{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example Data Details\n", "This notebook presents some ways to use the package to give insights of the data\n", "\n", "First, import the package" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\deap\\tools\\_hypervolume\\pyhv.py:33: ImportWarning: Falling back to the python version of hypervolume module. Expect this to be very slow.\n", " \"module. Expect this to be very slow.\", ImportWarning)\n", "C:\\ProgramData\\Anaconda3\\lib\\importlib\\_bootstrap_external.py:426: ImportWarning: Not importing directory C:\\ProgramData\\Anaconda3\\lib\\site-packages\\mpl_toolkits: missing __init__\n", " _warnings.warn(msg.format(portions[0]), ImportWarning)\n", "C:\\ProgramData\\Anaconda3\\lib\\importlib\\_bootstrap_external.py:426: ImportWarning: Not importing directory c:\\programdata\\anaconda3\\lib\\site-packages\\mpl_toolkits: missing __init__\n", " _warnings.warn(msg.format(portions[0]), ImportWarning)\n" ] } ], "source": [ "import dvb.datascience as ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Describe the data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Drawing diagram using blockdiag'" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Transform describe

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "85b96f3a06fb4156b011617c22c92801", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Progress:', max=2.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)target
count150.000000150.000000150.000000150.000000150.000000
mean5.8433333.0540003.7586671.1986671.000000
std0.8280660.4335941.7644200.7631610.819232
min4.3000002.0000001.0000000.1000000.000000
25%5.1000002.8000001.6000000.3000000.000000
50%5.8000003.0000004.3500001.3000001.000000
75%6.4000003.3000005.1000001.8000002.000000
max7.9000004.4000006.9000002.5000002.000000
\n", "
" ], "text/plain": [ " sepal length (cm) sepal width (cm) petal length (cm) \\\n", "count 150.000000 150.000000 150.000000 \n", "mean 5.843333 3.054000 3.758667 \n", "std 0.828066 0.433594 1.764420 \n", "min 4.300000 2.000000 1.000000 \n", "25% 5.100000 2.800000 1.600000 \n", "50% 5.800000 3.000000 4.350000 \n", "75% 6.400000 3.300000 5.100000 \n", "max 7.900000 4.400000 6.900000 \n", "\n", " petal width (cm) target \n", "count 150.000000 150.000000 \n", "mean 1.198667 1.000000 \n", "std 0.763161 0.819232 \n", "min 0.100000 0.000000 \n", "25% 0.300000 0.000000 \n", "50% 1.300000 1.000000 \n", "75% 1.800000 2.000000 \n", "max 2.500000 2.000000 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p = ds.Pipeline()\n", "p.addPipe('read', ds.data.SampleData('iris'))\n", "p.addPipe('describe', ds.eda.Describe(), [('read', 'df', 'df')])\n", "p.transform(name=\"describe\", close_plt=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dump the data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Drawing diagram using blockdiag'" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)target
05.13.51.40.20
14.93.01.40.20
24.73.21.30.20
34.63.11.50.20
45.03.61.40.20
55.43.91.70.40
64.63.41.40.30
75.03.41.50.20
84.42.91.40.20
94.93.11.50.10
105.43.71.50.20
114.83.41.60.20
124.83.01.40.10
134.33.01.10.10
145.84.01.20.20
155.74.41.50.40
165.43.91.30.40
175.13.51.40.30
185.73.81.70.30
195.13.81.50.30
205.43.41.70.20
215.13.71.50.40
224.63.61.00.20
235.13.31.70.50
244.83.41.90.20
255.03.01.60.20
265.03.41.60.40
275.23.51.50.20
285.23.41.40.20
294.73.21.60.20
304.83.11.60.20
315.43.41.50.40
325.24.11.50.10
335.54.21.40.20
344.93.11.50.10
355.03.21.20.20
365.53.51.30.20
374.93.11.50.10
384.43.01.30.20
395.13.41.50.20
405.03.51.30.30
414.52.31.30.30
424.43.21.30.20
435.03.51.60.60
445.13.81.90.40
454.83.01.40.30
465.13.81.60.20
474.63.21.40.20
485.33.71.50.20
495.03.31.40.20
507.03.24.71.41
516.43.24.51.51
526.93.14.91.51
535.52.34.01.31
546.52.84.61.51
555.72.84.51.31
566.33.34.71.61
574.92.43.31.01
586.62.94.61.31
595.22.73.91.41
605.02.03.51.01
615.93.04.21.51
626.02.24.01.01
636.12.94.71.41
645.62.93.61.31
656.73.14.41.41
665.63.04.51.51
675.82.74.11.01
686.22.24.51.51
695.62.53.91.11
705.93.24.81.81
716.12.84.01.31
726.32.54.91.51
736.12.84.71.21
746.42.94.31.31
756.63.04.41.41
766.82.84.81.41
776.73.05.01.71
786.02.94.51.51
795.72.63.51.01
805.52.43.81.11
815.52.43.71.01
825.82.73.91.21
836.02.75.11.61
845.43.04.51.51
856.03.44.51.61
866.73.14.71.51
876.32.34.41.31
885.63.04.11.31
895.52.54.01.31
905.52.64.41.21
916.13.04.61.41
925.82.64.01.21
935.02.33.31.01
945.62.74.21.31
955.73.04.21.21
965.72.94.21.31
976.22.94.31.31
985.12.53.01.11
995.72.84.11.31
1006.33.36.02.52
1015.82.75.11.92
1027.13.05.92.12
1036.32.95.61.82
1046.53.05.82.22
1057.63.06.62.12
1064.92.54.51.72
1077.32.96.31.82
1086.72.55.81.82
1097.23.66.12.52
1106.53.25.12.02
1116.42.75.31.92
1126.83.05.52.12
1135.72.55.02.02
1145.82.85.12.42
1156.43.25.32.32
1166.53.05.51.82
1177.73.86.72.22
1187.72.66.92.32
1196.02.25.01.52
1206.93.25.72.32
1215.62.84.92.02
1227.72.86.72.02
1236.32.74.91.82
1246.73.35.72.12
1257.23.26.01.82
1266.22.84.81.82
1276.13.04.91.82
1286.42.85.62.12
1297.23.05.81.62
1307.42.86.11.92
1317.93.86.42.02
1326.42.85.62.22
1336.32.85.11.52
1346.12.65.61.42
1357.73.06.12.32
1366.33.45.62.42
1376.43.15.51.82
1386.03.04.81.82
1396.93.15.42.12
1406.73.15.62.42
1416.93.15.12.32
1425.82.75.11.92
1436.83.25.92.32
1446.73.35.72.52
1456.73.05.22.32
1466.32.55.01.92
1476.53.05.22.02
1486.23.45.42.32
1495.93.05.11.82
\n", "
" ], "text/plain": [ " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n", "0 5.1 3.5 1.4 0.2 \n", "1 4.9 3.0 1.4 0.2 \n", "2 4.7 3.2 1.3 0.2 \n", "3 4.6 3.1 1.5 0.2 \n", "4 5.0 3.6 1.4 0.2 \n", "5 5.4 3.9 1.7 0.4 \n", "6 4.6 3.4 1.4 0.3 \n", "7 5.0 3.4 1.5 0.2 \n", "8 4.4 2.9 1.4 0.2 \n", "9 4.9 3.1 1.5 0.1 \n", "10 5.4 3.7 1.5 0.2 \n", "11 4.8 3.4 1.6 0.2 \n", "12 4.8 3.0 1.4 0.1 \n", "13 4.3 3.0 1.1 0.1 \n", "14 5.8 4.0 1.2 0.2 \n", "15 5.7 4.4 1.5 0.4 \n", "16 5.4 3.9 1.3 0.4 \n", "17 5.1 3.5 1.4 0.3 \n", "18 5.7 3.8 1.7 0.3 \n", "19 5.1 3.8 1.5 0.3 \n", "20 5.4 3.4 1.7 0.2 \n", "21 5.1 3.7 1.5 0.4 \n", "22 4.6 3.6 1.0 0.2 \n", "23 5.1 3.3 1.7 0.5 \n", "24 4.8 3.4 1.9 0.2 \n", "25 5.0 3.0 1.6 0.2 \n", "26 5.0 3.4 1.6 0.4 \n", "27 5.2 3.5 1.5 0.2 \n", "28 5.2 3.4 1.4 0.2 \n", "29 4.7 3.2 1.6 0.2 \n", "30 4.8 3.1 1.6 0.2 \n", "31 5.4 3.4 1.5 0.4 \n", "32 5.2 4.1 1.5 0.1 \n", "33 5.5 4.2 1.4 0.2 \n", "34 4.9 3.1 1.5 0.1 \n", "35 5.0 3.2 1.2 0.2 \n", "36 5.5 3.5 1.3 0.2 \n", "37 4.9 3.1 1.5 0.1 \n", "38 4.4 3.0 1.3 0.2 \n", "39 5.1 3.4 1.5 0.2 \n", "40 5.0 3.5 1.3 0.3 \n", "41 4.5 2.3 1.3 0.3 \n", "42 4.4 3.2 1.3 0.2 \n", "43 5.0 3.5 1.6 0.6 \n", "44 5.1 3.8 1.9 0.4 \n", "45 4.8 3.0 1.4 0.3 \n", "46 5.1 3.8 1.6 0.2 \n", "47 4.6 3.2 1.4 0.2 \n", "48 5.3 3.7 1.5 0.2 \n", "49 5.0 3.3 1.4 0.2 \n", "50 7.0 3.2 4.7 1.4 \n", "51 6.4 3.2 4.5 1.5 \n", "52 6.9 3.1 4.9 1.5 \n", "53 5.5 2.3 4.0 1.3 \n", "54 6.5 2.8 4.6 1.5 \n", "55 5.7 2.8 4.5 1.3 \n", "56 6.3 3.3 4.7 1.6 \n", "57 4.9 2.4 3.3 1.0 \n", "58 6.6 2.9 4.6 1.3 \n", "59 5.2 2.7 3.9 1.4 \n", "60 5.0 2.0 3.5 1.0 \n", "61 5.9 3.0 4.2 1.5 \n", "62 6.0 2.2 4.0 1.0 \n", "63 6.1 2.9 4.7 1.4 \n", "64 5.6 2.9 3.6 1.3 \n", "65 6.7 3.1 4.4 1.4 \n", "66 5.6 3.0 4.5 1.5 \n", "67 5.8 2.7 4.1 1.0 \n", "68 6.2 2.2 4.5 1.5 \n", "69 5.6 2.5 3.9 1.1 \n", "70 5.9 3.2 4.8 1.8 \n", "71 6.1 2.8 4.0 1.3 \n", "72 6.3 2.5 4.9 1.5 \n", "73 6.1 2.8 4.7 1.2 \n", "74 6.4 2.9 4.3 1.3 \n", "75 6.6 3.0 4.4 1.4 \n", "76 6.8 2.8 4.8 1.4 \n", "77 6.7 3.0 5.0 1.7 \n", "78 6.0 2.9 4.5 1.5 \n", "79 5.7 2.6 3.5 1.0 \n", "80 5.5 2.4 3.8 1.1 \n", "81 5.5 2.4 3.7 1.0 \n", "82 5.8 2.7 3.9 1.2 \n", "83 6.0 2.7 5.1 1.6 \n", "84 5.4 3.0 4.5 1.5 \n", "85 6.0 3.4 4.5 1.6 \n", "86 6.7 3.1 4.7 1.5 \n", "87 6.3 2.3 4.4 1.3 \n", "88 5.6 3.0 4.1 1.3 \n", "89 5.5 2.5 4.0 1.3 \n", "90 5.5 2.6 4.4 1.2 \n", "91 6.1 3.0 4.6 1.4 \n", "92 5.8 2.6 4.0 1.2 \n", "93 5.0 2.3 3.3 1.0 \n", "94 5.6 2.7 4.2 1.3 \n", "95 5.7 3.0 4.2 1.2 \n", "96 5.7 2.9 4.2 1.3 \n", "97 6.2 2.9 4.3 1.3 \n", "98 5.1 2.5 3.0 1.1 \n", "99 5.7 2.8 4.1 1.3 \n", "100 6.3 3.3 6.0 2.5 \n", "101 5.8 2.7 5.1 1.9 \n", "102 7.1 3.0 5.9 2.1 \n", "103 6.3 2.9 5.6 1.8 \n", "104 6.5 3.0 5.8 2.2 \n", "105 7.6 3.0 6.6 2.1 \n", "106 4.9 2.5 4.5 1.7 \n", "107 7.3 2.9 6.3 1.8 \n", "108 6.7 2.5 5.8 1.8 \n", "109 7.2 3.6 6.1 2.5 \n", "110 6.5 3.2 5.1 2.0 \n", "111 6.4 2.7 5.3 1.9 \n", "112 6.8 3.0 5.5 2.1 \n", "113 5.7 2.5 5.0 2.0 \n", "114 5.8 2.8 5.1 2.4 \n", "115 6.4 3.2 5.3 2.3 \n", "116 6.5 3.0 5.5 1.8 \n", "117 7.7 3.8 6.7 2.2 \n", "118 7.7 2.6 6.9 2.3 \n", "119 6.0 2.2 5.0 1.5 \n", "120 6.9 3.2 5.7 2.3 \n", "121 5.6 2.8 4.9 2.0 \n", "122 7.7 2.8 6.7 2.0 \n", "123 6.3 2.7 4.9 1.8 \n", "124 6.7 3.3 5.7 2.1 \n", "125 7.2 3.2 6.0 1.8 \n", "126 6.2 2.8 4.8 1.8 \n", "127 6.1 3.0 4.9 1.8 \n", "128 6.4 2.8 5.6 2.1 \n", "129 7.2 3.0 5.8 1.6 \n", "130 7.4 2.8 6.1 1.9 \n", "131 7.9 3.8 6.4 2.0 \n", "132 6.4 2.8 5.6 2.2 \n", "133 6.3 2.8 5.1 1.5 \n", "134 6.1 2.6 5.6 1.4 \n", "135 7.7 3.0 6.1 2.3 \n", "136 6.3 3.4 5.6 2.4 \n", "137 6.4 3.1 5.5 1.8 \n", "138 6.0 3.0 4.8 1.8 \n", "139 6.9 3.1 5.4 2.1 \n", "140 6.7 3.1 5.6 2.4 \n", "141 6.9 3.1 5.1 2.3 \n", "142 5.8 2.7 5.1 1.9 \n", "143 6.8 3.2 5.9 2.3 \n", "144 6.7 3.3 5.7 2.5 \n", "145 6.7 3.0 5.2 2.3 \n", "146 6.3 2.5 5.0 1.9 \n", "147 6.5 3.0 5.2 2.0 \n", "148 6.2 3.4 5.4 2.3 \n", "149 5.9 3.0 5.1 1.8 \n", "\n", " target \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 \n", "5 0 \n", "6 0 \n", "7 0 \n", "8 0 \n", "9 0 \n", "10 0 \n", "11 0 \n", "12 0 \n", "13 0 \n", "14 0 \n", "15 0 \n", "16 0 \n", "17 0 \n", "18 0 \n", "19 0 \n", "20 0 \n", "21 0 \n", "22 0 \n", "23 0 \n", "24 0 \n", "25 0 \n", "26 0 \n", "27 0 \n", "28 0 \n", "29 0 \n", "30 0 \n", "31 0 \n", "32 0 \n", "33 0 \n", "34 0 \n", "35 0 \n", "36 0 \n", "37 0 \n", "38 0 \n", "39 0 \n", "40 0 \n", "41 0 \n", "42 0 \n", "43 0 \n", "44 0 \n", "45 0 \n", "46 0 \n", "47 0 \n", "48 0 \n", "49 0 \n", "50 1 \n", "51 1 \n", "52 1 \n", "53 1 \n", "54 1 \n", "55 1 \n", "56 1 \n", "57 1 \n", "58 1 \n", "59 1 \n", "60 1 \n", "61 1 \n", "62 1 \n", "63 1 \n", "64 1 \n", "65 1 \n", "66 1 \n", "67 1 \n", "68 1 \n", "69 1 \n", "70 1 \n", "71 1 \n", "72 1 \n", "73 1 \n", "74 1 \n", "75 1 \n", "76 1 \n", "77 1 \n", "78 1 \n", "79 1 \n", "80 1 \n", "81 1 \n", "82 1 \n", "83 1 \n", "84 1 \n", "85 1 \n", "86 1 \n", "87 1 \n", "88 1 \n", "89 1 \n", "90 1 \n", "91 1 \n", "92 1 \n", "93 1 \n", "94 1 \n", "95 1 \n", "96 1 \n", "97 1 \n", "98 1 \n", "99 1 \n", "100 2 \n", "101 2 \n", "102 2 \n", "103 2 \n", "104 2 \n", "105 2 \n", "106 2 \n", "107 2 \n", "108 2 \n", "109 2 \n", "110 2 \n", "111 2 \n", "112 2 \n", "113 2 \n", "114 2 \n", "115 2 \n", "116 2 \n", "117 2 \n", "118 2 \n", "119 2 \n", "120 2 \n", "121 2 \n", "122 2 \n", "123 2 \n", "124 2 \n", "125 2 \n", "126 2 \n", "127 2 \n", "128 2 \n", "129 2 \n", "130 2 \n", "131 2 \n", "132 2 \n", "133 2 \n", "134 2 \n", "135 2 \n", "136 2 \n", "137 2 \n", "138 2 \n", "139 2 \n", "140 2 \n", "141 2 \n", "142 2 \n", "143 2 \n", "144 2 \n", "145 2 \n", "146 2 \n", "147 2 \n", "148 2 \n", "149 2 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p = ds.Pipeline()\n", "p.addPipe('read', ds.data.SampleData('iris'))\n", "p.addPipe('dump', ds.eda.Dump(), [('read', 'df', 'df')])\n", "p.transform(name=\"dump\", close_plt=True)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }