17.Seaborn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style=\"ticks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset(\"anscombe\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>dataset</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>I</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>I</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>I</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>I</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>I</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>I</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>II</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>II</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>II</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>II</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>II</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>II</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>II</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>II</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>II</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>II</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>II</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>III</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>III</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>III</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>III</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>III</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>III</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>III</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>III</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>III</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>III</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>III</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>IV</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dataset     x      y\n",
       "0        I  10.0   8.04\n",
       "1        I   8.0   6.95\n",
       "2        I  13.0   7.58\n",
       "3        I   9.0   8.81\n",
       "4        I  11.0   8.33\n",
       "5        I  14.0   9.96\n",
       "6        I   6.0   7.24\n",
       "7        I   4.0   4.26\n",
       "8        I  12.0  10.84\n",
       "9        I   7.0   4.82\n",
       "10       I   5.0   5.68\n",
       "11      II  10.0   9.14\n",
       "12      II   8.0   8.14\n",
       "13      II  13.0   8.74\n",
       "14      II   9.0   8.77\n",
       "15      II  11.0   9.26\n",
       "16      II  14.0   8.10\n",
       "17      II   6.0   6.13\n",
       "18      II   4.0   3.10\n",
       "19      II  12.0   9.13\n",
       "20      II   7.0   7.26\n",
       "21      II   5.0   4.74\n",
       "22     III  10.0   7.46\n",
       "23     III   8.0   6.77\n",
       "24     III  13.0  12.74\n",
       "25     III   9.0   7.11\n",
       "26     III  11.0   7.81\n",
       "27     III  14.0   8.84\n",
       "28     III   6.0   6.08\n",
       "29     III   4.0   5.39\n",
       "30     III  12.0   8.15\n",
       "31     III   7.0   6.42\n",
       "32     III   5.0   5.73\n",
       "33      IV   8.0   6.58\n",
       "34      IV   8.0   5.76\n",
       "35      IV   8.0   7.71\n",
       "36      IV   8.0   8.84\n",
       "37      IV   8.0   8.47\n",
       "38      IV   8.0   7.04\n",
       "39      IV   8.0   5.25\n",
       "40      IV  19.0  12.50\n",
       "41      IV   8.0   5.56\n",
       "42      IV   8.0   7.91\n",
       "43      IV   8.0   6.89"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f8f9b967208>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f9b967470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"x\", y=\"y\", col=\"dataset\", hue=\"dataset\", data=df,\n",
    "           col_wrap=2, ci=None, palette=\"muted\", scatter_kws={\"s\": 50, \"alpha\": 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset('iris')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f99559550>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f9b9b3be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot( df[\"sepal_length\"] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f9b9b3b38>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f99579358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot( df[\"sepal_length\"], bins=20 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset('iris')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f995792e8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f99255e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot( a=df[\"sepal_length\"], hist=True, kde=False, rug=False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f9920c390>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f99167550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot( a=df[\"sepal_length\"], hist=True, kde=True, rug=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f98f8f908>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f9919af98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot( a=df[\"sepal_length\"], rug=True,rug_kws={\"color\": \"r\", \"alpha\":0.3, \"linewidth\": 2, \"height\":0.2 })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f98d26588>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f98d965f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot( a=df[\"sepal_length\"], kde=True, kde_kws={\"color\": \"g\", \"alpha\":0.3, \"linewidth\": 5, \"shade\":True })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f98d43a90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f98f70ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot( df[\"sepal_length\"] , color=\"skyblue\", vertical=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f991ceba8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f98cb1438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot( df[\"sepal_length\"] , color=\"skyblue\", label=\"Sepal Length\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f98c73da0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f98c222b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot( df[\"sepal_width\"] , color=\"red\", label=\"Sepal Width\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/seaborn/categorical.py:462: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  box_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f98be0278>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f98c41128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot( y=df[\"species\"], x=df[\"sepal_length\"] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset('tips')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25.29</td>\n",
       "      <td>4.71</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.77</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26.88</td>\n",
       "      <td>3.12</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.04</td>\n",
       "      <td>1.96</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.78</td>\n",
       "      <td>3.23</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.27</td>\n",
       "      <td>1.71</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>35.26</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.42</td>\n",
       "      <td>1.57</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>18.43</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.83</td>\n",
       "      <td>3.02</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>21.58</td>\n",
       "      <td>3.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10.33</td>\n",
       "      <td>1.67</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16.29</td>\n",
       "      <td>3.71</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16.97</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.65</td>\n",
       "      <td>3.35</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>17.92</td>\n",
       "      <td>4.08</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20.29</td>\n",
       "      <td>2.75</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15.77</td>\n",
       "      <td>2.23</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>39.42</td>\n",
       "      <td>7.58</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>19.82</td>\n",
       "      <td>3.18</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>17.81</td>\n",
       "      <td>2.34</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>13.37</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>12.69</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>21.70</td>\n",
       "      <td>4.30</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>19.65</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>28.17</td>\n",
       "      <td>6.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>12.90</td>\n",
       "      <td>1.10</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>28.15</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>11.59</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>7.74</td>\n",
       "      <td>1.44</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>30.14</td>\n",
       "      <td>3.09</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>12.16</td>\n",
       "      <td>2.20</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>13.42</td>\n",
       "      <td>3.48</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>8.58</td>\n",
       "      <td>1.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>15.98</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>13.42</td>\n",
       "      <td>1.58</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>16.27</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>10.09</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>20.45</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>13.28</td>\n",
       "      <td>2.72</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>22.12</td>\n",
       "      <td>2.88</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>24.01</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>15.69</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>11.61</td>\n",
       "      <td>3.39</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>10.77</td>\n",
       "      <td>1.47</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>15.53</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>10.07</td>\n",
       "      <td>1.25</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>12.60</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>32.83</td>\n",
       "      <td>1.17</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>35.83</td>\n",
       "      <td>4.67</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size\n",
       "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
       "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
       "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
       "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
       "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
       "5         25.29  4.71    Male     No   Sun  Dinner     4\n",
       "6          8.77  2.00    Male     No   Sun  Dinner     2\n",
       "7         26.88  3.12    Male     No   Sun  Dinner     4\n",
       "8         15.04  1.96    Male     No   Sun  Dinner     2\n",
       "9         14.78  3.23    Male     No   Sun  Dinner     2\n",
       "10        10.27  1.71    Male     No   Sun  Dinner     2\n",
       "11        35.26  5.00  Female     No   Sun  Dinner     4\n",
       "12        15.42  1.57    Male     No   Sun  Dinner     2\n",
       "13        18.43  3.00    Male     No   Sun  Dinner     4\n",
       "14        14.83  3.02  Female     No   Sun  Dinner     2\n",
       "15        21.58  3.92    Male     No   Sun  Dinner     2\n",
       "16        10.33  1.67  Female     No   Sun  Dinner     3\n",
       "17        16.29  3.71    Male     No   Sun  Dinner     3\n",
       "18        16.97  3.50  Female     No   Sun  Dinner     3\n",
       "19        20.65  3.35    Male     No   Sat  Dinner     3\n",
       "20        17.92  4.08    Male     No   Sat  Dinner     2\n",
       "21        20.29  2.75  Female     No   Sat  Dinner     2\n",
       "22        15.77  2.23  Female     No   Sat  Dinner     2\n",
       "23        39.42  7.58    Male     No   Sat  Dinner     4\n",
       "24        19.82  3.18    Male     No   Sat  Dinner     2\n",
       "25        17.81  2.34    Male     No   Sat  Dinner     4\n",
       "26        13.37  2.00    Male     No   Sat  Dinner     2\n",
       "27        12.69  2.00    Male     No   Sat  Dinner     2\n",
       "28        21.70  4.30    Male     No   Sat  Dinner     2\n",
       "29        19.65  3.00  Female     No   Sat  Dinner     2\n",
       "..          ...   ...     ...    ...   ...     ...   ...\n",
       "214       28.17  6.50  Female    Yes   Sat  Dinner     3\n",
       "215       12.90  1.10  Female    Yes   Sat  Dinner     2\n",
       "216       28.15  3.00    Male    Yes   Sat  Dinner     5\n",
       "217       11.59  1.50    Male    Yes   Sat  Dinner     2\n",
       "218        7.74  1.44    Male    Yes   Sat  Dinner     2\n",
       "219       30.14  3.09  Female    Yes   Sat  Dinner     4\n",
       "220       12.16  2.20    Male    Yes   Fri   Lunch     2\n",
       "221       13.42  3.48  Female    Yes   Fri   Lunch     2\n",
       "222        8.58  1.92    Male    Yes   Fri   Lunch     1\n",
       "223       15.98  3.00  Female     No   Fri   Lunch     3\n",
       "224       13.42  1.58    Male    Yes   Fri   Lunch     2\n",
       "225       16.27  2.50  Female    Yes   Fri   Lunch     2\n",
       "226       10.09  2.00  Female    Yes   Fri   Lunch     2\n",
       "227       20.45  3.00    Male     No   Sat  Dinner     4\n",
       "228       13.28  2.72    Male     No   Sat  Dinner     2\n",
       "229       22.12  2.88  Female    Yes   Sat  Dinner     2\n",
       "230       24.01  2.00    Male    Yes   Sat  Dinner     4\n",
       "231       15.69  3.00    Male    Yes   Sat  Dinner     3\n",
       "232       11.61  3.39    Male     No   Sat  Dinner     2\n",
       "233       10.77  1.47    Male     No   Sat  Dinner     2\n",
       "234       15.53  3.00    Male    Yes   Sat  Dinner     2\n",
       "235       10.07  1.25    Male     No   Sat  Dinner     2\n",
       "236       12.60  1.00    Male    Yes   Sat  Dinner     2\n",
       "237       32.83  1.17    Male    Yes   Sat  Dinner     2\n",
       "238       35.83  4.67  Female     No   Sat  Dinner     3\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
       "\n",
       "[244 rows x 7 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/seaborn/categorical.py:490: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  box_data = remove_na(group_data[hue_mask])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f98ac6b70>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f98ab5da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"day\", y=\"total_bill\", hue=\"smoker\", data=df, palette=\"Set1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/seaborn/categorical.py:462: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  box_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f98d3c7b8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f98b6e8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = sns.load_dataset('iris')\n",
    "sns.boxplot( x=df[\"species\"], y=df[\"sepal_length\"], palette=\"Blues\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/usr/lib/python3/dist-packages/seaborn/categorical.py:462: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  box_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f989c3710>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f98a7b5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df.ix[:,0:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f989644e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.swarmplot(x='species', y='sepal_length', data=df, color=\"grey\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/seaborn/categorical.py:462: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  box_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f988f8cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1=sns.boxplot(x='species', y='sepal_length', data=df, order=[\"virginica\", \"versicolor\", \"setosa\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f98825128>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f98839a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=df[\"sepal_length\"], y=df[\"sepal_width\"], line_kws={\"color\":\"r\",\"alpha\":0.7,\"lw\":5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f988212e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import the necessary libraries\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "# Define a variable N\n",
    "N = 500\n",
    "\n",
    "# Construct the colormap\n",
    "current_palette = sns.color_palette(\"muted\", n_colors=5)\n",
    "cmap = ListedColormap(sns.color_palette(current_palette).as_hex())\n",
    "\n",
    "# Initialize the data\n",
    "data1 = np.random.randn(N)\n",
    "data2 = np.random.randn(N)\n",
    "# Assume that there are 5 possible labels\n",
    "colors = np.random.randint(0,5,N)\n",
    "\n",
    "# Create a scatter plot\n",
    "plt.scatter(data1, data2, c=colors, cmap=cmap)\n",
    "\n",
    "# Add a color bar\n",
    "plt.colorbar()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/seaborn/categorical.py:1460: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f9b9822b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"white\", context=\"talk\")\n",
    "rs = np.random.RandomState(8)\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(7, 5), sharex=True)\n",
    "\n",
    "# Generate some sequential data\n",
    "x = np.array(list(\"ABCDEFGHIJ\"))\n",
    "y1 = np.arange(1, 11)\n",
    "sns.barplot(x=x, y=y1, palette=\"rocket\", ax=ax1)\n",
    "ax1.axhline(0, color=\"k\", clip_on=False)\n",
    "ax1.set_ylabel(\"Sequential\")\n",
    "\n",
    "# Center the data to make it diverging\n",
    "y2 = y1 - 5.5\n",
    "sns.barplot(x=x, y=y2, palette=\"vlag\", ax=ax2)\n",
    "ax2.axhline(0, color=\"k\", clip_on=False)\n",
    "ax2.set_ylabel(\"Diverging\")\n",
    "\n",
    "# Randomly reorder the data to make it qualitative\n",
    "y3 = rs.choice(y1, len(y1), replace=False)\n",
    "sns.barplot(x=x, y=y3, palette=\"deep\", ax=ax3)\n",
    "ax3.axhline(0, color=\"k\", clip_on=False)\n",
    "ax3.set_ylabel(\"Qualitative\")\n",
    "\n",
    "# Finalize the plot\n",
    "sns.despine(bottom=True)\n",
    "plt.setp(f.axes, yticks=[])\n",
    "plt.tight_layout(h_pad=2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}