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Charlotte Weaver
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Added Lesson 9
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‎Lesson09_Project/MF Promotions Solution.ipynb‎

Lines changed: 243 additions & 0 deletions
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 139,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import random"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 148,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'new': True, 'level': 0, 'score': 61.38892117782677, 'sex': 'woman'}\n"
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]
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}
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],
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"source": [
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"def generateRandomPerson(level=0, new=True):\n",
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" score = 0\n",
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" sex = random.choice(['man', 'woman'])\n",
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" if sex=='man':\n",
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" score = random.normalvariate(50, 10) + 2.01\n",
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" else:\n",
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" score = random.normalvariate(50, 10)\n",
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" return {\n",
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" 'sex': sex,\n",
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" 'score': score,\n",
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" 'level': level,\n",
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" 'new': new\n",
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" }\n",
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"print generateRandomPerson()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 141,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"def generateStaff(levels):\n",
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" staff = []\n",
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" for idx, level in enumerate(levels):\n",
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" staff.append([generateRandomPerson(level=idx, new=False) for person in range(level)])\n",
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" return staff\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 142,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"def allAreNew(staff):\n",
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" for level in staff:\n",
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" for employee in level:\n",
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" if not employee['new']:\n",
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" return False\n",
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" return True"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 143,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def quit(staff, attrition):\n",
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" for idx, level in enumerate(staff):\n",
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" for idx2, employee in enumerate(level):\n",
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" rn = random.random()\n",
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" if rn <= attrition:\n",
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" staff[idx][idx2] = None\n",
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" return staff"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 144,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"def pickBest(level):\n",
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" best = None\n",
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" bestidx = None\n",
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" for idx, employee in enumerate(level):\n",
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" if best is None or (employee is not None and employee['score'] > best['score']):\n",
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" best = employee\n",
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" bestidx = idx\n",
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" return bestidx"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 145,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"def promote(staff):\n",
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" for level in reversed(range(len(staff))):\n",
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" newlevel = []\n",
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" for idx, employee in enumerate(staff[level]):\n",
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" if employee is None:\n",
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" if (level > 0):\n",
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" bestidx = pickBest(staff[level - 1])\n",
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" promotedemployee = staff[level - 1][bestidx]\n",
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" promotedemployee['level'] = level\n",
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" staff[level - 1][bestidx] = None\n",
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" newlevel.append(promotedemployee)\n",
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" else:\n",
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" newlevel.append(generateRandomPerson())\n",
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" else:\n",
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" newlevel.append(employee)\n",
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" staff[level] = newlevel\n",
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" return staff"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 146,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"def mfratio(level):\n",
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" m = 0\n",
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" f = 0\n",
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" for employee in level:\n",
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" if employee['sex'] == 'man':\n",
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" m += 1\n",
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" else:\n",
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" f += 1\n",
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" return m, f, m/float(m + f), f/float(m+ f)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 151,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(210, 290, 0.42, 0.58)\n",
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"(151, 199, 0.43142857142857144, 0.5685714285714286)\n",
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"(96, 104, 0.48, 0.52)\n",
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"(83, 67, 0.5533333333333333, 0.44666666666666666)\n",
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"(59, 41, 0.59, 0.41)\n",
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"(36, 39, 0.48, 0.52)\n",
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"(32, 8, 0.8, 0.2)\n",
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"(5, 5, 0.5, 0.5)\n"
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]
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}
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],
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"source": [
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"levels = [500, 350, 200, 150, 100, 75, 40, 10]\n",
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"attrition = 0.15\n",
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"staff = generateStaff(levels)\n",
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"# quit/promotion cycle until all employees are new\n",
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"while not allAreNew(staff):\n",
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" staff =quit(staff, attrition)\n",
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" staff = promote(staff)\n",
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"# print results\n",
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"for level in staff:\n",
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" print mfratio(level)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 135,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[{'new': True, 'level': 7, 'score': 88.12901630433419, 'sex': 'woman'}, {'new': True, 'level': 7, 'score': 78.34331335924162, 'sex': 'man'}, {'new': True, 'level': 7, 'score': 73.04051697978099, 'sex': 'woman'}, {'new': True, 'level': 7, 'score': 77.49551767908078, 'sex': 'man'}, {'new': True, 'level': 7, 'score': 71.64967624573703, 'sex': 'man'}, {'new': True, 'level': 7, 'score': 72.83227829828641, 'sex': 'woman'}, {'new': True, 'level': 7, 'score': 72.41219419646502, 'sex': 'man'}, {'new': True, 'level': 7, 'score': 73.96193729087702, 'sex': 'man'}, {'new': True, 'level': 7, 'score': 73.54940906075277, 'sex': 'woman'}, {'new': True, 'level': 7, 'score': 72.2103392854928, 'sex': 'man'}]\n"
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]
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}
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],
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"source": [
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"print staff[-1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

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