takemeout_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analisis Data Take Me Out\n",
    "##### Gagas Praharsa Bahar - 16520289\n",
    "##### Institut Teknologi Bandung\n",
    "##### Sumber dataset: Take me out\n",
    "##### Tanggal pengerjaan: 11 November 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing and Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"takemeout.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Timestamp</th>\n",
       "      <th>Siapa nama kamu?</th>\n",
       "      <th>Cewek atau cowok nih?</th>\n",
       "      <th>Seberapa penting quality time bareng calon pacar untuk kamu?</th>\n",
       "      <th>Seberapa penting physical touch sama calon pacar untuk kamu?</th>\n",
       "      <th>Seberapa penting word of affirmation dari calon pacar untuk kamu?</th>\n",
       "      <th>Seberapa penting dapet kado dari calon pacar untuk kamu?</th>\n",
       "      <th>Seberapa penting bantuan dari calon pacar untuk kamu?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020/10/31 3:39:25 PM GMT+7</td>\n",
       "      <td>A**************</td>\n",
       "      <td>Cowok</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020/10/31 3:39:36 PM GMT+7</td>\n",
       "      <td>L****</td>\n",
       "      <td>Cewek</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020/10/31 3:39:38 PM GMT+7</td>\n",
       "      <td>Y***********</td>\n",
       "      <td>Cowok</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020/10/31 3:39:42 PM GMT+7</td>\n",
       "      <td>a***</td>\n",
       "      <td>Cowok</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020/10/31 3:39:43 PM GMT+7</td>\n",
       "      <td>B****</td>\n",
       "      <td>Cowok</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Timestamp Siapa nama kamu? Cewek atau cowok nih?  \\\n",
       "0  2020/10/31 3:39:25 PM GMT+7  A**************                 Cowok   \n",
       "1  2020/10/31 3:39:36 PM GMT+7            L****                 Cewek   \n",
       "2  2020/10/31 3:39:38 PM GMT+7     Y***********                 Cowok   \n",
       "3  2020/10/31 3:39:42 PM GMT+7             a***                 Cowok   \n",
       "4  2020/10/31 3:39:43 PM GMT+7            B****                 Cowok   \n",
       "\n",
       "   Seberapa penting quality time bareng calon pacar untuk kamu?  \\\n",
       "0                                                  5              \n",
       "1                                                  5              \n",
       "2                                                  4              \n",
       "3                                                  5              \n",
       "4                                                  5              \n",
       "\n",
       "   Seberapa penting physical touch sama calon pacar untuk kamu?  \\\n",
       "0                                                  5              \n",
       "1                                                  5              \n",
       "2                                                  3              \n",
       "3                                                  5              \n",
       "4                                                  5              \n",
       "\n",
       "   Seberapa penting word of affirmation dari calon pacar untuk kamu?  \\\n",
       "0                                                  4                   \n",
       "1                                                  3                   \n",
       "2                                                  4                   \n",
       "3                                                  5                   \n",
       "4                                                  5                   \n",
       "\n",
       "   Seberapa penting dapet kado dari calon pacar untuk kamu?  \\\n",
       "0                                                  1          \n",
       "1                                                  2          \n",
       "2                                                  4          \n",
       "3                                                  2          \n",
       "4                                                  2          \n",
       "\n",
       "   Seberapa penting bantuan dari calon pacar untuk kamu?  \n",
       "0                                                  3      \n",
       "1                                                  2      \n",
       "2                                                  4      \n",
       "3                                                  3      \n",
       "4                                                  4      "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = [\"time\", \"name\", \"gender\", \"qt\", \"pt\", \"word\", \"gift\", \"service\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(df.columns[0], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>qt</th>\n",
       "      <th>pt</th>\n",
       "      <th>word</th>\n",
       "      <th>gift</th>\n",
       "      <th>service</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A**************</td>\n",
       "      <td>Cowok</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L****</td>\n",
       "      <td>Cewek</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Y***********</td>\n",
       "      <td>Cowok</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a***</td>\n",
       "      <td>Cowok</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B****</td>\n",
       "      <td>Cowok</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name gender  qt  pt  word  gift  service\n",
       "0  A**************  Cowok   5   5     4     1        3\n",
       "1            L****  Cewek   5   5     3     2        2\n",
       "2     Y***********  Cowok   4   3     4     4        4\n",
       "3             a***  Cowok   5   5     5     2        3\n",
       "4            B****  Cowok   5   5     5     2        4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of                       name gender  qt  pt  word  gift  service\n",
       "0          A**************  Cowok   5   5     4     1        3\n",
       "2             Y***********  Cowok   4   3     4     4        4\n",
       "3                     a***  Cowok   5   5     5     2        3\n",
       "4                    B****  Cowok   5   5     5     2        4\n",
       "5               M*********  Cowok   5   5     4     2        2\n",
       "..                     ...    ...  ..  ..   ...   ...      ...\n",
       "95                   N****  Cowok   5   4     4     4        5\n",
       "96               A********  Cowok   5   1     4     2        3\n",
       "98                      M*  Cowok   5   5     5     5        4\n",
       "99   K********************  Cowok   1   1     1     1        1\n",
       "100           B***********  Cowok   5   5     5     5        4\n",
       "\n",
       "[81 rows x 7 columns]>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_m = df[df['gender'] == 'Cowok']\n",
    "df_m.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_f = df[df['gender'] == 'Cewek']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "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>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>qt</th>\n",
       "      <th>pt</th>\n",
       "      <th>word</th>\n",
       "      <th>gift</th>\n",
       "      <th>service</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L****</td>\n",
       "      <td>Cewek</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>M**************</td>\n",
       "      <td>Cewek</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>F*************</td>\n",
       "      <td>Cewek</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>B***</td>\n",
       "      <td>Cewek</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>E***</td>\n",
       "      <td>Cewek</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               name gender  qt  pt  word  gift  service\n",
       "1             L****  Cewek   5   5     3     2        2\n",
       "19  M**************  Cewek   5   5     5     5        5\n",
       "20   F*************  Cewek   4   2     3     1        3\n",
       "23             B***  Cewek   5   2     4     5        4\n",
       "25             E***  Cewek   3   2     3     1        5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "fmean = df_f.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "mmean = df_m.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "qt         3.75\n",
       "pt         2.65\n",
       "word       3.30\n",
       "gift       2.70\n",
       "service    3.70\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fmean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "qt         4.185185\n",
       "pt         3.296296\n",
       "word       3.827160\n",
       "gift       2.740741\n",
       "service    3.765432\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mmean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name       w****\n",
       "gender     Cewek\n",
       "qt             5\n",
       "pt             5\n",
       "word           5\n",
       "gift           5\n",
       "service        5\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f.max(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name       u*********************\n",
       "gender                      Cowok\n",
       "qt                              5\n",
       "pt                              5\n",
       "word                            5\n",
       "gift                            5\n",
       "service                         5\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_m.max(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name          :*\n",
       "gender     Cowok\n",
       "qt             1\n",
       "pt             1\n",
       "word           1\n",
       "gift           1\n",
       "service        1\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_m.min(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name       A*****\n",
       "gender      Cewek\n",
       "qt              1\n",
       "pt              1\n",
       "word            1\n",
       "gift            1\n",
       "service         1\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f.min(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "qt         1.212476\n",
       "pt         1.334686\n",
       "word       1.209287\n",
       "gift       1.325829\n",
       "service    1.143730\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Jenis afeksi')"
      ]
     },
     "execution_count": 19,
     "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": [
    "fmean.plot(kind=\"bar\")\n",
    "plt.xticks(rotation=30, horizontalalignment=\"center\")\n",
    "plt.title(\"Rata-rata dari perempuan\")\n",
    "plt.xlabel(\"Jenis afeksi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Jenis afeksi')"
      ]
     },
     "execution_count": 20,
     "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": [
    "mmean.plot(kind=\"bar\")\n",
    "plt.xticks(rotation=30, horizontalalignment=\"center\")\n",
    "plt.title(\"Rata-rata dari laki-laki\")\n",
    "plt.xlabel(\"Jenis afeksi\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dapat dilihat dari kedua plot diatas, laki-laki dan perempuan sama-sama prefer quality time.\\\n",
    "Sedangkan untuk preferensi kedua, perempuan lebih memilih service, sedangkan laki-laki lebih memilih words of affirmation.\\\n",
    "Dan dari nilai tertingginya, terlihat bahwa laki-laki sampai nilai 4.0 pada qt, sehingga terbukti laki-laki sangat prefer quality time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kesimpulan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ada perbedaan preferensi love language dari kedua gender.\\\n",
    "Tingkat preferensi laki-laki terhadap quality time sangatlah tinggi.\\\n",
    "Laki-laki tidak terlalu mementingkan gift, perempuan tidak terlalu mementingkan physical touch.\\\n",
    "Perempuan mementingkan physical touch, laki-laki tidak."
   ]
  }
 ],
 "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": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}