myNB.ipynb

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       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {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-2.2.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2200 entries, 0 to 2199\n",
      "Data columns (total 14 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   world_rank            2200 non-null   int64  \n",
      " 1   institution           2200 non-null   object \n",
      " 2   country               2200 non-null   object \n",
      " 3   national_rank         2200 non-null   int64  \n",
      " 4   quality_of_education  2200 non-null   int64  \n",
      " 5   alumni_employment     2200 non-null   int64  \n",
      " 6   quality_of_faculty    2200 non-null   int64  \n",
      " 7   publications          2200 non-null   int64  \n",
      " 8   influence             2200 non-null   int64  \n",
      " 9   citations             2200 non-null   int64  \n",
      " 10  broad_impact          2000 non-null   float64\n",
      " 11  patents               2200 non-null   int64  \n",
      " 12  score                 2200 non-null   float64\n",
      " 13  year                  2200 non-null   int64  \n",
      "dtypes: float64(2), int64(10), object(2)\n",
      "memory usage: 240.8+ KB\n",
      "   world_rank                            institution         country  \\\n",
      "0           1                     Harvard University             USA   \n",
      "1           2  Massachusetts Institute of Technology             USA   \n",
      "2           3                    Stanford University             USA   \n",
      "3           4                University of Cambridge  United Kingdom   \n",
      "4           5     California Institute of Technology             USA   \n",
      "5           6                   Princeton University             USA   \n",
      "6           7                   University of Oxford  United Kingdom   \n",
      "7           8                        Yale University             USA   \n",
      "8           9                    Columbia University             USA   \n",
      "9          10     University of California, Berkeley             USA   \n",
      "\n",
      "   national_rank  quality_of_education  alumni_employment  quality_of_faculty  \\\n",
      "0              1                     7                  9                   1   \n",
      "1              2                     9                 17                   3   \n",
      "2              3                    17                 11                   5   \n",
      "3              1                    10                 24                   4   \n",
      "4              4                     2                 29                   7   \n",
      "5              5                     8                 14                   2   \n",
      "6              2                    13                 28                   9   \n",
      "7              6                    14                 31                  12   \n",
      "8              7                    23                 21                  10   \n",
      "9              8                    16                 52                   6   \n",
      "\n",
      "   publications  influence  citations  broad_impact  patents   score  year  \n",
      "0             1          1          1           NaN        5  100.00  2012  \n",
      "1            12          4          4           NaN        1   91.67  2012  \n",
      "2             4          2          2           NaN       15   89.50  2012  \n",
      "3            16         16         11           NaN       50   86.17  2012  \n",
      "4            37         22         22           NaN       18   85.21  2012  \n",
      "5            53         33         26           NaN      101   82.50  2012  \n",
      "6            15         13         19           NaN       26   82.34  2012  \n",
      "7            14          6         15           NaN       66   79.14  2012  \n",
      "8            13         12         14           NaN        5   78.86  2012  \n",
      "9             6          5          3           NaN       16   78.55  2012  \n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from plotly.offline import init_notebook_mode, iplot, plot\n",
    "import plotly as py\n",
    "init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pd.read_csv(\"./cwurData.csv\")\n",
    "data.info()\n",
    "print(data.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c8a26ba",
   "metadata": {},
   "source": [
    " # Line Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a0f8a33b",
   "metadata": {},
   "outputs": [
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and Teaching vs World Rank of Top 100 Universities\"},\"xaxis\":{\"ticklen\":4,\"title\":{\"text\":\"World Rank\"},\"zeroline\":false}},                        {\"responsive\": true}                    ).then(function(){\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataFrame = data.iloc[:100,:]\n",
    "#print(dataFrame)\n",
    "\n",
    "#Create first trace\n",
    "trace1 = go.Scatter(\n",
    "                    x = dataFrame.world_rank,\n",
    "                    y = dataFrame.citations,\n",
    "                    mode = \"lines\",\n",
    "                    name = \"citations\",\n",
    "                    marker = dict(color = 'rgba(80, 26, 80. 0.8)'),\n",
    "                    text = dataFrame.institution\n",
    "                    )\n",
    "\n",
    "trace2 = go.Scatter(\n",
    "                    x = dataFrame.world_rank,\n",
    "                    y = dataFrame.quality_of_education,\n",
    "                    mode = \"lines+markers\",\n",
    "                    name = \"quality of education\",\n",
    "                    marker = dict(color = 'rgba(70, 16, 70, 0.5)'),\n",
    "                    text = dataFrame.institution\n",
    ")\n",
    "\n",
    "\n",
    "layout = dict(title = 'Citation and Teaching vs World Rank of Top 100 Universities', xaxis = dict(title = \"World Rank\", ticklen = 4, zeroline = False))\n",
    "fig = dict(data = [trace1, trace2], layout =  layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8645bf89",
   "metadata": {},
   "source": [
    "# Scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "44d71ddc",
   "metadata": {},
   "outputs": [
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2014 = data[data.year == 2014].iloc[:100, :]\n",
    "df2015 = data[data.year == 2015].iloc[:100, :]\n",
    "df2013 = data[data.year == 2013].iloc[:100, :]\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.institution)\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(225, 128, 2, 0.8)'),\n",
    "                    text = df2015.institution)\n",
    "\n",
    "trace3 = go.Scatter(\n",
    "                    x = df2013.world_rank,\n",
    "                    y = df2013.citations,\n",
    "                    mode = \"markers\",\n",
    "                    name = '2013',\n",
    "                    marker = dict(color = 'rgba(0, 255, 200, 0.8)'),\n",
    "                    text = df2013.institution)\n",
    "\n",
    "traces = [trace1, trace2, trace3]\n",
    "layout = dict(title = 'Citation vs world rank of top 100 universities in 2013, 2014, 2015',\n",
    "                xaxis = dict(title = 'World Rank', ticklen = 5, zeroline = False),\n",
    "                yaxis = dict(title = 'Citations', ticklen = 5, zeroline = False))\n",
    "\n",
    "fig = dict(data = traces, layout = layout)\n",
    "iplot(fig)"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = data[data.year == 2014].iloc[:3,:]\n",
    "\n",
    "trace1 = go.Bar(\n",
    "                x = df.institution,\n",
    "                y = df.citations,\n",
    "                name = \"citations\",\n",
    "                marker = dict(color = 'rgba(255, 174, 255, 0.5)', line = dict(color= 'rgb(0,0,0)', width = 1.5)),\n",
    "                text = df.country)\n",
    "\n",
    "trace2 = go.Bar(\n",
    "                x = df.institution, \n",
    "                y = df. publications,\n",
    "                name = \"publications\",\n",
    "                marker = dict(color = 'rgba(255, 255, 128, 0.5)', line = dict(color = 'rgb(0,0,0)', width = 1.5)), \n",
    "                text = df.country)\n",
    "\n",
    "layout = go.Layout(barmode = 'group')\n",
    "fig = go.Figure(data = [trace1, trace2], layout = layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c359aa2",
   "metadata": {},
   "source": [
    "# Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "315240c2",
   "metadata": {},
   "outputs": [
    {
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    "pie1 = df2015.patents\n",
    "pie1_list = [int(each) for each in pie1]\n",
    "labels = df2015.institution\n",
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    "fig = {\n",
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    "                \"y\": 1\n",
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    "\n",
    "iplot(fig)"
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