first_stepic.ipynb

second_stepic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from collections import defaultdict, Counter\n",
    "from tqdm import tqdm_notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "events = pd.read_csv('course-217-events.csv')\n",
    "events.sort_values(\"time\", inplace=True)\n",
    "structure = pd.read_csv('course-217-structure.csv')\n",
    "structure.sort_values([\"module_position\", \"lesson_position\", \"step_position\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "step_line = list(structure.step_id.values)\n",
    "\n",
    "def next_step(step):\n",
    "    if step != step_line[-1]:\n",
    "        return step_line[step_line.index(step)+1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# сет возращений\n",
    "return_count = defaultdict(set)\n",
    "\n",
    "# вспомогательный список нужен для нормализации результата\n",
    "steps_by_user = defaultdict(set)\n",
    "\n",
    "for line in tqdm_notebook(events.groupby('user_id').step_id):\n",
    "    for i, step in enumerate(list(line[1])):\n",
    "        # Запомним то, сколько юзеров входило в сет\n",
    "        steps_by_user[step].add(line[0])\n",
    "        try:\n",
    "            # индекс следующего степа меньше текущего в списке до текущего индекса\n",
    "            if list(line[1])[:i].index(step) < i - list(line[1])[:i][::-1].index(next_step(step)) - 1:\n",
    "                return_count[step].add(line[0]) \n",
    "        except:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(41684, 0.7460106382978723),\n",
       " (41604, 0.7297605473204105),\n",
       " (41097, 0.6809314033983638),\n",
       " (41481, 0.6759465478841871),\n",
       " (42593, 0.6662468513853904),\n",
       " (38872, 0.6616337591947348),\n",
       " (41686, 0.6571815718157181),\n",
       " (39735, 0.6548804780876494),\n",
       " (39740, 0.6538882803943045),\n",
       " (39717, 0.6459863945578231)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter({i: len(return_count[i]) / len(steps_by_user[i]) for i in steps_by_user.keys()}).most_common(10)"
   ]
  }
 ],
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  "widgets": {
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third_kaggle.ipynb