OKCScrape_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Loading in the data\n",
    "The profiles are stored in a .pickle file, where each row of the DataFrame is iteratively appended to the file one at a time. load_profiles_df loops through the .pickle file, appending each results from pkl.load() to the output DataFrame until there is no more data in the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading file ...\n",
      "Finished loading file.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import okctools\n",
    "\n",
    "df_raw = okctools.load_profiles_df('profiles_old.pickle') # Defaults to 'profiles.pickle'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>age</th>\n",
       "      <th>build</th>\n",
       "      <th>cats</th>\n",
       "      <th>city</th>\n",
       "      <th>diet</th>\n",
       "      <th>dogs</th>\n",
       "      <th>drinks</th>\n",
       "      <th>drugs</th>\n",
       "      <th>ed_prefix</th>\n",
       "      <th>education</th>\n",
       "      <th>...</th>\n",
       "      <th>monogamous</th>\n",
       "      <th>name</th>\n",
       "      <th>orientation</th>\n",
       "      <th>region</th>\n",
       "      <th>religion</th>\n",
       "      <th>religion_pre</th>\n",
       "      <th>sign</th>\n",
       "      <th>smokes</th>\n",
       "      <th>status</th>\n",
       "      <th>username</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jakarta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>ruth</td>\n",
       "      <td>[straight]</td>\n",
       "      <td>indonesia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>single</td>\n",
       "      <td>ruth2044624212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>phoenix</td>\n",
       "      <td>omnivore</td>\n",
       "      <td>dogs</td>\n",
       "      <td>socially</td>\n",
       "      <td>None</td>\n",
       "      <td>attended</td>\n",
       "      <td>two-year college</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>shalena</td>\n",
       "      <td>[straight]</td>\n",
       "      <td>az</td>\n",
       "      <td>agnostic</td>\n",
       "      <td>it’s important</td>\n",
       "      <td>aquarius</td>\n",
       "      <td>NaN</td>\n",
       "      <td>single</td>\n",
       "      <td>sheenerb20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>beersheba</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>noa</td>\n",
       "      <td>[straight]</td>\n",
       "      <td>israel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>single</td>\n",
       "      <td>noache</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>crowley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>mimi</td>\n",
       "      <td>[straight]</td>\n",
       "      <td>tx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>single</td>\n",
       "      <td>mimi9108466544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>average</td>\n",
       "      <td>cats</td>\n",
       "      <td>parque industrial</td>\n",
       "      <td>vegetarian</td>\n",
       "      <td>dogs</td>\n",
       "      <td>socially</td>\n",
       "      <td>no</td>\n",
       "      <td>attended</td>\n",
       "      <td>university</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>mariane</td>\n",
       "      <td>[straight]</td>\n",
       "      <td>brazil</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>cancer</td>\n",
       "      <td>no</td>\n",
       "      <td>single</td>\n",
       "      <td>marian1026462024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  age    build  cats               city        diet  dogs    drinks drugs  \\\n",
       "0  26     None   NaN            jakarta         NaN   NaN       NaN   NaN   \n",
       "1  38     None   NaN            phoenix    omnivore  dogs  socially  None   \n",
       "2  30     None   NaN          beersheba        None  None       NaN    no   \n",
       "3  24     None   NaN            crowley         NaN   NaN       NaN   NaN   \n",
       "4  28  average  cats  parque industrial  vegetarian  dogs  socially    no   \n",
       "\n",
       "  ed_prefix         education        ...        monogamous     name  \\\n",
       "0       NaN               NaN        ...              None     ruth   \n",
       "1  attended  two-year college        ...              None  shalena   \n",
       "2       NaN               NaN        ...              None      noa   \n",
       "3       NaN               NaN        ...              None     mimi   \n",
       "4  attended        university        ...              None  mariane   \n",
       "\n",
       "  orientation     region  religion    religion_pre      sign smokes  status  \\\n",
       "0  [straight]  indonesia       NaN             NaN       NaN    NaN  single   \n",
       "1  [straight]         az  agnostic  it’s important  aquarius    NaN  single   \n",
       "2  [straight]     israel       NaN             NaN      None    NaN  single   \n",
       "3  [straight]         tx       NaN             NaN       NaN    NaN  single   \n",
       "4  [straight]     brazil      None            None    cancer     no  single   \n",
       "\n",
       "           username  \n",
       "0    ruth2044624212  \n",
       "1        sheenerb20  \n",
       "2            noache  \n",
       "3    mimi9108466544  \n",
       "4  marian1026462024  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a full list of parameter names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'build', 'cats', 'city', 'diet', 'dogs', 'drinks', 'drugs',\n",
       "       'ed_prefix', 'education', 'essays', 'ethnicity', 'gender', 'height',\n",
       "       'kids_future', 'kids_present', 'lang_primary', 'lang_secondary',\n",
       "       'monogamous', 'name', 'orientation', 'region', 'religion',\n",
       "       'religion_pre', 'sign', 'smokes', 'status', 'username'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'kids_present' label is using 'present' as in 'now', but it would be easy to misinterpret it as 'existing'. Let's fix that with a quick rename."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw.rename(columns={'kids_present': 'kids_current'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Null check\n",
    "\n",
    "The data's looking pretty sparse. Let's find out just how sparse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "numcols = len(df_raw.columns)\n",
    "nullcounts = list(map(lambda x: sum(df_raw.isnull().sum(axis=1) == x), \n",
    "                                range(numcols)))\n",
    "\n",
    "plt.figure(figsize=(10,5));\n",
    "plt.bar(range(numcols), nullcounts);\n",
    "plt.xticks(range(numcols+1));\n",
    "plt.title('Null values in profiles, (%s total profiles)' % len(df_raw));\n",
    "plt.xlabel('#nulls');\n",
    "plt.ylabel('#profiles');\n",
    "for xpos, ypos in enumerate(nullcounts):\n",
    "    plt.text(xpos, ypos + 3, str(ypos), ha='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A handful of profiles have only a non-null username field. This is because they turned up as \"not found\" on the OKC site. We have no way of knowing why (self-deletion/removal/etc.), but it's a surprisingly high percentage, given that the profile IDs were collected and then requested within the same day.\n",
    "\n",
    "As far as I know, the second largest grouping is due to minimally-completed profiles.\n",
    "\n",
    "So let's create subsets of the raw data:\n",
    "- one with no empty profiles\n",
    "- one with no empty or minimally-filled profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_no_empty = df_raw[df_raw.isnull().sum(axis=1) != (numcols-1)].reset_index(drop=True)\n",
    "df = df_no_empty[df_no_empty.isnull().sum(axis=1) != 19].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see how null each columns is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nullcols = df.isnull().sum(axis=0).sort_values()\n",
    "nullcols = nullcols.drop('username')\n",
    "fig2 = plt.figure(figsize=(10,5));\n",
    "plt.bar(range(len(nullcols)), nullcols.values, width=.8)\n",
    "for xpos, ypos in enumerate(nullcols.values):\n",
    "    plt.text(xpos + .06, ypos + 3, str(ypos), \n",
    "             ha='center', va='top', rotation=90, color='white')\n",
    "plt.xticks(range(len(nullcols)), nullcols.index, rotation=90)\n",
    "plt.title('# nulls for each column, out of %s profiles' % len(df))\n",
    "plt.xlabel('Column name')\n",
    "plt.ylabel('#nulls')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like there's a few interesting items, such as the unexpectedly high number of nulls in lang_primary.\n",
    "Note that, for some parameters, there is only a 'yes' option, and the alternative is to not answer, as opposed to an explicit 'no' (e.g. for cats, dogs).\n",
    "\n",
    "A unique parameter is 'essays', which is actually a list of dicts for each profile containing all of the free-response essay fields. Let's see how verbose things are around here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import regex\n",
    "\n",
    "def count_words(listofdicts):\n",
    "    words = 0\n",
    "    for essay in listofdicts:\n",
    "        words += len(regex.findall(r'\\b\\w+\\b', essay['content']))\n",
    "    return words\n",
    "\n",
    "wordcounts = np.fromiter(map(lambda essay: count_words(essay), df_no_empty.loc[:,'essays']), 'int32')\n",
    "\n",
    "fig3 = plt.figure(figsize=(10,5));\n",
    "plt.hist(wordcounts, bins=40)\n",
    "plt.title('Profile essay word count distribution')\n",
    "plt.xlabel('Word count in profile (All essays in each profile)')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be a pretty clear exponential tendancy to the word count distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "age_index = np.argsort(df['age'].values)\n",
    "\n",
    "fig4 = plt.figure(figsize=(10,5));\n",
    "plt.plot(df.loc[age_index,'age'].values, wordcounts[age_index.tolist()], 'o', alpha=0.5, markeredgewidth=0.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'build', 'cats', 'city', 'diet', 'dogs', 'drinks', 'drugs',\n",
       "       'ed_prefix', 'education', 'essays', 'ethnicity', 'gender', 'height',\n",
       "       'kids_future', 'kids_current', 'lang_primary', 'lang_secondary',\n",
       "       'monogamous', 'name', 'orientation', 'region', 'religion',\n",
       "       'religion_pre', 'sign', 'smokes', 'status', 'username'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_empty.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "feature_counts = df_no_empty.notnull().sum(axis=1)\n",
    "feature_count_index = np.argsort(feature_counts.values)\n",
    "fig5 = plt.figure(figsize=(10,5));\n",
    "plt.plot(feature_counts[feature_count_index].values, wordcounts[feature_count_index.tolist()], 'o', alpha=0.5, markeredgewidth=0.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fig6 = plt.figure(figsize=(10,5));\n",
    "#plt.hist(df_no_empty.age.values, bins=25)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}