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tvorogme/first_stepic.ipynb

Last active December 5, 2016 02:11
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first_stepic.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"events = pd.read_csv('course-217-events.csv')\n",
"structure = pd.read_csv('course-217-structure.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user_id</th>\n",
" <th>action</th>\n",
" <th>step_id</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>viewed</td>\n",
" <td>47435</td>\n",
" <td>1465181036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>viewed</td>\n",
" <td>47438</td>\n",
" <td>1465181034</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>viewed</td>\n",
" <td>47435</td>\n",
" <td>1465181021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>viewed</td>\n",
" <td>47436</td>\n",
" <td>1465181015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>viewed</td>\n",
" <td>41605</td>\n",
" <td>1465180037</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user_id action step_id time\n",
"0 1 viewed 47435 1465181036\n",
"1 1 viewed 47438 1465181034\n",
"2 1 viewed 47435 1465181021\n",
"3 1 viewed 47436 1465181015\n",
"4 1 viewed 41605 1465180037"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>course_id</th>\n",
" <th>module_id</th>\n",
" <th>module_position</th>\n",
" <th>lesson_id</th>\n",
" <th>lesson_position</th>\n",
" <th>step_id</th>\n",
" <th>step_position</th>\n",
" <th>step_type</th>\n",
" <th>step_cost</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>217</td>\n",
" <td>614</td>\n",
" <td>2</td>\n",
" <td>13228</td>\n",
" <td>2</td>\n",
" <td>38842</td>\n",
" <td>1</td>\n",
" <td>text</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>217</td>\n",
" <td>614</td>\n",
" <td>2</td>\n",
" <td>13228</td>\n",
" <td>2</td>\n",
" <td>39715</td>\n",
" <td>6</td>\n",
" <td>code</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>217</td>\n",
" <td>614</td>\n",
" <td>2</td>\n",
" <td>13228</td>\n",
" <td>2</td>\n",
" <td>39716</td>\n",
" <td>7</td>\n",
" <td>code</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>217</td>\n",
" <td>614</td>\n",
" <td>2</td>\n",
" <td>13228</td>\n",
" <td>2</td>\n",
" <td>39717</td>\n",
" <td>8</td>\n",
" <td>code</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>217</td>\n",
" <td>614</td>\n",
" <td>2</td>\n",
" <td>13228</td>\n",
" <td>2</td>\n",
" <td>39721</td>\n",
" <td>9</td>\n",
" <td>free-answer</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" course_id module_id module_position lesson_id lesson_position step_id \\\n",
"0 217 614 2 13228 2 38842 \n",
"1 217 614 2 13228 2 39715 \n",
"2 217 614 2 13228 2 39716 \n",
"3 217 614 2 13228 2 39717 \n",
"4 217 614 2 13228 2 39721 \n",
"\n",
" step_position step_type step_cost \n",
"0 1 text 0 \n",
"1 6 code 1 \n",
"2 7 code 1 \n",
"3 8 code 1 \n",
"4 9 free-answer 0 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"structure.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"events.sort_values(by='time', inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"costed = {x:y for x,y in zip(structure.step_id, structure.step_cost)}"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Тут храним все скоры\n",
"scores= {}\n",
"# Тут храним топовые значения\n",
"the_best = {}\n",
"\n",
"for event in (events.values):\n",
" user = event[0]\n",
" step_id = event[2]\n",
" \n",
" # Заполним значеия, если их ещё нет\n",
" # Нельзя юзать defaultdict, потому что events[3]\n",
" if (user not in scores.keys()):\n",
" scores[user] = [0, event[3]]\n",
" # Если чувак прошел степ\n",
" # Добавим ему за это денег\n",
" if (event[1] == 'passed'):\n",
" scores[user][0] += step_id_cost[step_id]\n",
" \n",
" # scores.get(user, [0,0])[0] <- замена deffaultdict\n",
" # user not in the_best < он должен там быть\n",
" if (scores.get(user, [0,0])[0] >= 24) and (user not in the_best):\n",
" # Сохранить чувака\n",
" the_best[user] = event[3] - scores[user][1]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Тут мы сортим по скору\n",
"data = pd.DataFrame(list(the_best.items())).sort_values(by=1)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4313,1264,821,1146,1124,2622,713,972,4280,1291,"
]
}
],
"source": [
"# Возьмём топ 10\n",
"for elem in data[:10][0].tolist():\n",
" print(elem, end=',')"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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second_stepic.ipynb
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third_kaggle.ipynb
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