{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"\"" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# development environment only\n", "lib = File.expand_path('../../lib', __FILE__)\n", "$LOAD_PATH.unshift(lib) unless $LOAD_PATH.include?(lib)\n", "''" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "true" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "require 'daru'\n", "require 'daru/plotly'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After including `Daru::Plotly::Methods`, you can use `plot` method and `generate_data` method." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Object" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "include Daru::Plotly::Methods # creates `plot` and `generate_data` method" ] }, { "cell_type": "code", "execution_count": 4, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "
Daru::DataFrame(5x4)
agemalefemaletotal_count
00430420850
11492457949
22498459957
33476441917
44491458949
" ], "text/plain": [ "#\n", " age male female total_coun\n", " 0 0 430 420 850\n", " 1 1 492 457 949\n", " 2 2 498 459 957\n", " 3 3 476 441 917\n", " 4 4 491 458 949" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# data source: http://www.data4citizen.jp/app/developer/app/GOvqzzYEoS/html/index.html\n", "\n", "df = Daru::DataFrame.from_csv('./O_TUKIBETSU_NENREI.csv')['age', 'male', 'female', 'total_count']\n", "df.first(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# plot DataFrame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## basic usage" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", "\n", "\n", "
\n", "\n", "" ], "text/plain": [ "#[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105], :y=>[850, 949, 957, 917, 949, 974, 1024, 996, 1067, 1042, 1044, 1061, 1159, 1138, 1173, 1265, 1284, 1306, 1298, 1153, 1140, 1111, 1066, 1063, 1038, 1120, 1095, 1152, 1207, 1256, 1217, 1212, 1278, 1335, 1265, 1320, 1314, 1395, 1375, 1516, 1544, 1556, 1622, 1653, 1641, 1509, 1564, 1542, 1501, 1706, 1158, 1567, 1593, 1617, 1631, 1676, 1677, 1674, 1697, 1634, 1768, 1808, 1774, 1789, 1902, 1897, 1986, 2140, 1924, 1943, 1277, 1044, 1494, 1470, 1341, 1391, 1414, 1224, 1260, 1336, 1134, 1281, 1076, 1001, 1109, 870, 837, 733, 728, 562, 516, 446, 355, 258, 183, 125, 126, 73, 48, 28, 22, 12, 9, 3, 6, 1], :type=>nil, :mode=>\"\", :name=>\"total_count\"}], @layout={:width=>1000, :height=>500, :title=>\"population by age\", :xaxis=>{:title=>\"age\"}, :yaxis=>{:title=>\"population\"}}, @config={:linkText=>\"Export to plot.ly\", :showLink=>true}, @embedded=true>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "#" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot(\n", " df,\n", " x: 'age',\n", " y: 'total_count',\n", " layout: {\n", " title: 'population by age',\n", " xaxis: { title: 'age' },\n", " yaxis: { title: 'population' }\n", " },\n", ").show" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## markers only / markers + lines" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", "\n", "\n", "
\n", "\n", "" ], "text/plain": [ "#[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105], :y=>[850, 949, 957, 917, 949, 974, 1024, 996, 1067, 1042, 1044, 1061, 1159, 1138, 1173, 1265, 1284, 1306, 1298, 1153, 1140, 1111, 1066, 1063, 1038, 1120, 1095, 1152, 1207, 1256, 1217, 1212, 1278, 1335, 1265, 1320, 1314, 1395, 1375, 1516, 1544, 1556, 1622, 1653, 1641, 1509, 1564, 1542, 1501, 1706, 1158, 1567, 1593, 1617, 1631, 1676, 1677, 1674, 1697, 1634, 1768, 1808, 1774, 1789, 1902, 1897, 1986, 2140, 1924, 1943, 1277, 1044, 1494, 1470, 1341, 1391, 1414, 1224, 1260, 1336, 1134, 1281, 1076, 1001, 1109, 870, 837, 733, 728, 562, 516, 446, 355, 258, 183, 125, 126, 73, 48, 28, 22, 12, 9, 3, 6, 1], :type=>nil, :mode=>\"markers\", :name=>\"total_count\"}], @layout={:width=>1000, :height=>500}, @config={:linkText=>\"Export to plot.ly\", :showLink=>true}, @embedded=true>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "#" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# markers only\n", "plot(df, x: 'age', y: 'total_count', mode: :markers).show" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", "\n", "\n", "
\n", "\n", "" ], "text/plain": [ "#[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105], :y=>[850, 949, 957, 917, 949, 974, 1024, 996, 1067, 1042, 1044, 1061, 1159, 1138, 1173, 1265, 1284, 1306, 1298, 1153, 1140, 1111, 1066, 1063, 1038, 1120, 1095, 1152, 1207, 1256, 1217, 1212, 1278, 1335, 1265, 1320, 1314, 1395, 1375, 1516, 1544, 1556, 1622, 1653, 1641, 1509, 1564, 1542, 1501, 1706, 1158, 1567, 1593, 1617, 1631, 1676, 1677, 1674, 1697, 1634, 1768, 1808, 1774, 1789, 1902, 1897, 1986, 2140, 1924, 1943, 1277, 1044, 1494, 1470, 1341, 1391, 1414, 1224, 1260, 1336, 1134, 1281, 1076, 1001, 1109, 870, 837, 733, 728, 562, 516, 446, 355, 258, 183, 125, 126, 73, 48, 28, 22, 12, 9, 3, 6, 1], :type=>nil, :mode=>\"markers+lines\", :name=>\"total_count\"}], @layout={:width=>1000, :height=>500}, @config={:linkText=>\"Export to plot.ly\", :showLink=>true}, @embedded=true>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "#" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# markers+lines only\n", "plot(df, x: 'age', y: 'total_count', mode: [:markers, :lines]).show" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multiple lines" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", "\n", "\n", "
\n", "\n", "" ], "text/plain": [ "#[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105], :y=>[430, 492, 498, 476, 491, 504, 532, 533, 543, 511, 541, 537, 601, 594, 586, 651, 641, 646, 657, 561, 554, 559, 537, 509, 552, 576, 565, 587, 604, 643, 619, 651, 666, 674, 658, 637, 672, 733, 698, 775, 785, 801, 815, 810, 813, 759, 759, 776, 712, 850, 588, 785, 806, 777, 794, 806, 821, 834, 846, 828, 861, 900, 894, 854, 906, 958, 954, 1068, 945, 918, 626, 494, 686, 671, 603, 588, 617, 486, 474, 513, 444, 488, 394, 350, 353, 300, 277, 243, 215, 157, 145, 112, 95, 54, 36, 24, 28, 10, 5, 1, 5, 4, 2, 1, 2, 0], :type=>nil, :mode=>\"\", :name=>\"male\"}, {:x=>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105], :y=>[420, 457, 459, 441, 458, 470, 492, 463, 524, 531, 503, 524, 558, 544, 587, 614, 643, 660, 641, 592, 586, 552, 529, 554, 486, 544, 530, 565, 603, 613, 598, 561, 612, 661, 607, 683, 642, 662, 677, 741, 759, 755, 807, 843, 828, 750, 805, 766, 789, 856, 570, 782, 787, 840, 837, 870, 856, 840, 851, 806, 907, 908, 880, 935, 996, 939, 1032, 1072, 979, 1025, 651, 550, 808, 799, 738, 803, 797, 738, 786, 823, 690, 793, 682, 651, 756, 570, 560, 490, 513, 405, 371, 334, 260, 204, 147, 101, 98, 63, 43, 27, 17, 8, 7, 2, 4, 1], :type=>nil, :mode=>\"\", :name=>\"female\"}], @layout={:width=>1000, :height=>500}, @config={:linkText=>\"Export to plot.ly\", :showLink=>true}, @embedded=true>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "#" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot(df, x: 'age', y: ['male', 'female']).show" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot types" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "
Daru::DataFrame(5x4)
malefemaletotal_countgeneration
000 ~501047159725000 ~
010 ~6015586611881010 ~
020 ~5686556211248020 ~
030 ~6783644413227030 ~
040 ~7880795815838040 ~
" ], "text/plain": [ "#\n", " male female total_coun generation\n", " 000 ~ 5010 4715 9725 000 ~\n", " 010 ~ 6015 5866 11881 010 ~\n", " 020 ~ 5686 5562 11248 020 ~\n", " 030 ~ 6783 6444 13227 030 ~\n", " 040 ~ 7880 7958 15838 040 ~" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_generation = df.dup['age', 'male', 'female', 'total_count']\n", "df_generation = df_generation.map_rows! do |row|\n", " generation = (row['age'] / 10) * 10\n", " row['age'] = \"#{'%03d' % generation} ~\"\n", " row\n", "end.group_by('age').sum\n", "df_generation.add_vector 'generation', df_generation.index.to_a\n", "df_generation.sort ['generation']\n", "df_generation.first(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### bar plot" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", "\n", "\n", "
\n", "\n", "" ], "text/plain": [ "#[\"000 ~\", \"010 ~\", \"020 ~\", \"030 ~\", \"040 ~\", \"050 ~\", \"060 ~\", \"070 ~\", \"080 ~\", \"090 ~\", \"100 ~\"], :y=>[5010, 6015, 5686, 6783, 7880, 7885, 9258, 5758, 3221, 510, 14], :type=>:bar, :mode=>\"\", :name=>\"male\"}, {:x=>[\"000 ~\", \"010 ~\", \"020 ~\", \"030 ~\", \"040 ~\", \"050 ~\", \"060 ~\", \"070 ~\", \"080 ~\", \"090 ~\", \"100 ~\"], :y=>[4715, 5866, 5562, 6444, 7958, 8039, 9673, 7493, 6110, 1648, 39], :type=>:bar, :mode=>\"\", :name=>\"female\"}], @layout={:width=>1000, :height=>500}, @config={:linkText=>\"Export to plot.ly\", :showLink=>true}, @embedded=true>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "#" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot(\n", " df_generation,\n", " x: 'generation',\n", " y: ['male', 'female'],\n", " type: :bar\n", ").show" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### pie plot" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", "\n", "\n", "
\n", "\n", "" ], "text/plain": [ "#:pie, :labels=>[\"000 ~\", \"010 ~\", \"020 ~\", \"030 ~\", \"040 ~\", \"050 ~\", \"060 ~\", \"070 ~\", \"080 ~\", \"090 ~\", \"100 ~\"], :values=>[9725, 11881, 11248, 13227, 15838, 15924, 18931, 13251, 9331, 2158, 53], :sort=>false, :direction=>\"clockwise\"}], @layout={:width=>1000, :height=>500}, @config={:linkText=>\"Export to plot.ly\", :showLink=>true}, @embedded=true>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "#" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot(\n", " df_generation,\n", " type: :pie,\n", " labels: 'generation',\n", " values: 'total_count',\n", " opts: {\n", " sort: false,\n", " direction: 'clockwise'\n", " }\n", ").show" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### heatmap" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", "\n", "\n", "
\n", "\n", "" ], "text/plain": [ "#[\"fixed acidity\", \"volatile acidity\", \"citric acid\", \"residual sugar\", \"chlorides\", \"free sulfur dioxide\", \"total sulfur dioxide\", \"density\", \"pH\", \"sulphates\", \"alcohol\", \"quality\"], :y=>[\"fixed acidity\", \"volatile acidity\", \"citric acid\", \"residual sugar\", \"chlorides\", \"free sulfur dioxide\", \"total sulfur dioxide\", \"density\", \"pH\", \"sulphates\", \"alcohol\", \"quality\"], :type=>:heatmap, :z=>[[1.0, 0.21900825635100266, 0.32443572544730104, -0.11198128107823678, 0.29819477170273795, -0.28273542836956794, -0.32905390129522527, 0.45890998228044255, -0.2527004683162333, 0.29956774438249995, -0.09545152256332995, -0.07674320790962286], [0.21900825635100266, 1.0000000000000002, -0.37798131705526566, -0.19601117434765503, 0.3771242764338664, -0.35255730641340716, -0.4144761946507196, 0.2712956478511821, 0.261454402742256, 0.2259836797410744, -0.03764038583468137, -0.26569947761148033], [0.32443572544730104, -0.37798131705526566, 1.0, 0.14245122598675725, 0.038998014089851825, 0.13312580951823125, 0.19524197598145304, 0.09615392906417021, -0.32980819113172016, 0.0561973001349725, -0.01049349217337923, 0.08553171718367827], [-0.11198128107823678, -0.19601117434765503, 0.14245122598675725, 0.9999999999999999, -0.12894049990326722, 0.4028706400566557, 0.4954815870066449, 0.552516950293483, -0.2673198368768104, -0.1859274052901841, -0.3594147708159969, -0.03698048458576987], [0.29819477170273795, 0.3771242764338664, 0.038998014089851825, -0.12894049990326722, 1.0000000000000002, -0.1950447852077002, -0.27963044744333765, 0.3626146565578091, 0.04470797955050543, 0.395593306547331, -0.2569155799729125, -0.20066550043510553], [-0.28273542836956794, -0.35255730641340716, 0.13312580951823125, 0.4028706400566557, -0.1950447852077002, 1.0000000000000002, 0.7209340813785245, 0.025716842144663805, -0.14585389640016552, -0.18845724880121598, -0.1798384348893394, 0.05546305861663346], [-0.32905390129522527, -0.4144761946507196, 0.19524197598145304, 0.4954815870066449, -0.27963044744333765, 0.7209340813785245, 1.0, 0.03239451234680212, -0.23841310290340784, -0.27572681991620573, -0.26573963910715914, -0.04138545385560974], [0.45890998228044255, 0.2712956478511821, 0.09615392906417021, 0.552516950293483, 0.3626146565578091, 0.025716842144663805, 0.03239451234680212, 1.0, 0.011686080687174244, 0.25947849534575335, -0.6867454216813362, -0.3058579060694189], [-0.2527004683162333, 0.261454402742256, -0.32980819113172016, -0.2673198368768104, 0.04470797955050543, -0.14585389640016552, -0.23841310290340784, 0.011686080687174244, 1.0000000000000002, 0.19212340657115304, 0.12124846709464465, 0.019505703714435736], [0.29956774438249995, 0.2259836797410744, 0.0561973001349725, -0.1859274052901841, 0.395593306547331, -0.18845724880121598, -0.27572681991620573, 0.25947849534575335, 0.19212340657115304, 0.9999999999999999, -0.003029194944255261, 0.038485445876515374], [-0.09545152256332995, -0.03764038583468137, -0.01049349217337923, -0.3594147708159969, -0.2569155799729125, -0.1798384348893394, -0.26573963910715914, -0.6867454216813362, 0.12124846709464465, -0.003029194944255261, 1.0, 0.44431852000752226], [-0.07674320790962286, -0.26569947761148033, 0.08553171718367827, -0.03698048458576987, -0.20066550043510553, 0.05546305861663346, -0.04138545385560974, -0.3058579060694189, 0.019505703714435736, 0.038485445876515374, 0.44431852000752226, 1.0]]}], @layout={:width=>500, :height=>500}, @config={:linkText=>\"Export to plot.ly\", :showLink=>true}, @embedded=true>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "#" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# heatmap of correlation matrix\n", "wine = Daru::DataFrame.from_csv('./wine.csv')\n", "plot(wine.corr, type: :heatmap, layout: { width: 500, height: 500 }).show" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Vector#plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`Vector#plot` assumes typically `index` to be \"xaxis\"." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## bar plot" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", "\n", "\n", "
\n", "\n", "" ], "text/plain": [ "#[\"000 ~\", \"010 ~\", \"020 ~\", \"030 ~\", \"040 ~\", \"050 ~\", \"060 ~\", \"070 ~\", \"080 ~\", \"090 ~\", \"100 ~\"], :y=>[9725, 11881, 11248, 13227, 15838, 15924, 18931, 13251, 9331, 2158, 53], :type=>:bar, :mode=>\"\"}], @layout={:width=>1000, :height=>500}, @config={:linkText=>\"Export to plot.ly\", :showLink=>true}, @embedded=true>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "#" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vector_generation = df_generation['total_count']\n", "plot(vector_generation, type: :bar).show" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## pie plot" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", "\n", "\n", "
\n", "\n", "" ], "text/plain": [ "#:pie, :labels=>[\"000 ~\", \"010 ~\", \"020 ~\", \"030 ~\", \"040 ~\", \"050 ~\", \"060 ~\", \"070 ~\", \"080 ~\", \"090 ~\", \"100 ~\"], :values=>[9725, 11881, 11248, 13227, 15838, 15924, 18931, 13251, 9331, 2158, 53], :sort=>false, :direction=>:clockwise}], @layout={:width=>1000, :height=>500}, @config={:linkText=>\"Export to plot.ly\", :showLink=>true}, @embedded=true>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "#" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot(vector_generation, type: :pie, opts: { direction: :clockwise, sort: false }).show" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## histogram" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", "\n", "\n", "
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171.2, 260.1, 78.4, 107.6, 37.4, 90.1, 518.3, 346.6, 305.1, 576.1, 46.2, 1061.2, 557.5, 557.5, 2055.2, 2109.2, 2020.6, 1689.2, 1564.1, 1920.7, 1205.5, 1367.1, 337.2, 3534.8, 4449.1, 5284.7, 7424.4, 1800.1, 1835.9, 7096.5, 836.7, 8243.5, 7111.6, 447.2, 932.1, 7899.2, 1501.7, 1722.0, 957.3, 930.1, 602.9, 833.3, 227.9, 242.3, 1662.2, 691.2, 531.6, 808.9, 226.0, 373.8, 553.0, 7838.5, 3516.2, 4833.0, 1229.7, 220.7, 761.6, 4648.1, 3701.6, 3103.0, 187.0, 198.7, 147.7, 5109.1, 279.7, 234.1, 9530.5, 2499.3, 635.9, 977.9, 712.2, 1225.8, 295.5, 371.8, 1704.2, 1704.2, 947.6, 947.6, 182.9, 198.7, 135.6, 132.6, 299.2, 316.0, 117.4, 159.2, 391.2, 379.6, 435.6, 315.9, 364.4, 387.1, 199.7, 170.5, 383.2, 494.4, 349.3, 445.7, 274.7, 260.1, 161.6, 288.4, 313.0, 851.1, 1116.4, 695.6, 271.2, 325.1, 220.2, 328.3, 524.1, 863.1, 759.0, 182.2, 368.8, 335.2, 402.0, 279.3, 522.8, 206.2, 520.9, 442.5, 170.2, 138.8, 119.0, 82.2, 310.5, 198.2, 198.2, 6015.7, 9299.5, 110.7, 14386.1, 4047.7, 12059.1, 10085.1, 17899.6, 18269.3, 17453.2, 18007.7, 11537.8, 16078.4, 18253.7, 11661.2, 15102.2, 13533.6, 20189.2, 16154.3, 21881.5, 16296.5, 19931.0, 16656.0, 14869.7, 12846.4, 12721.6, 13644.8, 3113.4, 7369.5, 12929.5, 11277.8, 1349.4, 8708.5, 6479.9, 10385.2, 5961.0, 10490.0, 9141.5, 6540.2, 8943.3, 10509.6, 9265.0, 5839.5, 12324.1, 6135.0, 7272.2, 9020.6, 4557.8, 7004.2, 4720.9, 5755.0, 1102.6, 12398.2, 12975.6, 11964.3, 156.3, 1990.3, 592.9, 24.3, 26.8, 95.7, 92.9, 82.8, 103.6, 100.1, 39.7, 48.2, 16.9, 101.1, 113.3, 33.6, 26.7, 26.7, 3745.4, 4832.5, 503.2, 8433.8, 8190.7, 9775.1, 13475.4, 7000.6, 15481.7, 9431.0, 8515.2, 6748.6, 10492.7, 7660.5, 11143.0, 7662.1, 6960.5, 7395.9, 6749.5, 6622.6, 8659.6, 7206.3, 9989.6, 5542.7, 15344.5, 15913.3, 13286.1, 10438.9, 11760.7, 7299.2, 2182.0, 7150.7, 1190.3, 226.0, 303.8, 156.8, 694.2, 7249.2, 7182.3, 4154.2, 3846.3, 4401.9, 5893.5, 1738.3, 6583.1, 3362.3, 1497.9, 1642.2, 2391.8, 8432.6, 1819.9, 4822.8, 7362.7, 572.2, 3732.8, 1920.6, 1920.6, 3552.3, 3552.3, 2379.4, 1919.3, 3251.3, 223.4, 500.0, 1247.2, 309.3, 52.4, 2495.3, 347.3, 149.2, 1169.8, 655.0, 431.4, 1227.4, 48.5, 188.7, 215.5, 43.1, 1118.2, 2441.0, 863.8, 501.8, 644.0, 574.2, 501.3, 2081.9, 259.5, 367.0, 480.2, 1053.6, 319.6, 248.1, 405.8, 300.4, 719.2, 3561.9, 4824.6, 919.2, 810.7, 465.6, 1719.0, 344.1, 317.3, 738.9, 102.5, 285.0, 239.6, 205.5, 339.6, 140.1, 184.7, 29.7, 97.2, 70.5, 236.8, 1065.9, 33.0, 247.6, 206.6, 261.8, 28.8, 106.0, 189.9, 179.3, 260.9, 180.3, 280.6, 248.8, 222.6, 99.9, 187.5, 98.2, 44.1, 39.5, 29.5, 537.0, 56.6, 205.4, 290.9, 114.2, 16.9, 23.1, 738.0, 1097.5, 367.5, 757.0, 63.9, 62.9, 61.9, 69.8, 79.6, 146.4, 42.0, 30.3, 155.0, 364.6, 73.2, 209.4, 540.3, 41.0, 74.4, 26.9, 37.8, 124.8, 609.7, 248.0, 62.2, 98.8, 110.2, 80.3, 150.6, 43.5, 236.4, 246.4, 291.0, 509.1, 95.0, 73.3, 63.1, 40.2, 194.2, 127.8, 64.6, 71.1, 82.4, 124.8, 73.4, 51.0, 42.6, 125.7, 391.9, 17.6, 76.9, 33.6, 5.8, 105.4, 100.0, 105.7, 112.5, 118.5, 303.2, 32.0, 361.3, 361.3, 341.1, 341.1, 402.6, 402.6, 14.0, 14.0, 54.1, 8.8, 7.8, 14.6, 110.6, 110.6, 23.5, 23.5, 63.9, 63.9, 182.6, 182.6, 22.0, 21.5, 37.1, 257.4, 293.9, 110.3, 339.8, 1553.4, 297.9, 19.3, 88.7, 587.5, 43.7, 37.8, 840.7, 1082.1, 223.8, 224.1, 224.4, 616.7, 98.2, 415.4, 17.1, 389.2, 446.2, 210.6, 239.1, 81.8, 141.3, 11.4, 7.4, 3.7, 351.1, 105.7, 446.1, 110.4, 857.2, 1078.3, 805.5, 592.0, 334.1, 1340.1, 95.7, 855.0, 92.8, 89.4, 137.2, 381.3, 60.3, 279.5, 308.6, 193.9, 988.4, 182.1, 300.5, 187.5, 64.2, 63.7, 292.2, 70.0, 86.6, 41.8, 65.9, 234.9, 417.4, 55.8, 281.0, 535.1, 391.9, 1850.5, 390.3, 146.3, 1150.1, 1416.4, 620.7, 14.3, 8.2, 37.9, 3.6, 4.3, 580.6, 1184.1, 1290.3, 273.8, 416.5, 416.5, 3826.3, 3826.3, 488.2, 334.5, 1321.2, 101.9, 90.1, 65.3, 115.0, 127.8, 108.7, 148.7, 207.4, 207.4, 201.6, 176.9, 316.2, 64.2, 53.1, 53.1, 71.5, 79.8, 36.5, 121.7, 192.4, 251.6, 74.7, 497.6, 736.2, 32.9, 96.9, 235.9, 269.7, 134.6, 40.5, 64.4, 1.6, 100.4, 795.9, 256.3, 334.3, 219.0, 371.0, 394.4, 275.8, 437.8, 474.0, 302.3, 1002.9, 408.3, 218.9, 218.9, 600.2, 122.8, 130.2, 15.6, 15.6, 33.6, 33.6, 102.8, 17.7, 37.1, 151.4, 151.4, 206.2, 117.9, 145.9, 121.6, 69.3, 69.3, 69.1, 153.3, 40.4, 17.6, 88.8, 90.1, 90.1, 85.7, 95.3, 193.3, 261.6, 76.5, 936.8, 1125.2, 252.2, 14.7, 415.5, 207.9, 127.1, 579.6, 49.2, 7.7, 100.3, 226.0, 275.1, 931.7, 509.2, 2.7, 1221.7, 457.0, 786.1, 77.9, 165.6, 36.6, 143.3, 222.9, 73.4, 177.4, 27.8, 53.0, 1026.0, 3089.7, 473.3, 805.7, 349.3, 534.2, 1839.5, 203.7, 346.7, 322.7, 210.1, 11.9, 158.9, 41.8, 201.0, 128.5, 128.2, 248.7, 30.3, 984.7, 259.5, 1363.4, 1646.4, 227.9, 227.9, 135.5, 301.4, 254.3, 41.7, 3.4, 47.9, 32.9, 83.1, 50.8, 44.9, 49.0, 39.8, 145.6, 191.1, 82.3, 1931.4, 1931.4, 116.1, 24.1, 299.1, 343.3, 100.8, 33.1, 160.7, 16.3, 333.2, 618.3, 52.7, 9.7, 15.5, 6.7, 158.7, 253.9, 65.1, 457.0, 884.6, 25.2, 69.2, 493.7, 30.0, 12.6, 67.8, 248.3, 781.7, 57.0, 255.7, 6.9, 5.4, 280.3, 289.1, 688.3, 222.8, 58.4, 43.2, 620.6, 159.9, 316.8, 4.0, 8.5, 469.5, 348.2, 445.9, 106.5, 302.6, 45.4, 6.0, 203.1, 407.3, 532.1, 129.5, 52.7, 59.8, 27.3, 16.4, 116.4, 211.7, 233.2, 376.2, 24.2, 237.2, 364.5, 175.2, 74.1, 319.8, 517.9, 484.1, 388.7, 703.7, 273.3, 199.9, 295.2, 290.8, 210.8, 712.2, 222.1, 455.6, 140.4, 36.1, 45.4, 23.7, 26.5, 17.0, 15.0, 64.2, 63.9, 64.6, 147.2, 121.9, 250.7, 115.3, 47.2, 80.7, 36.9, 48.4, 24.3, 174.4, 321.9, 106.0, 175.1, 166.4, 123.7, 304.4, 113.9, 355.6, 65.9, 164.6, 50.5, 187.6, 292.0, 44.2, 32.8, 14.8, 50.5, 11.7, 7.3, 12.6, 111.5, 15.1, 15.1, 29.6, 100.5, 88.7, 4.7, 20.1, 31.1, 22.3, 22.3, 3.1, 17.7, 26.8, 46.2, 44.1, 12.1, 12.5, 86.7, 86.4, 50.1, 337.8, 67.1, 52.0, 58.1, 64.1, 37.0, 60.3, 257.1, 214.4, 48.6, 12.0, 293.3, 293.3, 158.5, 580.6, 76.8, 53.1, 51.4, 49.7, 66.5, 85.4, 61.9, 52.4, 157.5, 173.7, 139.5, 8.2, 8.2, 195.9, 207.7, 149.8, 2036.3, 2048.7, 1723.4, 780.4, 1887.0, 48.9, 1367.6, 42.6, 458.2, 20.0, 7.0, 3.8, 50.4, 35.7, 10.0, 2.1, 91.1, 7.6, 1234.1, 1315.3, 764.1, 193.3, 743.6, 52.0, 6.9, 223.6, 54.8, 38.7, 119.6, 194.0, 167.7, 30.3, 26.9, 83.7, 83.0, 77.1, 74.2, 193.3, 230.8, 1252.7, 106.5, 200.9, 152.3, 76.6, 90.5, 21.7, 17.1, 731.6, 521.3, 1658.9, 1692.2, 1328.3, 1112.3, 1129.5, 551.0, 133.4, 472.6, 140.3, 48.2, 1842.9, 2353.2, 1126.8, 33.7, 156.2, 6.2, 4.2, 30.7, 93.6, 157.7, 1136.6, 761.0, 5.7, 43.2, 59.7, 44.1, 58.8, 30.9, 59.1, 11.9, 19.4, 42.7, 78.5, 14.2, 68.9, 41.1, 13.3, 337.8, 316.5, 472.6, 313.7, 2552.7, 3013.2, 2201.6, 434.3, 434.3, 343.9, 498.9, 164.3, 764.9, 1069.0, 448.5, 840.0, 82.8, 29.6, 374.2, 45.8, 10.4, 12.2, 1.2, 2.7, 698.0, 644.0, 3558.0, 3558.0, 86.2, 648.6, 327.9, 257.2, 49.6, 93.5, 40.1, 28.9, 332.5, 332.5, 4.9, 4.9, 483.9, 550.0, 174.2, 507.3, 408.7, 237.9, 2923.0, 933.1, 995.2, 834.1, 388.5, 514.0, 2325.4, 114.4, 1367.2, 20.5, 12.4, 27.0, 9.5, 2629.8, 1874.1, 649.1, 113.6, 203.1, 5391.7, 2735.1, 993.5, 2177.4, 320.3, 1369.8, 36.0, 1081.1, 1307.6, 95.2, 642.9, 1800.1, 339.3, 390.0, 124.2, 575.5, 317.4, 572.4, 308.0, 45.2, 1036.8, 1110.5, 526.7, 1027.7, 1387.9, 1173.4, 817.1, 1470.6, 289.3, 2030.5, 2625.9, 929.0, 438.1, 450.5, 353.5, 449.6, 732.6, 925.7, 222.7, 457.4, 781.6, 820.9, 670.9, 238.9, 394.1, 693.7, 785.1, 1251.7, 94.0, 220.1, 88.8, 49.5, 71.3, 526.9, 882.3, 447.2, 499.2, 493.7, 501.7, 520.2, 916.6, 561.4, 337.1, 438.9, 422.9, 458.6, 101.5, 180.7, 79.4, 86.0, 89.8, 89.7, 96.8, 78.0, 592.2, 592.2, 546.4, 3654.1, 1537.6, 151.5, 73.2, 1430.7, 16.3, 45.1, 6.9, 145.2, 145.2, 1434.8, 1646.7, 521.3, 6935.3, 8777.6, 9515.8, 9446.4, 8104.8, 8348.5, 8353.2, 9646.8, 9355.4, 7960.5, 6916.0, 3265.8, 7654.9, 4958.8, 6067.4, 8282.5, 7344.7, 1441.2, 961.6, 1602.6, 53.7, 3323.4, 3490.9, 2647.3, 3436.6, 1184.7, 2515.4, 3296.0, 1131.7, 1859.4, 304.2, 443.4, 2171.7, 718.4, 2602.0, 2008.3, 2889.6, 458.9, 1305.8, 296.2, 54.8, 44.6, 43.6, 36.2, 26.0, 2077.6, 1409.6, 1447.8, 1003.0, 986.1, 3305.6, 2342.1, 1720.6, 2092.7, 1000.0, 1488.1, 99.9, 296.2, 46.3, 23.3, 2483.6, 2531.1, 1852.4, 4185.9, 3858.3, 3386.7, 4512.9, 3008.8, 2413.7, 339.6, 457.7, 246.3, 248.2, 975.2, 1582.6, 316.7, 388.0, 2061.1, 3796.6, 5056.8, 2050.9, 4123.4, 4020.0, 4440.4, 5435.3, 3604.1, 884.5, 559.5, 971.0, 1871.6, 1871.6, 3143.0, 2745.5, 2407.0, 4283.7, 2372.3, 2321.2, 2415.1, 2327.1, 2327.1, 2261.9, 1652.9, 3001.6, 1767.9, 4535.8, 3305.2, 200.8, 987.5, 1063.7, 1602.3, 537.4, 542.7, 1642.4, 691.5, 1057.5, 619.7, 474.2, 668.0, 668.0, 19.6, 21.1, 20.1, 25.1, 30.4, 8.6, 9.9, 4.0, 321.0, 402.0, 133.8, 402.0, 1599.0, 640.0, 974.7, 134.4, 75.4, 297.7, 1266.2, 312.6, 109.2, 26.0, 1497.1, 1504.1, 1405.8, 624.7, 520.5, 762.0, 1640.7, 1496.5, 269.4, 595.1, 251.8, 740.2, 69.4, 18.0, 1026.9, 1979.3, 235.2, 494.3, 1023.7, 618.7, 103.7, 267.3, 395.7, 88.7, 198.5, 52.6, 70.3, 12.3, 208.1, 157.9, 379.8, 360.9, 104.5, 304.3, 174.5, 553.8, 716.3, 508.5, 105.2, 174.2, 311.9, 169.8, 104.7, 102.9, 55.6, 96.6, 436.1, 436.1, 1132.4, 1132.4, 523.1, 374.0, 1607.0, 1607.7, 96.0, 149.6, 212.9, 91.0, 558.0, 28.7, 125.8, 11.2, 74.5, 373.6, 64.4, 42.2, 46.7, 53.9, 22.7, 60.9, 78.7, 46.2, 72.4, 72.4, 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"metadata": {}, "source": [ "# generate_data\n", "\n", "You can build more complicated graphs with `generate_data` method." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", "\n", "\n", "
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5, 6, 7, 6, 6, 5, 5, 5, 6, 6, 6, 6, 6, 6, 5, 5, 5, 6, 5, 5, 4, 6, 5, 6, 5, 5, 7, 5, 5, 7, 6, 6, 7, 7, 7, 5, 6, 6, 5, 6, 5, 6, 4, 5, 6, 7, 7, 7, 7, 7, 7, 7, 5, 6, 5, 6, 7, 5, 5, 5, 8, 6, 5, 5, 5, 6, 6, 7, 5, 5, 8, 5, 5, 5, 6, 5, 4, 6, 6, 5, 5, 6, 5, 6, 7, 7, 5, 6, 6, 5, 6, 7, 5, 5, 5, 6, 5, 5, 6, 6, 5, 6, 6, 5, 5, 5, 5, 6, 5, 6, 5, 6, 5, 5, 6, 6, 3, 5, 5, 5, 5, 7, 5, 6, 6, 6, 5, 6, 6, 5, 7, 6, 6, 5, 6, 7, 7, 5, 5, 6, 5, 5, 6, 6, 5, 5, 6, 6, 6, 7, 6, 5, 6, 5, 6, 5, 6, 7, 6, 7, 6, 6, 6, 6, 6, 5, 5, 6, 6, 6, 7, 6, 6, 5, 6, 6, 6, 6, 4, 5, 5, 7, 5, 5, 5, 4, 6, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 5, 5, 5, 6, 6, 6, 6, 7, 5, 5, 4, 6, 6, 7, 7, 7, 7, 7, 7, 6, 6, 7, 6, 7, 7, 6, 5, 7, 6, 6, 5, 7, 5, 5, 5, 6, 5, 5, 5, 6, 6, 8, 4, 6, 5, 6, 5, 6, 6, 5, 5, 6, 6, 6, 6, 6, 7, 5, 5, 5, 5, 6, 7, 5, 6, 5, 5, 6, 6, 6, 5, 7, 7, 5, 4, 6, 4, 6, 6, 7, 6, 6, 4, 6, 7, 7, 6, 7, 6, 5, 7, 7, 6, 7, 6, 6, 6, 6, 6, 6, 5, 7, 7, 7, 5, 7, 7, 6, 6, 6, 5, 6, 6, 7, 6, 6, 5, 6, 6, 6, 7, 6, 6, 6, 5, 5, 6, 6, 5, 4, 5, 5, 6, 6, 6, 6, 5, 6, 6, 5, 5, 5, 6, 5, 6, 5, 5, 5, 4, 5, 5, 7, 7, 7, 7, 7, 6, 6, 6, 7, 6, 5, 7, 7, 6, 5, 6, 6, 6, 5, 5, 6, 5, 5, 8, 6, 5, 6, 6, 5, 6, 7, 5, 4, 6, 5, 6, 6, 6, 6, 6, 6, 5, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 6, 5, 6, 5, 6, 6, 5, 5, 7, 7, 7, 5, 6, 6, 6, 5, 6, 6, 6, 6, 6, 6, 6, 8, 7, 5, 6, 6, 6, 7, 5, 5, 5, 5, 7, 6, 5, 5, 6, 6, 5, 6, 5, 6, 7, 6, 6, 6, 6, 6, 6, 7, 7, 6, 7, 5, 6, 6, 5, 5, 5, 5, 7, 7, 7, 7, 7, 7, 5, 5, 7, 7, 8, 6, 7, 7, 5, 6, 5, 6, 7, 6, 6, 7, 5, 6, 6, 6, 7, 7, 5, 5, 6, 6, 5, 6, 5, 6, 6, 6, 8, 6, 5, 5, 6, 6, 6, 6, 5, 6, 6, 6, 7, 6, 6, 6, 6, 4, 4, 5, 5, 5, 4, 6, 5, 6, 6, 4, 4, 7, 5, 7, 7, 6, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 7, 6, 6, 7, 6, 5, 5, 6, 6, 6, 6, 6, 6, 6, 4, 5, 5, 5, 5, 6, 6, 6, 7, 6, 6, 6, 6, 7, 7, 6, 5, 6, 5, 6, 6, 6, 6, 7, 5, 4, 6, 6, 5, 5, 6, 6, 6, 6, 5, 6, 6, 5, 5, 6, 5, 5, 5, 6, 6, 6, 6, 5, 5, 6, 6, 6, 6, 6, 7, 6, 6, 6, 5, 6, 6, 7, 7, 5, 7, 7, 7, 5, 6, 7, 6, 6, 5, 5, 5, 5, 5, 5, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 7, 6, 5, 6, 6, 5, 6, 6, 6, 5, 6, 5, 6, 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6, 7, 7, 6, 7, 6, 5, 5, 6, 6, 7, 6, 6, 7, 6, 6, 6, 7, 6, 6, 6, 7, 6, 6, 6, 6, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 5, 5, 6, 6, 6, 6, 6, 6, 7, 6, 6, 5, 6, 6, 5, 5, 3, 6, 6, 5, 6, 5, 6, 7, 6, 6, 6, 7, 7, 6, 6, 6, 6, 7, 7, 6, 8, 8, 7, 6, 6, 6, 6, 5, 7, 4, 6, 6, 6, 6, 4, 5, 5, 6, 6, 5, 7, 8, 8, 5, 6, 6, 6, 6, 6, 5, 7, 7, 6, 5, 6, 7, 7, 8, 7, 4, 6, 6, 6, 5, 6, 5, 6, 7, 5, 6, 7, 6, 6, 6, 6, 5, 6, 6, 7, 5, 6, 6, 6, 5, 7, 6, 6, 5, 6, 7, 6, 5, 6, 6, 4, 7, 6, 6, 5, 6, 5, 6, 7, 6, 5, 6, 5, 5, 5, 6, 6, 6, 6, 5, 6, 6, 6, 6, 7, 5, 6, 6, 6, 6, 6, 7, 6, 5, 6, 6, 6, 7, 5, 4, 6, 6, 6, 5, 6, 5, 6, 7, 7, 5, 6, 6, 6, 5, 6, 5, 6, 7, 6], :type=>:histogram, :name=>\"white\", :marker=>{:color=>\"white\"}}], @layout={:paper_bgcolor=>\"gray\", :plot_bgcolor=>\"gray\", :font=>{:color=>\"white\"}}, @config={:linkText=>\"Export to plot.ly\", :showLink=>true}, @embedded=true>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "#" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reds = wine.where(wine['type'].eq 'red')\n", "whites = wine.where(wine['type'].eq 'white')\n", "\n", "reds_data = generate_data(reds['quality'], type: :histogram, opts: { name: 'red', marker: { color: 'red' }})\n", "whites_data = generate_data(whites['quality'], type: :histogram, opts: { name: 'white', marker: { color: 'white' }})\n", "\n", "Plotly::Plot.new(\n", " data: reds_data + whites_data,\n", " layout: { paper_bgcolor: 'gray', plot_bgcolor: 'gray', font: { color: 'white' } }\n", ").show" ] } ], "metadata": { "kernelspec": { "display_name": "Ruby 2.4.1", "language": "ruby", "name": "ruby" }, "language_info": { "file_extension": ".rb", "mimetype": "application/x-ruby", "name": "ruby", "version": "2.4.1" } }, "nbformat": 4, "nbformat_minor": 1 }