(O) Factory Farming Connections.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/peterhurford/.virtualenvs/dev/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3145: DtypeWarning: Columns (3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,78,79,80,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    },
    {
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       "      <th>agree_farming_harms_environment</th>\n",
       "      <th>agree_flies_animal_feed</th>\n",
       "      <th>...</th>\n",
       "      <th>college</th>\n",
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       "      <th>meta_straightlining_vote_measure</th>\n",
       "      <th>weight</th>\n",
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       "      <th>lv_weight</th>\n",
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       "      <td>Disagree</td>\n",
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       "      <td>Disagree</td>\n",
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       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
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       "      <td>0.794118</td>\n",
       "      <td>0.171455</td>\n",
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       "      <td>0.208401</td>\n",
       "      <td>0.195713</td>\n",
       "      <td>0.125348</td>\n",
       "      <td>0.224031</td>\n",
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       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
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       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
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       "      <td>Disagree</td>\n",
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       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.024394</td>\n",
       "      <td>0.044727</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>4933 rows × 133 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     agree_11hrs_sleep agree_40hrs_work agree_birth_control_immoral  \\\n",
       "0             Disagree         Disagree                    Disagree   \n",
       "1             Disagree            Agree                    Disagree   \n",
       "2                Agree       Don't know                       Agree   \n",
       "3             Disagree            Agree                    Disagree   \n",
       "4             Disagree       Don't know                    Disagree   \n",
       "...                ...              ...                         ...   \n",
       "4928        Don't know       Don't know                  Don't know   \n",
       "4929          Disagree         Disagree                    Disagree   \n",
       "4930          Disagree         Disagree                    Disagree   \n",
       "4931        Don't know            Agree                    Disagree   \n",
       "4932          Disagree            Agree                    Disagree   \n",
       "\n",
       "     agree_eat_animals_fed_insects agree_eat_animals_fed_insects_alt_reverse  \\\n",
       "0                         Disagree                                     Agree   \n",
       "1                         Disagree                                Don't know   \n",
       "2                       Don't know                                Don't know   \n",
       "3                         Disagree                                     Agree   \n",
       "4                       Don't know                                Don't know   \n",
       "...                            ...                                       ...   \n",
       "4928                           NaN                                       NaN   \n",
       "4929                           NaN                                       NaN   \n",
       "4930                           NaN                                       NaN   \n",
       "4931                           NaN                                       NaN   \n",
       "4932                           NaN                                       NaN   \n",
       "\n",
       "     agree_eat_insects agree_farming_climate_change agree_farming_covid  \\\n",
       "0           Don't know                     Disagree               Agree   \n",
       "1             Disagree                     Disagree               Agree   \n",
       "2             Disagree                        Agree            Disagree   \n",
       "3                Agree                     Disagree            Disagree   \n",
       "4                Agree                   Don't know            Disagree   \n",
       "...                ...                          ...                 ...   \n",
       "4928               NaN                          NaN                 NaN   \n",
       "4929               NaN                          NaN                 NaN   \n",
       "4930               NaN                          NaN                 NaN   \n",
       "4931               NaN                          NaN                 NaN   \n",
       "4932               NaN                          NaN                 NaN   \n",
       "\n",
       "     agree_farming_harms_environment agree_flies_animal_feed  ... college  \\\n",
       "0                              Agree                Disagree  ...    True   \n",
       "1                           Disagree                Disagree  ...    True   \n",
       "2                              Agree                   Agree  ...    True   \n",
       "3                           Disagree                   Agree  ...    True   \n",
       "4                           Disagree                Disagree  ...    True   \n",
       "...                              ...                     ...  ...     ...   \n",
       "4928                             NaN                     NaN  ...    True   \n",
       "4929                             NaN                     NaN  ...    True   \n",
       "4930                             NaN                     NaN  ...    True   \n",
       "4931                             NaN                     NaN  ...    True   \n",
       "4932                             NaN                     NaN  ...    True   \n",
       "\n",
       "     noncollege_white meta_straightlining_vote_measure    weight lv_index  \\\n",
       "0               False                         0.794118  0.532454     0.83   \n",
       "1               False                         0.794118  0.320096     0.83   \n",
       "2               False                         0.794118  0.171455     0.83   \n",
       "3               False                         0.794118  0.375608     0.83   \n",
       "4               False                         0.794118  0.120158     0.11   \n",
       "...               ...                              ...       ...      ...   \n",
       "4928            False                         0.676471  0.463365     0.11   \n",
       "4929            False                         0.676471  0.737776     0.83   \n",
       "4930            False                         0.676471  0.053500     0.59   \n",
       "4931            False                         0.676471  0.143413     0.83   \n",
       "4932            False                         0.676471  0.039550     0.83   \n",
       "\n",
       "     rv_index lv_weight rv_weight lv_weight_alt lv_weight_2020  \n",
       "0           1  0.647190  0.607788      0.199091       0.602161  \n",
       "1           1  0.389073  0.365385      0.204060       0.418252  \n",
       "2           1  0.208401  0.195713      0.125348       0.224031  \n",
       "3           1  0.456546  0.428751      0.324146       0.490787  \n",
       "4           0  0.019356  0.000000      0.033784       0.024351  \n",
       "...       ...       ...       ...           ...            ...  \n",
       "4928        0  0.074643  0.000000      0.031035       0.067751  \n",
       "4929        1  0.896757  0.842161      0.411416       0.834363  \n",
       "4930        1  0.046225  0.061070      0.075900       0.043009  \n",
       "4931        1  0.174316  0.163703      0.082386       0.162188  \n",
       "4932        1  0.048072  0.045145      0.024394       0.044727  \n",
       "\n",
       "[4933 rows x 133 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from surveyweights import normalize_weights \n",
    "\n",
    "survey = pd.read_csv('responses_processed_national_weighted.csv')\n",
    "survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## AGREEMENT, UNWEIGHTED ##\n",
      "Agree         59.656442\n",
      "Don't know    20.245399\n",
      "Disagree      20.098160\n",
      "Name: agree_farming_harms_environment, dtype: float64\n",
      "N = 4933\n"
     ]
    }
   ],
   "source": [
    "print('## AGREEMENT, UNWEIGHTED ##')\n",
    "print(survey['agree_farming_harms_environment'].value_counts(normalize=True) * 100)\n",
    "print('N = {}'.format(len(survey)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.dropna(subset=['agree_farming_harms_environment'], axis=0)['agree_farming_harms_environment'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## AGREEMENT, WEIGH ON GENDER + RACE + AGE + INCOME + REGION + EDUCATION + URBAN/RURAL + 2016 VOTE + SOCIAL TRUST + RELIGION + SPANKING ##\n",
      "#agree_farming_harms_environment\n",
      "Agree         40.978078\n",
      "Disagree      32.003801\n",
      "Don't know    27.018121\n",
      "dtype: float64\n",
      "Weighted N = 1716\n"
     ]
    }
   ],
   "source": [
    "def print_var\n",
    "print('## AGREEMENT, UNWEIGHTED ##')\n",
    "print(survey['agree_farming_harms_environment'].value_counts(normalize=True) * 100)\n",
    "print('N = {}'.format(len(survey)))\n",
    "\n",
    "survey['lv_weight_alt'] = survey['lv_weight'] / survey['lv_index']\n",
    "survey['lv_weight_alt'] = normalize_weights(survey['lv_weight_alt'])\n",
    "print('## AGREEMENT, WEIGH ON GENDER + RACE + AGE + INCOME + REGION + EDUCATION + URBAN/RURAL + 2016 VOTE + SOCIAL TRUST + RELIGION + SPANKING ##')\n",
    "print('#agree_farming_harms_environment')\n",
    "print(survey['agree_farming_harms_environment'].value_counts(normalize=True) * surve_.groupby('agree_farming_harms_environment')['lv_weight_alt'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## AGREEMENT, UNWEIGHTED ##\n",
      "Agree         57.521472\n",
      "Don't know    24.662577\n",
      "Disagree      17.815951\n",
      "Name: agree_farming_climate_change, dtype: float64\n",
      "N = 4933\n"
     ]
    }
   ],
   "source": [
    "print('## AGREEMENT, UNWEIGHTED ##')\n",
    "print(survey['agree_farming_climate_change'].value_counts(normalize=True) * 100)\n",
    "print('N = {}'.format(len(survey)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## AGREEMENT, WEIGH ON GENDER + RACE + AGE + INCOME + REGION + EDUCATION + URBAN/RURAL + 2016 VOTE + SOCIAL TRUST + RELIGION + SPANKING ##\n",
      "#agree_farming_climate_change\n",
      "Agree         44.124231\n",
      "Disagree      33.328352\n",
      "Don't know    29.361446\n",
      "dtype: float64\n",
      "Weighted N = 2067\n"
     ]
    }
   ],
   "source": [
    "survey['lv_weight_alt'] = survey['lv_weight'] / survey['lv_index']\n",
    "survey['lv_weight_alt'] = normalize_weights(survey['lv_weight_alt'])\n",
    "    \n",
    "print('## AGREEMENT, WEIGH ON GENDER + RACE + AGE + INCOME + REGION + EDUCATION + URBAN/RURAL + 2016 VOTE + SOCIAL TRUST + RELIGION + SPANKING ##')\n",
    "print('#agree_farming_climate_change')\n",
    "print(survey['agree_farming_climate_change'].value_counts(normalize=True) * survey.groupby('agree_farming_climate_change')['lv_weight_alt'].mean() * 100)\n",
    "weighted_n = int(np.round(survey['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## AGREEMENT, UNWEIGHTED ##\n",
      "Disagree      40.245399\n",
      "Don't know    37.938650\n",
      "Agree         21.815951\n",
      "Name: agree_farming_covid, dtype: float64\n",
      "N = 4933\n"
     ]
    }
   ],
   "source": [
    "print('## AGREEMENT, UNWEIGHTED ##')\n",
    "print(survey['agree_farming_covid'].value_counts(normalize=True) * 100)\n",
    "print('N = {}'.format(len(survey)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## AGREEMENT, WEIGH ON GENDER + RACE + AGE + INCOME + REGION + EDUCATION + URBAN/RURAL + 2016 VOTE + SOCIAL TRUST + RELIGION + SPANKING ##\n",
      "#agree_farming_covid\n",
      "Agree         20.153841\n",
      "Disagree      50.188447\n",
      "Don't know    36.471741\n",
      "dtype: float64\n",
      "Weighted N = 2067\n"
     ]
    }
   ],
   "source": [
    "survey['lv_weight_alt'] = survey['lv_weight'] / survey['lv_index']\n",
    "survey['lv_weight_alt'] = normalize_weights(survey['lv_weight_alt'])\n",
    "    \n",
    "print('## AGREEMENT, WEIGH ON GENDER + RACE + AGE + INCOME + REGION + EDUCATION + URBAN/RURAL + 2016 VOTE + SOCIAL TRUST + RELIGION + SPANKING ##')\n",
    "print('#agree_farming_covid')\n",
    "print(survey['agree_farming_covid'].value_counts(normalize=True) * survey.groupby('agree_farming_covid')['lv_weight_alt'].mean() * 100)\n",
    "weighted_n = int(np.round(survey['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## Interesting demographics ##\n",
      "## MALE ##\n",
      "## agree_farming_harms_environment ##\n",
      "Agree         47.030268\n",
      "Disagree      36.198338\n",
      "Don't know    27.237110\n",
      "dtype: float64\n",
      "Weighted N = 1015\n",
      "## agree_farming_climate_change ##\n",
      "Agree         49.722155\n",
      "Disagree      33.061622\n",
      "Don't know    27.681940\n",
      "dtype: float64\n",
      "Weighted N = 1015\n",
      "## agree_farming_covid ##\n",
      "Agree         27.942969\n",
      "Disagree      50.633535\n",
      "Don't know    31.889212\n",
      "dtype: float64\n",
      "Weighted N = 1015\n",
      "-\n",
      "-\n",
      "-\n",
      "## FEMALE ##\n",
      "## agree_farming_harms_environment ##\n",
      "Agree         43.246297\n",
      "Disagree      34.992226\n",
      "Don't know    26.926980\n",
      "dtype: float64\n",
      "Weighted N = 1018\n",
      "## agree_farming_climate_change ##\n",
      "Agree         39.846771\n",
      "Disagree      35.681950\n",
      "Don't know    29.636782\n",
      "dtype: float64\n",
      "Weighted N = 1018\n",
      "## agree_farming_covid ##\n",
      "Agree         16.752255\n",
      "Disagree      53.837775\n",
      "Don't know    34.575472\n",
      "dtype: float64\n",
      "Weighted N = 1018\n",
      "-\n",
      "-\n",
      "## BLACK OR AFRICAN AMERICAN ##\n",
      "## agree_farming_harms_environment ##\n",
      "Agree         43.360695\n",
      "Disagree      60.320462\n",
      "Don't know    42.251329\n",
      "dtype: float64\n",
      "Weighted N = 202\n",
      "## agree_farming_climate_change ##\n",
      "Agree         64.309407\n",
      "Disagree      44.195595\n",
      "Don't know    37.427484\n",
      "dtype: float64\n",
      "Weighted N = 202\n",
      "## agree_farming_covid ##\n",
      "Agree         25.863770\n",
      "Disagree      67.695750\n",
      "Don't know    52.372965\n",
      "dtype: float64\n",
      "Weighted N = 202\n",
      "-\n",
      "-\n",
      "## WHITE OR CAUCASIAN ##\n",
      "## agree_farming_harms_environment ##\n",
      "Agree         46.122796\n",
      "Disagree      36.589739\n",
      "Don't know    22.782838\n",
      "dtype: float64\n",
      "Weighted N = 1318\n",
      "## agree_farming_climate_change ##\n",
      "Agree         41.842018\n",
      "Disagree      36.965185\n",
      "Don't know    26.688170\n",
      "dtype: float64\n",
      "Weighted N = 1318\n",
      "## agree_farming_covid ##\n",
      "Agree         21.197273\n",
      "Disagree      55.577411\n",
      "Don't know    28.720689\n",
      "dtype: float64\n",
      "Weighted N = 1318\n",
      "-\n",
      "-\n",
      "## HISPANIC OR LATINO ##\n",
      "## agree_farming_harms_environment ##\n",
      "Agree         87.847603\n",
      "Disagree      39.744555\n",
      "Don't know    89.327205\n",
      "dtype: float64\n",
      "Weighted N = 197\n",
      "## agree_farming_climate_change ##\n",
      "Agree         99.699928\n",
      "Disagree      44.080185\n",
      "Don't know    73.139250\n",
      "dtype: float64\n",
      "Weighted N = 197\n",
      "## agree_farming_covid ##\n",
      "Agree         55.201650\n",
      "Disagree      67.841519\n",
      "Don't know    93.876194\n",
      "dtype: float64\n",
      "Weighted N = 197\n",
      "-\n",
      "-\n",
      "## ASIAN OR ASIAN AMERICAN ##\n",
      "## agree_farming_harms_environment ##\n",
      "Agree         17.726850\n",
      "Disagree       7.614892\n",
      "Don't know     9.233523\n",
      "dtype: float64\n",
      "Weighted N = 380\n",
      "## agree_farming_climate_change ##\n",
      "Agree         18.690196\n",
      "Disagree       6.524131\n",
      "Don't know     9.360938\n",
      "dtype: float64\n",
      "Weighted N = 380\n",
      "## agree_farming_covid ##\n",
      "Agree          6.525318\n",
      "Disagree      13.888626\n",
      "Don't know    14.161321\n",
      "dtype: float64\n",
      "Weighted N = 380\n",
      "-\n",
      "-\n",
      "## College ##\n",
      "## agree_farming_harms_environment ##\n",
      "Agree         35.253289\n",
      "Disagree      26.919372\n",
      "Don't know    18.102438\n",
      "dtype: float64\n",
      "Weighted N = 1907\n",
      "## agree_farming_climate_change ##\n",
      "Agree         34.215359\n",
      "Disagree      23.210133\n",
      "Don't know    22.849606\n",
      "dtype: float64\n",
      "Weighted N = 1907\n",
      "## agree_farming_covid ##\n",
      "Agree         14.749139\n",
      "Disagree      39.348310\n",
      "Don't know    26.177648\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-1a284943416e>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:39: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:47: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:69: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:77: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:85: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:98: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:106: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:114: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:129: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:137: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:145: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:158: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:166: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:174: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:186: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:194: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:202: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:214: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weighted N = 1907\n",
      "-\n",
      "-\n",
      "## NON-College ##\n",
      "## agree_farming_harms_environment ##\n",
      "Agree         103.662696\n",
      "Disagree       85.840454\n",
      "Don't know     83.464224\n",
      "dtype: float64\n",
      "Weighted N = 315\n",
      "## agree_farming_climate_change ##\n",
      "Agree         108.854099\n",
      "Disagree      101.281763\n",
      "Don't know     62.831512\n",
      "dtype: float64\n",
      "Weighted N = 315\n",
      "## agree_farming_covid ##\n",
      "Agree          66.892792\n",
      "Disagree      128.324042\n",
      "Don't know     77.750539\n",
      "dtype: float64\n",
      "Weighted N = 315\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-1a284943416e>:222: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
      "<ipython-input-8-1a284943416e>:230: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print('## Interesting demographics ##')\n",
    "print('## MALE ##')\n",
    "options = ['Don’t know / Undecided', 'Vote against / Oppose', 'Vote for / Support']\n",
    "options2 = ['Agree', 'Disagree', 'Don\\'t know']\n",
    "\n",
    "survey_ = survey[survey['gender'] == 'Male']\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_harms_environment ##')\n",
    "print(survey_['agree_farming_harms_environment'].value_counts(normalize=True) * survey_.groupby('agree_farming_harms_environment')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "\n",
    "survey_ = survey[survey['gender'] == 'Male']\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_climate_change ##')\n",
    "print(survey_['agree_farming_climate_change'].value_counts(normalize=True) * survey_.groupby('agree_farming_climate_change')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "\n",
    "survey_ = survey[survey['gender'] == 'Male']\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_covid ##')\n",
    "print(survey_['agree_farming_covid'].value_counts(normalize=True) * survey_.groupby('agree_farming_covid')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "print('-')\n",
    "\n",
    "print('-')\n",
    "print('-')\n",
    "\n",
    "print('## FEMALE ##')\n",
    "options = ['Don’t know / Undecided', 'Vote against / Oppose', 'Vote for / Support']\n",
    "options2 = ['Agree', 'Disagree', 'Don\\'t know']\n",
    "\n",
    "survey_ = survey[survey['gender'] == 'Female']\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_harms_environment ##')\n",
    "print(survey_['agree_farming_harms_environment'].value_counts(normalize=True) * survey_.groupby('agree_farming_harms_environment')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "\n",
    "survey_ = survey[survey['gender'] == 'Female']\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_climate_change ##')\n",
    "print(survey_['agree_farming_climate_change'].value_counts(normalize=True) * survey_.groupby('agree_farming_climate_change')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "\n",
    "survey_ = survey[survey['gender'] == 'Female']\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_covid ##')\n",
    "print(survey_['agree_farming_covid'].value_counts(normalize=True) * survey_.groupby('agree_farming_covid')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "print('-')\n",
    "print('-')\n",
    "\n",
    "print('## BLACK OR AFRICAN AMERICAN ##')\n",
    "options = ['Don’t know / Undecided', 'Vote against / Oppose', 'Vote for / Support']\n",
    "options2 = ['Agree', 'Disagree', 'Don\\'t know']\n",
    "\n",
    "survey_ = survey[survey['race'] == 'Black or African American']\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_harms_environment ##')\n",
    "print(survey_['agree_farming_harms_environment'].value_counts(normalize=True) * survey_.groupby('agree_farming_harms_environment')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "\n",
    "survey_ = survey[survey['race'] == 'Black or African American']\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_climate_change ##')\n",
    "print(survey_['agree_farming_climate_change'].value_counts(normalize=True) * survey_.groupby('agree_farming_climate_change')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "\n",
    "survey_ = survey[survey['race'] == 'Black or African American']\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_covid ##')\n",
    "print(survey_['agree_farming_covid'].value_counts(normalize=True) * survey_.groupby('agree_farming_covid')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "print('-')\n",
    "print('-')\n",
    "\n",
    "print('## WHITE OR CAUCASIAN ##')\n",
    "options = ['Don’t know / Undecided', 'Vote against / Oppose', 'Vote for / Support']\n",
    "options2 = ['Agree', 'Disagree', 'Don\\'t know']\n",
    "\n",
    "survey_ = survey[survey['race'] == 'White or Caucasian']\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_harms_environment ##')\n",
    "print(survey_['agree_farming_harms_environment'].value_counts(normalize=True) * survey_.groupby('agree_farming_harms_environment')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "\n",
    "survey_ = survey[survey['race'] == 'White or Caucasian']\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_climate_change ##')\n",
    "print(survey_['agree_farming_climate_change'].value_counts(normalize=True) * survey_.groupby('agree_farming_climate_change')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "\n",
    "survey_ = survey[survey['race'] == 'White or Caucasian']\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_covid ##')\n",
    "print(survey_['agree_farming_covid'].value_counts(normalize=True) * survey_.groupby('agree_farming_covid')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "\n",
    "print('-')\n",
    "print('-')\n",
    "\n",
    "print('## HISPANIC OR LATINO ##')\n",
    "options = ['Don’t know / Undecided', 'Vote against / Oppose', 'Vote for / Support']\n",
    "options2 = ['Agree', 'Disagree', 'Don\\'t know']\n",
    "\n",
    "survey_ = survey[survey['race'] == 'Hispanic or Latino']\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_harms_environment ##')\n",
    "print(survey_['agree_farming_harms_environment'].value_counts(normalize=True) * survey_.groupby('agree_farming_harms_environment')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "\n",
    "survey_ = survey[survey['race'] == 'Hispanic or Latino']\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_climate_change ##')\n",
    "print(survey_['agree_farming_climate_change'].value_counts(normalize=True) * survey_.groupby('agree_farming_climate_change')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "\n",
    "survey_ = survey[survey['race'] == 'Hispanic or Latino']\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_covid ##')\n",
    "print(survey_['agree_farming_covid'].value_counts(normalize=True) * survey_.groupby('agree_farming_covid')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "print('-')\n",
    "print('-')\n",
    "\n",
    "print('## ASIAN OR ASIAN AMERICAN ##')\n",
    "options = ['Don’t know / Undecided', 'Vote against / Oppose', 'Vote for / Support']\n",
    "options2 = ['Agree', 'Disagree', 'Don\\'t know']\n",
    "\n",
    "survey_ = survey[survey['race'] == 'Asian or Asian American']\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_harms_environment ##')\n",
    "print(survey_['agree_farming_harms_environment'].value_counts(normalize=True) * survey_.groupby('agree_farming_harms_environment')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "\n",
    "survey_ = survey[survey['race'] == 'Asian or Asian American']\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_climate_change ##')\n",
    "print(survey_['agree_farming_climate_change'].value_counts(normalize=True) * survey_.groupby('agree_farming_climate_change')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "\n",
    "survey_ = survey[survey['race'] == 'Asian or Asian American']\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_covid ##')\n",
    "print(survey_['agree_farming_covid'].value_counts(normalize=True) * survey_.groupby('agree_farming_covid')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "\n",
    "print('-')\n",
    "print('-')\n",
    "\n",
    "print('## College ##')\n",
    "survey_ = survey[survey['college']]\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_harms_environment ##')\n",
    "print(survey_['agree_farming_harms_environment'].value_counts(normalize=True) * survey_.groupby('agree_farming_harms_environment')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "\n",
    "survey_ = survey[survey['college']]\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_climate_change ##')\n",
    "print(survey_['agree_farming_climate_change'].value_counts(normalize=True) * survey_.groupby('agree_farming_climate_change')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "\n",
    "survey_ = survey[survey['college']]\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_covid ##')\n",
    "print(survey_['agree_farming_covid'].value_counts(normalize=True) * survey_.groupby('agree_farming_covid')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "                 \n",
    "\n",
    "print('-')\n",
    "print('-')\n",
    "print('## NON-College ##')\n",
    "\n",
    "survey_ = survey[~survey['college']]\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_harms_environment ##')\n",
    "print(survey_['agree_farming_harms_environment'].value_counts(normalize=True) * survey_.groupby('agree_farming_harms_environment')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "\n",
    "survey_ = survey[~survey['college']]\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_climate_change ##')\n",
    "print(survey_['agree_farming_climate_change'].value_counts(normalize=True) * survey_.groupby('agree_farming_climate_change')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n",
    "\n",
    "survey_ = survey[~survey['college']]\n",
    "survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "print('## agree_farming_covid ##')\n",
    "print(survey_['agree_farming_covid'].value_counts(normalize=True) * survey_.groupby('agree_farming_covid')['lv_weight'].mean() * 100)\n",
    "weighted_n = int(np.round(survey_['lv_weight_alt'].apply(lambda w: 1 if w > 1 else w).sum()))\n",
    "print('Weighted N = {}'.format(weighted_n))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "questions = {'agree_farming_harms_environment' , 'agree_farming_climate_change', 'agree_farming_covid'}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'set' object has no attribute 'items'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-934c03b89072>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquestion\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mquestions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\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[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'## {} ##'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquestion\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      3\u001b[0m     \u001b[0msurvey_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msurvey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msurvey\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\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      4\u001b[0m     \u001b[0msurvey_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lv_weight_alt'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnormalize_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msurvey_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lv_weight_alt'\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[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msurvey_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0msurvey_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lv_weight_alt'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'set' object has no attribute 'items'"
     ]
    }
   ],
   "source": [
    "for var, question in questions.items():\n",
    "    print('## {} ##'.format(question))\n",
    "    survey_ = survey.loc[survey[var].isin(options)].copy()\n",
    "    survey_['lv_weight_alt'] = normalize_weights(survey_['lv_weight_alt'])\n",
    "    print(survey_[var].value_counts(normalize=True) * survey_.groupby(var)['lv_weight_alt'].mean() * 100)\n",
    "    print('-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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