{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b4d18893",
   "metadata": {},
   "source": [
    "# Class 7: Visualization for EDA\n",
    "\n",
    "\n",
    "## Announcements\n",
    "\n",
    "Syllabus updated\n",
    "1. [rubric](https://github.com/rhodyprog4ds/BrownFall20/commit/315ce164bc4bd4b7d5ee321afd9143e08f05c07b#diff-1e595cb12e4db779fb0c857562c1e0dd) for summarize and visualize are slightly changed\n",
    "1. [Please accept assignments](https://github.com/rhodyprog4ds/BrownFall20/commit/f459e8b04dc0b09d2dadba10464f49b25de57190) if you plan to not complete for any reason\n",
    "\n",
    "\n",
    "Assignment updated to [clarify continuous and categorical variables](https://github.com/rhodyprog4ds/BrownFall20/commit/f719a92c4d5dd00cbe8bf646d9d63280fb4b1e50)\n",
    "\n",
    "\n",
    "## Loading Data\n",
    "\n",
    "Importing the libraries for today. We'll continue plotting with pandas and we'll use  [`seaborn`](https://seaborn.pydata.org/introduction.html) as well. Seaborn provides higher level plotting functions and [better formatting](https://seaborn.pydata.org/examples/index.html).\n",
    "\n",
    "````{margin}\n",
    "The alias for `seaborn` is `sns` the result of an [inside joke](https://github.com/mwaskom/seaborn/issues/229) among the developers in reference so [Samuel Norman Seaborn](https://en.wikipedia.org/wiki/Sam_Seaborn) on The West Wing, per [stackexchange](https://stackoverflow.com/questions/41499857/seaborn-why-import-as-sns)\n",
    "````"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "26e07b13",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e1d993b",
   "metadata": {},
   "source": [
    "Loading the data as usual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dd771246",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_url = 'https://raw.githubusercontent.com/brownsarahm/python-socialsci-files/master/data/SAFI_full_shortname.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa8de121",
   "metadata": {},
   "source": [
    "We know that the `key_id` column should be used as an index, not as data, so we'll use the `index_col` parameter t do that from the beginning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9f6137ea",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>interview_date</th>\n",
       "      <th>quest_no</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>province</th>\n",
       "      <th>district</th>\n",
       "      <th>ward</th>\n",
       "      <th>village</th>\n",
       "      <th>years_farm</th>\n",
       "      <th>agr_assoc</th>\n",
       "      <th>...</th>\n",
       "      <th>items_owned</th>\n",
       "      <th>items_owned_other</th>\n",
       "      <th>no_meals</th>\n",
       "      <th>months_lack_food</th>\n",
       "      <th>no_food_mitigation</th>\n",
       "      <th>gps_Latitude</th>\n",
       "      <th>gps_Longitude</th>\n",
       "      <th>gps_Altitude</th>\n",
       "      <th>gps_Accuracy</th>\n",
       "      <th>instanceID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key_id</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",
       "      <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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17 November 2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-03-23T09:49:57.000Z</td>\n",
       "      <td>2017-04-02T17:29:08.000Z</td>\n",
       "      <td>Manica</td>\n",
       "      <td>Manica</td>\n",
       "      <td>Bandula</td>\n",
       "      <td>God</td>\n",
       "      <td>11</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>['bicycle' ;  'television' ;  'solar_panel' ; ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>['Jan']</td>\n",
       "      <td>['na' ;  'rely_less_food' ;  'reduce_meals' ; ...</td>\n",
       "      <td>-19.112259</td>\n",
       "      <td>33.483456</td>\n",
       "      <td>698</td>\n",
       "      <td>14.0</td>\n",
       "      <td>uuid:ec241f2c-0609-46ed-b5e8-fe575f6cefef</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17 November 2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-04-02T09:48:16.000Z</td>\n",
       "      <td>2017-04-02T17:26:19.000Z</td>\n",
       "      <td>Manica</td>\n",
       "      <td>Manica</td>\n",
       "      <td>Bandula</td>\n",
       "      <td>God</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>['cow_cart' ;  'bicycle' ;  'radio' ;  'cow_pl...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>['Jan' ;  'Sept' ;  'Oct' ;  'Nov' ;  'Dec']</td>\n",
       "      <td>['na' ;  'reduce_meals' ;  'restrict_adults' ;...</td>\n",
       "      <td>-19.112477</td>\n",
       "      <td>33.483416</td>\n",
       "      <td>690</td>\n",
       "      <td>19.0</td>\n",
       "      <td>uuid:099de9c9-3e5e-427b-8452-26250e840d6e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17 November 2016</td>\n",
       "      <td>3</td>\n",
       "      <td>2017-04-02T14:35:26.000Z</td>\n",
       "      <td>2017-04-02T17:26:53.000Z</td>\n",
       "      <td>Manica</td>\n",
       "      <td>Manica</td>\n",
       "      <td>Bandula</td>\n",
       "      <td>God</td>\n",
       "      <td>40</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>['solar_torch']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>['Jan' ;  'Feb' ;  'Mar' ;  'Oct' ;  'Nov' ;  ...</td>\n",
       "      <td>['na' ;  'restrict_adults' ;  'lab_ex_food']</td>\n",
       "      <td>-19.112108</td>\n",
       "      <td>33.483450</td>\n",
       "      <td>674</td>\n",
       "      <td>13.0</td>\n",
       "      <td>uuid:193d7daf-9582-409b-bf09-027dd36f9007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17 November 2016</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-04-02T14:55:18.000Z</td>\n",
       "      <td>2017-04-02T17:27:16.000Z</td>\n",
       "      <td>Manica</td>\n",
       "      <td>Manica</td>\n",
       "      <td>Bandula</td>\n",
       "      <td>God</td>\n",
       "      <td>6</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>['bicycle' ;  'radio' ;  'cow_plough' ;  'sola...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>['Sept' ;  'Oct' ;  'Nov' ;  'Dec']</td>\n",
       "      <td>['na' ;  'reduce_meals' ;  'restrict_adults' ;...</td>\n",
       "      <td>-19.112229</td>\n",
       "      <td>33.483424</td>\n",
       "      <td>679</td>\n",
       "      <td>5.0</td>\n",
       "      <td>uuid:148d1105-778a-4755-aa71-281eadd4a973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17 November 2016</td>\n",
       "      <td>5</td>\n",
       "      <td>2017-04-02T15:10:35.000Z</td>\n",
       "      <td>2017-04-02T17:27:35.000Z</td>\n",
       "      <td>Manica</td>\n",
       "      <td>Manica</td>\n",
       "      <td>Bandula</td>\n",
       "      <td>God</td>\n",
       "      <td>18</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>['motorcyle' ;  'radio' ;  'cow_plough' ;  'mo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>['Aug' ;  'Sept' ;  'Oct' ;  'Nov']</td>\n",
       "      <td>['na' ;  'go_forest' ;  'migrate']</td>\n",
       "      <td>-19.112217</td>\n",
       "      <td>33.483425</td>\n",
       "      <td>689</td>\n",
       "      <td>10.0</td>\n",
       "      <td>uuid:2c867811-9696-4966-9866-f35c3e97d02d</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          interview_date  quest_no                     start  \\\n",
       "key_id                                                         \n",
       "1       17 November 2016         1  2017-03-23T09:49:57.000Z   \n",
       "2       17 November 2016         1  2017-04-02T09:48:16.000Z   \n",
       "3       17 November 2016         3  2017-04-02T14:35:26.000Z   \n",
       "4       17 November 2016         4  2017-04-02T14:55:18.000Z   \n",
       "5       17 November 2016         5  2017-04-02T15:10:35.000Z   \n",
       "\n",
       "                             end province district     ward village  \\\n",
       "key_id                                                                \n",
       "1       2017-04-02T17:29:08.000Z   Manica   Manica  Bandula     God   \n",
       "2       2017-04-02T17:26:19.000Z   Manica   Manica  Bandula     God   \n",
       "3       2017-04-02T17:26:53.000Z   Manica   Manica  Bandula     God   \n",
       "4       2017-04-02T17:27:16.000Z   Manica   Manica  Bandula     God   \n",
       "5       2017-04-02T17:27:35.000Z   Manica   Manica  Bandula     God   \n",
       "\n",
       "        years_farm agr_assoc  ...  \\\n",
       "key_id                        ...   \n",
       "1               11        no  ...   \n",
       "2                2       yes  ...   \n",
       "3               40        no  ...   \n",
       "4                6        no  ...   \n",
       "5               18        no  ...   \n",
       "\n",
       "                                              items_owned  items_owned_other  \\\n",
       "key_id                                                                         \n",
       "1       ['bicycle' ;  'television' ;  'solar_panel' ; ...                NaN   \n",
       "2       ['cow_cart' ;  'bicycle' ;  'radio' ;  'cow_pl...                NaN   \n",
       "3                                         ['solar_torch']                NaN   \n",
       "4       ['bicycle' ;  'radio' ;  'cow_plough' ;  'sola...                NaN   \n",
       "5       ['motorcyle' ;  'radio' ;  'cow_plough' ;  'mo...                NaN   \n",
       "\n",
       "        no_meals                                   months_lack_food  \\\n",
       "key_id                                                                \n",
       "1              2                                            ['Jan']   \n",
       "2              2       ['Jan' ;  'Sept' ;  'Oct' ;  'Nov' ;  'Dec']   \n",
       "3              2  ['Jan' ;  'Feb' ;  'Mar' ;  'Oct' ;  'Nov' ;  ...   \n",
       "4              2                ['Sept' ;  'Oct' ;  'Nov' ;  'Dec']   \n",
       "5              2                ['Aug' ;  'Sept' ;  'Oct' ;  'Nov']   \n",
       "\n",
       "                                       no_food_mitigation gps_Latitude  \\\n",
       "key_id                                                                   \n",
       "1       ['na' ;  'rely_less_food' ;  'reduce_meals' ; ...   -19.112259   \n",
       "2       ['na' ;  'reduce_meals' ;  'restrict_adults' ;...   -19.112477   \n",
       "3            ['na' ;  'restrict_adults' ;  'lab_ex_food']   -19.112108   \n",
       "4       ['na' ;  'reduce_meals' ;  'restrict_adults' ;...   -19.112229   \n",
       "5                      ['na' ;  'go_forest' ;  'migrate']   -19.112217   \n",
       "\n",
       "       gps_Longitude gps_Altitude gps_Accuracy  \\\n",
       "key_id                                           \n",
       "1          33.483456          698         14.0   \n",
       "2          33.483416          690         19.0   \n",
       "3          33.483450          674         13.0   \n",
       "4          33.483424          679          5.0   \n",
       "5          33.483425          689         10.0   \n",
       "\n",
       "                                       instanceID  \n",
       "key_id                                             \n",
       "1       uuid:ec241f2c-0609-46ed-b5e8-fe575f6cefef  \n",
       "2       uuid:099de9c9-3e5e-427b-8452-26250e840d6e  \n",
       "3       uuid:193d7daf-9582-409b-bf09-027dd36f9007  \n",
       "4       uuid:148d1105-778a-4755-aa71-281eadd4a973  \n",
       "5       uuid:2c867811-9696-4966-9866-f35c3e97d02d  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "safi_df = pd.read_csv(data_url,index_col='key_id')\n",
    "safi_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cd21bac",
   "metadata": {},
   "source": [
    "We can make scatter plots as we saw Friday."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "27d75c36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gps_Latitude', ylabel='gps_Longitude'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/home/runner/work/BrownFall20/BrownFall20/_build/jupyter_execute/notes/2020-09-23_7_1.png"
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "safi_df.plot.scatter('gps_Latitude','gps_Longitude')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58519c93",
   "metadata": {},
   "source": [
    "With seaborn, however, we can control it more, changing the color of the points based on a column of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c8aeaf45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f05c06aea50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 439.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/home/runner/work/BrownFall20/BrownFall20/_build/jupyter_execute/notes/2020-09-23_9_1.png"
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x= 'gps_Latitude',y='gps_Longitude',\n",
    "           data=safi_df, hue='village')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b945f56",
   "metadata": {},
   "source": [
    "We can also plot a single variable to see the quantiles (the box is 25%-50%) and see if there are outliers (the points outside the box)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5da88ff9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/home/runner/work/BrownFall20/BrownFall20/_build/jupyter_execute/notes/2020-09-23_11_1.png"
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "safi_df.boxplot('years_farm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc3c6042",
   "metadata": {},
   "source": [
    "We can do some more conditiioning, even with only pandas. Using the `by` parameter will do a `groupby` operation first and then make the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "12b430c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'years_farm'}, xlabel='village'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/home/runner/work/BrownFall20/BrownFall20/_build/jupyter_execute/notes/2020-09-23_13_1.png"
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "safi_df.boxplot('years_farm', by='village')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf3b8829",
   "metadata": {},
   "source": [
    "We can also make the figure larger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "22e90666",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'years_farm'}, xlabel='village'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/home/runner/work/BrownFall20/BrownFall20/_build/jupyter_execute/notes/2020-09-23_15_1.png"
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "safi_df.boxplot('years_farm', by='village',figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7578585",
   "metadata": {},
   "source": [
    "We can see how a single variable is distributed in more detail with the [seaborn displot](https://seaborn.pydata.org/generated/seaborn.displot.html#seaborn.displot)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "432814a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f05c0326c50>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/home/runner/work/BrownFall20/BrownFall20/_build/jupyter_execute/notes/2020-09-23_17_1.png"
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=safi_df, x='years_farm',col='village')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f8b00d42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f05c032d150>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/home/runner/work/BrownFall20/BrownFall20/_build/jupyter_execute/notes/2020-09-23_18_1.png"
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=safi_df, x='years_farm',col='village',kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29f2cf5a",
   "metadata": {},
   "source": [
    "## Updating Seaborn\n",
    "the `displot` is new in seaborn 0.11, on your terminal (mac, linux) or anaconda prompt(Windows):\n",
    "```\n",
    "pip install update seaborn\n",
    "```\n",
    "\n",
    "Then restart your notebook's kernel and re-run all cells\n",
    "\n",
    "or, in a notebook, you can update with\n",
    "```{margin}\n",
    "This is set to not run on the served site\n",
    "```\n",
    "\n",
    "```\n",
    "!pip install seaborn==0.11.0\n",
    "```\n",
    "\n",
    "## Regression plots\n",
    "\n",
    "We can also plot with some calculations done for us already."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4b34d373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f05bde67e50>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 439.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/home/runner/work/BrownFall20/BrownFall20/_build/jupyter_execute/notes/2020-09-23_20_1.png"
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='years_farm',y='years_liv', data=safi_df,\n",
    "           hue='village')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d3444a5",
   "metadata": {},
   "source": [
    "And we can make grids of plots with the `row` and `col` parameters. We can turn off the regression lines with the `fit_reg` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "546245c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f05c017bfd0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1519.5x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/home/runner/work/BrownFall20/BrownFall20/_build/jupyter_execute/notes/2020-09-23_22_1.png"
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='years_farm',y='years_liv', data=safi_df,\n",
    "           hue='village',  col='respondent_wall_type',row='respondent_floor_type',\n",
    "           fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "743bb15e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poultry\n",
       "no     5.894737\n",
       "yes    7.720430\n",
       "Name: members_count, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "safi_df.groupby('poultry')['members_count'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c908156",
   "metadata": {},
   "source": [
    "## Questions after class\n",
    "\n",
    "\n",
    "## More practice\n",
    "\n",
    "Test out the parameters of the plotting functions to see what they do.\n",
    "\n",
    "## Further Reading\n",
    "\n",
    "- [Pandas Plotting](https://pandas.pydata.org/docs/user_guide/visualization.html)\n",
    "- [Seaborn Gallery](https://seaborn.pydata.org/examples/index.html)\n",
    "- [Seaborn Tutorial](https://seaborn.pydata.org/tutorial.html)\n",
    "\n",
    "If you've made it this far, [let me know](https://forms.gle/PDATAmuRS5tAHiiZ8) how you found these notes."
   ]
  }
 ],
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