collect_users/analyze_users.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6617d56b",
   "metadata": {},
   "source": [
    "# Analysis of user collection\n",
    "\n",
    "In this notebook, the retrieved results of the user collection is analyzed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "635b6d3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import time\n",
    "import sys\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d921730",
   "metadata": {},
   "source": [
    "Configure modules like pandas and plotting libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e12160d",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = [12, 8]\n",
    "pd.set_option('display.min_rows', 24)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0efae96b",
   "metadata": {},
   "source": [
    "Folders and constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "74fd74bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "fp_figs = Path(\"figs\")\n",
    "\n",
    "annotated_users = Path(\"results\", \"users_enriched_summer2021.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49d80e8d",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "In this section, output data of the various methods is collected and combined. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a189654b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 methods\\github_search\\results\\github_search_repos.csv\n",
      "2 methods\\github_search\\results\\github_search_topic.csv\n",
      "3 methods\\github_search\\results\\github_search_users.csv\n",
      "4 methods\\papers_with_code\\results\\paperswithcode.csv\n",
      "5 methods\\profile_pages\\results\\profile_page_uu.csv\n",
      "6 methods\\pure\\results\\pure.csv\n"
     ]
    }
   ],
   "source": [
    "data_files = glob.glob(\"methods/*/results/*.csv\")\n",
    "\n",
    "for i, d in enumerate(data_files):\n",
    "    print(i+1, d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "206bba3c",
   "metadata": {},
   "source": [
    "All files is loaded and combined in a single dataset. The dataset contains the variables:\n",
    "- `source` The source is the method used to find the user, e.g. paperswithcode, github_search_users, github_search_topics. The name is derived from the file name of the results file.\n",
    "- `service` The service the user profile is found, e.g. github.com, github.warwick.ac.uk, gitlab.\n",
    "- `date` The date of collection. This can be useful when updating results.\n",
    "- `user_id` The user handle of the found user. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f16f7232",
   "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>source</th>\n",
       "      <th>service</th>\n",
       "      <th>user_id</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>github_search_repos</td>\n",
       "      <td>github.com</td>\n",
       "      <td>jonasmoons</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>github_search_repos</td>\n",
       "      <td>github.com</td>\n",
       "      <td>UtrechtUniversity</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>github_search_repos</td>\n",
       "      <td>github.com</td>\n",
       "      <td>gglucass</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>github_search_repos</td>\n",
       "      <td>github.com</td>\n",
       "      <td>MarcelRobeer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>github_search_repos</td>\n",
       "      <td>github.com</td>\n",
       "      <td>ekatrukha</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>github_search_repos</td>\n",
       "      <td>github.com</td>\n",
       "      <td>UtrechtUniversity</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>github_search_repos</td>\n",
       "      <td>github.com</td>\n",
       "      <td>MarcelRobeer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>github_search_repos</td>\n",
       "      <td>github.com</td>\n",
       "      <td>arnokamphuis</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>github_search_repos</td>\n",
       "      <td>github.com</td>\n",
       "      <td>UU-Hydro</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>github_search_repos</td>\n",
       "      <td>github.com</td>\n",
       "      <td>Peikos</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>github_search_repos</td>\n",
       "      <td>github.com</td>\n",
       "      <td>ruudkoot</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>github_search_repos</td>\n",
       "      <td>github.com</td>\n",
       "      <td>jkorb</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>pure</td>\n",
       "      <td>github.com</td>\n",
       "      <td>gerkovink</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>pure</td>\n",
       "      <td>github.com</td>\n",
       "      <td>gerkovink</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>pure</td>\n",
       "      <td>github.com</td>\n",
       "      <td>gerkovink</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>pure</td>\n",
       "      <td>github.com</td>\n",
       "      <td>gerkovink</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>pure</td>\n",
       "      <td>github.com</td>\n",
       "      <td>gerkovink</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>pure</td>\n",
       "      <td>github.com</td>\n",
       "      <td>gerkovink</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>pure</td>\n",
       "      <td>github.com</td>\n",
       "      <td>gerkovink</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>pure</td>\n",
       "      <td>github.com</td>\n",
       "      <td>keesmulder</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>pure</td>\n",
       "      <td>github.com</td>\n",
       "      <td>RianneSchouten</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>pure</td>\n",
       "      <td>github.com</td>\n",
       "      <td>ydluo</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>pure</td>\n",
       "      <td>github.com</td>\n",
       "      <td>marnixnaber</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>pure</td>\n",
       "      <td>github.com</td>\n",
       "      <td>gjlbeckers-uu</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>644 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  source     service            user_id date\n",
       "0    github_search_repos  github.com         jonasmoons  NaN\n",
       "1    github_search_repos  github.com  UtrechtUniversity  NaN\n",
       "2    github_search_repos  github.com           gglucass  NaN\n",
       "3    github_search_repos  github.com       MarcelRobeer  NaN\n",
       "4    github_search_repos  github.com          ekatrukha  NaN\n",
       "5    github_search_repos  github.com  UtrechtUniversity  NaN\n",
       "6    github_search_repos  github.com       MarcelRobeer  NaN\n",
       "7    github_search_repos  github.com       arnokamphuis  NaN\n",
       "8    github_search_repos  github.com           UU-Hydro  NaN\n",
       "9    github_search_repos  github.com             Peikos  NaN\n",
       "10   github_search_repos  github.com           ruudkoot  NaN\n",
       "11   github_search_repos  github.com              jkorb  NaN\n",
       "..                   ...         ...                ...  ...\n",
       "632                 pure  github.com          gerkovink  NaN\n",
       "633                 pure  github.com          gerkovink  NaN\n",
       "634                 pure  github.com          gerkovink  NaN\n",
       "635                 pure  github.com          gerkovink  NaN\n",
       "636                 pure  github.com          gerkovink  NaN\n",
       "637                 pure  github.com          gerkovink  NaN\n",
       "638                 pure  github.com          gerkovink  NaN\n",
       "639                 pure  github.com         keesmulder  NaN\n",
       "640                 pure  github.com     RianneSchouten  NaN\n",
       "641                 pure  github.com              ydluo  NaN\n",
       "642                 pure  github.com        marnixnaber  NaN\n",
       "643                 pure  github.com      gjlbeckers-uu  NaN\n",
       "\n",
       "[644 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user_names = pd.concat(\n",
    "    [pd.read_csv(fp) for fp in data_files], \n",
    "    axis=0, \n",
    "    keys=data_files, \n",
    "    names=[\"source\", \"row\"]\n",
    ") \\\n",
    "    .reset_index(\"source\") \\\n",
    "    .reset_index(drop=True)\n",
    "\n",
    "df_user_names[\"source\"] = df_user_names[\"source\"].apply(lambda x: Path(x).stem)\n",
    "df_user_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50e86f42",
   "metadata": {},
   "source": [
    "## Findability\n",
    "\n",
    "The following results shows how many times user handles are found with each retrieval method. If a user is found with multiple methods, the findability of the user can be consider to be better. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b7bb1a4",
   "metadata": {},
   "source": [
    "### Cross table user and method\n",
    "\n",
    "Create a cross table with service and user on one axis and the extraction method on the other axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1613a425",
   "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>source</th>\n",
       "      <th>github_search_repos</th>\n",
       "      <th>github_search_topic</th>\n",
       "      <th>github_search_users</th>\n",
       "      <th>paperswithcode</th>\n",
       "      <th>profile_page_uu</th>\n",
       "      <th>pure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>service</th>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"25\" valign=\"top\">github.com</th>\n",
       "      <th>19giorgosts</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4009509</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73616e646572</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AJueling</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARYENN</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AlCorreia</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Altijd-youri</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AmirPliev</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AndreasSaka</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AndriesBosma</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arccos390</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ArjenSimons</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wenkokke</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>whatsthehubbub</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wouter-swierstra</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wzwietering</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xavierpinho23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yaikohu</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ydluo</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yoepvanloenen</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ystouthart</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yuntaoj</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zievathustra</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zyumakuu</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>490 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "source                       github_search_repos  github_search_topic  \\\n",
       "service    user_id                                                      \n",
       "github.com 19giorgosts                         0                    0   \n",
       "           4009509                             0                    0   \n",
       "           73616e646572                        0                    0   \n",
       "           AJueling                            1                    0   \n",
       "           ARYENN                              0                    0   \n",
       "           AlCorreia                           1                    0   \n",
       "           Altijd-youri                        0                    0   \n",
       "           AmirPliev                           1                    0   \n",
       "           AndreasSaka                         1                    0   \n",
       "           AndriesBosma                        0                    0   \n",
       "           Arccos390                           0                    0   \n",
       "           ArjenSimons                         0                    0   \n",
       "...                                          ...                  ...   \n",
       "           wenkokke                            1                    0   \n",
       "           whatsthehubbub                      1                    0   \n",
       "           wouter-swierstra                    1                    0   \n",
       "           wzwietering                         0                    0   \n",
       "           xavierpinho23                       0                    0   \n",
       "           yaikohu                             0                    0   \n",
       "           ydluo                               0                    0   \n",
       "           yoepvanloenen                       1                    0   \n",
       "           ystouthart                          1                    1   \n",
       "           yuntaoj                             0                    0   \n",
       "           zievathustra                        2                    0   \n",
       "           zyumakuu                            0                    0   \n",
       "\n",
       "source                       github_search_users  paperswithcode  \\\n",
       "service    user_id                                                 \n",
       "github.com 19giorgosts                         1               0   \n",
       "           4009509                             1               0   \n",
       "           73616e646572                        0               0   \n",
       "           AJueling                            1               0   \n",
       "           ARYENN                              1               0   \n",
       "           AlCorreia                           0               0   \n",
       "           Altijd-youri                        1               0   \n",
       "           AmirPliev                           0               0   \n",
       "           AndreasSaka                         0               0   \n",
       "           AndriesBosma                        1               0   \n",
       "           Arccos390                           1               0   \n",
       "           ArjenSimons                         1               0   \n",
       "...                                          ...             ...   \n",
       "           wenkokke                            0               0   \n",
       "           whatsthehubbub                      0               0   \n",
       "           wouter-swierstra                    0               0   \n",
       "           wzwietering                         1               0   \n",
       "           xavierpinho23                       1               0   \n",
       "           yaikohu                             1               0   \n",
       "           ydluo                               0               0   \n",
       "           yoepvanloenen                       0               0   \n",
       "           ystouthart                          0               0   \n",
       "           yuntaoj                             1               0   \n",
       "           zievathustra                        0               0   \n",
       "           zyumakuu                            1               0   \n",
       "\n",
       "source                       profile_page_uu  pure  \n",
       "service    user_id                                  \n",
       "github.com 19giorgosts                     0     0  \n",
       "           4009509                         0     0  \n",
       "           73616e646572                    1     0  \n",
       "           AJueling                        0     0  \n",
       "           ARYENN                          0     0  \n",
       "           AlCorreia                       0     0  \n",
       "           Altijd-youri                    0     0  \n",
       "           AmirPliev                       0     0  \n",
       "           AndreasSaka                     0     0  \n",
       "           AndriesBosma                    0     0  \n",
       "           Arccos390                       0     0  \n",
       "           ArjenSimons                     0     0  \n",
       "...                                      ...   ...  \n",
       "           wenkokke                        0     0  \n",
       "           whatsthehubbub                  0     0  \n",
       "           wouter-swierstra                0     0  \n",
       "           wzwietering                     0     0  \n",
       "           xavierpinho23                   0     0  \n",
       "           yaikohu                         0     0  \n",
       "           ydluo                           0     1  \n",
       "           yoepvanloenen                   0     0  \n",
       "           ystouthart                      0     0  \n",
       "           yuntaoj                         0     0  \n",
       "           zievathustra                    1     0  \n",
       "           zyumakuu                        0     0  \n",
       "\n",
       "[490 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_name_method_crosstab = df_user_names \\\n",
    "    .drop(\"date\", axis=1) \\\n",
    "    .groupby([\"service\", \"user_id\", \"source\"]) \\\n",
    "    .size() \\\n",
    "    .unstack(\"source\") \\\n",
    "    .fillna(0) \\\n",
    "    .applymap(int)\n",
    "\n",
    "df_name_method_crosstab"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1a3ad6a",
   "metadata": {},
   "source": [
    "The following example can be uncommented to find the findabilty of a specific project. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "627cd832",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source\n",
      "github_search_repos    1\n",
      "github_search_topic    0\n",
      "github_search_users    0\n",
      "paperswithcode         0\n",
      "profile_page_uu        0\n",
      "pure                   0\n",
      "Name: (github.com, UiL-OTS-labs), dtype: int64\n",
      "source\n",
      "github_search_repos    1\n",
      "github_search_topic    0\n",
      "github_search_users    1\n",
      "paperswithcode         0\n",
      "profile_page_uu        2\n",
      "pure                   0\n",
      "Name: (github.com, UU-Hydro), dtype: int64\n",
      "source\n",
      "github_search_repos    1\n",
      "github_search_topic    0\n",
      "github_search_users    0\n",
      "paperswithcode         0\n",
      "profile_page_uu        0\n",
      "pure                   0\n",
      "Name: (github.com, UU-ComputerScience), dtype: int64\n",
      "source\n",
      "github_search_repos     6\n",
      "github_search_topic    10\n",
      "github_search_users     1\n",
      "paperswithcode          0\n",
      "profile_page_uu         0\n",
      "pure                    0\n",
      "Name: (github.com, UtrechtUniversity), dtype: int64\n",
      "source\n",
      "github_search_repos    0\n",
      "github_search_topic    0\n",
      "github_search_users    1\n",
      "paperswithcode         0\n",
      "profile_page_uu        1\n",
      "pure                   0\n",
      "Name: (github.com, UUDigitalHumanitieslab), dtype: int64\n",
      "source\n",
      "github_search_repos    0\n",
      "github_search_topic    0\n",
      "github_search_users    1\n",
      "paperswithcode         0\n",
      "profile_page_uu        0\n",
      "pure                   0\n",
      "Name: (github.com, haddocking), dtype: int64\n",
      "source\n",
      "github_search_repos    0\n",
      "github_search_topic    0\n",
      "github_search_users    1\n",
      "paperswithcode         0\n",
      "profile_page_uu        0\n",
      "pure                   0\n",
      "Name: (github.com, pcraster), dtype: int64\n"
     ]
    }
   ],
   "source": [
    "def print_findability(df, orgname):\n",
    "    print(df_name_method_crosstab.loc[('github.com', orgname)])\n",
    "\n",
    "\n",
    "# df_name_method_crosstab.loc[('github.com', 'UiL-OTS-labs')]\n",
    "# df_name_method_crosstab.loc[('github.com', 'UU-Hydro')]\n",
    "# df_name_method_crosstab.loc[('github.com', 'UU-ComputerScience')]\n",
    "# df_name_method_crosstab.loc[('github.com', 'UtrechtUniversity')]\n",
    "# df_name_method_crosstab.loc[('github.com', 'ictenmediaUU')]\n",
    "# df_name_method_crosstab.loc[('github.com', 'UniversiteitUtrecht')]\n",
    "# df_name_method_crosstab.loc[('github.com', 'UUDigitalHumanitieslab')]\n",
    "# df_name_method_crosstab.loc[('github.com', 'haddocking')]\n",
    "# df_name_method_crosstab.loc[('github.com', 'pcraster')]\n",
    "# df_name_method_crosstab.loc[('github.com', 'UiL-OTS')]\n",
    "\n",
    "orgs = [\n",
    "\"UiL-OTS-labs\",\n",
    "\"UU-Hydro\",\n",
    "\"UU-ComputerScience\",\n",
    "\"UtrechtUniversity\",\n",
    "# \"ictenmediaUU\",\n",
    "# \"UniversiteitUtrecht\",\n",
    "\"UUDigitalHumanitieslab\",\n",
    "\"haddocking\",\n",
    "\"pcraster\",\n",
    "# \"UiL-OTS\"\n",
    "]\n",
    "for org in orgs:\n",
    "    print_findability(df_name_method_crosstab, org)\n",
    "    \n",
    "    \n",
    "\"\"\"\n",
    "github_search_repos (1) \n",
    "github_search_users (1)\n",
    "profile_page_uu (2)\n",
    "\n",
    "github_search_repos (6)\n",
    "github_search_topic (10)\n",
    "github_search_users (1)\n",
    "\n",
    "github_search_users (1)\n",
    "profile_page_uu (1)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dce9541",
   "metadata": {},
   "source": [
    "### Findability per user\n",
    "\n",
    "The following table show which users are found with most collection strategies. The count indicates the number of methods, and the relative score the number relative to the total number of collection strategies. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "804c4aef",
   "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></th>\n",
       "      <th>count</th>\n",
       "      <th>relative</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>service</th>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"25\" valign=\"top\">github.com</th>\n",
       "      <th>kevin4998</th>\n",
       "      <td>3</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UU-Hydro</th>\n",
       "      <td>3</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UtrechtUniversity</th>\n",
       "      <td>3</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J535D165</th>\n",
       "      <td>3</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ekatrukha</th>\n",
       "      <td>3</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>matus-jan-lavko</th>\n",
       "      <td>2</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OscarKro</th>\n",
       "      <td>2</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MarcelRobeer</th>\n",
       "      <td>2</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casper-Smet</th>\n",
       "      <td>2</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fdekruijff</th>\n",
       "      <td>2</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SamOye95</th>\n",
       "      <td>2</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a-quei</th>\n",
       "      <td>2</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TabeaSonnenschein</th>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StanMey</th>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SquaredCanine</th>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sprouting-angiogenesis</th>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Southparkfan</th>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SonicwaveNL</th>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SolennWalstra</th>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SiefBlok</th>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shellydun</th>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShNadi?tab=repositories</th>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SarahWarnau</th>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zyumakuu</th>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>490 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    count  relative\n",
       "service    user_id                                 \n",
       "github.com kevin4998                    3  0.500000\n",
       "           UU-Hydro                     3  0.500000\n",
       "           UtrechtUniversity            3  0.500000\n",
       "           J535D165                     3  0.500000\n",
       "           ekatrukha                    3  0.500000\n",
       "           matus-jan-lavko              2  0.333333\n",
       "           OscarKro                     2  0.333333\n",
       "           MarcelRobeer                 2  0.333333\n",
       "           Casper-Smet                  2  0.333333\n",
       "           fdekruijff                   2  0.333333\n",
       "           SamOye95                     2  0.333333\n",
       "           a-quei                       2  0.333333\n",
       "...                                   ...       ...\n",
       "           TabeaSonnenschein            1  0.166667\n",
       "           StanMey                      1  0.166667\n",
       "           SquaredCanine                1  0.166667\n",
       "           Sprouting-angiogenesis       1  0.166667\n",
       "           Southparkfan                 1  0.166667\n",
       "           SonicwaveNL                  1  0.166667\n",
       "           SolennWalstra                1  0.166667\n",
       "           SiefBlok                     1  0.166667\n",
       "           Shellydun                    1  0.166667\n",
       "           ShNadi?tab=repositories      1  0.166667\n",
       "           SarahWarnau                  1  0.166667\n",
       "           zyumakuu                     1  0.166667\n",
       "\n",
       "[490 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute the count\n",
    "df_name_findability = (df_name_method_crosstab > 0) \\\n",
    "    .astype(int) \\\n",
    "    .sum(axis=1) \\\n",
    "    .sort_values(ascending=False) \\\n",
    "    .to_frame(name=\"count\")\n",
    "\n",
    "# compute relative score\n",
    "df_name_findability[\"relative\"] = df_name_findability[\"count\"] / len(data_files)\n",
    "\n",
    "df_name_findability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9fdc9cad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "retrieval_count = df_name_findability[\"count\"].value_counts().to_frame(name=\"Retrieval count\")\n",
    "\n",
    "sns.barplot(\n",
    "    x=retrieval_count.index.astype(str), \n",
    "    y=retrieval_count[\"Retrieval count\"]\n",
    ")\n",
    "plt.title(\"Retrieval count for each user in dataset\")\n",
    "plt.savefig(Path(fp_figs, 'user_collection_user_findability.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0934aae",
   "metadata": {},
   "source": [
    "### Findability per method\n",
    "\n",
    "This table indicates how succeful a method is collecting users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "23136aa1",
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>github_search_users</th>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>github_search_repos</th>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profile_page_uu</th>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>github_search_topic</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pure</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paperswithcode</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count\n",
       "source                    \n",
       "github_search_users    263\n",
       "github_search_repos    179\n",
       "profile_page_uu         64\n",
       "github_search_topic     17\n",
       "pure                    11\n",
       "paperswithcode           5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_method_findability = (df_name_method_crosstab > 0) \\\n",
    "    .astype(int) \\\n",
    "    .sum(axis=0) \\\n",
    "    .sort_values(ascending=False) \\\n",
    "    .to_frame(name=\"count\")\n",
    "\n",
    "df_method_findability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "21be8f84",
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>github_search_users</th>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>github_search_repos</th>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profile_page_uu</th>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>github_search_topic</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pure</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paperswithcode</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count\n",
       "source                    \n",
       "github_search_users    263\n",
       "github_search_repos    179\n",
       "profile_page_uu         64\n",
       "github_search_topic     17\n",
       "pure                    11\n",
       "paperswithcode           5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_method_findability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "68bddfe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(\n",
    "    x=df_method_findability.index, \n",
    "    y=df_method_findability[\"count\"]\n",
    ")\n",
    "plt.title(\"Retrieval count for each method in dataset\")\n",
    "plt.savefig(Path(fp_figs, 'user_collection_method_findability.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4311df7",
   "metadata": {},
   "source": [
    "## Filtering of users\n",
    "\n",
    "Not all users collected in the previous steps are relevant to the analysis. Some are not (or no longer) part of the organisation, or excluded for other reasons (for example students)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f2e9bb52",
   "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>user_id</th>\n",
       "      <th>is_student</th>\n",
       "      <th>is_employee</th>\n",
       "      <th>is_currently_employed</th>\n",
       "      <th>is_research_group</th>\n",
       "      <th>final_decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ReinierMaas</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rel=</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>jhellingman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LucaScorpion</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FloatingPoint</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M-thieu</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>marrykylie01</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ydluo</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>bricker0</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>cjvanlissa</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FPtje</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>sodascience</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>HU-ICT-TI-2</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>WouterDroogers</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>amices</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>D-score</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>ottenbri</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>stefvanbuuren</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>keesmulder</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>hanstimm</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>Jollyfant</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>growthcharts</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>gjlbeckers-uu</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>429 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id  is_student  is_employee  is_currently_employed  \\\n",
       "0       ReinierMaas       False          0.0                    0.0   \n",
       "1              rel=       False          NaN                    NaN   \n",
       "2       jhellingman       False          NaN                    NaN   \n",
       "3      LucaScorpion       False          NaN                    NaN   \n",
       "4     FloatingPoint       False          NaN                    NaN   \n",
       "5           M-thieu       False          1.0                    1.0   \n",
       "6               NaN       False          NaN                    NaN   \n",
       "7      marrykylie01       False          NaN                    NaN   \n",
       "8             ydluo       False          NaN                    NaN   \n",
       "9          bricker0       False          1.0                    1.0   \n",
       "10       cjvanlissa       False          1.0                    1.0   \n",
       "11            FPtje       False          NaN                    NaN   \n",
       "..              ...         ...          ...                    ...   \n",
       "417     sodascience       False          0.0                    0.0   \n",
       "418     HU-ICT-TI-2       False          NaN                    NaN   \n",
       "419  WouterDroogers       False          1.0                    1.0   \n",
       "420          amices       False          NaN                    NaN   \n",
       "421         D-score       False          NaN                    NaN   \n",
       "422        ottenbri       False          1.0                    0.0   \n",
       "423   stefvanbuuren       False          1.0                    1.0   \n",
       "424      keesmulder       False          1.0                    0.0   \n",
       "425        hanstimm       False          NaN                    NaN   \n",
       "426       Jollyfant       False          NaN                    NaN   \n",
       "427    growthcharts       False          NaN                    NaN   \n",
       "428   gjlbeckers-uu       False          1.0                    1.0   \n",
       "\n",
       "     is_research_group  final_decision  \n",
       "0                  0.0               0  \n",
       "1                  NaN               0  \n",
       "2                  NaN               0  \n",
       "3                  NaN               0  \n",
       "4                  NaN               0  \n",
       "5                  NaN               1  \n",
       "6                  NaN               0  \n",
       "7                  NaN               0  \n",
       "8                  NaN               0  \n",
       "9                  NaN               1  \n",
       "10                 NaN               1  \n",
       "11                 NaN               0  \n",
       "..                 ...             ...  \n",
       "417                1.0               1  \n",
       "418                0.0               0  \n",
       "419                NaN               1  \n",
       "420                1.0               1  \n",
       "421                1.0               1  \n",
       "422                NaN               0  \n",
       "423                NaN               1  \n",
       "424                NaN               0  \n",
       "425                0.0               0  \n",
       "426                NaN               1  \n",
       "427                1.0               1  \n",
       "428                NaN               1  \n",
       "\n",
       "[429 rows x 6 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_annotated_users = pd.read_excel(annotated_users)\n",
    "\n",
    "df_annotated_users[\n",
    "    [\"user_id\", \n",
    "     \"is_student\", \n",
    "     \"is_employee\", \n",
    "     \"is_currently_employed\", \n",
    "     \"is_research_group\", \n",
    "     \"final_decision\"\n",
    "    ]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ad800df2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of included users is 143\n"
     ]
    }
   ],
   "source": [
    "df_annotated_users_included = df_annotated_users[df_annotated_users[\"final_decision\"] == 1]\n",
    "\n",
    "print(\"The number of included users is\", len(df_annotated_users_included))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edc0ecfe",
   "metadata": {},
   "source": [
    "## Properties of users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8854f817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    113\n",
       "1.0     30\n",
       "Name: is_research_group, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_annotated_users_included \\\n",
    "    .loc[df_annotated_users_included[\"final_decision\"] == 1, \"is_research_group\"] \\\n",
    "    .fillna(0) \\\n",
    "    .value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "837dc7d9",
   "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>user_id</th>\n",
       "      <th>public_repos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>brentp</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>UiL-OTS-labs</td>\n",
       "      <td>113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>CLARIAH</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>CLARIAH</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>UtrechtUniversity</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>cjvanlissa</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>UUDigitalHumanitieslab</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>UUDigitalHumanitieslab</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>MycrofD</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>J535D165</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   user_id  public_repos\n",
       "69                  brentp         182.0\n",
       "84            UiL-OTS-labs         113.0\n",
       "44                 CLARIAH         111.0\n",
       "43                 CLARIAH         111.0\n",
       "62       UtrechtUniversity          68.0\n",
       "10              cjvanlissa          68.0\n",
       "50  UUDigitalHumanitieslab          63.0\n",
       "49  UUDigitalHumanitieslab          63.0\n",
       "93                 MycrofD          47.0\n",
       "27                J535D165          45.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a boxplot with swarm\n",
    "ax = sns.boxplot(x='public_repos', data=df_annotated_users_included)\n",
    "ax = sns.swarmplot(x='public_repos', data=df_annotated_users_included, color=\".25\")\n",
    "\n",
    "# output users with most public repos\n",
    "df_annotated_users_included[['user_id', 'public_repos']].sort_values('public_repos', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "237f17b4",
   "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>user_id</th>\n",
       "      <th>public_gists</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>brentp</td>\n",
       "      <td>184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>ekatrukha</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>daob</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>J535D165</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>norm2782</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>japhir</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>wouter-swierstra</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>AJueling</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>leonardovida</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>davidcortesortuno</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               user_id  public_gists\n",
       "69              brentp         184.0\n",
       "85           ekatrukha           8.0\n",
       "103               daob           7.0\n",
       "27            J535D165           7.0\n",
       "140           norm2782           6.0\n",
       "253             japhir           5.0\n",
       "79    wouter-swierstra           5.0\n",
       "87            AJueling           4.0\n",
       "34        leonardovida           4.0\n",
       "113  davidcortesortuno           2.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='public_gists', data=df_annotated_users_included)\n",
    "df_annotated_users_included[['user_id', 'public_gists']].sort_values('public_gists', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0ac0bf4a",
   "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>user_id</th>\n",
       "      <th>followers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>brentp</td>\n",
       "      <td>924.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>J535D165</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>stefvanbuuren</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>wouter-swierstra</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>jbikker</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>norm2782</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>cjvanlissa</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>PABalland</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>alexandrosstergiou</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>bozso</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                user_id  followers\n",
       "69               brentp      924.0\n",
       "27             J535D165      135.0\n",
       "423       stefvanbuuren      124.0\n",
       "79     wouter-swierstra      106.0\n",
       "92              jbikker      104.0\n",
       "140            norm2782       59.0\n",
       "10           cjvanlissa       39.0\n",
       "240           PABalland       37.0\n",
       "74   alexandrosstergiou       35.0\n",
       "26                bozso       33.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x='followers', data=df_annotated_users_included, showfliers=False)\n",
    "\n",
    "df_annotated_users_included[['user_id', 'followers']].sort_values('followers', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e85a1a02",
   "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>user_id</th>\n",
       "      <th>following</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>bozso</td>\n",
       "      <td>296.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>brentp</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>J535D165</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>bricker0</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>hanneoberman</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>leonardovida</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>bmatern</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>japhir</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>MycrofD</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>AJueling</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id  following\n",
       "26          bozso      296.0\n",
       "69         brentp      181.0\n",
       "27       J535D165       77.0\n",
       "9        bricker0       73.0\n",
       "20   hanneoberman       38.0\n",
       "34   leonardovida       38.0\n",
       "59        bmatern       36.0\n",
       "253        japhir       25.0\n",
       "93        MycrofD       23.0\n",
       "87       AJueling       22.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df_annotated_users_included['following'], showfliers = False)\n",
    "df_annotated_users_included[['user_id', 'following']].sort_values('following', ascending=False).head(10)\n"
   ]
  }
 ],
 "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,
 "nbformat_minor": 5
}