diff --git a/module2/exo2/exercice.ipynb b/module2/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..52369c5865e6b10b8042b83f00718e241302c4ff 100644 --- a/module2/exo2/exercice.ipynb +++ b/module2/exo2/exercice.ipynb @@ -1,5 +1,89 @@ { - "cells": [], + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Standard Deviation: 4.3124\n", + "Minimum: 2.8\n", + "Median: 14.5\n", + "Maximum: 23.4\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "data = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,\n", + " 12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2,\n", + " 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0,\n", + " 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6,\n", + " 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0,\n", + " 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4,\n", + " 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n", + "\n", + "# Calculate statistics\n", + "std_dev = np.std(data)\n", + "minimum = np.min(data)\n", + "median = np.median(data)\n", + "maximum = np.max(data)\n", + "\n", + "print(f\"Standard Deviation: {std_dev:.4f}\")\n", + "print(f\"Minimum: {minimum:.1f}\")\n", + "print(f\"Median: {median:.1f}\")\n", + "print(f\"Maximum: {maximum:.1f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average (Mean): 14.1130\n", + "Standard Deviation: 4.3124\n", + "Minimum: 2.8\n", + "Median: 14.5\n", + "Maximum: 23.4\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "data = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n", + "\n", + "# 计算统计量\n", + "mean = np.mean(data)\n", + "std_dev = np.std(data)\n", + "minimum = np.min(data)\n", + "median = np.median(data)\n", + "maximum = np.max(data)\n", + "\n", + "# 输出结果（保留适当小数位）\n", + "print(f\"Average (Mean): {mean:.4f}\") # 平均值保留4位小数\n", + "print(f\"Standard Deviation: {std_dev:.4f}\")\n", + "print(f\"Minimum: {minimum:.1f}\")\n", + "print(f\"Median: {median:.1f}\")\n", + "print(f\"Maximum: {maximum:.1f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +100,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } - diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5b58a3ae26346460dd39e62a39c55243d7..27de31d89ae26612d5041247f5352bd02674052d 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -28,10 +28,8 @@ }, { "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\"" @@ -61,11 +59,978 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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202506 3 273519 258159.0 288879.0 408 385.0 \n", + "19 202505 3 334395 318416.0 350374.0 499 475.0 \n", + "20 202504 3 350043 332885.0 367201.0 522 496.0 \n", + "21 202503 3 252772 238917.0 266627.0 377 356.0 \n", + "22 202502 3 257247 242991.0 271503.0 384 363.0 \n", + "23 202501 3 231549 214627.0 248471.0 345 320.0 \n", + "24 202452 3 201726 185870.0 217582.0 302 278.0 \n", + "25 202451 3 201697 187843.0 215551.0 302 281.0 \n", + "26 202450 3 136694 126369.0 147019.0 205 190.0 \n", + "27 202449 3 108487 99037.0 117937.0 163 149.0 \n", + "28 202448 3 87381 78687.0 96075.0 131 118.0 \n", + "29 202447 3 76286 67626.0 84946.0 114 101.0 \n", + "... ... ... ... ... ... ... ... \n", + "2090 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2091 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2092 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2093 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2094 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2095 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2096 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2097 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2098 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2099 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2100 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2101 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2102 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2103 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2104 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2105 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2106 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2107 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2108 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2109 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2110 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2111 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2112 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2113 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2114 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2115 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2116 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2117 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2118 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2119 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 42.0 FR France \n", + "1 45.0 FR France \n", + "2 35.0 FR France \n", + "3 43.0 FR France \n", + "4 37.0 FR France \n", + "5 30.0 FR France \n", + "6 33.0 FR France \n", + "7 40.0 FR France \n", + "8 52.0 FR France \n", + "9 62.0 FR France \n", + "10 65.0 FR France \n", + "11 68.0 FR France \n", + "12 88.0 FR France \n", + "13 100.0 FR France \n", + "14 101.0 FR France \n", + "15 140.0 FR France \n", + "16 220.0 FR France \n", + "17 331.0 FR France \n", + "18 431.0 FR France \n", + "19 523.0 FR France \n", + "20 548.0 FR France \n", + "21 398.0 FR France \n", + "22 405.0 FR France \n", + "23 370.0 FR France \n", + "24 326.0 FR France \n", + "25 323.0 FR France \n", + "26 220.0 FR France \n", + "27 177.0 FR France \n", + "28 144.0 FR France \n", + "29 127.0 FR France \n", + "... ... ... ... \n", + "2090 59.0 FR France \n", + "2091 64.0 FR France \n", + "2092 97.0 FR France \n", + "2093 93.0 FR France \n", + "2094 80.0 FR France \n", + "2095 116.0 FR France \n", + "2096 149.0 FR France \n", + "2097 281.0 FR France \n", + "2098 395.0 FR France \n", + "2099 485.0 FR France \n", + "2100 544.0 FR France \n", + "2101 689.0 FR France \n", + "2102 722.0 FR France \n", + "2103 762.0 FR France \n", + "2104 926.0 FR France \n", + "2105 1113.0 FR France \n", + "2106 1236.0 FR France \n", + "2107 832.0 FR France \n", + "2108 459.0 FR France \n", + "2109 207.0 FR France \n", + "2110 190.0 FR France \n", + "2111 198.0 FR France \n", + "2112 224.0 FR France \n", + "2113 266.0 FR France \n", + "2114 219.0 FR France \n", + "2115 176.0 FR France \n", + "2116 163.0 FR France \n", + "2117 195.0 FR France \n", + "2118 308.0 FR France \n", + "2119 213.0 FR France \n", + "\n", + "[2120 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 = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n", "raw_data" ] }, @@ -78,11 +1043,75 @@ }, { "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
18831989193-NaNNaN-NaNNaNFRFrance
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21071985043424937390794.0459080.0770708.0832.0FRFrance
21081985033213901174689.0253113.0388317.0459.0FRFrance
210919850239758680949.0114223.0177147.0207.0FRFrance
211019850138548965918.0105060.0155120.0190.0FRFrance
211119845238483060602.0109058.0154110.0198.0FRFrance
2112198451310172680242.0123210.0185146.0224.0FRFrance
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211519844837862060634.096606.0143110.0176.0FRFrance
211619844737202954274.089784.013199.0163.0FRFrance
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211919844436842220056.0116788.012537.0213.0FRFrance
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2119 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202524 3 22816 17621.0 28011.0 34 26.0 \n", + "1 202523 3 24564 19382.0 29746.0 37 29.0 \n", + "2 202522 3 18755 14333.0 23177.0 28 21.0 \n", + "3 202521 3 23760 18671.0 28849.0 35 27.0 \n", + "4 202520 3 20265 15814.0 24716.0 30 23.0 \n", + "5 202519 3 16264 12394.0 20134.0 24 18.0 \n", + "6 202518 3 18115 13975.0 22255.0 27 21.0 \n", + "7 202517 3 22150 17291.0 27009.0 33 26.0 \n", + "8 202516 3 28564 22550.0 34578.0 43 34.0 \n", + "9 202515 3 35721 29592.0 41850.0 53 44.0 \n", + "10 202514 3 37579 31232.0 43926.0 56 47.0 \n", + "11 202513 3 39673 33686.0 45660.0 59 50.0 \n", + "12 202512 3 52543 45627.0 59459.0 78 68.0 \n", + "13 202511 3 59469 52154.0 66784.0 89 78.0 \n", + "14 202510 3 60334 53048.0 67620.0 90 79.0 \n", + "15 202509 3 84531 74994.0 94068.0 126 112.0 \n", + "16 202508 3 136020 124824.0 147216.0 203 186.0 \n", + "17 202507 3 208952 195988.0 221916.0 312 293.0 \n", + "18 202506 3 273519 258159.0 288879.0 408 385.0 \n", + "19 202505 3 334395 318416.0 350374.0 499 475.0 \n", + "20 202504 3 350043 332885.0 367201.0 522 496.0 \n", + "21 202503 3 252772 238917.0 266627.0 377 356.0 \n", + "22 202502 3 257247 242991.0 271503.0 384 363.0 \n", + "23 202501 3 231549 214627.0 248471.0 345 320.0 \n", + "24 202452 3 201726 185870.0 217582.0 302 278.0 \n", + "25 202451 3 201697 187843.0 215551.0 302 281.0 \n", + "26 202450 3 136694 126369.0 147019.0 205 190.0 \n", + "27 202449 3 108487 99037.0 117937.0 163 149.0 \n", + "28 202448 3 87381 78687.0 96075.0 131 118.0 \n", + "29 202447 3 76286 67626.0 84946.0 114 101.0 \n", + "... ... ... ... ... ... ... ... \n", + "2090 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2091 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2092 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2093 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2094 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2095 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2096 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2097 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2098 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2099 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2100 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2101 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2102 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2103 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2104 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2105 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2106 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2107 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2108 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2109 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2110 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2111 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2112 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2113 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2114 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2115 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2116 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2117 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2118 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2119 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 42.0 FR France \n", + "1 45.0 FR France \n", + "2 35.0 FR France \n", + "3 43.0 FR France \n", + "4 37.0 FR France \n", + "5 30.0 FR France \n", + "6 33.0 FR France \n", + "7 40.0 FR France \n", + "8 52.0 FR France \n", + "9 62.0 FR France \n", + "10 65.0 FR France \n", + "11 68.0 FR France \n", + "12 88.0 FR France \n", + "13 100.0 FR France \n", + "14 101.0 FR France \n", + "15 140.0 FR France \n", + "16 220.0 FR France \n", + "17 331.0 FR France \n", + "18 431.0 FR France \n", + "19 523.0 FR France \n", + "20 548.0 FR France \n", + "21 398.0 FR France \n", + "22 405.0 FR France \n", + "23 370.0 FR France \n", + "24 326.0 FR France \n", + "25 323.0 FR France \n", + "26 220.0 FR France \n", + "27 177.0 FR France \n", + "28 144.0 FR France \n", + "29 127.0 FR France \n", + "... ... ... ... \n", + "2090 59.0 FR France \n", + "2091 64.0 FR France \n", + "2092 97.0 FR France \n", + "2093 93.0 FR France \n", + "2094 80.0 FR France \n", + "2095 116.0 FR France \n", + "2096 149.0 FR France \n", + "2097 281.0 FR France \n", + "2098 395.0 FR France \n", + "2099 485.0 FR France \n", + "2100 544.0 FR France \n", + "2101 689.0 FR France \n", + "2102 722.0 FR France \n", + "2103 762.0 FR France \n", + "2104 926.0 FR France \n", + "2105 1113.0 FR France \n", + "2106 1236.0 FR France \n", + "2107 832.0 FR France \n", + "2108 459.0 FR France \n", + "2109 207.0 FR France \n", + "2110 190.0 FR France \n", + "2111 198.0 FR France \n", + "2112 224.0 FR France \n", + "2113 266.0 FR France \n", + "2114 219.0 FR France \n", + "2115 176.0 FR France \n", + "2116 163.0 FR France \n", + "2117 195.0 FR France \n", + "2118 308.0 FR France \n", + "2119 213.0 FR France \n", + "\n", + "[2119 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -122,7 +2118,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -152,13 +2148,11 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ - "sorted_data = data.set_index('period').sort_index()" + " sorted_data = data.set_index('period').sort_index()" ] }, { @@ -179,9 +2173,17 @@ }, { "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", @@ -199,9 +2201,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "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()" ] @@ -253,9 +2272,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", @@ -341,9 +2358,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } @@ -364,7 +2379,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4,