\u30c7\u30fc\u30bf\u53ef\u8996\u5316\u30b9\u30ad\u30eb\u306f\u3001\u73fe\u4ee3\u306e\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30c6\u30a3\u30b9\u30c8\u3084\u30a8\u30f3\u30b8\u30cb\u30a2\u306b\u3068\u3063\u3066\u4e0d\u53ef\u6b20\u306e\u80fd\u529b\u3067\u3059\u3002\u4e2d\u3067\u3082\u3001Plotly\u306f\u6ce8\u76ee\u3092\u96c6\u3081\u3066\u3044\u308b\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u4e00\u3064\u3067\u3059\u3002\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001Plotly\u306e\u57fa\u672c\u304b\u3089\u5b9f\u8df5\u7684\u306a\u6d3b\u7528\u65b9\u6cd5\u307e\u3067\u3001Plotly\u30b9\u30ad\u30eb\u3092\u8eab\u306b\u3064\u3051\u308b\u305f\u3081\u306e\u30dd\u30a4\u30f3\u30c8\u3092\u8a73\u3057\u304f\u89e3\u8aac\u3057\u307e\u3059\u3002Plotly\u3092\u4f7f\u3044\u3053\u306a\u305b\u3070\u3001\u30c7\u30fc\u30bf\u53ef\u8996\u5316\u3067\u4e00\u6b69\u5148\u3078\u9032\u307f\u3001\u30ad\u30e3\u30ea\u30a2\u30a2\u30c3\u30d7\u3092\u5b9f\u73fe\u3067\u304d\u308b\u306f\u305a\u3067\u3059\u3002\u305c\u3072\u3001\u3053\u306e\u8a18\u4e8b\u3092\u53c2\u8003\u306b\u3001Plotly\u306e\u5b66\u7fd2\u3092\u59cb\u3081\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n\n\n\n
\u76ee\u6b21 <\/p>\n
\u30c7\u30fc\u30bf\u53ef\u8996\u5316\u306e\u91cd\u8981\u6027\u304c\u9ad8\u307e\u308b\u4e2d\u3001Python\u30e6\u30fc\u30b6\u30fc\u306e\u9593\u3067\u6ce8\u76ee\u3092\u96c6\u3081\u3066\u3044\u308b\u306e\u304cPlotly\u3067\u3059\u3002Plotly\u306f\u3001\u30a4\u30f3\u30bf\u30e9\u30af\u30c6\u30a3\u30d6\u3067\u7f8e\u3057\u3044\u30b0\u30e9\u30d5\u3084\u30c1\u30e3\u30fc\u30c8\u3092\u4f5c\u6210\u3059\u308b\u305f\u3081\u306e\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u306ePython\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u3059\u3002\u30a6\u30a7\u30d6\u30d6\u30e9\u30a6\u30b6\u4e0a\u3067\u52d5\u4f5c\u3057\u3001\u30e6\u30fc\u30b6\u30fc\u304c\u30b0\u30e9\u30d5\u3068\u76f4\u611f\u7684\u306b\u5bfe\u8a71\u3067\u304d\u308b\u306e\u304c\u5927\u304d\u306a\u7279\u5fb4\u3067\u3059\u3002<\/p>\n\n\n\n
Plotly\u306f\u3001\u30c7\u30fc\u30bf\u306e\u63a2\u7d22\u3084\u7406\u89e3\u3092\u6df1\u3081\u308b\u305f\u3081\u306e\u30d1\u30ef\u30d5\u30eb\u306a\u30c4\u30fc\u30eb\u3067\u3059\u3002\u6563\u5e03\u56f3\u3001\u6298\u308c\u7dda\u56f3\u3001\u68d2\u30b0\u30e9\u30d5\u3001\u30d2\u30fc\u30c8\u30de\u30c3\u30d7\u30013D\u30b0\u30e9\u30d5\u306a\u3069\u3001\u591a\u69d8\u306a\u30b0\u30e9\u30d5\u30bf\u30a4\u30d7\u306b\u5bfe\u5fdc\u3057\u3066\u304a\u308a\u3001\u3042\u3089\u3086\u308b\u30c7\u30fc\u30bf\u3092\u8996\u899a\u7684\u306b\u8868\u73fe\u3067\u304d\u307e\u3059\u3002\u307e\u305f\u3001\u30b0\u30e9\u30d5\u306e\u30c7\u30b6\u30a4\u30f3\u3084\u8272\u3001\u30ec\u30a4\u30a2\u30a6\u30c8\u306a\u3069\u3092\u7d30\u304b\u304f\u30ab\u30b9\u30bf\u30de\u30a4\u30ba\u3067\u304d\u308b\u305f\u3081\u3001\u81ea\u5206\u597d\u307f\u306e\u53ef\u8996\u5316\u3092\u4f5c\u6210\u3067\u304d\u308b\u306e\u3082\u9b45\u529b\u306e\u4e00\u3064\u3067\u3059\u3002<\/p>\n\n\n\n
Plotly\u3067\u4f5c\u6210\u3057\u305f\u30b0\u30e9\u30d5\u306fHTML\u5f62\u5f0f\u3067\u51fa\u529b\u3055\u308c\u308b\u305f\u3081\u3001\u30a6\u30a7\u30d6\u4e0a\u3067\u7c21\u5358\u306b\u5171\u6709\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u3055\u3089\u306b\u3001Python\u306e\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30c6\u30a3\u30b9\u30c8\u3084Jupyter Notebook\u30e6\u30fc\u30b6\u30fc\u306b\u4eba\u6c17\u306a\u306e\u304c\u3001pandas\u3001numpy\u3001scipy \u306a\u3069\u306e\u4e3b\u8981\u306a\u30c7\u30fc\u30bf\u51e6\u7406\u30e9\u30a4\u30d6\u30e9\u30ea\u3068\u30b7\u30fc\u30e0\u30ec\u30b9\u306b\u9023\u643a\u3067\u304d\u308b\u70b9\u3067\u3059\u3002<\/p>\n\n\n\n
\u4ee5\u4e0b\u306f\u3001Plotly\u3092\u4f7f\u3063\u305f\u57fa\u672c\u7684\u306a\u6298\u308c\u7dda\u30b0\u30e9\u30d5\u306e\u4f8b\u3067\u3059\u3002<\/p>\n\n\n\n
import plotly.graph_objects as go\n\nx = [1, 2, 3, 4, 5]\ny = [1, 3, 2, 4, 5]\n\nfig = go.Figure(data=go.Scatter(x=x, y=y, mode='lines'))\nfig.update_layout(\n title='Basic Line Plot',\n xaxis_title='X Axis',\n yaxis_title='Y Axis'\n)\n\nfig.show()<\/pre>\n\n\n\nPlotly\u304c\u652f\u6301\u3055\u308c\u308b5\u3064\u306e\u7406\u7531<\/h3>\n\n\n\n
\n
- \u30a4\u30f3\u30bf\u30e9\u30af\u30c6\u30a3\u30d6\u53ef\u8996\u5316\u306b\u3088\u308a\u3001\u30c7\u30fc\u30bf\u306e\u63a2\u7d22\u3084\u7406\u89e3\u304c\u6df1\u307e\u308b<\/li>\n\n\n\n
- \u30b0\u30e9\u30d5\u306e\u30c7\u30b6\u30a4\u30f3\u6027\u304c\u9ad8\u304f\u3001\u30d7\u30ec\u30bc\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u3084\u30ec\u30dd\u30fc\u30c8\u306b\u9069\u3057\u3066\u3044\u308b<\/li>\n\n\n\n
- Python\u3084Jupyter Notebook\u3068\u306e\u89aa\u548c\u6027\u304c\u9ad8\u304f\u3001\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30c6\u30a3\u30b9\u30c8\u306b\u597d\u307e\u308c\u308b<\/li>\n\n\n\n
- \u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u3067\u6d3b\u767a\u306a\u30b3\u30df\u30e5\u30cb\u30c6\u30a3\u304c\u3042\u308a\u3001\u7d99\u7d9a\u7684\u306b\u6a5f\u80fd\u304c\u62e1\u5f35\u3055\u308c\u3066\u3044\u308b<\/li>\n\n\n\n
- \u7121\u6599\u306e\u30d7\u30e9\u30f3\u3067\u3082\u5341\u5206\u306a\u6a5f\u80fd\u304c\u4f7f\u7528\u3067\u304d\u3001\u624b\u8efd\u306b\u59cb\u3081\u3089\u308c\u308b<\/li>\n<\/ol>\n\n\n\n
Plotly\u306f\u3001\u30c7\u30fc\u30bf\u53ef\u8996\u5316\u306e\u65b0\u3057\u3044\u53ef\u80fd\u6027\u3092\u5207\u308a\u62d3\u304f\u30c4\u30fc\u30eb\u3068\u3057\u3066\u3001\u4eca\u5f8c\u3055\u3089\u306b\u6ce8\u76ee\u304c\u96c6\u307e\u308b\u3067\u3057\u3087\u3046\u3002\u6b21\u7ae0\u3067\u306f\u3001Plotly\u3092\u30bc\u30ed\u304b\u3089\u59cb\u3081\u308b\u305f\u3081\u306e3\u30b9\u30c6\u30c3\u30d7\u30ac\u30a4\u30c9\u3092\u3054\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
Plotly\u3092\u30bc\u30ed\u304b\u3089\u59cb\u3081\u308b3\u30b9\u30c6\u30c3\u30d7\u30ac\u30a4\u30c9<\/h2>\n\n\n\n
Plotly\u3092\u4f7f\u3063\u305f\u30c7\u30fc\u30bf\u53ef\u8996\u5316\u306b\u6311\u6226\u3057\u3066\u307f\u305f\u3044\u3051\u3069\u3001\u3069\u3053\u304b\u3089\u59cb\u3081\u308c\u3070\u3044\u3044\u306e\u304b\u308f\u304b\u3089\u306a\u3044\u65b9\u306e\u305f\u3081\u306b\u3001\u3053\u3053\u3067\u306fPlotly\u3092\u5c0e\u5165\u304b\u3089\u30a4\u30f3\u30bf\u30e9\u30af\u30c6\u30a3\u30d6\u53ef\u8996\u5316\u306e\u4f5c\u6210\u307e\u3067\u30923\u3064\u306e\u30b9\u30c6\u30c3\u30d7\u3067\u89e3\u8aac\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
Step1: \u74b0\u5883\u8a2d\u5b9a \u2013 \u305f\u3063\u305f15\u5206\u3067\u5b8c\u4e86\uff01<\/h3>\n\n\n\n
\u307e\u305a\u306f\u3001Plotly\u3092\u4f7f\u3048\u308b\u74b0\u5883\u3092\u6574\u3048\u307e\u3057\u3087\u3046\u3002Python\u3068Plotly\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u306f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306bpip\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3059\u308b\u3060\u3051\u3067\u3059\u3002<\/p>\n\n\n\n
pip install plotly<\/pre>\n\n\n\n\u6b21\u306b\u3001Jupyter Notebook\u306a\u3069\u306e\u958b\u767a\u74b0\u5883\u3092\u6e96\u5099\u3057\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306bPlotly\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
import plotly.graph_objects as go<\/pre>\n\n\n\n\u3053\u308c\u3067\u3001Plotly\u3092\u4f7f\u3046\u305f\u3081\u306e\u74b0\u5883\u8a2d\u5b9a\u306f\u5b8c\u4e86\u3067\u3059\u3002<\/p>\n\n\n\n
Step2: \u57fa\u672c\u7684\u306a\u30b0\u30e9\u30d5\u4f5c\u6210 \u2013 1\u6642\u9593\u3067\u30de\u30b9\u30bf\u30fc<\/h3>\n\n\n\n
Plotly\u3067\u306f\u3001
go.Scatter()<\/code>\u3001go.Bar()<\/code>\u3001go.Line()<\/code>\u306a\u3069\u306e\u95a2\u6570\u3092\u4f7f\u3063\u3066\u3001\u6563\u5e03\u56f3\u3001\u68d2\u30b0\u30e9\u30d5\u3001\u6298\u308c\u7dda\u56f3\u306a\u3069\u306e\u57fa\u672c\u7684\u306a\u30b0\u30e9\u30d5\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002\u4f8b\u3048\u3070\u3001\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u306f\u7c21\u5358\u306a\u68d2\u30b0\u30e9\u30d5\u3092\u4f5c\u6210\u3059\u308b\u4f8b\u3067\u3059\u3002<\/p>\n\n\n\nimport plotly.graph_objects as go\n\nx = ['A', 'B', 'C', 'D', 'E']\ny = [10, 15, 13, 17, 12]\n\nfig = go.Figure(data=[go.Bar(x=x, y=y)])\nfig.update_layout(\n title='Basic Bar Chart',\n xaxis_title='Categories',\n yaxis_title='Values'\n)\n\nfig.show()<\/pre>\n\n\n\n
fig.update_layout()<\/code>\u3092\u4f7f\u3046\u3053\u3068\u3067\u3001\u30b0\u30e9\u30d5\u306e\u30bf\u30a4\u30c8\u30eb\u3084\u8ef8\u30e9\u30d9\u30eb\u306a\u3069\u306e\u30ec\u30a4\u30a2\u30a6\u30c8\u3092\u81ea\u7531\u306b\u30ab\u30b9\u30bf\u30de\u30a4\u30ba\u3067\u304d\u307e\u3059\u3002fig.show()<\/code>\u3067\u30b0\u30e9\u30d5\u3092\u8868\u793a\u3059\u308b\u3068\u3001\u30a6\u30a7\u30d6\u30d6\u30e9\u30a6\u30b6\u4e0a\u3067\u5bfe\u8a71\u7684\u306b\u64cd\u4f5c\u3067\u304d\u308b\u30b0\u30e9\u30d5\u304c\u73fe\u308c\u307e\u3059\u3002<\/p>\n\n\n\nStep3: \u30a4\u30f3\u30bf\u30e9\u30af\u30c6\u30a3\u30d6\u6a5f\u80fd\u306e\u8ffd\u52a0 \u2013 \u672c\u683c\u7684\u306a\u53ef\u8996\u5316\u306b\u6311\u6226<\/h3>\n\n\n\n
Plotly\u306e\u771f\u9ac4\u306f\u3001\u30a4\u30f3\u30bf\u30e9\u30af\u30c6\u30a3\u30d6\u6027\u306b\u3042\u308a\u307e\u3059\u3002\u4f8b\u3048\u3070\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u30c7\u30fc\u30bf\u70b9\u306b\u30db\u30d0\u30fc\u3057\u305f\u969b\u306b\u8a73\u7d30\u60c5\u5831\u3092\u8868\u793a\u3055\u305b\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
import plotly.graph_objects as go\n\nx = [1, 2, 3, 4, 5]\ny = [1, 3, 2, 4, 5]\ntext = ['A', 'B', 'C', 'D', 'E']\n\nfig = go.Figure(data=go.Scatter(x=x, y=y, mode='markers', text=text, hoverinfo='text+x+y'))\nfig.update_layout(title='Scatter Plot with Hover Text')\n\nfig.show()<\/pre>\n\n\n\n\u307e\u305f\u3001
fig.update_layout()<\/code>\u3067x\u8ef8\u3001y\u8ef8\u306e\u30aa\u30d7\u30b7\u30e7\u30f3\u3092\u8a2d\u5b9a\u3059\u308b\u3053\u3068\u3067\u3001\u30ba\u30fc\u30e0\u3001\u30d1\u30f3\u3001\u9078\u629e\u6a5f\u80fd\u3092\u6709\u52b9\u306b\u3057\u305f\u308a\u3001frames<\/code>\u3068animation<\/code>\u30aa\u30d7\u30b7\u30e7\u30f3\u3092\u4f7f\u3063\u3066\u30a2\u30cb\u30e1\u30fc\u30b7\u30e7\u30f3\u3092\u8ffd\u52a0\u3057\u305f\u308a\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n\u3055\u3089\u306b\u3001
go.Figure()<\/code>\u306b\u8907\u6570\u306e\u30b0\u30e9\u30d5\u3092\u8ffd\u52a0\u3057\u305f\u308a\u3001make_subplots()<\/code>\u3067\u8907\u6570\u306e\u30b0\u30e9\u30d5\u3092\u914d\u7f6e\u3057\u305f\u308a\u3059\u308b\u3053\u3068\u3067\u3001\u8907\u96d1\u306a\u30c0\u30c3\u30b7\u30e5\u30dc\u30fc\u30c9\u3092\u4f5c\u6210\u3059\u308b\u3053\u3068\u3082\u53ef\u80fd\u3067\u3059\u3002<\/p>\n\n\n\nPlotly\u306e\u57fa\u672c\u7684\u306a\u4f7f\u3044\u65b9\u3092\u30de\u30b9\u30bf\u30fc\u3059\u308c\u3070\u3001\u3042\u306a\u305f\u306e\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u306b\u65b0\u3057\u3044\u6d1e\u5bdf\u3092\u3082\u305f\u3089\u3059\u3053\u3068\u304c\u3067\u304d\u308b\u3067\u3057\u3087\u3046\u3002\u6b21\u7ae0\u3067\u306f\u3001Plotly\u3092\u4f7f\u3063\u305f\u5b9f\u8df5\u7684\u306a\u30c7\u30fc\u30bf\u53ef\u8996\u5316\u306e\u4e8b\u4f8b\u3092\u3054\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
Plotly\u3092\u4f7f\u3063\u305f\u5b9f\u8df5\u7684\u306a\u30c7\u30fc\u30bf\u53ef\u8996\u5316\u4e8b\u4f8b<\/h2>\n\n\n\n
Plotly\u306f\u3001\u30d3\u30b8\u30cd\u30b9\u3084\u7814\u7a76\u306e\u73fe\u5834\u3067\u5e45\u5e83\u304f\u6d3b\u7528\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u3053\u3053\u3067\u306f\u3001Plotly\u3092\u4f7f\u3063\u305f\u5b9f\u8df5\u7684\u306a\u30c7\u30fc\u30bf\u53ef\u8996\u5316\u306e\u4e8b\u4f8b\u30923\u3064\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
\u4e8b\u4f8b1: \u4f01\u696d\u306eKPI\u7ba1\u7406\u30c0\u30c3\u30b7\u30e5\u30dc\u30fc\u30c9<\/h3>\n\n\n\n
\u4f01\u696d\u306e\u7d4c\u55b6\u306b\u304a\u3044\u3066\u3001\u58f2\u4e0a\u3001\u5229\u76ca\u3001\u9867\u5ba2\u6570\u3001\u5f93\u696d\u54e1\u6e80\u8db3\u5ea6\u306a\u3069\u306e\u91cd\u8981\u696d\u7e3e\u8a55\u4fa1\u6307\u6a19\uff08KPI\uff09\u3092\u5e38\u306b\u628a\u63e1\u3057\u3066\u304a\u304f\u3053\u3068\u306f\u6b20\u304b\u305b\u307e\u305b\u3093\u3002Plotly\u3092\u4f7f\u3048\u3070\u3001\u3053\u308c\u3089\u306e\u6307\u6a19\u3092\u4e00\u76ee\u3067\u628a\u63e1\u3067\u304d\u308b\u30c0\u30c3\u30b7\u30e5\u30dc\u30fc\u30c9\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
\u4ee5\u4e0b\u306f\u3001\u58f2\u4e0a\u3068\u5229\u76ca\u306e\u30c7\u30fc\u30bf\u3092\u8868\u793a\u3059\u308b\u30c0\u30c3\u30b7\u30e5\u30dc\u30fc\u30c9\u306e\u4f8b\u3067\u3059\u3002\u30c9\u30ed\u30c3\u30d7\u30c0\u30a6\u30f3\u30e1\u30cb\u30e5\u30fc\u3092\u4f7f\u3063\u3066\u3001\u8868\u793a\u3059\u308b\u6307\u6a19\u3092\u5207\u308a\u66ff\u3048\u3089\u308c\u308b\u3088\u3046\u306b\u306a\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
import plotly.graph_objects as go\n\n# \u30c7\u30fc\u30bf\u306e\u6e96\u5099\nsales = {'2021\u5e74\u7b2c1\u56db\u534a\u671f': 100, '2021\u5e74\u7b2c2\u56db\u534a\u671f': 120, '2021\u5e74\u7b2c3\u56db\u534a\u671f': 110, '2021\u5e74\u7b2c4\u56db\u534a\u671f': 130}\nprofit = {'2021\u5e74\u7b2c1\u56db\u534a\u671f': 20, '2021\u5e74\u7b2c2\u56db\u534a\u671f': 25, '2021\u5e74\u7b2c3\u56db\u534a\u671f': 22, '2021\u5e74\u7b2c4\u56db\u534a\u671f': 28}\n\n# \u30ec\u30a4\u30a2\u30a6\u30c8\u306e\u4f5c\u6210\nlayout = go.Layout(\n title='\u4f01\u696d\u306eKPI\u7ba1\u7406\u30c0\u30c3\u30b7\u30e5\u30dc\u30fc\u30c9',\n xaxis=dict(title='\u56db\u534a\u671f'),\n yaxis=dict(title='\u5024'),\n updatemenus=[dict(\n active=0,\n buttons=list([\n dict(label=\"\u58f2\u4e0a\",\n method=\"update\",\n args=[{\"y\": [list(sales.values())]}]),\n dict(label=\"\u5229\u76ca\",\n method=\"update\",\n args=[{\"y\": [list(profit.values())]}])\n ])\n )]\n)\n\n# \u30b0\u30e9\u30d5\u306e\u4f5c\u6210\nfig = go.Figure(data=[go.Bar(x=list(sales.keys()), y=list(sales.values()))], layout=layout)\nfig.show()<\/pre>\n\n\n\n\u3053\u306e\u3088\u3046\u306a\u30c0\u30c3\u30b7\u30e5\u30dc\u30fc\u30c9\u3092\u4f5c\u6210\u3059\u308b\u3053\u3068\u3067\u3001\u7d4c\u55b6\u9663\u3084\u793e\u54e1\u304c\u4f1a\u793e\u306e\u73fe\u72b6\u3092\u7d20\u65e9\u304f\u628a\u63e1\u3057\u3001\u30c7\u30fc\u30bf\u306b\u57fa\u3065\u3044\u305f\u610f\u601d\u6c7a\u5b9a\u3092\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
\u4e8b\u4f8b2: \u6c17\u8c61\u30c7\u30fc\u30bf\u306e\u53ef\u8996\u5316\u3068\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3<\/h3>\n\n\n\n
Plotly\u306f\u3001\u6c17\u8c61\u30c7\u30fc\u30bf\u306e\u53ef\u8996\u5316\u306b\u3082\u5a01\u529b\u3092\u767a\u63ee\u3057\u307e\u3059\u3002\u6c17\u6e29\u3001\u6e7f\u5ea6\u3001\u98a8\u901f\u3001\u964d\u6c34\u91cf\u306a\u3069\u306e\u30c7\u30fc\u30bf\u3092\u3001\u30d2\u30fc\u30c8\u30de\u30c3\u30d7\u30843D\u30b5\u30fc\u30d5\u30a7\u30b9\u30d7\u30ed\u30c3\u30c8\u3067\u8868\u73fe\u3059\u308b\u3053\u3068\u3067\u3001\u5929\u6c17\u306e\u30d1\u30bf\u30fc\u30f3\u3084\u5909\u5316\u3092\u76f4\u611f\u7684\u306b\u7406\u89e3\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
\u307e\u305f\u3001\u904e\u53bb\u306e\u30c7\u30fc\u30bf\u3092\u57fa\u306b\u3001\u672a\u6765\u306e\u6c17\u8c61\u30d1\u30bf\u30fc\u30f3\u3092\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3057\u3001\u30a2\u30cb\u30e1\u30fc\u30b7\u30e7\u30f3\u3067\u8868\u793a\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002\u3053\u306e\u3088\u3046\u306a\u53ef\u8996\u5316\u306f\u3001\u8fb2\u696d\u3084\u518d\u751f\u53ef\u80fd\u30a8\u30cd\u30eb\u30ae\u30fc\u306e\u5206\u91ce\u3067\u3001\u610f\u601d\u6c7a\u5b9a\u3092\u30b5\u30dd\u30fc\u30c8\u3059\u308b\u305f\u3081\u306b\u6d3b\u7528\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u4e8b\u4f8b3: SNS\u5206\u6790\u306e\u53ef\u8996\u5316\u3068\u4eca\u5f8c\u306e\u5c55\u958b\u4e88\u6e2c<\/h3>\n\n\n\n
SNS\u306e\u666e\u53ca\u306b\u4f34\u3044\u3001Twitter\u3084Facebook\u306a\u3069\u306e\u30d7\u30e9\u30c3\u30c8\u30d5\u30a9\u30fc\u30e0\u304b\u3089\u5f97\u3089\u308c\u308b\u30c7\u30fc\u30bf\u3092\u5206\u6790\u3059\u308b\u3053\u3068\u304c\u91cd\u8981\u306b\u306a\u3063\u3066\u3044\u307e\u3059\u3002Plotly\u3092\u4f7f\u3048\u3070\u3001SNS\u30c7\u30fc\u30bf\u304b\u3089\u5f97\u3089\u308c\u308b\u81a8\u5927\u306a\u60c5\u5831\u3092\u3001\u30ef\u30fc\u30c9\u30af\u30e9\u30a6\u30c9\u3084\u30d0\u30d6\u30eb\u30c1\u30e3\u30fc\u30c8\u306a\u3069\u3067\u8996\u899a\u7684\u306b\u8868\u73fe\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
\u4f8b\u3048\u3070\u3001\u30e6\u30fc\u30b6\u30fc\u306e\u30bb\u30f3\u30c1\u30e1\u30f3\u30c8\u5206\u6790\u306e\u7d50\u679c\u3092\u68d2\u30b0\u30e9\u30d5\u3084\u30d1\u30a4\u30c1\u30e3\u30fc\u30c8\u3067\u8868\u793a\u3057\u305f\u308a\u3001\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u304b\u3089\u5c06\u6765\u306e\u30c8\u30ec\u30f3\u30c9\u3092\u4e88\u6e2c\u3057\u3066\u30a8\u30ea\u30a2\u30c1\u30e3\u30fc\u30c8\u3067\u53ef\u8996\u5316\u3057\u305f\u308a\u3059\u308b\u3053\u3068\u3067\u3001\u30de\u30fc\u30b1\u30c6\u30a3\u30f3\u30b0\u6226\u7565\u306e\u7acb\u6848\u3084\u5546\u54c1\u958b\u767a\u306b\u5f79\u7acb\u3066\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
\u4ee5\u4e0a\u306e\u4e8b\u4f8b\u304b\u3089\u308f\u304b\u308b\u3088\u3046\u306b\u3001Plotly\u306f\u69d8\u3005\u306a\u5206\u91ce\u3067\u5b9f\u8df5\u7684\u306a\u30c7\u30fc\u30bf\u53ef\u8996\u5316\u306b\u6d3b\u7528\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u6b21\u7ae0\u3067\u306f\u3001Python\u30e6\u30fc\u30b6\u30fc\u306e\u305f\u3081\u306b\u3001Plotly\u3092\u4f7f\u3044\u3053\u306a\u3059\u305f\u3081\u306e\u30b3\u30c4\u3068Tips\u3092\u3054\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
Python\u30e6\u30fc\u30b6\u30fc\u5fc5\u898b\uff01Plotly\u6d3b\u7528\u306e\u30b3\u30c4\u3068Tips<\/h2>\n\n\n\n
Python\u30e6\u30fc\u30b6\u30fc\u306b\u3068\u3063\u3066Plotly\u306f\u975e\u5e38\u306b\u9b45\u529b\u7684\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u3059\u304c\u3001\u4f7f\u3044\u3053\u306a\u3059\u306b\u306f\u3044\u304f\u3064\u304b\u306e\u30b3\u30c4\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u3053\u3067\u306f\u3001Plotly\u3092\u52b9\u679c\u7684\u306b\u6d3b\u7528\u3059\u308b\u305f\u3081\u306eTips\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
\u4ed6\u306e\u30e9\u30a4\u30d6\u30e9\u30ea(Matplotlib, Seaborn\u306a\u3069)\u3068\u306e\u6bd4\u8f03<\/h3>\n\n\n\n
Plotly\u306f\u3001Matplotlib\u306a\u3069\u306e\u4ed6\u306e\u53ef\u8996\u5316\u30e9\u30a4\u30d6\u30e9\u30ea\u3068\u6bd4\u3079\u3066\u3001\u30a4\u30f3\u30bf\u30e9\u30af\u30c6\u30a3\u30d6\u6027\u3068\u30b0\u30e9\u30d5\u306e\u30c7\u30b6\u30a4\u30f3\u6027\u304c\u512a\u308c\u3066\u3044\u307e\u3059\u3002\u4e00\u65b9\u3067\u3001\u30ec\u30f3\u30c0\u30ea\u30f3\u30b0\u304c\u91cd\u304f\u306a\u308a\u304c\u3061\u306a\u3053\u3068\u304c\u77ed\u6240\u3068\u3057\u3066\u6319\u3052\u3089\u308c\u307e\u3059\u3002<\/p>\n\n\n\n
\u65e2\u306bMatplotlib\u3084Seaborn\u3092\u4f7f\u3063\u3066\u3044\u308b\u5834\u5408\u3001\u305d\u308c\u3089\u306e\u30b3\u30fc\u30c9\u3092Plotly\u306b\u79fb\u884c\u3059\u308b\u306e\u306f\u6bd4\u8f03\u7684\u7c21\u5358\u3067\u3059\u3002\u4f8b\u3048\u3070\u3001Matplotlib\u306e\u30b0\u30e9\u30d5\u3092Plotly\u306b\u5909\u63db\u3059\u308b\u306b\u306f\u3001
plotly.tools.mpl_to_plotly()<\/code>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n\n\n\n\u307e\u305f\u3001Plotly\u3068Matplotlib\u3092\u4f75\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u305d\u308c\u305e\u308c\u306e\u9577\u6240\u3092\u6d3b\u304b\u3057\u305f\u30c7\u30fc\u30bf\u53ef\u8996\u5316\u304c\u53ef\u80fd\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
\u30e9\u30c3\u30d7\u95a2\u6570\u3092\u4f5c\u3063\u3066Plotly\u3092\u3082\u3063\u3068\u4f7f\u3044\u3084\u3059\u304f<\/h3>\n\n\n\n
Plotly\u306f\u975e\u5e38\u306b\u67d4\u8edf\u3067\u5f37\u529b\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u3059\u304c\u3001\u30b3\u30fc\u30c9\u304c\u9577\u304f\u306a\u308a\u304c\u3061\u3067\u3059\u3002\u3088\u304f\u4f7f\u3046\u30b0\u30e9\u30d5\u306e\u4f5c\u6210\u3092\u95a2\u6570\u5316\u3057\u3001\u518d\u5229\u7528\u6027\u3092\u9ad8\u3081\u308b\u3053\u3068\u3067\u3001\u30b3\u30fc\u30c9\u306e\u53ef\u8aad\u6027\u3068\u4fdd\u5b88\u6027\u3092\u5411\u4e0a\u3055\u305b\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
\u4ee5\u4e0b\u306f\u3001\u6298\u308c\u7dda\u30b0\u30e9\u30d5\u3092\u4f5c\u6210\u3059\u308b\u30e9\u30c3\u30d7\u95a2\u6570\u306e\u4f8b\u3067\u3059\u3002<\/p>\n\n\n\n
import plotly.graph_objects as go\n\ndef create_line_chart(x, y, title, xaxis_title, yaxis_title):\n fig = go.Figure(data=go.Scatter(x=x, y=y))\n fig.update_layout(\n title=title,\n xaxis_title=xaxis_title,\n yaxis_title=yaxis_title\n )\n return fig\n\n# \u4f7f\u7528\u4f8b\nx = [1, 2, 3, 4, 5]\ny = [1, 3, 2, 4, 5]\nfig = create_line_chart(x, y, 'Sample Line Chart', 'X Axis', 'Y Axis')\nfig.show()<\/pre>\n\n\n\n\u3053\u306e\u3088\u3046\u306a\u30e9\u30c3\u30d7\u95a2\u6570\u3092\u4f5c\u6210\u3057\u3066\u304a\u304f\u3053\u3068\u3067\u3001\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u6307\u5b9a\u3059\u308b\u3060\u3051\u3067\u76ee\u7684\u306e\u30b0\u30e9\u30d5\u3092\u7c21\u5358\u306b\u4f5c\u6210\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
\u5927\u898f\u6a21\u30c7\u30fc\u30bf\u306e\u51e6\u7406\u3068\u30ec\u30f3\u30c0\u30ea\u30f3\u30b0\u306e\u9ad8\u901f\u5316\u30c6\u30af\u30cb\u30c3\u30af<\/h3>\n\n\n\n
Plotly\u306f\u5927\u898f\u6a21\u306a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u53ef\u8996\u5316\u306b\u3082\u5bfe\u5fdc\u3057\u3066\u3044\u307e\u3059\u304c\u3001\u30c7\u30fc\u30bf\u91cf\u304c\u591a\u3044\u5834\u5408\u306f\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u304c\u4f4e\u4e0b\u3059\u308b\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002\u4ee5\u4e0b\u306f\u3001\u5927\u898f\u6a21\u30c7\u30fc\u30bf\u306e\u51e6\u7406\u3068\u30ec\u30f3\u30c0\u30ea\u30f3\u30b0\u306e\u9ad8\u901f\u5316\u30c6\u30af\u30cb\u30c3\u30af\u306e\u4e00\u4f8b\u3067\u3059\u3002<\/p>\n\n\n\n
import plotly.graph_objects as go\nimport numpy as np\n\n# \u5927\u898f\u6a21\u306a\u30c7\u30fc\u30bf\u3092\u751f\u6210\nx = np.random.randn(100000)\ny = np.random.randn(100000)\n\n# \u30b0\u30e9\u30d5\u306e\u4f5c\u6210\nfig = go.Figure(data=go.Scatter(x=x, y=y, mode='markers', marker=dict(size=2)))\nfig.update_layout(title='Large Dataset Scatter Plot')\n\n# \u30ec\u30f3\u30c0\u30ea\u30f3\u30b0\u306e\u9ad8\u901f\u5316\nfig.update_layout(\n xaxis=dict(rangeslider=dict(visible=True)),\n yaxis=dict(fixedrange=False)\n)\n\nfig.show()<\/pre>\n\n\n\n\u3053\u306e\u30b3\u30fc\u30c9\u3067\u306f\u300110\u4e07\u500b\u306e\u30c7\u30fc\u30bf\u70b9\u3092\u6301\u3064\u6563\u5e03\u56f3\u3092\u4f5c\u6210\u3057\u3066\u3044\u307e\u3059\u3002
marker=dict(size=2)<\/code>\u3067\u30de\u30fc\u30ab\u30fc\u306e\u30b5\u30a4\u30ba\u3092\u5c0f\u3055\u304f\u3059\u308b\u3053\u3068\u3067\u3001\u30ec\u30f3\u30c0\u30ea\u30f3\u30b0\u306e\u901f\u5ea6\u3092\u5411\u4e0a\u3055\u305b\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n\u307e\u305f\u3001
fig.update_layout()<\/code>\u3092\u4f7f\u3063\u3066\u3001x\u8ef8\u306b\u30ec\u30f3\u30b8\u30b9\u30e9\u30a4\u30c0\u30fc\u3092\u8ffd\u52a0\u3057\u3001y\u8ef8\u306e\u7bc4\u56f2\u3092\u53ef\u5909\u306b\u3059\u308b\u3053\u3068\u3067\u3001\u5927\u898f\u6a21\u30c7\u30fc\u30bf\u306e\u63a2\u7d22\u3092\u5bb9\u6613\u306b\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n\u4ee5\u4e0a\u306e\u3088\u3046\u306a\u30b3\u30c4\u3068Tips\u3092\u6d3b\u7528\u3059\u308b\u3053\u3068\u3067\u3001Python\u30e6\u30fc\u30b6\u30fc\u306fPlotly\u3092\u3088\u308a\u52b9\u679c\u7684\u306b\u4f7f\u3044\u3053\u306a\u3059\u3053\u3068\u304c\u3067\u304d\u308b\u3067\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
\u307e\u3068\u3081\uff1aPlotly\u30b9\u30ad\u30eb\u3067\u30ad\u30e3\u30ea\u30a2\u306b\u5dee\u3092\u3064\u3051\u308b<\/h2>\n\n\n\n
\u30c7\u30fc\u30bf\u53ef\u8996\u5316\u306e\u91cd\u8981\u6027\u304c\u9ad8\u307e\u308b\u4e2d\u3001Plotly\u3092\u4f7f\u3044\u3053\u306a\u3059\u30b9\u30ad\u30eb\u306f\u3001\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30c6\u30a3\u30b9\u30c8\u3084\u30a8\u30f3\u30b8\u30cb\u30a2\u306b\u3068\u3063\u3066\u5927\u304d\u306a\u6b66\u5668\u306b\u306a\u308a\u307e\u3059\u3002\u3053\u3053\u3067\u306f\u3001Plotly\u30b9\u30ad\u30eb\u304c\u30ad\u30e3\u30ea\u30a2\u30a2\u30c3\u30d7\u306b\u3064\u306a\u304c\u308b\u7406\u7531\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30c6\u30a3\u30b9\u30c8\u3084\u30a8\u30f3\u30b8\u30cb\u30a2\u306b\u6c42\u3081\u3089\u308c\u308bPlotly\u30b9\u30ad\u30eb<\/h3>\n\n\n\n
\u73fe\u4ee3\u306e\u30d3\u30b8\u30cd\u30b9\u74b0\u5883\u3067\u306f\u3001\u30c7\u30fc\u30bf\u30c9\u30ea\u30d6\u30f3\u306a\u610f\u601d\u6c7a\u5b9a\u304c\u4e0d\u53ef\u6b20\u3067\u3059\u3002\u305d\u306e\u305f\u3081\u3001\u30c7\u30fc\u30bf\u304b\u3089\u4fa1\u5024\u3042\u308b\u77e5\u898b\u3092\u5f15\u304d\u51fa\u3057\u3001\u308f\u304b\u308a\u3084\u3059\u304f\u4f1d\u3048\u3089\u308c\u308b\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30c6\u30a3\u30b9\u30c8\u3084\u30a8\u30f3\u30b8\u30cb\u30a2\u304c\u6c42\u3081\u3089\u308c\u3066\u3044\u307e\u3059\u3002Plotly\u3092\u4f7f\u3044\u3053\u306a\u305b\u308b\u3053\u3068\u306f\u3001\u305d\u3046\u3057\u305f\u4eba\u6750\u306e\u5e02\u5834\u4fa1\u5024\u3092\u9ad8\u3081\u308b\u91cd\u8981\u306a\u30b9\u30ad\u30eb\u306e\u4e00\u3064\u3067\u3059\u3002<\/p>\n\n\n\n
\u5b9f\u969b\u306b\u3001\u30c7\u30fc\u30bf\u53ef\u8996\u5316\u306e\u30b9\u30ad\u30eb\u3092\u6c42\u3081\u308b\u6c42\u4eba\u60c5\u5831\u306f\u5897\u52a0\u50be\u5411\u306b\u3042\u308a\u3001\u591a\u304f\u306e\u4f01\u696d\u304cPlotly\u3092\u6d3b\u7528\u3057\u305f\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u3092\u9032\u3081\u3066\u3044\u307e\u3059\u3002Plotly\u306e\u30b9\u30ad\u30eb\u3092\u8eab\u306b\u3064\u3051\u308b\u3053\u3068\u3067\u3001\u30ad\u30e3\u30ea\u30a2\u306e\u9078\u629e\u80a2\u304c\u5e83\u304c\u308b\u3067\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
Plotly\u306e\u7fd2\u5f97\u304c\u4ed5\u4e8b\u3084\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u306b\u4e0e\u3048\u308b\u30a4\u30f3\u30d1\u30af\u30c8<\/h3>\n\n\n\n
Plotly\u3092\u4f7f\u3063\u305f\u30a4\u30f3\u30bf\u30e9\u30af\u30c6\u30a3\u30d6\u306a\u53ef\u8996\u5316\u306f\u3001\u30c7\u30fc\u30bf\u304b\u3089\u306e\u77e5\u898b\u767a\u898b\u3084\u610f\u601d\u6c7a\u5b9a\u306e\u30b9\u30d4\u30fc\u30c9\u3092\u5927\u5e45\u306b\u5411\u4e0a\u3055\u305b\u307e\u3059\u3002\u307e\u305f\u3001\u9b45\u529b\u7684\u306a\u30d3\u30b8\u30e5\u30a2\u30eb\u3092\u7528\u3044\u308b\u3053\u3068\u3067\u3001\u30d7\u30ec\u30bc\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u3084\u30ec\u30dd\u30fc\u30c8\u306e\u8aac\u5f97\u529b\u304c\u5897\u3057\u3001\u30b9\u30c6\u30fc\u30af\u30db\u30eb\u30c0\u30fc\u3068\u306e\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\u304c\u30b9\u30e0\u30fc\u30ba\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
\u4f8b\u3048\u3070\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u30a4\u30f3\u30bf\u30e9\u30af\u30c6\u30a3\u30d6\u306a3D\u30b0\u30e9\u30d5\u3092\u4f7f\u3048\u3070\u3001\u8907\u96d1\u306a\u30c7\u30fc\u30bf\u306e\u69cb\u9020\u3092\u76f4\u611f\u7684\u306b\u4f1d\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
import plotly.graph_objects as go\nimport numpy as np\n\n# \u30c7\u30fc\u30bf\u306e\u6e96\u5099\nx, y, z = np.mgrid[-8:8:40j, -8:8:40j, -8:8:40j]\nvalues = np.sin(x*y*z) \/ (x*y*z)\n\n# \u30b0\u30e9\u30d5\u306e\u4f5c\u6210\nfig = go.Figure(data=go.Volume(\n x=x.flatten(),\n y=y.flatten(),\n z=z.flatten(),\n value=values.flatten(),\n isomin=0.1,\n isomax=0.8,\n opacity=0.1,\n surface_count=17,\n colorscale='RdBu',\n reversescale=True\n))\n\nfig.update_layout(scene_xaxis_showticklabels=False,\n scene_yaxis_showticklabels=False,\n scene_zaxis_showticklabels=False)\n\nfig.show()<\/pre>\n\n\n\n\u3053\u306e\u3088\u3046\u306aPlotly\u30b9\u30ad\u30eb\u306f\u3001\u500b\u4eba\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3060\u3051\u3067\u306a\u304f\u3001\u30c1\u30fc\u30e0\u3084\u7d44\u7e54\u5168\u4f53\u306e\u751f\u7523\u6027\u5411\u4e0a\u306b\u3082\u5bc4\u4e0e\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
Plotly\u30b3\u30df\u30e5\u30cb\u30c6\u30a3\u304b\u3089\u60c5\u5831\u3092\u5f97\u3066\u3001\u5e38\u306b\u30a2\u30c3\u30d7\u30c7\u30fc\u30c8\u3057\u7d9a\u3051\u308b\u91cd\u8981\u6027<\/h3>\n\n\n\n
Plotly\u306f\u3001\u975e\u5e38\u306b\u6d3b\u767a\u306a\u30b3\u30df\u30e5\u30cb\u30c6\u30a3\u3092\u6301\u3064\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u3059\u3002\u30b3\u30df\u30e5\u30cb\u30c6\u30a3\u3067\u306f\u3001\u65b0\u6a5f\u80fd\u3084\u4f7f\u7528\u4f8b\u3001Tips\uff06Tricks\u306a\u3069\u306e\u6709\u76ca\u306a\u60c5\u5831\u304c\u65e5\u3005\u5171\u6709\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u3053\u3046\u3057\u305f\u60c5\u5831\u3092\u7a4d\u6975\u7684\u306b\u53d6\u308a\u5165\u308c\u3001\u5e38\u306b\u30b9\u30ad\u30eb\u3092\u30a2\u30c3\u30d7\u30c7\u30fc\u30c8\u3057\u7d9a\u3051\u308b\u3053\u3068\u304c\u3001Plotly\u3092\u4f7f\u3044\u3053\u306a\u3059\u305f\u3081\u306b\u91cd\u8981\u3067\u3059\u3002<\/p>\n\n\n\n
\u307e\u305f\u3001\u30b3\u30df\u30e5\u30cb\u30c6\u30a3\u3078\u306e\u53c2\u52a0\u306f\u3001\u4ed6\u306e\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30c6\u30a3\u30b9\u30c8\u3084\u30a8\u30f3\u30b8\u30cb\u30a2\u3068\u306e\u30cd\u30c3\u30c8\u30ef\u30fc\u30ad\u30f3\u30b0\u306e\u6a5f\u4f1a\u306b\u3082\u306a\u308a\u307e\u3059\u3002\u305d\u308c\u304c\u65b0\u305f\u306a\u30ad\u30e3\u30ea\u30a2\u30c1\u30e3\u30f3\u30b9\u306b\u3064\u306a\u304c\u308b\u53ef\u80fd\u6027\u3082\u3042\u308b\u3067\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
\u4ee5\u4e0a\u3001Plotly\u306e\u57fa\u672c\u304b\u3089\u5b9f\u8df5\u7684\u306a\u6d3b\u7528\u65b9\u6cd5\u307e\u3067\u3001Plotly\u30b9\u30ad\u30eb\u3092\u8eab\u306b\u3064\u3051\u308b\u305f\u3081\u306e\u30dd\u30a4\u30f3\u30c8\u3092\u8a73\u3057\u304f\u89e3\u8aac\u3057\u3066\u304d\u307e\u3057\u305f\u3002Plotly\u3092\u4f7f\u3044\u3053\u306a\u305b\u3070\u3001\u30c7\u30fc\u30bf\u53ef\u8996\u5316\u3067\u4e00\u6b69\u5148\u3078\u9032\u307f\u3001\u30ad\u30e3\u30ea\u30a2\u30a2\u30c3\u30d7\u3092\u5b9f\u73fe\u3067\u304d\u308b\u306f\u305a\u3067\u3059\u3002\u305c\u3072\u3001\u4eca\u65e5\u304b\u3089Plotly\u306e\u5b66\u7fd2\u3092\u59cb\u3081\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
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