21_user_study.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Usage Data Graphs\n",
    "- Acceptance Rate per filter;\n",
    "- n manual invocations per filter;\n",
    "- Acceptance Rate per user over a session; to see whether there are temporal changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Authenticated to larisa.ewi.tudelft.nl ([130.161.252.12]:22) using \"publickey\".\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "receiving file list ... done\n",
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      "data_aral/f337d126502344b582c0bbfaabc8b1f8/fed2c01ceba2476bbd5556cb4fc87a50.json\n",
      "data_aral/f337d126502344b582c0bbfaabc8b1f8/fee99a85f9cb4dc59b464ae16dd82346.json\n",
      "data_aral/f337d126502344b582c0bbfaabc8b1f8/fef2795c5d0c406f8aa0bd5fa1c50e39.json\n",
      "data_aral/f337d126502344b582c0bbfaabc8b1f8/ff04d147c028445588dd5895fa343341.json\n",
      "data_aral/f337d126502344b582c0bbfaabc8b1f8/ff3379ec5df642b298f374c8ef645d26.json\n",
      "data_aral/f337d126502344b582c0bbfaabc8b1f8/ffa4d05434a944aa964ec3f7abbcac05.json\n",
      "data_aral/f337d126502344b582c0bbfaabc8b1f8/ffc5585c2dfc4df5888610e90bc09fb6.json\n",
      "data_aral/f337d126502344b582c0bbfaabc8b1f8/ffe1b76414ba47babd2636e328c43f33.json\n",
      "\n",
      "sent 147972 bytes  received 13709871 bytes  3959383.71 bytes/sec\n",
      "total size is 1176024649  speedup is 84.86\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Transferred: sent 165688, received 13722796 bytes, in 2.6 seconds\n",
      "Bytes per second: sent 63180.5, received 5232803.1\n"
     ]
    }
   ],
   "source": [
    "import os, subprocess, json, pandas as pd, matplotlib.pyplot as plt, datetime as dt\n",
    "while not os.getcwd().endswith('-analysis'): os.chdir('..')\n",
    "\n",
    "SERVER_DATA_DIR = 'REDACTED' \n",
    "DATA_DIR = os.path.join('..', 'data')\n",
    "\n",
    "_ = subprocess.run(['rsync', '-avz', f'larisa:{SERVER_DATA_DIR}', DATA_DIR])\n",
    "DATA_DIR = os.path.join(DATA_DIR, SERVER_DATA_DIR.split('/')[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt, json\n",
    "\n",
    "from pprint import pprint\n",
    "from dataclasses import dataclass\n",
    "from typing import Optional\n",
    "\n",
    "@dataclass\n",
    "class Query:\n",
    "\n",
    "    user            : str\n",
    "    prefix          : str\n",
    "    suffix          : str\n",
    "    trigger         : str\n",
    "    language        : str\n",
    "    ide             : str\n",
    "    version         : str\n",
    "    store           : bool\n",
    "    timestamp       : dt.datetime\n",
    "    predictions     : dict\n",
    "    predict_time    : float\n",
    "    survey          : bool\n",
    "\n",
    "    time_since_last_completion: float\n",
    "    filter_type     : str\n",
    "    filter_time     : float\n",
    "    should_filter   : bool\n",
    "    study_version   : str\n",
    "\n",
    "    @classmethod \n",
    "    def from_dict(cls, data: dict) -> 'Query':\n",
    "        ''' Parse json dict into pythonic types, returning Query or VerifiedQuery '''\n",
    "\n",
    "        try: \n",
    "            data['timestamp'] = dt.datetime.fromisoformat(data['timestamp'])\n",
    "            return VerifiedQuery.from_dict(data) \\\n",
    "                if 'verifyToken' in data else cls(**data)\n",
    "        except Exception as e:\n",
    "            print(f'Error parsing query: {e}')\n",
    "            pprint(data)\n",
    "\n",
    "@dataclass \n",
    "class VerifiedQuery(Query):\n",
    "    verify_token    : str\n",
    "    chosen_model    : str\n",
    "    ground_truth    : str\n",
    "    shown_times     : Optional[list[dt.datetime]]\n",
    "    accept_time     : dt.datetime\n",
    "\n",
    "    @classmethod\n",
    "    def from_dict(cls, data: dict):\n",
    "        ''' parse json dict into pythonic types '''\n",
    "\n",
    "        data['verify_token'] = data.pop('verifyToken')\n",
    "        data['accept_time'] = dt.datetime.fromisoformat(data['accept_time'])\n",
    "        \n",
    "        # pushed bugfix for shown_times field on client-side\n",
    "        data['shown_times'] = [dt.datetime.fromisoformat(t) for t in data['shown_times']] \\\n",
    "            if 'shown_times' in data else None\n",
    "\n",
    "        return cls(**data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "unfiltered_queries_per_user = {\n",
    "    user: sorted([\n",
    "        Query.from_dict({\n",
    "            **json.load(open(os.path.join(DATA_DIR, user, query))),\n",
    "            'user': user\n",
    "        }) for query in os.listdir(os.path.join(DATA_DIR, user))\n",
    "    ], key=lambda q: q.timestamp, reverse=True) \n",
    "    for user in os.listdir(DATA_DIR) if user != '.DS_Store'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15047, 71592)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oops_date = dt.datetime(2024, 3, 21, 17, 17, 50)\n",
    "queries_per_user = {\n",
    "    user: [q for q in queries if q.timestamp >= oops_date] \\\n",
    "    for user, queries in unfiltered_queries_per_user.items()\n",
    "}\n",
    "oops_queries = [q for q in sum(unfiltered_queries_per_user.values(), []) if q.timestamp < oops_date]\n",
    "len(oops_queries), len(sum(queries_per_user.values(), []))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recovering Logs\n",
    "I accidentally forgot to save filtered-out queries in the first 24 hours of the user study; which corresponds to about 9253 data points. Luckily I have a log file, which i pre-process with the following pipeline: \n",
    "\n",
    "```sh\n",
    "cat two_clean.log | grep -v 'POST' | sed 's/-1  | //g' | sed '/^Traceback/,/^RuntimeError/d' | awk '/^\\[2024/ {if (NR>1) print \"\"; print} !/^\\[2024/ {printf \" %s\", $0}' > two_cleaner.log\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9115/9115 [00:00<00:00, 481900.33it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recovered 9112 queries\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import tqdm \n",
    "with open('notebooks/paper/two_cleaner.log', 'r') as f: \n",
    "    logs = f.readlines()\n",
    "\n",
    "# if the line does not start with [2024, append it to the previous line\n",
    "clean_logs = [] \n",
    "for line in logs: \n",
    "    if line.startswith('['): \n",
    "        clean_logs.append(line)\n",
    "    else: \n",
    "        clean_logs[-1] += line\n",
    "\n",
    "# remove 'WARNING in api: ' from each line \n",
    "clean_logs = [line.replace('WARNING in api: ', '') for line in clean_logs]\n",
    "\n",
    "# remove any lines that contain 'Error' \n",
    "clean_logs = [line for line in clean_logs if 'Error' not in line]\n",
    "\n",
    "recovered_queries = [] \n",
    "fixed_kwargs = dict(user='rec', prefix='', suffix='', trigger='', language='', ide='', version='', store=False, \n",
    "                    predictions=dict(), predict_time=0.0, survey=False, time_since_last_completion=0.0,\n",
    "                    filter_time=0.0, study_version='')\n",
    "\n",
    "for line in tqdm.tqdm(clean_logs): \n",
    "    for filter_type in ['no_filter', 'feature', 'context', 'joint_a', 'joint_h']: \n",
    "        if filter_type in line: \n",
    "\n",
    "            filter_type = filter_type\n",
    "            timestamp = dt.datetime.fromisoformat(line[1:24].replace(' ', 'T'))\n",
    "            should_filter = 'filter' in line[:34]\n",
    "\n",
    "            query = Query(**fixed_kwargs, timestamp=timestamp, filter_type=filter_type, should_filter=should_filter)\n",
    "            recovered_queries.append(query)\n",
    "            break\n",
    "\n",
    "print(f'Recovered {len(recovered_queries)} queries')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first oops query: 2024-03-17 15:48:44.953718\n",
      "first recovered query: 2024-03-20 09:43:43.963000\n",
      "Maintained 6744 oops queries (0 filtered, 6744 unfiltered)\n",
      "Merging with 9112 recovered queries (2372 filtered, 6740 unfiltered)\n",
      "Final count: 9116 queries\n"
     ]
    }
   ],
   "source": [
    "# So, recovered_queries contains both the filtered and unfiltered queries. (9'112 from the last log)\n",
    "# while, oops_queries contains only the unfiltered queries. (14'047 including but also before the last log)\n",
    "# to maintain the same data distribution, we need to merge them. \n",
    "\n",
    "# NOTE: oops queries contains 18 filtered queries (one for each user) which are overriden each time\n",
    "# let's filter those out first to avoid duplicates. \n",
    "\n",
    "unfiltered_queries = [q for q in oops_queries if not q.should_filter]\n",
    "\n",
    "# NOTE: oops_queries starts much before recovered_queries. \n",
    "unfiltered_queries = sorted(unfiltered_queries, key=lambda q: q.timestamp)\n",
    "recovered_queries = sorted(recovered_queries, key=lambda q: q.timestamp)\n",
    "\n",
    "print(f'first oops query: {unfiltered_queries[0].timestamp}')\n",
    "print(f'first recovered query: {recovered_queries[0].timestamp}')\n",
    "\n",
    "# modify timestamp as the os time on larisa is not set correctly \n",
    "filter_timestamp = recovered_queries[0].timestamp + dt.timedelta(minutes=26)\n",
    "\n",
    "# So, let's remove the oops_queries with timestamp < first recovered query timestamp\n",
    "unfiltered_queries = [q for q in unfiltered_queries if q.timestamp >= filter_timestamp]\n",
    "\n",
    "n_oops_filtered = len([q for q in unfiltered_queries if q.should_filter])\n",
    "n_oops_unfiltered = len([q for q in unfiltered_queries if not q.should_filter])\n",
    "print(f'Maintained {len(unfiltered_queries)} oops queries ({n_oops_filtered} filtered, {n_oops_unfiltered} unfiltered)')\n",
    "n_rec_filtered = len([q for q in recovered_queries if q.should_filter])\n",
    "n_rec_unfiltered = len([q for q in recovered_queries if not q.should_filter])\n",
    "print(f'Merging with {len(recovered_queries)} recovered queries ({n_rec_filtered} filtered, {n_rec_unfiltered} unfiltered)')\n",
    "\n",
    "# add the filtered recovered queires to the oops_queries\n",
    "unfiltered_queries += [q for q in recovered_queries if q.should_filter]\n",
    "\n",
    "print(f'Final count: {len(unfiltered_queries)} queries') # off by ~4/9000 but good enough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 12 users (excl the recovered one which we cannot match )\n"
     ]
    }
   ],
   "source": [
    "n_users = set(q.user for q in unfiltered_queries)\n",
    "print(f'Found {len(n_users)-1} users (excl the recovered one which we cannot match )')\n",
    "\n",
    "# add unfiltred queries to the queries_per_user dict\n",
    "for query in unfiltered_queries: \n",
    "    if query.user not in queries_per_user: \n",
    "        queries_per_user[query.user] = []\n",
    "    queries_per_user[query.user].append(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23376 queries have empty predictions\n"
     ]
    }
   ],
   "source": [
    "# exclude queries where ground-truth is empty \n",
    "# exclude queries where the predictions are empty\n",
    "n_empty = 0\n",
    "for user, queries in queries_per_user.items(): \n",
    "    n_empty += len([q for q in queries if not q.predictions or len(q.predictions) == 0])\n",
    "print(f'{n_empty} queries have empty predictions')\n",
    "\n",
    "queries_per_user = {\n",
    "    user: [q for q in queries if \n",
    "        #    q.ground_truth and \n",
    "           len(q.predictions) > 0\n",
    "        ]\n",
    "    for user, queries in queries_per_user.items()\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Computing Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    57332 completions for 45 users  (10 filtered out in total)\n",
      "    726 accepted in total (1.27%)\n",
      "\n",
      "    Filter            | \u001b[1mcontext        \tfeature        \tjoint_a        \tjoint_h        \tno_filter       \u001b[0m\n",
      "    queries           | 14056.0        \t7804.0         \t8652.0         \t11683.0        \t15137.0        \n",
      "    last query        | 10:09:11 ago   \t3:00:58 ago    \t2:03:04 ago    \t10:07:13 ago   \t1:00:28 ago    \n",
      "\n",
      "    filtered out      | 8     \u001b[37m(0.06%)\u001b[0m\t0     \u001b[37m(0.00%)\u001b[0m\t1     \u001b[37m(0.01%)\u001b[0m\t1     \u001b[37m(0.01%)\u001b[0m\t0     \u001b[37m(0.00%)\u001b[0m\n",
      "    kept              | 14048 \u001b[37m(99.94%)\u001b[0m\t7804  \u001b[37m(100.00%)\u001b[0m\t8651  \u001b[37m(99.99%)\u001b[0m\t11682 \u001b[37m(99.99%)\u001b[0m\t15137 \u001b[37m(100.00%)\u001b[0m\n",
      "\n",
      "    avg. filter time  | 32.14          \t0.19           \t35.31          \t35.36          \t0.02           \n",
      "    med. filter time  | 23.64          \t0.16           \t24.42          \t18.56          \t0.02           \n",
      "    ± std.            | 27.86          \t0.07           \t30.23          \t41.42          \t0.01           \n",
      "    5th perc.         | 11.21          \t0.13           \t11.89          \t11.30          \t0.01           \n",
      "    95th perc.        | 75.10          \t0.32           \t89.49          \t106.55         \t0.02           \n",
      "\n",
      "    accs              | 179   \u001b[37m(1.27%)\u001b[0m\t53    \u001b[37m(0.68%)\u001b[0m\t86    \u001b[37m(0.99%)\u001b[0m\t184   \u001b[37m(1.57%)\u001b[0m\t224   \u001b[37m(1.48%)\u001b[0m\n",
      "    perc. accs        | 179   \u001b[37m(1.27%)\u001b[0m\t53    \u001b[37m(0.68%)\u001b[0m\t86    \u001b[37m(0.99%)\u001b[0m\t184   \u001b[37m(1.58%)\u001b[0m\t224   \u001b[37m(1.48%)\u001b[0m\n",
      "    rel. to no_filter | 86.1%          \t45.9%          \t67.2%          \t106.4%         \t100.0%         \n",
      "\n",
      "    avg. shown length | 28.0           \t26.1           \t28.5           \t23.1           \t23.3           \n",
      "    med. shown length | 18.3           \t16.7           \t17.3           \t14.7           \t13.0           \n",
      "\n",
      "    avg. acc. length  | 20.6           \t16.2           \t20.2           \t16.6           \t21.9           \n",
      "    med. acc. length  | 12.7           \t12.0           \t13.3           \t10.7           \t15.3           \n",
      "\n",
      "    man               | 17    \u001b[37m(0.12%)\u001b[0m\t0     \u001b[37m(0.00%)\u001b[0m\t1     \u001b[37m(0.01%)\u001b[0m\t6     \u001b[37m(0.05%)\u001b[0m\t32    \u001b[37m(0.21%)\u001b[0m\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "n_queries = sum(len(queries) for queries in queries_per_user.values())\n",
    "n_users = len(queries_per_user)\n",
    "n_verified = len(verified := \\\n",
    "                [q for queries in queries_per_user.values() \n",
    "                for q in queries if isinstance(q, VerifiedQuery)]\n",
    "            )\n",
    "n_filtered = len(filtered := \\\n",
    "                 [q for queries in queries_per_user.values()\n",
    "                 for q in queries if q.should_filter])\n",
    "\n",
    "all_queries = sum(queries_per_user.values(), [])\n",
    "queries_per_filter = {} \n",
    "for query in all_queries: \n",
    "    filter_type = query.filter_type\n",
    "    if filter_type not in queries_per_filter: \n",
    "        queries_per_filter[filter_type] = [query]\n",
    "    else: \n",
    "        queries_per_filter[filter_type].append(query)\n",
    "\n",
    "# create a sorted_dict based on the keys \n",
    "queries_per_filter = dict(sorted(queries_per_filter.items(), key=lambda x: x[0]))\n",
    "\n",
    "def tab(list_to_tabulate: list) -> str:\n",
    "    return \"\\t\".join([f'{i:<15}' if isinstance(i, str) else f'{i:<15.1f}' for i in list_to_tabulate])\n",
    "\n",
    "def perc(list_1, list_2) -> list:\n",
    "    return [f'{v:<5} \\033[37m({(100*v/n) if n > 0 else 0 :.2f}%)\\033[0m' for v, n in zip(list_1, list_2)]\n",
    "\n",
    "queries_per_filter = dict(sorted(queries_per_filter.items(), key=lambda x: x[0]))\n",
    "\n",
    "filters = tab(queries_per_filter.keys())\n",
    "n_per_filter = [len(v) for v in queries_per_filter.values()]\n",
    "f_per_filter = [len([q for q in v if q.should_filter == True]) for v in queries_per_filter.values()]\n",
    "i_per_filter = [len([q for q in v if not q.should_filter]) for v in queries_per_filter.values()]\n",
    "v_per_filter = [len([q for q in v if isinstance(q, VerifiedQuery)]) for v in queries_per_filter.values()]\n",
    "\n",
    "# NOTE: Filtering out any queries with <10 chars\n",
    "not_pre_filter_per_filter = [len([q for q in v if (len(q.prefix) + len(q.suffix)) < 10]) for v in queries_per_filter.values()]\n",
    "pre_filter_per_filter = [len([q for q in v if (len(q.prefix) + len(q.suffix)) >= 10]) for v in queries_per_filter.values()]\n",
    "passed_per_filter = [len([q for q in v_2 if not q.should_filter or q.trigger == 'manual']) for v_2 in \\\n",
    "                     [[q for q in v if (len(q.prefix) + len(q.suffix)) >= 10] for v in queries_per_filter.values()]]\n",
    "perceived_v_per_filter = perc([len([q for q in v if isinstance(q, VerifiedQuery)]) for v in queries_per_filter.values()], passed_per_filter)\n",
    "\n",
    "man_per_filter  = [len([q for q in v if q.trigger == 'manual']) for v in queries_per_filter.values()]\n",
    "idle_per_filter = [len([q for q in v if q.trigger == 'idle']) for v in queries_per_filter.values()]\n",
    "auto_per_filter = [len([q for q in v if q.trigger == 'auto']) for v in queries_per_filter.values()]\n",
    "\n",
    "man_filtered_out = [len([q for q in v if q.trigger == 'manual' and q.should_filter]) for v in queries_per_filter.values()]\n",
    "idle_filtered_out = [len([q for q in v if q.trigger == 'idle' and q.should_filter]) for v in queries_per_filter.values()]\n",
    "auto_filtered_out = [len([q for q in v if q.trigger == 'auto' and q.should_filter]) for v in queries_per_filter.values()]\n",
    "\n",
    "avg_filter_time = [sum([q.filter_time for q in v]) / len(v) for v in queries_per_filter.values()]\n",
    "med_filter_time = [sorted([q.filter_time for q in v])[len(v)//2] for v in queries_per_filter.values()]\n",
    "std_filter_time = [pd.Series([q.filter_time for q in v]).std() for v in queries_per_filter.values()]\n",
    "\n",
    "# compute the 2.5th and 97.5th percentile for the filter time\n",
    "low_filter_time = [sorted([q.filter_time for q in v])[int(0.05*len(v))] for v in queries_per_filter.values()]\n",
    "high_filter_time = [sorted([q.filter_time for q in v])[int(0.95*len(v))] for v in queries_per_filter.values()]\n",
    " \n",
    "# get the last query's timestamp per filter \n",
    "last_query_per_filter = {k: max(v, key=lambda q: q.timestamp) for k, v in queries_per_filter.items()}\n",
    "# compute timedelta compared to now\n",
    "time_since_last_query = {k: dt.datetime.now() - v.timestamp - dt.timedelta(hours=1) \\\n",
    "                         for k, v in last_query_per_filter.items()}\n",
    "# convert to nice string repr with h:m but no s\n",
    "time_since_last_query = {k: str(time_since_last_query[k]).split('.')[0] for k in time_since_last_query}\n",
    "time_per_filter = [time_since_last_query[k] for k in queries_per_filter.keys()]\n",
    "# remove the seconds from the time_since_last_query\n",
    "# time_per_filter = [':'.join(t.split(':')[:2]) for t in time_per_filter]\n",
    "time_per_filter = [f'{t} ago' for t in time_per_filter]\n",
    "\n",
    "acc_rate_per_filter = [100*v/i if i > 0 else 0 for v, i in zip(v_per_filter, i_per_filter)]\n",
    "rel_acc_per_filter = [f'{100*acc_rate / acc_rate_per_filter[-1]:.1f}%' for acc_rate in acc_rate_per_filter]\n",
    "\n",
    "shown_suggestion_lengths = [[sum(len(prediction) for prediction in q.predictions.values()) / len(q.predictions)\\\n",
    "                                for q in queries if q.predictions and len(q.predictions) > 0] \\\n",
    "                                    for queries in queries_per_filter.values()]\n",
    "med_suggestion_lengths = [sorted(lengths)[len(lengths)//2] for lengths in shown_suggestion_lengths]\n",
    "avg_suggestion_lengths = [sum(lengths) / len(lengths) for lengths in shown_suggestion_lengths]\n",
    "\n",
    "acc_suggestion_lengths = [[sum(len(prediction) for prediction in q.predictions.values()) / len(q.predictions)\\\n",
    "                                for q in queries if isinstance(q, VerifiedQuery) and len(q.predictions) > 0] \\\n",
    "                                    for queries in queries_per_filter.values()]\n",
    "med_acc_suggestion_lengths = [sorted(lengths)[len(lengths)//2] for lengths in acc_suggestion_lengths]\n",
    "avg_acc_suggestion_lengths = [sum(lengths) / len(lengths) for lengths in acc_suggestion_lengths]\n",
    "\n",
    "print(f'''\n",
    "    {n_queries} completions for {n_users} users  ({n_filtered} filtered out in total)\n",
    "    {n_verified} accepted in total ({100*n_verified/n_queries:.2f}%)\n",
    "\n",
    "    Filter            | \\033[1m{filters} \\033[0m\n",
    "    queries           | {tab(n_per_filter)}\n",
    "    last query        | {tab(time_per_filter)}\n",
    "\n",
    "    filtered out      | {tab(perc(f_per_filter, n_per_filter))}\n",
    "    kept              | {tab(perc(i_per_filter, n_per_filter))}\n",
    "\n",
    "    avg. filter time  | {tab([f'{t:.2f}' for t in avg_filter_time])}\n",
    "    med. filter time  | {tab([f'{t:.2f}' for t in med_filter_time])}\n",
    "    ± std.            | {tab([f'{t:.2f}' for t in std_filter_time])}\n",
    "    5th perc.         | {tab([f'{t:.2f}' for t in low_filter_time])}\n",
    "    95th perc.        | {tab([f'{t:.2f}' for t in high_filter_time])}\n",
    "\n",
    "    accs              | {tab(perc(v_per_filter, n_per_filter))}\n",
    "    perc. accs        | {tab(perc(v_per_filter, i_per_filter))}\n",
    "    rel. to no_filter | {tab(rel_acc_per_filter)}\n",
    "\n",
    "    avg. shown length | {tab(avg_suggestion_lengths)}\n",
    "    med. shown length | {tab(med_suggestion_lengths)}\n",
    "\n",
    "    avg. acc. length  | {tab(avg_acc_suggestion_lengths)}\n",
    "    med. acc. length  | {tab(med_acc_suggestion_lengths)}\n",
    "\n",
    "    man               | {tab(perc(man_per_filter, n_per_filter))}\n",
    "    ''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#     < 10chr           | {tab(perc(not_pre_filter_per_filter, n_per_filter))}\n",
    "#     >= 10chr          | {tab(perc(pre_filter_per_filter, n_per_filter))}\n",
    "#     passed            | {tab(perc(passed_per_filter, pre_filter_per_filter))}\n",
    "#     perceived accs    | {tab(perceived_v_per_filter)}\n",
    "# \n",
    "#     auto              | {tab(perc(auto_per_filter, n_per_filter))}\n",
    "#     idle              | {tab(perc(idle_per_filter, n_per_filter))}\n",
    "# \n",
    "#     n man filtered    | {tab(perc(man_filtered_out, man_per_filter))}\n",
    "#     n auto filtered   | {tab(perc(auto_filtered_out, auto_per_filter))}\n",
    "#     n idle filtered   | {tab(perc(idle_filtered_out, idle_per_filter))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = {} \n",
    "# print three most recent users, and the number of queries in the last 24 hours \n",
    "# for user, queries in sorted({k: v for k, v in queries_per_user.items() if len(v) > 0}, key=lambda x: len(x[1])):\n",
    "for user, queries in sorted(queries_per_user.items(), key=lambda x: len(x[1]), reverse=True):\n",
    "                            # key=lambda x: x[1][0].timestamp, reverse=True):\n",
    "    most_recent = (str(dt.datetime.now() - dt.timedelta(hours=1) - queries[0].timestamp).split('.')[0] )\\\n",
    "          if len(queries) > 0 else None\n",
    "    user_uuid = user[:4]\n",
    "    vqs = [q for q in queries if isinstance(q, VerifiedQuery)]\n",
    "    last_day_qs = [q for q in queries if q.timestamp > dt.datetime.now() - dt.timedelta(days=1)]\n",
    "    last_day_vqs = [q for q in last_day_qs if isinstance(q, VerifiedQuery)]\n",
    "    last_day_acc_rate = len(last_day_vqs) / len(last_day_qs) if last_day_qs else 0\n",
    "    last_day_top_three_langs = sorted(\n",
    "        {q.language for q in queries if q.timestamp > dt.datetime.now() - dt.timedelta(days=1)},\n",
    "        key=lambda lang: len([q for q in queries if q.language == lang]),\n",
    "        reverse=True\n",
    "    )[:3]\n",
    "    top_langs = sorted(\n",
    "        {q.language for q in queries},\n",
    "        key=lambda lang: len([q for q in queries if q.language == lang]),\n",
    "        reverse = True\n",
    "    )\n",
    "\n",
    "    stats[user_uuid] = {\n",
    "        ('','most recent'): most_recent,\n",
    "        ('total', 'n'): len(queries),\n",
    "        ('total', 'acc'): len(vqs),\n",
    "        ('total', 'acc_rate'): (100*len(vqs) / len(queries)) if len(queries) > 0 else 0,\n",
    "        ('total', 'top langs'): top_langs,\n",
    "        ('last day', 'n'): len(last_day_qs),\n",
    "        ('last day', 'acc'): len(last_day_vqs),\n",
    "        ('last day', 'rate'): 100*last_day_acc_rate,\n",
    "        ('last day', 'top langs'): last_day_top_three_langs\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t34 users with 50 or more queries\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">total</th>\n",
       "      <th colspan=\"4\" halign=\"left\">last day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>most recent</th>\n",
       "      <th>n</th>\n",
       "      <th>acc</th>\n",
       "      <th>acc_rate</th>\n",
       "      <th>top langs</th>\n",
       "      <th>n</th>\n",
       "      <th>acc</th>\n",
       "      <th>rate</th>\n",
       "      <th>top langs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1ae4</th>\n",
       "      <td>3 days, 8:11:53</td>\n",
       "      <td>13876</td>\n",
       "      <td>23</td>\n",
       "      <td>0.165754</td>\n",
       "      <td>[latex, scminput, python]</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8cc7</th>\n",
       "      <td>3 days, 8:56:56</td>\n",
       "      <td>12736</td>\n",
       "      <td>51</td>\n",
       "      <td>0.40044</td>\n",
       "      <td>[python, latex, markdown, typescript, plaintex...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
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       "    <tr>\n",
       "      <th>a926</th>\n",
       "      <td>6:31:38</td>\n",
       "      <td>7706</td>\n",
       "      <td>88</td>\n",
       "      <td>1.141967</td>\n",
       "      <td>[python, yaml, ignore, latex, jsonc, propertie...</td>\n",
       "      <td>401</td>\n",
       "      <td>5</td>\n",
       "      <td>1.246883</td>\n",
       "      <td>[python, yaml, ignore]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed93</th>\n",
       "      <td>2 days, 15:09:49</td>\n",
       "      <td>6252</td>\n",
       "      <td>6</td>\n",
       "      <td>0.095969</td>\n",
       "      <td>[latex, python, scminput]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5747</th>\n",
       "      <td>18:11:36</td>\n",
       "      <td>5410</td>\n",
       "      <td>87</td>\n",
       "      <td>1.608133</td>\n",
       "      <td>[python, typescriptreact, typescript, plaintex...</td>\n",
       "      <td>644</td>\n",
       "      <td>1</td>\n",
       "      <td>0.15528</td>\n",
       "      <td>[typescriptreact, plaintext, jsonc]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f337</th>\n",
       "      <td>6:24:25</td>\n",
       "      <td>4342</td>\n",
       "      <td>33</td>\n",
       "      <td>0.760018</td>\n",
       "      <td>[python, http, markdown, csv, dynamic csv, pip...</td>\n",
       "      <td>382</td>\n",
       "      <td>2</td>\n",
       "      <td>0.52356</td>\n",
       "      <td>[python, http, dockercompose]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7f5d</th>\n",
       "      <td>7:02:05</td>\n",
       "      <td>3345</td>\n",
       "      <td>129</td>\n",
       "      <td>3.856502</td>\n",
       "      <td>[php, html, css, cpp, python, pip-requirements]</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[css]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0787</th>\n",
       "      <td>21:56:16</td>\n",
       "      <td>2540</td>\n",
       "      <td>15</td>\n",
       "      <td>0.590551</td>\n",
       "      <td>[typescript, lua, scss, typescriptreact, prope...</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>[typescript, sqlite]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a420</th>\n",
       "      <td>6:26:30</td>\n",
       "      <td>2419</td>\n",
       "      <td>28</td>\n",
       "      <td>1.157503</td>\n",
       "      <td>[python, markdown, java, search-result, json]</td>\n",
       "      <td>350</td>\n",
       "      <td>3</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>[python, markdown, json]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rec</th>\n",
       "      <td>15 days, 8:49:18</td>\n",
       "      <td>2372</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cc39</th>\n",
       "      <td>5 days, 7:21:11</td>\n",
       "      <td>2165</td>\n",
       "      <td>49</td>\n",
       "      <td>2.263279</td>\n",
       "      <td>[html, javascript]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f47e</th>\n",
       "      <td>3 days, 1:35:25</td>\n",
       "      <td>2013</td>\n",
       "      <td>11</td>\n",
       "      <td>0.546448</td>\n",
       "      <td>[css, html, python, javascript, markdown, json...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6064</th>\n",
       "      <td>12 days, 3:03:23</td>\n",
       "      <td>1145</td>\n",
       "      <td>7</td>\n",
       "      <td>0.611354</td>\n",
       "      <td>[python, markdown]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2f6a</th>\n",
       "      <td>8 days, 5:06:00</td>\n",
       "      <td>1048</td>\n",
       "      <td>16</td>\n",
       "      <td>1.526718</td>\n",
       "      <td>[markdown, python, cpp, ini]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c9b7</th>\n",
       "      <td>8 days, 8:07:27</td>\n",
       "      <td>841</td>\n",
       "      <td>10</td>\n",
       "      <td>1.189061</td>\n",
       "      <td>[python, markdown, scminput]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04db</th>\n",
       "      <td>3 days, 20:33:08</td>\n",
       "      <td>816</td>\n",
       "      <td>11</td>\n",
       "      <td>1.348039</td>\n",
       "      <td>[javascript, json, scminput, markdown, html, p...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c715</th>\n",
       "      <td>12 days, 1:37:06</td>\n",
       "      <td>711</td>\n",
       "      <td>3</td>\n",
       "      <td>0.421941</td>\n",
       "      <td>[typescriptreact, typescript, css, json]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0178</th>\n",
       "      <td>5 days, 13:30:42</td>\n",
       "      <td>585</td>\n",
       "      <td>9</td>\n",
       "      <td>1.538462</td>\n",
       "      <td>[typescriptreact, yaml, json, typescript, pyth...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250b</th>\n",
       "      <td>1 day, 2:02:22</td>\n",
       "      <td>570</td>\n",
       "      <td>5</td>\n",
       "      <td>0.877193</td>\n",
       "      <td>[python, jsonc, shellscript, yaml, log]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7cf7</th>\n",
       "      <td>4 days, 18:08:36</td>\n",
       "      <td>401</td>\n",
       "      <td>7</td>\n",
       "      <td>1.745636</td>\n",
       "      <td>[python, arduino-output]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>df7d</th>\n",
       "      <td>23:53:21</td>\n",
       "      <td>346</td>\n",
       "      <td>4</td>\n",
       "      <td>1.156069</td>\n",
       "      <td>[python, scminput, yaml, json]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8c9f</th>\n",
       "      <td>9 days, 7:47:55</td>\n",
       "      <td>314</td>\n",
       "      <td>2</td>\n",
       "      <td>0.636943</td>\n",
       "      <td>[python]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7dd3</th>\n",
       "      <td>11 days, 3:51:48</td>\n",
       "      <td>308</td>\n",
       "      <td>7</td>\n",
       "      <td>2.272727</td>\n",
       "      <td>[python]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62a9</th>\n",
       "      <td>3 days, 4:33:14</td>\n",
       "      <td>301</td>\n",
       "      <td>9</td>\n",
       "      <td>2.990033</td>\n",
       "      <td>[python, javascriptreact, typescript, json, pl...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e575</th>\n",
       "      <td>6 days, 21:09:05</td>\n",
       "      <td>268</td>\n",
       "      <td>1</td>\n",
       "      <td>0.373134</td>\n",
       "      <td>[latex, bibtex, markdown, python]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8caf</th>\n",
       "      <td>6:24:35</td>\n",
       "      <td>209</td>\n",
       "      <td>2</td>\n",
       "      <td>0.956938</td>\n",
       "      <td>[python, javascript, properties, javascriptrea...</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[javascript, css, plaintext]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6832</th>\n",
       "      <td>1 day, 3:30:30</td>\n",
       "      <td>189</td>\n",
       "      <td>1</td>\n",
       "      <td>0.529101</td>\n",
       "      <td>[python]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ebdc</th>\n",
       "      <td>12 days, 2:44:24</td>\n",
       "      <td>145</td>\n",
       "      <td>6</td>\n",
       "      <td>4.137931</td>\n",
       "      <td>[python]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53ed</th>\n",
       "      <td>9:06:29</td>\n",
       "      <td>134</td>\n",
       "      <td>2</td>\n",
       "      <td>1.492537</td>\n",
       "      <td>[java, http, json, markdown]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[json]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1c67</th>\n",
       "      <td>4 days, 5:58:58</td>\n",
       "      <td>123</td>\n",
       "      <td>2</td>\n",
       "      <td>1.626016</td>\n",
       "      <td>[python]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
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       "    <tr>\n",
       "      <th>cff8</th>\n",
       "      <td>1 day, 16:55:28</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[python, code-runner-output, go, plaintext]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4849</th>\n",
       "      <td>7 days, 10:06:33</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[ini, python]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49ee</th>\n",
       "      <td>1 day, 23:12:26</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>3.571429</td>\n",
       "      <td>[java]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c961</th>\n",
       "      <td>13 days, 11:26:00</td>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>3.773585</td>\n",
       "      <td>[javascript]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90b0</th>\n",
       "      <td>5 days, 8:57:07</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[latex, jsonc, log]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a57c</th>\n",
       "      <td>8 days, 11:48:39</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[html, javascript, css, python]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b509</th>\n",
       "      <td>5 days, 23:33:15</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[python, markdown]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8166</th>\n",
       "      <td>13 days, 8:48:09</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[python]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1d4</th>\n",
       "      <td>3 days, 19:54:51</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[python]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cbe6</th>\n",
       "      <td>14 days, 0:09:31</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[php, html]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5d8f</th>\n",
       "      <td>11 days, 17:57:54</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[javascript]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7869</th>\n",
       "      <td>6 days, 3:03:20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[python]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cbb6</th>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3a63</th>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4217</th>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         total                 \\\n",
       "            most recent      n  acc  acc_rate   \n",
       "1ae4    3 days, 8:11:53  13876   23  0.165754   \n",
       "8cc7    3 days, 8:56:56  12736   51   0.40044   \n",
       "a926            6:31:38   7706   88  1.141967   \n",
       "ed93   2 days, 15:09:49   6252    6  0.095969   \n",
       "5747           18:11:36   5410   87  1.608133   \n",
       "f337            6:24:25   4342   33  0.760018   \n",
       "7f5d            7:02:05   3345  129  3.856502   \n",
       "0787           21:56:16   2540   15  0.590551   \n",
       "a420            6:26:30   2419   28  1.157503   \n",
       "rec    15 days, 8:49:18   2372    0       0.0   \n",
       "cc39    5 days, 7:21:11   2165   49  2.263279   \n",
       "f47e    3 days, 1:35:25   2013   11  0.546448   \n",
       "6064   12 days, 3:03:23   1145    7  0.611354   \n",
       "2f6a    8 days, 5:06:00   1048   16  1.526718   \n",
       "c9b7    8 days, 8:07:27    841   10  1.189061   \n",
       "04db   3 days, 20:33:08    816   11  1.348039   \n",
       "c715   12 days, 1:37:06    711    3  0.421941   \n",
       "0178   5 days, 13:30:42    585    9  1.538462   \n",
       "250b     1 day, 2:02:22    570    5  0.877193   \n",
       "7cf7   4 days, 18:08:36    401    7  1.745636   \n",
       "df7d           23:53:21    346    4  1.156069   \n",
       "8c9f    9 days, 7:47:55    314    2  0.636943   \n",
       "7dd3   11 days, 3:51:48    308    7  2.272727   \n",
       "62a9    3 days, 4:33:14    301    9  2.990033   \n",
       "e575   6 days, 21:09:05    268    1  0.373134   \n",
       "8caf            6:24:35    209    2  0.956938   \n",
       "6832     1 day, 3:30:30    189    1  0.529101   \n",
       "ebdc   12 days, 2:44:24    145    6  4.137931   \n",
       "53ed            9:06:29    134    2  1.492537   \n",
       "1c67    4 days, 5:58:58    123    2  1.626016   \n",
       "cff8    1 day, 16:55:28     70    0       0.0   \n",
       "4849   7 days, 10:06:33     70    0       0.0   \n",
       "49ee    1 day, 23:12:26     56    2  3.571429   \n",
       "c961  13 days, 11:26:00     53    2  3.773585   \n",
       "90b0    5 days, 8:57:07     46    0       0.0   \n",
       "a57c   8 days, 11:48:39     23    0       0.0   \n",
       "b509   5 days, 23:33:15     21    0       0.0   \n",
       "8166   13 days, 8:48:09      9    0       0.0   \n",
       "f1d4   3 days, 19:54:51      3    0       0.0   \n",
       "cbe6   14 days, 0:09:31      2    0       0.0   \n",
       "5d8f  11 days, 17:57:54      2    0       0.0   \n",
       "7869    6 days, 3:03:20      1    0       0.0   \n",
       "cbb6               None      0    0         0   \n",
       "3a63               None      0    0         0   \n",
       "4217               None      0    0         0   \n",
       "\n",
       "                                                        last day      \\\n",
       "                                              top langs        n acc   \n",
       "1ae4                          [latex, scminput, python]        0   0   \n",
       "8cc7  [python, latex, markdown, typescript, plaintex...        0   0   \n",
       "a926  [python, yaml, ignore, latex, jsonc, propertie...      401   5   \n",
       "ed93                          [latex, python, scminput]        0   0   \n",
       "5747  [python, typescriptreact, typescript, plaintex...      644   1   \n",
       "f337  [python, http, markdown, csv, dynamic csv, pip...      382   2   \n",
       "7f5d    [php, html, css, cpp, python, pip-requirements]        2   0   \n",
       "0787  [typescript, lua, scss, typescriptreact, prope...      120   1   \n",
       "a420      [python, markdown, java, search-result, json]      350   3   \n",
       "rec                                                  []        0   0   \n",
       "cc39                                 [html, javascript]        0   0   \n",
       "f47e  [css, html, python, javascript, markdown, json...        0   0   \n",
       "6064                                 [python, markdown]        0   0   \n",
       "2f6a                       [markdown, python, cpp, ini]        0   0   \n",
       "c9b7                       [python, markdown, scminput]        0   0   \n",
       "04db  [javascript, json, scminput, markdown, html, p...        0   0   \n",
       "c715           [typescriptreact, typescript, css, json]        0   0   \n",
       "0178  [typescriptreact, yaml, json, typescript, pyth...        0   0   \n",
       "250b            [python, jsonc, shellscript, yaml, log]        0   0   \n",
       "7cf7                           [python, arduino-output]        0   0   \n",
       "df7d                     [python, scminput, yaml, json]        0   0   \n",
       "8c9f                                           [python]        0   0   \n",
       "7dd3                                           [python]        0   0   \n",
       "62a9  [python, javascriptreact, typescript, json, pl...        0   0   \n",
       "e575                  [latex, bibtex, markdown, python]        0   0   \n",
       "8caf  [python, javascript, properties, javascriptrea...       45   0   \n",
       "6832                                           [python]        0   0   \n",
       "ebdc                                           [python]        0   0   \n",
       "53ed                       [java, http, json, markdown]        1   0   \n",
       "1c67                                           [python]        0   0   \n",
       "cff8        [python, code-runner-output, go, plaintext]        0   0   \n",
       "4849                                      [ini, python]        0   0   \n",
       "49ee                                             [java]        0   0   \n",
       "c961                                       [javascript]        0   0   \n",
       "90b0                                [latex, jsonc, log]        0   0   \n",
       "a57c                    [html, javascript, css, python]        0   0   \n",
       "b509                                 [python, markdown]        0   0   \n",
       "8166                                           [python]        0   0   \n",
       "f1d4                                           [python]        0   0   \n",
       "cbe6                                        [php, html]        0   0   \n",
       "5d8f                                       [javascript]        0   0   \n",
       "7869                                           [python]        0   0   \n",
       "cbb6                                                 []        0   0   \n",
       "3a63                                                 []        0   0   \n",
       "4217                                                 []        0   0   \n",
       "\n",
       "                                                     \n",
       "          rate                            top langs  \n",
       "1ae4         0                                   []  \n",
       "8cc7         0                                   []  \n",
       "a926  1.246883               [python, yaml, ignore]  \n",
       "ed93         0                                   []  \n",
       "5747   0.15528  [typescriptreact, plaintext, jsonc]  \n",
       "f337   0.52356        [python, http, dockercompose]  \n",
       "7f5d       0.0                                [css]  \n",
       "0787  0.833333                 [typescript, sqlite]  \n",
       "a420  0.857143             [python, markdown, json]  \n",
       "rec          0                                   []  \n",
       "cc39         0                                   []  \n",
       "f47e         0                                   []  \n",
       "6064         0                                   []  \n",
       "2f6a         0                                   []  \n",
       "c9b7         0                                   []  \n",
       "04db         0                                   []  \n",
       "c715         0                                   []  \n",
       "0178         0                                   []  \n",
       "250b         0                                   []  \n",
       "7cf7         0                                   []  \n",
       "df7d         0                                   []  \n",
       "8c9f         0                                   []  \n",
       "7dd3         0                                   []  \n",
       "62a9         0                                   []  \n",
       "e575         0                                   []  \n",
       "8caf       0.0         [javascript, css, plaintext]  \n",
       "6832         0                                   []  \n",
       "ebdc         0                                   []  \n",
       "53ed       0.0                               [json]  \n",
       "1c67         0                                   []  \n",
       "cff8         0                                   []  \n",
       "4849         0                                   []  \n",
       "49ee         0                                   []  \n",
       "c961         0                                   []  \n",
       "90b0         0                                   []  \n",
       "a57c         0                                   []  \n",
       "b509         0                                   []  \n",
       "8166         0                                   []  \n",
       "f1d4         0                                   []  \n",
       "cbe6         0                                   []  \n",
       "5d8f         0                                   []  \n",
       "7869         0                                   []  \n",
       "cbb6         0                                   []  \n",
       "3a63         0                                   []  \n",
       "4217         0                                   []  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert tuples to layered headings in dataframe\n",
    "df = pd.DataFrame(stats).T\n",
    "df.columns = pd.MultiIndex.from_tuples(df.columns)\n",
    "df.sort_values(('', 'most recent'), ascending=False)\n",
    "\n",
    "# print n users with >50 queries \n",
    "n_with_50 = len([q for q in queries_per_user.values() if len(q) >= 50])\n",
    "print(f'\\t{n_with_50} users with 50 or more queries')\n",
    "\n",
    "df"
   ]
  }
 ],
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   "mimetype": "text/x-python",
   "name": "python",
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