From 28cebeca920f12b848f3e5016e1c4d7709fefb76 Mon Sep 17 00:00:00 2001 From: 3447a552022e9b60e8a4c5d442e78097 <3447a552022e9b60e8a4c5d442e78097@app-learninglab.inria.fr> Date: Thu, 14 Nov 2024 18:47:22 +0000 Subject: [PATCH] no commit message --- .../influenza-like-illness-analysis.ipynb | 3248 ++++++++++++++++- 1 file changed, 3199 insertions(+), 49 deletions(-) diff --git a/module3/exo1/influenza-like-illness-analysis.ipynb b/module3/exo1/influenza-like-illness-analysis.ipynb index 87092fc..d9e00f8 100644 --- a/module3/exo1/influenza-like-illness-analysis.ipynb +++ b/module3/exo1/influenza-like-illness-analysis.ipynb @@ -9,10 +9,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 1, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -30,13 +28,11 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ - "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + "data_url = \"https://www.sentiweb.fr/datasets/all/inc-3-PAY.csv\"" ] }, { @@ -63,9 +59,976 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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2089 rows × 10 columns

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FR France \n", + "26 29.0 FR France \n", + "27 41.0 FR France \n", + "28 49.0 FR France \n", + "29 51.0 FR France \n", + "... ... ... ... \n", + "2059 59.0 FR France \n", + "2060 64.0 FR France \n", + "2061 97.0 FR France \n", + "2062 93.0 FR France \n", + "2063 80.0 FR France \n", + "2064 116.0 FR France \n", + "2065 149.0 FR France \n", + "2066 281.0 FR France \n", + "2067 395.0 FR France \n", + "2068 485.0 FR France \n", + "2069 544.0 FR France \n", + "2070 689.0 FR France \n", + "2071 722.0 FR France \n", + "2072 762.0 FR France \n", + "2073 926.0 FR France \n", + "2074 1113.0 FR France \n", + "2075 1236.0 FR France \n", + "2076 832.0 FR France \n", + "2077 459.0 FR France \n", + "2078 207.0 FR France \n", + "2079 190.0 FR France \n", + "2080 198.0 FR France \n", + "2081 224.0 FR France \n", + "2082 266.0 FR France \n", + "2083 219.0 FR France \n", + "2084 176.0 FR France \n", + "2085 163.0 FR France \n", + "2086 195.0 FR France \n", + "2087 308.0 FR France \n", + "2088 213.0 FR France \n", + "\n", + "[2089 rows x 10 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data" @@ -80,9 +1043,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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120244433667130600.042742.05546.064.0FRFrance
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420244137943571386.087484.0119107.0131.0FRFrance
520244038496576555.093375.0127114.0140.0FRFrance
620243939166082937.0100383.0137124.0150.0FRFrance
720243839178682903.0100669.0138125.0151.0FRFrance
820243735646049319.063601.08574.096.0FRFrance
920243633365727906.039408.05041.059.0FRFrance
1020243532740422036.032772.04133.049.0FRFrance
1120243432671721003.032431.04031.049.0FRFrance
1220243332062315349.025897.03123.039.0FRFrance
1320243232318717532.028842.03527.043.0FRFrance
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1720242835434245781.062903.08168.094.0FRFrance
1820242734736440234.054494.07160.082.0FRFrance
1920242634421936956.051482.06655.077.0FRFrance
2020242534720440300.054108.07161.081.0FRFrance
2120242434111034671.047549.06252.072.0FRFrance
2220242333587530610.041140.05446.062.0FRFrance
2320242233377228274.039270.05143.059.0FRFrance
2420242132196317556.026370.03326.040.0FRFrance
2520242032005715780.024334.03024.036.0FRFrance
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2720241832240917653.027165.03427.041.0FRFrance
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2088 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202445 3 61119 51039.0 71199.0 92 77.0 \n", + "1 202444 3 36671 30600.0 42742.0 55 46.0 \n", + "2 202443 3 46572 39928.0 53216.0 70 60.0 \n", + "3 202442 3 67785 60009.0 75561.0 102 90.0 \n", + "4 202441 3 79435 71386.0 87484.0 119 107.0 \n", + "5 202440 3 84965 76555.0 93375.0 127 114.0 \n", + "6 202439 3 91660 82937.0 100383.0 137 124.0 \n", + "7 202438 3 91786 82903.0 100669.0 138 125.0 \n", + "8 202437 3 56460 49319.0 63601.0 85 74.0 \n", + "9 202436 3 33657 27906.0 39408.0 50 41.0 \n", + "10 202435 3 27404 22036.0 32772.0 41 33.0 \n", + "11 202434 3 26717 21003.0 32431.0 40 31.0 \n", + "12 202433 3 20623 15349.0 25897.0 31 23.0 \n", + "13 202432 3 23187 17532.0 28842.0 35 27.0 \n", + "14 202431 3 26035 20267.0 31803.0 39 30.0 \n", + "15 202430 3 36393 28593.0 44193.0 55 43.0 \n", + "16 202429 3 39560 32592.0 46528.0 59 49.0 \n", + "17 202428 3 54342 45781.0 62903.0 81 68.0 \n", + "18 202427 3 47364 40234.0 54494.0 71 60.0 \n", + "19 202426 3 44219 36956.0 51482.0 66 55.0 \n", + "20 202425 3 47204 40300.0 54108.0 71 61.0 \n", + "21 202424 3 41110 34671.0 47549.0 62 52.0 \n", + "22 202423 3 35875 30610.0 41140.0 54 46.0 \n", + "23 202422 3 33772 28274.0 39270.0 51 43.0 \n", + "24 202421 3 21963 17556.0 26370.0 33 26.0 \n", + "25 202420 3 20057 15780.0 24334.0 30 24.0 \n", + "26 202419 3 15375 11274.0 19476.0 23 17.0 \n", + "27 202418 3 22409 17653.0 27165.0 34 27.0 \n", + "28 202417 3 27042 21410.0 32674.0 41 33.0 \n", + "29 202416 3 28882 23305.0 34459.0 43 35.0 \n", + "... ... ... ... ... ... ... ... \n", + "2059 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2060 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2061 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2062 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2063 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2064 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2065 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2066 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2067 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2068 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2069 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2070 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2071 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2072 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2073 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2074 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2075 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2076 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2077 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2078 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2079 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2080 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2081 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2082 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2083 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2084 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2085 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2086 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2087 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2088 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 107.0 FR France \n", + "1 64.0 FR France \n", + "2 80.0 FR France \n", + "3 114.0 FR France \n", + "4 131.0 FR France \n", + "5 140.0 FR France \n", + "6 150.0 FR France \n", + "7 151.0 FR France \n", + "8 96.0 FR France \n", + "9 59.0 FR France \n", + "10 49.0 FR France \n", + "11 49.0 FR France \n", + "12 39.0 FR France \n", + "13 43.0 FR France \n", + "14 48.0 FR France \n", + "15 67.0 FR France \n", + "16 69.0 FR France \n", + "17 94.0 FR France \n", + "18 82.0 FR France \n", + "19 77.0 FR France \n", + "20 81.0 FR France \n", + "21 72.0 FR France \n", + "22 62.0 FR France \n", + "23 59.0 FR France \n", + "24 40.0 FR France \n", + "25 36.0 FR France \n", + "26 29.0 FR France \n", + "27 41.0 FR France \n", + "28 49.0 FR France \n", + "29 51.0 FR France \n", + "... ... ... ... \n", + "2059 59.0 FR France \n", + "2060 64.0 FR France \n", + "2061 97.0 FR France \n", + "2062 93.0 FR France \n", + "2063 80.0 FR France \n", + "2064 116.0 FR France \n", + "2065 149.0 FR France \n", + "2066 281.0 FR France \n", + "2067 395.0 FR France \n", + "2068 485.0 FR France \n", + "2069 544.0 FR France \n", + "2070 689.0 FR France \n", + "2071 722.0 FR France \n", + "2072 762.0 FR France \n", + "2073 926.0 FR France \n", + "2074 1113.0 FR France \n", + "2075 1236.0 FR France \n", + "2076 832.0 FR France \n", + "2077 459.0 FR France \n", + "2078 207.0 FR France \n", + "2079 190.0 FR France \n", + "2080 198.0 FR France \n", + "2081 224.0 FR France \n", + "2082 266.0 FR France \n", + "2083 219.0 FR France \n", + "2084 176.0 FR France \n", + "2085 163.0 FR France \n", + "2086 195.0 FR France \n", + "2087 308.0 FR France \n", + "2088 213.0 FR France \n", + "\n", + "[2088 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -123,10 +2117,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "def convert_week(year_and_week_int):\n", @@ -154,13 +2146,994 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
period
1984-10-29/1984-11-0419844436842220056.0116788.012537.0213.0FRFrance
1984-11-05/1984-11-111984453135223101414.0169032.0246184.0308.0FRFrance
1984-11-12/1984-11-1819844638733067686.0106974.0159123.0195.0FRFrance
1984-11-19/1984-11-2519844737202954274.089784.013199.0163.0FRFrance
1984-11-26/1984-12-0219844837862060634.096606.0143110.0176.0FRFrance
1984-12-03/1984-12-09198449310107381684.0120462.0184149.0219.0FRFrance
1984-12-10/1984-12-161984503123680101401.0145959.0225184.0266.0FRFrance
1984-12-17/1984-12-23198451310172680242.0123210.0185146.0224.0FRFrance
1984-12-24/1984-12-3019845238483060602.0109058.0154110.0198.0FRFrance
1984-12-31/1985-01-0619850138548965918.0105060.0155120.0190.0FRFrance
1985-01-07/1985-01-1319850239758680949.0114223.0177147.0207.0FRFrance
1985-01-14/1985-01-201985033213901174689.0253113.0388317.0459.0FRFrance
1985-01-21/1985-01-271985043424937390794.0459080.0770708.0832.0FRFrance
1985-01-28/1985-02-031985053637302592795.0681809.011551074.01236.0FRFrance
1985-02-04/1985-02-101985063565825518011.0613639.01026939.01113.0FRFrance
1985-02-11/1985-02-171985073471852432599.0511105.0855784.0926.0FRFrance
1985-02-18/1985-02-241985083389886359529.0420243.0707652.0762.0FRFrance
1985-02-25/1985-03-031985093369895341109.0398681.0670618.0722.0FRFrance
1985-03-04/1985-03-101985103353231326279.0380183.0640591.0689.0FRFrance
1985-03-11/1985-03-171985113276205252399.0300011.0501458.0544.0FRFrance
1985-03-18/1985-03-241985123245240223304.0267176.0445405.0485.0FRFrance
1985-03-25/1985-03-311985133197206176080.0218332.0357319.0395.0FRFrance
1985-04-01/1985-04-071985143134545114400.0154690.0244207.0281.0FRFrance
1985-04-08/1985-04-1419851536388145538.082224.011683.0149.0FRFrance
1985-04-15/1985-04-2119851635036236451.064273.09166.0116.0FRFrance
1985-04-22/1985-04-2819851733405324366.043740.06244.080.0FRFrance
1985-04-29/1985-05-0519851834055529935.051175.07455.093.0FRFrance
1985-05-06/1985-05-1219851934315432821.053487.07859.097.0FRFrance
1985-05-13/1985-05-1919852032789620885.034907.05138.064.0FRFrance
1985-05-20/1985-05-2619852132609619621.032571.04735.059.0FRFrance
.................................
2024-04-15/2024-04-2120241632888223305.034459.04335.051.0FRFrance
2024-04-22/2024-04-2820241732704221410.032674.04133.049.0FRFrance
2024-04-29/2024-05-0520241832240917653.027165.03427.041.0FRFrance
2024-05-06/2024-05-1220241931537511274.019476.02317.029.0FRFrance
2024-05-13/2024-05-1920242032005715780.024334.03024.036.0FRFrance
2024-05-20/2024-05-2620242132196317556.026370.03326.040.0FRFrance
2024-05-27/2024-06-0220242233377228274.039270.05143.059.0FRFrance
2024-06-03/2024-06-0920242333587530610.041140.05446.062.0FRFrance
2024-06-10/2024-06-1620242434111034671.047549.06252.072.0FRFrance
2024-06-17/2024-06-2320242534720440300.054108.07161.081.0FRFrance
2024-06-24/2024-06-3020242634421936956.051482.06655.077.0FRFrance
2024-07-01/2024-07-0720242734736440234.054494.07160.082.0FRFrance
2024-07-08/2024-07-1420242835434245781.062903.08168.094.0FRFrance
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2088 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 \\\n", + "period \n", + "1984-10-29/1984-11-04 198444 3 68422 20056.0 116788.0 125 \n", + "1984-11-05/1984-11-11 198445 3 135223 101414.0 169032.0 246 \n", + "1984-11-12/1984-11-18 198446 3 87330 67686.0 106974.0 159 \n", + "1984-11-19/1984-11-25 198447 3 72029 54274.0 89784.0 131 \n", + "1984-11-26/1984-12-02 198448 3 78620 60634.0 96606.0 143 \n", + "1984-12-03/1984-12-09 198449 3 101073 81684.0 120462.0 184 \n", + "1984-12-10/1984-12-16 198450 3 123680 101401.0 145959.0 225 \n", + "1984-12-17/1984-12-23 198451 3 101726 80242.0 123210.0 185 \n", + "1984-12-24/1984-12-30 198452 3 84830 60602.0 109058.0 154 \n", + "1984-12-31/1985-01-06 198501 3 85489 65918.0 105060.0 155 \n", + "1985-01-07/1985-01-13 198502 3 97586 80949.0 114223.0 177 \n", + "1985-01-14/1985-01-20 198503 3 213901 174689.0 253113.0 388 \n", + "1985-01-21/1985-01-27 198504 3 424937 390794.0 459080.0 770 \n", + "1985-01-28/1985-02-03 198505 3 637302 592795.0 681809.0 1155 \n", + "1985-02-04/1985-02-10 198506 3 565825 518011.0 613639.0 1026 \n", + "1985-02-11/1985-02-17 198507 3 471852 432599.0 511105.0 855 \n", + "1985-02-18/1985-02-24 198508 3 389886 359529.0 420243.0 707 \n", + "1985-02-25/1985-03-03 198509 3 369895 341109.0 398681.0 670 \n", + "1985-03-04/1985-03-10 198510 3 353231 326279.0 380183.0 640 \n", + "1985-03-11/1985-03-17 198511 3 276205 252399.0 300011.0 501 \n", + "1985-03-18/1985-03-24 198512 3 245240 223304.0 267176.0 445 \n", + "1985-03-25/1985-03-31 198513 3 197206 176080.0 218332.0 357 \n", + "1985-04-01/1985-04-07 198514 3 134545 114400.0 154690.0 244 \n", + "1985-04-08/1985-04-14 198515 3 63881 45538.0 82224.0 116 \n", + "1985-04-15/1985-04-21 198516 3 50362 36451.0 64273.0 91 \n", + "1985-04-22/1985-04-28 198517 3 34053 24366.0 43740.0 62 \n", + "1985-04-29/1985-05-05 198518 3 40555 29935.0 51175.0 74 \n", + "1985-05-06/1985-05-12 198519 3 43154 32821.0 53487.0 78 \n", + "1985-05-13/1985-05-19 198520 3 27896 20885.0 34907.0 51 \n", + "1985-05-20/1985-05-26 198521 3 26096 19621.0 32571.0 47 \n", + "... ... ... ... ... ... ... \n", + "2024-04-15/2024-04-21 202416 3 28882 23305.0 34459.0 43 \n", + "2024-04-22/2024-04-28 202417 3 27042 21410.0 32674.0 41 \n", + "2024-04-29/2024-05-05 202418 3 22409 17653.0 27165.0 34 \n", + "2024-05-06/2024-05-12 202419 3 15375 11274.0 19476.0 23 \n", + "2024-05-13/2024-05-19 202420 3 20057 15780.0 24334.0 30 \n", + "2024-05-20/2024-05-26 202421 3 21963 17556.0 26370.0 33 \n", + "2024-05-27/2024-06-02 202422 3 33772 28274.0 39270.0 51 \n", + "2024-06-03/2024-06-09 202423 3 35875 30610.0 41140.0 54 \n", + "2024-06-10/2024-06-16 202424 3 41110 34671.0 47549.0 62 \n", + "2024-06-17/2024-06-23 202425 3 47204 40300.0 54108.0 71 \n", + "2024-06-24/2024-06-30 202426 3 44219 36956.0 51482.0 66 \n", + "2024-07-01/2024-07-07 202427 3 47364 40234.0 54494.0 71 \n", + "2024-07-08/2024-07-14 202428 3 54342 45781.0 62903.0 81 \n", + "2024-07-15/2024-07-21 202429 3 39560 32592.0 46528.0 59 \n", + "2024-07-22/2024-07-28 202430 3 36393 28593.0 44193.0 55 \n", + "2024-07-29/2024-08-04 202431 3 26035 20267.0 31803.0 39 \n", + "2024-08-05/2024-08-11 202432 3 23187 17532.0 28842.0 35 \n", + "2024-08-12/2024-08-18 202433 3 20623 15349.0 25897.0 31 \n", + "2024-08-19/2024-08-25 202434 3 26717 21003.0 32431.0 40 \n", + "2024-08-26/2024-09-01 202435 3 27404 22036.0 32772.0 41 \n", + "2024-09-02/2024-09-08 202436 3 33657 27906.0 39408.0 50 \n", + "2024-09-09/2024-09-15 202437 3 56460 49319.0 63601.0 85 \n", + "2024-09-16/2024-09-22 202438 3 91786 82903.0 100669.0 138 \n", + "2024-09-23/2024-09-29 202439 3 91660 82937.0 100383.0 137 \n", + "2024-09-30/2024-10-06 202440 3 84965 76555.0 93375.0 127 \n", + "2024-10-07/2024-10-13 202441 3 79435 71386.0 87484.0 119 \n", + "2024-10-14/2024-10-20 202442 3 67785 60009.0 75561.0 102 \n", + "2024-10-21/2024-10-27 202443 3 46572 39928.0 53216.0 70 \n", + "2024-10-28/2024-11-03 202444 3 36671 30600.0 42742.0 55 \n", + "2024-11-04/2024-11-10 202445 3 61119 51039.0 71199.0 92 \n", + "\n", + " inc100_low inc100_up geo_insee geo_name \n", + "period \n", + "1984-10-29/1984-11-04 37.0 213.0 FR France \n", + "1984-11-05/1984-11-11 184.0 308.0 FR France \n", + "1984-11-12/1984-11-18 123.0 195.0 FR France \n", + "1984-11-19/1984-11-25 99.0 163.0 FR France \n", + "1984-11-26/1984-12-02 110.0 176.0 FR France \n", + "1984-12-03/1984-12-09 149.0 219.0 FR France \n", + "1984-12-10/1984-12-16 184.0 266.0 FR France \n", + "1984-12-17/1984-12-23 146.0 224.0 FR France \n", + "1984-12-24/1984-12-30 110.0 198.0 FR France \n", + "1984-12-31/1985-01-06 120.0 190.0 FR France \n", + "1985-01-07/1985-01-13 147.0 207.0 FR France \n", + "1985-01-14/1985-01-20 317.0 459.0 FR France \n", + "1985-01-21/1985-01-27 708.0 832.0 FR France \n", + "1985-01-28/1985-02-03 1074.0 1236.0 FR France \n", + "1985-02-04/1985-02-10 939.0 1113.0 FR France \n", + "1985-02-11/1985-02-17 784.0 926.0 FR France \n", + "1985-02-18/1985-02-24 652.0 762.0 FR France \n", + "1985-02-25/1985-03-03 618.0 722.0 FR France \n", + "1985-03-04/1985-03-10 591.0 689.0 FR France \n", + "1985-03-11/1985-03-17 458.0 544.0 FR France \n", + "1985-03-18/1985-03-24 405.0 485.0 FR France \n", + "1985-03-25/1985-03-31 319.0 395.0 FR France \n", + "1985-04-01/1985-04-07 207.0 281.0 FR France \n", + "1985-04-08/1985-04-14 83.0 149.0 FR France \n", + "1985-04-15/1985-04-21 66.0 116.0 FR France \n", + "1985-04-22/1985-04-28 44.0 80.0 FR France \n", + "1985-04-29/1985-05-05 55.0 93.0 FR France \n", + "1985-05-06/1985-05-12 59.0 97.0 FR France \n", + "1985-05-13/1985-05-19 38.0 64.0 FR France \n", + "1985-05-20/1985-05-26 35.0 59.0 FR France \n", + "... ... ... ... ... \n", + "2024-04-15/2024-04-21 35.0 51.0 FR France \n", + "2024-04-22/2024-04-28 33.0 49.0 FR France \n", + "2024-04-29/2024-05-05 27.0 41.0 FR France \n", + "2024-05-06/2024-05-12 17.0 29.0 FR France \n", + "2024-05-13/2024-05-19 24.0 36.0 FR France \n", + "2024-05-20/2024-05-26 26.0 40.0 FR France \n", + "2024-05-27/2024-06-02 43.0 59.0 FR France \n", + "2024-06-03/2024-06-09 46.0 62.0 FR France \n", + "2024-06-10/2024-06-16 52.0 72.0 FR France \n", + "2024-06-17/2024-06-23 61.0 81.0 FR France \n", + "2024-06-24/2024-06-30 55.0 77.0 FR France \n", + "2024-07-01/2024-07-07 60.0 82.0 FR France \n", + "2024-07-08/2024-07-14 68.0 94.0 FR France \n", + "2024-07-15/2024-07-21 49.0 69.0 FR France \n", + "2024-07-22/2024-07-28 43.0 67.0 FR France \n", + "2024-07-29/2024-08-04 30.0 48.0 FR France \n", + "2024-08-05/2024-08-11 27.0 43.0 FR France \n", + "2024-08-12/2024-08-18 23.0 39.0 FR France \n", + "2024-08-19/2024-08-25 31.0 49.0 FR France \n", + "2024-08-26/2024-09-01 33.0 49.0 FR France \n", + "2024-09-02/2024-09-08 41.0 59.0 FR France \n", + "2024-09-09/2024-09-15 74.0 96.0 FR France \n", + "2024-09-16/2024-09-22 125.0 151.0 FR France \n", + "2024-09-23/2024-09-29 124.0 150.0 FR France \n", + "2024-09-30/2024-10-06 114.0 140.0 FR France \n", + "2024-10-07/2024-10-13 107.0 131.0 FR France \n", + "2024-10-14/2024-10-20 90.0 114.0 FR France \n", + "2024-10-21/2024-10-27 60.0 80.0 FR France \n", + "2024-10-28/2024-11-03 46.0 64.0 FR France \n", + "2024-11-04/2024-11-10 77.0 107.0 FR France \n", + "\n", + "[2088 rows x 10 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "sorted_data = data.set_index('period').sort_index()" + "sorted_data = data.set_index('period').sort_index()\n", + "sorted_data" ] }, { @@ -180,9 +3153,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" + ] + } + ], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", @@ -200,10 +3181,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "Empty 'DataFrame': no numeric data to plot", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\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[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\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 1730\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\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 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\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/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot" + ] + } + ], + "source": [ + "sorted_data['inc'].plot()" + ] + }, + { + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ + "La Collumn 'inc' is represented by a chain of characters, so it will have a problem in the week 19 of 1989. To correct that we need to change the type of the variable 'inc' to int." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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9zENxnPLB11X+mVrS1o4Jw7n6RFL5CRQVo1DrwYGf1tTIfPvPKzDr8vvrXQxGgwXKKKHmDmy1lleM+wYFecV5pO17hjA0ki853pKymvzwSH0ESjaEhlLrEHBVpmYTKz9/ytIyC82kno9yWKA0CQ3qQol036JJx2PHxgpEec3774X41G2LS45nlEDJlQqbWhBGG6jbumxNOv1l0DBwYOoDC5RRQjOu5RXiyrLztvK30v11dWnkUiZpNfl6TR70G02rbZVr7UNRNFtUnRoc9GdzdS4Jo2CB0iQ02p7y9n2rmCbqM/ktXZJMkLwmWt5xCbOsSr0sOM027ydl/5ZNVvBRDAsUJhJxXuFgrUbEuoefWSmVVJ1QNImyadcAVm7dEykt4K992HvM1Klnr5dmFBeWJ40DC5QmoWH3EImxllegUz7iM2VlBJcp/4Q8mIvYC5123WN4zw//Gq1gCNdpRyna0Ege9yzdHEsYNWu/zE75xiHufihMjQjqKJrJ7h5c1Hi+BBW1lE6UjpdS8li9zCRhbhulTr/38Cr87K/rML4tgzMOnxKhZM2roTCNA2soEfDq3G9/bkPdJonVTUGJdOOQJq+oGooSKMlSFUX5UKJqKHHxE2TFmfLl57ttzzAAaxmVyNShSv751hdw3V9ejZS2XisLMN6wQImA1wv/jT+9gvf/+KnaFkZSvyiv8tOGXnolYg83KKN+WuUkRh1lBqufhuLnQ5GaWYyyxWkG9aiSR1Zuxw2Pr42VB1u8GgcWKBFoxKiSJnKhVD3KayBrzUswCRSVZ700lDDPFOW5bYd+jJbQbGHDtgBmDaVhYIESgXo04IYNG67CPJS4zzJoC5TS5l2vCKpyNKMoHXsltlIxVc2mXQN1W0gzLPvazp6NDAuUCDTiiKjWL1V1w4bVddHyV9pHOmkQKNGyjE059v44ylM8k1dp4tOuewz/cNOz0TOtAQ1oMNhnYYESgXqYvMKu0Ftr4pi8gtfyivZUfp12cVmX2tZYIoR5Jo6T2d5Bs4LCSC1Ps2pbX/RMa0ADju/2WVigRMA0J67asfCNa/KqfBp1Ouoz+aWrl58g1ErCMSY2xtnwTOG+baOvQsxRXo0HC5QI5A0N2HSsltT6pSoqFxF8KCHTRH2iMBpKrVHrdFVt6ZUKzLJ3/y7N0lE3Szn3BVigRMDUKVTbDNaoM+Uj3dcOG/a3eUXtHO2wZINNrW6dj62hhHDKR4nykjeopIbS8P10BbZQYCoLC5QImDq6eo+S6rfBVuXTqPqNq6H4iata/1xh5u3E86GUnaQE932bJXqq3u8eU4QFSgSMJi/2oVQ8TdRn8vspbKd8TAFcbmdLZWgosTrICiZtln66Wcq5L8ACJQIm4RFx8dqKUeuXKpZpJfzUxmj5Kw3FMGqvlCZXbn2HuT5OpFbRKR/Dh+K6cbOM/JulnPsCLFAiYBIe1XbKB4cN18vkFX1io5eZJm6Ul5+Gon67uD9X1ORh7hvH5FXJsOFatqg4kW08D6VxYIESgVqavMJ2FLV+qWKt5VXh60rS+WoolaHcTl+4Pn2vrZf50vV/LUf+8QQhS5RGgQVKBIwmr3o75eu2fH2ENFWeKa9+HlMUme3wj1ld1fi9y/GzlKSNEeXled8aNqk49ckaSuMQW6AQUZKIlhLRn+X/XUS0gIhWy88J2rVXENEaIlpFRPO14ycS0TJ57kckjclE1EJEd8rji4holpbmInmP1UR0UdznKAdTh1jvBSPrdfdIa3kF5hk9byt/n3kokXI05BMxozDPFKUpxTF5eWmbtWzS8ZabYYnSKFRCQ/kigJXa/5cDWCiEmANgofwfRHQkgPMBHAXgHAA3EJFaDvZGAJcCmCP/zpHHLwHQI4Q4BMD1AK6VeXUBuBLASQDmAbhSF1zVph5RXkE01UtlaxChLisbW0MxmbzskOS4UV7lJgi+pNix1zZs2GvV3lr65eIsN8MaSuMQS6AQ0QwA7wPwc+3wuQBuld9vBXCedvwOIcSwEGIdgDUA5hHRNABjhRDPCutNus2VRuV1N4AzpfYyH8ACIcQuIUQPgAUoCqGqUw+TV1An00xhw/Y8kYBeMGqd+tVVpeopctlC5R0pa5l/nCivypUj7r3LS8sSpVGIq6H8AMBXAOhxT1OFEFsBQH6q/UinA9ikXbdZHpsuv7uPO9IIIXIAegFM9MmrJhijvOo8TLrpiXibFNWS0O9/ZB+KFFg+WdY6yqucjj5aBxl/ccjKlCMacSLbWENpHCILFCJ6P4DtQoglYZMYjgmf41HTOG9KdCkRLSaixd3d3aEKGoTJ5FV9DcX//CMrt1f1/l5EChuu8HUl6XwS1ltD8XuoOCac4gZbEdLKz9KZ8hEyi0g8p3xtJYoQAjc/uRZbewdret9mII6G8nYAf09E6wHcAeBdRPRrANukGQvyU/V0mwHM1NLPAPCGPD7DcNyRhohSAMYB2OWTVwlCiJuFEHOFEHMnT54c7UldmBpwvsoTGxt1EBZtpnxtorxMjgX128WtT1Hm7x1qYqN9bZQorzJu5E7r4dCvZUcdR8uotUDZsHMA//3Aq/j07WHH0vsOkQWKEOIKIcQMIcQsWM72R4UQHwdwHwAVdXURgHvl9/sAnC8jt2bDcr4/L81ifUR0svSPXOhKo/L6sLyHAPAQgLOJaIJ0xp8tj9UE01L19TZ51YtqzEOJ6zgPY/KKK1Gili1MutprKB5O+Zr6UGL4fipYjjCoDdz2DuVqfOfGJ1WFPK8BcBcRXQJgI4CPAIAQYjkR3QVgBYAcgMuEEHmZ5rMAfgWgDcCD8g8AfgHgdiJaA0szOV/mtYuIvgXgBXndVUKIXVV4FiP1ccpXPs/uvmEs3diDs4/aL3IeUYpV7bW8/J3yIUPMAii30y/n8jjzULzYMzQCIYBxbWnPa9x3bXQNJY5Gx1SHiggUIcTjAB6X33cCONPjuqsBXG04vhjA0YbjQ5ACyXDuFgC3RC1zHBoxbDgKn/jFIrz6Zh9WXnUO2jLJ4AQGor3MKsorzFXl4x82HDHTkntEjUALcU2knP3zP+a/HgYArL/mfaHT1rJFx/Kh1GsdvQqs8Dza4JnyEWjEtbyisKXHcipmYziAqqGhFCOxKt9pV2rUXf7ikMEJYs2Uj7PBlkfaxo/yCt5WmaktLFAioPba1qn2FsDVIJW0XshctSMKXFQ7ysvPh1Kpn6kawq4Y+hvDKR8Br+2Da+tDiZ629q9e873rtYIFSkj0l3wgWypQ6r0fShRSSevnj7V3eKQoL+szcHZ3TB+KaeJkpUbdVfWhxJDvkZzySjNyPVRtl16JZeirWDmYeLBACYne3geypdEd9d5TPgoZW6DEMXlFmYcSzuYVPcrL71yFTF6Ro7wqc42bOHupeN23tkuv1CctU1lYoIREb7NDI6UdcLUcg/ZCiVXIW5m8hnMxBEoMDaUaeQMBYcMVc8qXd32oeSgxfCj2fSKnNMxDqfbcKu2GUUzGcbZMrgTsky+FBUpI9MZvMm9VXUOpQv7JCjg1o8d4hbgussnL+1ylRrNRfWZhTG41XxzSw3dTbQ2lXsK92e7XTLBACYnehsxbAFenldWi7cYSKJE0FKVBmHtB4fosl+Lik97n4lKN8UMldiCMI4xq7ZTXs4/XBmvbw4dd3HRfhAVKSPQ2W495KNXIXb0PccpezVFs3Egqvw224lJNH0o1Jjb6pzXft5YraDfT0ivNOOesVrBAiUAtTV7VHH1VwpEbhdA+lIj5+3UwFTN5VbHOIk0lqcBguWRiY5XbhV6H8ebeVKhAIWnC+JuawQIlJPqItJYmL/v+Vcy+5iavkKIi/uKQpnOVChuOpz2ZiLcFcPS0ipprKFo7iDNwqr0PhSWKFyxQQqK3oVyVNJTuvuGSSYbVbLqJipi8IqRRJimPUXWxc/HP/e4lm/HjhasNZfJOFzLrQKrZp8TRUKItLGnej77a3ab+nNGaYH1myrPJyxsWKBGoxmrDe4dzeOvVj+CqP68wnq+Gr6K4ymyMTCK8zGGTBJXr33//Er634DXP/M1hw9bJ+FsAR03vnc5r1d9yiKPduItWbWe3U6A0k1Pe+mSXfCksUEISpKHEHSWpyZIPLNtqvK9X9pWxnddYQwk4H9e3owS+OcrL+RmVag5S40V5RUjkYWoLk9etz6zH6dc9FmlApd8vzpwXNnk1DixQQqKPaKuxwZaaE+J+MYOabjoR/SeME+UVp9Mvhg37n6/mTPm4o9rIUV4hkkUL/Y2+Dljxvs7/wzSLb/15BTbuGkDf0Ej593Pcq/J+vMFsHid+awEee7Wyu5k247p9tYIFSkgcGoph7augRtY7OIJlm3s9z6sOwaT9AD6CJc6EthhbztZCq4k/U960Y6PzMyrVmEVeiailSP4X+VmqoQRnptprlBWr9fzj+Y3MiTfs6sfO/iy+8+DKsvJ9YNlWfPJXL3ieb8ZllmpFNTbYGpUEjaaCGtmlty3GonW7sObq99iLMuqoPEsEU1DjrUDbjjeqjeJDUSYpf2kY10th0iLUsbgaSuQoL59zlYnUipzUsMFW+LSmQVYQccOGFUG/ZblZf+43fwuVH89rLIU1lAgY56EEvH0rtu4BAOwayBrPqxfKU0OpwqDIjvLyyXz1tj6serPP83wkH0pIGRl9YqMSGt73bmSrRTyhUDnzZZi8VBvyW2D0oeVvYmvvYOmJmAIlaGWBOJM9/eAoL29YoIRE79yiOOU7MpYy6LUPtUruzjuo6caJVgoTavru65/E/B886X3/KOYydf/yk4bC9pOYzsmHjetYreaOjVHy9hOi5eZR/D84TVJKFD+B8unbl+CDNzxTctzhlI9Rbq/68lpSJnS+HoXyM6nu67BACYnD5BUjbNir8Qc5i70ERyU0l5ovDhkyUdyJjaa6LJ4zp83mCrjwlud9/V16PtUgTp2anjlI0/MSwGHqX+2ls3h9jzlvWVFbe4dKy1VGGf3w+i2UGS5q3l7WAo7y8oYFSkgCJzYG9DDFiCrzebuj87qvR/ZxmrY97yFWlFeE0XRAqdW4z69Od3uYDgFdOHuf8+oUXtvWhydf68ZX/vCybxkjm+P85qHEqlPnp07gYMc2AzqvC9NxjmmxNO9HPSKp/MyplVrLy6u+cjJyImrWXvVWtz3smwAWKGEJsPeGHbV4N9JoJos4I7s4s6vjYBfZw2KQUCHUPs/2oRtLTSjFGzg+XPf2r+ewe7NXdR5KhMyFh1AAvEfairxHnYRp0+84dDIA4NCpnea8fe5d7bW8/MxwSzf2BIYTe7U/1lC84SivkOgjS7OGEi6fIJOX1329mnA8DcX/3n5UM2zYa0tanbXd/Z7n/MyHwnVNyb1D2sWrs6e8RaQwbpiFgpVfRJNXGfeN0vkGze0Ki1daZY5LJUp/0w9In876a97nmW/eI3KNBYo3rKGERG9Dpo4ubCPztstGKlY8KLrJSxHp3QpIRCE0FD+8zIfWOX+TV1iq+XN5le3pNTuwYadZkAqfZw7SULz8L2GEptJAvDQRPw1Fzz7a/Bn/eVTKh5KIGN+b87Bt2UuvsE++BNZQQqK3WVND018cIUTJHIsgv4CnhuLxsrvPR6EiGzpF6FqDUiRCaCh+FIWG6Zx/GcJOLoy8Y6PPuaAO6mM/XwTAPKoWPoIyqKzFNmY+7ofKOq5AqYqGIt/TpEFDCUOUZ9rXYQ0lJM4tgEvP640szNIffvnXiorsYR6Q9MnXunHKdxZiaCRfksbrNU94LENTdpl8Ip4CfSgBYq8afYq6d5BGYUKUfCkSlJ+X1qYn8x7QRNdQKhU27IXSUCILlAbzodz5wkZ8895X6nLvsLBAiUDeoKEUHALHu8F5ayjm671Gj9axeA07jg/Fa8lzN1ffvxJbe4ewbkfRVBNUbjsiLuLjFdcCK0X9dEE+lKAqib6Wl3e6lFyXzb2FQRj8THlBGopXVFyYKKxAk5evD6W0DFHwej7llHebvByDP5+68Zr9Xy+B8p9/WIbbnt1Ql3uHhQVKSPQmZGpnTg3FZ1RWrsnLcH/7XMx2XVzLq3oaismME3Q3iunb8ZtrogRBucERJfl4XLZkQw/uf3mr+WQAxVnnETQUn2cO1lBkWo/j1nf/gZB4TL3xAAAgAElEQVTnaN5HNjrX8ooyqHGWwY0SKG4NRY/+8hN4HDZcPuxDCYne7kwaSt4xmvMZ9ZTZSKu6BXDAvcMQZffFoEdSKy9HMf0AetSSYbTu0/E604a7hxsVzvy+Y7yjh4LwC3f1wm/gEX3SbXCb9lyDTt3bT0PRBVas5evN97BNXq5RzXBOEygFgXTSnG+jmbyaAdZQQlLOFsCm80GRS8FOeVOZ4lELH4oxjX1/s207brnsFYUNnZTfpEc9TaUXHAyDyjKSD8XP5BVWE3CdKoTo8IPWoPPTMuM65YMCVlTwjHuHB11gm+4btK0DCxRvWKCEJeDl0geVfqMtrxfMq436b2cbr2GXs0Ogu9wJOxoqpIaC0k7NK218p7y3lqFu6fXMeZ+0OtVYy0udy+YiaCghTV7lBIyE6fDVb+SpoVTRKe8XzQcUTYd+Ji+TIFRXe/tQyizoPgQLlJDobcgUNuxwykexywZpKMal2MOxdGMPlm40rLVUxkz5Edczh90+OGiJej/iRnmZ1/LyHsnr9wwqdTXCilV5veY/hCmP8ZlD+vfcZ8KYcVVRvTQUP21LPxdvuRkvk5fZKa8LCtPvFDSgaVQNRQiBPyzZ7IiorDWRBQoRzSSix4hoJREtJ6IvyuNdRLSAiFbLzwlamiuIaA0RrSKi+drxE4lomTz3I5K9EBG1ENGd8vgiIpqlpblI3mM1EV0U9TnC4vChGNpTPsDkpQgaGZfcN2SZ/PjADc/YM4N1yonyco/WyjVLmXwo1dICfDtNW0PxOl++xmXCLYDDpFNnRnLlP7ef7ycXVqAI9/96x2tOo9ptFH+DLjgjrQ4QUkNxz5QPeleLUYbNpaE8/lo3vvz7l/C9h1fVrQxxNJQcgC8LIY4AcDKAy4joSACXA1gohJgDYKH8H/Lc+QCOAnAOgBuISLnDbgRwKYA58u8cefwSAD1CiEMAXA/gWplXF4ArAZwEYB6AK3XBVQ2cPpTynfJBcwy8OrIgm38cwixxooiq/ptG+n7LhFjHZScVM8rLz5/gde/QDuwAJSLahlNWGi9h5Ic9Wg8Y7Pi1I3d9hYlcVO05isnLoSl45D/r8vtx/YLXjOeCzJd/XmZF27lNXvo7aBIatr/Ta6Z8QBtZv6Mfv1lU+/DePYPWNsxv7hmu+b0VkQWKEGKrEOJv8nsfgJUApgM4F8Ct8rJbAZwnv58L4A4hxLAQYh2ANQDmEdE0AGOFEM8Kqye5zZVG5XU3gDOl9jIfwAIhxC4hRA+ABSgKoaqgtyFTEE6QUz7onGcSn7YbZy8UILzZCijt5IIcom7CTvwEio8cdukVryVDTPcJKnf41XH9yxZlozR1zhQ2HHYJeuNuoiHbpvtMqLBheZHX8/oKlJCa0w8XrjbfO2Bw8NKm3QBKTV5Bc8aCfSj+v8WHb3oWX7vnlUjRenGIs1p1paiID0Waoo4HsAjAVCHEVsASOgCmyMumA9ikJdssj02X393HHWmEEDkAvQAm+uRlKtulRLSYiBZ3d3dHe0A4fySjhhLT5OXprDfcv1gmz9uEIshs5dhUzPVyBc3ncN/D1Hl4+SnskadPPZ4uV7nVr3fn77fmWpBPwK9cQLC5LsrkRFugGJzyQZqTnxkxbEi7u40VAkbyernKnV8FOOvIdFngkjEhr3O78fS27GvyKnfwJ1FbK0TRUpud2AKFiMYA+AOAfxVC7PG71HBM+ByPmsZ5UIibhRBzhRBzJ0+ebLokFA4fiqFF6S+cXx8btB9K6X1FSf6VopwXx2u0FdYqFXZ2spWnfGafPnlMS3HygNeSIX47a3oVIazTOmgU6FWn/hqKGu2bgj58b6eN1g3PHFI79BLMpnMKe6Z8gMAxEaShBE7IlOeDmqA7a2dbLL0+nbS6xWGvNh9SgGVrraHIz3qKsVgChYjSsITJb4QQf5SHt0kzFuSn2nRgM4CZWvIZAN6Qx2cYjjvSEFEKwDgAu3zyqhpBIY5hRnOAt13We6dGlc6UxvM2oQhaZkR/8dwCJch+bd/DILTsrDxUFHvk6ZO3Xo3uq4p1ZhIo/nmr384UnVbOhlAjUaK85GfWMLINqmf1rKbLwkZTuc+F0Wxsp3wkDcW/PgO1soB7zOxqM57XBbZJeLekrG5xKGuOlir4tBGg+F5FNXkNZHO45FcvYNOuAfP9w9qM60CcKC8C8AsAK4UQ39dO3QfgIvn9IgD3asfPl5Fbs2E535+XZrE+IjpZ5nmhK43K68MAHpV+locAnE1EE6Qz/mx5rGrobdK42nCABhN2x8bS4/4mhTgERbPoz+G1132QUFMvl9HB66mVld7fTcGnsytOtiutbPtSr/r2vachHw+8TF5+yWynvMHkFSRQir4hQ74+GorvuYA2raf3XhzSo8Bw+uXMGop/h+z3zAAwmC3IvN1l8heUGamhDHqE3waHylufUQXKwpXbsfDV7bjmwVeN54MiQuu5rH6cpVfeDuATAJYR0Yvy2FcBXAPgLiK6BMBGAB8BACHEciK6C8AKWBFilwkh1C/2WQC/AtAG4EH5B1gC63YiWgNLMzlf5rWLiL4F4AV53VVCiF0xniUQ1fAyyYR5BnZIB2PUmfKm83Gd8kHx9voLXaqhlCfoTKNdr5RF00+0zt0vSizQh+LzOOVsCBVttrv1aWojQaN1v+dyRDW58nGYat15hogOywf8Vn5CYdixAnX5z+y3OgBgjfRN5531UZquRa7Fsnc4Z8w3rFYeJfwbKL6XfnOlTMvFKOtH1P1fKkFkgSKEeAreftUzPdJcDeBqw/HFAI42HB+CFEiGc7cAuCVseeOi2mAqSYH7ofiOrMs0Dfh1MnFNXokAH4pDQylxyluEHa3ppj7bkRsw0vJftsNHQyk476NTvLc530rt3+HlkPU1Oflc4xSgpfvt+Alpp3/PJVB8zGFbdg+W5O9GPadnu/YZpA+N+DvlgxbJDDJf2mXz0cpM73JXRwbrdvQ7Vsj2Sm9CdehRfShBe/J4Pa9aYaGeGgrPlA+N9SOmEmTeD0X7kaOFDQe8PIZ0MeWJ3fCfXrPDeD7n9+IFvMxuHEvTBKQNE4jg59PyW18qWEOxjhujPsqo8EgLPPppVgEDFnU7Y9iwj6/Cz/F+85Ove6Zzp/c0m/pUmj6jO5oPxX9woNqsW1A6ggF8fqZBTx+Kb7Hs9yqqycveYK7MQIdszB0qKwELlJCo3zCdTJj3Qwlr8ipzJFd8YUvPxY03V6kXrTNbC51O+fJH3IC+U2Wpvdz7meVnSJNXyRpU9j29NZQwgQil9wz3G/vl45eqqI3639skKO3fwdQx+5TboaH4lC5Iiw3ysZjQV/2N4kNRWZt+CiGE58DFYU3wMbV5+1BUGzI/W9A8liCC9hryqhalobBAaQJUI0olKTBs2PQO2c7pqD6UKmgo5XSKUU1esP002n2DTF5laijC9YKpcps6XlugeNRe2LDhwJnysWa7+7cv08jXz+TlN9gpGDRHY9kC2m2UHRv1RTBN+Qd2yLZA8f6dgdLfyulDMQVuSIESEOXlRdywYTv8t0ytj01eTYT6DVOJhO/I1/3d7zodryRhJhC2phORtjkNUnAcGkrJTPngcgHaaE33oQSk9dMwFH7agupw/Z3ywfn6nQuSo56doU9Cv85ZL1a54dB+Jh79d/H7Kb3NSgECxSdTvcM1JY+6dbE7bamGovvzSvNVZQ7SULzaCsU0eRVzNb/TXnX9Zu+gT6rawAIlJKrxpJNkfEnCmkO8l6/311D8OhmK2IQCO8VKaCj2dXqn5t+p21qZbz2a8waKI7V8QXjaz73yVqGmfuUKKpt+n3LwMx8FmR/9liHxa5thZ9FXw+SVDTB5BflQigMTw319fEN+UW9AsW0F+VC8ylfceTOaQLFXvPZ4rU119eKm3bhVbg8coelVDBYoIbE1FI+w4bAaiudGRAEDWuPLLg8lKJo/JXi2tzYBzGtiY0DrtZdeMTiGg4Rr1L00/Ea+9uxqj6y/es8yR7lN5XJ/N+HVmfj5KfzK5vSheJu8jKN1n0UYHRNEXeemjWv1TOcus5cm4idYgzQUvw65UBC+Aw9/DSVAoMjrF28wbPmA4DZkR3lF2NcGKD631zDRVOYVb+zRztd2hr4OC5QQbNw5gMdetSb8pxLmsOFCQYvOiBFd5Mavc1WdU9Q9R4JkkP5Sumd+h13LS2FyhAabvLzz05O6O0LnBkrOTPxCaINw3tP/2igrJfstBa8/hsmcZu806ZMvUNpxO6MTi8d/vHA1tvYO2f97PW+QycuvfQT5UEIPzAyX5X2EaJBTXr/etLdIsMnL+gwKe/bC9PsGCUHHnLE6qii8p3wIzrr+Cbvxp5MJFETpXIC8EEgnExjOFTw6hGgvnm3yMiko8hiRX0SId+Ny76LoFkx6w/aa+R3Udu0orzJMXkEvrPtciYbi2jNcxx2+nPSQxaZIGT2noOfWOxOnZuOdxk9zC4ry8lt9wG+uid7xZvPFzvNHjzpX+A0KJonilB/JF5BJJZDNFQK1DL98jeYyXVC6Tjt9Sqa6LH7vGchi2rg243mv4sX1oSjhoDfBnCFKUkdv81EWJq0UrKGEQP+xlPO7ZKRXEPaSDX5RYJ6mAa+wXPXp0xP56Se+kVL6qDegE/NayyswbNgwGz9IYKjD/jPltU7S1Yvqnbk7j7Dh3aYgh6hhw2H9KX7mI/2YqcPwM//4jcid0WPF722uqdhBcyKi7IeSzRXQKtfNMl3mG9ziM6Bwp/WbzGls99qxfsNs+aCJuarlRBUoaj6J7hsNWiFZ9/fUc5VjFihlkk6alyspCIG0/XJ4j3rKffH8TV4WRBRpXoVz0zD/EaJbfQ/jOPcqh3rPvJKqw2HnobjL7nD2ujWUkELBJFD8zGzuY46oNr1z87yjvylQz3ubYQOl4iS+0nydARGue2pl0+utLeMUKF7Pq5J7+wZ9BEq+YC9zYvb9eHfIfiYtd3n8l14xD6Q65PPrs/kVQcsOmfyG5WB67qCVmYdyeeO1tYYFSpkUNRTnj/by5l67IRijcOSPXO7e234RJaph+0UM+zUu/RFMoynnPJRoJi+TQAwyk4RxyvttT5t1+FC8NRQ/WWg0eZUxKvbTkkwIoTmZjUEfxe/b+4YM572FkcN06cpcv16vN7eGYgyvDaHt+Q3Ss7kCWtNWF2RK7ucLCAp3dmii7lDpvL/5KC8E2lssb4ApdDjQ5CU1C72TLwf1e+lNUH8/TXWqRydGmQNVKViglEkqUaqFrN7WBwDYM2Spx0anWsCoJmhZez+tx28OSt5nlKRnadRQfDrFsPNQTAIxrMnLf2KjVk5X3WVzBU/zo9++NfpvY6pS5z39R8WOuQ5aPfbIzZfchI08AsxmGHVvU405hIYr8kj/jUccGorTvRrkp/DcsdHgO7Pvly+gJZU0njOV1ZGvQ+srT0MJNnnB1lBMocNB7Ve1nW/eu9yr+L6YtoDWVxUwDf50wRc1GKASsEApk1Sy1IeiBInCb/HIcsMr1VG/TkaNpo2zjX1GK/qLaGqEfnMf1H9BFi912mRmiGfy8u4URvLFka/7nN+oWh+hm+4c5MfwMhHqv8F1f1llyDnYFKeXe+9waSenBIOpTvVyuU04XhpKeybYh6I3rTDzUNy/ha6hmJL7+SCcgspw3keDCXLKCyHQLgWqf5SXuWytmnZnSh+EWqVY11D8Ak0Aa+XmmV1tOPWQSeyUbyZSCZMPxd2hmV4+KVA8fmtP846Peq0atq+G4vPyBGooPvNQ3GXwwmRvDvK/2CaxsBpK3l3/BdsH4NbQ/ATKsI/vxZ2XcVkXj6i4MCHEfhM13cdMI3evhRDd9x92mWGcg4Zivh0tLg3F8PM7Io88nnHEpx5G8qKooQRELpXkm/P+Ha17wfN80G+TFwIdckdQo8nL9gEG/65Rll9R9arX+XCAj2RwJI+2dBLJBLEPpZmwfSiOjsl5janzVUKmbKe8fQ/vjqKooZSmd2gGrgv8orjcZSqZhxIgFNx5mDQUz42C5OHewRHPfIUQRuFeKAiM5IXtAyiJavL53Zwztw3PElBfegc7pIdxhnjBg1ar1u+nh/e605jupO874tZQ8h6CakJ7GgBw1hFTAfhrKJlkwtvk5dBQzKZJ8piY6ydQdP+E32Aok0xEcMrD1lCGDU75oAGPnn+UyY2qr/DK50M3PlOSZnAkj9Z0EukkcZRXMzG21XrRTEuJKEo7X2GPVMp3yivNxqSaW58Jn1/RzzYvPK4zHSuZKR+wdLg7D72+1IsoRGlHov+/uWcQXhSEsPf+dk7qsr4rH4DbN+VcksOtofh3Un6LZbrP676SUBpKgMM/q43ITZ2cPWAxdHL6KNutoeht17318AFd7fjcGQd75qt+x3SSPAcWfuuIZfMFpFMJJIiMz6zv6e5+x3RTkkkLUHVulc1VpnxpW9QpFPw1lCB/qP6uRAkdHrH7imLaYZdgcrfdISlQUomEI93SjT04/brH8Jr081YbFihlMl6O3FSjWvT6Tvzjzc85rnF3viMBIY7WNeaG5zcT2TZ5+Sx37btbn8/oEXA24lKnvPr07yzVc4Wd6Kf+VxPevGzQ+YIwhnCrkVxbCB+K+97lrC1lcpzq99rdP2I87kVei+wxdVT67+PXgZp+jkEfDcU5CnaaVdJJ8t09UN0zkzIvmAr4r1qgNJQEmfPXgwTc9a07y01tRJWnJZ0sebfChA37+VCKbb/klJ1/Rk4hiLJro+o/dMHn1nTcu0kOjhTQlk5aGwBq6f7yypvYuGsAj8qVPqoNC5QymdnVDqA42lqo/VBXnXsUALNNX+H14u0eKHZAjvkMMq2vycvXh6L7QVyqf4BPQH9pSyY2ys+gvlLl62W3dj+X+k9pgn1D5m1Y8wVhOz9NpgHlQ3E/c74gPDcwUgKUPDq4qBpKGCep6jBbU0njqNlh8jKYUd7cY4USm36OwWzBdrJ7aSjpJDmEfj4vkEok7MGK3/p1mVRx9Qg3jk6xZKBVQCZlCS1T+8v6vDdKSI5rS5eM3vXr29JJQ7v3fx8tk5ePDyUgyitXKJpcTebJIJSFwzGIcD1jvyswYyibR2s6gXQy4RC+Y9us90jvX6oJC5QyuPjts9DimryoO8SPnTEeQOlo6qHlb9rfveyuK7fqi7sVVWrV3s1Oeesz6RvlpZlK8qUO2YTPJCy1J7fpfFgfihplOoSqlsT9XOoZxrZZI8S+IfOLkCsItCgtRMvw/156AwDw7Nqd1r1cNygIa4FP073VS9ueTvrupWLdM0BDGShPQ1GdeWu61OZvlc17tLpkQ3GDNFMbGBrJY0J7Rn43+1BaU0mXNiGQTJAdaeRv8vJeIUJ/F9z3zuYtDaU1nXT4eezzjlBZt4nHOje+PW00Aao6b0knytdQ5LNnUgnjxMaiGbfklCxrUYBnK6ShuAcCd7yw0fG/7pTXg0PUoMvPH1lJWKCUwZwpnSVLiSS12D4VGePufP/trpfs76Z5IXuGRhy7JqoXdSQgisYt1IwmL32egXu0LopRNqZOr19qKGNbUyXmiuI8FMNNNdRoK+th8ipZ/Vb+G6Sh5PICrXbZi2Xb3mfNIle5mpzy6YRZAKuR7ri2NIYM8w8cTnmjwCmWY5s2+TCMD0V1Ii2ppFEb0DvFYVcHuX7HgP3dy+Q1To5U3SYcVbbWTNK5HlShgFSAyUu1yUzSbF60nqt4TB+gANZgI51MoDVt7rj9NPshh4bibfJqTSVLNCN9mSSviLokEVpTCaPJSz2T1+BweKSAztZUyTOEoVAQdpt3DAZdg4gfPFJca+3DNz6DjbsG0JaxnPJZxztvpesdNM9/qjQsUEJw2pxJAIDz3zqzaAIwaCitaStixT16nTHBWlyuLZ00Nv5NuwYc/6sGG+g8dM1DMaHfbyRX+mKpeQCmiZWD2TyIgDEtqZIJXqqhBvlQVF14xdF7TTpTUUaeAkUzeen5HT5tLADgq+85ouQcYNWj13psL2/ebd27I2MLU1PZ9OdylwkADpzYjg07B+zOSO8YJne2GJ9H1WdLOmH8rZXQbHd1/AAwcUzG/m50ymfz6GhJIiMXLzU9U1s6WWJiSibIritTv6ieK+Oz5JBeT+7OOSsXh2xLJ42mJb8FD3WTl0kYqbRjWlKlJq9CwbY0GDVRIZBIENoySePExqxP288XrAAcNSAqN2z4qj+vwIOvvGmXU2Ey6ynUMvstqSTGtKTQNzRil02983sGze9RpWGBEoJrPnQMnviPdyKRIHtio+oAUppAmT6+DelEomT0ut/YVrzt4IkYHMnjsVXdJZ3cD+Vo47zj9gdQbDx6Y3fbTIGiXdvWUAx9u/6ylar+xZnKprkzA9k8OjIpjGlNYZMr4ipogTz7HjJfr9FmSQSOfKgJHVYn6W3yMncKqtNSwRPuzqRQKEaHuWdYf/v+lda92zMlo2l3uU0mQnVs5gTLz6ZmtCthvf+4VodGq6NMI62ppIeD2jrWnkmVCBT1G3dkkp4aSms6iZZ06YjbHsmnE64OXCCdSNjt3W+yrhIoxkULtfu5Ny/L2hpK0qgJ6Mfc75Q6N6E9YxykqY68vcXglM8L3zJbW1GQVS5T3jnl1yw5ZZdF+S7cg7ggfvXMekc53ff84fnHeaZtyyTR1dGC4VwBA1IQqnp4as0OvN69t6yyRIEFSgimj2/DgRM7AAAdMvpjwDVymXvgBBCRjLJwNqKBbN4x81i3Z27bM4SHV2wDABw9fRyAYuNR+bRnktgzOFIyIlIdj9/Lob9sJap/vuiHMKUdyObQlkniwIkd2OvSFNQLHrRskDLbue3z7mewz8mXqKtdCRQPp7xH2ZUwNpkchBCW7yVV6nvRGd+exkhelHRUYSOtlHlJtRF1n7aMWUPVy9mSThjDqdX9xrSUaigqz7ZMyrxwoLSvt6SSJSNddX2bKxpKaSjKNOQ3H8Nvle1BLZ1bSGfz1qCgJZ10zNtR6CsCuE3FQU559SwdmRRyBefOnXoUVulkX2GXqy1t1lBUfZvr2tn+ou4rr8pZvKeVzzsPnYKj9h9rl1V/roHhHCbKgdiu/iyefK0bS7RNwqJsE14uLFDKRPlJVNjeQyss9fTWT84DYGks7tF+7+CIPWIBgN1aBNB2beXYLtkYVINVjejQqZ3I5gt4fUe/I1/VoFUHaQpl1UeF7nLlCkU/hMnWu7M/i4kdGbRnSkdqYTWUEYOGon//2M8W4V3ffbx4zqWh7PFxyrca/D/DtoZipdcFv3q51W/h1bkrE+JLm3odx3Ut0SSMlP9GBRSoNqLKML494znRTXWQHfb8GWf+I/bgIlXSSalOrz2TNPrRXn2zDy3ppPRVuH9H67Mtk3Ros8qHooS2228DFIWMWkjR1HkOyugj/RmLz2Rpi16+Cn3NMnfbVmUd25byFShqIOderNPL5KXyyvgIOvUbmgS/eg7b5BVx10bAaeZTgT2ZVAJ/d+z+8l4Fh2l2z1DO7kN29mdx4S3P4xkZnALAtkZUExYoZaImPK3dbqmPr2zZI49bL5Vlw3SOxHYPZDGhPYNfXvxWAECPjADqHRzB3/3kKfs6NWpSjfrd338CAPDWWRMAABt2OgXK/cu2OtK51eu9wzlc9tu/2f+7G3e+4K+hbO8bxuTOFrSmnCYJIYRxBrybbK5gX5f1CA5YtqXXISjV9cpk5Y63V+QKBS3Kq9TWrF4sfVSsnl+97CbbOwC8+qb1m/7DT591HFc+lo5M0mgC+tRti4337t5rCZr9x7d52sK/JrceViPbElOgrLMxLSaTlzKzlJ776+puAFb02+aeQdyzdIvjvOrkJ3e2on84Z3eQuYJAgggtSRWtZNIgrOebJH04bi1GCIFHVm6z61lvQ0MjeduH5xXl1a/9diVhw9kcWtOWFpEviJIBkSqvei8dGnLeMrWZwsOVULQ0lIQxOGO3ZmFw/56qPosacnBAhrvMdjnlM2/bM4TnZdBOJpXAGPlMfcMjDmtHMkHokr/F/S+/UZK/6ieqCQuUMlEN9Bv3LsfCldtKzk+f0IZV24ohwNv7htCfzWNCe9oO3VQRF1ffv8KRVo0ghkcKEELYo4+pY629vXVBtWFnP376xOsAvKNs3M5+90s3kM3bjdPd8Idzeazf0W8JFFcUjtO34+3s27anGOnkmKSWL6CzxbxZqOo4M8kEOjJJo8nL6kCKk8/cPhQiYLzUQvQRnHr5lQahdwZ66O0vL7a0zTlTxjju++NH1wAApo5rxa5+76iZrg7L8a5MNjv3Wtce2NWOXEEYR+Nru/tl2aTvxyWwrn7A8u90GExeyqw0pbO1xOf0xm7v1QYAq2MGgKmdLcgVhC0kBrOWmUx1Qn4axOQxLcZr3GZh/X/VNmd2tVuDMEM70gcT7ra7fucA9h/fVnxnXHWyadcgkgmygyBMIdFJohJBpeq2JZXw9KF09w3b75y7/ReFe7rkvn95ZSvWu6wMOu6VqNW7sEX7DZMJsoXV3qEceuXglAj4yvzDbJPXX1fvKMm/hQVK49GhLet9ya3WiPSDx0+3jx07YzzWbN9r2zfnXb0QgLUpkurkeuQsatWJKNQPns3nHR34W2d1AXAKlJ1ahzbGI0Rxi8uRrr/ww7k8Bkfy9gvnNle8vLkXvYMjeMehk9GacUbh6C/hHp/49tOue8z+7jZ5TQqIdkonE+hsTRud8v/zkLVir/JVuH0oLamELfgHhks1lE6poeij4tuf3WB/nz6+DW+dNQGTxpjLOG9WF1Zv2+sZ4aZG7OreO/YOoz2TxMwuK9pvp48wmj7eukY3r+nP195iMHkpLWNMS8nK1yoS7v3HTLOP6eVWAveo6ZZd/rVte+X9c+hoSaE1ncCkMRmsNTh0ixqKEiiuTr3HEhrve8s0RzkBq1MGLCE4dWwrtvWW7vGydyhXHCy5Bjxv9g7hgK72oknOJcx2D2Yxri2tdcWQYjwAABoBSURBVOyuNqLmbHiYvFpSSaMPZe9wDgPZPGZP6nDUgULNf3KbvEbyBXzm13/D/B88WfKcigelxUGxs38Yx131MO570alt2BrKUM4WQr/955MxZWyrrR2v3l76e7GG0oCoH0xHNWrAChEeGilgZ3/WYco54cDxthlHqcy6wwwoCpThkYLdUL/x/iNxiBwp66Mh9UICxY7V/dJt7nFqKP/1f8X9GW58fC0A2C9Gj9bJ9Q2N4CM3WeaeI6aNtWL5cwXbgal3aO4OTKE/20GTO5wCJVewO1036sVPJQljWkvNh/mCwE1PWGW3TX1a3v3DObSlrc6AqNhhZnMF/GHJZgDWvBrAPxSzPZMyRnoBwCFTxqBvOOeYvKh3TEpIq9/wF0+tw0A2j4lSc9m517njoq5xqJUYVFohBDZqmmZLKlHqlB/JoyWVwPj2dImAVyadL599GL5yzmElz907OIJUgjBLBp30DmbxzNodeKN3CGNakiAiHDRpDP78srOz08s4qdP6Ld0+knN+8FcAwHnHTwcRHOYjZQac3JnB+PY0+rN5h+kym7N8hvuPt7Rzt/a9Y+8wJo1psd8Zt6/j189txK7+rD3nSG8jQ9k82tIJo0BRA5gxrSm0pZMlWtZ2qXUfNLlDXu9sIypSUIVyq3dFrUvn1+YeWu60eIzkBXYPjNiRX584+UAARXNwz0DWfsfV1IQxLSlMG9fqeK4xLSnccenJdnRjNWGBUiaZVAIfO+kAe5QAAFu10dV0GTK6uWfQ0RGcd9x0jG1Ngwh4ft1Ox8x4PW/AanTqZZ3QnkZ7xuoc9dHQdoNAcTuwVSM+Us7N2LTL+r9vaMSeGDVrYgeInKPmP2kjogO62u377ui37jkgR89TOluwdzhnnJOhr4h68OQxjhdpOFfAlM5Wx/VKe1LPMLY1jU6DQNE7eRWyrdT+J1/rxj1Lt2BCRwaJBKE9nbS1hNuf24DvLXjNytswyc/9ou/qz+Klzb3oHRjBYDaPM2TgwNHTx9oRf/rvqzqiI6aNxaFTO2VZnZ2RMl0uer1oXlu/ox+Hfv1B+38l7NTg4YbH19r3vu5Dx8hOzr2OUx5tmSQ6Wy0Htf5cyuzW0ZJEu9RW9HJ191kdsyrb2u39+OjPFgEoanLPr9+FbK5QYuZT7UIJSq911ya0p63fQrvvDmkGnDSmxX6XdK3s6TU7kC8IHDfTWn1Cb2Nru/dia+8QJne22JqALkh1v4LqRHUhPDiSR3smZa0X52q7ar7G2NY0xrdnHAE0QPG9UwLFy+R7kByoKUH2gKZ9vLKlFxfc/Bx+KgdGKp8lG3vs1Z3dtKYT9tJO+0stdvkbe7C5xzLtTRtn/X5E5NBGAUu4nXzQRGO+lYYFSgTecehkR+c+b3aX/V2NFLb0DNompwe+cBqICIkEYWxrGg8t34b3/PCvJfkqn0B337DdUDtaUiAidGRSjhdaaSipBOGYGVa48d//5Gn7/K3PrMfPn1qHw/frxANfPA3Tx7fh3UdajVXZ9K17JjGhPeMYNee1l6w1ncSyLVa0k1LnX1hvdYgHyNG024Sjm1S+fd7R6GrP2Ne8uGk3dvZn7Q5MoezESjiMb08jSYSn1jhtwV+75xX7+0dOnInOlhT+9OIW9A2N4MJbnsdANm+HHLe3pGzHrlpIEiiaI9xCTkeZp55aswOf+80SrJPa5j++9QAcLDsTfVDw6+csk9nn33WI3UHuHc7Zy9v8yxmH4IhplqBR/hAAuOHxNfb3//3oCbb5UgnS66UQBIC/O3Z/zOxqR89A0Rl74+NrcduzG9CaSmKGHMwo/8RIvoBv/Mmqr45Mym5fukDasdcKvNh/fJtt2tpfdk5nHD7ZUSe6P+aN3YO45sFXARS1dl1DueKPy+zvR+4/1pokKM8/s3YHvvVny384ri3tcDIrLv7VCwCA0w+1yrBBPtNwLo8zv2cFq0wa02LfW70bQyN5fFW7t/I36IOtnoEsOlpSJUIOKA4MOltTmDjGmuCqC8oXN1mBGQdPHiPzcmqp7ZkkLpg3E9NlP6DMgMpMCwDv//FTePb1nfiOrD8A+MyvlyCbK2ByZwarvn0Orv3QWxzl2m9sq71Kx7RxbZjc2YIfPrIaG3cNYL+xrfZyQgBw4oFdjrRvkdMRakFTCxQiOoeIVhHRGiK6vFb3Pckl7T9z+sH2dyVQvn3/CmzZPYhUgnDYfp32eX30NGtiu2M0cciUMdhvbCseXrHN7lD0zuk3izbiS3e+iKGRPLr7htHVkcELXzsLx82cYOehOvMr77PMW8qGPqmzBQtWbMNwLm/nnUoQTp0zCRM7Mo6l4tUkxqcvfxcA4MvvPhRAsSP+/O+WAgCOkWuXPaU5AB99dRtmX/EAAOCiUw7Ex08+EGNaU+juG0Z33zDO+19L6K3e3of5R021X3jVCa6Sy2yPa0vb4ai/WWR11gtWbMN9cq2u7//DsThgYjuGcnms7e7H+35UjJZTIccdmaQ96tVj8JUZ6PdLNuEROQfIbSv/zgeOARFw2W//hsdWddvHD9+vE7MndWBsawqX/3EZbnt2Pb52zzJ892Gr4z9y2li0pCxzykA2h3U79qIgLDOY/tKrMFBHe5jU7vi9Aefuf22ZpG2i3LjTqq9r/2J1SuPb0/bIVS0UqYIIAGueSbtakj3r1ASUCWXGhHZs6hnAG1LjVh3T7z51MgCnCVUJBFUuoKih9A2N4HfPF9eaas+k0KrNhv+6NiggIluInnrtY3hp025HgMT8o/bDhPY0lm60TKjbeosDn8mdpQLlt4s22tGP33z/kZgo/TtqQHPrM+uxuWcQb5luCTlduL6ypdf2i45tKwbR6M7y1dLHpATdOs2svW5HPwayeZx4YBfa0kl0dWRKIjPdKM1LOdFTiQRaUskS/536bRWZpKVd3fviG3ZYtl4vgDXw/dp7j8A33n+kbxkqSdMKFCJKAvhfAO8BcCSAC4ioJjU3ri2NH19wvP2/vtqvMhNs7R3CjY+vtSNKFBmtU1m/cwAHTmy3/08mCEdM68QjK7fhgp9ZS+K7fTb3LN2Cw7/xF/xp6RZM6WzBhI4MujoyOFaaBv788lbHi3/1B44GABw21RpRXfGHZXaHc+enT0F7JoUpY1vwxGvdWLPd6sxf2rQbR08fazuI1ZyO/35gJdZozr6vnHMYOltSeHJ1N/qHcxgayePJ14rC5V/eNceuLwC27wMAzjhsCn76ibl45N/eAQBYsXUPNuzst0dy49sy+Nw7LUGttJLFWkejtCPlc9HNT2ccNgWApd3tHhxB/3DOodmoeSZPr9mJf75tMbb2DmLP0AjaM0ks+fpZVpnb0/YoVGdqpzVSVC/tN+9djt8sKnaeB3S1g8gyt/3iqXV4XAoj/XcGgE/fvgRfvGOpLTDa0paw2E9qB3cv2eSYuPY/Hz5G1otVl0+u7nZogu97yzTbL7Vw5Xb0DY3gYSm0Tj90MhIJstvm5t2D2LF3GDc9sRYjeWFroFPHtuDpNcV5C4pjZ45DMkF47NVubNw5gOVv9Do0umnjWtGWTtrr0a16s7j3xrNXWIOS9kwSe4csjc09n0o3H9+9ZDM+dOOzdp11tKQws6sdr77Zh4FsDr/TFkV8x6GT7fdDRRQqje/w/TrxyVNn23Wyc28WQgh7oPW2gychnUzggWVv2uaw7zxY1ByndLagq8OqLyWs3tg9iD/8bTNOmzNJmglb7PehUBA4S4b5H76ftebf4ft1Yvkbe2yzlzKT6fzu+Y2Ydfn99v+fP/MQAMDbD5nkuM7t/7jx4yfY39WitIrjZo7HF8+cg+9+5Fh86vSDSqwB1cQcu9kczAOwRgjxOgAQ0R0AzgWwwjdVhXj/MdPw+d8tNaqTnz79IPz0ydeN6R760um2TRwAjtp/HJ674kzbJPOBE2Y4RsRqZLLsv87GW/7rYfv44EjeYTf+yQXH47TrHrO1BwD44plzcNT+Vvn+9axDcdfizfjj0i34o5yLMFNqU18661A8veZZnPX9YgSKmjwFABPki7V6+177pbnln+aiNZ3ExDEZ3PviG7jXFYny20+dZHe6n33nwfj+gtfwi6fWybLMwSdPnQ3A8iscOnUMrvvLKnu/9Y5MEuPa0zj+gOKLor90AHDigZZWNn18myOs8qaPn4hzjt4PgCWgn3ytG0dd+ZAj7fyj9nOYnU75zqMALNPlRG1k6I5suv4fLa0IMC+78dR/nmEPLlQYrHLSHi+1yAe/eJpt7lR1dvqhk3GbnBirzFIPLS9qev8x/zB8ZO5MAMWIvv95aJUjMOPc46ZjvPydfvXMetuRO7Y1Zed9nOx4Lv7lC45y3/ixEwEAJ82eaDuGv/8Px9rn2zMpHDtjHO5cvAl3Lt7kSPsvZxyC9kwKp82ZhN8u2ojfasIVsMwzgCXcH16xDQd/9QH73LrvvBcA7KATwPJ1KW6V4ds9A1ls2jWII79Z/B2f/9qZGNeWtoXqt+9ficFsHjv2ZjGzqw13f/ZtAGD76v799y/h339fXKT1sP068a7Dp+DVN/vw0Z89h1xBaOasDnS0pOzw7/f96Cl86axDcf0jlhZ6wgHWb3nQpDH4w9824/gDxjvMgcqHduKBE/DjR9dgztcsH9nFb5uFvcN5nHRQF6Z0tuDUax/DN+4tBsr89Stn2OVtTSfxwBdOw+6BLD7680W2RqQ4ZsZ4fPadB+O513fiW+cd7TiXTBC+JK0KtaZpNRQA0wHorXuzPFYTiAiv/L/5uPuzp5Scu+K9R9jff/qJEx3nZk/qwJ2XWiaEs4+civlH7Yf9xrXaHdnfH7s/TpI+mU++fbY9uu9sTWPdd96LL51VbCiXa/dRpjadEw4smsL2H9+GX19ykv1/Jpmw1eq5s7pw8kFOu+s/vW2W/X1KZ2vJnAzl5Pu81EJ0Dp06Bm87uDjCSicTuPjtxfzeKTUIxfvesr/j/xevPBuAFbr5g390rl3U1ZHBuu+817YnX3XuUfjQCTO0vIsv3nnHOZvDB0+YjvXXvA8HTGzHS988u6TcJ2r1BQBfcD3bB44v3ufKv3MqwxeecqDtwwCAT7/jIMd5NffliGljcfl7DnecO801Gm1LO2c064L1oEnF30EJjT989m04YGK7cW6PbjoZ1562TYyKDx4/HaccbP2Wnzx1Nv7t3Yfik2+fjQ9qdQoAP7twbkned336FPz7fCty7OMyAklxzIxxWHHVfPv/U13P+Nt/Psn+Dd3mHMDyJ82S5r0PnzDTce6CeTPtjpeIbE1aBV1c8Z4jbK2nLZN0tGXAMuVabdIa1Cze0GMLk787dn/c9y+nAnDOQ1LC5NPvOAifeYelOaslUL7+p1dww+Nr0ZZO4m/feLcdXKMGNorTD52Mz77zYJxwwATMmNBut4ML5s3Ek/9xhh3hpzhy/7F42yGT8PTl78LFrmcAgP8853Dc87m32yHyjQAFrRbbqBDRRwDMF0L8s/z/EwDmCSE+77ruUgCXAsABBxxw4oYNG0ryqgabewbwxGvdOP+tBxjX0BnI5uzRaLk89/pOzJ7UUaLKvrx5N55ZuxMPL38Tbzt4Er589qH2S6voGxrBr5/biPcfM62kAefyBSSIkNe213Wn7e4bxuxJHY58h0byuPLe5djZn8Ulp87GvNldxme+/+WtaE0ncKYrkkUIgcUbevDr5zbg74/dv+T8qjf78PLm3dixN4tPn36QcUOxx1Ztx2FTO0s6p+FcHr0DI9jeN2yvlaazvW8Ib7/mUVzxniPw8ZMPLInV3z2QxZfufBFfPvuwkvRCCCxcuR2v79iLC+YdYJuUAMsE8vqOfry8eTfGtqZx1pGl0TuFgvXcb501wVGfuXwB/cN53PHCRrxl+ji8zdUZq+e9+Jcv4LIzDsZ/zC8KqGyugBVb9+D//d9yDAzncdMnTrT9LoDlOL7t2fXobE1j2rhWnHjgBIefxo+tvYPYO5TDPUu34LiZ43H2Uc4Os6c/iyde60YqSXjv0dMcv1M2V8Dq7X0Q0p/kbruvbOlFz0AWr3f349CpnbaQUwxmLb9hQQhMHdtq+20UvQMjWLRuJ17Z0ovPnXFIyTP1DY3g3hffwCkHT3SYMn/3/Ea8tGk35s3uwtwDuzB9Qpuj7T75WjceWbkNZxw2Ba3ppKNcI/kC7nhhE1a8sQczJrThrCOmOvylAPD4qu1IJRKY2VVcD1CnUBC+G+Q1CkS0RAhROqpwX9fEAuUUAP8lhJgv/78CAIQQ3/FKM3fuXLF48eIalZBhGGZ0EFagNLPJ6wUAc4hoNhFlAJwP4L46l4lhGGafpXGMb2UihMgR0b8AeAhAEsAtQojlAckYhmGYKtG0AgUAhBAPAHgg8EKGYRim6jSzyYthGIZpIFigMAzDMBWBBQrDMAxTEVigMAzDMBWBBQrDMAxTEZp2YmMUiKgPwJsAen0uG+dz/gAAGz3OBaUNOh8nLZeLy8XlapxyBZWtGct1mBCi0+NcEbWi6b7wB2AxgJsDrvE8D6A7atoQeXO5uFxcrlFQrqCyNWO5ACz2y1f97Ysmr/+LcX53FfPmcpV3nstV3nkuV3nn46QF/MvWrOUKZF8zeS0WIdajqVb6asHlKg8uV3lwucqnUcsWtVxh0+1rGsrNdU5fLbhc5cHlKg8uV/k0atmilitUun1KQ2EYhmGqx76moTAMwzBVYp8XKER0CxFtJ6JXtGPHEtGzRLSMiP6PiMbK42kiulUeX6n2YJHnHieiVUT0ovybYrpflcqVIaJfyuMvEdE7tTQnyuNriOhH5N5xq37lqlh9EdFMInpM/ibLieiL8ngXES0gotXyc4KW5gpZJ6uIaL52vGL1VeFy1a2+iGiivH4vEf3ElVfd6iugXJV+H8st27uJaImsmyVE9C4tr3rWmV+54tdZmFCw0fwH4HQAJwB4RTv2AoB3yO+fBPAt+f2jAO6Q39sBrAcwS/7/OIC5dSrXZQB+Kb9PAbAEQEL+/zyAUwAQgAcBvKdBylWx+gIwDcAJ8nsngNcAHAngOgCXy+OXA7hWfj8SwEsAWgDMBrAWQLLS9VXhctWzvjoAnArgMwB+4sqrnvXlV66K1VfEsh0PYH/5/WgAWxqkzvzKFbvOKlLZzf4HYBacHeQeFP1LMwGskN8vgBValwIwUf54XZX6MWKU638BfFy7biGAebKxvaodvwDAT+tdrmrVl3afewG8G8AqANPksWkAVsnvVwC4Qrv+IfmCV6W+4par3vWlXfdP0DrueteXV7mqXV/llE0eJwA7YQ0UGqLO3OWqVJ3t8yYvD14B8Pfy+0dgdZIAcDeAfgBbYc02/a4QYpeW7pdSVfxGHDU2QrleAnAuEaWIaDaAE+W56QA2a+k3y2P1Lpei4vVFRLNgjcIWAZgqhNgKAPJTqfDTAWzSkql6qVp9xSyXol715UW96yuIqryPEcr2IQBLhRDDaKw608uliFVnLFDMfBLAZUS0BJYamZXH5wHIA9gflkniy0R0kDz3MSHEWwCcJv8+UcNy3QKrYS4G8AMAzwDIwRqBuKlGWF+55QKqUF9ENAbAHwD8qxBij9+lhmPC53i9ywXUt748szAcq2V9+VGV97HcshHRUQCuBfBpdchwWc3rzFAuoAJ1xgLFgBDiVSHE2UKIEwH8DpYtG7B8KH8RQowIIbYDeBrAXJlmi/zsA/BbWMKnJuUSQuSEEF8SQhwnhDgXwHgAq2F15jO0LGYAeKMBylXx+iKiNKwX6jdCiD/Kw9uIaJo8Pw3Adnl8M5yakqqXitdXhcpV7/ryot715Uk13sdyy0ZEMwDcA+BCIYTqQ+peZx7lqkidsUAxoKIbiCgB4OsAbpKnNgJ4F1l0ADgZwKvSpDNJpkkDeD8sM1BNykVE7bI8IKJ3A8gJIVZIVbePiE6W6uuFsGysdS1XpetLPtsvAKwUQnxfO3UfgIvk94tQfPb7AJxPRC3SFDcHwPOVrq9KlasB6stIA9SXVz4Vfx/LLRsRjQdwPyyf2NPq4nrXmVe5KlZnlXIGNesfrBH1VgAjsEYPlwD4IiyH+2sArkHR4TwGwO8BLAewAsB/yOMdsCKYXpbnfggZnVOjcs2C5YRbCeARAAdq+cyVDWMtgJ+oNPUsV6XrC1akj5D5vSj/3gsrcGIhLK1oIWQAhUzzNVknq6BF2VSyvipVrgapr/UAdgHYK3/3IxukvkrKVen6ilI2WAOrfu3aFwFMqXedeZWrUnXGM+UZhmGYisAmL4ZhGKYisEBhGIZhKgILFIZhGKYisEBhGIZhKgILFIZhGKYisEBhmAaBiD5DRBeWcf0s0lZ9Zph6k6p3ARiGsSaWCSFuCr6SYRoXFigMUyHk4nx/gbU43/GwJnpeCOAIAN+HNTF2B4B/EkJsJaLHYa1v9nYA9xFRJ4C9QojvEtFxsFYcaIc1Ae6TQogeIjoR1hppAwCeqt3TMUwwbPJimMpyGICbhRDHwFrW/zIAPwbwYWGtdXYLgKu168cLId4hhPieK5/bAPynzGcZgCvl8V8C+IIQ4pRqPgTDRIE1FIapLJtEcY2kXwP4KqyNjBbI1cCTsJauUdzpzoCIxsESNE/IQ7cC+L3h+O0A3lP5R2CYaLBAYZjK4l7LqA/Ach+Nor+MvMmQP8M0DGzyYpjKcgARKeFxAYDnAExWx4goLfei8EQI0Qugh4hOk4c+AeAJIcRuAL1EdKo8/rHKF59hosMaCsNUlpUALiKin8Ja6fXHsLby/ZE0WaVgbTa2PCCfiwDcRETtAF4HcLE8fjGAW4hoQObLMA0DrzbMMBVCRnn9WQhxdJ2LwjB1gU1eDMMwTEVgDYVhGIapCKyhMAzDMBWBBQrDMAxTEVigMAzDMBWBBQrDMAxTEVigMAzDMBWBBQrDMAxTEf4/RLPnvXtLhaMAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'] = sorted_data['inc'].astype(int)\n", "sorted_data['inc'].plot()" ] }, @@ -216,9 +3254,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
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\n", + "text/plain": [ + "
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4111392\n", + "2013 4182691\n", + "1986 5115251\n", + "1990 5235827\n", + "1989 5466192\n", + "dtype: int64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -332,9 +3461,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "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_incidence.hist(xrot=20)" ] @@ -342,9 +3494,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } @@ -365,7 +3515,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4, -- 2.18.1