yehjames/[資料分析&機器學習] 第2.5講資料視覺化.ipynb

## [資料分析&機器學習] 第2.5講資料視覺化.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target_names: ['setosa' 'versicolor' 'virginica']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = datasets.load_iris()\n",
    "x = pd.DataFrame(iris['data'], columns=iris['feature_names'])\n",
    "print(\"target_names: \"+str(iris['target_names']))\n",
    "y = pd.DataFrame(iris['target'], columns=['target'])\n",
    "iris_data = pd.concat([x,y], axis=1)\n",
    "iris_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x114831f60>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zwSD5016k88L/4mLKye14H/5bHyOhtv76SdXRny6RKrTp+8WUTB5KxpFiFtftTtIt4wh0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\niEgcUrmLiMQhlbuISBz6H3wkkebQyimNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11143b198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1148a9358>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RU6SmpfNV7lGGd2vL7FsHkhgd6XRZ0ggo3EXqQaXb8uYXO/nDB1sJDQnh6fFJ3D40Xo2+\npMEo3EV8bOvBqkZfm/ee4Jo+7Xlq/ABio9ToSxqWwl3ER8oq3LzySQ4vr8mhZXgof/7xYG4aGKtG\nX+IIhbuID3y39wRTF7rYeqiIcYM6MeOm/rSNDHO6LGnEFO4iF6CkrJIXPtzK65/vpH3LcF6/K5lr\n+qrRlzhP4S5ynr7ccYTUtHT2HCvmjpQEUkf3oVW4Gn2Jf1C4i9RRYWk5z6zM5u2v99ClXQRv3zec\nS7q3c7oskf+gcBepg4+yDvHYknQOF51m8hXd+PW1vWge1sTpskS+R+Eu4oWjJ08z870s3tt8gD4d\nWzJvYjIXxbd2uiyRs6o13I0x8cACoANggXnW2pfOGDMSWArs9GxaZK190relijQ8ay3LNh9g5rJM\nTp6u4DfX9eL+K7ur0Zf4PW/O3CuA31prNxljWgIbjTEfWmuzzhi31lo71vclijjjwIkSHl+SwcfZ\n+QyKb81zEwbSq0NLp8sS8Uqt4W6tzQPyPM+LjDFbgM7AmeEuEhTcbss/vt7D7FXZVLot08f24+4R\niWr0JQGlTtfcjTGJwGBgfQ27RxhjXMB+4GFrbeYFVyfSwHYeOUVqmov1O49xaY92PDN+IAntIpwu\nS6TOvA53Y0wLIA34lbW28Izdm4AEa+1JY8wYYAnQs4bPMRmYDJCQkHDeRYv4WkWlm9c/38kLH24j\nrGkIz96WxA+T49U6QAKWsdbWPsiYUGA5sNpa+4IX43cBydbaI2cbk5ycbDds2FCHUkXqx5a8Qqam\nuXDtK+C6fh146pYBdGgV7nRZIjUyxmy01ibXNs6bu2UM8Dqw5WzBbozpCByy1lpjzDAgBDhax5pF\nGtTpikpe/jiHVz7ZQVTzUObcMZgbk9ToS4KDN5dlLgUmAunGmO8826YBCQDW2rnABOABY0wFUALc\nbr35L4GIQzbtOc7UhS6255/k1sGdmT62H23U6EuCiDd3y3wOnPNUxlo7B5jjq6JE6ktxWQV/WL2N\nN7/cSWyrcN68ZyhX9W7vdFkiPqffUJVG4/PtR0hd5GLf8RImDu/ClFG9aalGXxKkFO4S9ApKyvn9\niize3bCPrtGR/O/k4aR0U6MvCW4KdwlqqzMPMn1JBkdPlXH/ld351bU9CQ9Voy8Jfgp3CUqHi04z\nc1kmK9Lz6BvbitfvGkpSXJTTZYk0GIW7BBVrLYu/3c+Ty7MoPl3Jw9f34udXdie0iRp9SeOicJeg\nsf9ECdMWpfPptsMMSahq9NWjvRp9SeOkcJeA53Zb3lq/m2dXZWOBmTf1Y+IlavQljZvCXQLajsMn\nSU1z8c2u41zeM5qnxycR31aNvkQU7hKQKirdzFuby4sfbSe8aQjPTxjIhIvj1DpAxEPhLgEn80AB\nU9NcZOwv5Ib+HZg1bgDt1ehL5D8o3CVglJZX8t8fb2fup7m0iQjj1TuHMDop1umyRPySwl0CwoZd\nx5ia5mLH4VPcNiSO6WP70jpCjb5EzkbhLn7t1OkKnl+9lflf7aJTVHPmTxrGlb1inC5LxO8p3MVv\nfbbtMI8uSudAQQk/Hd6FR0b1oUUzfcmKeEPfKeJ3ThSX8dSKLSzcuI9uMZG8+/NLGJrY1umyRAKK\nwl38yqr0PKYvzeR4cRn/NbI7v7hGjb5EzofCXfxCflEpM5ZmsirjIP1iW/HXe4YyoLMafYmcL4W7\nOMpay8KN+3hqxRZKyit55IbeTL6imxp9iVwghbs4Zu+xYqYtTmft9iMkd2nD7NsG0qN9C6fLEgkK\nCndpcG63ZcFXu3hu9VYM8OS4/vwkpQshavQl4jMKd2lQOflFTE1LZ+Pu41zRK4anxw8gro0afYn4\nmsJdGkR5pZt5n+Xy0kfbaR7WhD/+4CJuHdJZjb5E6onCXepdxv4Cpix0kZVXyJikjjxx8wBiWjZz\nuiyRoKZwl3pTWl7JS//azrzPcmkbGcbcnwxh1AA1+hJpCLWGuzEmHlgAdAAsMM9a+9IZYwzwEjAG\nKAbuttZu8n25Eii+3nmM1DQXuUdO8YOL43j8xn5ERYQ6XZZIo+HNmXsF8Ftr7SZjTEtgozHmQ2tt\nVrUxo4GenkcK8KrnT2lkTp6u4NlV2fxt3W7i2jTnbz8bxuU91ehLpKHVGu7W2jwgz/O8yBizBegM\nVA/3ccACa60F1hljWhtjYj0fK43Emq35PLYonbzCUu65NJGHr+9NpBp9iTiiTt95xphEYDCw/oxd\nnYG91V7v82xTuDcCx0+VMWt5Fou+3U+P9i1YeP8ILu7SxumyRBo1r8PdGNMCSAN+Za0tPJ+DGWMm\nA5MBEhISzudTiB+x1rIy/SAzlmVworich67qwf+7pgfNmqrRl4jTvAp3Y0woVcH+d2vtohqG7Afi\nq72O82z7D9baecA8gOTkZFvnasVvHCosZfqSDD7IOkRS5ygWTEqhX6dWTpclIh7e3C1jgNeBLdba\nF84ybBnwkDHmHap+kFqg6+3ByVrLuxv28tSKLZRVuEkd3Yd7L+tKUzX6EvEr3py5XwpMBNKNMd95\ntk0DEgCstXOBlVTdBplD1a2Q9/i+VHHanqPFPLrYxRc5RxnWtS2zb02iW4wafYn4I2/ulvkcOOfv\niHvuknnQV0WJf6l0W/765S7+sHorTUIMT90ygDuGJajRl4gf031qck7bDxUxJc3Ft3tOMLJ3DE+P\nT6JT6+ZOlyUitVC4S43KKtzM/XQHcz7OIbJZE1780SDGDeqkRl8iAULhLt+zee8Jpqa5yD5YxNiB\nscy8uT/RLdToSySQKNzl/5SUVfLiR9t4bW0u0S2aMW/ixVzfv6PTZYnIeVC4CwDrco+SmuZi19Fi\nfjwsntTRfYlqrkZfIoFK4d7IFZWWM3tVNn9fv4eEthH8494URvSIdrosEblACvdG7OPsQzy2OIND\nhaXce1lXfnN9LyLC9CUhEgz0ndwIHT15mieXZ7H0uwP0bN+CVx4YweAENfoSCSYK90bEWst7rjxm\nLsuksKScX17Tk/+6qrsafYkEIYV7I3GwoJTHl6Tz0ZZ8LoqL4tn7UujTUY2+RIKVwj3IWWt555u9\nPL1iC+VuN4+N6cuky7rSRK0DRIKawj2I7T56itS0dL7KPcrwbm2ZfetAEqMjnS5LRBqAwj0IVbot\nb36xkz98sJXQkBCeHp/E7UPj1ehLpBFRuAeZrQerGn1t3nuCa/q056nxA4iNUqMvkcZG4R4kyirc\nvLwmh1c+yaFleCgv3T6Imy9Soy+RxkrhHgS+23uCKQs3s+3QScYN6sTvxvajnRp9iTRqCvcAVlJW\nyR8/2MobX+ykfctwXr8rmWv6dnC6LBHxAwr3APXljiOkpqWz51gxd6QkkDq6D63C1ehLRKoo3ANM\nYWk5z6zcwttf76VLuwjevm84l3Rv53RZIuJnFO4B5MOsQzy+JJ3DRaeZfEU3fn1tL5qHqXWAiHyf\nwj0AHDl5mpnLMlnuyqNPx5bMm5jMRfGtnS5LRPyYwt2PWWtZ+t0Bnngvk5OnK/jNdb24/8ruhDUN\ncbo0EfFzCnc/deBECY8vyeDj7HwGxbfmuQkD6dWhpdNliUiAULj7Gbfb8o+v9zB7VTaVbsv0sf24\ne0SiGn2JSJ3UGu7GmDeAsUC+tXZADftHAkuBnZ5Ni6y1T/qyyMZi55FTpKa5WL/zGJf2aMcz4weS\n0C7C6bJEJAB5c+b+V2AOsOAcY9Zaa8f6pKJGqKLSzeuf7+SFD7cR1jSEZ29L4ofJ8WodICLnrdZw\nt9Z+ZoxJrP9SGqesA4VMTXORvr+A6/p14KlbBtChVbjTZYlIgPPVNfcRxhgXsB942Fqb6aPPG7RO\nV1Qy5+McXv1kB60jQnn5jiGMSeqos3UR8QlfhPsmIMFae9IYMwZYAvSsaaAxZjIwGSAhIcEHhw5M\nG3cfZ2qai5z8k9w6uDPTx/ajTWSY02WJSBC54HC31hZWe77SGPOKMSbaWnukhrHzgHkAycnJ9kKP\nHWiKyyp4fvVW/vrlLmJbhfPmPUO5qnd7p8sSkSB0weFujOkIHLLWWmPMMCAEOHrBlQWZz7cfIXWR\ni33HS5g4vAtTRvWmpRp9iUg98eZWyLeBkUC0MWYfMAMIBbDWzgUmAA8YYyqAEuB2a22jOys/m4Li\ncn6/Mot3N+yja3Qk/zt5OCnd1OhLROqXN3fL/LiW/XOoulVSzvB+xkGmL83g2KkyHhjZnV9e05Pw\nUDX6EpH6p99QrQeHi6oafa1Iz6NvbCveuGsoSXFRTpclIo2Iwt2HrLUs2rSfJ5dnUVJWySM39Gby\nFd0IbaJGXyLSsBTuPrL/RAnTFqXz6bbDDEmoavTVo70afYmIMxTuF8jttry1fjfPrsrGAjNv6sfE\nS9ToS0ScpXC/ADsOnyQ1zcU3u45zec9onh6fRHxbNfoSEecp3M9DeaWb19bm8uJH2wlvGsLzEwYy\n4eI4tQ4QEb+hcK+jjP0FTE1zkXmgkFH9O/LkLf1p31KNvkTEvyjcvVRaXsl/f7yduZ/m0iYijFfv\nHMLopFinyxIRqZHC3Qsbdh1jSpqL3MOnuG1IHNPH9qV1hBp9iYj/Urifw6nTVY2+5n+1i05RzZk/\naRhX9opxuiwRkVop3M/i022HmbYonQMFJdx1SSKP3NCbyGb66xKRwKC0OsOJ4jJmLd9C2qZ9dIuJ\n5J8/v4TkxLZOlyUiUicK92pWpecxfWkmx4vLePCq7vy/q9XoS0QCk8IdyC8s5XdLM3k/8yD9O7Vi\n/qSh9O+kRl8iErgadbhba1m4cR+zlmdRWuFmyqje3He5Gn2JSOBrtOG+91gx0xans3b7EYYmtmH2\nbQPpHtPC6bJERHyi0YV7pduy4KtdPL96KwaYNa4/d6Z0IUSNvkQkiDSqcM/JL2JqWjobdx/nyl4x\n/H78AOLaqNGXiASfRhHu5ZVu/vLpDv78rxwimjXhhR9exPjBndXoS0SCVtCHe8b+Ah5Z6GJLXiE3\nJsUy8+b+xLRs5nRZIiL1KmjDvbS8khc/2s5ra3NpGxnG3J9czKgBHZ0uS0SkQQRluH+98xipaS5y\nj5ziR8nxTBvTl6iIUKfLEhFpMEEV7kWl5Tz3/lb+tm43cW2a89bPUrisZ7TTZYmINLigCfc1W/N5\nbFE6eYWlTLq0Kw/f0IuIsKCZnohIndSafsaYN4CxQL61dkAN+w3wEjAGKAbuttZu8nWhZ3P8VBmz\nlmex6Nv99GjfgoX3j+DiLm0a6vAiIn7Jm1PbvwJzgAVn2T8a6Ol5pACvev6sV9ZaVqTnMWNpJgUl\n5fzi6h48eHUPmjVVoy8RkVrD3Vr7mTEm8RxDxgELrLUWWGeMaW2MibXW5vmoxu85VFjK9CUZfJB1\niKTOUbx1bwp9Y1vV1+FERAKOLy5Kdwb2Vnu9z7OtXsJ9TXY+v3jnW8oq3Dw6ug8/u6wrTdXoS0Tk\nPzToTxyNMZOByQAJCQnn9Tm6RkcyJKENM2/uT9foSF+WJyISNHxxyrsfiK/2Os6z7XustfOstcnW\n2uSYmPN7L9LE6EjmTxqmYBcROQdfhPsy4KemynCgoD6vt4uISO28uRXybWAkEG2M2QfMAEIBrLVz\ngZVU3QaZQ9WtkPfUV7EiIuIdb+6W+XEt+y3woM8qEhGRC6bbTEREgpDCXUQkCCncRUSCkMJdRCQI\nKdxFRIKQqbrZxYEDG3MY2H2eHx4NHPFhOU7SXPxTsMwlWOYBmsu/dbHW1vpboI6F+4Uwxmyw1iY7\nXYcvaC7+KVjmEizzAM2lrnRZRkQkCCncRUSCUKCG+zynC/AhzcU/BctcgmUeoLnUSUBecxcRkXML\n1DN3ERE5B78Od2PMKGPMVmNMjjEmtYb9xhjzZ89+lzFmiBN1esOLuYw0xhQYY77zPH7nRJ21Mca8\nYYzJN8ZknGV/IK1JbXMJlDWJN8asMcZkGWMyjTG/rGFMQKyLl3MJlHUJN8Z8bYzZ7JnLEzWMqb91\nsdb65QNoAuwAugFhwGag3xljxgCrAAMMB9Y7XfcFzGUksNzpWr2YyxXAECDjLPsDYk28nEugrEks\nMMTzvCWwLYC/V7yZS6CsiwFaeJ6HAuuB4Q21Lv585j4MyLHW5lpry4B3qHoz7ur+7825rbXrgNbG\nmNiGLtRNiOaHAAAB+UlEQVQL3swlIFhrPwOOnWNIoKyJN3MJCNbaPGvtJs/zImALVe9jXF1ArIuX\ncwkInr/rk56XoZ7HmT/krLd18edwP9sbb9d1jD/wts4Rnv+arTLG9G+Y0nwuUNbEWwG1JsaYRGAw\nVWeJ1QXcupxjLhAg62KMaWKM+Q7IBz601jbYujToG2TLOW0CEqy1J40xY4AlQE+Ha2rsAmpNjDEt\ngDTgV9baQqfruRC1zCVg1sVaWwkMMsa0BhYbYwZYa2v8GY+v+fOZuzdvvO31m3M7rNY6rbWF//4v\nnLV2JRBqjIluuBJ9JlDWpFaBtCbGmFCqwvDv1tpFNQwJmHWpbS6BtC7/Zq09AawBRp2xq97WxZ/D\n/RugpzGmqzEmDLidqjfjri5Q3py71rkYYzoaY4zn+TCq1uZog1d64QJlTWoVKGviqfF1YIu19oWz\nDAuIdfFmLgG0LjGeM3aMMc2B64DsM4bV27r47WUZa22FMeYhYDVVd5u8Ya3NNMbc79kfMG/O7eVc\nJgAPGGMqgBLgduv5cbo/MUH0hulezCUg1gS4FJgIpHuu7wJMAxIg4NbFm7kEyrrEAvONMU2o+gfo\nXWvt8obKMP2GqohIEPLnyzIiInKeFO4iIkFI4S4iEoQU7iIiQUjhLiIShBTuIiJBSOEuIhKEFO4i\nIkHo/wPo3JZCMiwAKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11485cba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1149a5f98>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cVUztWsbZPdswMjuDs3umU6e21voXkdSRUkGwcN12Jufk89q8dezcW0HnVg25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\n7+cRmgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114a534e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])\n",
    "plt.ylabel('y label')\n",
    "plt.xlabel('x label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x114b55c18>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7q+pwVS0C1wGfr6obNnXb0zmZqe8UTXILvbvZh5KcAW6id4OEqno/8Gl6d4m/DWwAb5xO\npbsbYCyvBd6c5FHgf4Drqn8bfMZcDbwO+Eb/GifAu4AjMHfzMshY5mVeLgU+lOQieuH2saq6Pcnv\nw9zNyyBjmZd5+Rn7OSe+KSpJjZi3Sy6SpG0Y6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrok\nNeL/AdGa6K1bQ8T4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114a080b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 6, 0, 20]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WAaamxleXpCZ5xX8m3HzzT0L/pPn5QbsknWEG/5nw7LOn1y5Ja8jgPxOW+2EZf3BG0hgY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\n0H3+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114cc6208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')\n",
    "plt.axis([0, 6, 0, 20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t: [ 0.   0.2  0.4  0.6  0.8  1.   1.2  1.4  1.6  1.8  2.   2.2  2.4  2.6  2.8\n",
      "  3.   3.2  3.4  3.6  3.8  4.   4.2  4.4  4.6  4.8]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x114dcf828>,\n",
       " <matplotlib.lines.Line2D at 0x114dcfa20>,\n",
       " <matplotlib.lines.Line2D at 0x114dd7390>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114c6d978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evenly sampled time at 200ms intervals\n",
    "t = np.arange(0., 5., 0.2)\n",
    "print(\"t: \" + str(t))\n",
    "# red dashes, blue squares and green triangles\n",
    "plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 50 artists>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NM+glqXH/B6Jbxz9BrBEzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110b3c4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [4, 4, 7, 7, 8, 9, 10, 10, 10, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 15, 15, 15, 16, 16, 17, 17, 17, 18, 18, 18, 18, 19, 19, 19, 20, 20, 20, 20, 20, 22, 23, 24, 24, 24, 24, 25]\n",
    "y = [2, 10, 4, 22, 16, 10, 18, 26, 34, 17, 28, 14, 20, 24, 28, 26, 34, 34, 46, 26, 36, 60, 80, 20, 26, 54, 32, 40, 32, 40, 50, 42, 56, 76, 84, 36, 46, 68, 32, 48, 52, 56, 64, 66, 54, 70, 92, 93, 120, 85]\n",
    "plt.bar(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1151241d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WLYfUjbUvq1QlsHSzbkrq0mhjZWPdckjdWPuySlUCV8auC1d9jnbl3rsHtlfObpzhP/e8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\n8d5VjQ8HAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1150ca2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [4, 4, 7, 7, 8, 9, 10, 10, 10, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 15, 15, 15, 16, 16, 17, 17, 17, 18, 18, 18, 18, 19, 19, 19, 20, 20, 20, 20, 20, 22, 23, 24, 24, 24, 24, 25]\n",
    "y = [2, 10, 4, 22, 16, 10, 18, 26, 34, 17, 28, 14, 20, 24, 28, 26, 34, 34, 46, 26, 36, 60, 80, 20, 26, 54, 32, 40, 32, 40, 50, 42, 56, 76, 84, 36, 46, 68, 32, 48, 52, 56, 64, 66, 54, 70, 92, 93, 120, 85]\n",
    "\n",
    "plt.scatter(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114e3bd30>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5l156aYNjnDNnDg8++CBut5vy8nJ+/etfc//99+Pz+Zg+fTqvv/46TqcTVWX1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114cf3358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_data.plot.scatter(x='sepal length (cm)', y='sepal width (cm)', c='target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "color = {\n",
    "    0:'r',\n",
    "    1:'g',\n",
    "    2:'b'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_data['color'] = iris_data['target'].map(color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>r</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target color  \n",
       "0       0     r  \n",
       "1       0     r  \n",
       "2       0     r  \n",
       "3       0     r  \n",
       "4       0     r  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114e79240>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fwTffwP77u1xI6bB1K8yc6VKGHXZYdHMduLrTWbNcA/ywYcmlKUulRD5qX30F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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114e80ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_data.plot.scatter(x='sepal length (cm)', y='sepal width (cm)', c=iris_data['color'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115d710b8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1152bb4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7TqDOzBaWPdBkHAAOuPt/hq+3kv8SHKua95+C/VPl+8+oi4CH3P2ZCZbFuv8o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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1152bb828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115d34b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_data[iris_data['target']==0].plot.scatter(x='sepal length (cm)', y='petal width (cm)',color='r', label='setosa')\n",
    "iris_data[iris_data['target']==1].plot.scatter(x='sepal length (cm)', y='petal width (cm)',color='g', label='versicolor')\n",
    "iris_data[iris_data['target']==2].plot.scatter(x='sepal length (cm)', y='petal width (cm)',color='b', label='virginica')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1155f6f98>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4GPiqiLwhIg+LSEnAcZkMl2wylZfzEyXI+cGSwkym8ZK8tgZYADwIvIJzG+mW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\npmcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11596d128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = iris_data[iris_data['target']==0].plot.scatter(x='sepal length (cm)', y='petal width (cm)',color='r', label='setosa')\n",
    "ax = iris_data[iris_data['target']==1].plot.scatter(x='sepal length (cm)', y='petal width (cm)',color='g', label='versicolor', ax=ax)\n",
    "iris_data[iris_data['target']==2].plot.scatter(x='sepal length (cm)', y='petal width (cm)',color='b', label='virginica', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5e+g1THvi9Q5KbSqnz74duDLm56vAzluOWQ04hBBvAgHg76WU37n1QkKI54Dn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1153c2e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 50\n",
    "x = np.random.rand(N)\n",
    "y = np.random.rand(N)\n",
    "colors = np.random.rand(N)\n",
    "area = np.pi * (15 * np.random.rand(N))**2  # 0 to 15 point radii\n",
    "\n",
    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['_classic_test',\n",
       " 'bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fivethirtyeight',\n",
       " 'ggplot',\n",
       " 'grayscale',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn-dark',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-deep',\n",
       " 'seaborn-muted',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-paper',\n",
       " 'seaborn-pastel',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-ticks',\n",
       " 'seaborn-white',\n",
       " 'seaborn-whitegrid',\n",
       " 'seaborn']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m/QFse2nBxvaZHHntEp96ai+WVVkDrZImShuMPSSVO/lDqFVc1TE4OFjS862X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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1161529b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 50\n",
    "x = np.random.rand(N)\n",
    "y = np.random.rand(N)\n",
    "colors = np.random.rand(N)\n",
    "area = np.pi * (15 * np.random.rand(N))**2  # 0 to 15 point radii\n",
    "\n",
    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.style.use('classic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3Oo8lOTkZm83GF198gcPhwGq18sknn2AymYiJ6f4GT8Jz2O120nd+TVDkhC5d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\nJ18DxZ/jRdfBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1161156a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 50\n",
    "x = np.random.rand(N)\n",
    "y = np.random.rand(N)\n",
    "colors = np.random.rand(N)\n",
    "area = np.pi * (15 * np.random.rand(N))**2  # 0 to 15 point radii\n",
    "\n",
    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x115c2e320>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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WLVuGwYMHY/78+cjNzbXLkv1EABAXORTdA7pB66ZF94BuiIuUv7JMXORQRAR0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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11630a828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('sepal length (cm)','petal width (cm)',data=iris_data, fit_reg=False, hue='target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "target_name = {\n",
    "    0:'setosa',\n",
    "    1:'versicolor',\n",
    "    2:'virginica'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris_data['target_name'] = iris_data['target'].map(target_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x115a3e860>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A3LlzcfHiRSgUCowbNw5RUVG4du0aZs6ciYqKCtja2mL27Nno3LkzDh48iE2b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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115c78320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('sepal length (cm)','petal width (cm)', data=iris_data, fit_reg=False, hue='target_name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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"
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "type": "scatter",
         "x": [
          1,
          2,
          3,
          4
         ],
         "y": [
          4,
          3,
          2,
          1
         ]
        }
       ],
       "layout": {
        "title": "hello world"
       }
      },
      "text/html": [
       "<div id=\"a017d2bd-f31b-49b8-b000-f42eb1f59b11\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"a017d2bd-f31b-49b8-b000-f42eb1f59b11\", [{\"type\": \"scatter\", \"x\": [1, 2, 3, 4], \"y\": [4, 3, 2, 1]}], {\"title\": \"hello world\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"a017d2bd-f31b-49b8-b000-f42eb1f59b11\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"a017d2bd-f31b-49b8-b000-f42eb1f59b11\", [{\"type\": \"scatter\", \"x\": [1, 2, 3, 4], \"y\": [4, 3, 2, 1]}], {\"title\": \"hello world\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly\n",
    "from plotly.graph_objs import Scatter, Layout\n",
    "plotly.offline.init_notebook_mode(connected=True)\n",
    "plotly.offline.iplot({\n",
    "    \"data\": [Scatter(x=[1, 2, 3, 4], y=[4, 3, 2, 1])],\n",
    "    \"layout\": Layout(title=\"hello world\")\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "mode": "markers",
         "type": "scatter",
         "x": [
          -1.1858911232388252,
          -0.20768869862084127,
          -0.08704831818634184,
          -1.8917631337315564,
          -1.8701193884023362,
          0.34475760245126236,
          1.0480932342928446,
          -0.1135467815460186,
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "# Create random data with numpy\n",
    "import numpy as np\n",
    "\n",
    "N = 1000\n",
    "random_x = np.random.randn(N)\n",
    "random_y = np.random.randn(N)\n",
    "\n",
    "# Create a trace\n",
    "trace = go.Scatter(\n",
    "    x = random_x,\n",
    "    y = random_y,\n",
    "    mode = 'markers'\n",
    ")\n",
    "\n",
    "data = [trace]\n",
    "\n",
    "# Plot and embed in ipython notebook!\n",
    "plotly.offline.iplot(data, filename='basic-scatter')\n",
    "# or plot with: plot_url = py.plot(data, filename='basic-line')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 100\n",
    "random_x = np.linspace(0, 1, N)\n",
    "random_y0 = np.random.randn(N)+5\n",
    "random_y1 = np.random.randn(N)\n",
    "random_y2 = np.random.randn(N)-5\n",
    "\n",
    "# Create traces\n",
    "trace0 = go.Scatter(\n",
    "    x = random_x,\n",
    "    y = random_y0,\n",
    "    mode = 'markers',\n",
    "    name = 'markers'\n",
    ")\n",
    "trace1 = go.Scatter(\n",
    "    x = random_x,\n",
    "    y = random_y1,\n",
    "    mode = 'lines+markers',\n",
    "    name = 'lines+markers'\n",
    ")\n",
    "trace2 = go.Scatter(\n",
    "    x = random_x,\n",
    "    y = random_y2,\n",
    "    mode = 'lines',\n",
    "    name = 'lines'\n",
    ")\n",
    "\n",
    "data = [trace0, trace1, trace2]\n",
    "plotly.offline.iplot(data, filename='scatter-mode')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace0 = go.Scatter(\n",
    "    x = iris_data[iris_data['target']==0]['sepal length (cm)'],\n",
    "    y = iris_data[iris_data['target']==0]['petal width (cm)'],\n",
    "    mode = 'markers',\n",
    "    name = 'setosa'\n",
    ")\n",
    "trace1 = go.Scatter(\n",
    "    x = iris_data[iris_data['target']==1]['sepal length (cm)'],\n",
    "    y = iris_data[iris_data['target']==1]['petal width (cm)'],\n",
    "    mode = 'markers',\n",
    "    name = 'versicolor'\n",
    ")\n",
    "trace2 = go.Scatter(\n",
    "    x = iris_data[iris_data['target']==2]['sepal length (cm)'],\n",
    "    y = iris_data[iris_data['target']==2]['petal width (cm)'],\n",
    "    mode = 'markers',\n",
    "    name = 'virginica'\n",
    ")\n",
    "\n",
    "data = [trace0, trace1, trace2]\n",
    "plotly.offline.iplot(data, filename='basic-scatter')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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