Data Visualisation with Plotly.ipynb

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    "# Data Visualisation with Plotly\n",
    "\n",
    "Plotly documentation has many good examples of different types of plots. The examples that use *go* are often the best. This notebook reproduces a few examples but there are many different types of plots and many different ways of styling plots.\n",
    "\n",
    "https://plotly.com/python/\n",
    "\n",
    "\n",
    "The examples in this notebook just use dummy data that is typed in explicitly. It will be more common to plot data that is stored in CSV files and loaded use Pandas, for more on how to do that with Plotly see here: https://plotly.com/python/plot-data-from-csv/"
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   "source": [
    "import plotly.graph_objects as go\n",
    "from random import random"
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   "source": [
    "## Line Plots\n",
    "\n",
    "https://plotly.com/python/line-charts/"
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\"xaxis\": {\"title\": {\"text\": \"X data\"}}, \"yaxis\": {\"title\": {\"text\": \"Y data\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('6750b6b3-e5ae-4010-b782-240769cf95bb');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\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",
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       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_data = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
    "y_data_1 = [2 * x + 10 * random() for x in x_data]\n",
    "y_data_2 = [3 * x - 1 + 20 * random() for x in x_data]\n",
    "y_data_3 = [x + 1 - 30 * random() for x in x_data]\n",
    "\n",
    "fig = go.Figure(data=[\n",
    "    go.Scatter(x=x_data,  y=y_data_1, name='y 1'),\n",
    "    go.Scatter(x=x_data,  y=y_data_2, name='y 2'),\n",
    "    go.Scatter(x=x_data,  y=y_data_3, name='y 3')\n",
    "])\n",
    "fig.update_layout(\n",
    "    title=\"Some line plots\",\n",
    "    xaxis_title=\"X data\",\n",
    "    yaxis_title=\"Y data\"\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Plots\n",
    "\n",
    "https://plotly.com/python/bar-charts/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "config": {
        "plotlyServerURL": "https://plot.ly"
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         "type": "bar",
         "x": [
          "giraffes",
          "orangutans",
          "monkeys"
         ],
         "y": [
          20,
          14,
          23
         ]
        }
       ],
       "layout": {
        "template": {
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           {
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             "color": "#2a3f5f"
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            "error_y": {
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            "marker": {
             "line": {
              "color": "#E5ECF6",
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             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
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          ],
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            "type": "contour"
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            "type": "contourcarpet"
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          "heatmapgl": [
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          ],
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          "histogram2dcontour": [
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     "metadata": {},
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   ],
   "source": [
    "animals=['giraffes', 'orangutans', 'monkeys']\n",
    "\n",
    "fig = go.Figure([go.Bar(x=animals, y=[20, 14, 23])])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouped Bar Plots\n",
    "\n",
    "https://plotly.com/python/bar-charts/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
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       "                            \n",
       "var gd = document.getElementById('c5feca25-19dc-4300-9446-13ab39c07795');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "animals=['giraffes', 'orangutans', 'monkeys']\n",
    "\n",
    "fig = go.Figure(data=[\n",
    "    go.Bar(name='SF Zoo', x=animals, y=[20, 14, 23]),\n",
    "    go.Bar(name='LA Zoo', x=animals, y=[12, 18, 29])\n",
    "])\n",
    "# Change the bar mode\n",
    "fig.update_layout(barmode='group')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacked Bar Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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         "y": [
          20,
          14,
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        },
        {
         "name": "LA Zoo",
         "type": "bar",
         "x": [
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          "orangutans",
          "monkeys"
         ],
         "y": [
          12,
          18,
          29
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        }
       ],
       "layout": {
        "barmode": "stack",
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              "width": 0.5
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              "color": "#E5ECF6",
              "width": 0.5
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            "type": "barpolar"
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          ],
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             "startlinecolor": "#2a3f5f"
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   "source": [
    "animals=['giraffes', 'orangutans', 'monkeys']\n",
    "\n",
    "fig = go.Figure(data=[\n",
    "    go.Bar(name='SF Zoo', x=animals, y=[20, 14, 23]),\n",
    "    go.Bar(name='LA Zoo', x=animals, y=[12, 18, 29])\n",
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    "# Change the bar mode\n",
    "fig.update_layout(barmode='stack')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Colored and Styled Bar Chart\n",
    "\n",
    "https://plotly.com/python/bar-charts/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "years = [1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n",
    "         2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012]\n",
    "\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Bar(x=years,\n",
    "                y=[219, 146, 112, 127, 124, 180, 236, 207, 236, 263,\n",
    "                   350, 430, 474, 526, 488, 537, 500, 439],\n",
    "                name='Rest of world',\n",
    "                marker_color='rgb(55, 83, 109)'\n",
    "                ))\n",
    "fig.add_trace(go.Bar(x=years,\n",
    "                y=[16, 13, 10, 11, 28, 37, 43, 55, 56, 88, 105, 156, 270,\n",
    "                   299, 340, 403, 549, 499],\n",
    "                name='China',\n",
    "                marker_color='rgb(26, 118, 255)'\n",
    "                ))\n",
    "\n",
    "fig.update_layout(\n",
    "    title='US Export of Plastic Scrap',\n",
    "    xaxis_tickfont_size=14,\n",
    "    yaxis=dict(\n",
    "        title='USD (millions)',\n",
    "        titlefont_size=16,\n",
    "        tickfont_size=14,\n",
    "    ),\n",
    "    legend=dict(\n",
    "        x=0,\n",
    "        y=1.0,\n",
    "        bgcolor='rgba(255, 255, 255, 0)',\n",
    "        bordercolor='rgba(255, 255, 255, 0)'\n",
    "    ),\n",
    "    barmode='group',\n",
    "    bargap=0.15, # gap between bars of adjacent location coordinates.\n",
    "    bargroupgap=0.1 # gap between bars of the same location coordinate.\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pie Chart\n",
    "\n",
    "https://plotly.com/python/pie-charts/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "labels": [
          "Oxygen",
          "Hydrogen",
          "Carbon_Dioxide",
          "Nitrogen"
         ],
         "type": "pie",
         "values": [
          4500,
          2500,
          1053,
          500
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       ],
       "layout": {
        "template": {
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           {
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       "var gd = document.getElementById('73b601ea-be09-4b74-ab19-a65d64fb6b36');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['Oxygen','Hydrogen','Carbon_Dioxide','Nitrogen']\n",
    "values = [4500, 2500, 1053, 500]\n",
    "\n",
    "fig = go.Figure(data=[go.Pie(labels=labels, values=values)])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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