STA141B-2022WQ/Week 3-1.ipynb

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    "# Lecture 5\n",
    "\n",
    "January 18, 2022\n",
    "\n",
    "### Announcements\n",
    "\n",
    "* Homework due this Wednesday\n",
    "* Group member due this Sunday\n",
    "\n",
    "### Topics\n",
    "\n",
    "* Pandas\n",
    "* Static Visualizations\n",
    "    + matplotlib\n",
    "    + plotnine\n",
    "    + seaborn\n",
    "\n",
    "### Data Sets\n",
    "\n",
    "* Dogs Dataset \n",
    "* [Milk Sales Dataset](https://www.ers.usda.gov/webdocs/DataFiles/48685/fluidmilk.xlsx?v=5010.6)\n",
    "\n",
    "\n",
    "### References\n",
    "\n",
    "* Python for Data Analysis, Ch. 8, 9\n",
    "* [Python Data Science Handbook][PDSH], Ch. 3, 4\n",
    "\n",
    "[PDSH]: https://jakevdp.github.io/PythonDataScienceHandbook/\n",
    "[ProGit]: https://git-scm.com/book/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Data\n",
    "\n",
    "Pandas represents missing data with `NaN` and `None`, but these values do not exclusively mean missing data. For instance, `NaN` stands for \"Not a Number\" and is also the result of undefined computations. Pay attention to your data and code to determine whether values are missing or have some other meaning.\n",
    "\n",
    "You can create `NaN` values with NumPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "     x  y     z\n",
       "0  1.0  0     x\n",
       "1  NaN  1     y\n",
       "2  2.0  2  None"
      ]
     },
     "execution_count": 5,
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   "source": [
    "df = pd.DataFrame({\"x\": [1, np.nan, 2], \"y\": np.arange(3), \"z\": [\"x\", \"y\", None]})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the `.isna()` or `.isnull()` methods to detect missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       x      y      z\n",
       "0  False  False  False\n",
       "1   True  False  False\n",
       "2  False  False   True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "       x      y      z\n",
       "0  False  False  False\n",
       "1   True  False  False\n",
       "2  False  False   True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(~df.isna()).sum().sum() # number not missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.notna().sum().sum() # number not missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Alignment\n",
    "\n",
    "Pandas supports vectorized operations, but elements are <b>automatically</b> aligned by index. **Beware!!** This is a major difference compared to R."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.Series([1, 2, 3], index = [\"a\", \"b\", \"c\"])\n",
    "y = pd.Series([1, 2, 3, 1], index = [\"b\", \"a\", \"d\", \"c\"])\n",
    "\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1\n",
       "a    2\n",
       "d    3\n",
       "c    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    2.0\n",
       "b    2.0\n",
       "c    3.0\n",
       "d    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x * y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the `.reset_index()` method to reset the indexes on a series or data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>0</th>\n",
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       "      <th>0</th>\n",
       "      <td>a</td>\n",
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       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
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      "text/plain": [
       "  index  0\n",
       "0     a  1\n",
       "1     b  2\n",
       "2     c  3"
      ]
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     "metadata": {},
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   "source": [
    "x.reset_index() # keep old index as a new column\n",
    "\n",
    "# x.reset_index(drop = True) # throw away the old index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading Data\n",
    "\n",
    "Pandas provides functions for reading (and writing) a variety of common formats. Most of their names begin with `read_`. For instance, we can read the dogs data from a CSV file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dogs = pd.read_csv(\"dogs_full.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>breed</th>\n",
       "      <th>group</th>\n",
       "      <th>datadog</th>\n",
       "      <th>popularity_all</th>\n",
       "      <th>popularity</th>\n",
       "      <th>lifetime_cost</th>\n",
       "      <th>intelligence_rank</th>\n",
       "      <th>longevity</th>\n",
       "      <th>ailments</th>\n",
       "      <th>price</th>\n",
       "      <th>food_cost</th>\n",
       "      <th>grooming</th>\n",
       "      <th>kids</th>\n",
       "      <th>megarank_kids</th>\n",
       "      <th>megarank</th>\n",
       "      <th>size</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Border Collie</td>\n",
       "      <td>herding</td>\n",
       "      <td>3.64</td>\n",
       "      <td>45</td>\n",
       "      <td>39.0</td>\n",
       "      <td>20143.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.52</td>\n",
       "      <td>2.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>weekly</td>\n",
       "      <td>low</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Border Terrier</td>\n",
       "      <td>terrier</td>\n",
       "      <td>3.61</td>\n",
       "      <td>80</td>\n",
       "      <td>61.0</td>\n",
       "      <td>22638.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>833.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>weekly</td>\n",
       "      <td>high</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>small</td>\n",
       "      <td>13.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brittany</td>\n",
       "      <td>sporting</td>\n",
       "      <td>3.54</td>\n",
       "      <td>30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22589.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>618.0</td>\n",
       "      <td>466.0</td>\n",
       "      <td>weekly</td>\n",
       "      <td>medium</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>35.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cairn Terrier</td>\n",
       "      <td>terrier</td>\n",
       "      <td>3.53</td>\n",
       "      <td>59</td>\n",
       "      <td>48.0</td>\n",
       "      <td>21992.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>13.84</td>\n",
       "      <td>2.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>weekly</td>\n",
       "      <td>high</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>small</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Welsh Springer Spaniel</td>\n",
       "      <td>sporting</td>\n",
       "      <td>3.34</td>\n",
       "      <td>130</td>\n",
       "      <td>81.0</td>\n",
       "      <td>20224.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>12.49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>weekly</td>\n",
       "      <td>high</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>English Cocker Spaniel</td>\n",
       "      <td>sporting</td>\n",
       "      <td>3.33</td>\n",
       "      <td>63</td>\n",
       "      <td>51.0</td>\n",
       "      <td>18993.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>weekly</td>\n",
       "      <td>high</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>30.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    breed     group  datadog  popularity_all  popularity  \\\n",
       "0           Border Collie   herding     3.64              45        39.0   \n",
       "1          Border Terrier   terrier     3.61              80        61.0   \n",
       "2                Brittany  sporting     3.54              30        30.0   \n",
       "3           Cairn Terrier   terrier     3.53              59        48.0   \n",
       "4  Welsh Springer Spaniel  sporting     3.34             130        81.0   \n",
       "5  English Cocker Spaniel  sporting     3.33              63        51.0   \n",
       "\n",
       "   lifetime_cost  intelligence_rank  longevity  ailments  price  food_cost  \\\n",
       "0        20143.0                1.0      12.52       2.0  623.0      324.0   \n",
       "1        22638.0               30.0      14.00       0.0  833.0      324.0   \n",
       "2        22589.0               19.0      12.92       0.0  618.0      466.0   \n",
       "3        21992.0               35.0      13.84       2.0  435.0      324.0   \n",
       "4        20224.0               31.0      12.49       1.0  750.0      324.0   \n",
       "5        18993.0               18.0      11.66       0.0  800.0      324.0   \n",
       "\n",
       "  grooming    kids  megarank_kids  megarank    size  weight  height  \n",
       "0   weekly     low            1.0      29.0  medium     NaN    20.0  \n",
       "1   weekly    high            2.0       1.0   small    13.5     NaN  \n",
       "2   weekly  medium            3.0      11.0  medium    35.0    19.0  \n",
       "3   weekly    high            4.0       2.0   small    14.0    10.0  \n",
       "4   weekly    high            5.0       4.0  medium     NaN    18.0  \n",
       "5   weekly    high            6.0       5.0  medium    30.0    16.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dogs.head()\n",
    "dogs.head(n = 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>breed</th>\n",
       "      <th>group</th>\n",
       "      <th>datadog</th>\n",
       "      <th>popularity_all</th>\n",
       "      <th>popularity</th>\n",
       "      <th>lifetime_cost</th>\n",
       "      <th>intelligence_rank</th>\n",
       "      <th>longevity</th>\n",
       "      <th>ailments</th>\n",
       "      <th>price</th>\n",
       "      <th>food_cost</th>\n",
       "      <th>grooming</th>\n",
       "      <th>kids</th>\n",
       "      <th>megarank_kids</th>\n",
       "      <th>megarank</th>\n",
       "      <th>size</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Border Collie</td>\n",
       "      <td>herding</td>\n",
       "      <td>3.64</td>\n",
       "      <td>45</td>\n",
       "      <td>39.0</td>\n",
       "      <td>20143.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.52</td>\n",
       "      <td>2.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>weekly</td>\n",
       "      <td>low</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Border Terrier</td>\n",
       "      <td>terrier</td>\n",
       "      <td>3.61</td>\n",
       "      <td>80</td>\n",
       "      <td>61.0</td>\n",
       "      <td>22638.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>833.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>weekly</td>\n",
       "      <td>high</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>small</td>\n",
       "      <td>13.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brittany</td>\n",
       "      <td>sporting</td>\n",
       "      <td>3.54</td>\n",
       "      <td>30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22589.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>618.0</td>\n",
       "      <td>466.0</td>\n",
       "      <td>weekly</td>\n",
       "      <td>medium</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>35.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            breed     group  datadog  popularity_all  popularity  \\\n",
       "0   Border Collie   herding     3.64              45        39.0   \n",
       "1  Border Terrier   terrier     3.61              80        61.0   \n",
       "2        Brittany  sporting     3.54              30        30.0   \n",
       "\n",
       "   lifetime_cost  intelligence_rank  longevity  ailments  price  food_cost  \\\n",
       "0        20143.0                1.0      12.52       2.0  623.0      324.0   \n",
       "1        22638.0               30.0      14.00       0.0  833.0      324.0   \n",
       "2        22589.0               19.0      12.92       0.0  618.0      466.0   \n",
       "\n",
       "  grooming    kids  megarank_kids  megarank    size  weight  height  \n",
       "0   weekly     low            1.0      29.0  medium     NaN    20.0  \n",
       "1   weekly    high            2.0       1.0   small    13.5     NaN  \n",
       "2   weekly  medium            3.0      11.0  medium    35.0    19.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>breed</th>\n",
       "      <th>group</th>\n",
       "      <th>datadog</th>\n",
       "      <th>popularity_all</th>\n",
       "      <th>popularity</th>\n",
       "      <th>lifetime_cost</th>\n",
       "      <th>intelligence_rank</th>\n",
       "      <th>longevity</th>\n",
       "      <th>ailments</th>\n",
       "      <th>price</th>\n",
       "      <th>food_cost</th>\n",
       "      <th>grooming</th>\n",
       "      <th>kids</th>\n",
       "      <th>megarank_kids</th>\n",
       "      <th>megarank</th>\n",
       "      <th>size</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Weimaraner</td>\n",
       "      <td>sporting</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>weekly</td>\n",
       "      <td>high</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>large</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Welsh Terrier</td>\n",
       "      <td>terrier</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>843.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>weekly</td>\n",
       "      <td>high</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>small</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Wire Fox Terrier</td>\n",
       "      <td>terrier</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.0</td>\n",
       "      <td>13.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>668.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>small</td>\n",
       "      <td>17.5</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Wirehaired Pointing Griffon</td>\n",
       "      <td>sporting</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>8.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>755.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Xoloitzcuintli</td>\n",
       "      <td>non-sporting</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>717.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           breed         group  datadog  popularity_all  \\\n",
       "167                   Weimaraner      sporting      NaN              32   \n",
       "168                Welsh Terrier       terrier      NaN              99   \n",
       "169             Wire Fox Terrier       terrier      NaN             100   \n",
       "170  Wirehaired Pointing Griffon      sporting      NaN              92   \n",
       "171               Xoloitzcuintli  non-sporting      NaN             155   \n",
       "\n",
       "     popularity  lifetime_cost  intelligence_rank  longevity  ailments  price  \\\n",
       "167         NaN            NaN               21.0        NaN       1.0  562.0   \n",
       "168         NaN            NaN               53.0        NaN       0.0  843.0   \n",
       "169         NaN            NaN               51.0      13.17       0.0  668.0   \n",
       "170         NaN            NaN               46.0       8.80       0.0  755.0   \n",
       "171         NaN            NaN                NaN        NaN       NaN  717.0   \n",
       "\n",
       "     food_cost grooming  kids  megarank_kids  megarank    size  weight  height  \n",
       "167        NaN   weekly  high            NaN       NaN   large     NaN    25.0  \n",
       "168        NaN   weekly  high            NaN       NaN   small    20.0    15.0  \n",
       "169        NaN      NaN   NaN            NaN       NaN   small    17.5    15.0  \n",
       "170        NaN      NaN   NaN            NaN       NaN  medium     NaN    22.0  \n",
       "171        NaN      NaN   NaN            NaN       NaN  medium     NaN    16.5  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspecting Data\n",
    "\n",
    "Series and data frames provide many of the same methods and attributes as NumPy arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a data frame, the `.dtypes` attribute gives the column types.\n",
    "\n",
    "The type \"object\" means some non-numeric Python object, often a string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "breed                 object\n",
       "group                 object\n",
       "datadog              float64\n",
       "popularity_all         int64\n",
       "popularity           float64\n",
       "lifetime_cost        float64\n",
       "intelligence_rank    float64\n",
       "longevity            float64\n",
       "ailments             float64\n",
       "price                float64\n",
       "food_cost            float64\n",
       "grooming              object\n",
       "kids                  object\n",
       "megarank_kids        float64\n",
       "megarank             float64\n",
       "size                  object\n",
       "weight               float64\n",
       "height               float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are also several methods for quickly summarizing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datadog</th>\n",
       "      <th>popularity_all</th>\n",
       "      <th>popularity</th>\n",
       "      <th>lifetime_cost</th>\n",
       "      <th>intelligence_rank</th>\n",
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       "      <th>megarank_kids</th>\n",
       "      <th>megarank</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>86.00000</td>\n",
       "      <td>159.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.603678</td>\n",
       "      <td>87.122093</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>19819.538462</td>\n",
       "      <td>40.924242</td>\n",
       "      <td>10.956741</td>\n",
       "      <td>1.216216</td>\n",
       "      <td>876.815068</td>\n",
       "      <td>489.597701</td>\n",
       "      <td>43.954023</td>\n",
       "      <td>43.942529</td>\n",
       "      <td>44.97093</td>\n",
       "      <td>19.089623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.570288</td>\n",
       "      <td>50.205335</td>\n",
       "      <td>25.258662</td>\n",
       "      <td>3102.475382</td>\n",
       "      <td>19.603560</td>\n",
       "      <td>1.995742</td>\n",
       "      <td>1.549810</td>\n",
       "      <td>461.172524</td>\n",
       "      <td>204.266894</td>\n",
       "      <td>25.288065</td>\n",
       "      <td>25.278153</td>\n",
       "      <td>35.52707</td>\n",
       "      <td>6.012400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.990000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12653.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.290000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>283.000000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.185000</td>\n",
       "      <td>43.750000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>17816.500000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>587.250000</td>\n",
       "      <td>324.000000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>17.50000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.710000</td>\n",
       "      <td>87.500000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>20087.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>11.290000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>466.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>35.00000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.035000</td>\n",
       "      <td>130.250000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>21798.000000</td>\n",
       "      <td>54.250000</td>\n",
       "      <td>12.365000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1042.250000</td>\n",
       "      <td>466.000000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>62.50000</td>\n",
       "      <td>24.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.640000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>26686.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3460.000000</td>\n",
       "      <td>1349.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>175.00000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         datadog  popularity_all  popularity  lifetime_cost  \\\n",
       "count  87.000000      172.000000   87.000000      91.000000   \n",
       "mean    2.603678       87.122093   44.000000   19819.538462   \n",
       "std     0.570288       50.205335   25.258662    3102.475382   \n",
       "min     0.990000        1.000000    1.000000   12653.000000   \n",
       "25%     2.185000       43.750000   22.500000   17816.500000   \n",
       "50%     2.710000       87.500000   44.000000   20087.000000   \n",
       "75%     3.035000      130.250000   65.500000   21798.000000   \n",
       "max     3.640000      173.000000   87.000000   26686.000000   \n",
       "\n",
       "       intelligence_rank   longevity    ailments        price    food_cost  \\\n",
       "count         132.000000  135.000000  148.000000   146.000000    87.000000   \n",
       "mean           40.924242   10.956741    1.216216   876.815068   489.597701   \n",
       "std            19.603560    1.995742    1.549810   461.172524   204.266894   \n",
       "min             1.000000    6.290000    0.000000   283.000000   270.000000   \n",
       "25%            27.000000    9.700000    0.000000   587.250000   324.000000   \n",
       "50%            42.000000   11.290000    1.000000   795.000000   466.000000   \n",
       "75%            54.250000   12.365000    2.000000  1042.250000   466.000000   \n",
       "max            80.000000   16.500000    9.000000  3460.000000  1349.000000   \n",
       "\n",
       "       megarank_kids   megarank     weight      height  \n",
       "count      87.000000  87.000000   86.00000  159.000000  \n",
       "mean       43.954023  43.942529   44.97093   19.089623  \n",
       "std        25.288065  25.278153   35.52707    6.012400  \n",
       "min         1.000000   1.000000    5.00000    5.000000  \n",
       "25%        22.500000  22.500000   17.50000   14.000000  \n",
       "50%        44.000000  44.000000   35.00000   19.000000  \n",
       "75%        65.500000  65.500000   62.50000   24.125000  \n",
       "max        87.000000  87.000000  175.00000   32.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datadog</th>\n",
       "      <th>popularity</th>\n",
       "      <th>lifetime_cost</th>\n",
       "      <th>intelligence_rank</th>\n",
       "      <th>longevity</th>\n",
       "      <th>ailments</th>\n",
       "      <th>price</th>\n",
       "      <th>food_cost</th>\n",
       "      <th>megarank_kids</th>\n",
       "      <th>megarank</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>86.00000</td>\n",
       "      <td>159.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.603678</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>19819.538462</td>\n",
       "      <td>40.924242</td>\n",
       "      <td>10.956741</td>\n",
       "      <td>1.216216</td>\n",
       "      <td>876.815068</td>\n",
       "      <td>489.597701</td>\n",
       "      <td>43.954023</td>\n",
       "      <td>43.942529</td>\n",
       "      <td>44.97093</td>\n",
       "      <td>19.089623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.570288</td>\n",
       "      <td>25.258662</td>\n",
       "      <td>3102.475382</td>\n",
       "      <td>19.603560</td>\n",
       "      <td>1.995742</td>\n",
       "      <td>1.549810</td>\n",
       "      <td>461.172524</td>\n",
       "      <td>204.266894</td>\n",
       "      <td>25.288065</td>\n",
       "      <td>25.278153</td>\n",
       "      <td>35.52707</td>\n",
       "      <td>6.012400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.990000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12653.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.290000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>283.000000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.185000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>17816.500000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>587.250000</td>\n",
       "      <td>324.000000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>17.50000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.710000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>20087.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>11.290000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>466.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>35.00000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.035000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>21798.000000</td>\n",
       "      <td>54.250000</td>\n",
       "      <td>12.365000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1042.250000</td>\n",
       "      <td>466.000000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>62.50000</td>\n",
       "      <td>24.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.640000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>26686.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3460.000000</td>\n",
       "      <td>1349.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>175.00000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         datadog  popularity  lifetime_cost  intelligence_rank   longevity  \\\n",
       "count  87.000000   87.000000      91.000000         132.000000  135.000000   \n",
       "mean    2.603678   44.000000   19819.538462          40.924242   10.956741   \n",
       "std     0.570288   25.258662    3102.475382          19.603560    1.995742   \n",
       "min     0.990000    1.000000   12653.000000           1.000000    6.290000   \n",
       "25%     2.185000   22.500000   17816.500000          27.000000    9.700000   \n",
       "50%     2.710000   44.000000   20087.000000          42.000000   11.290000   \n",
       "75%     3.035000   65.500000   21798.000000          54.250000   12.365000   \n",
       "max     3.640000   87.000000   26686.000000          80.000000   16.500000   \n",
       "\n",
       "         ailments        price    food_cost  megarank_kids   megarank  \\\n",
       "count  148.000000   146.000000    87.000000      87.000000  87.000000   \n",
       "mean     1.216216   876.815068   489.597701      43.954023  43.942529   \n",
       "std      1.549810   461.172524   204.266894      25.288065  25.278153   \n",
       "min      0.000000   283.000000   270.000000       1.000000   1.000000   \n",
       "25%      0.000000   587.250000   324.000000      22.500000  22.500000   \n",
       "50%      1.000000   795.000000   466.000000      44.000000  44.000000   \n",
       "75%      2.000000  1042.250000   466.000000      65.500000  65.500000   \n",
       "max      9.000000  3460.000000  1349.000000      87.000000  87.000000   \n",
       "\n",
       "          weight      height  \n",
       "count   86.00000  159.000000  \n",
       "mean    44.97093   19.089623  \n",
       "std     35.52707    6.012400  \n",
       "min      5.00000    5.000000  \n",
       "25%     17.50000   14.000000  \n",
       "50%     35.00000   19.000000  \n",
       "75%     62.50000   24.125000  \n",
       "max    175.00000   32.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, get the string columns (\"object\"), then describe\n",
    "dogs.select_dtypes(include = [\"object\"]).describe()\n",
    "dogs.select_dtypes(include = [\"float64\"]).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregation\n",
    "\n",
    "Pandas also provides several methods for aggregating data, such as `.mean()`, `.median()`, `.std()`, and `.value_counts()`. They ignore missing values by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datadog                  2.71\n",
       "popularity_all          87.50\n",
       "popularity              44.00\n",
       "lifetime_cost        20087.00\n",
       "intelligence_rank       42.00\n",
       "longevity               11.29\n",
       "ailments                 1.00\n",
       "price                  795.00\n",
       "food_cost              466.00\n",
       "megarank_kids           44.00\n",
       "megarank                44.00\n",
       "weight                  35.00\n",
       "height                  19.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "795.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"price\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sporting        28\n",
       "terrier         28\n",
       "working         27\n",
       "hound           26\n",
       "herding         25\n",
       "toy             19\n",
       "non-sporting    19\n",
       "Name: group, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"group\"].value_counts() # like R's table() with 1 arg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For counting one group against another (crosstabulating), use `pd.crosstab()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>kids</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>medium</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>herding</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hound</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-sporting</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sporting</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>terrier</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toy</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>working</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "kids          high  low  medium\n",
       "group                          \n",
       "herding         11    2       3\n",
       "hound            8    1       6\n",
       "non-sporting     7    0       4\n",
       "sporting        17    1       3\n",
       "terrier          9    0       9\n",
       "toy              3    5       5\n",
       "working         12    1       5"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(dogs[\"group\"], dogs[\"kids\"]) # like R's table() with 2+ arg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying Functions\n",
    "\n",
    "You can also use Pandas to apply your own aggregation functions to columns or rows.\n",
    "\n",
    "* `.apply()` applies a function column-by-column or row-by-row.\n",
    "* `.applymap()` applies a function element-by-element.\n",
    "\n",
    "This is another way to vectorizing code, but only works for data frame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datadog                  2.65\n",
       "popularity_all         172.00\n",
       "popularity              86.00\n",
       "lifetime_cost        14033.00\n",
       "intelligence_rank       79.00\n",
       "longevity               10.21\n",
       "ailments                 9.00\n",
       "price                 3177.00\n",
       "food_cost             1079.00\n",
       "megarank_kids           86.00\n",
       "megarank                86.00\n",
       "weight                 170.00\n",
       "height                  27.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def spread(x):\n",
    "    # Input is a single column (or row)\n",
    "    return x.max() - x.min()\n",
    "    \n",
    "dogs.select_dtypes(include = [\"float64\", \"int64\"]).apply(spread)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping\n",
    "\n",
    "Use the `.groupby()` method to group data before computing aggregate statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>breed</th>\n",
       "      <th>group</th>\n",
       "      <th>datadog</th>\n",
       "      <th>popularity_all</th>\n",
       "      <th>popularity</th>\n",
       "      <th>lifetime_cost</th>\n",
       "      <th>intelligence_rank</th>\n",
       "      <th>longevity</th>\n",
       "      <th>ailments</th>\n",
       "      <th>price</th>\n",
       "      <th>food_cost</th>\n",
       "      <th>grooming</th>\n",
       "      <th>kids</th>\n",
       "      <th>megarank_kids</th>\n",
       "      <th>megarank</th>\n",
       "      <th>size</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Border Collie</td>\n",
       "      <td>herding</td>\n",
       "      <td>3.64</td>\n",
       "      <td>45</td>\n",
       "      <td>39.0</td>\n",
       "      <td>20143.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.52</td>\n",
       "      <td>2.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>weekly</td>\n",
       "      <td>low</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Border Terrier</td>\n",
       "      <td>terrier</td>\n",
       "      <td>3.61</td>\n",
       "      <td>80</td>\n",
       "      <td>61.0</td>\n",
       "      <td>22638.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>833.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>weekly</td>\n",
       "      <td>high</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>small</td>\n",
       "      <td>13.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brittany</td>\n",
       "      <td>sporting</td>\n",
       "      <td>3.54</td>\n",
       "      <td>30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22589.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>618.0</td>\n",
       "      <td>466.0</td>\n",
       "      <td>weekly</td>\n",
       "      <td>medium</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>35.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cairn Terrier</td>\n",
       "      <td>terrier</td>\n",
       "      <td>3.53</td>\n",
       "      <td>59</td>\n",
       "      <td>48.0</td>\n",
       "      <td>21992.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>13.84</td>\n",
       "      <td>2.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>weekly</td>\n",
       "      <td>high</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>small</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Welsh Springer Spaniel</td>\n",
       "      <td>sporting</td>\n",
       "      <td>3.34</td>\n",
       "      <td>130</td>\n",
       "      <td>81.0</td>\n",
       "      <td>20224.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>12.49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>weekly</td>\n",
       "      <td>high</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    breed     group  datadog  popularity_all  popularity  \\\n",
       "0           Border Collie   herding     3.64              45        39.0   \n",
       "1          Border Terrier   terrier     3.61              80        61.0   \n",
       "2                Brittany  sporting     3.54              30        30.0   \n",
       "3           Cairn Terrier   terrier     3.53              59        48.0   \n",
       "4  Welsh Springer Spaniel  sporting     3.34             130        81.0   \n",
       "\n",
       "   lifetime_cost  intelligence_rank  longevity  ailments  price  food_cost  \\\n",
       "0        20143.0                1.0      12.52       2.0  623.0      324.0   \n",
       "1        22638.0               30.0      14.00       0.0  833.0      324.0   \n",
       "2        22589.0               19.0      12.92       0.0  618.0      466.0   \n",
       "3        21992.0               35.0      13.84       2.0  435.0      324.0   \n",
       "4        20224.0               31.0      12.49       1.0  750.0      324.0   \n",
       "\n",
       "  grooming    kids  megarank_kids  megarank    size  weight  height  \n",
       "0   weekly     low            1.0      29.0  medium     NaN    20.0  \n",
       "1   weekly    high            2.0       1.0   small    13.5     NaN  \n",
       "2   weekly  medium            3.0      11.0  medium    35.0    19.0  \n",
       "3   weekly    high            4.0       2.0   small    14.0    10.0  \n",
       "4   weekly    high            5.0       4.0  medium     NaN    18.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datadog</th>\n",
       "      <th>popularity_all</th>\n",
       "      <th>popularity</th>\n",
       "      <th>lifetime_cost</th>\n",
       "      <th>intelligence_rank</th>\n",
       "      <th>longevity</th>\n",
       "      <th>ailments</th>\n",
       "      <th>price</th>\n",
       "      <th>food_cost</th>\n",
       "      <th>megarank_kids</th>\n",
       "      <th>megarank</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th>herding</th>\n",
       "      <td>2.732000</td>\n",
       "      <td>99.880000</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>20691.818182</td>\n",
       "      <td>21.812500</td>\n",
       "      <td>11.728824</td>\n",
       "      <td>2.235294</td>\n",
       "      <td>814.941176</td>\n",
       "      <td>490.900000</td>\n",
       "      <td>40.300000</td>\n",
       "      <td>42.600000</td>\n",
       "      <td>36.666667</td>\n",
       "      <td>19.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hound</th>\n",
       "      <td>2.373077</td>\n",
       "      <td>104.769231</td>\n",
       "      <td>52.692308</td>\n",
       "      <td>19365.769231</td>\n",
       "      <td>54.904762</td>\n",
       "      <td>10.793529</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>746.571429</td>\n",
       "      <td>514.538462</td>\n",
       "      <td>54.769231</td>\n",
       "      <td>56.153846</td>\n",
       "      <td>63.833333</td>\n",
       "      <td>22.543478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-sporting</th>\n",
       "      <td>2.488000</td>\n",
       "      <td>82.210526</td>\n",
       "      <td>38.600000</td>\n",
       "      <td>19315.800000</td>\n",
       "      <td>46.714286</td>\n",
       "      <td>10.976000</td>\n",
       "      <td>1.352941</td>\n",
       "      <td>930.500000</td>\n",
       "      <td>409.200000</td>\n",
       "      <td>46.300000</td>\n",
       "      <td>42.800000</td>\n",
       "      <td>27.928571</td>\n",
       "      <td>14.984375</td>\n",
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       "    <tr>\n",
       "      <th>sporting</th>\n",
       "      <td>2.976000</td>\n",
       "      <td>87.428571</td>\n",
       "      <td>46.066667</td>\n",
       "      <td>20299.312500</td>\n",
       "      <td>27.782609</td>\n",
       "      <td>10.895600</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>760.125000</td>\n",
       "      <td>510.866667</td>\n",
       "      <td>27.466667</td>\n",
       "      <td>17.266667</td>\n",
       "      <td>51.966667</td>\n",
       "      <td>21.276786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>terrier</th>\n",
       "      <td>2.787500</td>\n",
       "      <td>100.250000</td>\n",
       "      <td>58.416667</td>\n",
       "      <td>20504.333333</td>\n",
       "      <td>44.750000</td>\n",
       "      <td>11.480000</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>905.760000</td>\n",
       "      <td>389.916667</td>\n",
       "      <td>37.166667</td>\n",
       "      <td>39.583333</td>\n",
       "      <td>23.413043</td>\n",
       "      <td>13.780000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toy</th>\n",
       "      <td>2.805385</td>\n",
       "      <td>54.052632</td>\n",
       "      <td>36.769231</td>\n",
       "      <td>19506.076923</td>\n",
       "      <td>48.647059</td>\n",
       "      <td>11.672941</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>686.894737</td>\n",
       "      <td>343.230769</td>\n",
       "      <td>35.076923</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>9.818182</td>\n",
       "      <td>10.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>working</th>\n",
       "      <td>2.065000</td>\n",
       "      <td>71.111111</td>\n",
       "      <td>32.285714</td>\n",
       "      <td>19164.687500</td>\n",
       "      <td>41.529412</td>\n",
       "      <td>9.465909</td>\n",
       "      <td>1.772727</td>\n",
       "      <td>1235.708333</td>\n",
       "      <td>721.500000</td>\n",
       "      <td>66.571429</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>105.000000</td>\n",
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      "text/plain": [
       "               datadog  popularity_all  popularity  lifetime_cost  \\\n",
       "group                                                               \n",
       "herding       2.732000       99.880000   43.500000   20691.818182   \n",
       "hound         2.373077      104.769231   52.692308   19365.769231   \n",
       "non-sporting  2.488000       82.210526   38.600000   19315.800000   \n",
       "sporting      2.976000       87.428571   46.066667   20299.312500   \n",
       "terrier       2.787500      100.250000   58.416667   20504.333333   \n",
       "toy           2.805385       54.052632   36.769231   19506.076923   \n",
       "working       2.065000       71.111111   32.285714   19164.687500   \n",
       "\n",
       "              intelligence_rank  longevity  ailments        price   food_cost  \\\n",
       "group                                                                           \n",
       "herding               21.812500  11.728824  2.235294   814.941176  490.900000   \n",
       "hound                 54.904762  10.793529  0.833333   746.571429  514.538462   \n",
       "non-sporting          46.714286  10.976000  1.352941   930.500000  409.200000   \n",
       "sporting              27.782609  10.895600  1.040000   760.125000  510.866667   \n",
       "terrier               44.750000  11.480000  0.653846   905.760000  389.916667   \n",
       "toy                   48.647059  11.672941  1.000000   686.894737  343.230769   \n",
       "working               41.529412   9.465909  1.772727  1235.708333  721.500000   \n",
       "\n",
       "              megarank_kids   megarank      weight     height  \n",
       "group                                                          \n",
       "herding           40.300000  42.600000   36.666667  19.730000  \n",
       "hound             54.769231  56.153846   63.833333  22.543478  \n",
       "non-sporting      46.300000  42.800000   27.928571  14.984375  \n",
       "sporting          27.466667  17.266667   51.966667  21.276786  \n",
       "terrier           37.166667  39.583333   23.413043  13.780000  \n",
       "toy               35.076923  49.000000    9.818182  10.533333  \n",
       "working           66.571429  62.000000  105.000000  25.388889  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.groupby(\"group\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the groups become the index. You can keep them as regular columns by setting `as_index = False` when grouping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>datadog</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>herding</td>\n",
       "      <td>2.732000</td>\n",
       "      <td>99.880000</td>\n",
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       "      <th>1</th>\n",
       "      <td>hound</td>\n",
       "      <td>2.373077</td>\n",
       "      <td>104.769231</td>\n",
       "      <td>52.692308</td>\n",
       "      <td>19365.769231</td>\n",
       "      <td>54.904762</td>\n",
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       "      <td>514.538462</td>\n",
       "      <td>54.769231</td>\n",
       "      <td>56.153846</td>\n",
       "      <td>63.833333</td>\n",
       "      <td>22.543478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>non-sporting</td>\n",
       "      <td>2.488000</td>\n",
       "      <td>82.210526</td>\n",
       "      <td>38.600000</td>\n",
       "      <td>19315.800000</td>\n",
       "      <td>46.714286</td>\n",
       "      <td>10.976000</td>\n",
       "      <td>1.352941</td>\n",
       "      <td>930.500000</td>\n",
       "      <td>409.200000</td>\n",
       "      <td>46.300000</td>\n",
       "      <td>42.800000</td>\n",
       "      <td>27.928571</td>\n",
       "      <td>14.984375</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sporting</td>\n",
       "      <td>2.976000</td>\n",
       "      <td>87.428571</td>\n",
       "      <td>46.066667</td>\n",
       "      <td>20299.312500</td>\n",
       "      <td>27.782609</td>\n",
       "      <td>10.895600</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>760.125000</td>\n",
       "      <td>510.866667</td>\n",
       "      <td>27.466667</td>\n",
       "      <td>17.266667</td>\n",
       "      <td>51.966667</td>\n",
       "      <td>21.276786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>terrier</td>\n",
       "      <td>2.787500</td>\n",
       "      <td>100.250000</td>\n",
       "      <td>58.416667</td>\n",
       "      <td>20504.333333</td>\n",
       "      <td>44.750000</td>\n",
       "      <td>11.480000</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>905.760000</td>\n",
       "      <td>389.916667</td>\n",
       "      <td>37.166667</td>\n",
       "      <td>39.583333</td>\n",
       "      <td>23.413043</td>\n",
       "      <td>13.780000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>toy</td>\n",
       "      <td>2.805385</td>\n",
       "      <td>54.052632</td>\n",
       "      <td>36.769231</td>\n",
       "      <td>19506.076923</td>\n",
       "      <td>48.647059</td>\n",
       "      <td>11.672941</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>686.894737</td>\n",
       "      <td>343.230769</td>\n",
       "      <td>35.076923</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>9.818182</td>\n",
       "      <td>10.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>working</td>\n",
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       "      <td>71.111111</td>\n",
       "      <td>32.285714</td>\n",
       "      <td>19164.687500</td>\n",
       "      <td>41.529412</td>\n",
       "      <td>9.465909</td>\n",
       "      <td>1.772727</td>\n",
       "      <td>1235.708333</td>\n",
       "      <td>721.500000</td>\n",
       "      <td>66.571429</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>25.388889</td>\n",
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      "text/plain": [
       "          group   datadog  popularity_all  popularity  lifetime_cost  \\\n",
       "0       herding  2.732000       99.880000   43.500000   20691.818182   \n",
       "1         hound  2.373077      104.769231   52.692308   19365.769231   \n",
       "2  non-sporting  2.488000       82.210526   38.600000   19315.800000   \n",
       "3      sporting  2.976000       87.428571   46.066667   20299.312500   \n",
       "4       terrier  2.787500      100.250000   58.416667   20504.333333   \n",
       "5           toy  2.805385       54.052632   36.769231   19506.076923   \n",
       "6       working  2.065000       71.111111   32.285714   19164.687500   \n",
       "\n",
       "   intelligence_rank  longevity  ailments        price   food_cost  \\\n",
       "0          21.812500  11.728824  2.235294   814.941176  490.900000   \n",
       "1          54.904762  10.793529  0.833333   746.571429  514.538462   \n",
       "2          46.714286  10.976000  1.352941   930.500000  409.200000   \n",
       "3          27.782609  10.895600  1.040000   760.125000  510.866667   \n",
       "4          44.750000  11.480000  0.653846   905.760000  389.916667   \n",
       "5          48.647059  11.672941  1.000000   686.894737  343.230769   \n",
       "6          41.529412   9.465909  1.772727  1235.708333  721.500000   \n",
       "\n",
       "   megarank_kids   megarank      weight     height  \n",
       "0      40.300000  42.600000   36.666667  19.730000  \n",
       "1      54.769231  56.153846   63.833333  22.543478  \n",
       "2      46.300000  42.800000   27.928571  14.984375  \n",
       "3      27.466667  17.266667   51.966667  21.276786  \n",
       "4      37.166667  39.583333   23.413043  13.780000  \n",
       "5      35.076923  49.000000    9.818182  10.533333  \n",
       "6      66.571429  62.000000  105.000000  25.388889  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.groupby(\"group\", as_index = False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can group by multiple columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>datadog</th>\n",
       "      <th>popularity_all</th>\n",
       "      <th>popularity</th>\n",
       "      <th>lifetime_cost</th>\n",
       "      <th>intelligence_rank</th>\n",
       "      <th>longevity</th>\n",
       "      <th>ailments</th>\n",
       "      <th>price</th>\n",
       "      <th>food_cost</th>\n",
       "      <th>megarank_kids</th>\n",
       "      <th>megarank</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th>kids</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">herding</th>\n",
       "      <th>high</th>\n",
       "      <td>2.576667</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>21171.142857</td>\n",
       "      <td>26.125000</td>\n",
       "      <td>11.702000</td>\n",
       "      <td>2.111111</td>\n",
       "      <td>848.875000</td>\n",
       "      <td>531.166667</td>\n",
       "      <td>48.833333</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>19.909091</td>\n",
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       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>3.445000</td>\n",
       "      <td>52.500000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>20269.000000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>12.095000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>576.500000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>40.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.250000</td>\n",
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       "      <th>medium</th>\n",
       "      <td>2.485000</td>\n",
       "      <td>61.666667</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>19437.000000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>10.936667</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>692.500000</td>\n",
       "      <td>466.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">hound</th>\n",
       "      <th>high</th>\n",
       "      <td>2.295000</td>\n",
       "      <td>98.250000</td>\n",
       "      <td>54.166667</td>\n",
       "      <td>20165.500000</td>\n",
       "      <td>56.625000</td>\n",
       "      <td>10.678571</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1028.714286</td>\n",
       "      <td>601.500000</td>\n",
       "      <td>60.666667</td>\n",
       "      <td>49.166667</td>\n",
       "      <td>72.500000</td>\n",
       "      <td>22.785714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>3.190000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>20113.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>12.630000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>423.000000</td>\n",
       "      <td>324.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medium</th>\n",
       "      <td>2.315000</td>\n",
       "      <td>82.500000</td>\n",
       "      <td>58.500000</td>\n",
       "      <td>18441.500000</td>\n",
       "      <td>61.666667</td>\n",
       "      <td>10.535000</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>841.000000</td>\n",
       "      <td>459.333333</td>\n",
       "      <td>55.666667</td>\n",
       "      <td>63.500000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">non-sporting</th>\n",
       "      <th>high</th>\n",
       "      <td>2.723333</td>\n",
       "      <td>70.714286</td>\n",
       "      <td>42.333333</td>\n",
       "      <td>20368.000000</td>\n",
       "      <td>50.428571</td>\n",
       "      <td>11.844286</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>896.333333</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>27.666667</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>13.950000</td>\n",
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       "    <tr>\n",
       "      <th>medium</th>\n",
       "      <td>2.135000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>17737.500000</td>\n",
       "      <td>54.750000</td>\n",
       "      <td>9.890000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1308.750000</td>\n",
       "      <td>430.500000</td>\n",
       "      <td>57.250000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>34.500000</td>\n",
       "      <td>16.666667</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">sporting</th>\n",
       "      <th>high</th>\n",
       "      <td>2.935714</td>\n",
       "      <td>70.176471</td>\n",
       "      <td>47.214286</td>\n",
       "      <td>20146.666667</td>\n",
       "      <td>24.125000</td>\n",
       "      <td>10.986250</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>705.875000</td>\n",
       "      <td>514.071429</td>\n",
       "      <td>29.214286</td>\n",
       "      <td>17.714286</td>\n",
       "      <td>55.791667</td>\n",
       "      <td>21.794118</td>\n",
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       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>NaN</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medium</th>\n",
       "      <td>3.540000</td>\n",
       "      <td>108.333333</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>22589.000000</td>\n",
       "      <td>31.333333</td>\n",
       "      <td>11.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>618.000000</td>\n",
       "      <td>466.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>20.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">terrier</th>\n",
       "      <th>high</th>\n",
       "      <td>3.024000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>57.200000</td>\n",
       "      <td>21680.600000</td>\n",
       "      <td>48.875000</td>\n",
       "      <td>12.213333</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>380.800000</td>\n",
       "      <td>28.400000</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>20.187500</td>\n",
       "      <td>12.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medium</th>\n",
       "      <td>2.618571</td>\n",
       "      <td>89.111111</td>\n",
       "      <td>59.285714</td>\n",
       "      <td>19664.142857</td>\n",
       "      <td>43.250000</td>\n",
       "      <td>11.391429</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>1042.555556</td>\n",
       "      <td>396.428571</td>\n",
       "      <td>43.428571</td>\n",
       "      <td>53.142857</td>\n",
       "      <td>23.875000</td>\n",
       "      <td>13.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">toy</th>\n",
       "      <th>high</th>\n",
       "      <td>2.740000</td>\n",
       "      <td>19.333333</td>\n",
       "      <td>19.333333</td>\n",
       "      <td>19439.333333</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>11.830000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>689.666667</td>\n",
       "      <td>351.000000</td>\n",
       "      <td>40.333333</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>14.666667</td>\n",
       "      <td>12.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>2.844000</td>\n",
       "      <td>36.800000</td>\n",
       "      <td>30.800000</td>\n",
       "      <td>20409.800000</td>\n",
       "      <td>54.400000</td>\n",
       "      <td>12.674000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>785.600000</td>\n",
       "      <td>313.200000</td>\n",
       "      <td>33.600000</td>\n",
       "      <td>66.400000</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>9.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medium</th>\n",
       "      <td>2.806000</td>\n",
       "      <td>77.400000</td>\n",
       "      <td>53.200000</td>\n",
       "      <td>18642.400000</td>\n",
       "      <td>37.400000</td>\n",
       "      <td>11.150000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>746.000000</td>\n",
       "      <td>368.600000</td>\n",
       "      <td>33.400000</td>\n",
       "      <td>45.400000</td>\n",
       "      <td>9.666667</td>\n",
       "      <td>9.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">working</th>\n",
       "      <th>high</th>\n",
       "      <td>1.992222</td>\n",
       "      <td>53.333333</td>\n",
       "      <td>30.111111</td>\n",
       "      <td>18153.272727</td>\n",
       "      <td>48.444444</td>\n",
       "      <td>8.947273</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1292.083333</td>\n",
       "      <td>684.000000</td>\n",
       "      <td>67.222222</td>\n",
       "      <td>56.333333</td>\n",
       "      <td>124.583333</td>\n",
       "      <td>25.958333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>1.950000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>20994.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>10.160000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1202.000000</td>\n",
       "      <td>710.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medium</th>\n",
       "      <td>2.257500</td>\n",
       "      <td>57.400000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>21488.750000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>10.172000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>982.000000</td>\n",
       "      <td>808.750000</td>\n",
       "      <td>63.750000</td>\n",
       "      <td>68.750000</td>\n",
       "      <td>90.833333</td>\n",
       "      <td>25.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      datadog  popularity_all  popularity  lifetime_cost  \\\n",
       "group        kids                                                          \n",
       "herding      high    2.576667       96.000000   53.500000   21171.142857   \n",
       "             low     3.445000       52.500000   44.000000   20269.000000   \n",
       "             medium  2.485000       61.666667   13.000000   19437.000000   \n",
       "hound        high    2.295000       98.250000   54.166667   20165.500000   \n",
       "             low     3.190000        9.000000    9.000000   20113.000000   \n",
       "             medium  2.315000       82.500000   58.500000   18441.500000   \n",
       "non-sporting high    2.723333       70.714286   42.333333   20368.000000   \n",
       "             medium  2.135000       42.000000   33.000000   17737.500000   \n",
       "sporting     high    2.935714       70.176471   47.214286   20146.666667   \n",
       "             low          NaN      161.000000         NaN            NaN   \n",
       "             medium  3.540000      108.333333   30.000000   22589.000000   \n",
       "terrier      high    3.024000       98.000000   57.200000   21680.600000   \n",
       "             medium  2.618571       89.111111   59.285714   19664.142857   \n",
       "toy          high    2.740000       19.333333   19.333333   19439.333333   \n",
       "             low     2.844000       36.800000   30.800000   20409.800000   \n",
       "             medium  2.806000       77.400000   53.200000   18642.400000   \n",
       "working      high    1.992222       53.333333   30.111111   18153.272727   \n",
       "             low     1.950000       47.000000   41.000000   20994.000000   \n",
       "             medium  2.257500       57.400000   35.000000   21488.750000   \n",
       "\n",
       "                     intelligence_rank  longevity  ailments        price  \\\n",
       "group        kids                                                          \n",
       "herding      high            26.125000  11.702000  2.111111   848.875000   \n",
       "             low              5.500000  12.095000  1.500000   576.500000   \n",
       "             medium          22.500000  10.936667  5.000000   692.500000   \n",
       "hound        high            56.625000  10.678571  1.000000  1028.714286   \n",
       "             low             49.000000  12.630000  2.000000   423.000000   \n",
       "             medium          61.666667  10.535000  1.166667   841.000000   \n",
       "non-sporting high            50.428571  11.844286  0.571429   896.333333   \n",
       "             medium          54.750000   9.890000  2.250000  1308.750000   \n",
       "sporting     high            24.125000  10.986250  1.470588   705.875000   \n",
       "             low                   NaN  11.170000  0.000000          NaN   \n",
       "             medium          31.333333  11.190000  0.000000   618.000000   \n",
       "terrier      high            48.875000  12.213333  0.750000   800.000000   \n",
       "             medium          43.250000  11.391429  0.888889  1042.555556   \n",
       "toy          high            57.000000  11.830000  1.333333   689.666667   \n",
       "             low             54.400000  12.674000  1.200000   785.600000   \n",
       "             medium          37.400000  11.150000  1.400000   746.000000   \n",
       "working      high            48.444444   8.947273  2.300000  1292.083333   \n",
       "             low             54.000000  10.160000  1.000000  1202.000000   \n",
       "             medium          23.000000  10.172000  2.500000   982.000000   \n",
       "\n",
       "                      food_cost  megarank_kids   megarank      weight  \\\n",
       "group        kids                                                       \n",
       "herding      high    531.166667      48.833333  37.500000   31.000000   \n",
       "             low     395.000000       5.000000  40.500000         NaN   \n",
       "             medium  466.000000      50.000000  60.000000         NaN   \n",
       "hound        high    601.500000      60.666667  49.166667   72.500000   \n",
       "             low     324.000000      14.000000  54.000000   24.000000   \n",
       "             medium  459.333333      55.666667  63.500000   67.000000   \n",
       "non-sporting high    395.000000      39.000000  27.666667   18.000000   \n",
       "             medium  430.500000      57.250000  65.500000   34.500000   \n",
       "sporting     high    514.071429      29.214286  17.714286   55.791667   \n",
       "             low            NaN            NaN        NaN   40.000000   \n",
       "             medium  466.000000       3.000000  11.000000   35.000000   \n",
       "terrier      high    380.800000      28.400000  20.600000   20.187500   \n",
       "             medium  396.428571      43.428571  53.142857   23.875000   \n",
       "toy          high    351.000000      40.333333  26.000000   14.666667   \n",
       "             low     313.200000      33.600000  66.400000    6.250000   \n",
       "             medium  368.600000      33.400000  45.400000    9.666667   \n",
       "working      high    684.000000      67.222222  56.333333  124.583333   \n",
       "             low     710.000000      72.000000  86.000000         NaN   \n",
       "             medium  808.750000      63.750000  68.750000   90.833333   \n",
       "\n",
       "                        height  \n",
       "group        kids               \n",
       "herding      high    19.909091  \n",
       "             low     19.250000  \n",
       "             medium  21.000000  \n",
       "hound        high    22.785714  \n",
       "             low           NaN  \n",
       "             medium  23.000000  \n",
       "non-sporting high    13.950000  \n",
       "             medium  16.666667  \n",
       "sporting     high    21.794118  \n",
       "             low     14.000000  \n",
       "             medium  20.500000  \n",
       "terrier      high    12.937500  \n",
       "             medium  13.312500  \n",
       "toy          high    12.750000  \n",
       "             low      9.333333  \n",
       "             medium   9.916667  \n",
       "working      high    25.958333  \n",
       "             low     26.000000  \n",
       "             medium  25.600000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.groupby([\"group\", \"kids\"]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On groups, the `.apply()` method computes group-by-group. It is the most general form of two other methods:\n",
    "\n",
    "* `.agg()`, which applies a function to each group to compute summary statistics\n",
    "* `.transform()`, which applies a function to each group to compute transformations (such as standardization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dogs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-18aa23a329e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mbreakpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdogs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"group\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'dogs' is not defined"
     ]
    }
   ],
   "source": [
    "def f(x):\n",
    "    ''' Like R's browser() function, the breakpoint() function runs the debugger.\n",
    "    The debugger is especially useful for checking what's going on in a function.'''\n",
    "    breakpoint()\n",
    "    \n",
    "dogs.groupby(\"group\").apply(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python Ipdb Cheatsheet [here](https://kapeli.com/cheat_sheets/Python_Debugger.docset/Contents/Resources/Documents/index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tidying a Dataset\n",
    "\n",
    "Do Americans prefer low fat milk over whole milk?\n",
    "\n",
    "The USDA publishes data about dairy production. We can answer the question with the [Milk Sales Dataset](https://www.ers.usda.gov/webdocs/DataFiles/48685/fluidmilk.xlsx?v=5010.6).\n",
    "\n",
    "Many of Python's visualization packages expect [tidy data](https://vita.had.co.nz/papers/tidy-data.pdf), which means:\n",
    "\n",
    "1. Each feature must have its own column.\n",
    "2. Each observation must have its own row.\n",
    "3. Each value must have its own cell.\n",
    "\n",
    "Let's tidy up the Milk Sales Dataset so we can make a line plot that shows how milk sales have changed over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Year</th>\n",
       "      <th>Kind</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1975</td>\n",
       "      <td>Whole</td>\n",
       "      <td>36188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1975</td>\n",
       "      <td>Reduced-fat \\n(2% milk fat)</td>\n",
       "      <td>8726.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1975</td>\n",
       "      <td>Low-fat \\n(1% milk fat)</td>\n",
       "      <td>2742.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1975</td>\n",
       "      <td>Skim</td>\n",
       "      <td>2480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1975</td>\n",
       "      <td>Flavored \\nwhole 1</td>\n",
       "      <td>1366.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year                         Kind    Sales\n",
       "0  1975                        Whole  36188.0\n",
       "1  1975  Reduced-fat \\n(2% milk fat)   8726.0\n",
       "2  1975      Low-fat \\n(1% milk fat)   2742.0\n",
       "3  1975                         Skim   2480.0\n",
       "4  1975           Flavored \\nwhole 1   1366.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "milk = pd.read_excel(\"fluidmilk.xlsx\", skiprows = 1)\n",
    "\n",
    "# quantities in millions of pounds\n",
    "\n",
    "# Rename first column and fix last column name\n",
    "# Also see .rename({\"Whole\": \"Whole Milk\"})\n",
    "milk.columns.values[0] = \"Year\"\n",
    "\n",
    "milk.columns.values[-1] = milk.columns.values[-1].strip(\"1\")\n",
    "\n",
    "# Slice to get rid of extra rows at the end\n",
    "milk = milk[:-3]\n",
    "\n",
    "# Check data types\n",
    "milk.dtypes\n",
    "\n",
    "# Move columns in tidy format into the index\n",
    "milk = milk.set_index(\"Year\")\n",
    "\n",
    "milk = milk.stack()\n",
    "\n",
    "# You can use .reset_index() to get a regular data frame\n",
    "milk = milk.reset_index()\n",
    "milk.columns.values[[1, 2]] = [\"Kind\", \"Sales\"]\n",
    "\n",
    "# MultiIndexes are convenient for subsetting the data\n",
    "milk.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization in Python\n",
    "\n",
    "We will use the plotnine package, an implementation of ggplot2 for Python. Unlike packages we've seen so far, plotnine is not included with Anaconda. To install the package:\n",
    "\n",
    "* On Windows, run `conda install -c conda-forge plotnine` in an Anaconda Prompt (find it in the start menu)\n",
    "* On MacOS or Linux, run `conda install -c conda-forge plotnine` in the Terminal\n",
    "\n",
    "You may have to restart Jupyter after installing. To test your installation, try running"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.8.0'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotnine\n",
    "\n",
    "plotnine.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Python visualization landscape](img/visualization_landscape.png)\n",
    "_Image from [Jake VanderPlas](https://speakerdeck.com/jakevdp/pythons-visualization-landscape-pycon-2017). See [here](https://rougier.github.io/python-visualization-landscape/landscape-colors.html) for a version with links to all of the packages!_\n",
    "\n",
    "Our focus right now is _static_ visualization, where the visualization is a still image. So what packages should you actually use?\n",
    "\n",
    "* __plotnine__ is convenient if you already know ggplot2. It's relatively new, so there are some bugs and missing features.\n",
    "\n",
    "* __seaborn__ is designed specifically for making statistical plots. It's well-documented and stable. Most of the package's functions expect tidy data as input.\n",
    "\n",
    "* __matplotlib__ is useful to know, since many other packages use matplotlib under the hood. That said, using matplotlib alone to create plots is painful; matplotlib is _low-level_, so it's flexible but simple plots may take [5 lines of code or more][ex]. The matplotlib PyPlot tools may be convenient if you already know MATLAB.\n",
    "\n",
    "* __pandas__ provides built-in plotting functions, which can be convenient but are more limited than the packages above. They're also inconsistent about the expected format of the data.\n",
    "\n",
    "We don't have time to exhaustively cover visualization packages for Python. You're welcome to explore other packages while doing the assignments for this class.\n",
    "\n",
    "Later in the quarter, we'll see some of Python's _interactive_ visualization packages.\n",
    "\n",
    "[ex]: https://dsaber.com/2016/10/02/a-dramatic-tour-through-pythons-data-visualization-landscape-including-ggplot-and-altair/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jupyter and matplotlib\n",
    "\n",
    "Jupyter notebooks can display most static visualizations and some interactive visualizations. If you're going to use visualization packages that depend on matplotlib, it's a good idea to set up your notebook by running:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize matplotlib\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Change the size of the plot\n",
    "plt.rcParams[\"figure.figsize\"] = [10, 8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplot cheat sheet [see here][link1] \n",
    "\n",
    "[link1]:https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Matplotlib_Cheat_Sheet.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Milk Dataset with plotnine\n",
    "\n",
    "See the [plotnine documentation](https://plotnine.readthedocs.io/en/latest/)! Also see the [ggplot2 documentation](https://ggplot2.tidyverse.org/reference/) and the [ggplot2 cheatsheet](https://www.maths.usyd.edu.au/u/UG/SM/STAT3022/r/current/Misc/data-visualization-2.1.pdf). If you run into a bug, you may want to check for a work-around on the [plotnine bug tracker](https://github.com/has2k1/plotnine/issues).\n",
    "\n",
    "We can avoid typing `plotnine.` over and over again by importing all of plotnine's objects directly into the global namespace:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotnine import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Year</th>\n",
       "      <th>Kind</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1975</td>\n",
       "      <td>Whole</td>\n",
       "      <td>36188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1975</td>\n",
       "      <td>Reduced-fat \\n(2% milk fat)</td>\n",
       "      <td>8726.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1975</td>\n",
       "      <td>Low-fat \\n(1% milk fat)</td>\n",
       "      <td>2742.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1975</td>\n",
       "      <td>Skim</td>\n",
       "      <td>2480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1975</td>\n",
       "      <td>Flavored \\nwhole 1</td>\n",
       "      <td>1366.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1975</td>\n",
       "      <td>Flavored, \\nother than whole</td>\n",
       "      <td>719.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1975</td>\n",
       "      <td>Buttermilk</td>\n",
       "      <td>1011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1975</td>\n",
       "      <td>Eggnog</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1975</td>\n",
       "      <td>Total 2</td>\n",
       "      <td>53308.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1976</td>\n",
       "      <td>Whole</td>\n",
       "      <td>35241.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year                          Kind    Sales\n",
       "0  1975                         Whole  36188.0\n",
       "1  1975   Reduced-fat \\n(2% milk fat)   8726.0\n",
       "2  1975       Low-fat \\n(1% milk fat)   2742.0\n",
       "3  1975                          Skim   2480.0\n",
       "4  1975            Flavored \\nwhole 1   1366.0\n",
       "5  1975  Flavored, \\nother than whole    719.0\n",
       "6  1975                    Buttermilk   1011.0\n",
       "7  1975                        Eggnog     76.0\n",
       "8  1975                       Total 2  53308.0\n",
       "9  1976                         Whole  35241.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "milk.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing other packages this way is not recommended, since names of objects in a package may overlap with names of objects you've defined.\n",
    "\n",
    "\n",
    "The syntax of plotnine closely follows the syntax of R's ggplot2. In R, we would write\n",
    "\n",
    "```r\n",
    "ggplot2(milk, aes(x = Year, y = Sales, color = Kind)) + geom_line() \n",
    "```\n",
    "\n",
    "One important difference is that plotnine requires that we quote variable names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yuchien/opt/anaconda3/lib/python3.8/site-packages/plotnine/geoms/geom_path.py:81: PlotnineWarning: geom_path: Each group consist of only one observation. Do you need to adjust the group aesthetic?\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8775391345098)>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    ggplot(milk, aes(x = \"Year\", y = \"Sales\", color = \"Kind\")) + geom_line()\n",
    "    + labs(title = \"US Milk Sales\", y = \"Sales (millions of pounds)\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year      object\n",
       "Kind      object\n",
       "Sales    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "milk.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "milk['Year'] = pd.to_numeric(milk['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year       int64\n",
       "Kind      object\n",
       "Sales    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "milk.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8775399017111)>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    ggplot(milk, aes(x = \"Year\", y = \"Sales\", color = \"Kind\")) + geom_line()\n",
    "    + labs(title = \"US Milk Sales\", y = \"Sales (millions of pounds)\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plotnine includes the familiar `ggsave()` function for saving a visualization to an image file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Milk Dataset with seaborn\n",
    "\n",
    "Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.\n",
    "\n",
    "Seaborn library is included as part of the Anaconda distribution.\n",
    "\n",
    "See the [seaborn documentation](https://seaborn.pydata.org/)!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.11.1'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In seaborn, the __hue__ parameter determines which column in the data frame should be used for colour encoding. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'US Milk Sales')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.lineplot(x = \"Year\", y = \"Sales\", hue = \"Kind\", data = milk)\n",
    "ax.set_title(\"US Milk Sales\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to adjust the size and layout, we have to learn more about matplotlib."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Basics of matplotlib\n",
    "\n",
    "See the [matplotlib documentation](https://matplotlib.org/contents.html)!\n",
    "\n",
    "First, let's change the size of the figures in the notebook. To do that, we need to go back to the code we used to initialize matplotlib, and adjust `rcParams`, matplotlib's default settings.\n",
    "\n",
    "### Jargon\n",
    "\n",
    "The most important thing to know is matplotlib's jargon:\n",
    "\n",
    "* _Figure_: Container for plots.\n",
    "* _Axes_: Container for components of a plot (\"primitives\"). In other words, an axes is a single plot.\n",
    "* _Axis_: Container for components of an axis. An axis is a single axis.\n",
    "* _Tick_: A container for tick marks on an axis.\n",
    "\n",
    "All of the containers and the primitives are called _Artists_.\n",
    "\n",
    "### Saving Figures\n",
    "\n",
    "You can save figures to an image file with the `.savefig()` method.\n",
    "\n",
    "You can also get the Figure that contains an Axes with the `.get_figure()` method. So to save our seaborn plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.lineplot(x = \"Year\", y = \"Sales\", hue = \"Kind\", data = milk)\n",
    "ax.set_title(\"US Milk Sales\")\n",
    "\n",
    "plt.savefig('seabornplot.png')\n",
    "\n",
    "ax.get_figure().savefig('output.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing Packages\n",
    "\n",
    "Let's use the familiar dogs dataset to further compare the different plotting packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "dogs = pd.read_csv(\"dogs_full.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dot Plots\n",
    "\n",
    "Plot the number of dogs in each category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8775366119385)>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plotnine\n",
    "\n",
    "p = ggplot(dogs, aes(x = \"group\")) + geom_point(stat = \"count\")\n",
    "p + labs(title = \"Dog Groups\", x = \"Group\", y = \"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'terrier'),\n",
       " Text(1, 0, 'sporting'),\n",
       " Text(2, 0, 'working'),\n",
       " Text(3, 0, 'hound'),\n",
       " Text(4, 0, 'herding'),\n",
       " Text(5, 0, 'non-sporting'),\n",
       " Text(6, 0, 'toy')]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn\n",
    "counts = dogs[\"group\"].value_counts()\n",
    "# Pandas calls the rownames an \"index\"\n",
    "\n",
    "ax = sns.stripplot(x = counts.index, y = counts)\n",
    "ax.set(title = \"Dog Groups\", xlabel = \"Group\", ylabel = \"Count\")\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Dog Groups'), Text(0.5, 0, 'Group'), Text(0, 0.5, 'Count')]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pandas\n",
    "counts = dogs[\"group\"].value_counts()\n",
    "\n",
    "ax = counts.plot(style = \"o\")\n",
    "ax.set(title = \"Dog Groups\", xlabel = \"Group\", ylabel = \"Count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box Plots\n",
    "\n",
    "Plot the distribution of dog longevity, grouped by category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yuchien/opt/anaconda3/lib/python3.8/site-packages/plotnine/layer.py:324: PlotnineWarning: stat_boxplot : Removed 37 rows containing non-finite values.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8775366608095)>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plotnine\n",
    "\n",
    "p = ggplot(dogs, aes(\"group\", \"longevity\")) + geom_boxplot()\n",
    "p + labs(title = \"Dog Longevity\", x = \"Group\", y = \"Years\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'herding'),\n",
       " Text(1, 0, 'terrier'),\n",
       " Text(2, 0, 'sporting'),\n",
       " Text(3, 0, 'toy'),\n",
       " Text(4, 0, 'working'),\n",
       " Text(5, 0, 'non-sporting'),\n",
       " Text(6, 0, 'hound')]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn\n",
    "\n",
    "ax = sns.boxplot(x = \"group\", y = \"longevity\", data = dogs)\n",
    "ax.set(title = \"Dog Longevity\", xlabel = \"Group\", ylabel = \"Years\")\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pandas\n",
    "\n",
    "ax = dogs.boxplot(by = \"group\", column = \"longevity\", rot = 45)\n",
    "# Set title and axis labels.\n",
    "ax.set(title = \"Dog Longevity\", xlabel = \"Group\", ylabel = \"Years\")\n",
    "# Hide grouping title Pandas adds.\n",
    "ax.get_figure().suptitle(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot popularity against datadog score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yuchien/opt/anaconda3/lib/python3.8/site-packages/plotnine/layer.py:401: PlotnineWarning: geom_point : Removed 85 rows containing missing values.\n"
     ]
    },
    {
     "data": {
      "image/png": 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DDXJ0dMyzXaPRWKzYsJeipajgsBERFVm///47QkND0bdvX7Rv3x4dOnRAWlqa1LHoH1xdXfHOO++YzE1RqVQYMmQIi44iisULERVJCQkJiI6OhlarhV6vhyiKOH36NCZOnCh1NMrDjBkzMHDgQPj4+MDPzw+ffPIJvvzyS6ljkUR4fZSIiqTz58/DYDCYtGm1Whw5ckSiRPRvHBwcMGnSJEyaNEnqKGQDWLwQUZFUrFixPCeIFi9evPDDkF27ceMGjh49CgcHBzRt2hQeHh5SR5I9Fi9EVCSFhoYiLCwMf//9N7RaLYBnd6/85z//kTgZ2ZPdu3ejX79+AJ7d0VSsWDFs2bIFZcuWlTiZvHHOCxEVSUqlEqtXr0bfvn1RuXJl1KtXD7/++iuaNm0qdTSyE5mZmRg8eDBycnKQk5MDrVaLx48fY+jQoVJHkz1eeSGiIsvFxQXffPON1DHITt29exfp6ekmbTqdDhcvXpQokf1g8UJEZGVPnjzBhg0bkJKSglq1aqFJkyZSR6JC8LItBjw9PQs5if1h8UJEZEX3799HixYtkJSUBEEQoNVq8dFHH2HUqFFSRyMr8/DwwNChQzF//nzj5HBBEDBhwgSJk8kfixciIiuaOHEiHj58aJwUDDxbs6RTp06oUqWKhMmoMIwbNw4hISHYvn07HB0d0adPH86rsgAWL0REVnTp0iWTwgV4tnNxbGwsi5ciQBAE9O3bF3379pU6il1h8UJEZEVBQUG4ePGiyZoyOp0O/v7+BT6mwWDAr7/+itOnT6NEiRLo168fSpUqZYm4RLLA4oWIyIpGjx6Nffv2ITs7GzqdDkqlEp06dUKtWrUKdDxRFDFs2DBs3LgRoihCoVBg8eLF2LNnDwICAiycnsg2sXghIrKismXLYv/+/fjpp5+Mdxv16tWrwBsK/vnnn1i3bh1EUQQA6PV6pKamYsqUKZg7d64loxPZLBYvRERWFhgYiLFjx1rkWLdv34ZarUZOTo6xTafTIT4+3iLHJ5IDFi9ElG87duzA+vXrAQBRUVFo3bq1xImKnpCQkDwnAFeoUEGiRESFj9sDEFG+LF++HH369MHGjRuxceNG9OvXD0uXLpU6VpFTq1YtDBw4EEqlEhqNBhqNBl5eXlw3hooUQXw+cGpHkpKSrHp8pVIJDw8PpKSk5LkrLRWMm5sbUlNTpY5hNyzZT0VRRFBQEDIzM03aHRwccOvWLSgURedzkC30U1EUsX37dpw6dQqenp7o3r27bHfD5vupddhCPy0ILy+vfL2Ow0ZE9EqZmZm5ChcAyM7ORnp6Otzc3CRIVXQJgoDWrVtz2I6KrKLzcYmICszZ2Rm+vr4mV1gEQYC3tzdcXV0lTEYEZGVlYcKECWjevDm6du2Kw4cPSx2JrIxXXogoXxYvXox33nkHOp0OgiAY1xcp6C2/RJZgMBjQs2dPHDt2DFqtFoIgYP/+/Vi3bh3Cw8OljkdWwuKFiPKlTp06OHz4MPbu3QsAaNKkCUqXLi1xKirqTp06hYMHDxofP5/G+d1337F4sWMsXogo3wICAtCnTx+pYxAZpaSkQKlUmkz2FUURycnJEqYia+OcFyIimXn48CEGDBiA6tWrIyIiArt375Y6kmSqVKmSa+hSrVajTp06EiWiwsDihYhIRjIzM9GhQwfs2LEDCQkJOHfuHHr16oUDBw5IHU0Sfn5+mDNnDpRKJdRqNRQKBSpXrowJEyZIHY2siMNGREQycvDgQcTHx+caJpk3bx4aNWokYTLpdO7cGaGhoTh79iyKFSuGevXqQa1WSx2LrEjy4qVr164mj3NyclC7dm2MHj0aADB48GA8fvzYeItmyZIlMWfOnELPSURkC9LT0/Oc4yHHBcksKSgoCEFBQVLHoEIiefGyZs0a49/1ej0GDRqEBg0amLxm5MiRCA0NLexoRGRHrl27hg8++ACXLl1CiRIlMGHCBHTq1EnqWGarVasW/rkwulqtRpMmTaQJRCQBm5rzcvLkSWRlZaF+/fpSRyEiO5KcnIz27dvj3LlzyM7Oxr179/Duu+8ab/uWk6CgICxcuBAajcbY1qpVK3z00UcSpiIqXJJfeXlRTEwMwsPD4eDgYNL+ww8/QBRFBAYGonfv3qhSpYrJ8wkJCUhISDA+dnBwgJ+fn9VyKpVKk/+SZQiCwJ+pBbGf/s++ffvw9OnTXEMtK1asQGRkpFnHsoV+2rFjRzRs2BDXrl2Dp6cnypcvL9vFAtlPrcMW+qk12Uzx8vTpU/z555/4+uuvTdo/+eQTlC1bFsCz4mbChAmYNWsWvL29ja9ZsGCByczyUaNGYfLkyVbP7O7ubvVzFDUvfpoky2A/hfEulH8yGAzw8PAw+3i20E89PDxQrlw5qWNYzIv9VKfTQavVwsnJScJE8mcL/dRabKZ42bdvH3x9fVGxYkWT9hevsrRp0wYHDx7E33//bbIhWXR0NDp06GB87ODggJSUFKtlVSqVcHd3z/VJjl6Pi4sL0tPTpY5hN9hP/6dmzZq55okolUpERkaa/V7BfmpZL/bTrKwsfPHFF1i2bBkMBgNq1aqFZcuWwd/fX+qYsiPXfprfDxM2U7zExMTk6/KtQqHI9Sbk6+sLX19f4+OkpKRCebPW6/VF/peCJYmiyJ+nFbCfAqVLl8ayZcswZMgQpKWlQRAEDBs2DD179jT7Z8N+ah16vR5fffUVVqxYAYPBAAA4e/Ys3n77bezfvx8qlc38upIFe++nNtEb4uLicOvWrVyz5R8+fIgHDx6gQoUKAIA9e/bg2rVrGDZsmAQpiUjOIiMjcenSJdy7dw8lSpRAsWLFpI5E/7BmzRpotVrjY51Oh6tXryIuLi7XVXkq2myieNm9ezdq166d63JRZmYmFi5ciISEBKhUKpQuXRpjxowxucpCRJRfjo6OCAkJkToGvcTzKy5Er2ITxUt0dHSe7YGBgZg5c2YhpyEiosI2f/58PHr0yKRNpVIhODjYeNMG0XM2tc4LEREVPVu3bsWXX36Zaz5jSEgI1q5dy/kulAuLFyIiktS2bdty3cru4OCArl278k4jyhPLWSIiK0hPT8fly5fh4OCAypUr2/WCYa9Lo9HkWmRPFEVurkgvxSsvREQWdubMGYSGhqJVq1Zo2rQpWrVqZdW1p+SuV69eJkNGCoUCSqUSbdu2lTAV2TIWL0REFpSdnY3u3bubFCsXLlzAiBEjJExl22rXro3Vq1ejdOnS0Gg0KFu2LDZt2sRdoumlOGxERGRBN2/eRFJSkkmbVqvFwYMHJUokD82aNcPJkyeljkEyweKFyEZkZWXh+PHjSE9Px5tvvsn1jGTK2dnZrHYiMh+LFyIbkJSUhA4dOiAuLs443v/TTz+ZveMxSc/f3x8tWrTA3r17javFKpVKDB8+XOJkRPaDc16IbMB//vMf3LhxAwaDATqdDtnZ2Rg4cCCePHkidTQykyAIWLx4MXr37g1/f3+EhIRgypQpGDJkiNTRiOwGr7wQ2YATJ06Y7OkCPNseIzY2FqGhoRKlooJycnLCd999h++++07qKER2icULkQ3w8PDAw4cPc7UXL1688MPYAVEUsX//fsTFxaF06dKIjIzMtQgaEckXixciG/DFF19g0KBBxrUu1Go1WrRowU0EC0AURQwbNgzr1q2DRqOBVqtFkyZNsGLFCi4zT2Qn+FGEyAa0b98eK1asQMOGDVGjRg18+OGHWLRoUa5VR+nVtmzZgvXr18NgMCArKwt6vR4HDhzA8uXLpY5GRBbCjyFENqJFixZo0aKF1DFk7+LFi1CpVNDr9cY2vV6PixcvSpiKiCyJxQsR2ZVSpUrBYDCYtKlUKnh7e0uUyPpu3LiB3377zThExkneZO84bEREduWdd95BUFCQcVM/tVqN4sWLY9CgQRIns46TJ08iPDwc3377LWbMmIHWrVvj119/lToWkVWxeCEiu+Li4oIdO3bgvffeQ4sWLTBgwADs3bsXJUqUkDqaVQwfPhw5OTnIyclBdnY2RFHEiBEjkJ6eLnU0IqvhsBER2R13d3eMHTtW6hiF4ubNm7mGybRaLRITE1G2bFmJUhFZF4sXIqJ80uv1WLhwIU6cOAEvLy8MHDgQPj4+kmby9fXFzZs3jbfZA4BCoUCpUqUkTEVkXSxeiIjywWAwoH///oiJiYFOp4NKpcKyZcuwd+9e+Pn5SZZr+vTp6Nq1KwRBgCiK0Ov1+Oqrr+Dq6ipZJiJrY/FCRJQPBw4cwM6dO41DNFqtFk+fPsV3332HH374QbJc4eHh2LVrF9atW4ecnBw0a9YMzZo1kywPUWFg8UJElA937tyBWq1Gdna2sU2n0+HmzZsSpnqmatWqqFq1qtQxiAoNixcionwoV64ccnJyTNrUajUqVapUqDk2b96MjRs3AgDefvtttG/fvlDPT2QLeKs0EVE+hIWFoV+/flAqlXBwcIBGo4G/vz8+++yzQsuwaNEivPvuu9i6dSu2bt2KwYMHY8mSJYV2fiJbIYgvTlG3E0lJSVY9vlKphIeHB1JSUkyWIKfX4+bmhtTUVKlj2A32U8sTRREHDx7En3/+CS8vL3Tp0qXQJsYaDAYEBgaaDFsBgKOjI27duiXbfbDYT61Dru+nXl5e+Xodh42IiPJJEAS0bdsWjRo1KvRzZ2Rk5CpcACArKwsZGRlwcXEp9ExEUuGwEREBeLbY2YABAxAeHo4BAwbYxERU+h9XV1f4+vqaXGFRKBTw8/Nj4UJFDq+8EBESExMRGRmJtLQ06HQ6xMbG4vDhwzhw4IDki7DR/yxZsgTvvPMOtFotgGcThn/88UeJUxEVPhYvRIRffvkFGRkZ0Ol0AJ7dApyeno7Vq1fj448/ljYcGb311ls4evQo9u/fDwBo3LgxfH19JU5FVPhYvBARnjx5gn/O3RdFEU+ePJEoEb2Mr68vunfvLnUMIklxzgsRITQ0NNedHnq9HrVq1ZIoEeXHkSNHEBERgcqVKyMqKgrx8fFSRyIqFCxeiAjt2rXDkCFDAAAq1bMLskOGDEG7du2kjEX/4syZM3j77bdx/vx5JCUl4dixY2jTpg0ePXokdTQiq+OwERFBEARMmjQJPXr0wK1btxAYGIg33nhD6lj0L5YvXw6DwWAc7tPpdHj69Cm2bduGPn36SJyOyLpYvBCR0RtvvMGiRSbS0tKMm0Q+p1AokJGRIVEiosLDYSMiIhlq3LgxlEqlSVtOTg6cnZ0RGhoKHx8f1KpVCwcPHpQoIZH1sHghIpKhHj16YPDgwcbHKpUKn3/+OT7//HPcvn0ber0ed+7cQbdu3XDp0iUJkxJZHosXIiIZej5P6dSpU9i+fTvOnz8PrVYLQRCM82BEUYQgCNi0aZO0YYksjHNeiIqY5+u3uLq6Gu8sIvkKCAhAQEAAABhX3n2RKIrGxQeJ7AWvvBAVIadPn0bNmjVRvnx5lC5dGtOnT8+1OB3JV/PmzXMVMDqdDpGRkRIlIrIOFi9ERcTDhw/x9ttvIzExEcCzX2rfffcdVq5cKXEyspS6deviv//9LzQaDQBAo9Fg5syZqFevnsTJiCyLxQtREXH06FFkZmaa3F6r1+uxfv16CVORpXXv3h1xcXE4efIk4uLi0KNHD6kjEVkcB7yJigiFQpHnEJFCwc8w/6TVanHr1i04OTnB19cXgiBIHcksjo6OKF26tNQxiKyG71pERUT9+vXh5uZmsjaIQqHgJn//cPnyZbz11lsICwtDjRo10L17d6SlpUkdi4heIIh2OFvv6dOncHBwsNrxBUGARqNBTk4OJztakEql4l0RFpRXPz137hx69OiB2NhYODk5YcKECfjwww8lTmo7cnJyULlyZSQmJho3qtRoNOjWrRsWLVoEgP3U0vh+ah1y7af5/d1tl8VLUlKSVY+vVCrh4eGBlJSUXDvxUsG5ubkhNTVV6hh249/6aVZWFhwcHGQ3HGJtly5dQqNGjXK1e3p64sqVKwDYTy2N76fWIdd+6uXlla/Xcc4LURHk6OgodQSb9LJPfda8kktkq548eYILFy7AxcUFb7zxhk2tC8U5L0RE/19wcDDq1q0LtVptbFOpVBg0aJCEqYgK37Fjx/Dmm2+iU6dOiIyMRNu2bfHkyROpYxmxeCEi+v8EQcDKlSvRsmVLuLq6okSJEvjss88wfPhwqaMRFZq0tDT07t0baWlpJvPlPv30U4mT/Y/tXAMiIrIBxYoVw9KlS6WOQSSZ2NjYXFdZtFotDh8+LFGi3Fi8UJHy+PFjHDlyBAaDAWFhYfmeHEZEVFS4ubnl2e7q6lrISV6OxQsVGVeuXEHHjh2NnyicnZ2xbt06vPnmmxInIyKyHSEhIWjatCkOHTpk3CtLoVDg448/ljbYCzjnhYqMQYMG4fHjx9DpdNDpdEhLS0Pfvn25tgQR0QsEQcBPP/2E3r17IygoCJUrV8asWbNsaqsJXnmhIkGn0+Hq1asmhYrBYEBiYiKSk5NRokQJCdMREdkWZ2dnfPfdd1LHeCkWL1QkKJVKODs7Iz093aRdoVC8dHyXyF7dvn0bMTExEEURERERCAoKkjoSkVk4bERFgiAIGDlypMkmhEqlEiNGjIBGo5EwGVHhOn78OBo0aIDRo0djzJgxaNiwoU3dRUKUH2YXL9u2bfvX5ydOnFjgMETWFB0djdmzZ6Nhw4aoX78+vv32W3z++edSxyIqVEOGDEFWVhays7ONfwYPHsy5XyQrZu9t5OzsjD/++AONGzfO9dwXX3yBGTNmIDs722IBC4J7G8mTXPfisFXsp9Yh536akZHx0iGiuLg4uLu7F3Ii9lNrkWs/tdreRl9++SU6dOiAXbt2oU6dOsb2jz/+GPPnz8eaNWvMPSQRkc3R6XRYs2YNYmNj4e/vj549e8LJyUnqWK/FyckJzs7OyMjIMGl3cHCwqTU8iF6lQMVLamoqWrdujX379qFatWqIjo7Gzz//jE2bNqFVq1bWyElEVGj0ej169OiBw4cPQxAEiKKIZcuW4Y8//pD1BG9BEDBlyhSMGDHCpH3y5Mkm88GIbF2B7jb65ptv8PTpU7Ro0QINGzbE9u3bsXXrVjRt2tTS+YiICt2mTZtw6NAh6HQ6Y1tcXBwWLVqEMWPGSJjs9fXq1QulSpXCxo0bAQAdOnRAy5YtJU5FZJ4C3yo9d+5cpKen47fffsPOnTtRr149S+YiIpLMjRs3oFQqTYoXrVaLGzduSBfKgiIjIxEZGSl1DKICy1fx4ubmBkEQcrWLoojs7GyTql0QBJvaNpuIyFyBgYG5Jo+q1WqULl0638fYvHkz9uzZA0dHR/Ts2RM1atSwdEyiIitfxct//vOfPIsXIiJ7FBUVhRUrVuDEiRMQRREKhQKBgYGIjo7O19d///33mDp1qvFrly1bhjVr1qBRo0ZWTk5UNJh9q7Qc8FZpeZLrrX22iv309Wi1WqxYsQLXrl1DQEAA+vbtC1dX11f20+TkZFSqVMlk3RRBEFCuXDkcOXKkMKLLCvupdcj1/dRqt0oTERUFarUaAwYMMPvrEhMTcy34JooiEhISLBWNqMgzu3gxGAxYvHgx1q1bhzt37iArKyvXa65fv26RcEREclO6dGmo1WpotVpjm1KpRLly5SRMlT96vR7z5s3D3r174e7ujiFDhqB+/fpSxyLKxezi5fPPP8e0adPQoEEDhIeHc18YIqIXuLm5YebMmRg2bBjUajVEUYSjoyP++9//Sh3tX4miiGHDhmHTpk3Q6XQQBAHbtm3D6tWruQwG2Ryz57z4+fnhvffew9ixY62V6bVxzos8yXWM1laxn1pHfvvpmTNncOjQITg4OKBdu3bw8fEphHQFFxcXh7CwsFzt1apVw549e6x2XvZT65Dr+6nV5rxkZWWhQYMGZgciIipKatSoIavbo1/2oe/hw4eFnITo1cxeD7pXr174/fffrZGFiIisTBRFzJ8/H02aNEGTJk0wZ84cGAwGlCtXLtc0ALVajZo1a0oTlOhfmH3lJSwsDKNHj8b9+/fRvHlzFC9ePNdrOnfubIlsRERkYd9++y1mzpxpXD34ypUrePToEcaOHYs5c+bgvffeg0qlgl6vh5+fH6ZOnSpxYqLczJ7z8qrNuwRBkHzcknNe5EmuY7S2iv3UOuTcTw0GAwICAkzuhAKe9ZU7d+5ApVIhLi4Of/31F1xcXNCkSROr7zbNfmodcu2nVpvzEh8fb3YYIiKSXnZ2dq7CBXh2i3RWVhZcXV1RtmxZlC1bVoJ0RPlndvESFBRkjRxEsqDX6zF16lSsXLkSer0eHTp0wPjx4+Ho6Ch1NKJXcnJyQqVKlXDt2jXjVQ6lUokyZcpY/QoLkSW91gq7GRkZeS5S5+np+TqHJbJZkyZNwvz5843zBZYvX46UlBQsWLBA4mRE+fPTTz8hKioKiYmJEAQBXl5eWL58udSxiMxi9pwXURQxefJkzJ8//6XLXUs9bsk5L/Jk62O0oigiMDAwz4I9Pj7e5j65sp9ah6330/xIS0vD6dOnAQA1a9aUtO+yn1qHXPup1ea8zJgxA9OmTcNnn32GL7/8EqNHj4ZSqcTq1auRk5ODL7/80uywRHIgimKe8wUAGOcLkP0yGAyvvGFBLlxdXdGwYUOpYxAVmNn/EpcsWYIJEybgs88+AwB06tQJ48aNw4ULF1C5cmXExsZaPCSRLVAoFKhfvz7UarWxTalUonz58ihRooSEychaRFHEvHnzEBwcDF9fXzRr1gxxcXFSxyIq8swuXm7cuIGaNWtCqVRCrVbj8ePHzw6kUGDo0KH46aefLByRyHbMnz8flSpVMj4uXbo0Vq5cCUEQJExF1rJy5UqMHz8eaWlpMBgMuHDhAlq0aIG0tDSpoxEVaWYPG5UoUcL4DzcwMBAnT55EREQEgGfLSGdkZFg2IZEN8fb2xq5duxAfHw+9Xo+QkBCTKzFkX1auXAmDwWB8rNfrkZiYiBMnTnCzQiIJmV28NGjQACdOnECbNm3Qs2dPjB8/HomJiVCr1Vi0aBGaNWtmjZxENkOpVKJcuXJSx3htOTk5ePz4Mby8vOxmLoelvWyOEyeWEknL7OJl/PjxuHv3LgBg1KhRePz4MX755RdkZmaiefPmmDVrlsVDEpFlLVy4EOPGjYNOp0OxYsWwaNEiXknIQ1RUFC5cuGC8NV6hUMDd3R2hoaESJyMq2sy+VfpVRFGUfPyft0rLk1xv7bNVL+un27Ztw4ABA0yGQzQaDQ4cOMCVVf/BYDBg3LhxWLBgAURRhI+PDzZs2IDy5ctLHc1u8P3UOuT6fprfW6Utdq04JycHCxcuRMWKFS11SCKygi1btuCfn1kEQcC+ffukCWTDFAoFvvrqK9y8eRMXLlzA2bNnUatWLaljERV5+R42io+Px5o1a3Dr1i2UK1cO/fv3h4eHB7KysjBr1izMmDEDiYmJaNCggTXzEtFrUiqVZrXTs2X1nZycpI5BMiOKIm7fvo20tDSEhIRwGxELylfxcuzYMTRv3hzp6enGtjlz5mDdunXo2rUrYmNj0aBBAyxfvhyRkZFWC0tEr69Lly749ddfjY8VCgVUKhWaN28uYSoi+5KZmYnBgwdj586dAJ7dqbtq1SpeubOQfA0bjRs3Dn5+fjh69CgyMjJw6dIllClTBuHh4Xj48CE2bNiAgwcPsnAhkoHGjRtj3rx5KF68OIBnSx5s2LAB/v7+0gYjsiPjx4/H3r17jY+Tk5PRvXt3rhFkIfm68nL27FnMmDEDdevWBQBUrFgR8+bNQ8WKFbFw4UJ06tSpwAF++OEHHDhwACrV/6LMmTMHJUuWBADcvHkTs2bNwo0bN+Dj44P3338fb7zxRoHPR0TA22+/jbfffhs6nc7k3x4RWcauXbtMbrUXRREpKSm4ePEi6tSpI2Ey+5Cvd6379+8jODjYpO354xo1arx2iI4dO6Jfv3652nU6HSZNmoRWrVrh66+/xqFDhzB58mQsXLiQ+8gQWQALF6L/efDgAS5evAg3NzfjSvIF5eDgkGc7571YRr7vNvrnIlbPb4e25pvfuXPnkJ2djaioKKjVajRt2hSlSpXCkSNHrHZOIiIqenbu3InQ0FB069YNrVq1QseOHV9riOf99983KX7UajVq1qzJkQMLyXfl0bNnzzxn23fr1s2kkhQEAWfOnDErxI4dO7Bjxw54eXmhffv2xomDt27dQlBQkEnhFBwcjFu3bpl1fCIiopd5+PAhBg0ahKysLGPbyZMnMX78eHz//fcFOmafPn2g0+kwc+ZMZGRkoGHDhpg+fTrv6rOQfBUveQ3pALDIKpPt27fHwIED4eLigosXL+Kbb76Bi4sL6tevj8zMTLi4uJi83sXFJdf+SQkJCUhISDA+dnBwgJ+f32tne5nnnY+d0LIEQeDP1ILYT62D/dSybKGfXr58GTk5OSZtWq0Whw8ffq1cQ4YMwZAhQ143XoHYez/NV/GydOlSqwV4cUXPatWqoW3btjh8+DDq168PJyenXIVKRkZGritACxYswIQJE4yPR40ahcmTJ1st83Pu7u5WP0dRo9FopI5gd9hPLY/91PKk7Kf+/v4mK04/V6JECXh4eAAA/vzzT1y6dAkBAQFo2rSpLPYDs+d+anOz9QRBMK7++fwWToPBYOwo8fHxaNWqlcnXREdHo0OHDsbHDg4OSElJsVpGpVIJd3d3PH36lMtZW5CLi4vJWkL0ethPrYP91LJsoZ+GhIQgLCwMf//9t/EOIUEQ8OGHHyIlJQVjx47FnDlzoNFooNVq0bx5c/z88882PeFdrv30ebH4KpL/5A8dOoRatWrB0dERly9fxtatW/Huu+8CeHYlRq1WY9OmTWjfvj2OHDmCxMRE1KtXz+QYvr6+8PX1NT5OSkoqlH8Eer2evxQsSBRF/jytgP3UsthPrUPqfvrLL79g/PjxOHToEIoVK4YRI0agRYsW2L17N+bOnQtRFJGdnQ0A2LNnDxYtWmT8XWWL7L2fSl68bNmyBXPmzIHBYICXlxd69eqFRo0aAXh2J9Po0aMxe/ZsrFq1CqVKlcKoUaPg5uYmcWoiIrInrq6ueU7OPXv2LNRqtbFwAZ7NhzH3xhSyLMmLl2+++eZfny9TpkyBZ3sTERG9jhIlSuSaD6NWq40LqcpBSkoKfv/9d6SmpuKtt96yi0XyJC9eiIiIbFVUVBRmz56NW7duQavVQqVSwcnJSbK7iMx19+5dtGzZEikpKVAoFMjOzsbEiRPx3nvvSR3ttZg9XXr27Nl4/PixFaIQERHZFhcXF2zfvh39+vVDvXr18Pbbb2PPnj2y2Qts1KhRSE5ORk5ODrKysiCKIsaOHSv79dLMvvLy+eef47PPPkOnTp0wePBgREREWCMXERGRTShevDi+/vprqWMUyMWLF032WAKe3UkVFxeHwMBAiVK9PrOvvCQkJGD69OmIi4tDZGQkQkJCMGnSJNy5c8ca+YhkKzExEdOmTcPYsWOxefNm4xIARESFJSAgINeaNAaDwaoLuRYGs4sXd3d3vPfeezh+/DjOnj2Ljh07YtasWQgODkabNm2wfv36XFUeUVFz48YNhIeHY/r06Vi8eDGio6MxevRoqWMRUREzfvx4qNVqqFQq46q7ffr0QcWKFaWO9loE0QIfB+/du4eePXviwIEDAAAvLy+8//77+OKLL/LcD8nakpKSrHp8pVIJDw8PpKSk2PV99IXNzc0NqampUsewiP79+2PHjh3Q6XQm7fv27Su0jdnYT63DnvqpLWA/tY4X++nVq1fx008/4cmTJwgLC0Pv3r2NmyvbGi8vr3y9rsB3G4miiO3bt2PJkiXYsmULihcvjk8//RRRUVHYtm0bZs2ahfPnz2P9+vUFPQWRbMXFxeUqXFQqFW7dusVdZYmoUFWoUAFTpkyROoZFmV28xMXF4ccff8Ty5ctx7949NG/eHCtXrkTHjh2NSyWHhYWhdu3a6N69u8UDE8lBuXLlEBsba1LA6HQ6BAUFSZiKyD4ZDAYsXboUf/75J4oVK4bBgwejQoUKUsciKzK7eClfvjz8/f0xYMAADBo06KVvxpUqVULdunVfOyCRHE2YMAGHDx827i2i0+kQHR2NKlWqSJyMyL6IooihQ4di06ZN0Ol0UCqVWLVqFf744w9Uq1ZN6nhkJWbPefn999/Rtm1bm95Rk3Ne5Mne5hLcv38fq1evxpMnT1C7dm20bt26UMeZ2U+tw976qdRet5+eO3cu15IdCoUCjRo1wtq1ay0VU3bk2k+tNudl48aNqFq1KoKDg3M9d/PmTUyYMAE//vijuYclsjulSpXCRx99JHUMIruWkJAApVJpUvgYDAYu32HnzL58smzZMjx8+DDP55KSkrBs2bLXDkVERY9Op8OMGTPQvn179OjRA/v375c6EslA+fLl89x7qGrVqhIlosJgdvEiiuJLL31fu3YNJUqUeO1QRFS0PJ+3MHXqVBw7dgy7d+9G165dsWPHDqmjkY0LDg7GhAkTIAgCHB0dodFo4O3tja+++krqaGRF+Ro2mjdvHubNmwfg2bLCPXv2zLV+S1ZWFm7cuIF33nnH8imJyK7Fx8djw4YNJm0GgwGTJk1Cy5YtJUpFcvH++++jdu3a+Pvvv+Hu7o527drB3d1d6lhkRfkqXvz8/BAaGgoAOH/+PCpWrJhrO3CNRoPKlStj0KBBlk9JRHYtOTk5z/ZHjx4VchKSq7feegtvvfWW1DGokOSreOnYsSM6duxofDxmzBiEhIRYLRSRvTp+/DgmTpyIxMREVK9eHV9//TV8fHykjiW5cuXKwcHBAdnZ2cY2tVqNWrVqSZiKiGyV2XcbLV261Bo5iOzemTNn0KlTJ+j1eoiiiISEBJw5cwYHDhyAq6ur1PEkVbx4cSxatAiDBg2CIAjQ6/UICAjA999/L3U0IrJB+SpePvzwQ/zf//0fAgMD8eGHH/7rawVBwMyZMy0SjsieLFq0CAaDwbi7tFarRWJiInbu3InOnTtLnE56rVu3xrFjx/D333/DxcUFDRs2hLOzs9SxiMgG5at4+f333zFo0CAEBgbit99++9eFtli8EOXt8ePHuW7pVCqVslxIyloCAwMRGBgodQwisnH5Kl7i4+ONf79x44a1spCMiaKIxYsXY+bMmcjIyED9+vUxc+ZM3jr/goYNGyImJsZkv6OcnBzUrl1bwlRERPJj1jovWVlZGD58OP78809r5SGZWr58OcaMGYP79+8jNTUVe/bsQZcuXXLtrFyUDRkyxDjxXRAEKJVKTJs2jbtMExGZyazixdHREUuXLkVmZqa18pBMLViwwGR5bq1Wi/Pnz+PChQsSprItSqUS8+bNw8GDB7Fu3TqcOnUKvXv3ljoWEZHsmL3Cbv369XH8+HFrZCEZe/EW1/y0S0mv10t2RUgQBFSqVAmNGjWCr6+vJBmIiOTO7OJl4sSJWLhwIaZPn45Lly7h0aNHSE5ONvlDRU/Lli2hVquNjwVBgKenJ6pUqSJhKlMZGRl477334O/vD39/f/Tu3RtPnz6VOhYREZlJEJ/ft5lPCsX/6p2X3XVUkG3NLSkpKcmqx3/dLdztUVZWFt5991388ccfAJ5ta/7LL7+gZs2a+T6Gtbdwf++99/Dbb79Bq9UCeLYIWnh4OH799VernVNK7KfWYe1+WtSwn1qHXPupl5dXvl5n9iJ1P/7447/eKk1Fk6OjI5YvX4579+4hPT0dgYGBcHBwkDqWkcFgwObNm02Gi7RaLfbs2YO0tLQiv0gcEZGcmF289O/f3woxyF74+flJHSFPoijiZRcZzbz4aBMyMjKQkpICb29vk+E6IqKiwOw5L0RypFQqc83LUavVqFevHtzc3CRMZr6ZM2ciODgYNWvWRMWKFRETEyN1JCKiQmX2lRcAOHDgABYuXIirV68iKysr1/Nnz5597WBEljZ79mxER0dj165dAJ7tQvvjjz9KnMo8GzduxJQpU4wr9aampqJPnz44dOgQN0sloiLD7CsvO3bsQEREBJKSkvDXX3+hdOnS8PLywpUrV5Cens7VQslmubm5YdWqVbhx4wbi4+OxefNm2a0AvG3btlxbDCgUChw8eFCiREREhc/s4mXcuHH4+OOPsXXrVgDAV199hT179uDq1avQaDSIiIiweEgiS3JxcZHtBF21Wp1rwrwoilAqlRIlIrlITk7GqVOncO/ePamjEL02s4uXS5cuoXXr1lAoFBAEAenp6QCAoKAgjB8/HpMmTbJ4SCJ6plu3biaPlUolHB0dERkZKVEikoM1a9bgjTfeQIsWLVCjRg2MGTNGlhPViZ4zu3hxdHSEwWCAIAjw9fVFXFyc8TlXV1fcvn3bogGJ6H8aN26MBQsWoGTJklAoFChbtiw2bdoEHx8fqaORjTp//jyGDx9uskzAokWL8Msvv0iYiuj1mD1ht0aNGrhy5QqaN2+OZs2aYfLkyfDy8oJarcbo0aNRrVo1a+Qkov8vKioKUVFREEWRay7RKx05cgQajcbk5gq9Xo99+/ahZ8+eEiYjKjizi5ePP/4Y8fHxAIApU6agffv26NChAwAgICAAGzdutGxCIsoTCxfKDycnpzwnebu4uEiUiOTs8ePHOHnyJFQqFUJDQyXrR2YXL23atDH+3d/fH3///TdiY2ORmZmJSpUqQaPRWDQgEREVXJs2bTBlyhQ8fvwYOp0OgiBAEAT07dtX6mgkM2fOnEGXLl2Me8L5+flh06ZNCAoKKvQsr71InSAIKF++PKpXr87ChYjIxpQoUQLbtm1DnTp14OnpicqVK2PdunV48803pY5GMmIwGIyb2RoMBhgMBiQmJuLdd9+VJE++rrxMnz493wcUBAEjRowocCAiIrKs4OBgbN68WeoYJGMPHjxAYmKiSZtOp8Pp06clmX+Xr+Ll//7v//J9QBYvRJZx7NgxXLt2DX5+fmjatKnJju5ERIXJ1dUVgiDkusXexcVFkvl3+Spe/jnZi4isRxRFjBw5EkuXLoVarYZOp0Pjxo2xcuVKqFQF2tGDiOi1uLq6YuDAgVi2bJnxtnulUolPP/1Ukjx8JySyMTExMfjpp59gMBiQnZ0NADh48CCWLFmC6OhoidMRUVE1efJk+Pr6YvPmzVCpVOjTpw969+4tSRazi5dbt2698jWBgYEFCkNEwLlz56BWq6HX641tWq0W58+flzAVERV1SqUSH330ET766COpo5hfvJQpU+aV41svvukSkXlKliyZ69+QWq1GyZIlrXbOo0eP4tixY3B1dUWnTp2sei4iotdldvGydu3aXG3JycnYuXMnTpw4gcmTJ1skGFFRFRUVhTlz5uDmzZvQarVQqVRwdXW12i2J8+bNw7hx46DRaCCKIqZNm4bt27ejTJkyVjkfEdHrEkQL7s41YsQIZGdnY+7cuZY6ZIE8ffoUDg4OVju+IAjQaDTIycnh5mYWpFKpTPZfKcqePHmCKVOm4Pz58yhTpgy++OILlC5d2qxj5Kef3r59GxUqVDB5XqVSoWnTpvj9999f63uwV+ynlsX3U+uQaz/N7+9uixYvu3fvRteuXZGcnGypQxZIUlKSVY+vVCrh4eGBlJQUDpFZkJubG1JTU6WOYTfy008PHTqEzp075/ql4e/vj9OnTxdCSvlhP7Usvp9ah1z7qZeXV75eZ9G7jY4cOQJHR0dLHpKIrMjPzy9X4aJQKMy+ylOUiaKITZs24c8//4S7uzt69erFmxaIrMzs4uXDDz/M1ZaTk4NLly7h0KFDZi1oR0TSCgkJQXR0NBYvXgxRFKFQKKBUKjFp0iSpo8nGmDFjsHjxYgDPCr+FCxdix44dqFChgsTJiOyX2cNGwcHBudocHR0REBCALl26YPDgwVAqlRYLWBAcNpInuV7mtFX57aeiKGL9+vU4cuQI3Nzc0Lt3b5QvX74Qk8rLi/306tWraNCggcnzSqUSjRs3xq+//ipFPNnh+6l1yPX91GrDRvHx8WaHISLbJQgCunTpgi5dukgdRXZu374NhUJhsgq5Xq/n+ySRlXGFXbJrmZmZmD17Ns6ePQs/Pz8MGzaM8znIYsqUKZNr+xSVSsUhIyIrK9BObxcvXkTv3r1Rrlw5uLi4oFy5cujduzcuXLhg6XxEBabVatG5c2fMmDED27dvx/Lly9G0aVPcvn1b6mhkJ8qWLYvPPvsMCoUCDg4O0Gg0cHNz45whIisz+8rL1q1b0blzZ/j7+6NDhw7w9vbGgwcP8Ntvv6FWrVrYsGED2rZta42sRGbZsWMHTp8+bVzrQKfTIT09HbNmzcJ3330ncTqyF59++ilCQ0ONdxt16dIF3t7eUscismtmFy+ffvopWrVqhY0bN0Kh+N+Fm+nTp6Njx4749NNPWbyQTbh//36uhZp0Oh3u3r0rYSqyRxEREYiIiJA6BlGRYfawUXx8PN5//32TwgV4dovg0KFDOVGNbEblypWRk5Nj0qZWq1G9enWJEhHRq2zfvh1RUVFo1aoVpk2bJstVYsn6zL7yUr169ZcWKPHx8ahateprhyKyhPr16yM6OhoLFiyARqOBTqdD1apVMXz4cKmjEVEeNm3ahHfffde4cOLZs2cRGxuLefPmSZyMbI3ZxcucOXPQvXt3ODs7o1OnTihWrBiePHmCjRs3Ytq0afjll1+skZOoQCZOnIiWLVvi4sWL8Pb2RuvWraHRaKSORUR5mDx5ssmKz1qtFuvWrcOXX36JgIAACZORrTF7kTo3NzdotVpotVoAzy7Dv/j3F38xCIKAJ0+eWDBu/nCROnmS66JKtor91DrYTy3rxX5atmxZpKSk5HpNTEwMh3vNJNd+arVF6v7zn/9AEASzAxEREf2bWrVq4cCBA8YPxADg4uKCkJAQCVP9z86dOzFlyhQkJyejTp06+Pbbb1GiRAmpYxVJFt1V2lbwyos8yfWTgq1iP7UO9lPLerGf3r17Fx06dMDNmzehVCqhVCqN6zNJbc+ePejRo4dxUUK1Wo2QkBDExMTAwcFB4nS5ybWfWn1X6YyMDJw6dQrJycnw9PRErVq14OTkVNDDERFREefj44P9+/fj6NGjyMzMRK1ateDr6yt1LADA3LlzTVZT1mq1uHbtGo4ePYomTZpIF6yIKlDxMnnyZHz77bdIT083Tq5ydXXFF198gVGjRlk0IBERFR1OTk42uWbO06dPc7UplUqkpaVJkIbMXudl5syZGDNmDHr27Ik9e/bg0qVL2Lt3L3r16oWxY8fiv//9rzVyEhERITY2Fi1atEBAQABq1KiBjRs3Fsp5mzZtCrVabdImCALefPPNQjk/mTJ7zkuFChUQFRWFb7/9Ntdzn3/+OTZu3IirV69aLGBBcM6LPMl1jNZWsZ9aB/upZZnTT5OTk9GgQQOT1wqCgNWrV1v9ak1OTg4GDRqE7du3AwAcHR2xaNEitGrVyqrnLSi59tP8znkx+8rLrVu30Lx58zyfi4yMxK1bt8w9JBER0Svt3bsXT548MSlyRFHEzz//bPVzazQaLF++HIcOHcLvv/+OM2fO2GzhUhSYXbz4+fnh0KFDeT53+PBh+Pn5vXYoInsmiiLS09OljkEkOzk5OXku1ZGdnV0o5xcEARUrVkRYWBg8PT0L5ZyUN7OLl8GDB2Py5MkYOXIkTp06hYSEBJw+fRqjRo3ClClTMGTIEGvkJLILS5cuRVBQEMqUKYMaNWrgxIkTUkciko369evnalMqlWjdurUEaUhKZs95EUUR//d//4fZs2ebbJilUqnw4YcfYurUqRYPaS7OeZEnuY7R5tfWrVsxcOBA4+2WgiDA2dkZR44cscoVS/ZT67D3flrYzO2nMTExGDx4MNLS0iAIAoYPH47Ro0dz8dR/kGs/tdo6L4IgYNq0aRg1ahSOHz+OlJQUeHp6ok6dOlxpkOhfbNiwwWSdCFEUodVqsW/fPvTs2VPCZETy0axZM1y+fBl3795FiRIlUKxYMakjkQTyXbxcuHABCxYsQHx8PPz9/dGlSxe0adPGmtmIiIhycXBwsJktA+TEYDDg3r17UKlUKFWqlKyvVuVrzsuhQ4dQq1YtzJkzB3/++SeWLFmCli1bYv78+dbOR2Q3oqKioFD875+cQqGASqVC48aNJUxFREXB7du3ER4ejjfffBPVqlVD586dJdk42VLyVbyMHz8eVapUwY0bN3D//n08evQInTp1wujRo62dj8hutGvXDlOmTDHug+Lt7Y21a9fC399f4mREZM9EUUSPHj1w/fp1Y9vx48fx0UcfSZjq9eRrwq63tzfmz5+Pzp07G9tu3LiBkJAQ3Lx5E6VLl7ZqSHNxwq48yXWCmbkMBgPS09Ph5uZm1fOwn1pHUemnhYX91Dpe7KeJiYmoVq1artc4ODjgzp07hR3tX1l0kbqkpCQEBASYtD0vWKxdKBDZG4VCYfXChagouH79Oo4fP87fQ6+gUuU9vfVl7XKQ73Ve5Dyxh4iI7IfBYMBHH32EunXrol27dqhWrRrWr18vdSyb5eXlhYiICJO9mdRqNfr06SNhqteT7+KladOmcHd3N/7x8PAAAISHh5u087Y1IiKypsWLF2PNmjXGxzqdDkOHDpV8Xz1btmTJErRt2xbOzs5wdXXF4MGDMXbsWKljFVi+rhmNGzfO2jmIiIjyZf/+/SaLpALPhkCOHz+OChUqSJTKtrm6umLRokVSx7AYFi8kOzdu3MD58+fh4eGBunXrynrclojM5+rqCkEQ8OL9JgaDAa6urhKmosJk9t5GRFL69ddfERYWhnfffRdRUVHo2LEjNzkkKmIGDx4MQRCMczHVajW8vb0RGRkpcTIqLCxeSDbi4+Px0UcfQa/XQ6vVQhRFnDp1Cl9//bXU0YioEL311ltYt24dqlWrhlKlSqFJkybYtm0b7+IrQni9nWTj/PnzUCgUJmtBaLVaHD16VMJURCSF8PBwxMTESB2DJMLihWTDw8Mj1yJWgiDke1EjIiJrefjwIWJiYqDVatGgQQPuvWRlLF5INsLCwhAaGoozZ84gJycHgiBAoVDgk08+kToaERVhly9fRvv27ZGRkQFBEGAwGLBs2TI0b95c6mh2i3NeSDZUKhXWrFmDfv36oVq1amjSpAk2b96MunXrSh2NiIqw9957D6mpqcjJyUF2dja0Wi2GDBmC7OxsqaPZLV55IVlxdXXFlClTpI5BRGR07dq1XEPa6enpuHfvHoKDgyVKZd9YvBARyVRGRgbWrFmDhIQEVKpUCR07doRCwQvqhc3T0xOJiYkmbZyPZ10sXoiIZCgtLQ0tW7ZEfHy8cZ7Fli1bsHjxYu5FV8gmTZqEIUOGAABEUYRCocB//vMf3rptRSxeiIhkaPbs2YiPj4dWqzW2bd26FTt37kTLli0lTFb0dOzYER4eHli5ciW0Wi1atWqFd955R+pYdk3y4kWr1WL+/Pk4c+YMUlNT4eXlhXfeeQdNmjQB8GwlxcePHxsvhZYsWRJz5syRMDERkfRiY2NNChfg2Uqz169flyhR0daoUSM0atRI6hhFhuTFi16vh6enJyZNmgRvb29cvnwZEydOhI+PDypVqgQAGDlyJEJDQyVOSkT2KjU1FQsXLsSdO3dQtmxZDBo0CE5OTlLH+leBgYFQq9UmBYxOp4O/v7+EqYgKh+TFi6OjI3r16mV8XKVKFVSuXBmXLl0yFi9ERNaSmpqKZs2a4e7du9BqtVCpVNi8eTO2bNkCBwcHqeO91LBhw7Bx40Y8ePAAer0eCoUCYWFhaNu2rdTRiKxO8uLln7KyshAbG4v27dsb23744QeIoojAwED07t0bVapUMfmahIQEJCQkGB87ODjAz8/PahmVSqXJf8kyBEHgz9SC2E/z58cff8Tdu3eRk5MD4NlQ9oULF7Bu3Tr07ds31+ttpZ+WLFkSBw4cwOLFi5GYmIhKlSqhX79+UKvVUkczC/upddhKP7UWmypeRFHEzJkzUb58ebz55psAgE8++QRly5YFAMTExGDChAmYNWsWvL29jV+3YMECTJgwwfh41KhRmDx5stXzuru7W/0cRY1Go5E6gt1hP/13Dx8+hE6nM2lTKBRITk6Gh4dHnl9jK/3Uw8OjUN7rCgP7qeXZSj+1BpspXkRRxNy5c/Ho0SNMnDjReKvfi1dZ2rRpg4MHD+Lvv/9G69atje3R0dHo0KGD8bGDgwNSUlKsllWpVMLd3R1Pnz7NtTARFZyLiwvS09OljmE32E/zp3Tp0lAqlTAYDMY2vV4Pf3//PN9H2E//JycnB7Nnz8axY8fg5eWFoUOH4o033jDrGOyn1iHXfvqyDwz/ZBPFiyiKmD9/Pq5fv46vvvoKjo6OL32tQqGAKIombb6+vvD19TU+TkpKKpR/BHq9nv/YLEgURf48rYD99N/1798fGzduxNmzZ427ljdp0gQdO3bM8+fGfvqMwWBA7969sX//fmi1WigUCmzYsAF//PEHqlevbvbx2E8ty977qU0ULwsWLMCVK1cwadIkODs7G9sfPnyIBw8eoEKFCgCAPXv24Nq1axg2bJhUUYnIzjg4OOC3337D+vXrjXcbdezY0a7nC1jCiRMnEBMTY/wwaTAYoNPp8M0332DVqlUSpyN7J3nx8uDBA2zbtg1qtRoDBw40tnfp0gVhYWFYuHAhEhISoFKpULp0aYwZM8bkKgsR0evSaDTo0aOH1DFk5eHDh1CpVCa3ahsMBpObJ4isRfLixdvbG7/99ttLn585c2YhpiEiovyoXLlyronOSqUS9+/fR0REBFq1aoURI0bI7u4nkgfu4EVERGYrW7YsJk2aBEEQ4OjoaJwv9PDhQ5w7dw4//PADhg8fLnVMslMsXoiIqEDeffdd7N69G1OmTEHx4sVNntNqtVi/fj2HkcgqWLwQEVGBVa9eHX369Mk1hPTc48ePCzcQFQksXoiIClFiYiJ69uyJKlWqIDw8HDt37pQ6kkWEhobmmt/i7u6OoKAgiRKRPWPxQkRUSNLS0tC2bVvs27cPDx8+xOXLl9GnTx8cOHBA6miv7b///S8CAwOhUCigVCrh4uKC5cuXmyx/QWQpkt9tRERUVBw4cAD37t0zGWIRRRGLFi1Co0aNJEz2+nx8fLBv3z6cOHECmZmZePPNN1GyZEmpY5GdYvFCRFRIMjIyoFCYXvAWRRGpqakSJbIsR0dHhIeHSx2DigAOGxERWVhycjJ69uwJf39/BAcH46uvvoJer0ft2rVzbW+iUqnQrFkziZISyROLFyIiCzIYDOjVqxf27duHnJwcpKWlYe7cuZg6dSrKlCmDRYsWwcHBwfj6zp0744MPPpAwMZH8COI/PwbYgaSkJKseX6lUwsPDAykpKXa98VVhc3Nzs5vL57aA/dQ6XtVPb9y4gbfeeitXu5eXFy5dugTg2e3D169fR4kSJYr83Tjsp9Yh1/dTLy+vfL2Oc16IiCzoZeudvPiLuXjx4qhVq1ZhRSIzGQwGPHnyBMWLF4cgCFLHoTxw2IiIyILKlCmDMmXKmOxKrVar0bp1awlTUX5t2LABwcHBqFChAsqWLYtt27ZJHYnywOKFiMiCVCoV1qxZgzJlyhjbmjVrhq+//lq6UJQvx48fx/vvv4+MjAwAQGpqKgYOHIizZ89KnIz+icNGREQWFhwcjCNHjuDBgwfQaDTw9PSUOhLlw7Zt26BUKmEwGIxtSqUSO3bsQPXq1SVMRv/E4oVskk6nw61bt6DRaODv789xZ5IdhUIBHx8fqWOQGf65Bs+r2kk6/D9CNic+Ph7169dH3bp18eabb6Jjx4548uSJ1LGIyM61b98+1x1PBoOB85VsEIsXsikGgwE9evTArVu3jG1//fUXPvzwQwlTEVFRUKtWLSxduhTFixcH8Oy23RUrVqBKlSrSBqNcOGxENuXevXuIi4szadNqtdizZ49EiYioKGnTpg3atGmDrKwsODo6Sh2HXoLFC9mUF1cefZFGoynkJNJ5+vQpLly4AEdHR1StWhVqtVrqSERFDgsX28ZhI7IpXl5eiIyMNPmFrVKpMGDAAAlTFZ6///4btWrVQseOHdGiRQu0aNECjx49kjoWEZFNYfFCNkUQBCxatAidOnWCu7s7PD09MXz4cIwcOVLqaFaXlZWFnj174unTp8bN+65cuYKPP/5Y2mBERDaGw0Zkc1xdXTF37lypYxS6GzduIDk52aRNq9XiyJEjEiUiIrJNLF6IbISbm1ue7a6uroWchKhg0tPTcfjwYWRmZuKtt96Cn5+f1JHITrF4IbIRfn5+aN26NXbv3g2tVgvg2eqeHDYiObh79y7at2+Pe/fuQaFQQKlUYvny5WjatKnU0cgOcc4LkY0QBAELFy7EgAEDUKZMGVSqVAlTp05F//79pY5G9ErDhw9HQkIC9Ho9tFotsrKyMHDgQKSnp0sdjewQr7wQ2RBHR0dMnjwZkydPljoKkVnOnDkDnU5n0paWloabN29ykTeyOBYvRET02ooXL46nT5/mapfbppQGgwF//PEHbt++jeDgYLRo0YJ7q9kgFi9ERPTaRo8ejejoaONt/iqVCl27dpXV5pQ6nQ69evXC/v37oVarodVq0bp1ayxZsoSbM9oYFi9ERPTaoqKi4OzsjCVLliAjIwMtW7bEBx98IHUss6xcuRIHDx6EXq83btC4fft2bNiwAV26dJE4Hb2IxQsREVlEy5Yt0bJlS6ljFNilS5dgMBhM2hQKBS5fvixRInoZFi9ERGQzbt68ifXr1yMrKwuNGjVCw4YNC+3cPj4+UCqVxqsuL7aTbeEgHhER2YSzZ8+iUaNGmDZtGubMmYPOnTtj0aJFhXb+AQMGwNvb27i3mlqtRmBgILp3715oGSh/eOWFiIhswieffILs7GyTKx+jR49G586dUaJECaufv1ixYtizZw9mzZqFGzduoFy5chg2bBhXubZBLF6IiMgm3LhxI9eQjcFgwJ07dwqleAEADw8PjB07tlDORQXH4oWIiGxCYGAgLly4YDJpVhAE+Pv7F/iYmZmZWLBgAa5duwZ/f3+8//778PDwsERckhCLFyIisgnff/89OnToAFEUIYoidDodxo4dCy8vrwIdLzs7G+3bt8fFixeh1WqhVquxZs0a7N27lwWMzLF4ISIim1CrVi3s378fa9asQWZmJho3boxmzZoV+Hjr1683Fi4AoNVq8fDhQyxcuBCff/65pWKTBFi8EBGRzShbtixGjhxpkWMlJCTkWhlXq9Xi3r17Fjk+SYfFCxGRlZ09exazZs1CUlIS6tatixEjRsDBwUHqWHavfPnyuSYAq1QqlC9fXqJEZCksXoiIrOj06dNo06YN9Ho9DAYDjh8/jj///BPr1q3jfjlW1q5dO7Rp0wZbt26FWq2GXq9HjRo1MHjwYKmj0Wti8UJEZEXfffedsXABng1bHDp0CMePH0e9evUkTmffFAoFFi9ejG3bthnvNurUqZNxETqSLxYvRERWlJiYmGu/HJVKhaSkJIkSFS2CIKBt27ZSxyAL4zVLIiIrCg0NzfVJX6/Xo0qVKhIloudOnTqFzp07IywsDNHR0Xjw4IHUkSifBFEURalDWNrTp0+tOhlOEARoNBrk5OTADn98klGpVNDpdFLHsBvsp9Zhbj998uQJmjdvjgsXLkClUkGr1WLGjBmIjo62Ykr5kKqfnj17Fg0aNDAO6anVavj7++Ovv/6yi+0A5Pp+mt/f3XY5bJSTk4OcnByrHV+pVEKj0SA9PT3XTHYqODc3N6Smpkodw26wn1qHuf1UoVBg27Zt2Lt3L1JSUlCjRg1UqVKFff3/k6qf/vDDD7nmIt29exfr169H586dCy2Htcj1/bRIFy9ERLbEwcEBrVq1kjoGvSAlJSXXXCSlUoknT55IlIjMwTkvRET02kRRxNy5c1GjRg1UqlQJQ4cORVpamtSxXqp+/fpQqUw/v2dnZ6NWrVoSJSJzsHghIqLXNnfuXEycOBH37t3Do0ePsHHjRvTv399m51sNGTLEeBeSUqmEQqHAN998gxo1akicjPLDLifsWvsWRKVSCQ8PD6SkpHAugQXJdYzWVrGfWgf7ad6qVq2K+/fv52r/+++/ERgY+NKvk7KfiqKIc+fO4cGDB6hQocK/5pQbufbT/G7CyTkvRCQZg8HAVWbtRHZ2dp7tmZmZhZwk/wRBQPXq1aWOQQXAdw0iKnRxcXGIiIiAj48PQkJCsGDBAqkj0Wtq0qSJyXo2CoUCPj4+CA4OljAV2SsWL0RUqFJTU9GpUydcvHgRoigiNTUVY8eOxerVq6WORq9h2rRpqF27tvFxyZIlsXr1amg0GglTkb3isBERFarjx4/j4cOHJvMbDAYDVqxYge7du0uYjF6Hu7s7Nm/ejPj4eGRlZaFs2bLcOZushsULERUqvV4PQRDybCd5EwQBISEhUscwS05ODh4/fgwvLy/Ov5IR/p8iokJVu3ZtuLi4mPyiUKlU6Nixo4SpqCiaO3cugoKC8MYbb6BSpUo4ePCg1JEon1i8EFGhKlGiBNatW4eSJUsCePZpPTo6Gu+++67Eyago2bRpEyZMmGDc/yclJQU9evTAzZs3JU5G+cFhIyIqdDVr1sTZs2fx8OFDuLu7w8nJSepIVMT8/vvvubYHAIADBw6gT58+EiQic7B4ISJJKBQKlCpVSuoYZKOys7MRHx8PR0dHBAUF5TlP6nX8c2sA4NmidZz3Ig/8v0RERDbl3LlzqFWrFsLDw/HWW2/hnXfesfg+Se+8845JQaRQKODg4IBmzZpZ9DxkHSxeiIjIZmRlZaFbt24m27wcOXIEI0eOtOh5IiMjMWvWLBQrVgyCICAoKAgbN26Ej4+PRc9D1sFhIyIishmxsbF4+PChSZtWq0VMTIzFz9WtWzd069YNOp0uz2Eksl38v0VkZWlpaThz5gwEQUDNmjXh7OwsdSQim+Xo6JhnuzUXvGPhIj/8P0ZkRZcuXcLbb79tvAQeEBCAjRs3IigoSOJkRLYpJCQEYWFh+Pvvv6HVagE823k6Ojpa4mRkSzjnhchKRFFE79698ejRI4iiCFEUkZCQgMGDB0sdjchmKRQKrFy5Eu3atYOHhwd8fX0xfvx4Fi9kgldeiKwkOTkZt27dMmnT6XQ4c+YMx9iJ/oW7uzsWLlwodQyyYXz3zKdHjx7h4MGDEEUR4eHh8PDwkDoS2ThnZ2cIggBRFE3aHR0doVQqJUpFRCR/LF7y4cKFC+jUqRPS09MBPJs4tmvXLlSsWFHiZGTLnJycMGjQIPz000/GJciVSiU++ugjiy+4RURUlLB4yYdBgwYhNTXVuOutTqdDVFQULly4IHEysnWTJk1CyZIlsXHjRigUCvTu3ZtzXoiIXpMg/vOath14cXGj16XVauHn55fnc7GxsShWrJjFzlXUubm5ITU1VeoYdkOpVMLDwwMpKSnGwpteH/upZbGfWodc+6mXl1e+XscrL6+gUqng4uJiHDJ6TqlUws3NTaJURETWYzAYsHXrVsTHxyMwMBDt27fnPC2yKSxeXkEQBIwZMwajRo0y7kCqUCgwfvx4qNVqflIgIrui1+vRt29f7NmzByqVCjqdDj///DN+/fVX3iFHNoM9MR8GDRoET09P/PrrrzAYDOjcuTOGDh2Kx48fSx2NiMii1q1bhz179kCn0xknmh89ehQrVqxA//79pQ1H9P+xeMmnqKgoREVFAXg2ZMS7RYjIHl25ciXX+5soirh69apEiYhyY/FCRFTEnD59Glu2bIEoimjTpg1CQ0ONz/n5+eUqXhQKBXx9fQs7JtFLcXsAIqIi5I8//kDLli0xd+5czJs3D23atMFvv/1mfL5Hjx4oU6YM1Go1AECj0cDPz49DRmRTeKt0AfDWPuuQ6619tor91Drk3E9FUUSFChVyzddzdXXF9evXjVdc0tLSMG/ePFy/fh1BQUH44IMP4O7ubpVM7KfWIdd+yluliYjIRGZmZp43GqSlpSE1NdVYoLi6uuLTTz8t5HRE+cdhIyKiIuL+/ftwcHDI1e7m5sZ1q0hWWLwQERUBcXFxiIiIMN7+DDxbx0oQBMyaNYt3UJKssHghIioCvv32W2RmZuaaVzJ16lS0bdtWolREBcPihYioCLhz506uwkWj0cDZ2VmiREQFx+KFqAjbtm0bunbtio4dO2LBggXGLTDI/lSrVs14+/NzOTk5qFixokSJiAqOdxsRFVG//vorhg8fjuerJfz555+4efMmpkyZInEysoaRI0fiwIEDuHnzJpRKJXJycvDxxx+jevXqUkcjMhvXeSkArktgHXJdl8BWvaqfVq1aFffv38/VfvXqVXh4eBRGRFmScz9NT0/Hli1bkJycjOrVq6NBgwZSR+L7qZXItZ9ynRci+lcv21g0JSWFxYudcnFxQbdu3aSOQfTaOOeFqIiqXr06VCrTzy/u7u7w9/eXKBHZK51Oh6+//hphYWFo2LAhFi1aBDu86E+FiFdeiIqouXPnokOHDnj48CEEQYBKpcKyZcvyXMSM6HV8+umn+PXXX6HVagEAY8aMQUZGBj766COJk5Fccc5LAXCM1jrkOkZrq/LTT1NTU3Hw4EHk5OSgbt263Dk4H9hPzZOWlobg4OBc7W5ubrh+/TrfT61Erv3Urua8pKWlYc6cOTh58iScnJzQtWtXtGnTRupYRLLn5ubGf0tkVWlpaXm2Z2ZmcuiICkwWxcuCBQug1+uxdOlSJCQkYOzYsQgICOAtfkRENs7b2xv+/v5ITEw0XllRqVQIDQ3llgRUYDY/YTcrKwuHDx9G79694ezsjLJlyyIiIgK7d++WOhoREb2CQqHAypUr4eHhYSxWgoKCsGDBAomTkZzZ/JWXu3fvAgACAwONbSEhIdi0aZPxcUJCAhISEoyPHRwc4OfnZ7VMSqXS5L9kGYIg8GdqQeyn1sF+ar7q1avj5MmTOHfuHJRKJWrUqGGcGM5+ah323k9tvnjJysqCk5OTSZuLiwsyMzONjxcsWIAJEyYYH48aNQqTJ0+2ejZ3d3ern6Oo0Wg0UkewO+ynlsd+aj4PDw+ULl36pc+zn1qePfdTmy9eHB0dTQoV4NkqkS8WNNHR0ejQoYPxsYODA1JSUqyWSalUwt3dHU+fPuXseAtycXFBenq61DHsBvupdbCfWhb7qXXItZ/md4FMmy9eni+Ydfv2bWPVHh8fj6CgIONrfH19TW7xTEpKKpR/BHq9nv/YLEgURf48rYD91LLYT62D/dSy7L2f2vyEXUdHRzRo0AArV65ERkYG4uPjERMTg2bNmkkdjYiIiCRg81degGfDQrNnz0b//v3h7OyMXr16oUaNGlLHIiIiIgnIonhxdXXFF198IXUMIiIisgE2P2xERERE9CIWL0RERCQrLF6IiIhIVli8EBERkayweCEiIiJZYfFCREREssLihYiIiGRFEEVRlDqE3CQkJGDBggWIjo422ZaAyJawn5IcsJ9SQfDKSwEkJCRgwoQJSEhIkDoK0Uuxn5IcsJ9SQbB4ISIiIllh8UJERESywuKlAHx9fTFu3DiOz5JNYz8lOWA/pYLghF0iIiKSFV55ISIiIllh8UJERESywuKFiIiIZEUldQA52bJlC/bs2YMbN26gXr16+PTTT6WORJSLVqvF/PnzcebMGaSmpsLLywvvvPMOmjRpInU0IhOzZ8/GX3/9hczMTLi5uaFFixbo2rWr1LFIBli8mMHT0xNdu3bF6dOnkZqaKnUcojzp9Xp4enpi0qRJ8Pb2xuXLlzFx4kT4+PigUqVKUscjMurQoQOGDBkCBwcHPHz4EOPHj4efnx8aNmwodTSycSxezFC/fn0AwPXr11m8kM1ydHREr169jI+rVKmCypUr49KlSyxeyKYEBgaaPBYEAffu3ZMoDckJixciO5eVlYXY2Fi0b99e6ihEuSxbtgxbtmxBdnY2vL290bRpU6kjkQyweCGyY6IoYubMmShfvjzefPNNqeMQ5dKvXz/07dsXsbGxOH78OFxcXKSORDLAu42I7JQoipg7dy4ePXqEzz77DIIgSB2JKE+CIKB8+fJQqVT45ZdfpI5DMsArL0R2SBRFzJ8/H9evX8dXX30FR0dHqSMRvZLBYODu0pQvvPJiBr1ej5ycHBgMBhgMBuTk5ECn00kdiyiXBQsW4MqVK5gwYQKcnZ2ljkOUS3p6Ovbu3YuMjAwYDAZcvHgRf/zxB2rWrCl1NJIB7m1khlWrVmH16tUmbREREfj444+lCUSUhwcPHmDw4MFQq9VQKpXG9i5dunANDbIZGRkZmDJlCuLi4mAwGODp6YnIyEh07tyZQ5z0SixeiIiISFY4bERERESywuKFiIiIZIXFCxEREckKixciIiKSFRYvREREJCssXoiIiEhWWLwQERGRrLB4ISIiIllh8UIkM+PHj4cgCBAEAQqFAsWKFUP16tUxbNgwXLp0qUDH3LdvH6ZMmVLgTM/zCIIABwcH+Pn5oVWrVliyZAm0Wm2Bj1tQx44dQ+vWreHj4wMnJyeUKVMGXbp0wfHjxws9CxFZHosXIhlycnLC0aNHceTIEaxbtw79+/fH7t27UbNmTaxYscLs471u8QIAw4cPx9GjRxETE4MffvgBfn5+eO+99xAeHo7U1NTXOrY5Dh06hPDwcKhUKsyfPx9bt27FyJEjkZ6ejj///LPQchCR9XBXaSIZUigUCAsLMz5u3rw5PvjgA7Rt2xaDBg1C/fr1ERISUqiZAgMDTTJ17doVXbt2Rdu2bfHJJ59g0aJFhZJj3rx5KFOmDDZt2mTc2ykiIgLR0dEwGAxWP79er4fBYIBarbb6uYiKKl55IbITjo6OmDVrFnJycrB48WJj+/Lly9GwYUN4enrCw8MDTZo0MbkCMX78eEyYMAHp6enGoZ8mTZoAAC5fvozu3bujdOnScHZ2RpUqVTBt2rR8FwGtWrXC22+/jeXLl5tcfTl//jxatWoFV1dXuLu7o2PHjoiNjTX52idPnqB3795wc3NDyZIl8dlnn+Gbb7555aZ9jx8/hre3t8mmlM8pFKZveUePHkWLFi3g7u4ONzc31K1bF7t27TI+n5ycjMGDB6NkyZJwcnJCnTp1sHPnTpNjNGnSBO3atcOyZctQsWJFODg44PTp0wCArVu3om7dunByckLJkiXx/vvvIz09PV8/OyJ6OV55IbIjVapUgb+/P44ePWpsu3HjBvr27YuyZcsiJycHq1atQqNGjXD27FlUqFABgwcPxp07d7Bq1Srs2bMHAODu7g4AuHv3LipWrIhevXrBzc0Np0+fxrhx45Ceno6xY8fmK1OLFi2wdu1anDx5Eo0bN8bt27cRHh6OMmXKYNmyZdDr9Rg3bhzCw8Nx9uxZlCxZEgAwYMAA7NmzB9999x2CgoKwYMECnDx58pXnCw0NxVdffYUxY8agV69eqFSpUp6vO3z4MCIiIhAWFobFixejePHi+Ouvv3Dr1i0Az66gtG7dGrGxsfj6668REBCAefPmoU2bNti1axeaNm1qPNbzr/vqq69QvHhxlC5dGuvWrUO3bt0wYMAATJgwAQkJCfjiiy+QkpKSa3d6IjKTSESyMm7cONHFxeWlz4eFhYmVKlXK8zm9Xi9qtVqxYsWK4siRI/N9TFEURYPBIGq1WnHy5Mmir6+vyXMAxKlTp+b5ddu3bxcBiKtXrxZFURRHjBghOjs7iw8ePDC+5saNG6JarRbHjRsniqIoXrhwQQQgLl++3PganU4nhoSEiK9623r69KnYvHlzEYAIQPT09BR79uwpHjhwwOR19evXF6tUqSLqdLo8j7N582YRgLh161Zjm16vFytXriw2btzY2Na4cWNRo9GIt2/fNrYZDAYxKChI7NGjh8kxt27dKgqCIJ4/f/5fvwci+nccNiKyM6IomgytXLp0CVFRUShVqhSUSiXUajWuXLmCq1evvvJYWVlZGDduHMqVKwcHBweo1Wp8+eWXSEhIQFpaWr7zADBmOnjwICIiIoxXWAAgKCgI9evXx8GDBwEAJ06cAAB06NDB+BqlUol27dq98nxubm7YuXMnjh8/jrFjx6JmzZpYu3YtGjdubBxOy8jIwLFjx9CvX788h5ee53Rzc0ObNm2MbQqFAl27dsWRI0eg1+uN7dWrV0dAQIDx8dWrV3Hz5k107doVOp3O+Kdx48YQBAF//fXXK78PIno5Fi9EdubOnTvw8fEBAKSmpqJFixa4efMmpk+fjoMHD+LEiROoUaMGsrKyXnmszz//HFOnTsWQIUOwbds2nDhxAqNHjwaAfH398zwAjJlSUlKMf3+Rj48PkpOTAQAJCQlQq9UoVqyYyWu8vb3zdU4AqFOnDiZMmICYmBhcuXIFAQEB+Oyzz4wZDAYD/Pz8Xvr1KSkpKFWqVJ45tVqtSfH2z1xJSUkAgKioKKjVauMfV1dXGAwG3L59O9/fBxHlxjkvRHbkwoULuHv3Lvr37w/g2YTUO3fuYMuWLahRo4bxdU+ePDG5UvAya9euRXR0ND7//HNj29atW83KtGPHDjg4OCA0NBQA4Onpifv37+d6XWJiIjw9PQEAvr6+0Gq1ePLkiUkB8+DBA7PO/VxwcDDeeecdTJ8+Hffv30fx4sWhUChw7969l37Nv+V8Xog8989JxM+/j9mzZ6Nu3bq5jvFvRRMRvRqvvBDZiaysLAwfPhwODg4YPHgwACAzMxMAoNFojK87cuQIbty4YfK1Go0G2dnZuY6ZmZlp8rV6vd6syabbt2/Hhg0b0L9/f7i4uAAAGjZsiJiYGDx69Mj4utu3b+PIkSMIDw8HALz11lsAgM2bN5uc+/fff3/lOfMqOIBnQzkODg4oXrw4XFxcUK9ePSxfvtxk+OdFDRs2RGpqKrZv325sMxgMWLt2LerXr//S4SYAqFSpEgICAnD9+nXUrl071x8WL0Svh1deiGTIYDDg2LFjAIC0tDScO3cOCxcuxPXr1/HTTz+hTJkyAICwsDC4urpi6NCh+OKLL3D37l2MHz8e/v7+JserXLkydDodZs6cifr168Pd3R0VK1ZE8+bNsWjRIlSpUgUlS5bEnDlz8ixyAODWrVs4duwY9Ho9EhIS8Mcff2D58uWoW7cuvv/+e+PrRowYgaVLl6JFixb48ssvjXcbeXp6YujQoQCe3TUVFRWFDz/8EBkZGQgKCsL8+fOh1Wpfeav0kCFDoNPp8Pbbb6N8+fJ4+vQp1q9fjy1btuDjjz+Gg4MDAOCbb75BREQEIiMj8cEHH8DDwwMnT56El5cXBg4ciLZt26JOnTro06cPpkyZgoCAAMyfPx9XrlzBnDlz/jWDIAiYPn06evbsifT0dLRt2xYuLi64efMmtm7diilTpqBChQr/egwi+hdSzxgmIvOMGzfOeCeNIAiim5ubWLVqVXHo0KHipUuXcr3+jz/+EN944w3R0dFRrF69urht2zaxcePGYtu2bY2v0Wq14gcffCCWKlVKFATBeDdNYmKi2KlTJ9HNzU0sVaqU+Pnnn4uLFi0SAYgPHz40fv3zPABEtVot+vj4iC1bthSXLFkiarXaXJnOnj0rtmjRQnR2dhZdXV3F9u3bi1evXjV5TUpKitirVy/RxcVFLFGihPjJJ5+IX3zxhVi8ePF//fls375d7NmzpxgSEiI6OTmJJUqUEOvUqSMuWbIk151Fhw8fFps2bSo6OzuLbm5uYlhYmLh7927j848ePRIHDhwolihRQnRwcBDfeustcfv27SbH+OfP8kU7d+4UGzduLLq4uIguLi7iG2+8If7nP/8RHz9+/K/fAxH9O0EU//+tAERENq5hw4ZQq9XYu3ev1FGISEIcNiIim7R+/XrcunUL1atXR3p6OlatWoXDhw9j48aNUkcjIomxeCEim+Tq6oqff/4Z165dQ05ODipVqoQVK1agU6dOUkcjIolx2IiIiIhkhbdKExERkayweCEiIiJZYfFCREREssLihYiIiGSFxQsRERHJCosXIiIikhUWL0RERCQrLF6IiIhIVv4fHM1BV4hp6o4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8775367040835)>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plotnine\n",
    "\n",
    "p = ggplot(dogs, aes(\"datadog\", \"popularity\")) + geom_point()\n",
    "p + labs(title = \"Best in Show\", x = \"DataDog Score\", y = \"Popularity Rank\")\n",
    "# A bug in the latest version of plotnine prevents this line from working:\n",
    "# p + ylim(95, -5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91.3, -3.3)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn\n",
    "\n",
    "ax = sns.regplot(x = \"datadog\", y = \"popularity\", data = dogs, fit_reg = False)\n",
    "ax.set(title = \"Best in Show\", xlabel = \"DataDog Score\", ylabel = \"Popularity Rank\")\n",
    "ylim = reversed(ax.get_ylim())\n",
    "ax.set_ylim(ylim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91.3, -3.3)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pandas\n",
    "\n",
    "ax = dogs.plot.scatter(x = \"datadog\", y = \"popularity\")\n",
    "ax.set(title = \"Best in Show\", xlabel = \"DataDog Score\", ylabel = \"Popularity Rank\")\n",
    "ylim = reversed(ax.get_ylim())\n",
    "ax.set_ylim(ylim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More About Packages and Modules\n",
    "\n",
    "Which of the built-in modules are important?\n",
    "\n",
    "Module      | Description\n",
    "----------- | -----------\n",
    "sys         | info about Python (version, etc)\n",
    "pdb         | Python debugger\n",
    "pathlib     | tools for file paths\n",
    "collections | additional data structures\n",
    "string      | string processing\n",
    "re          | regular expressions\n",
    "datetime    | date processing\n",
    "urlparse    | tools for URLs\n",
    "itertools   | tools for iterators\n",
    "functools   | tools for functions\n",
    "\n",
    "Python's built-in `math` and `statistics` modules are missing features we need for serious scientific computing, so we use the \"SciPy Stack\" instead, which includes `numpy`, `scipy`, and `pandas`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}