diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..40ab9114c9619f2ee5fe22881319a49e04ebbb84 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,2225 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analyse de l'incidence de la varicelle" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importation des données brutes" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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26202053711978840615550181323FRFrance
27202052712012828515739181224FRFrance
28202051710564757413554161121FRFrance
29202050770634744938211715FRFrance
.................................
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\n", + "

1596 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202126 7 6779 2197 11361 10 3 \n", + "1 202125 7 9254 6439 12069 14 10 \n", + "2 202124 7 12034 8937 15131 18 13 \n", + "3 202123 7 9116 6420 11812 14 10 \n", + "4 202122 7 4817 2752 6882 7 4 \n", + "5 202121 7 6092 3458 8726 9 5 \n", + "6 202120 7 7485 4601 10369 11 7 \n", + "7 202119 7 6654 4370 8938 10 7 \n", + "8 202118 7 3912 2110 5714 6 3 \n", + "9 202117 7 4686 2878 6494 7 4 \n", + "10 202116 7 4780 2891 6669 7 4 \n", + "11 202115 7 11215 7627 14803 17 12 \n", + "12 202114 7 11197 7994 14400 17 12 \n", + "13 202113 7 9714 6289 13139 15 10 \n", + "14 202112 7 11520 8415 14625 17 12 \n", + "15 202111 7 9386 6678 12094 14 10 \n", + "16 202110 7 9056 6452 11660 14 10 \n", + "17 202109 7 10988 7938 14038 17 12 \n", + "18 202108 7 11281 8361 14201 17 13 \n", + "19 202107 7 13561 10315 16807 21 16 \n", + "20 202106 7 13401 9810 16992 20 15 \n", + "21 202105 7 12210 8988 15432 18 13 \n", + "22 202104 7 12026 8826 15226 18 13 \n", + "23 202103 7 8913 6375 11451 13 9 \n", + "24 202102 7 7795 5430 10160 12 8 \n", + "25 202101 7 10525 7750 13300 16 12 \n", + "26 202053 7 11978 8406 15550 18 13 \n", + "27 202052 7 12012 8285 15739 18 12 \n", + "28 202051 7 10564 7574 13554 16 11 \n", + "29 202050 7 7063 4744 9382 11 7 \n", + "... ... ... ... ... ... ... ... \n", + "1566 199126 7 17608 11304 23912 31 20 \n", + "1567 199125 7 16169 10700 21638 28 18 \n", + "1568 199124 7 16171 10071 22271 28 17 \n", + "1569 199123 7 11947 7671 16223 21 13 \n", + "1570 199122 7 15452 9953 20951 27 17 \n", + "1571 199121 7 14903 8975 20831 26 16 \n", + "1572 199120 7 19053 12742 25364 34 23 \n", + "1573 199119 7 16739 11246 22232 29 19 \n", + "1574 199118 7 21385 13882 28888 38 25 \n", + "1575 199117 7 13462 8877 18047 24 16 \n", + "1576 199116 7 14857 10068 19646 26 18 \n", + "1577 199115 7 13975 9781 18169 25 18 \n", + "1578 199114 7 12265 7684 16846 22 14 \n", + "1579 199113 7 9567 6041 13093 17 11 \n", + "1580 199112 7 10864 7331 14397 19 13 \n", + "1581 199111 7 15574 11184 19964 27 19 \n", + "1582 199110 7 16643 11372 21914 29 20 \n", + "1583 199109 7 13741 8780 18702 24 15 \n", + "1584 199108 7 13289 8813 17765 23 15 \n", + "1585 199107 7 12337 8077 16597 22 15 \n", + "1586 199106 7 10877 7013 14741 19 12 \n", + "1587 199105 7 10442 6544 14340 18 11 \n", + "1588 199104 7 7913 4563 11263 14 8 \n", + "1589 199103 7 15387 10484 20290 27 18 \n", + "1590 199102 7 16277 11046 21508 29 20 \n", + "1591 199101 7 15565 10271 20859 27 18 \n", + "1592 199052 7 19375 13295 25455 34 23 \n", + "1593 199051 7 19080 13807 24353 34 25 \n", + "1594 199050 7 11079 6660 15498 20 12 \n", + "1595 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 17 FR France \n", + "1 18 FR France \n", + "2 23 FR France \n", + "3 18 FR France \n", + "4 10 FR France \n", + "5 13 FR France \n", + "6 15 FR France \n", + "7 13 FR France \n", + "8 9 FR France \n", + "9 10 FR France \n", + "10 10 FR France \n", + "11 22 FR France \n", + "12 22 FR France \n", + "13 20 FR France \n", + "14 22 FR France \n", + "15 18 FR France \n", + "16 18 FR France \n", + "17 22 FR France \n", + "18 21 FR France \n", + "19 26 FR France \n", + "20 25 FR France \n", + "21 23 FR France \n", + "22 23 FR France \n", + "23 17 FR France \n", + "24 16 FR France \n", + "25 20 FR France \n", + "26 23 FR France \n", + "27 24 FR France \n", + "28 21 FR France \n", + "29 15 FR France \n", + "... ... ... ... \n", + "1566 42 FR France \n", + "1567 38 FR France \n", + "1568 39 FR France \n", + "1569 29 FR France \n", + "1570 37 FR France \n", + "1571 36 FR France \n", + "1572 45 FR France \n", + "1573 39 FR France \n", + "1574 51 FR France \n", + "1575 32 FR France \n", + "1576 34 FR France \n", + "1577 32 FR France \n", + "1578 30 FR France \n", + "1579 23 FR France \n", + "1580 25 FR France \n", + "1581 35 FR France \n", + "1582 38 FR France \n", + "1583 33 FR France \n", + "1584 31 FR France \n", + "1585 29 FR France \n", + "1586 26 FR France \n", + "1587 25 FR France \n", + "1588 20 FR France \n", + "1589 36 FR France \n", + "1590 38 FR France \n", + "1591 36 FR France \n", + "1592 45 FR France \n", + "1593 43 FR France \n", + "1594 28 FR France \n", + "1595 5 FR France \n", + "\n", + "[1596 rows x 10 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_url = 'https://www.sentiweb.fr/datasets/incidence-PAY-7.csv'\n", + "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Recherche d'erreur ou de données manquantes" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", + "Index: []" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il n'y a pas de données manquante. Convertissons désormais le champ \"week\" en un format approprié pour l'analyse de pandas.\n", + "## Conversion des dates et tri des données par date" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
period
1990-12-03/1990-12-091990497114302610205FRFrance
1990-12-10/1990-12-16199050711079666015498201228FRFrance
1990-12-17/1990-12-231990517190801380724353342543FRFrance
1990-12-24/1990-12-301990527193751329525455342345FRFrance
1990-12-31/1991-01-061991017155651027120859271836FRFrance
1991-01-07/1991-01-131991027162771104621508292038FRFrance
1991-01-14/1991-01-201991037153871048420290271836FRFrance
1991-01-21/1991-01-271991047791345631126314820FRFrance
1991-01-28/1991-02-03199105710442654414340181125FRFrance
1991-02-04/1991-02-10199106710877701314741191226FRFrance
1991-02-11/1991-02-17199107712337807716597221529FRFrance
1991-02-18/1991-02-24199108713289881317765231531FRFrance
1991-02-25/1991-03-03199109713741878018702241533FRFrance
1991-03-04/1991-03-101991107166431137221914292038FRFrance
1991-03-11/1991-03-171991117155741118419964271935FRFrance
1991-03-18/1991-03-24199112710864733114397191325FRFrance
1991-03-25/1991-03-3119911379567604113093171123FRFrance
1991-04-01/1991-04-07199114712265768416846221430FRFrance
1991-04-08/1991-04-14199115713975978118169251832FRFrance
1991-04-15/1991-04-211991167148571006819646261834FRFrance
1991-04-22/1991-04-28199117713462887718047241632FRFrance
1991-04-29/1991-05-051991187213851388228888382551FRFrance
1991-05-06/1991-05-121991197167391124622232291939FRFrance
1991-05-13/1991-05-191991207190531274225364342345FRFrance
1991-05-20/1991-05-26199121714903897520831261636FRFrance
1991-05-27/1991-06-02199122715452995320951271737FRFrance
1991-06-03/1991-06-09199123711947767116223211329FRFrance
1991-06-10/1991-06-161991247161711007122271281739FRFrance
1991-06-17/1991-06-231991257161691070021638281838FRFrance
1991-06-24/1991-06-301991267176081130423912312042FRFrance
.................................
2020-12-07/2020-12-13202050770634744938211715FRFrance
2020-12-14/2020-12-20202051710564757413554161121FRFrance
2020-12-21/2020-12-27202052712012828515739181224FRFrance
2020-12-28/2021-01-03202053711978840615550181323FRFrance
2021-01-04/2021-01-10202101710525775013300161220FRFrance
2021-01-11/2021-01-172021027779554301016012816FRFrance
2021-01-18/2021-01-242021037891363751145113917FRFrance
2021-01-25/2021-01-31202104712026882615226181323FRFrance
2021-02-01/2021-02-07202105712210898815432181323FRFrance
2021-02-08/2021-02-14202106713401981016992201525FRFrance
2021-02-15/2021-02-212021077135611031516807211626FRFrance
2021-02-22/2021-02-28202108711281836114201171321FRFrance
2021-03-01/2021-03-07202109710988793814038171222FRFrance
2021-03-08/2021-03-1420211079056645211660141018FRFrance
2021-03-15/2021-03-2120211179386667812094141018FRFrance
2021-03-22/2021-03-28202112711520841514625171222FRFrance
2021-03-29/2021-04-0420211379714628913139151020FRFrance
2021-04-05/2021-04-11202114711197799414400171222FRFrance
2021-04-12/2021-04-18202115711215762714803171222FRFrance
2021-04-19/2021-04-2520211674780289166697410FRFrance
2021-04-26/2021-05-0220211774686287864947410FRFrance
2021-05-03/2021-05-092021187391221105714639FRFrance
2021-05-10/2021-05-16202119766544370893810713FRFrance
2021-05-17/2021-05-232021207748546011036911715FRFrance
2021-05-24/2021-05-3020212176092345887269513FRFrance
2021-05-31/2021-06-0620212274817275268827410FRFrance
2021-06-07/2021-06-1320212379116642011812141018FRFrance
2021-06-14/2021-06-20202124712034893715131181323FRFrance
2021-06-21/2021-06-2720212579254643912069141018FRFrance
2021-06-28/2021-07-042021267677921971136110317FRFrance
\n", + "

1596 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 \\\n", + "period \n", + "1990-12-03/1990-12-09 199049 7 1143 0 2610 2 \n", + "1990-12-10/1990-12-16 199050 7 11079 6660 15498 20 \n", + "1990-12-17/1990-12-23 199051 7 19080 13807 24353 34 \n", + "1990-12-24/1990-12-30 199052 7 19375 13295 25455 34 \n", + "1990-12-31/1991-01-06 199101 7 15565 10271 20859 27 \n", + "1991-01-07/1991-01-13 199102 7 16277 11046 21508 29 \n", + "1991-01-14/1991-01-20 199103 7 15387 10484 20290 27 \n", + "1991-01-21/1991-01-27 199104 7 7913 4563 11263 14 \n", + "1991-01-28/1991-02-03 199105 7 10442 6544 14340 18 \n", + "1991-02-04/1991-02-10 199106 7 10877 7013 14741 19 \n", + "1991-02-11/1991-02-17 199107 7 12337 8077 16597 22 \n", + "1991-02-18/1991-02-24 199108 7 13289 8813 17765 23 \n", + "1991-02-25/1991-03-03 199109 7 13741 8780 18702 24 \n", + "1991-03-04/1991-03-10 199110 7 16643 11372 21914 29 \n", + "1991-03-11/1991-03-17 199111 7 15574 11184 19964 27 \n", + "1991-03-18/1991-03-24 199112 7 10864 7331 14397 19 \n", + "1991-03-25/1991-03-31 199113 7 9567 6041 13093 17 \n", + "1991-04-01/1991-04-07 199114 7 12265 7684 16846 22 \n", + "1991-04-08/1991-04-14 199115 7 13975 9781 18169 25 \n", + "1991-04-15/1991-04-21 199116 7 14857 10068 19646 26 \n", + "1991-04-22/1991-04-28 199117 7 13462 8877 18047 24 \n", + "1991-04-29/1991-05-05 199118 7 21385 13882 28888 38 \n", + "1991-05-06/1991-05-12 199119 7 16739 11246 22232 29 \n", + "1991-05-13/1991-05-19 199120 7 19053 12742 25364 34 \n", + "1991-05-20/1991-05-26 199121 7 14903 8975 20831 26 \n", + "1991-05-27/1991-06-02 199122 7 15452 9953 20951 27 \n", + "1991-06-03/1991-06-09 199123 7 11947 7671 16223 21 \n", + "1991-06-10/1991-06-16 199124 7 16171 10071 22271 28 \n", + "1991-06-17/1991-06-23 199125 7 16169 10700 21638 28 \n", + "1991-06-24/1991-06-30 199126 7 17608 11304 23912 31 \n", + "... ... ... ... ... ... ... \n", + "2020-12-07/2020-12-13 202050 7 7063 4744 9382 11 \n", + "2020-12-14/2020-12-20 202051 7 10564 7574 13554 16 \n", + "2020-12-21/2020-12-27 202052 7 12012 8285 15739 18 \n", + "2020-12-28/2021-01-03 202053 7 11978 8406 15550 18 \n", + "2021-01-04/2021-01-10 202101 7 10525 7750 13300 16 \n", + "2021-01-11/2021-01-17 202102 7 7795 5430 10160 12 \n", + "2021-01-18/2021-01-24 202103 7 8913 6375 11451 13 \n", + "2021-01-25/2021-01-31 202104 7 12026 8826 15226 18 \n", + "2021-02-01/2021-02-07 202105 7 12210 8988 15432 18 \n", + "2021-02-08/2021-02-14 202106 7 13401 9810 16992 20 \n", + "2021-02-15/2021-02-21 202107 7 13561 10315 16807 21 \n", + "2021-02-22/2021-02-28 202108 7 11281 8361 14201 17 \n", + "2021-03-01/2021-03-07 202109 7 10988 7938 14038 17 \n", + "2021-03-08/2021-03-14 202110 7 9056 6452 11660 14 \n", + "2021-03-15/2021-03-21 202111 7 9386 6678 12094 14 \n", + "2021-03-22/2021-03-28 202112 7 11520 8415 14625 17 \n", + "2021-03-29/2021-04-04 202113 7 9714 6289 13139 15 \n", + "2021-04-05/2021-04-11 202114 7 11197 7994 14400 17 \n", + "2021-04-12/2021-04-18 202115 7 11215 7627 14803 17 \n", + "2021-04-19/2021-04-25 202116 7 4780 2891 6669 7 \n", + "2021-04-26/2021-05-02 202117 7 4686 2878 6494 7 \n", + "2021-05-03/2021-05-09 202118 7 3912 2110 5714 6 \n", + "2021-05-10/2021-05-16 202119 7 6654 4370 8938 10 \n", + "2021-05-17/2021-05-23 202120 7 7485 4601 10369 11 \n", + "2021-05-24/2021-05-30 202121 7 6092 3458 8726 9 \n", + "2021-05-31/2021-06-06 202122 7 4817 2752 6882 7 \n", + "2021-06-07/2021-06-13 202123 7 9116 6420 11812 14 \n", + "2021-06-14/2021-06-20 202124 7 12034 8937 15131 18 \n", + "2021-06-21/2021-06-27 202125 7 9254 6439 12069 14 \n", + "2021-06-28/2021-07-04 202126 7 6779 2197 11361 10 \n", + "\n", + " inc100_low inc100_up geo_insee geo_name \n", + "period \n", + "1990-12-03/1990-12-09 0 5 FR France \n", + "1990-12-10/1990-12-16 12 28 FR France \n", + "1990-12-17/1990-12-23 25 43 FR France \n", + "1990-12-24/1990-12-30 23 45 FR France \n", + "1990-12-31/1991-01-06 18 36 FR France \n", + "1991-01-07/1991-01-13 20 38 FR France \n", + "1991-01-14/1991-01-20 18 36 FR France \n", + "1991-01-21/1991-01-27 8 20 FR France \n", + "1991-01-28/1991-02-03 11 25 FR France \n", + "1991-02-04/1991-02-10 12 26 FR France \n", + "1991-02-11/1991-02-17 15 29 FR France \n", + "1991-02-18/1991-02-24 15 31 FR France \n", + "1991-02-25/1991-03-03 15 33 FR France \n", + "1991-03-04/1991-03-10 20 38 FR France \n", + "1991-03-11/1991-03-17 19 35 FR France \n", + "1991-03-18/1991-03-24 13 25 FR France \n", + "1991-03-25/1991-03-31 11 23 FR France \n", + "1991-04-01/1991-04-07 14 30 FR France \n", + "1991-04-08/1991-04-14 18 32 FR France \n", + "1991-04-15/1991-04-21 18 34 FR France \n", + "1991-04-22/1991-04-28 16 32 FR France \n", + "1991-04-29/1991-05-05 25 51 FR France \n", + "1991-05-06/1991-05-12 19 39 FR France \n", + "1991-05-13/1991-05-19 23 45 FR France \n", + "1991-05-20/1991-05-26 16 36 FR France \n", + "1991-05-27/1991-06-02 17 37 FR France \n", + "1991-06-03/1991-06-09 13 29 FR France \n", + "1991-06-10/1991-06-16 17 39 FR France \n", + "1991-06-17/1991-06-23 18 38 FR France \n", + "1991-06-24/1991-06-30 20 42 FR France \n", + "... ... ... ... ... \n", + "2020-12-07/2020-12-13 7 15 FR France \n", + "2020-12-14/2020-12-20 11 21 FR France \n", + "2020-12-21/2020-12-27 12 24 FR France \n", + "2020-12-28/2021-01-03 13 23 FR France \n", + "2021-01-04/2021-01-10 12 20 FR France \n", + "2021-01-11/2021-01-17 8 16 FR France \n", + "2021-01-18/2021-01-24 9 17 FR France \n", + "2021-01-25/2021-01-31 13 23 FR France \n", + "2021-02-01/2021-02-07 13 23 FR France \n", + "2021-02-08/2021-02-14 15 25 FR France \n", + "2021-02-15/2021-02-21 16 26 FR France \n", + "2021-02-22/2021-02-28 13 21 FR France \n", + "2021-03-01/2021-03-07 12 22 FR France \n", + "2021-03-08/2021-03-14 10 18 FR France \n", + "2021-03-15/2021-03-21 10 18 FR France \n", + "2021-03-22/2021-03-28 12 22 FR France \n", + "2021-03-29/2021-04-04 10 20 FR France \n", + "2021-04-05/2021-04-11 12 22 FR France \n", + "2021-04-12/2021-04-18 12 22 FR France \n", + "2021-04-19/2021-04-25 4 10 FR France \n", + "2021-04-26/2021-05-02 4 10 FR France \n", + "2021-05-03/2021-05-09 3 9 FR France \n", + "2021-05-10/2021-05-16 7 13 FR France \n", + "2021-05-17/2021-05-23 7 15 FR France \n", + "2021-05-24/2021-05-30 5 13 FR France \n", + "2021-05-31/2021-06-06 4 10 FR France \n", + "2021-06-07/2021-06-13 10 18 FR France \n", + "2021-06-14/2021-06-20 13 23 FR France \n", + "2021-06-21/2021-06-27 10 18 FR France \n", + "2021-06-28/2021-07-04 3 17 FR France \n", + "\n", + "[1596 rows x 10 columns]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def convert_week(yw):\n", + " yw_string = str(yw)\n", + " year = int(yw_string[:4])\n", + " week = int(yw_string[4:])\n", + " monday = isoweek.Week(year, week).day(0)\n", + " period = pd.Period(monday,'W') \n", + " return period\n", + "raw_data['period'] = [convert_week(yw) for yw in raw_data['week']]\n", + "data = raw_data.set_index('period').sort_index()\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calcul des années les plus intense et les plus faibles" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Period('1990-08-27/1990-09-02', 'W-SUN')" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "week_start" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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LL5/+yQ5eOtqe6KUETY/bd9FvXFgS9JxUyzWJh/PdpNyWeVW2zYTF+cU+zQTAOZhZfwN/5hQmInKZiBzwe/SLyF+LyDdF5Lzf+Ef95twpIo0iclJEbvQb3yQih43X7hcRMcatIvKUMb5LRBb7zdkmIqeMx7bobn92KmxWRscVLt1FblZ2nu4B4HSXO8ErCZ5e4w6yxPgnEAxTwiQ1NJN4ON9NKjNQMzGz/ivtVsoKfUEIzsHM/V8xpzBRSp1USm1QSm0ANgFDwLPGy/earymlfgsgIquBrcAa4CbgRyJi6tgPAHcAK4zHTcb47YBLKbUcuBf4jnGuUuAu4CpgM3CXiDgi3HPQTPaCz7CLJFT2nHUC0OxMjTt2mDJHlBYGL0zKi6wplWsSD+e7SYXNSkeGZcGbpecr7Xk4CnMBrZmEwvVAk1Lq3CzH3Az8XCk1opQ6AzQCm0WkCrArpXYo3zfuSeCTfnOeMJ4/A1xvaC03AtuVUk6llAvYzpQAijmVOnFxTiYmFHvPuYDUEiZm6QtHCJqJiFDtyOd8igiTeDjfTSrtVoZHx3GnUHBCpJilVCpseVozIfR+JluBn/n9/jURuQ3YC/yt8Q+/Gtjpd0yrMTZqPJ8+jvGzBUApNSYifUCZ/3iAOZOIyB34NB4qKytpaGgIcVuB6RzyteH8/Z4DTLTlRuWcweB2u6O2h1hz3j1B79Aolmx4t80V9LoTvcd3Gn3C5OCet8nJkqDnFSgPx5qH51x7ovenlOI3+zwsKBR2vvVGTN7Df4/dbT4h8uuX36CqKD1csXN9hvtPjGC3wNtv/h4ASzYcPNFIw0X/spKXaH9HgxYmImIBPgHcaQw9ANwNKOPnvwFfBAJdmWqWccKcMzWg1IPAgwB1dXWqvr5+pm2ExLB3nL/7/YuUVi+hvn55VM4ZDA0NDURrD7HmP3edA47wB+uq+e8D57n6mvdjzZk7cijRe2zoP4qttZUPXffBkOb9znWYl460z7n2RO/vVwfbON23n3/5w8upnyN/Jlz892hp7ObBQ7tYtGod7102LybvF2/m+gyfPLuHmnEP9fXvB6B856sUlJZSX78hTiuMjGh/R0O5hfgI8I5SqgNAKdWhlBpXSk0AD+HzaYBPe6j1m1cDtBnjNQHGL5ojIjlAMeCc5VxxQXeRm5u9Z12U26y8b/k8lCJlTECuIW9I/hKTGkc+PYNehrzJa84Z9o7z7d8eZ221nVvraueeEAXMxMWuDMrLau/zlVIxMRMXM5VQhMln8DNxGT4Qk08BR4znzwNbjQitJfgc7buVUheAARHZYvhDbgOe85tjRmrdArxq+FVeAm4QEYfheL/BGIsb5XZrRl0gobL7jJMrFztYVObLwUgVv4lraDSkSC6TyVyTJBaaP/l9E219Hu76+BqyQzDhRcJksEoG3Xh19Hsm/aoAjgyvzxWUmUtECoAPA1/yG/6uiGzAZ3Y6a76mlDoqIk8Dx4Ax4KtKqXFjzleAx4F84AXjAfAI8FMRacSnkWw1zuUUkbuBPcZx31JKOUPfZvj4olR0NFcg2nqHOd87zJ+9fwmLSlNMmAx6mVcUnmYCvvDgFZW2aC8rYs73DvPj15v4+PoFXLm4NG7va7PmkJeblTHXysjYOD2DXub7CZOyQgtnulMnPD7aBCVMlFJD+Bzi/mNfmOX4e4B7AozvBdYGGPcAt85wrkeBR4NZZyyosOVxoKU3UW+f1JghwVcuLqXc5gubbe5JDWHiHPSyorIo5Hk1JcmduPgvL5wA4BsfWRnX9xURI9ckMzQT01pRaWhk4IsMdLozVzNJj7CLGFJhs9I54Mmo+Plg2XvWRZE1h5XzbYgIC0sLUkYz6R3yhhQWbDKvyIolSXNNdp9x8quDbXz5A8uoNoRePDGvlUzA1MAq/XwmZUUWBr3jeEbHZ5qW1mhhMgcVdiue0QkGMih+Plj2nHVyxcIScrJ9X6NUESae0XEGveNhOeCzsoSakuSrHjw+ofjnXx1lQXEeX7p2WULWUGHLyxifSXufb5/+Zi7z++TK0PpcWpjMQYVNJy4Gom94lJMdAxfZ5ReW+YRJsmtxvUZ5nHA0EzBL0SeX0HxmXwtH2/q586OryLfEtqjjTGRSlW0zYdFfmJjfp54MNXVpYTIHmdz4ZzbeOedCKS4WJqUFDHl9jslkxjmZ/R5eImqyNcnq94zy/790kisXO/jYuqq5J8SIClse7pGxlCrRHy6d/R4sOVmU+H2HzMrBWjPRBCQT4+eDYfdZJ7nZwobaqaq7C1Mkosss8ugIw8wFyZdr8oNXG+kZ9HLXx9dg1E5NCFM3Xul/rbT3+zos+v+9pyoHa2GiCUAmxs8Hw96zTtZWF19kUjGFSUuSC5Nwijz6Y4YHJ0OuyekuN4+9dYY/rquNS0HH2ZiqZZf+Wnx7n+ciExf4QoNBCxPNDJjx89rMNYVndJyDLX2X5DHUGsLkXJKHB4dT5NEfM3ExGUxd9/zmOHk52fztDZcleil+VbbT/8ZresIiQHF+LlmihYlmBkTEF6WSARdIsBw+34d3fOISYZKXm02l3Zr0Zi6zP01JmD6T2iRpktVwspNXTnTyl9evoNxmnXtCjDHNXOmeuGi2650uTLKyBEeBJel9hrFCC5MgqMjALnKzsfuML1lx06JLW8ukQniwc9CLLS+H3Ozwvv7JkGsyOj7B3b8+xpJ5hWx77+KErcOf4vxcLDlZae9f7PeMMTw6fomZCzK7PpcWJkHgC3lM77utUNh71snyiqKAPofa0oKk95mEW+TRZDLXJIG94H+64xxNXYP8r4+twpKTHJexT4tP//JDgRIWTRyFWjPRzEImJWPNhdkMa6a6T4tKC2nv9yR1FnC4RR798eWaJEaY9LhHuPfld/nAe8r54GUVCVnDTGRCSZXJ3u8BNJMyrZloZqPcZmVgZIxhb/L+g4wXJzsGGPCMceXiwN2TF5blo1RyOKdnwjXopTRMf4lJjaOA8wnymXxv+7sMe8f5Xx9bldBQ4ED4SqqkuTAJkLBoksmVg7UwCQKduDjFXr/ijoFIhfBg56A37BwTkxpHPt1ub9xvMI619fOz3c3cdvVillckX9XiTDBzmaHPFfZLgx7KCi24hrxMTCR3FYhYoIVJEJiJi+l+xxUMu8+6mG/Pm8y1mE5tCiQuhlvk0Z/JXJPe+O7z7l8fo6TAwl9dvyKu7xssFfY8BjxjSW3mjJT2fg8lBbnk5V5atsZRYGFC+coNZRpamATBpGaS4X4TpRR7zjipW+yY0bxSXmQlPzc7aXNNIiny6I8pTFriaM7rHPCw43QPt1+zhOIIzXSxIhOulfa+kYAmLpgqqZKJTngtTIJAm7l8tLqGae/3sHnJzE2Xkr0UfaRFHk0Skbi4v9nXV2fL0rI5jkwcphbfkcbXSqCERZNMrhyshUkQOAos5GZLxpu59p7z+UvqFs3ewS+Zw4PNizzcIo8m5UVWLNlZcU1c3N/ciyU7i7XV9ri9Z6hUZkD5oQ6jLlcgMrlysBYmQZCVJZQX6cTFPWdd2PJyuGz+7I5fUzNJxlL0k6VUIjRzZWVJ3MOD32l2sXqBHWtOYkrMB8Nky4Y01UzGxifods9t5tKaiWZGyu15aXuBBMueM042LXKQnTV7OOqisgKGR8fpcief8I20yKM/NXEUJmPjExxq7eWKhSVzH5xAHAW55GYLHWl649XlHmFCBU5YhMyuHKyFSZBU2KxpXyZiNlyDXk51umcMCfYnmcODI63L5U+NIz9uuSYn2gfwjE5wxcLA+T3JwlQtu/S88ZotYRF89ekKLdlamGhmJhOSsWZj7zkXMHN+iT/JHB4cacVgf2ocBXHLNdnf4nO+b0xyzQR8Sb7peuNlalwzOeAhcxMX5xQmInKZiBzwe/SLyF+LSKmIbBeRU8ZPh9+cO0WkUUROisiNfuObROSw8dr9YsSXiohVRJ4yxneJyGK/OduM9zglItuiu/3gqbDl4Rz04h2bSNQSEsres04s2Vmsq5m7Z0aNIx8RaO5Jviz4SIs8+hPPXJP951yU26xUlwTO70km0jlxcbIu1yzCpCxD63PNeUUppU4qpTYopTYAm4Ah4FngG8ArSqkVwCvG74jIamArsAa4CfiRiJgewweAO4AVxuMmY/x2wKWUWg7cC3zHOFcpcBdwFbAZuMtfaMUTM9s1Gf0A8WD3WSfraooDJmpNJy83m/n2PM45B+OwstCIRsKiSTxzTfa39HJFbUnSlU8JRDrX52rv95CbLZONsAKRqZWDQ709ux5oUkqdA24GnjDGnwA+aTy/Gfi5UmpEKXUGaAQ2i0gVYFdK7VC+MJ8np80xz/UMcL2htdwIbFdKOZVSLmA7UwIorkwlY6XnHddsDHvHOXK+j7ogTFwmyRoe7BwajTiSyyReuSauQS9nugeT3l9iUmGz0js0mpZZ8B19HipseWTNEoSizVzBsRX4mfG8Uil1AcD4aZYvrQZa/Oa0GmPVxvPp4xfNUUqNAX1A2SznijtTIY/pecc1GwdbexkdV2xeEvw/s2RNXIxGkUcTM9ck1u17D6SQvwT8tPg4XCvxroHVMeAJWJPLn7IMFSY5wR4oIhbgE8Cdcx0aYEzNMh7uHP+13YHPfEZlZSUNDQ1zLDF0XB6fr+StfYexdp2I+vn9cbvdMdlDuDzf5LswhluO0dB+PKg5431eOvpH+d0rr2HJvvRjTNQeLziHsKnsqL23w6p45+RZGvLbLxqP5v5+ecpLlkDv6UM0NCePmWumPXZ0jQHw0us7WO6IXU7Mke5x/v0dDx9fmssfLM0lZ46Q9VAJtL/TF4aoLsqa9bN1dXgZHh3npVdewxrgu58sRPsaDFqYAB8B3lFKdRi/d4hIlVLqgmHC6jTGW4Fav3k1QJsxXhNg3H9Oq4jkAMWA0xivnzanYfrClFIPAg8C1NXVqfr6+umHRMzY+AR/8/oLlMxfSH19bPttNzQ0EIs9hMujp3dzWaWHP/jwtUHP6S0+z7ONB1iyto4VlZcmOSZqj8OvvsiqpbXU16+OyvlWNO7CPTJGff37LhqP5v4ebtzFqiovN37o/VE5X7SYaY/lbX18b9+bVC9fTf3lVTF7/+3PHmZcNfNs4yiNwwX826fX854A37VwCbS/gddeYu2yGurr18w4r6OwmWfePczaTVuSOmAi2tdgKGauzzBl4gJ4HjCjq7YBz/mNbzUitJbgc7TvNkxhAyKyxfCH3DZtjnmuW4BXDb/KS8ANIuIwHO83GGNxJyc7i3lFmRcePD6heOeciytDMHEBLCxLvvDgkbHoFHn0J9aJi+MTigMtyZ+s6E9lnKps7zjdQ/17ynngcxs53zvMx+5/kwcamhiPkenLPTKGe2SM+TMkLJpMJi5mWEmVoISJiBQAHwZ+6Tf8L8CHReSU8dq/ACiljgJPA8eAF4GvKqVMT9xXgIfxOeWbgBeM8UeAMhFpBP4GIzJMKeUE7gb2GI9vGWMJIRNzTY5f6Mc9MhZUfok/C5Mw16Q3igmLJr6+JiMxczY3dblxj4yxMUWc7wClBRZysiSmiYud/R5Odw2yZWkZH7m8it99/VquW1nBd148wS0/fpvTXe6ov+dUWPAcPpPJysGZ9b8iKDOXUmoIn0Pcf6wHX3RXoOPvAe4JML4XWBtg3APcOsO5HgUeDWadscYnTDIrmmvPHM2wZqKs0EKBJTuphInpFC2NUmgwXBzRtbyiKGrnNXnHSBZNlUgu8NUtm1dkjWlJlR2ne4CpCsrziqw88PmNPH+wjX967igfue8N/v6mlfzJexfPGnkVCh19c+eYAJQW+oRNptXn0hnwIZCJveD3nnVRXZLPghBtv5Ol6JOor0m0ijz6Y+aaxKp68P7mXkoKcllsmA1ThUp7bLX4naed2Kw5rFkwVUFZRLh5QzW/+/q1vG/5PL7162N85qGdUfsOztau15/SDK0crIVJCFTYrXS7R2Jmk002lFLsPutrhhUOyRYe7IpSLxN/Yp1rsr/FlTLJiv6U2/JimpO163QPVy4pJSdAJYNKex6PbKvju7es42hbPzfd93v+Y+e5iKtYtweR/Q5gz88hO0u0ZqKZmQqblQmVObbQZucQXQMjIZu4TJKtFL1XApBBAAAgAElEQVRZMdhRGD2fSYXNSm62xESY9HtGOdXpTikTl0lFDDWTjn4Pp7sHuXqWJmEiwqfrannp69eycaGD//nfR7jt0d30RvAPvrN/BJs1h0Lr7N4BEcFRkHm5JlqYhEC5mbiYwqauUP6x7zkbfHHHQCwsK2BkbCJpiv5Fs8ijSVaWUF2SHxMz18GWXpQipZzvJpUxrGW3c5q/ZDaqS/L56e2bufuTa3mzsZv/2Hku7Pdt7/PMWHp+OmWFFm3m0syMmfmaqk74f3j2MBvv3s7fPH2AFw5fwD0yNuvxe844Kc7PZUWYjuVkqx7sGvJis0anyKM/NY6CmGgm+5t7EYF1tXMX10w2zGulOwa17HY09WDLy2H1guA6TooIX9iyiBUVRZPVr8Ohvd8zp7/ExFGYm3FmrlCSFjOeqfpcyXGnHSpvN3aTm53FK8c7+eU757FkZ3H1sjI+tLqSD62qoKr4Yif7nnNO6hY5wo6GWWQIk3M9QyHV9YoVrkFvVJ3vJjWOfF4+3jn3gSGyv9nFiooi7HnRM8vFC/Na6ej3hBy8MRc7T/dw1ZLSOZu0TWfTolJ+c6iNiQkV1ne6o9/DsmXzgjq2rNDK8fb+kN8jldGaSQiUm8IkScw2oTAyNk6zc4g/vrKWff/zQ/z8ji3cdvUizvYM8r/++whXf/tVPvb9N/j3l9/laFsf3e4RTncNcuWS8IVAtVmKPkk0k2gWefQnFrkmSimjUnDqmbggdomLF/qGOdszFJSJazqbFjno94zRGEYOysSEonNgZM4cE5NMrBysNZMQsOZkU1KQm5JmrnM9Q0woWFZeRE52FluWlrFlaRn/+AeraOpys/1YJy8f7+C+V07x7y+fwp7n+2pcGWYkF/j+XlX2vKSpHuwa9E4mlEWTWOSanOkepHdolI2LUifz3Z+KGN14heIvmc6mRb7v8r5zrpDLrnQP+qI458p+N3EUWugdHmV8QoWsQaUqWpiESIXNmpJmrqZO393Y9H92IsLyChvLK2x8pX4Z3e4RXj3RycvHOugdHmVtdWT2+tokCg92DXnD9v/MRrVfrkm0hMn+Zl+l4FSM5AIoK7KSJdFv2bCjqYfi/FxWVwXnL/FncVkBZYUW9p1z8ZnNC0Oa29E3d4dFf8oKLSjl659TVhScNpPqaGESIqna+KfJUO2XzCuc9bh5RVY+XVfLp+tqZz0uWBaVFfDaya6onCtSYukzATjfGz0n/P4WFzZrDsvLoy/84kG2kQUf7RuvnaedbF5SGpbPQ0TYuMjBvjCc8MEmLJqY3zPnYOYIE+0zCZFU7W/d1DXIguK8OWPko83C0gK6Bkbi0id9Nswij44o1uUyqbDlRT3XZH9zL+trS6JWCiQRVNitdETRJHy+d5hm59Cs+SVzUbfIwZnuQXpCjDILpl2vP2Ynxkxq36uFSYhU2PLoGhhJmkS8YGnsdLMsBiaeuTDDg1tiVG4kWMwij7HQTLKzhAUl0asePOQd40T7QMo0w5qJaJcf2tkUvr/ExN9vEgod/R6yBOYF6XMzc5kyyQmvhUmIVNiseMcnJv85pQJKKZq63CxLgMlksnpwgmt0xaLIoz++UvTR2eOh1j7GJ1TK+ktMol2fa+fpHkoKclk5P/yeJWuri7FkZ7GvOTRh0t7nodxmDVi+JRBTlYO1MNHMwFTiYuqYutr7PQx5x1lWPru/JBYsKvO957kEO+HNO8SSWAmTkuglLprO9w21qa2ZlNvy6BkcYWw8OlnwO4z8kkhMf3m52ayttrPvbIjCJISERdCaiSYIpnrBp054cFPnIEBCNBNHQS5F1pyEhwebRR6j2RjLnxpHPl0D0ck1eafZxZJ5hTExycWTSrsVpaA7CmVFWpxDtLqGIzJxmWxa5ODQ+T5GxoL/rDr7R6gIQZhYcrKwWXO0ZqKZmanM3tTRTMxIrkT4TEQkKcKDY1Hk0Z+a0uhEdCml2N+cWp0VZyKaN15mfsnVy6IhTErxjk1wtC34DPVQNROA0iJLRpVU0cIkRFKxPldTlxubNWdSEMabhaX5CRcmk2au/FhpJtEpRd/qGqbbPZLy/hKI7o3XztNOHAW5vKci8h7vk074IE1dntFx+oZHg05YNMm0ysFamIRIgSWHImtOSFEqr7/bxTefPxqTVqLB0NTlZmlFUcJ6YiwqK6TZOcREAvvAmEUeLTmx+cpHq0nW/hYjWTHF/SXgX1IlshsvpRQ7T/ewZWlZVEKly21WFpUVBB3R1R5kh8XpZFrlYC1MwqAihFyTFw5f4PbH9/D422e5/nuv87X/eofjF+JbAK6pczAhzneT2tICvGMTCQ1aiFXCokm0ck3eOeciPzc7ooilZGFekQWRyAujtrqGOd8bHX+JyaaFDvaecwUV4t8eZO/36ZQWajOXZg7Kg+wF/9yB83ztZ/tZX1vCa/9vPV+6dhkNJ7v4yH1v8GdP7GF/iOGJ4eAeGaO935MQ57vJwiQoRe8cGo1JwqJJtHJN9rf0sq6mOOgQ1GQmJzuLskJLxJrJjqbo+UtMNi5y0O0eocU59+fVEWL2u0lpoYWeQW/K5aSFS+p/YxNAMCVVnt7bwl8/dYArFzt48oubWTKvkG98ZCVv/f11fP1D72HvORef+tHbfO7hnexo6onZF840rWW6MOkdiq1mAkTcJMszOs6xtr608JeYRCNxcefpHsoKLVGtq2a2ot57zjnnsZPZ7yH6TEoLLXjHJhhKcPWHeBGUMBGREhF5RkROiMhxEblaRL4pIudF5IDx+Kjf8XeKSKOInBSRG/3GN4nIYeO1+8Uw4ouIVUSeMsZ3ichivznbROSU8dgWva2Hj1nscSYB8J+7zvF3zxzimuXzeOxPNl9UwqS4IJe/+tAK3vz76/iHj67k3Q43n3loJ7f8eAevneiMulBpnCzwmDgzV3VJPlkCzT2DCVuDc9Abs4RFE1/iYviaydG2fkbHVVpEcplE2r5XKcUOw18STZ/figobNmtOUH6T9r4RCizZ2EIsReRfnysTCFYzuQ94USm1ElgPHDfG71VKbTAevwUQkdXAVmANcBPwIxHJNo5/ALgDWGE8bjLGbwdcSqnlwL3Ad4xzlQJ3AVcBm4G7RCTht20VdivDo+MBOxU+9tYZ/vHZI1y3soKHbqsj35Id4AxQZM3hjmuX8cbffZC7b15De5+HP318Dx/7/pvs65i9A2IoNHW5yc4SFpYmTphYcrKoKk5sRJdr0BuzhEWTGkdBRLkmptkzrYSJzTp5Zx8Ozc4hLvR52LI0us3VsrOEK4Is+tjR76HSnheyMCvTwuRiRMQOXAs8AqCU8iqlemeZcjPwc6XUiFLqDNAIbBaRKsCulNqhfLffTwKf9JvzhPH8GeB6Q2u5EdiulHIqpVzAdqYEUMKYip+/+I7rJ6838c+/OsaNayr58ec3kZcbWJD4k5ebzReuXkzD/6jnu7esY8g7zvf3j4RV2TQQTZ2DLCotiFkUU7AsTGCuiVnksTRGOSYmkVYP3t/cS40jf/L7lQ5U2vPodvt6gYRDNPNLprNpoYOTHQP0e2YvjeQTJqGH1WvN5FKWAl3AYyKyX0QeFhHzNvdrInJIRB710xiqgRa/+a3GWLXxfPr4RXOUUmNAH1A2y7kSSqD2vfe/copvv3CCj62r4gef3RjyP+/c7Cw+XVfLL7/yXgD2nJ3blhsMTV1uliZBGXOfMIl+n/RgiGWRR3/MXJPzYZq69je70spfAr5rZUJBz2B4pq4dTT3MK7LGxOdXt9iBUnCgebZ74/ASFiHzKgcHYwTMATYCf6GU2iUi9wHfAH4A3A0o4+e/AV8EAumCapZxwpwziYjcgc98RmVlJQ0NDbNsJ3La3L5aQ6/v2o+nOZtfnhrlV6dHed+CHP6wqo+33vh9ROefl6fY/s4pVqqWuQ+ehfEJxemuIVYUjsT8bzLnWvq8dLtHefHl18jLEdxud9zW1DLg+7zazpyiYfhMzN6nZ9j3Pq/uOkCdI7S/ucszQVufh/qR7oR/VsESzGfYaZhsf/vqWywunltT90cpxevHh1nhyOL1118Pd5kzMjymEOD/vL6fibZLbzTcbjevvfYa7b3DeEvGQv5chsd8/6r2HDrOvIHGKKw4ukT7GgxGmLQCrUqpXcbvzwDfUEp1mAeIyEPAr/2O9++sVAO0GeM1Acb957SKSA5QDDiN8fppcxqmL1Ap9SDwIEBdXZ2qr6+ffkhU6Rse5R/e/B1lNUt5e8DDr06fYeuVtfzvT10elaSq5Qdf5NxQLpHu40z3IOO/a+CDm1ZRH6VmV+Ey4GjjmVP7Wbx2Eyvn22loaIh4f8HydlM3vLWLa668IibmEpPxCcXfv/ECBeW1FOW1h7S/Fw5fAN7h1uvqUkY7CeYzLG528f39b7PwsrXUr6wM6fxnugdxvdTAJ7ason7LoghWOjOrjr5Bj1ior7/qktcaGhq4vO5qxl56mbo1K6i/ZklI51ZKkfvaCzjm11JfvzJaS44a0b4G57TFKKXagRYRucwYuh44ZvhATD4FHDGePw9sNSK0luBztO9WSl0ABkRki+EPuQ14zm+OGal1C/Cq4Vd5CbhBRByGGe0GYyyh2PNysOZk8ePXm3jojTPcdvWiqAkSgKXF2Vzo80TkuISpVr2JqMk1nUSWoncNxrbIo0kkuSb7W3qx5GSxZkFkbZKTDbM4YjglVSLp9x4sdYsd7G92zejTMdcdaikV8NWlKy20ZEzl4GAN+38B/KeIHAI2AP8b+K4R5nsI+CDwdQCl1FHgaeAY8CLwVaWUGd7yFeBhfE75JuAFY/wRoExEGoG/wWdGQynlxGdC22M8vmWMJRQRocJupWfQy59ds4R//sSaqHbEW1rs+1gOtMxuy52LyQKP85JImCTACT9Z5DGGSYsm4fY12d/sYu0Ce8IDJaJNedGl/sVg2dHUQ7nNGtPqDZsWORj0jnOiPXBVilA7LE7HUWDRPhN/lFIHgLppw1+Y5fh7gHsCjO8F1gYY9wC3znCuR4FHg1lnPPncVT61+0vXLo16zauF9ixysoSDLb3cuGZ+2Odp6nIzr8hKcRz+ic5FSUEutrychAiT3hj3MvGnxpFPw8kugry0ABgdn+BQax+fj5EpJ5FYcrIot1l59WQnt79/CUVB5mr41+OKZU05s+jjO+dcAbXCyd7vYWgm4GuS5Qwz+CDVSK/boDjy5Q8s48sfWBaTL7olW1hZZeNga2SaSWOnO6E1ufwRkYSFBztjXOTRnxpHAZ0DI3jHgw+FPX6hn5GxibTKL/Hnnz62miPn+/jCI7voGw6uQ+np7kE6B0Yi6vceDNUl+VTareydIRTfLPJoalih4iiwTPbSSXe0MElSNtSWcKilL+xKu75WvYNJ4S8xSZQwcQ16KYlxjomJmWvi9AT/uZmdFTemiOM9VD6+fgE//OxGjpzv47MP7Qwq72LKXxLdZMXpiAibZkle7BzwMK/IEvaNiK9ysNZMNAlkfU0JAyNjnO4Or2x9z6CXvuHRhNbkms7C0gJancNxL0XvGhqNeSkVEzPXpHs4+Fa1+5tdVNqtVIVpSkkFblo7n4duq6Ox080f/2QHnXMEl+xo6qHSbmXJvNhr1psWldLqGg4Y8NLe5wnbXwJQWmil3zPGaJRaFyczWpgkKWb/7wMtfWHNb5qsyZU8wqS2tADv+MSkHTpeuOJQ5NGk2tBMuodD0Exaermi1pGwfjPxov6yCh7/082c7x3m0z/ZMWOlAJ+/xBlzf4nJZLOsANpJe/9IWAmLJmbVhUwoRa+FSZKyrLyIImsOB8OM6GrqMvu+J4fPBGBRWWIiupyDXhxx0kwqbVZysiRoYXKqY4BzPUNp6y+ZztXLyvjp7VfRM+jl0z/ewbkAxT+bugbpdsfeX2KyZoGdvNysgMKko98TUu/36ZQW+nwtZnh6OhNaGUxN3MjKEtbVFIcdHtzU5SYvN4sFxflRXln4+IcHV8TxfXuHRuMmTHKys6gqyaN7+NI70YkJxbudA+w542TPWRd7zzppMxy8V8XpH2cysGmRg5/9+Ra+8Mgubv3xDv7rz69iuV873h1xyC/xJzc7i3U1JZc44UcnFM5Bb0SaicPQTHzlZFK/4dlsaGGSxKyvLeGh35/GMzoeVNFIf5q63CydVxTV/JdIWWCUom9xDlERn//tjIz5qjvHusijPzUlBXQ5fdWDD7X2seesk71nnew952LA4ysvUmm3cuXiUr60uJQtS8u4LA06K4bC2upinvrS1Xzu4V388U928tPbr2L1Ajvgc75XFedNarLxoG6RgwenXWu9RhDF/OLwIrkAygzNJBOKPWphksRsqC1hbEJx7EJ/yJE+TV1uNtQmV3RQbnYWC0ryOdczxKaquY+PBmaRx3jkmJjUOPLZfaaHdd/8HV7D8bq8ooiPrVvAlYsdXLm4lBpHftr7SObiPZU2nv7S1XzuoZ1sfXAHT95+Fetritl1uof3ryiP699n0yIHYxOKgy29k1pi74hPmETigDc1k0zIgtfCJImZdMI394YkTDyj47S6hvmjjTVzHxxnFpUZ4cFxEiam4zPWpVT8+cjl8zl8po1r1y7mysWlbFrkiOv7pxJL5hXy9Jev5rMP7eLzD+/izo+upNvtjZu/xMS8vvY1uyaFicsTBWFSkDmVg7UwSWIq7XnMt+eFnLx4pnsQpRLbqncmFpYW8LujHUB8zE6meSFePhOA61ZWktWeT339qri9ZypT4yjgF1/2mbz+8Vlfib94+UtMHIUWlpUXsu/slN/EZWgmkfhMcrOzKM7PzQjNREdzJTkbaktCjugyW/UmozCpLS2gZ9A7WZ471phRNI44+kw0oVNpz+OpO7awusrOiooiakvjHziyaZGDfc2uydbZLo/CkpNFSYTliEoLM6M+lxYmSc762hLO9gyFdGfT1OVGBJYmUViwiRnR1TUUnyQus8hjvJIWNeFTVmTlV39xDc9+9X0J8SfVLSqld2iU092+cOXekQnmh9GudzqlhRadZ6JJPOtrfcXnQjF1NXUNUuPIDzkCLB4sMnrRd4WQ1BcJ8SzyqImc7CwJuhhktNloJi8api6XR4XVrnc6jgILPe70FybaZ5LkXF5djAgcbOmj/rLgsjOaOt1JaeKCKc3kdO8EB1p6cQ16cQ56cQ1N+zk4imvI9/vGhQ4evG160ergcA55KYpTkUdNarOsvJCSglz2nXPx6Str6R1RrIjAX2JSVmjhUIRFW1MBLUySHFteLisqioLWTCYmFKe73THtKBgJxQW5OApy+c2ZUX7zw7cuei0nS3AUWigtsOAozGV5RRHne4d55URnWLk24AvJ1P4STTCICJsWOth7zolSCpdHReR8N3EYZi6lVFqHg2thkgKsrynhlROdQX0Z2/qG8YxOJK1mAvDgbXW89NY+3rtpHY4CC6WFFhyFFmzWnEv29+tDbXztv/bT2OlmbXXoXQjjWeRRk/psXOTglROdNDuH8E6E38fEn7JCC6PjioGRMex56Xtjo3X/FGB9bQnOQW9Q7WCTsSbXdK5cXMo11blct7KSKxY6WFRWiD0vN6CgXFXly4o+fiFwJ7y5cA15tb9EEzR1ht/kt4fbASKqy2Vi5hile3iwFiYpwFQF4blNXcnU9z0aLC4rJC83ixPtA2HNdw56dcKgJmjW1ZSQkyX89vAFILIcExPz+5fu4cFamKQAl823Yc3JCk6YdLkpzs+lLE3+gWZnCZdV2sLWTOJZ5FGT+uRbsllTXczh877WD9EUJloz0SSc3Ows1lYXB5W8aLbqTSdH38r5do5f6J9MJgsWs8ijI8KkM01mscmvdFFFFEKDtWaiSSo21JZwpK1vzo5tTV2DSdUQKxqsqrLhGhqlayC09qdmkcd4NcbSpAdms6zCXKKSq2UKk3SvHKyFSYqwvrYEz+gEJ2fxHfQNjdLtHknqSK5wWGk44Y+FaOpKRJFHTepTt9gnTBzW6Gj3BZZsLDlZ2swFICIlIvKMiJwQkeMicrWIlIrIdhE5Zfx0+B1/p4g0ishJEbnRb3yTiBw2XrtfDFuMiFhF5CljfJeILPabs814j1Misi16W08tNtT4nPCz5Zs0dSdvTa5IWDXfJ0xCdcI7J7PftZlLEzyV9jxqS/Mpy4/OvbaIUJYB9bmC/WvdB7yolFoJrAeOA98AXlFKrQBeMX5HRFYDW4E1wE3Aj0TE1BUfAO4AVhiPm4zx2wGXUmo5cC/wHeNcpcBdwFXAZuAuf6GVSdSW5lNaaJnVb5JukVwmxQW5LCjOC9kJbxZ51JqJJlQe+Nwmtl4Wve9NaaFFayYiYgeuBR4BUEp5lVK9wM3AE8ZhTwCfNJ7fDPxcKTWilDoDNAKbRaQKsCuldiifJ/XJaXPMcz0DXG9oLTcC25VSTqWUC9jOlADKKESE9TXFHGzpm/GYpq5BcrOFWkfytOqNFiur7Jy4EJpm4tJFHjVhsra6mKqi6HkBMqFycDAZ8EuBLuAxEVkP7AP+CqhUSl0AUEpdEBGzcFQ1sNNvfqsxNmo8nz5uzmkxzjUmIn1Amf94gDmTiMgd+DQeKisraWhoCGJbyYvb7Q64h+JxLw0do7zw8mvk51xqz9113EN5Prz5xu/jsMrImGmPM1Hg9dLYOcr2V18jN8hWxO80+S7eQ3t3kBPn9sWh7i8VSfc9RnN/o24P53snkurvFe3PLxhhkgNsBP5CKbVLRO7DMGnNQKCrVs0yHu6cqQGlHgQeBKirq1P19fWzLC/5aWhoINAeVFUn/924h5Il6wLW3vrWvgbWLbJRX78pDquMjJn2OBMDjjZ+fXo/C1ZuZM2C4MqqvD5wlKLmVj503QfDXGX4hLq/VCTd9xjN/TX0H+Xovtak+ntF+/MLRo9rBVqVUruM35/BJ1w6DNMVxs9Ov+Nr/ebXAG3GeE2A8YvmiEgOUAw4ZzlXRjKbE350fILmniGWVSRvGZVImCqrErypq3doVBd51CQFZYUWBkbGGBkbT/RSYsacwkQp1Q60iMhlxtD1wDHgecCMrtoGPGc8fx7YakRoLcHnaN9tmMQGRGSL4Q+5bdoc81y3AK8afpWXgBtExGE43m8wxjISR6GFRWUFHGi+VJic6xlibEKlXSSXyeKyAqw5WZwIwQnvHPRqf4kmKTBznczcp3Qk2KrBfwH8p4hYgNPAn+ITRE+LyO1AM3ArgFLqqIg8jU/gjAFfVUqZ4vgrwONAPvCC8QCfc/+nItKITyPZapzLKSJ3A3uM476llHKGude0YH1NCXvOXvonMFv1plvCoklOdhaXzbdxvD14YeIa8upSKpqkwCxv1OP2UhmFEi3JSFDCRCl1AAjUnej6GY6/B7gnwPheYG2AcQ+GMArw2qPAo8GsMxPYUFvC8wfb6Oj3XPSlbOryCZOlaaqZAKycb+Pl48GV4gefMElXTU2TWkzW50rj9r06Az7FWD9DBeGmLjfz7XkJa3kaD1ZV2XEOeoMuq+Ia1EUeNclBJtTn0sIkxVizwE5OllySvNjUNZi2zneTlUYm/PEgMuG9YxO6yKMmaZisz+UOrb5cKqGFSYqRl5vNqir7RRFdSilOJ3Hf92ixqsoGEJQTvtcwJ+gij5pkoKTAggg409gBr4VJCrK+tphDLX1MTPhSbroGRhgYGUt7YVJSYKEqyLIqTl3kUZNEZGcJJfm5OAdjp5l898UT3PXckZBbNUQLLUxSkPU1JQyMjHHaKOzY2JWeBR4DsXK+LaiCj7rIoybZcBRaJuvFRZt+zyhPvH2WvuHRhPUy0sIkBblioemE99Xpmuz7nuY+E/A54Rs73XMmf5nx/Foz0SQLvsrBsdFMntrdwqB3nNuvWRqT8weDFiYpyNJ5RRRZcyad8E2dbgot2VFpMZrsrKyyMzahaOocnPU4UzPRSYuaZKE0RprJ2PgEj799lquWlHJ5TXClhmKBFiYpSFaWsK6meDI8uKnLzbKKorRq1TsTqw0n/Fx+E9ekmUsLE01yEKvKwS8ebed87zB/9v7EaSWghUnKsr62hOMX+vGMjtOUAZFcJovLCrHkZHFijkx419AoRdYcLDn6K65JDkoLLbiGvJOBM9FAKcVDb5xhcVkB16+smHtCDNFXWoqyobaEsQnF3rMu2vo8LCtPf38JGGVVKm1zFnx0DXl1kUdNUuEosDA+oRjwjEXtnO80uzjY0svt1ywhK85tFqajhUmKssHIhP/lfl+LmEzRTMCM6JpdM3EO6rpcmuSirMjMgo+eE/7hN85QnJ/LH22qmfvgGKOFSYpSac9jvj2PF4+0A+nXqnc2VlXZ6XZ76RzwzHhMry7yqEkyzO9jtOpzNfcM8dLRdj571UIKLIkvo6SFSQqzobaEIe84WQKLygoSvZy4sXIyE35mU5dzyKvDgjVJRVmhFfBVDo4Gj719hiwRtl29OCrnixQtTFIYs+jjwtICrDnZCV5N/Fhl1OiazdTlGhzVCYuapKK0KHqaSb9nlKf3tPDx9QuYX5wcKQFamKQw62t9MeXp2sNkJhyFFubb82Z0wptFHnWOiSaZML+P0QgPnkpSXBLxuaKFFiYpzLqaEnKyhBWVtkQvJe6srLLNmGuiizxqkpF8Szb5udk4IzRzjY1P8NhbZ9iytJS11YlLUpxO4r02mrApsubw8zu2ZFQkl8mqKjtvNXbjHZu4JJfELPKoHfCaZKO00DL5/QyXF46009bn4Vs3X9JnMKFozSTFqVtcmpF34Cvn2xgdV5MdJv0xS6noPBNNslFaaJn8foaDUoqH3zjNknmFXJfgJMXpaGGiSUlWVxmNsgKYunSRR02y4qscHL4w2XfOxcHWPr6YBEmK09HCRJOSLJlnllW51Ak/qZloM5cmySiLsD7Xw2+coaQglz/aWB3FVUUHLUw0KUlOdhbvqSwKqJm4dC8TTZISiZnrXM8gLx1r57ObkyNJcTpBCeyhGgIAAAvYSURBVBMROSsih0XkgIjsNca+KSLnjbEDIvJRv+PvFJFGETkpIjf6jW8yztMoIveLUeZWRKwi8pQxvktEFvvN2SYip4zHtmhtXJP6rJxvDxgebBZ5zKTcG01qUFpoYcg7jmd09n48gXjsrbPkZAnb3rs4+guLAqFoJh9USm1QStX5jd1rjG1QSv0WQERWA1uBNcBNwI9ExLyqHwDuAFYYj5uM8dsBl1JqOXAv8B3jXKXAXcBVwGbgLhFxhLFPTRriK6syQtfAxbWOXENerZVokhLTjxeqdtI3PMov9rbw8XULqEzSvkWxMHPdDPxcKTWilDoDNAKbRaQKsCuldihfk+IngU/6zXnCeP4McL2htdwIbFdKOZVSLmA7UwJIk+Gsmm+UVZmWCe8c1KVUNMmJ6cd7s7E7pF7tT+1pZtA7zheTKElxOsEKEwX8TkT2icgdfuNfE5FDIvKon8ZQDbT4HdNqjFUbz6ePXzRHKTUG9AFls5xLo2HlDBFdusijJlmpW+ygxpHP3z1ziI/c9wbPHTjP2PjErHNGxyd4/K2zXL20LKmSFKcTrBfnfUqpNhGpALaLyAl8Jqu78Qmau4F/A74IBIpXU7OME+acSQwBdwdAZWUlDQ0Ns24m2XG73Sm/h7mI1h5LrELDgUbeMzF1z9HWM0TBeFZC/4b6M0x9YrW/b14p7Lxg4ben3fzVzw/w/z13kI8syeWa6hws2Zf+y9t5YYy2vhE+vUxFdT3R3l9QwkQp1Wb87BSRZ4HNSqnfm6+LyEPAr41fW4Fav+k1QJsxXhNg3H9Oq4jkAMWA0xivnzanIcD6HgQeBKirq1P19fXTD0kpGhoaSPU9zEW09rj+9G46B0aor3//5Njway+xcmkN9fVrIj5/uOjPMPWJ5f4+BPzDhOLl4x38qKGJJ4/18ttm4YvXLObzWxZhz/P5/JRSfO+Hb7F0Xg5/ecsHoppbEu39zWnmEpFCEbGZz4EbgCOGD8TkU8AR4/nzwFYjQmsJPkf7bqXUBWBARLYY/pDbgOf85piRWrcArxp+lZeAG0TEYZjRbjDGNBrA54Rv7BzAO+YzFegij5pUIStLuGHNfJ79f97Lz/58C6uqbHz3xZO879uv8p0XT9A54GHvOReHkjRJcTrBaCaVwLNGFG8O8F9KqRdF5KcisgGf2eks8CUApdRREXkaOAaMAV9VSplxcF8BHgfygReMB8AjwE9FpBGfRrLVOJdTRO4G9hjHfUsp5Qx/u5p0Y1WVr6zK6W43K+fbJ4s8lmgHvCZFEBGuXlbG1cvKOHK+jwdeb+LHrzfxyJtnqLBZjSTFxHdSnIs5hYlS6jSwPsD4F2aZcw9wT4DxvcAl1cmUUh7g1hnO9Sjw6Fzr1GQmq/yc8Cvn2yeL6GnNRJOKrK0u5oef3ciZ7kEe/H0T/2ffeb523XLyLcmfM5V8aZQaTQgsmVeIJTvL13XxCl9TLNBFHjWpzZJ5hXz7D9dx18fXYM1JjUIlWphoUprc7CxWVBZxzAgPduny85o0Ii83+TUSk9QQeRrNLKycb58s+GhmFuukRY0mvmhhokl5VlXZ6BoYods9MuWA1+VUNJq4ooWJJuUxnfAnLgzgHByl0JKtizxqNHFGCxNNyrPSqNF1/EI/riFvRnae1GgSjRYmmpSnrMhKhc3K8XafMNH+Eo0m/uhoLk1asLLK19vEki2U6EgujSbuaM1EkxasqrLR2DlA58AIpdr5rtHEHS1MNGnBqvl2RscVF/o82mei0SQALUw0aYEZ0QU6YVGjSQRamGjSgqXlvrIqgNZMNJoEoIWJJi3Izc5ieUURoIs8ajSJQAsTTdqwssqXb+LQDniNJu5oYaJJG1YbfhMdGqzRxB+dZ6JJGz6xYQFd7hHeU1mU6KVoNBmHFiaatKHClsedH1mV6GVoNBmJNnNpNBqNJmK0MNFoNBpNxGhhotFoNJqI0cJEo9FoNBETlDARkbMiclhEDojIXmOsVES2i8gp46fD7/g7RaRRRE6KyI1+45uM8zSKyP0iIsa4VUSeMsZ3ichivznbjPc4JSLborVxjUaj0USPUDSTDyqlNiil6ozfvwG8opRaAbxi/I6IrAa2AmuAm4AfiYjZ9u4B4A5ghfG4yRi/HXAppZYD9wLfMc5VCtwFXAVsBu7yF1oajUajSQ4iMXPdDDxhPH8C+KTf+M+VUiNKqTNAI7BZRKoAu1Jqh1JKAU9Om2Oe6xngekNruRHYrpRyKqVcwHamBJBGo9FokoRghYkCfici+0TkDmOsUil1AcD4WWGMVwMtfnNbjbFq4/n08YvmKKXGgD6gbJZzaTQajSaJCDZp8X1KqTYRqQC2i8iJWY6VAGNqlvFw50y9oU/AmULOLSInZ1lfKjAP6E70ImJMuu8x3fcH6b/HTNvfokhOFpQwUUq1GT87ReRZfP6LDhGpUkpdMExYncbhrUCt3/QaoM0Yrwkw7j+nVURygGLAaYzXT5vTEGB9DwIPBrOXVEBE9vr5ptKSdN9juu8P0n+Pen+hMaeZS0QKRcRmPgduAI4AzwNmdNU24Dnj+fPAViNCawk+R/tuwxQ2ICJbDH/IbdPmmOe6BXjV8Ku8BNwgIg7D8X6DMabRaDSaJCIYzaQSeNaI4s0B/ksp9aKI7AGeFpHbgWbgVgCl1FEReRo4BowBX1VKjRvn+grwOJAPvGA8AB4Bfioijfg0kq3GuZwicjewxzjuW0opZwT71Wg0Gk0MEJ8CoEkmROQOw3SXtqT7HtN9f5D+e9T7C/F8WphoNBqNJlJ0ORWNRqPRRIwWJnFCRB4VkU4ROeI3tl5EdhglZn4lInZj3CIijxnjB0Wk3m9Og1Gm5oDxqAjwdnFHRGpF5DUROS4iR0Xkr4zxqJXdSSRR3l9afIYiUmYc7xaRH0w7V8p/hnPsL+k+wzD292Hx5Q4eNn5e53eu0D8/pZR+xOEBXAtsBI74je0BPmA8/yJwt/H8q8BjxvMKYB+QZfzeANQlej8B9lcFbDSe24B3gdXAd4FvGOPfAL5jPF8NHASswJL/2975w9YUR3H8c9BE1NCIGETCJimGppOEkYSIDo1JGBhIDBgFu0lIOlgwYcIkBro0SAhaUqqkiaVpmEQ18afJ13B+L314r/Xufem9rvNJbvp7v3t7c7/9vuT093vnnAdMAEvTuSfAVrzO6C6wq2L6quJhJ7ANOAoM/HavKng4n77SeZhBXw+wNo03A5N5/IuVySIhaQjPVKtnIzCUxveA/jTuxvudIekj8Akodb67pClJz9N4GhjDuxW0s+1OYbRL3+I+dWu0qlHSjKQHwNf6+1TFw2b6ykoGfcNKNYTAK2C5eUlHJv8imBTLKLA3jfcxV+z5Augzs2XmtTq9/FoIejUtrc+WYfvgd8y7PvcAj2lv251SkFNfjSp42IyqeLgQpfUwg75+YFjSNzL6F8GkWA4Bx8zsGb4s/Z7mr+AGPgUuAI/wmh2A/ZK2ANvTcWBRn3gBzGwlcBM4IenzfJc2mPvrFjpF0QZ9UB0Pm96iwdy/6OF8lNbDVvWZ2Sa8U/uR2lSDyxb0L4JJgUh6I2mnpF7gBr6vjqRZSSflLf/7gC7gXTo3mX5OA9cp0daJmXXgb+Jrkm6l6Q9p2Vzb/sjTdqdQ2qSvSh42oyoeNqWsHraqz8zWAbeBg5Im0nQm/yKYFEgtA8TMlgBngEvp9Qrz1jWY2Q5gVtLrtO21Os13AHvwrbLCScv8y8CYpPN1p9rZdqcw2qWvYh42pEIeNrtPKT1sVZ+ZdQF3gFOSHtYuzuxfUZkH/9uBrzymgB945D8MHMczLt4C55grIt0AjOMfoN0H1qf5Tjyz6yX+gdlFUoZQ0Qee9aL0bCPp2I1/lcAgvrIaBFbV/c5pfDU2Tl22CJ5sMJrODdT+LlXQV0EP3+OJJV/S+7q7Yh7+oa+sHraqD/8Hdqbu2hFgTVb/ogI+CIIgyE1scwVBEAS5iWASBEEQ5CaCSRAEQZCbCCZBEARBbiKYBEEQBLmJYBIEQRDkJoJJEARBkJsIJkEQBEFufgL/tSj0/Dy20wAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "first_week_of_september = [pd.Period(pd.Timestamp(y,9,1),'W') \n", + " for y in range(data.index[0].year,\n", + " data.index[-1].year)]\n", + "years = []\n", + "total_inc = []\n", + "for week_start, week_end in zip(first_week_of_september_trunc[:-1],\n", + " first_week_of_september_trunc[1:]):\n", + " data_extract = data['inc'][week_start : week_end-1]\n", + " assert data_extract.size in range(51,54)\n", + " total_inc.append(data_extract.sum())\n", + " years.append(week_end.year)\n", + "yearly_inc = pd.Series(total_inc,index=years)\n", + "yearly_inc.plot()\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_inc.hist()" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2002 516689\n", + "2018 542312\n", + "2017 551041\n", + "1996 564901\n", + "2019 584066\n", + "2015 604382\n", + "2000 617597\n", + "2001 619041\n", + "2012 624573\n", + "2005 628464\n", + "2006 632833\n", + "2011 642368\n", + "1993 643387\n", + "1995 652478\n", + "1994 661409\n", + "1998 677775\n", + "1997 683434\n", + "2014 685769\n", + "2013 698332\n", + "2007 717352\n", + "2008 749478\n", + "1999 756456\n", + "2003 758363\n", + "2004 777388\n", + "2016 782114\n", + "2010 829911\n", + "1992 832939\n", + "2009 842373\n", + "dtype: int64" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_inc.sort_values()" + ] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +2236,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } -