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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202339713901182662204FRFrance
1202338716702783062315FRFrance
2202337711222232021213FRFrance
32023367726101442102FRFrance
42023357961961826102FRFrance
52023347116892327204FRFrance
62023337330811845432528FRFrance
72023327799611201487212222FRFrance
82023317331813985238528FRFrance
920233075821326983739513FRFrance
10202329713558829718819201228FRFrance
11202328767004043935710614FRFrance
12202327772534599990711715FRFrance
1320232679192622312161141018FRFrance
14202325711498825714739171222FRFrance
15202324711115796814262171222FRFrance
1620232371256361341899219929FRFrance
17202322712184812516243181224FRFrance
18202321711349759815100171123FRFrance
192023207900046151338514721FRFrance
202023197934460911259714919FRFrance
21202318710671729114051161121FRFrance
222023177918461621220614919FRFrance
23202316711387801414760171222FRFrance
24202315714040761320467211131FRFrance
252023147152471103219462231729FRFrance
26202313713322970016944201525FRFrance
27202312710374721813530161121FRFrance
2820231174919288069587410FRFrance
2920231074854273169777410FRFrance
.................................
16831991267176081130423912312042FRFrance
16841991257161691070021638281838FRFrance
16851991247161711007122271281739FRFrance
1686199123711947767116223211329FRFrance
1687199122715452995320951271737FRFrance
1688199121714903897520831261636FRFrance
16891991207190531274225364342345FRFrance
16901991197167391124622232291939FRFrance
16911991187213851388228888382551FRFrance
1692199117713462887718047241632FRFrance
16931991167148571006819646261834FRFrance
1694199115713975978118169251832FRFrance
1695199114712265768416846221430FRFrance
169619911379567604113093171123FRFrance
1697199112710864733114397191325FRFrance
16981991117155741118419964271935FRFrance
16991991107166431137221914292038FRFrance
1700199109713741878018702241533FRFrance
1701199108713289881317765231531FRFrance
1702199107712337807716597221529FRFrance
1703199106710877701314741191226FRFrance
1704199105710442654414340181125FRFrance
17051991047791345631126314820FRFrance
17061991037153871048420290271836FRFrance
17071991027162771104621508292038FRFrance
17081991017155651027120859271836FRFrance
17091990527193751329525455342345FRFrance
17101990517190801380724353342543FRFrance
1711199050711079666015498201228FRFrance
17121990497114302610205FRFrance
\n", + "

1713 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202339 7 1390 118 2662 2 0 \n", + "1 202338 7 1670 278 3062 3 1 \n", + "2 202337 7 1122 223 2021 2 1 \n", + "3 202336 7 726 10 1442 1 0 \n", + "4 202335 7 961 96 1826 1 0 \n", + "5 202334 7 1168 9 2327 2 0 \n", + "6 202333 7 3308 1184 5432 5 2 \n", + "7 202332 7 7996 1120 14872 12 2 \n", + "8 202331 7 3318 1398 5238 5 2 \n", + "9 202330 7 5821 3269 8373 9 5 \n", + "10 202329 7 13558 8297 18819 20 12 \n", + "11 202328 7 6700 4043 9357 10 6 \n", + "12 202327 7 7253 4599 9907 11 7 \n", + "13 202326 7 9192 6223 12161 14 10 \n", + "14 202325 7 11498 8257 14739 17 12 \n", + "15 202324 7 11115 7968 14262 17 12 \n", + "16 202323 7 12563 6134 18992 19 9 \n", + "17 202322 7 12184 8125 16243 18 12 \n", + "18 202321 7 11349 7598 15100 17 11 \n", + "19 202320 7 9000 4615 13385 14 7 \n", + "20 202319 7 9344 6091 12597 14 9 \n", + "21 202318 7 10671 7291 14051 16 11 \n", + "22 202317 7 9184 6162 12206 14 9 \n", + "23 202316 7 11387 8014 14760 17 12 \n", + "24 202315 7 14040 7613 20467 21 11 \n", + "25 202314 7 15247 11032 19462 23 17 \n", + "26 202313 7 13322 9700 16944 20 15 \n", + "27 202312 7 10374 7218 13530 16 11 \n", + "28 202311 7 4919 2880 6958 7 4 \n", + "29 202310 7 4854 2731 6977 7 4 \n", + "... ... ... ... ... ... ... ... \n", + "1683 199126 7 17608 11304 23912 31 20 \n", + "1684 199125 7 16169 10700 21638 28 18 \n", + "1685 199124 7 16171 10071 22271 28 17 \n", + "1686 199123 7 11947 7671 16223 21 13 \n", + "1687 199122 7 15452 9953 20951 27 17 \n", + "1688 199121 7 14903 8975 20831 26 16 \n", + "1689 199120 7 19053 12742 25364 34 23 \n", + "1690 199119 7 16739 11246 22232 29 19 \n", + "1691 199118 7 21385 13882 28888 38 25 \n", + "1692 199117 7 13462 8877 18047 24 16 \n", + "1693 199116 7 14857 10068 19646 26 18 \n", + "1694 199115 7 13975 9781 18169 25 18 \n", + "1695 199114 7 12265 7684 16846 22 14 \n", + "1696 199113 7 9567 6041 13093 17 11 \n", + "1697 199112 7 10864 7331 14397 19 13 \n", + "1698 199111 7 15574 11184 19964 27 19 \n", + "1699 199110 7 16643 11372 21914 29 20 \n", + "1700 199109 7 13741 8780 18702 24 15 \n", + "1701 199108 7 13289 8813 17765 23 15 \n", + "1702 199107 7 12337 8077 16597 22 15 \n", + "1703 199106 7 10877 7013 14741 19 12 \n", + "1704 199105 7 10442 6544 14340 18 11 \n", + "1705 199104 7 7913 4563 11263 14 8 \n", + "1706 199103 7 15387 10484 20290 27 18 \n", + "1707 199102 7 16277 11046 21508 29 20 \n", + "1708 199101 7 15565 10271 20859 27 18 \n", + "1709 199052 7 19375 13295 25455 34 23 \n", + "1710 199051 7 19080 13807 24353 34 25 \n", + "1711 199050 7 11079 6660 15498 20 12 \n", + "1712 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 4 FR France \n", + "1 5 FR France \n", + "2 3 FR France \n", + "3 2 FR France \n", + "4 2 FR France \n", + "5 4 FR France \n", + "6 8 FR France \n", + "7 22 FR France \n", + "8 8 FR France \n", + "9 13 FR France \n", + "10 28 FR France \n", + "11 14 FR France \n", + "12 15 FR France \n", + "13 18 FR France \n", + "14 22 FR France \n", + "15 22 FR France \n", + "16 29 FR France \n", + "17 24 FR France \n", + "18 23 FR France \n", + "19 21 FR France \n", + "20 19 FR France \n", + "21 21 FR France \n", + "22 19 FR France \n", + "23 22 FR France \n", + "24 31 FR France \n", + "25 29 FR France \n", + "26 25 FR France \n", + "27 21 FR France \n", + "28 10 FR France \n", + "29 10 FR France \n", + "... ... ... ... \n", + "1683 42 FR France \n", + "1684 38 FR France \n", + "1685 39 FR France \n", + "1686 29 FR France \n", + "1687 37 FR France \n", + "1688 36 FR France \n", + "1689 45 FR France \n", + "1690 39 FR France \n", + "1691 51 FR France \n", + "1692 32 FR France \n", + "1693 34 FR France \n", + "1694 32 FR France \n", + "1695 30 FR France \n", + "1696 23 FR France \n", + "1697 25 FR France \n", + "1698 35 FR France \n", + "1699 38 FR France \n", + "1700 33 FR France \n", + "1701 31 FR France \n", + "1702 29 FR France \n", + "1703 26 FR France \n", + "1704 25 FR France \n", + "1705 20 FR France \n", + "1706 36 FR France \n", + "1707 38 FR France \n", + "1708 36 FR France \n", + "1709 45 FR France \n", + "1710 43 FR France \n", + "1711 28 FR France \n", + "1712 5 FR France \n", + "\n", + "[1713 rows x 10 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_week(year_and_week_int):\n", + " year_and_week_str = str(year_and_week_int)\n", + " year = int(year_and_week_str[:4])\n", + " week = int(year_and_week_str[4:])\n", + " w = isoweek.Week(year, week)\n", + " return pd.Period(w.day(0), 'W')\n", + "\n", + "data['period'] = [convert_week(yw) for yw in data['week']]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "periods = sorted_data.index\n", + "for p1, p2 in zip(periods[:-1], periods[1:]):\n", + " delta = p2.to_timestamp() - p1.end_time\n", + " if delta > pd.Timedelta('1s'):\n", + " print(p1, p2)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'].plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'3'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2524\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2525\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2526\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: '3'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mMAX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'3'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mMIN\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'3'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'max='\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mMAX\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'min='\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mMIN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2137\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2138\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2139\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2141\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2144\u001b[0m \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2145\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2146\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2148\u001b[0m \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 1840\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1841\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1842\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1843\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1844\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m 3841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3842\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3843\u001b[0;31m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3844\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3845\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2525\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2526\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2527\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2529\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: '3'" + ] + } + ], + "source": [ + "MAX=data['3'].max()\n", + "MIN=data['3'].min()\n", + "print('max=',MAX )\n", + "print('min=',MIN)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +2175,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } - -- 2.18.1