ScatterPlots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": [
    "import plotly.offline as offline\n",
    "\n",
    "offline.init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import plotly.graph_objs as go\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate a sets of 200 x and y coordinates\n",
    "These will be used in our scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n = 200\n",
    "\n",
    "random_x = np.random.randn(n)\n",
    "\n",
    "random_y = np.random.randn(n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating a trace from a Plotly Scatter object\n",
    "This will generate points using the X and Y coordinates which we supply. The X and Y coordinates are paired by their index. \n",
    "\n",
    "Setting the mode to 'markers' will ensure that only the points are plotted, and are not connected by a line. The default mode is 'lines' where the points are connected by a line in the order of their index. i.e. (x0, y0) to (x1, y1) to (x2, y2) etc.\n",
    "\n",
    "The trace or graph is effectively a JSON object containing the details needed for a plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mode': 'markers',\n",
       " 'type': 'scatter',\n",
       " 'x': array([ 0.31091208,  0.42908134,  0.04421788,  0.97892626,  0.45999711,\n",
       "         0.59787322,  1.09449802,  2.22819381, -0.49942157,  0.21207045,\n",
       "         0.72911391,  0.80989105,  0.5591782 ,  1.06719178, -0.12231344,\n",
       "         0.2722153 , -0.51457655,  1.01811094, -0.26720136,  0.22058155,\n",
       "         0.28254196,  0.11482878,  0.28079731,  0.00398614,  1.78307698,\n",
       "         1.12661995, -0.78714718, -0.12400523,  0.85070084,  0.69241158,\n",
       "        -0.0237722 ,  1.18129892, -0.63813104,  1.29995027,  0.17610542,\n",
       "        -0.440565  ,  0.05291311,  0.92527737, -0.03614502,  0.83693615,\n",
       "        -0.2867586 ,  0.6268299 , -1.05501201,  0.15693239,  0.96052052,\n",
       "        -0.03954197,  0.23586542, -2.03369828, -0.14887309, -0.6472578 ,\n",
       "         0.38106071, -0.88661415, -0.90590422,  0.38237946, -0.2034882 ,\n",
       "         0.02013261, -0.35102805,  0.69434042, -0.14132585, -0.16049498,\n",
       "         0.01634312, -0.025559  , -0.17687119,  0.70653244,  0.78560299,\n",
       "        -0.22789259,  0.19560952, -1.09310517,  0.2377787 ,  0.54377856,\n",
       "         0.14069149,  1.24968599,  0.84118978, -0.78086651, -0.66756209,\n",
       "         0.67833491, -1.46270283,  0.56546026, -0.3044802 , -1.4874782 ,\n",
       "        -0.00861877,  0.93388394,  0.56737197, -0.24570504, -1.69786624,\n",
       "         0.61658347,  0.21390269, -0.54288346,  1.104666  ,  0.01629294,\n",
       "         0.35260332, -0.07547981,  1.17405771, -0.7987889 , -0.29826733,\n",
       "        -2.37156063, -0.27612987,  0.48987276, -1.33409653,  0.32272842,\n",
       "         0.46714367, -1.04596088, -0.92707066, -0.51858321,  0.28845735,\n",
       "        -0.34397516, -0.94331799, -0.75899992,  0.0985499 , -0.65245568,\n",
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       "        -0.39531653, -1.12196065,  0.25622106, -2.97159762,  0.29537461,\n",
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       "        -0.6640525 , -0.76503364,  0.42224973,  0.02630651, -0.74500312,\n",
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       "        -0.62993328,  0.61388772, -1.75164723,  0.76315463,  1.03193133,\n",
       "        -0.27204641,  0.08926445,  0.7632912 , -1.53504453, -1.68192814,\n",
       "        -0.7246956 ,  0.00991425, -0.85268313,  0.32158247,  0.95922283,\n",
       "         0.95384551,  0.17315168, -0.18757969,  0.5901003 , -0.91860036,\n",
       "         1.91197353, -0.48155324, -1.78774156, -1.943219  ,  1.03714697,\n",
       "        -0.18454133,  0.49776598, -2.10724195, -0.55764702, -1.64900171,\n",
       "         0.88573711, -0.07434533, -0.60390157,  1.22056913, -0.18890698,\n",
       "        -0.16941941, -1.41527655,  1.54644629,  0.74029398, -1.52658539,\n",
       "         0.29519829,  0.81543416, -2.45207861, -0.09581572, -0.46730514,\n",
       "         1.02974038,  1.13555483, -1.14085424,  1.20991365, -0.30830344,\n",
       "         0.13268205,  0.76986964, -0.91669708, -0.85313479, -1.71606104,\n",
       "         0.87757152,  1.20145258,  1.01466268,  0.1281788 ,  0.53975716,\n",
       "        -1.54318622, -0.30359576, -0.84044352,  1.00272642,  0.15375128]),\n",
       " 'y': array([ 0.02966213, -0.7566736 ,  0.33930961, -0.08556928,  0.05474966,\n",
       "        -0.47305681, -0.20625819, -0.26004748, -0.73485122, -0.01850299,\n",
       "         0.80219933,  1.06597036, -0.6314206 , -1.03830272,  1.98989586,\n",
       "        -0.20966247, -1.65993423, -1.1124418 , -1.05980168,  1.73086593,\n",
       "        -0.87852197,  0.624229  ,  1.63853818,  1.41096044, -0.67203418,\n",
       "        -0.47086636,  0.85324726, -0.7837849 ,  0.88533271,  0.45062546,\n",
       "         0.3408176 ,  0.27052186,  1.48649648,  1.23813884,  1.3602683 ,\n",
       "         1.19842712, -1.96894833,  0.98823274, -0.11682963,  0.50762281,\n",
       "        -0.70108878,  0.97431522, -1.1506061 , -0.52662836,  0.67440393,\n",
       "         0.99171129,  0.43181464, -1.49068952,  0.86780862,  0.83997387,\n",
       "         0.67881982, -0.18813325,  0.6491904 , -0.12674376, -0.17291975,\n",
       "         1.05845479, -0.98986212, -0.97348567, -0.99061111,  1.76496207,\n",
       "        -1.37743197,  1.50345229,  1.05635382, -0.50500802, -1.44471867,\n",
       "        -0.32674144, -0.71004687, -0.02138235,  1.27262174, -0.08818368,\n",
       "         0.10200359,  0.18689741, -0.51528869,  2.47989698, -0.95479425,\n",
       "        -0.24160886,  1.28330594, -1.60943881, -0.53431439, -0.41665901,\n",
       "         0.47913453, -0.69478864,  0.17310412, -0.21016649, -1.14124952,\n",
       "         0.60241982,  0.9401389 , -1.2359742 , -1.17528184,  0.70268569,\n",
       "        -1.27039543, -0.50347482, -1.44372682, -0.82932939, -0.9162895 ,\n",
       "        -0.47441875,  0.37616838, -0.61917046, -2.11770683, -0.04992867,\n",
       "         0.02056518,  0.64497215, -0.17928308,  0.2315892 , -1.29200842,\n",
       "         0.03603364,  1.17882618, -0.60222716,  0.27057776,  1.56448733,\n",
       "        -0.49782131, -0.41054576,  0.86532279,  0.47705125, -0.61526144,\n",
       "        -0.26298131, -0.31888107, -0.16966796, -0.02641988, -1.04751217,\n",
       "        -1.24099344,  0.55317009, -0.29319223, -0.34108598,  0.18587086,\n",
       "         0.50040137,  0.29993311,  0.68667142, -0.05234853,  0.86465632,\n",
       "        -0.58505406,  0.35054462, -0.26582846, -0.16492876, -0.11030539,\n",
       "         1.19615447, -0.07783162,  2.2761104 ,  1.94039453,  1.23690729,\n",
       "        -0.32286509,  0.75076076, -0.15047439, -0.03754253,  0.86236001,\n",
       "         0.59741601, -0.4204151 , -0.27151323, -1.04092256,  1.62613048,\n",
       "         0.36098538, -0.23786398, -1.04826587, -1.6194534 ,  0.17172439,\n",
       "        -0.98893629,  0.86471275,  0.84637936,  2.75381032,  2.38185649,\n",
       "        -1.24695323,  1.66880827,  0.11666944, -0.69751122, -1.41894402,\n",
       "        -1.51791556, -0.41075374, -0.64868019,  0.91068243, -0.58784477,\n",
       "         0.81523881, -0.7703237 ,  0.37996149,  0.70552628,  0.15713237,\n",
       "         1.91183785, -0.84639117,  1.1353432 , -0.77091066,  0.28187152,\n",
       "        -1.17547324, -1.67112317,  1.41001695, -0.35860106,  0.59395766,\n",
       "        -0.13253486, -0.01192205, -0.95307137,  0.72575523,  1.11301819,\n",
       "        -1.3604067 , -0.38068516, -0.53480136,  1.41720889, -0.57749875,\n",
       "         0.60391723, -1.61010936, -1.94822476,  0.93159701, -0.27815365])}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace = go.Scatter(\n",
    "    x = random_x,\n",
    "    y = random_y,\n",
    "    mode = 'markers'\n",
    ")\n",
    "\n",
    "trace"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The data supplied for a plotly plot needs to be a list\n",
    "This is because we can have multiple traces on the same plot (as we shall soon see)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "data=[trace]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Produce the plot using iplot\n",
    "Hover over each point to view the X and Y coordinates of the point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
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         "mode": "markers",
         "type": "scatter",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "offline.iplot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple Scatter plots in one plot\n",
    "Here we produce multiple graphs on the same plot. We do that by having a single set of x coordinates shared by 3 sets of y coordinates. We space out the y values for each graph so that they do not overlap on the plot. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate the X coordinates\n",
    "We will create 50 points in each graph. Start off by creating 50 evenly spaced x values in the interval (0,1). These values are ordered (so that lines connecting them move left to right rather than all over the place)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n = 50\n",
    "\n",
    "random_x = np.linspace(0, 1, n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate 3 sets of Y coordinates\n",
    "These are spaced with offsets of 5 so that they do not overlap. There is no ordering of values here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "random_y0 = np.random.randn(n)+5\n",
    "\n",
    "random_y1 = np.random.randn(n)\n",
    "\n",
    "random_y2 = np.random.randn(n)-5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The first trace/graph contains only markers\n",
    "These will only be points which are not connected by a line. A name is assigned to the trace - this will appear in the legend of the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trace0 = go.Scatter(\n",
    "    x = random_x,\n",
    "    y = random_y0,\n",
    "    mode = 'markers'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The second trace has only a line\n",
    "The points are connected by a line, but without markers for the points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "trace1 = go.Scatter(\n",
    "    x = random_x,\n",
    "    y = random_y1,\n",
    "    mode = 'lines'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The third trace has both markers and a connecting line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trace2 = go.Scatter(\n",
    "    x = random_x,\n",
    "    y = random_y2,\n",
    "    mode = 'lines+markers'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The data for our plot now comprises three traces\n",
    "These are all contained in a list which is used by the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = [trace0, trace1, trace2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate the plot\n",
    "Plotly is smart enough to create a legend automatically, though it has assigned some default names for the traces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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    "offline.iplot(data)"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Style Scatter Plots\n",
    "We can customize the look of the scatter plots by configuring each of its components. Here, we show how we can define the markers, lines, graph names, the axes and set a title for our plot.\n",
    "\n",
    "We also define a layout and figure instead of plotting with only the data. A figure object comprises the data plus a layout."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define a style for the marker\n",
    "Among other attributes, we can set the size and color of markers. There are different ways to set the colors, but here we just use the names of some colors. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "marker_style = dict(size = 10,\n",
    "                    color = 'orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the line style\n",
    "Here, we set the color and width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "line_style = dict(width = 3, \n",
    "                  color = 'khaki')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the first trace\n",
    "It's the same as the previous one, but with a customised marker style. We also set a name for the graph which will appear in the legend (instead of \"trace 0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trace0 = go.Scatter(\n",
    "    x = random_x,\n",
    "    y = random_y0,\n",
    "    mode = 'markers',\n",
    "    marker = marker_style,\n",
    "    name = 'Markers only'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The second trace uses the line style\n",
    "We also set a name for this trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trace1 = go.Scatter(\n",
    "    x = random_x,\n",
    "    y = random_y1,\n",
    "    mode = 'lines',\n",
    "    line = line_style,\n",
    "    name = 'Lines only'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the third trace\n",
    "This one uses both the line style and marker style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trace2 = go.Scatter(\n",
    "    x = random_x,\n",
    "    y = random_y2,\n",
    "    mode = 'lines+markers',\n",
    "    marker = marker_style,\n",
    "    line = line_style,\n",
    "    name = 'Markers + Lines'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Each plot contains a layout which can be configured\n",
    "The layout has several configurable attributes including the title and the axes. For the axes, we set the width for the zero line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "layout = dict(title = 'Styled Scatter',\n",
    "              yaxis = dict(zerolinewidth = 4),\n",
    "              xaxis = dict(zerolinewidth = 4)\n",
    "             )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the data for our plot\n",
    "Just as before, it contains the 3 traces we have created. This time with the additional styling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = [trace0, trace1, trace2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define a figure object\n",
    "A figure is simply an object which contains the data and the layout definition for a plot. This time we will plot a figure object instead of a data object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig = dict(data = data, \n",
    "           layout = layout)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the figure\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using a color scale\n",
    "A color scale will set the color of an object (e.g. a marker) according to a particular value. This will allow the visualization of an extra dimension in a plot (a 3rd dimension in a 2D and 4th in a 3D plot). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define x, y and z values\n",
    "Similar to the scatter plot we plotted right in the beginning, this time with an added Z dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n = 200\n",
    "\n",
    "random_x = np.random.randn(n)\n",
    "\n",
    "random_y = np.random.randn(n)\n",
    "\n",
    "random_z = np.random.randn(n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Colorscales\n",
    "Plotly uses the Matplotlib colormaps for its colorscales. The list of available colorscales can be found here:\n",
    "https://matplotlib.org/examples/color/colormaps_reference.html\n",
    "\n",
    "The way this works is that for a given range of values (in our example, the range of Z values) the colorscale is created such that a value will correspond to a color on the colorscale. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate the trace with the details for the colorscale\n",
    "Here, the we define our scatter trace to color the markers according to the value of the Z coordinate, and using the colorscale 'Magma'.\n",
    "\n",
    "showscale = True will ensure the colorscale is portrayed on the plot.\n",
    "\n",
    "We also define the text which will be visible when we hover over a point. We set this to the Z value of the point so that we can match it to the color in the colorscale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "trace = go.Scatter(\n",
    "    x = random_x,\n",
    "    y = random_y,\n",
    "    mode = 'markers',\n",
    "    marker= dict(size = 14,\n",
    "                 color = random_z,\n",
    "                 colorscale = 'Magma',\n",
    "                 showscale = True),\n",
    "    text = random_z\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = [trace]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the layout\n",
    "The Layout can also be defined using the go.Layout object rather than a dictionary.\n",
    "\n",
    "By default, when hovering over a graph, the y value is displayed near the cursor, and the x value near the X axis. This is when the hovermode is 'x'.\n",
    "\n",
    "Setting the hovermode to 'y' will cause the x value to be displayed at the cursor and y value on the Y axis. We use the 'closest' hovermode to display the (x,y) and hover text of the closest marker from the cursor. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "layout = go.Layout(title = 'Scatter with Colorscale',\n",
    "                   hovermode = 'closest')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the figure\n",
    "Like the layout, there is also a go.Figure object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig = go.Figure(data = data, \n",
    "                layout = layout)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the data\n",
    "Hover over the markers to view the Z value and match it to the colorscale on the right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import the Pandas library\n",
    "This will help us load a dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Honey Production dataset\n",
    "\n",
    "The dataset can be downloaded from here: https://www.kaggle.com/jessicali9530/honey-production"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "honey_data = pd.read_csv('./datasets/honeyproduction.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preview the data\n",
    "\n",
    "The honey dataset shows total honey production, yield per colony etc. for each state In the USA from 1998 to 2012"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>state</th>\n",
       "      <th>numcol</th>\n",
       "      <th>yieldpercol</th>\n",
       "      <th>totalprod</th>\n",
       "      <th>stocks</th>\n",
       "      <th>priceperlb</th>\n",
       "      <th>prodvalue</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1136000.0</td>\n",
       "      <td>159000.0</td>\n",
       "      <td>0.72</td>\n",
       "      <td>818000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AZ</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>60</td>\n",
       "      <td>3300000.0</td>\n",
       "      <td>1485000.0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>2112000.0</td>\n",
       "      <td>1998</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>65</td>\n",
       "      <td>3445000.0</td>\n",
       "      <td>1688000.0</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2033000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>83</td>\n",
       "      <td>37350000.0</td>\n",
       "      <td>12326000.0</td>\n",
       "      <td>0.62</td>\n",
       "      <td>23157000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CO</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>72</td>\n",
       "      <td>1944000.0</td>\n",
       "      <td>1594000.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1361000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state    numcol  yieldpercol   totalprod      stocks  priceperlb  \\\n",
       "0    AL   16000.0           71   1136000.0    159000.0        0.72   \n",
       "1    AZ   55000.0           60   3300000.0   1485000.0        0.64   \n",
       "2    AR   53000.0           65   3445000.0   1688000.0        0.59   \n",
       "3    CA  450000.0           83  37350000.0  12326000.0        0.62   \n",
       "4    CO   27000.0           72   1944000.0   1594000.0        0.70   \n",
       "\n",
       "    prodvalue  year  \n",
       "0    818000.0  1998  \n",
       "1   2112000.0  1998  \n",
       "2   2033000.0  1998  \n",
       "3  23157000.0  1998  \n",
       "4   1361000.0  1998  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "honey_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate the trace\n",
    "\n",
    "The data we will plot includes:\n",
    "* State on the X axis\n",
    "* Year on the Y axis\n",
    "* Honey production (lbs) represented by a color from the colorscale\n",
    "\n",
    "We use the Rainbox colorscale in this example. The opacity of each of the markers can also be set.\n",
    "\n",
    "This allows us to assess just by looking at the plot what the honey production was for a particular year for a state. So we can spot any changes in honey production quite easily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trace= go.Scatter(\n",
    "    \n",
    "         x = honey_data['state'],\n",
    "        \n",
    "         y = honey_data['year'],\n",
    "    \n",
    "         mode = 'markers',\n",
    "    \n",
    "         marker = dict(size= 14,\n",
    "                    color=honey_data['totalprod'],\n",
    "                    colorscale='Rainbow',\n",
    "                    showscale=True,\n",
    "                    opacity= 0.5\n",
    "                   ),\n",
    "    \n",
    "        text = honey_data['totalprod']\n",
    ")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "data=[trace]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "layout= go.Layout(\n",
    "    \n",
    "    title= 'Honey production the United States',\n",
    "    \n",
    "    hovermode= 'closest',\n",
    "    \n",
    "    xaxis= dict(title= 'States'),\n",
    "    \n",
    "    yaxis=dict(title= 'Honey Production')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig= go.Figure(data=data, \n",
    "               layout=layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
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