Plotly and Cufflinks.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.10.0\n"
     ]
    }
   ],
   "source": [
    "from plotly import __version__\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "\n",
    "print(__version__) # requires version >= 1.9.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.10.0'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotly\n",
    "plotly.__version__\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cufflinks as cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <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-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For Notebooks\n",
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <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-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For offline use\n",
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fake Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(100,4),columns='A B C D'.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.182076</td>\n",
       "      <td>0.367782</td>\n",
       "      <td>-1.157464</td>\n",
       "      <td>1.368032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.195745</td>\n",
       "      <td>0.919077</td>\n",
       "      <td>-2.011852</td>\n",
       "      <td>0.379701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.228726</td>\n",
       "      <td>-0.694182</td>\n",
       "      <td>-0.301690</td>\n",
       "      <td>0.511220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.217693</td>\n",
       "      <td>-0.917686</td>\n",
       "      <td>0.490469</td>\n",
       "      <td>-1.262535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.656136</td>\n",
       "      <td>2.350028</td>\n",
       "      <td>-0.030796</td>\n",
       "      <td>0.672610</td>\n",
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       "          A         B         C         D\n",
       "0  1.182076  0.367782 -1.157464  1.368032\n",
       "1 -1.195745  0.919077 -2.011852  0.379701\n",
       "2 -0.228726 -0.694182 -0.301690  0.511220\n",
       "3 -0.217693 -0.917686  0.490469 -1.262535\n",
       "4  0.656136  2.350028 -0.030796  0.672610"
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     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame({'Category':['A','B','C'],'Values':[32,43,50]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  Category  Values\n",
       "0        A      32\n",
       "1        B      43\n",
       "2        C      50"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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       "                        [{\"line\": {\"color\": \"rgba(255, 153, 51, 1.0)\", \"dash\": \"solid\", \"shape\": \"linear\", \"width\": 1.3}, \"marker\": {\"size\": 10, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"B\", \"text\": \"\", \"type\": \"scatter\", \"uid\": \"dcb51c4e-98b3-43b4-bcf5-86c21caadbd6\", \"x\": [1.1820761317595112, -1.1957449848553816, -0.22872597771264847, -0.21769315233327416, 0.6561357200292962, -0.707722812390429, 0.14700301826458662, 0.42681751690466435, -0.044331356767010166, -0.9916980838634194, -0.6580267433832421, 0.6642413307709566, -0.09813372049561793, -0.06387115554962547, -1.5313408253818008, 0.425262350070461, -1.9267400486849793, 0.08067836016333314, 2.0965644512631987, 2.713019580867971, -2.2618326782166056, -1.1594690121570992, 1.138829910030225, -0.25668751274667345, 0.9195940697740286, 1.1290730413918906, -0.2555555968352573, -0.31310623726035997, -0.3910027576221044, 1.8929049200002512, -0.007892586550403542, 0.7542872650681782, 1.9382777096305177, 1.1876096473100592, 0.8051535905109284, 0.15231371625706594, 0.7093191881831092, 0.8582184110688298, 0.004644903501815296, -0.6927324100538997, 0.25088427122592966, -0.05271552145085384, 0.9789095715895554, 0.7189875367544704, 0.809755257444939, 0.2552242887745342, 1.589832910928995, 0.6412823857795971, 0.23536121870581259, -0.2378557183789376, 0.4275154966389168, 0.052700487393593, -0.7506341720833639, -0.10696289007698384, 0.3465798502323297, 0.9283706412084939, 0.09534937393970791, 1.1079844335976319, 0.562746252525166, 0.9893799420655445, 0.2699760888688912, 0.271450988585568, 1.7518303646703381, 0.8497178254549088, -1.2704011562858815, -0.38087177796396293, 0.7910258016979438, -0.6543945655852774, 0.7402536113498233, -0.637341521479548, -1.0518837202511633, 1.0578841927498812, -0.1516477298165657, 0.8266332732841642, -1.266010464743975, 0.5298101568323579, 0.7595277433555689, 0.09642638918078085, 0.9443791272324935, -0.07286357663085076, 0.9365574546616722, -0.3793789622083632, 0.6661809080077449, -0.1984060794004996, -1.2071651762279367, -1.445069349190326, -0.11711733211241306, 0.6484954293974587, 0.944563797790363, -0.7922355449019604, 0.9905430717938278, 0.11644551787156983, -0.42890921480172245, 1.2974452460749781, 0.5431552724032924, 0.27827583339412104, -0.4343425215883538, -0.7447182437602055, -0.6126781758248733, -0.37530002860904044], \"y\": [0.36778186186469397, 0.919076969287833, -0.6941822711824219, -0.9176856334189007, 2.3500280682249257, -0.5702575179504427, 1.3615565581292062, -0.8574324362921482, -1.0871874810716333, -0.3597112140083064, 0.8990451886427763, -0.44567077158383456, 0.19624112348878783, 1.9018993610873836, 2.2180777211718747, -2.5136129810135595, 0.7220025555454592, -1.1704430273533395, -0.9252305338856738, 1.0799095612660092, 2.1162518912953985, 0.8297005139149413, 0.3197346353911437, -0.2801664298196646, 1.2826051005924892, 1.7145500276231855, 0.6342442812497291, 1.9471198003895538, 1.2487187474470565, 0.9809152670185036, -1.0361238311233567, 1.289652639275663, -0.37974793607961854, 0.2982222245429276, -1.0154859259976412, -0.3208246450508227, 0.11902799740862566, 1.1625433376954595, -1.9029521517583687, -0.29091519300690466, 0.6080936016410933, 0.548363443469096, 1.702840005510032, 1.033323949056093, -0.8296117821755975, -0.5913051351323906, -0.4032664038325869, -0.7312652002418988, -1.7046490979280615, -1.5241747975511244, -0.36114607970106005, 0.27267047018567325, -0.6258654062473908, -1.072428545132449, 0.30985222469323703, -0.2451567159748494, -1.2807644359941066, 1.373166060362144, -0.1202492728650289, -2.01203095456615, 1.0436157189825732, -0.8671487426857799, 0.8059559882757161, 1.1837812097421028, -0.16166331307917217, 0.2434480096741748, 0.05701525942048205, 1.6369354999554275, -0.5028573284511287, -1.584269237558665, -1.002069408664375, -0.2263718537408452, 0.8559907349523389, -0.6416881546272165, 0.7069812206087409, -0.35527827739461, -1.0947847463370945, 0.018941715790661263, -0.27954934809471566, 0.17122092385297427, -0.014474566461938183, 0.3075978120222689, -1.5714268859620908, -0.7509913093348213, -0.6797818748306629, 0.3642923931504285, 0.30202287648459825, -0.6904986251844727, 0.43660035781915363, 0.1721853695505766, -0.5844065553294266, -0.4906235023101047, -0.004215039555858531, 0.7969708845868069, 1.352020974324154, -0.6637834983792144, -0.11489049993046979, 1.5803290454651546, 1.6383212285395818, -0.4289757093106136]}],\n",
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       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
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       "            observer.disconnect();\n",
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       "\n",
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       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
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       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "            </script>\n",
       "        </div>"
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   ],
   "source": [
    "df.iplot(kind='scatter',x='A',y='B',mode='markers',size=10)"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Plots"
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
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        "plotlyServerURL": "https://plot.ly",
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         "type": "bar",
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         "x": [
          "A",
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          "C"
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       "            <script type=\"text/javascript\">\n",
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       "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
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       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('173bff49-24c7-47f4-9a72-8c2a7dc94626');\n",
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       "            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",
       "}}\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",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.iplot(kind='bar',x='Category',y='Values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
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    {
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      "application/vnd.plotly.v1+json": {
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        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
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         "orientation": "v",
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         "type": "bar",
         "uid": "0f150d2a-97d5-4e7c-9429-b17140a06808",
         "x": [
          "A",
          "B",
          "C",
          "D"
         ],
         "y": [
          100,
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          100,
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       "layout": {
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         "bgcolor": "#F5F6F9",
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        "paper_bgcolor": "#F5F6F9",
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        "xaxis": {
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       "<div>\n",
       "        \n",
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       "            <script type=\"text/javascript\">\n",
       "                require([\"plotly\"], function(Plotly) {\n",
       "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
       "                    window.PLOTLYENV.BASE_URL='https://plot.ly';\n",
       "                    \n",
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       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('fbe0e6f0-4f3a-4e9b-b78e-1b4c29b38d52');\n",
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       "if (notebookContainer) {{\n",
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       "\n",
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       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
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       "            </script>\n",
       "        </div>"
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     "metadata": {},
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   ],
   "source": [
    "df.count().iplot(kind='bar')"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boxplots"
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  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
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    "df.iplot(kind='box')"
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   "source": [
    "## 3d Surface"
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       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
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    "df3 = pd.DataFrame({'x':[1,2,3,4,5],'y':[10,20,30,20,10],'z':[5,4,3,2,1]})\n",
    "df3.iplot(kind='surface',colorscale='rdylbu')"
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   "metadata": {},
   "source": [
    "## Spread"
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  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
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   "source": [
    "## histogram"
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   "execution_count": 18,
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