\": [7, 5, 9], \"type\": \"scatter\", \"uid\": \"4b5288c3-88ae-4ae1-a6a2-a8fb2685c936\"}], {\"title\": \"First Plot\", \"xaxis\": {\"title\": \"x1\"}, \"yaxis\": {\"title\": \"x2\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"d7a5539b-c50f-4251-b5dc-289ae735ba27\"));});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace_test = go.Scatter(x=[1,2,3], y=[7,5,9], marker={'color': 'red', 'symbol': 104, 'size': 10}, \n",
    "                        mode=\"markers+lines\",  text=[\"one\",\"two\",\"three\"], name='1st Trace')\n",
    "                                               \n",
    "data = [trace_test]\n",
    "layout = go.Layout(title=\"First Plot\", xaxis={'title':'x1'}, yaxis={'title':'x2'})\n",
    "figure = go.Figure(data=data, layout=layout)\n",
    "iplot(figure, filename='pyguide_1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Figure({\n",
       "    'data': [{'marker': {'color': 'red', 'size': 10, 'symbol': 104},\n",
       "              'mode': 'markers+lines',\n",
       "              'name': '1st Trace',\n",
       "              'text': [one, two, three],\n",
       "              'type': 'scatter',\n",
       "              'uid': '4b5288c3-88ae-4ae1-a6a2-a8fb2685c936',\n",
       "              'x': [1, 2, 3],\n",
       "              'y': [7, 5, 9]}],\n",
       "    'layout': {'title': 'First Plot', 'xaxis': {'title': 'x1'}, 'yaxis': {'title': 'x2'}}\n",
       "})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "marker": {
          "color": "green",
          "size": 10,
          "symbol": 103
         },
         "mode": "markers+lines",
         "name": "1st Trace",
         "text": [
          "one",
          "two",
          "three"
         ],
         "type": "scatter",
         "uid": "4b5288c3-88ae-4ae1-a6a2-a8fb2685c936",
         "x": [
          1,
          2,
          3
         ],
         "y": [
          7,
          5,
          9
         ]
        }
       ],
       "layout": {
        "autosize": true,
        "title": "Plot Update",
        "xaxis": {
         "autorange": true,
         "range": [
          0.8697212255037261,
          3.130278774496274
         ],
         "title": "x1",
         "type": "linear"
        },
        "yaxis": {
         "autorange": true,
         "range": [
          4.657266811279826,
          9.342733188720173
         ],
         "title": "x2",
         "type": "linear"
        }
       }
      },
      "image/png": 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",
      "text/html": [
       "<div id=\"038921d9-de51-49a8-b9df-f32f09653e15\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"038921d9-de51-49a8-b9df-f32f09653e15\", [{\"marker\": {\"color\": \"green\", \"size\": 10, \"symbol\": 103}, \"mode\": \"markers+lines\", \"name\": \"1st Trace\", \"text\": [\"one\", \"two\", \"three\"], \"x\": [1, 2, 3], \"y\": [7, 5, 9], \"type\": \"scatter\", \"uid\": \"4b5288c3-88ae-4ae1-a6a2-a8fb2685c936\"}], {\"title\": \"Plot Update\", \"xaxis\": {\"title\": \"x1\"}, \"yaxis\": {\"title\": \"x2\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"038921d9-de51-49a8-b9df-f32f09653e15\"));});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"038921d9-de51-49a8-b9df-f32f09653e15\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"038921d9-de51-49a8-b9df-f32f09653e15\", [{\"marker\": {\"color\": \"green\", \"size\": 10, \"symbol\": 103}, \"mode\": \"markers+lines\", \"name\": \"1st Trace\", \"text\": [\"one\", \"two\", \"three\"], \"x\": [1, 2, 3], \"y\": [7, 5, 9], \"type\": \"scatter\", \"uid\": \"4b5288c3-88ae-4ae1-a6a2-a8fb2685c936\"}], {\"title\": \"Plot Update\", \"xaxis\": {\"title\": \"x1\"}, \"yaxis\": {\"title\": \"x2\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"038921d9-de51-49a8-b9df-f32f09653e15\"));});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure.update({'layout' : {'title': 'Plot Update'}, 'data': [{'marker': {'color': 'green', 'symbol': 103}}]})\n",
    "iplot(figure, filename='pyguide_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'pandas'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-7dd3504c366f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pandas'"
     ]
    }
   ],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('world-university-rankings/timesData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_df = df.iloc[:100,:]\n",
    "time_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trace1 = go.Scatter(\n",
    "                    x = time_df.world_rank,\n",
    "                    y = time_df.citations,\n",
    "                    mode = \"lines\",\n",
    "                    name = \"citations\",\n",
    "                    marker = dict(color = 'rgba(16, 112, 2, 0.8)'),\n",
    "                    text= time_df.university_name)\n",
    "trace2 = go.Scatter(\n",
    "                    x = time_df.world_rank,\n",
    "                    y = time_df.teaching,\n",
    "                    mode = \"lines+markers\",\n",
    "                    name = \"teaching\",\n",
    "                    marker = dict(color = 'rgba(80, 26, 80, 0.8)'),\n",
    "                    text= time_df.university_name)\n",
    "data = [trace1, trace2]\n",
    "layout = dict(title = 'Citation and Teaching vs World Rank of Top 100 Universities',\n",
    "              xaxis= dict(title= 'World Rank',ticklen= 5,zeroline= False)\n",
    "             )\n",
    "fig = dict(data = data, layout = layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cufflinks as cf\n",
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_df.iplot(x='world_rank', y=['citations', 'teaching'], \n",
    "              kind='scatter', xTitle=\"World Rank\", text='university_name',\n",
    "              mode=['lines', 'lines+markers'],\n",
    "              filename='cufflinks/cf-simple-line')\n"
   ]
  }
 ],
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