DashBoard.ipynb

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   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "from plotly.offline import init_notebook_mode, iplot, plot\n",
    "\n",
    "init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "timesData = pd.read_csv(\"https://raw.githubusercontent.com/ApoorvGit/Data-Science/main/timesData.csv\")"
   ]
  },
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2603 entries, 0 to 2602\n",
      "Data columns (total 14 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   world_rank              2603 non-null   object \n",
      " 1   university_name         2603 non-null   object \n",
      " 2   country                 2603 non-null   object \n",
      " 3   teaching                2603 non-null   float64\n",
      " 4   international           2603 non-null   object \n",
      " 5   research                2603 non-null   float64\n",
      " 6   citations               2603 non-null   float64\n",
      " 7   income                  2603 non-null   object \n",
      " 8   total_score             2603 non-null   object \n",
      " 9   num_students            2544 non-null   object \n",
      " 10  student_staff_ratio     2544 non-null   float64\n",
      " 11  international_students  2536 non-null   object \n",
      " 12  female_male_ratio       2370 non-null   object \n",
      " 13  year                    2603 non-null   int64  \n",
      "dtypes: float64(4), int64(1), object(9)\n",
      "memory usage: 284.8+ KB\n"
     ]
    }
   ],
   "source": [
    "timesData.info()"
   ]
  },
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   ],
   "source": [
    "df = timesData.iloc[:100,:]\n",
    "\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "trace1 = go.Scatter(\n",
    "                    x = df.world_rank,\n",
    "                    y = df.citations,\n",
    "                    mode = \"lines\",\n",
    "                    name = \"citations\",\n",
    "                    marker = dict(color = 'rgba(16, 112, 2, 0.8)'),\n",
    "                    text= df.university_name)\n",
    "trace2 = go.Scatter(\n",
    "                    x = df.world_rank,\n",
    "                    y = df.teaching,\n",
    "                    mode = \"lines+markers\",\n",
    "                    name = \"teaching\",\n",
    "                    marker = dict(color = 'rgba(80, 26, 80, 0.8)'),\n",
    "                    text= df.university_name)\n",
    "data = [trace1, trace2]\n",
    "layout = dict(title = 'Citation Score and Teaching Score vs World Rank of Top 100 Universities 2011',\n",
    "              xaxis= dict(title= 'World Rank',ticklen= 5,zeroline= False),\n",
    "              yaxis=dict(title='Citation Score and Teaching Score',ticklen= 5,zeroline= False)\n",
    "             )\n",
    "fig = dict(data = data, layout = layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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          "Delft University of Technology",
          "Erasmus University Rotterdam",
          "Wageningen University and Research Center",
          "University of Bristol",
          "University of Basel",
          "University of Southern California",
          "University of Amsterdam",
          "École Normale Supérieure",
          "Utrecht University",
          "Humboldt University of Berlin",
          "Free University of Berlin",
          "Michigan State University",
          "Durham University",
          "Monash University",
          "Middle East Technical University",
          "University of Arizona",
          "University of Notre Dame",
          "Tufts University",
          "University of California, Irvine",
          "Ghent University",
          "University of Massachusetts",
          "University of Pittsburgh",
          "Emory University",
          "McMaster University",
          "University of Glasgow",
          "Vanderbilt University",
          "University of Colorado Boulder",
          "Stockholm University",
          "Technical University of Munich",
          "Uppsala University"
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          "2",
          "3",
          "4",
          "5",
          "6",
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          "12",
          "13",
          "14",
          "15",
          "16",
          "17",
          "18",
          "19",
          "20",
          "21",
          "22",
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          "25",
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          "27",
          "28",
          "29",
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          "32",
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          "42",
          "43",
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          "45",
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          94,
          97.3,
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          83.5,
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          93.6,
          94.4,
          88.9,
          96.6,
          91.5,
          83,
          96.9,
          85.1,
          74.7,
          92,
          66,
          95,
          85.8,
          91.5,
          83,
          77.8,
          87.7,
          85.3,
          80.6,
          71.3,
          95,
          88.3,
          99.2,
          89.5,
          76,
          88.3,
          96.4,
          97.1,
          65.1,
          76.8,
          71.1,
          82.9,
          91,
          63.7,
          65,
          48.7,
          72.9,
          71.4,
          74.3,
          92,
          71.8,
          80.4,
          94.4,
          76,
          57,
          69,
          75.9,
          79.9,
          96.4,
          82.3,
          74.2,
          84.4,
          92.3,
          80.4,
          99.9,
          81.4,
          42.6,
          82.3,
          77.5,
          85.1,
          81.4,
          81.3,
          76.6,
          84.8,
          80,
          71.9,
          67.6,
          74,
          88.9,
          65.7,
          92,
          74,
          90,
          92.9,
          89.5,
          73.8,
          78.7,
          84.4,
          89.5,
          78.8,
          83.7,
          87.7,
          97.4,
          90.9,
          78.8,
          70.9
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          "Stanford University",
          "University of Cambridge",
          "Massachusetts Institute of Technology",
          "Harvard University",
          "Princeton University",
          "Imperial College London",
          "ETH Zurich – Swiss Federal Institute of Technology Zurich",
          "University of Chicago",
          "Johns Hopkins University",
          "Yale University",
          "University of California, Berkeley",
          "University College London",
          "Columbia University",
          "University of California, Los Angeles",
          "University of Pennsylvania",
          "Cornell University",
          "University of Toronto",
          "Duke University",
          "University of Michigan",
          "Carnegie Mellon University",
          "London School of Economics and Political Science",
          "University of Edinburgh",
          "Northwestern University",
          "National University of Singapore",
          "King’s College London",
          "Karolinska Institute",
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          "McGill University",
          "University of California, San Diego",
          "University of California, Santa Barbara",
          "Georgia Institute of Technology",
          "Peking University",
          "University of Tokyo",
          "University of California, Davis",
          "University of Hong Kong",
          "University of Texas at Austin",
          "Tsinghua University",
          "Wageningen University and Research Center",
          "Humboldt University of Berlin",
          "University of Wisconsin-Madison",
          "Brown University",
          "Australian National University",
          "Technical University of Munich",
          "École Normale Supérieure",
          "Nanyang Technological University",
          "University of Manchester",
          "University of Sydney",
          "University of Amsterdam",
          "Hong Kong University of Science and Technology",
          "The University of Queensland",
          "Washington University in St Louis",
          "Utrecht University",
          "University of North Carolina at Chapel Hill",
          "Boston University",
          "Delft University of Technology",
          "University of Minnesota",
          "Leiden University",
          "University of Southern California",
          "University of Bristol",
          "Durham University",
          "Erasmus University Rotterdam",
          "Free University of Berlin",
          "Monash University",
          "University of Groningen",
          "Pennsylvania State University",
          "University of Glasgow",
          "University of Helsinki",
          "University of Tübingen",
          "University of Pittsburgh",
          "University of Warwick",
          "Uppsala University",
          "University of Copenhagen",
          "University of New South Wales",
          "University of Freiburg",
          "Seoul National University",
          "University of St Andrews",
          "Vanderbilt University",
          "Kyoto University",
          "Maastricht University",
          "Emory University",
          "Lund University",
          "Ohio State University",
          "University of Exeter",
          "University of Bonn",
          "Georgetown University",
          "McMaster University",
          "University of Sheffield",
          "Queen Mary University of London",
          "University of Göttingen",
          "Michigan State University"
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          "12",
          "13",
          "14",
          "15",
          "16",
          "17",
          "18",
          "19",
          "20",
          "21",
          "22",
          "23",
          "24",
          "25",
          "26",
          "27",
          "28",
          "29",
          "30",
          "31",
          "32",
          "33",
          "34",
          "35",
          "36",
          "37",
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          "=39",
          "=39",
          "41",
          "42",
          "43",
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          "=47",
          "=47",
          "49",
          "50",
          "51",
          "52",
          "53",
          "54",
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          "=56",
          "58",
          "59",
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          "=60",
          "62",
          "63",
          "64",
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          "=65",
          "67",
          "68",
          "69",
          "70",
          "71",
          "72",
          "73",
          "74",
          "75",
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          "78",
          "79",
          "80",
          "81",
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          "84",
          "85",
          "86",
          "87",
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          "=88",
          "=90",
          "=90",
          "=90",
          "93",
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          "=94",
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          "97",
          "98",
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          73.3,
          84,
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          92.3,
          82.1,
          79.3,
          92.3,
          78.2,
          80.9,
          86.2,
          82.1,
          80.5,
          50,
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          95.7,
          46.6,
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          96.7,
          81.3,
          83.2,
          94.3,
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          80.1,
          93.3,
          72.7,
          76.8
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   ],
   "source": [
    "\n",
    "df2014 = timesData[timesData.year == 2014].iloc[:100,:]\n",
    "df2015 = timesData[timesData.year == 2015].iloc[:100,:]\n",
    "df2016 = timesData[timesData.year == 2016].iloc[:100,:]\n",
    "\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "trace1 =go.Scatter(\n",
    "                    x = df2014.world_rank,\n",
    "                    y = df2014.citations,\n",
    "                    mode = \"markers\",\n",
    "                    name = \"2014\",\n",
    "                    marker = dict(color = 'rgba(255, 128, 255, 0.8)'),\n",
    "                    text= df2014.university_name)\n",
    "\n",
    "trace2 =go.Scatter(\n",
    "                    x = df2015.world_rank,\n",
    "                    y = df2015.citations,\n",
    "                    mode = \"markers\",\n",
    "                    name = \"2015\",\n",
    "                    marker = dict(color = 'rgba(255, 128, 2, 0.8)'),\n",
    "                    text= df2015.university_name)\n",
    "\n",
    "trace3 =go.Scatter(\n",
    "                    x = df2016.world_rank,\n",
    "                    y = df2016.citations,\n",
    "                    mode = \"markers\",\n",
    "                    name = \"2016\",\n",
    "                    marker = dict(color = 'rgba(0, 255, 200, 0.8)'),\n",
    "                    text= df2016.university_name)\n",
    "\n",
    "data = [trace1, trace2, trace3]\n",
    "layout = dict(title = 'Citation vs world rank of top 100 universities during 2014, 2015 and 2016 years',\n",
    "              xaxis= dict(title= 'World Rank',ticklen= 5,zeroline= False),\n",
    "              yaxis= dict(title= 'Citation',ticklen= 5,zeroline= False)\n",
    "             )\n",
    "fig = dict(data = data, layout = layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "\n",
    "df2014 = timesData[timesData.year == 2014].iloc[:3,:]\n",
    "\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "trace1 = go.Bar(\n",
    "                x = df2014.university_name,\n",
    "                y = df2014.citations,\n",
    "                name = \"citations\",\n",
    "                marker = dict(color = 'rgba(255, 174, 255, 0.5)',\n",
    "                             line=dict(color='rgb(0,0,0)',width=1.5)),\n",
    "                text = \"Citation Score\")\n",
    "\n",
    "trace2 = go.Bar(\n",
    "                x = df2014.university_name,\n",
    "                y = df2014.teaching,\n",
    "                name = \"teaching\",\n",
    "                marker = dict(color = 'rgba(255, 255, 128, 0.5)',\n",
    "                              line=dict(color='rgb(0,0,0)',width=1.5)),\n",
    "                text = \"Teaching Score\")\n",
    "data = [trace1, trace2]\n",
    "layout = go.Layout(barmode = \"group\")\n",
    "fig = go.Figure(data = data, layout = layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\apoorv\\anaconda3\\lib\\site-packages\\plotly\\tools.py:465: DeprecationWarning:\n",
      "\n",
      "plotly.tools.make_subplots is deprecated, please use plotly.subplots.make_subplots instead\n",
      "\n"
     ]
    },
    {
     "data": {
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    "# import graph objects as \"go\" and import tools\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "from plotly import tools\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# prepare data frames\n",
    "df2016 = timesData[timesData.year == 2016].iloc[:10,:]\n",
    "\n",
    "y_saving = [each for each in df2016.research]\n",
    "y_net_worth  = [float(each) for each in df2016.income]\n",
    "x_saving = [each for each in df2016.university_name]\n",
    "x_net_worth  = [each for each in df2016.university_name]\n",
    "\n",
    "trace0 = go.Bar(\n",
    "                x=y_saving,\n",
    "                y=x_saving,\n",
    "                marker=dict(color='rgba(171, 50, 96, 0.6)',line=dict(color='rgba(171, 50, 96, 1.0)',width=1)),\n",
    "                name='research',\n",
    "                orientation='h',\n",
    ")\n",
    "\n",
    "trace1 = go.Scatter(\n",
    "                x=y_net_worth,\n",
    "                y=x_net_worth,\n",
    "                mode='lines+markers',\n",
    "                line=dict(color='rgb(63, 72, 204)'),\n",
    "                name='income',\n",
    ")\n",
    "layout = dict(\n",
    "                title='Comparing Income of Top 10 Institute with respect to Citations',\n",
    "                yaxis=dict(showticklabels=True,domain=[0, 0.85]),\n",
    "                yaxis2=dict(showline=True,showticklabels=False,linecolor='rgba(102, 102, 102, 0.8)',linewidth=2,domain=[0, 0.85]),\n",
    "                xaxis=dict(zeroline=False,showline=False,showticklabels=True,showgrid=True,domain=[0, 0.42]),\n",
    "                xaxis2=dict(zeroline=False,showline=False,showticklabels=True,showgrid=True,domain=[0.47, 1],side='top',dtick=25),\n",
    "                legend=dict(x=0.029,y=1.038,font=dict(size=10) ),\n",
    "                margin=dict(l=200, r=20,t=70,b=70),\n",
    "                paper_bgcolor='rgb(248, 248, 255)',\n",
    "                plot_bgcolor='rgb(248, 248, 255)',\n",
    ")\n",
    "\n",
    "annotations = []\n",
    "y_s = np.round(y_saving, decimals=2)\n",
    "y_nw = np.rint(y_net_worth)\n",
    "\n",
    "for ydn, yd, xd in zip(y_nw, y_s, x_saving):\n",
    "    annotations.append(dict(xref='x2', yref='y2', y=xd, x=ydn - 4,text='{:,}'.format(ydn),font=dict(family='Arial', size=12,color='rgb(63, 72, 204)'),showarrow=False))\n",
    "    annotations.append(dict(xref='x1', yref='y1', y=xd, x=yd + 3,text=str(yd),font=dict(family='Arial', size=12,color='rgb(171, 50, 96)'),showarrow=False))\n",
    "\n",
    "layout['annotations'] = annotations\n",
    "\n",
    "\n",
    "fig = tools.make_subplots(rows=1, cols=2, specs=[[{}, {}]], shared_xaxes=True,\n",
    "                          shared_yaxes=False, vertical_spacing=0.001)\n",
    "\n",
    "fig.append_trace(trace0, 1, 1)\n",
    "fig.append_trace(trace1, 1, 2)\n",
    "\n",
    "fig['layout'].update(layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df2016 = timesData[timesData.year == 2016].iloc[:30,:]\n",
    "x2016 = df2016.country\n",
    "\n",
    "plt.subplots(figsize=(8,8))\n",
    "#!pip install wordcloud\n",
    "from wordcloud import WordCloud\n",
    "wordcloud = WordCloud(\n",
    "                          background_color='white',\n",
    "                          width=512,\n",
    "                          height=384\n",
    "                         ).generate(\" \".join(x2016))\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis('off')\n",
    "plt.savefig('graph.png')\n",
    "plt.title(\"Countries in Top 30\", fontsize=23)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "name": "research",
         "type": "scatter",
         "x": [
          "1",
          "2",
          "3",
          "4",
          "5",
          "6",
          "7",
          "8",
          "9",
          "10",
          "11",
          "12",
          "13",
          "14",
          "15",
          "16",
          "17",
          "18",
          "19",
          "20",
          "21",
          "22",
          "23",
          "24",
          "25",
          "26",
          "27",
          "28",
          "29",
          "30",
          "31",
          "32",
          "33",
          "34",
          "35",
          "36",
          "37",
          "38",
          "=39",
          "=39",
          "41",
          "42",
          "43",
          "=44",
          "=44",
          "46",
          "=47",
          "=47",
          "49",
          "50"
         ],
         "y": [
          97.6,
          98.9,
          96.2,
          96.7,
          88.6,
          99,
          91.9,
          88.5,
          95,
          88.9,
          90.4,
          87.8,
          91.1,
          91,
          82.2,
          88.6,
          86.9,
          86.1,
          89.3,
          78,
          85.2,
          88.8,
          80.6,
          77.2,
          78.4,
          84.5,
          75.8,
          81.1,
          77.4,
          72.3,
          67.5,
          70,
          75.5,
          73.2,
          76.9,
          81.2,
          69.6,
          72.1,
          69.8,
          66,
          72.7,
          72.4,
          83,
          72.7,
          72.8,
          69.7,
          83,
          66.9,
          77,
          68.2
         ]
        },
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citation, income and total score VS World Rank of Top 50 Universities in 2016\"},\"xaxis\":{\"domain\":[0,0.45]},\"yaxis\":{\"domain\":[0,0.45]},\"xaxis2\":{\"domain\":[0.55,1]},\"xaxis3\":{\"anchor\":\"y3\",\"domain\":[0,0.45]},\"xaxis4\":{\"anchor\":\"y4\",\"domain\":[0.55,1]},\"yaxis2\":{\"anchor\":\"x2\",\"domain\":[0,0.45]},\"yaxis3\":{\"domain\":[0.55,1]},\"yaxis4\":{\"anchor\":\"x4\",\"domain\":[0.55,1]}},                        {\"responsive\": true}                    ).then(function(){\n",
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      ]
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     "metadata": {},
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    }
   ],
   "source": [
    "\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "dataframe = timesData[timesData.year == 2016].iloc[:50,:]\n",
    "trace1 = go.Scatter(\n",
    "    x=dataframe.world_rank,\n",
    "    y=dataframe.research,\n",
    "    name = \"research\"\n",
    ")\n",
    "trace2 = go.Scatter(\n",
    "    x=dataframe.world_rank,\n",
    "    y=dataframe.citations,\n",
    "    xaxis='x2',\n",
    "    yaxis='y2',\n",
    "    name = \"citations\"\n",
    ")\n",
    "trace3 = go.Scatter(\n",
    "    x=dataframe.world_rank,\n",
    "    y=dataframe.income,\n",
    "    xaxis='x3',\n",
    "    yaxis='y3',\n",
    "    name = \"income\"\n",
    ")\n",
    "trace4 = go.Scatter(\n",
    "    x=dataframe.world_rank,\n",
    "    y=dataframe.total_score,\n",
    "    xaxis='x4',\n",
    "    yaxis='y4',\n",
    "    name = \"total_score\"\n",
    ")\n",
    "data = [trace1, trace2, trace3, trace4]\n",
    "layout = go.Layout(\n",
    "    xaxis=dict(\n",
    "        domain=[0, 0.45]\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        domain=[0, 0.45]\n",
    "    ),\n",
    "    xaxis2=dict(\n",
    "        domain=[0.55, 1]\n",
    "    ),\n",
    "    xaxis3=dict(\n",
    "        domain=[0, 0.45],\n",
    "        anchor='y3'\n",
    "    ),\n",
    "    xaxis4=dict(\n",
    "        domain=[0.55, 1],\n",
    "        anchor='y4'\n",
    "    ),\n",
    "    yaxis2=dict(\n",
    "        domain=[0, 0.45],\n",
    "        anchor='x2'\n",
    "    ),\n",
    "    yaxis3=dict(\n",
    "        domain=[0.55, 1]\n",
    "    ),\n",
    "    yaxis4=dict(\n",
    "        domain=[0.55, 1],\n",
    "        anchor='x4'\n",
    "    ),\n",
    "    title = 'Research, citation, income and total score VS World Rank of Top 50 Universities in 2016'\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#                                           Useful Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Name of Universities With Total Score More then 90.0 in 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1803       California Institute of Technology\n",
       "1804                     University of Oxford\n",
       "1805                      Stanford University\n",
       "1806                  University of Cambridge\n",
       "1807    Massachusetts Institute of Technology\n",
       "1808                       Harvard University\n",
       "1809                     Princeton University\n",
       "Name: university_name, dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timesData[(timesData.total_score >=\"90.0\") & (timesData.year==2016)].university_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Name of Top  University in years 2011 - 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>university_name</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Harvard University</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>California Institute of Technology</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>California Institute of Technology</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>California Institute of Technology</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1402</th>\n",
       "      <td>California Institute of Technology</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1803</th>\n",
       "      <td>California Institute of Technology</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         university_name  year\n",
       "0                     Harvard University  2011\n",
       "200   California Institute of Technology  2012\n",
       "602   California Institute of Technology  2013\n",
       "1002  California Institute of Technology  2014\n",
       "1402  California Institute of Technology  2015\n",
       "1803  California Institute of Technology  2016"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timesData[(timesData.world_rank=='1')].iloc[:,[1,13]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Top 5 Universities of India "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>university_name</th>\n",
       "      <th>world_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2070</th>\n",
       "      <td>Indian Institute of Science</td>\n",
       "      <td>251-300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2166</th>\n",
       "      <td>Indian Institute of Technology Bombay</td>\n",
       "      <td>351-400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2239</th>\n",
       "      <td>Indian Institute of Technology Delhi</td>\n",
       "      <td>401-500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2240</th>\n",
       "      <td>Indian Institute of Technology Kharagpur</td>\n",
       "      <td>401-500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2241</th>\n",
       "      <td>Indian Institute of Technology Madras</td>\n",
       "      <td>401-500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               university_name world_rank\n",
       "2070               Indian Institute of Science    251-300\n",
       "2166     Indian Institute of Technology Bombay    351-400\n",
       "2239      Indian Institute of Technology Delhi    401-500\n",
       "2240  Indian Institute of Technology Kharagpur    401-500\n",
       "2241     Indian Institute of Technology Madras    401-500"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timesData[(timesData.country=='India') & (timesData.year==2016)].iloc[:5,[1,0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Indian University with Highest Number of Students "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>world_rank</th>\n",
       "      <th>university_name</th>\n",
       "      <th>country</th>\n",
       "      <th>teaching</th>\n",
       "      <th>international</th>\n",
       "      <th>research</th>\n",
       "      <th>citations</th>\n",
       "      <th>income</th>\n",
       "      <th>total_score</th>\n",
       "      <th>num_students</th>\n",
       "      <th>student_staff_ratio</th>\n",
       "      <th>international_students</th>\n",
       "      <th>female_male_ratio</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2240</th>\n",
       "      <td>401-500</td>\n",
       "      <td>Indian Institute of Technology Kharagpur</td>\n",
       "      <td>India</td>\n",
       "      <td>33.2</td>\n",
       "      <td>13.3</td>\n",
       "      <td>30.5</td>\n",
       "      <td>41.4</td>\n",
       "      <td>31.7</td>\n",
       "      <td>-</td>\n",
       "      <td>9,928</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0%</td>\n",
       "      <td>15 : 85</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     world_rank                           university_name country  teaching  \\\n",
       "2240    401-500  Indian Institute of Technology Kharagpur   India      33.2   \n",
       "\n",
       "     international  research  citations income total_score num_students  \\\n",
       "2240          13.3      30.5       41.4   31.7           -        9,928   \n",
       "\n",
       "      student_staff_ratio international_students female_male_ratio  year  \n",
       "2240                 17.5                     0%           15 : 85  2016  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxi=timesData[timesData.country==\"India\"].iloc[:,9].max()\n",
    "timesData[(timesData.year==2016) & (timesData.country==\"India\") & (timesData.num_students==maxi) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Indian University with Highest number of International Students "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>country</th>\n",
       "      <th>international_students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2412</th>\n",
       "      <td>India</td>\n",
       "      <td>8%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country international_students\n",
       "2412   India                     8%"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxi=timesData[(timesData.country==\"India\") & (timesData.year==2016)].iloc[:,11].max()\n",
    "timesData[(timesData.year==2016) & (timesData.country==\"India\") & (timesData.international_students==maxi) ].iloc[:,[2,11]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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