fear_vs_horror_preference.ipynb

{
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   "source": [
    "How do people's general propensity toward phobias relate to their movie preferences? Specifically, do people who have strong perceptions of fear tend to avoid horror movies? In this analysis, I will use the \"Young People Survey\" dataset from Kaggle. For more info on this dataset, please visit: https://www.kaggle.com/miroslavsabo/young-people-survey?select=responses.csv\n",
    "\n",
    "The responses of interest from this dataset are 10 questions about phobias that responders rated from 1-5 (\"not afraid at all\", to \"very afraid of\"). There are also questions about movie preferences which contained a question about rating their preference toward horror movies from 1-5 (\"don't enjoy at all\", to \"enjoy very much\").\n",
    "\n",
    "Let's examine the relationship between these responses!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a06b2159",
   "metadata": {},
   "outputs": [
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       "   Music  Slow songs or fast songs  Dance  Folk  Country  Classical music  \\\n",
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       "4    5.0                       3.0    4.0   3.0      2.0              4.0   \n",
       "\n",
       "   Musical  Pop  Rock  Metal or Hardrock  ...   Age  Height  Weight  \\\n",
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       "1      2.0  3.0   5.0                4.0  ...  19.0   163.0    58.0   \n",
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       "\n",
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       "0                 1.0  female         right handed  college/bachelor degree   \n",
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       "4                 1.0  female         right handed         secondary school   \n",
       "\n",
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       "0          no         village          block of flats  \n",
       "1          no            city          block of flats  \n",
       "2          no            city          block of flats  \n",
       "3         yes            city          house/bungalow  \n",
       "4          no         village          house/bungalow  \n",
       "\n",
       "[5 rows x 150 columns]"
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     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('datasets/responses.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cad55aa2",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Flying  Storm  Darkness  Heights  Spiders  Snakes  Rats  Ageing  \\\n",
       "0     1.0    1.0       1.0      1.0      1.0       5   3.0     1.0   \n",
       "1     1.0    1.0       1.0      2.0      1.0       1   1.0     3.0   \n",
       "2     1.0    1.0       1.0      1.0      1.0       1   1.0     1.0   \n",
       "3     2.0    1.0       1.0      3.0      5.0       5   5.0     4.0   \n",
       "4     1.0    2.0       1.0      1.0      1.0       1   2.0     2.0   \n",
       "\n",
       "   Dangerous dogs  Fear of public speaking  Horror  \n",
       "0             3.0                      2.0     4.0  \n",
       "1             1.0                      4.0     2.0  \n",
       "2             1.0                      2.0     3.0  \n",
       "3             5.0                      5.0     4.0  \n",
       "4             4.0                      3.0     4.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select columns of interest\n",
    "\n",
    "cols = ['Flying','Storm','Darkness','Heights','Spiders','Snakes','Rats','Ageing','Dangerous dogs','Fear of public speaking','Horror']\n",
    "df2 = df[cols]\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "14c99b00",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/b7/kbc9pm217t92p0l_p84jfqhc0000gn/T/ipykernel_8266/3072028357.py:6: 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",
      "  df2['fear_avg'] = fear_avg\n"
     ]
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      "text/plain": [
       "   Flying  Storm  Darkness  Heights  Spiders  Snakes  Rats  Ageing  \\\n",
       "0     1.0    1.0       1.0      1.0      1.0       5   3.0     1.0   \n",
       "1     1.0    1.0       1.0      2.0      1.0       1   1.0     3.0   \n",
       "2     1.0    1.0       1.0      1.0      1.0       1   1.0     1.0   \n",
       "3     2.0    1.0       1.0      3.0      5.0       5   5.0     4.0   \n",
       "4     1.0    2.0       1.0      1.0      1.0       1   2.0     2.0   \n",
       "\n",
       "   Dangerous dogs  Fear of public speaking  Horror  fear_avg  \n",
       "0             3.0                      2.0     4.0       1.9  \n",
       "1             1.0                      4.0     2.0       1.6  \n",
       "2             1.0                      2.0     3.0       1.1  \n",
       "3             5.0                      5.0     4.0       3.6  \n",
       "4             4.0                      3.0     4.0       1.8  "
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# take an average of fear responses - add as a new column\n",
    "\n",
    "fear_avg = []\n",
    "for row in range(len(df2)):\n",
    "    fear_avg.append(df2.loc[row, :'Fear of public speaking'].mean())\n",
    "df2['fear_avg'] = fear_avg\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e834ba7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>fear_avg</th>\n",
       "      <th>Horror</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.9</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.6</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.6</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.8</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fear_avg  Horror\n",
       "0       1.9     4.0\n",
       "1       1.6     2.0\n",
       "2       1.1     3.0\n",
       "3       3.6     4.0\n",
       "4       1.8     4.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2[['fear_avg', 'Horror']]\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3ee1644a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 5 artists>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.sort(df3['Horror'].dropna().unique())\n",
    "y = df3.groupby('Horror').mean()['fear_avg']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_xlabel('Horror Preferences')\n",
    "ax.set_ylabel('Average Fear Rating')\n",
    "ax.bar(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f0e8a8e",
   "metadata": {},
   "source": [
    "Judging by the looks of this bar graph, there does appear to be a negative relationship with fear ratings and horror preferences, although it is quite small. Unfortunately, since we do not have continuous data for horror preferences, we cannot test for a correlation, but we can test for a significant difference in the means of fear ratings for the highest and lowest horror preferences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c613f2a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import ttest_ind_from_stats as ttest\n",
    "\n",
    "horror_1_mean = df3[df3['Horror'] == 1.0].mean()['fear_avg']\n",
    "horror_1_std = df3[df3['Horror'] == 1.0].std()['fear_avg']\n",
    "horror_1_num_obs = len(df3[df3['Horror'] == 1.0])\n",
    "\n",
    "horror_5_mean = df3[df3['Horror'] == 5.0].mean()['fear_avg']\n",
    "horror_5_std = df3[df3['Horror'] == 5.0].std()['fear_avg']\n",
    "horror_5_num_obs = len(df3[df3['Horror'] == 5.0])\n",
    "\n",
    "statistic, pvalue = ttest(horror_1_mean, horror_1_std, horror_1_num_obs, horror_5_mean, horror_5_std, horror_5_num_obs, alternative='greater')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8aa34f91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.4656639535926113"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "eeee3738",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00029196595562085224"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pvalue"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81f3ed17",
   "metadata": {},
   "source": [
    "The result is significant! It turns out that our respondents with low preference toward horror movies have significantly higher general fear ratings than respondents with high preference toward horror movies. Wow!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
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}