{ "cells": [ { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "import plotly.graph_objs as go\n", "import plotly.plotly as py\n", "# from plotly import tools\n", "from plotly.offline import init_notebook_mode\n", "import plotly.offline as offline\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "from matplotlib.colors import rgb2hex" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/vnd.plotly.v1+html": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "init_notebook_mode(connected=False)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "# tools.set_credentials_file(username='your_username', api_key='your_apikey')" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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is python the main language you use for your current projects?none:what other language(s) do you use?java:what other language(s) do you use?javascript:what other language(s) do you use?c/c++:what other language(s) do you use?php:what other language(s) do you use?c#:what other language(s) do you use?ruby:what other language(s) do you use?bash / shell:what other language(s) do you use?objective-c:what other language(s) do you use?...technical support:which of the following best describes your job role(s)?data analyst:which of the following best describes your job role(s)?business analyst:which of the following best describes your job role(s)?team lead:which of the following best describes your job role(s)?product manager:which of the following best describes your job role(s)?cio / ceo / cto:which of the following best describes your job role(s)?systems analyst:which of the following best describes your job role(s)?other - write in::which of the following best describes your job role(s)?could you tell us your age range?what country do you live in?
0YesNaNNaNJavaScriptNaNPHPNaNNaNBash / ShellNaN...NaNNaNNaNNaNNaNNaNNaNNaN60 or olderItaly
1YesNaNNaNJavaScriptNaNNaNNaNNaNNaNNaN...NaNNaNNaNTeam leadNaNNaNNaNNaN40-49United Kingdom
2YesNaNNaNJavaScriptNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaN40-49France
3No, I don’t use Python for my current projectsNaNNaNNaNNaNNaNC#NaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaN17 or youngerSpain
4YesNaNJavaNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaN18-20Israel
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5 rows × 162 columns

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" ], "text/plain": [ " is python the main language you use for your current projects? \\\n", "0 Yes \n", "1 Yes \n", "2 Yes \n", "3 No, I don’t use Python for my current projects \n", "4 Yes \n", "\n", " none:what other language(s) do you use? \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", " java:what other language(s) do you use? \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 Java \n", "\n", " javascript:what other language(s) do you use? \\\n", "0 JavaScript \n", "1 JavaScript \n", "2 JavaScript \n", "3 NaN \n", "4 NaN \n", "\n", " c/c++:what other language(s) do you use? \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", " php:what other language(s) do you use? \\\n", "0 PHP \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", " c#:what other language(s) do you use? \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 C# \n", "4 NaN \n", "\n", " ruby:what other language(s) do you use? \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", " bash / shell:what other language(s) do you use? \\\n", "0 Bash / Shell \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", " objective-c:what other language(s) do you use? ... \\\n", "0 NaN ... \n", "1 NaN ... \n", "2 NaN ... \n", "3 NaN ... \n", "4 NaN ... \n", "\n", " technical support:which of the following best describes your job role(s)? \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", " data analyst:which of the following best describes your job role(s)? \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", " business analyst:which of the following best describes your job role(s)? \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", " team lead:which of the following best describes your job role(s)? \\\n", "0 NaN \n", "1 Team lead \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", " product manager:which of the following best describes your job role(s)? \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", " cio / ceo / cto:which of the following best describes your job role(s)? \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", " systems analyst:which of the following best describes your job role(s)? \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", " other - write in::which of the following best describes your job role(s)? \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", " could you tell us your age range? what country do you live in? \n", "0 60 or older Italy \n", "1 40-49 United Kingdom \n", "2 40-49 France \n", "3 17 or younger Spain \n", "4 18-20 Israel \n", "\n", "[5 rows x 162 columns]" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "survey_df = pd.read_csv('pythondevsurvey2017_raw_data.csv')\n", "survey_df.columns = [c.lower() for c in survey_df.columns]\n", "survey_df.head()" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "def find_cols(df, kws):\n", " '''找到 df 中含有 kws 的列'''\n", " return [item for item in df.columns if all ([w in item for w in kws])]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 散点图/折线图" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": "red" }, "mode": "lines+markers", "type": "scatter", "uid": "8cb44f1c-ea75-11e8-8482-d8c4973dfcaa", "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 ], "y": [ 0.9716512779319738, 0.8456386975545167, 0.48480067400456117, 0.770398050401701, 0.23284543577018268, 0.10554137897395932, 0.049855439663064605, 0.10895935323758432, 0.3562052572025035, 0.5363346289889088, 0.16768718890664136, 0.38823350577813787, 0.8711933163021941, 0.5104439534784287, 0.458760896773429, 0.9866129907169772, 0.8552936945231427, 0.8093668439466623, 0.856409086358952, 0.39645899972911014, 0.4998502895416449, 0.4153595186110006, 0.26109957737737644, 0.8969650131097293, 0.3013973908589037, 0.5597656510898611, 0.8603109854527045, 0.272433624644406, 0.781348873531537, 0.13398243378897778, 0.2361945293241886, 0.49730876007938507, 0.39301399764002076, 0.9201696974361896, 0.6044080348487139, 0.4062898718928051, 0.5369137313103775, 0.13297560185644364, 0.5799563662826495, 0.7267963448956315, 0.7396366719577046, 0.020109046231340688, 0.7314919504821047, 0.1904647036691225, 0.7178426517353867, 0.5256157370046654, 0.4236932824062992, 0.7134616399770779, 0.1144823531938145, 0.01456631082362947, 0.0655975033688303, 0.5381252762748181, 0.9289341998154816, 0.17276833436434524, 0.8418567001676033, 0.5083971929795241, 0.2580651633906016, 0.5562884144052586, 0.5332648437624022, 0.2150119538432511, 0.0680067530269769, 0.5601425656108872, 0.621413091043735, 0.28795006601045914, 0.10587298822587954, 0.8269689916716478, 0.9780256539717129, 0.7263585356593383, 0.5185631953692592, 0.56278190542258, 0.35520403207617324, 0.9657470041550605, 0.38744572815318057, 0.26597977981740417, 0.06836966500226538, 0.8870966986795674, 0.976167558787473, 0.17327018117333237, 0.3824895760127771, 0.8503342844994518, 0.6792440685590809, 0.0978767537338796, 0.15738922422631063, 0.34546788881498713, 0.7687208735370491, 0.5395095831217798, 0.7478890204215055, 0.06946164676996658, 0.1969903074354542, 0.06158093202786652, 0.4315358679837695, 0.5396764511896047, 0.6026585848025823, 0.012998100029235626, 0.2758930973972443, 0.5964986034867495, 0.017387563465794287, 0.43504004107099337, 0.2179625248555077, 0.19385941195278 ] } ], "layout": { "title": "散点+折线" } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# mode 可以是 ['lines', 'markers', 'text'] 三者的任意组合\n", "# 颜色支持 rgb 和 十六进制格式\n", "# 对于大数据量可以使用 go.Scattergl 进行绘制\n", "trace1 = go.Scatter(x=np.arange(100), y = np.random.rand(100), mode='lines+markers',\n", " marker={\n", " 'color': 'red'\n", " })\n", "data = [trace1]\n", "layout = {\n", " 'title': '散点+折线'\n", "}\n", "# go.FigureWidget(data=data, layout=layout)\n", "\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig, show_link=False)\n", "\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 使用 Python 2 和 Python 3 的开发者的比例？" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Python 3 6046\n", "Python 2 2066\n", "Name: which version of python do you use the most?, dtype: int64" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "python_version = survey_df['which version of python do you use the most?']\n", "counts = python_version.value_counts()\n", "counts" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "hoverinfo": "label+value", "labels": [ "Python 3", "Python 2" ], "marker": { "colors": [ "#386df9", "#12c8e6" ] }, "rotation": 0, "type": "pie", "uid": "8cbb2cfe-ea75-11e8-9c66-d8c4973dfcaa", "values": [ 6046, 2066 ] } ], "layout": { "title": "Python 2 VS Python 3" } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels = counts.index\n", "values = counts.values\n", "colors = [rgb2hex(i) for i in sns.color_palette('rainbow')[:2]]\n", "\n", "trace = go.Pie(labels=labels, values=values,\n", " marker={\n", " 'colors': colors\n", " },\n", " rotation=0,\n", " hoverinfo='label+value')\n", "data = [trace]\n", "layout = {\n", " 'title': 'Python 2 VS Python 3'\n", "}\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 做数据分析和机器学习的人中分别有多少人使用的是 Python 3？" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "python_da_ml = survey_df[['machine learning:\\xa0what do you use python for?', 'data analysis:\\xa0what do you use python for?', 'which version of python do you use the most?']]\n", "python_da = pd.crosstab(python_da_ml['which version of python do you use the most?'], python_da_ml['data analysis:\\xa0what do you use python for?'], normalize=True)\n", "python_ml = pd.crosstab(python_da_ml['which version of python do you use the most?'], python_da_ml['machine learning:\\xa0what do you use python for?'], normalize=True)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Data analysisMachine learning
which version of python do you use the most?
Python 20.2331770.193548
Python 30.7668230.806452
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" ], "text/plain": [ " Data analysis Machine learning\n", "which version of python do you use the most? \n", "Python 2 0.233177 0.193548\n", "Python 3 0.766823 0.806452" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "da_ml = pd.concat([python_da, python_ml], axis=1)\n", "da_ml" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": "#386df9" }, "name": "Python 2", "type": "bar", "uid": "8cd45ba8-ea75-11e8-832e-d8c4973dfcaa", "x": [ "Data analysis", "Machine learning" ], "y": [ 0.2331772245501602, 0.1935483870967742 ] }, { "marker": { "color": "#12c8e6" }, "name": "Python 3", "type": "bar", "uid": "8cd45ba9-ea75-11e8-978f-d8c4973dfcaa", "x": [ "Data analysis", "Machine learning" ], "y": [ 0.7668227754498398, 0.8064516129032258 ] } ], "layout": {} }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colors = [rgb2hex(i) for i in sns.color_palette('rainbow')[:2]]\n", "\n", "python2 = go.Bar(x=da_ml.columns, y=da_ml.loc['Python 2'], name='Python 2', marker={'color': colors[0]})\n", "python3 = go.Bar(x=da_ml.columns, y=da_ml.loc['Python 3'], name='Python 3', marker={'color': colors[1]})\n", "\n", "data = [python2, python3]\n", "# go.FigureWidget(data)\n", "fig = go.Figure(data=data)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 做数据分析和机器学习的人常用的框架？" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "django 3363\n", "numpy / pandas / matplotlib / scipy and similar 3163\n", "requests 2769\n", "flask 2607\n", "cloud platforms (google app engine, aws, rackspace, heroku and similar) 1960\n", "dtype: int64" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cols = find_cols(survey_df, 'what framework(s) do you use in addition to python?')\n", "frameworks = survey_df[cols[1:]]\n", "count_df = frameworks.count().sort_values(ascending=False)\n", "count_df.index = [item.split(':')[0] for item in count_df.index]\n", "count_df.head()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": [ "#6c1fff", "#583efd", "#445cfb", "#3079f7", "#1996f3", "#06aeed", "#0fc4e7", "#22d6e0", "#38e7d7", "#4df3ce", "#60fac5", "#74feba", "#8bfeae", "#9efaa2", "#b2f396", "#c6e789", "#dcd67a", "#f0c46c", "#ffae5e", "#ff964f", "#ff793e", "#ff5c2f", "#ff3e1f", "#ff1f10" ] }, "type": "bar", "uid": "8cddf746-ea75-11e8-95f2-d8c4973dfcaa", "x": [ "django", "numpy / pandas / matplotlib / scipy and similar", "requests", "flask", "cloud platforms (google app engine, aws, rackspace, heroku and similar)", "jupyter notebook", "keras / theano / tensorflow / scikit-learn and similar", "pillow", "pyqt / pygtk / wxpython", "tkinter", "pygame", "asyncio", "other - write in", "six", "tornado", "aiohttp", "kivy", "web2py", "twisted", "bottle", "pyramid", "cherrypy", "komodo ide", "komodo editor" ], "y": [ 3363, 3163, 2769, 2607, 1960, 1740, 1360, 1257, 1129, 938, 791, 759, 649, 626, 510, 439, 389, 332, 303, 282, 278, 224, 185, 164 ] } ], "layout": { "title": "Framework Usage" } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colors = [rgb2hex(i) for i in sns.color_palette('rainbow', len(count_df))]\n", "trace = go.Bar(x=count_df.index, y=count_df.values, marker={'color': colors})\n", "\n", "data = [trace]\n", "layout = {'title': 'Framework Usage'}\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": [ "#ff1f10", "#ff3e1f", "#ff5c2f", "#ff793e", "#ff964f", "#ffae5e", "#f0c46c", "#dcd67a", "#c6e789", "#b2f396", "#9efaa2", "#8bfeae", "#74feba", "#60fac5", "#4df3ce", "#38e7d7", "#22d6e0", "#0fc4e7", "#06aeed", "#1996f3", "#3079f7", "#445cfb", "#583efd", "#6c1fff" ] }, "orientation": "h", "type": "bar", "uid": "8ce63614-ea75-11e8-a394-d8c4973dfcaa", "x": [ 164, 185, 224, 278, 282, 303, 332, 389, 439, 510, 626, 649, 759, 791, 938, 1129, 1257, 1360, 1740, 1960, 2607, 2769, 3163, 3363 ], "y": [ "komodo editor", "komodo ide", "cherrypy", "pyramid", "bottle", "twisted", "web2py", "kivy", "aiohttp", "tornado", "six", "other - write in", "asyncio", "pygame", "tkinter", "pyqt / pygtk / wxpython", "pillow", "keras / theano / tensorflow / scikit-learn and similar", "jupyter notebook", "cloud platforms (google app engine, aws, rackspace, heroku and similar)", "flask", "requests", "numpy / pandas / matplotlib / scipy and similar", "django" ] } ], "layout": { "height": 1000, "margin": { "r": 10 }, "title": "Framework Usage", "yaxis": { "automargin": true } } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 对于 Y 轴刻度标签太长的情况，可以设置 layout 中 yaxis 的 automargin 属性为 True\n", "# 也可以自定义 margin\n", "trace = go.Bar(y=count_df.index[::-1], x=count_df.values[::-1], marker={'color': colors[::-1]}, orientation='h')\n", "\n", "data = [trace]\n", "layout = go.Layout(\n", " title='Framework Usage',\n", " margin={'r': 10},\n", " height=1000,\n", " yaxis={'automargin': True}\n", ")\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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djangoflasktornadobottleweb2pynumpy / pandas / matplotlib / scipy and similarkeras / theano / tensorflow / scikit-learn and similarpillowpyqt / pygtk / wxpythontkinter...requestsasynciokivysixaiohttpother - write incloud platforms (google app engine, aws, rackspace, heroku and similar)jupyter notebookkomodo editorkomodo ide
which version of python do you use the most?
Python 20.2500740.2600690.2823530.2943260.2921690.2298450.1941180.2649160.2648360.186567...0.2755510.1251650.1799490.3785940.1002280.3436060.2811220.1988510.2621950.318919
Python 30.7499260.7399310.7176470.7056740.7078310.7701550.8058820.7350840.7351640.813433...0.7244490.8748350.8200510.6214060.8997720.6563940.7188780.8011490.7378050.681081
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" ], "text/plain": [ " django flask tornado \\\n", "which version of python do you use the most? \n", "Python 2 0.250074 0.260069 0.282353 \n", "Python 3 0.749926 0.739931 0.717647 \n", "\n", " bottle web2py \\\n", "which version of python do you use the most? \n", "Python 2 0.294326 0.292169 \n", "Python 3 0.705674 0.707831 \n", "\n", " numpy / pandas / matplotlib / scipy and similar \\\n", "which version of python do you use the most? \n", "Python 2 0.229845 \n", "Python 3 0.770155 \n", "\n", " keras / theano / tensorflow / scikit-learn and similar \\\n", "which version of python do you use the most? \n", "Python 2 0.194118 \n", "Python 3 0.805882 \n", "\n", " pillow \\\n", "which version of python do you use the most? \n", "Python 2 0.264916 \n", "Python 3 0.735084 \n", "\n", " pyqt / pygtk / wxpython \\\n", "which version of python do you use the most? \n", "Python 2 0.264836 \n", "Python 3 0.735164 \n", "\n", " tkinter ... requests \\\n", "which version of python do you use the most? ... \n", "Python 2 0.186567 ... 0.275551 \n", "Python 3 0.813433 ... 0.724449 \n", "\n", " asyncio kivy six \\\n", "which version of python do you use the most? \n", "Python 2 0.125165 0.179949 0.378594 \n", "Python 3 0.874835 0.820051 0.621406 \n", "\n", " aiohttp other - write in \\\n", "which version of python do you use the most? \n", "Python 2 0.100228 0.343606 \n", "Python 3 0.899772 0.656394 \n", "\n", " cloud platforms (google app engine, aws, rackspace, heroku and similar) \\\n", "which version of python do you use the most? \n", "Python 2 0.281122 \n", "Python 3 0.718878 \n", "\n", " jupyter notebook komodo editor \\\n", "which version of python do you use the most? \n", "Python 2 0.198851 0.262195 \n", "Python 3 0.801149 0.737805 \n", "\n", " komodo ide \n", "which version of python do you use the most? \n", "Python 2 0.318919 \n", "Python 3 0.681081 \n", "\n", "[2 rows x 24 columns]" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frameworks_pyver = frameworks.apply(lambda col: pd.crosstab(index=python_version, columns=col).iloc[:, 0])\n", "frameworks_pyver = frameworks_pyver / frameworks_pyver.sum(axis=0)\n", "frameworks_pyver.columns = [item.split(':')[0] for item in frameworks.columns]\n", "frameworks_pyver" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": "#386df9" }, "name": "Python 2", "type": "bar", "uid": "8d1dbf64-ea75-11e8-9470-d8c4973dfcaa", "x": [ "django", "flask", "tornado", "bottle", "web2py", "numpy / pandas / matplotlib / scipy and similar", "keras / theano / tensorflow / scikit-learn and similar", "pillow", "pyqt / pygtk / wxpython", "tkinter", "pygame", "cherrypy", "twisted", "pyramid", "requests", "asyncio", "kivy", "six", "aiohttp", "other - write in", "cloud platforms (google app engine, aws, rackspace, heroku and similar)", "jupyter notebook", "komodo editor", "komodo ide" ], "y": [ 0.25007433838834375, 0.2600690448791715, 0.2823529411764706, 0.29432624113475175, 0.2921686746987952, 0.22984508378122037, 0.19411764705882353, 0.2649164677804296, 0.26483613817537643, 0.1865671641791045, 0.16434892541087232, 0.3169642857142857, 0.39933993399339934, 0.29136690647482016, 0.27555074033947274, 0.1251646903820817, 0.17994858611825193, 0.37859424920127793, 0.10022779043280182, 0.3436055469953775, 0.2811224489795918, 0.19885057471264367, 0.2621951219512195, 0.31891891891891894 ] }, { "marker": { "color": "#12c8e6" }, "name": "Python 3", "type": "bar", "uid": "8d1dbf65-ea75-11e8-a4d3-d8c4973dfcaa", "x": [ "django", "flask", "tornado", "bottle", "web2py", "numpy / pandas / matplotlib / scipy and similar", "keras / theano / tensorflow / scikit-learn and similar", "pillow", "pyqt / pygtk / wxpython", "tkinter", "pygame", "cherrypy", "twisted", "pyramid", "requests", "asyncio", "kivy", "six", "aiohttp", "other - write in", "cloud platforms (google app engine, aws, rackspace, heroku and similar)", "jupyter notebook", "komodo editor", "komodo ide" ], "y": [ 0.7499256616116563, 0.7399309551208285, 0.7176470588235294, 0.7056737588652482, 0.7078313253012049, 0.7701549162187796, 0.8058823529411765, 0.7350835322195705, 0.7351638618246236, 0.8134328358208955, 0.8356510745891277, 0.6830357142857143, 0.6006600660066007, 0.7086330935251799, 0.7244492596605273, 0.8748353096179183, 0.8200514138817481, 0.6214057507987221, 0.8997722095671982, 0.6563944530046225, 0.7188775510204082, 0.8011494252873563, 0.7378048780487805, 0.6810810810810811 ] } ], "layout": { "title": "Python 2 and Python 3 Usage among Frameworks" } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colors = [rgb2hex(i) for i in sns.color_palette('rainbow')[:2]]\n", "\n", "py2 = go.Bar(x=frameworks_pyver.columns, y=frameworks_pyver.loc['Python 2'], marker={'color': colors[0]}, name='Python 2')\n", "py3 = go.Bar(x=frameworks_pyver.columns, y=frameworks_pyver.loc['Python 3'], marker={'color': colors[1]}, name='Python 3')\n", "\n", "data = [py2, py3]\n", "layout = go.Layout(title='Python 2 and Python 3 Usage among Frameworks')\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": "#386df9" }, "name": "Python 2", "orientation": "h", "type": "bar", "uid": "8d27d1a8-ea75-11e8-9282-d8c4973dfcaa", "x": [ 0.25007433838834375, 0.2600690448791715, 0.2823529411764706, 0.29432624113475175, 0.2921686746987952, 0.22984508378122037, 0.19411764705882353, 0.2649164677804296, 0.26483613817537643, 0.1865671641791045, 0.16434892541087232, 0.3169642857142857, 0.39933993399339934, 0.29136690647482016, 0.27555074033947274, 0.1251646903820817, 0.17994858611825193, 0.37859424920127793, 0.10022779043280182, 0.3436055469953775, 0.2811224489795918, 0.19885057471264367, 0.2621951219512195, 0.31891891891891894 ], "y": [ "django", "flask", "tornado", "bottle", "web2py", "numpy / pandas / matplotlib / scipy and similar", "keras / theano / tensorflow / scikit-learn and similar", "pillow", "pyqt / pygtk / wxpython", "tkinter", "pygame", "cherrypy", "twisted", "pyramid", "requests", "asyncio", "kivy", "six", "aiohttp", "other - write in", "cloud platforms (google app engine, aws, rackspace, heroku and similar)", "jupyter notebook", "komodo editor", "komodo ide" ] }, { "marker": { "color": "#12c8e6" }, "name": "Python 3", "orientation": "h", "type": "bar", "uid": "8d27d1a9-ea75-11e8-84f4-d8c4973dfcaa", "x": [ 0.7499256616116563, 0.7399309551208285, 0.7176470588235294, 0.7056737588652482, 0.7078313253012049, 0.7701549162187796, 0.8058823529411765, 0.7350835322195705, 0.7351638618246236, 0.8134328358208955, 0.8356510745891277, 0.6830357142857143, 0.6006600660066007, 0.7086330935251799, 0.7244492596605273, 0.8748353096179183, 0.8200514138817481, 0.6214057507987221, 0.8997722095671982, 0.6563944530046225, 0.7188775510204082, 0.8011494252873563, 0.7378048780487805, 0.6810810810810811 ], "y": [ "django", "flask", "tornado", "bottle", "web2py", "numpy / pandas / matplotlib / scipy and similar", "keras / theano / tensorflow / scikit-learn and similar", "pillow", "pyqt / pygtk / wxpython", "tkinter", "pygame", "cherrypy", "twisted", "pyramid", "requests", "asyncio", "kivy", "six", "aiohttp", "other - write in", "cloud platforms (google app engine, aws, rackspace, heroku and similar)", "jupyter notebook", "komodo editor", "komodo ide" ] } ], "layout": { "height": 1000, "title": "Python 2 and Python 3 Usage among Frameworks", "yaxis": { "automargin": true } } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colors = [rgb2hex(i) for i in sns.color_palette('rainbow')[:2]]\n", "\n", "py2 = go.Bar(y=frameworks_pyver.columns, x=frameworks_pyver.loc['Python 2'], marker={'color': colors[0]}, orientation='h', name='Python 2')\n", "py3 = go.Bar(y=frameworks_pyver.columns, x=frameworks_pyver.loc['Python 3'], marker={'color': colors[1]}, orientation='h', name='Python 3')\n", "\n", "data = [py2, py3]\n", "layout = go.Layout(title='Python 2 and Python 3 Usage among Frameworks', height=1000, yaxis={'automargin': True})\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "import plotly.figure_factory as ff" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { 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0.7, "type": "histogram", "uid": "8d36ecad-ea75-11e8-a21c-d8c4973dfcaa", "x": [ 0.7499256616116563, 0.7399309551208285, 0.7176470588235294, 0.7056737588652482, 0.7078313253012049, 0.7701549162187796, 0.8058823529411765, 0.7350835322195705, 0.7351638618246236, 0.8134328358208955, 0.8356510745891277, 0.6830357142857143, 0.6006600660066007, 0.7086330935251799, 0.7244492596605273, 0.8748353096179183, 0.8200514138817481, 0.6214057507987221, 0.8997722095671982, 0.6563944530046225, 0.7188775510204082, 0.8011494252873563, 0.7378048780487805, 0.6810810810810811 ], "xaxis": "x", "xbins": { "end": 0.8997722095671982, "size": 0.05, "start": 0.6006600660066007 }, "yaxis": "y" }, { "legendgroup": "Python 2", "marker": { "color": "#386df9" }, "mode": "lines", "name": "Python 2", "showlegend": false, "type": "scatter", "uid": "8d36ecae-ea75-11e8-8b08-d8c4973dfcaa", "x": [ 0.10022779043280182, 0.10082601471992302, 0.10142423900704421, 0.10202246329416541, 0.1026206875812866, 0.1032189118684078, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colors = [rgb2hex(i) for i in sns.color_palette('rainbow')[:2]]\n", "\n", "py2 = go.Scatter(x=frameworks_pyver.columns, y=frameworks_pyver.loc['Python 2'], mode='markers', marker={'color': colors[0]}, name='Python 2')\n", "py3 = go.Scatter(x=frameworks_pyver.columns, y=frameworks_pyver.loc['Python 3'], mode='markers', marker={'color': colors[1]}, name='Python 3')\n", "\n", "data = [py2, py3]\n", "layout = go.Layout(title='Python 2 and Python 3 Usage among Frameworks')\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": "#386df9" }, "mode": "markers", "name": "Python 2", "orientation": "h", "type": "scatter", "uid": "8d4f569c-ea75-11e8-9149-d8c4973dfcaa", "x": [ 0.31891891891891894, 0.2621951219512195, 0.19885057471264367, 0.2811224489795918, 0.3436055469953775, 0.10022779043280182, 0.37859424920127793, 0.17994858611825193, 0.1251646903820817, 0.27555074033947274, 0.29136690647482016, 0.39933993399339934, 0.3169642857142857, 0.16434892541087232, 0.1865671641791045, 0.26483613817537643, 0.2649164677804296, 0.19411764705882353, 0.22984508378122037, 0.2921686746987952, 0.29432624113475175, 0.2823529411764706, 0.2600690448791715, 0.25007433838834375 ], "y": [ "komodo ide", "komodo editor", "jupyter notebook", "cloud platforms (google app engine, aws, rackspace, heroku and similar)", "other - write in", "aiohttp", "six", "kivy", "asyncio", "requests", "pyramid", "twisted", "cherrypy", "pygame", "tkinter", "pyqt / pygtk / wxpython", "pillow", "keras / theano / tensorflow / scikit-learn and similar", "numpy / pandas / matplotlib / scipy and similar", "web2py", "bottle", "tornado", "flask", "django" ] }, { "marker": { "color": "#12c8e6" }, "mode": "markers", "name": "Python 3", "orientation": "h", "type": "scatter", "uid": "8d4f569d-ea75-11e8-8451-d8c4973dfcaa", "x": [ 0.6810810810810811, 0.7378048780487805, 0.8011494252873563, 0.7188775510204082, 0.6563944530046225, 0.8997722095671982, 0.6214057507987221, 0.8200514138817481, 0.8748353096179183, 0.7244492596605273, 0.7086330935251799, 0.6006600660066007, 0.6830357142857143, 0.8356510745891277, 0.8134328358208955, 0.7351638618246236, 0.7350835322195705, 0.8058823529411765, 0.7701549162187796, 0.7078313253012049, 0.7056737588652482, 0.7176470588235294, 0.7399309551208285, 0.7499256616116563 ], "y": [ "komodo ide", "komodo editor", "jupyter notebook", "cloud platforms (google app engine, aws, rackspace, heroku and similar)", "other - write in", "aiohttp", "six", "kivy", "asyncio", "requests", "pyramid", "twisted", "cherrypy", "pygame", "tkinter", "pyqt / pygtk / wxpython", "pillow", "keras / theano / tensorflow / scikit-learn and similar", "numpy / pandas / matplotlib / scipy and similar", "web2py", "bottle", "tornado", "flask", "django" ] } ], "layout": { "height": 1000, "margin": { "r": 10 }, "title": "Python 2 and Python 3 Usage among Frameworks", "yaxis": { "automargin": true } } }, "text/html": [ "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colors = [rgb2hex(i) for i in sns.color_palette('rainbow')[:2]]\n", "\n", "py2 = go.Scatter(y=frameworks_pyver.columns[::-1], x=frameworks_pyver.loc['Python 2'][::-1], mode='markers', marker={'color': colors[0]}, orientation='h', name='Python 2')\n", "py3 = go.Scatter(y=frameworks_pyver.columns[::-1], x=frameworks_pyver.loc['Python 3'][::-1], mode='markers', marker={'color': colors[1]}, orientation='h', name='Python 3')\n", "\n", "data = [py2, py3]\n", "layout = go.Layout(\n", " title='Python 2 and Python 3 Usage among Frameworks',\n", " margin={'r': 10},\n", " height=1000,\n", " yaxis={'automargin': True}\n", ")\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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djangoflasktornadobottleweb2pynumpy / pandas / matplotlib / scipy and similarkeras / theano / tensorflow / scikit-learn and similarpillowpyqt / pygtk / wxpythontkinter...requestsasynciokivysixaiohttpother - write incloud platforms (google app engine, aws, rackspace, heroku and similar)jupyter notebookkomodo editorkomodo ide
which version of python do you use the most?
Python 20.2500740.2600690.2823530.2943260.2921690.2298450.1941180.2649160.2648360.186567...0.2755510.1251650.1799490.3785940.1002280.3436060.2811220.1988510.2621950.318919
Python 30.7499260.7399310.7176470.7056740.7078310.7701550.8058820.7350840.7351640.813433...0.7244490.8748350.8200510.6214060.8997720.6563940.7188780.8011490.7378050.681081
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" ], "text/plain": [ " django flask tornado \\\n", "which version of python do you use the most? \n", "Python 2 0.250074 0.260069 0.282353 \n", "Python 3 0.749926 0.739931 0.717647 \n", "\n", " bottle web2py \\\n", "which version of python do you use the most? \n", "Python 2 0.294326 0.292169 \n", "Python 3 0.705674 0.707831 \n", "\n", " numpy / pandas / matplotlib / scipy and similar \\\n", "which version of python do you use the most? \n", "Python 2 0.229845 \n", "Python 3 0.770155 \n", "\n", " keras / theano / tensorflow / scikit-learn and similar \\\n", "which version of python do you use the most? \n", "Python 2 0.194118 \n", "Python 3 0.805882 \n", "\n", " pillow \\\n", "which version of python do you use the most? \n", "Python 2 0.264916 \n", "Python 3 0.735084 \n", "\n", " pyqt / pygtk / wxpython \\\n", "which version of python do you use the most? \n", "Python 2 0.264836 \n", "Python 3 0.735164 \n", "\n", " tkinter ... requests \\\n", "which version of python do you use the most? ... \n", "Python 2 0.186567 ... 0.275551 \n", "Python 3 0.813433 ... 0.724449 \n", "\n", " asyncio kivy six \\\n", "which version of python do you use the most? \n", "Python 2 0.125165 0.179949 0.378594 \n", "Python 3 0.874835 0.820051 0.621406 \n", "\n", " aiohttp other - write in \\\n", "which version of python do you use the most? \n", "Python 2 0.100228 0.343606 \n", "Python 3 0.899772 0.656394 \n", "\n", " cloud platforms (google app engine, aws, rackspace, heroku and similar) \\\n", "which version of python do you use the most? \n", "Python 2 0.281122 \n", "Python 3 0.718878 \n", "\n", " jupyter notebook komodo editor \\\n", "which version of python do you use the most? \n", "Python 2 0.198851 0.262195 \n", "Python 3 0.801149 0.737805 \n", "\n", " komodo ide \n", "which version of python do you use the most? \n", "Python 2 0.318919 \n", "Python 3 0.681081 \n", "\n", "[2 rows x 24 columns]" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frameworks_pyver" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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which version of python do you use the most?
Python 20.1002280.1251650.1643490.1799490.1865670.1941180.1988510.2298450.2500740.260069...0.2811220.2823530.2913670.2921690.2943260.3169640.3189190.3436060.3785940.39934
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" ], "text/plain": [ " aiohttp asyncio pygame \\\n", "which version of python do you use the most? \n", "Python 2 0.100228 0.125165 0.164349 \n", "Python 3 0.899772 0.874835 0.835651 \n", "\n", " kivy tkinter \\\n", "which version of python do you use the most? \n", "Python 2 0.179949 0.186567 \n", "Python 3 0.820051 0.813433 \n", "\n", " keras / theano / tensorflow / scikit-learn and similar \\\n", "which version of python do you use the most? \n", "Python 2 0.194118 \n", "Python 3 0.805882 \n", "\n", " jupyter notebook \\\n", "which version of python do you use the most? \n", "Python 2 0.198851 \n", "Python 3 0.801149 \n", "\n", " numpy / pandas / matplotlib / scipy and similar \\\n", "which version of python do you use the most? \n", "Python 2 0.229845 \n", "Python 3 0.770155 \n", "\n", " django flask ... \\\n", "which version of python do you use the most? ... \n", "Python 2 0.250074 0.260069 ... \n", "Python 3 0.749926 0.739931 ... \n", "\n", " cloud platforms (google app engine, aws, rackspace, heroku and similar) \\\n", "which version of python do you use the most? \n", "Python 2 0.281122 \n", "Python 3 0.718878 \n", "\n", " tornado pyramid web2py \\\n", "which version of python do you use the most? \n", "Python 2 0.282353 0.291367 0.292169 \n", "Python 3 0.717647 0.708633 0.707831 \n", "\n", " bottle cherrypy komodo ide \\\n", "which version of python do you use the most? \n", "Python 2 0.294326 0.316964 0.318919 \n", "Python 3 0.705674 0.683036 0.681081 \n", "\n", " other - write in six \\\n", "which version of python do you use the most? \n", "Python 2 0.343606 0.378594 \n", "Python 3 0.656394 0.621406 \n", "\n", " twisted \n", "which version of python do you use the most? \n", "Python 2 0.39934 \n", "Python 3 0.60066 \n", "\n", "[2 rows x 24 columns]" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_frameworks_pyver = frameworks_pyver.sort_values(by='Python 3', axis=1, ascending=False)\n", "sorted_frameworks_pyver" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": "#386df9" }, "mode": "markers", "name": "Python 2", "orientation": "h", "type": "scatter", "uid": "8d617f34-ea75-11e8-bb0c-d8c4973dfcaa", "x": [ 0.39933993399339934, 0.37859424920127793, 0.3436055469953775, 0.31891891891891894, 0.3169642857142857, 0.29432624113475175, 0.2921686746987952, 0.29136690647482016, 0.2823529411764706, 0.2811224489795918, 0.27555074033947274, 0.2649164677804296, 0.26483613817537643, 0.2621951219512195, 0.2600690448791715, 0.25007433838834375, 0.22984508378122037, 0.19885057471264367, 0.19411764705882353, 0.1865671641791045, 0.17994858611825193, 0.16434892541087232, 0.1251646903820817, 0.10022779043280182 ], "y": [ "twisted", "six", "other - write in", "komodo ide", "cherrypy", "bottle", "web2py", "pyramid", "tornado", "cloud platforms (google app engine, aws, rackspace, heroku and similar)", "requests", "pillow", "pyqt / pygtk / wxpython", "komodo editor", "flask", "django", "numpy / pandas / matplotlib / scipy and similar", "jupyter notebook", "keras / theano / tensorflow / scikit-learn and similar", "tkinter", "kivy", "pygame", "asyncio", "aiohttp" ] }, { "marker": { "color": "#12c8e6" }, "mode": "markers", "name": "Python 3", "orientation": "h", "type": "scatter", "uid": "8d617f35-ea75-11e8-9c16-d8c4973dfcaa", "x": [ 0.6006600660066007, 0.6214057507987221, 0.6563944530046225, 0.6810810810810811, 0.6830357142857143, 0.7056737588652482, 0.7078313253012049, 0.7086330935251799, 0.7176470588235294, 0.7188775510204082, 0.7244492596605273, 0.7350835322195705, 0.7351638618246236, 0.7378048780487805, 0.7399309551208285, 0.7499256616116563, 0.7701549162187796, 0.8011494252873563, 0.8058823529411765, 0.8134328358208955, 0.8200514138817481, 0.8356510745891277, 0.8748353096179183, 0.8997722095671982 ], "y": [ "twisted", "six", "other - write in", "komodo ide", "cherrypy", "bottle", "web2py", "pyramid", "tornado", "cloud platforms (google app engine, aws, rackspace, heroku and similar)", "requests", "pillow", "pyqt / pygtk / wxpython", "komodo editor", "flask", "django", "numpy / pandas / matplotlib / scipy and similar", "jupyter notebook", "keras / theano / tensorflow / scikit-learn and similar", "tkinter", "kivy", "pygame", "asyncio", "aiohttp" ] } ], "layout": { "height": 1000, "margin": { "r": 10 }, "title": "Python 2 and Python 3 Usage among Frameworks", "yaxis": { "automargin": true } } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colors = [rgb2hex(i) for i in sns.color_palette('rainbow')[:2]]\n", "\n", "py2 = go.Scatter(y=sorted_frameworks_pyver.columns[::-1], x=sorted_frameworks_pyver.loc['Python 2'][::-1], mode='markers', marker={'color': colors[0]}, orientation='h', name='Python 2')\n", "py3 = go.Scatter(y=sorted_frameworks_pyver.columns[::-1], x=sorted_frameworks_pyver.loc['Python 3'][::-1], mode='markers', marker={'color': colors[1]}, orientation='h', name='Python 3')\n", "\n", "data = [py2, py3]\n", "layout = go.Layout(\n", " title='Python 2 and Python 3 Usage among Frameworks',\n", " margin={'r': 10},\n", " height=1000,\n", " yaxis={'automargin': True}\n", ")\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 做数据分析和机器学习的人常用的框架？" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", "
" ], "text/plain": [ " django flask tornado \\\n", "Computer graphics 20 15 7 \n", "Data analysis 424 395 73 \n", "Desktop development 151 114 17 \n", "DevOps / System administration / Writing automa... 271 289 41 \n", "Educational purposes 160 91 13 \n", "\n", " bottle web2py \\\n", "Computer graphics 3 3 \n", "Data analysis 38 49 \n", "Desktop development 13 21 \n", "DevOps / System administration / Writing automa... 33 28 \n", "Educational purposes 16 21 \n", "\n", " numpy / pandas / matplotlib / scipy and similar \\\n", "Computer graphics 47 \n", "Data analysis 926 \n", "Desktop development 156 \n", "DevOps / System administration / Writing automa... 230 \n", "Educational purposes 186 \n", "\n", " keras / theano / tensorflow / scikit-learn and similar \\\n", "Computer graphics 11.0 \n", "Data analysis 397.0 \n", "Desktop development 28.0 \n", "DevOps / System administration / Writing automa... 58.0 \n", "Educational purposes 53.0 \n", "\n", " pillow \\\n", "Computer graphics 23 \n", "Data analysis 159 \n", "Desktop development 79 \n", "DevOps / System administration / Writing automa... 83 \n", "Educational purposes 55 \n", "\n", " pyqt / pygtk / wxpython \\\n", "Computer graphics 34 \n", "Data analysis 200 \n", "Desktop development 193 \n", "DevOps / System administration / Writing automa... 106 \n", "Educational purposes 68 \n", "\n", " tkinter ... \\\n", "Computer graphics 16 ... \n", "Data analysis 154 ... \n", "Desktop development 139 ... \n", "DevOps / System administration / Writing automa... 80 ... \n", "Educational purposes 115 ... \n", "\n", " requests asyncio kivy \\\n", "Computer graphics 16 3.0 9 \n", "Data analysis 376 85.0 44 \n", "Desktop development 119 20.0 51 \n", "DevOps / System administration / Writing automa... 343 97.0 20 \n", "Educational purposes 68 17.0 35 \n", "\n", " six aiohttp \\\n", "Computer graphics 3.0 1 \n", "Data analysis 94.0 46 \n", "Desktop development 26.0 8 \n", "DevOps / System administration / Writing automa... 64.0 53 \n", "Educational purposes 6.0 9 \n", "\n", " other - write in \\\n", "Computer graphics 8 \n", "Data analysis 81 \n", "Desktop development 41 \n", "DevOps / System administration / Writing automa... 68 \n", "Educational purposes 22 \n", "\n", " cloud platforms (google app engine, aws, rackspace, heroku and similar) \\\n", "Computer graphics 16 \n", "Data analysis 279 \n", "Desktop development 61 \n", "DevOps / System administration / Writing automa... 227 \n", "Educational purposes 80 \n", "\n", " jupyter notebook \\\n", "Computer graphics 10 \n", "Data analysis 594 \n", "Desktop development 71 \n", "DevOps / System administration / Writing automa... 113 \n", "Educational purposes 96 \n", "\n", " komodo editor komodo ide \n", "Computer graphics 5 3 \n", "Data analysis 27 25 \n", "Desktop development 13 15 \n", "DevOps / System administration / Writing automa... 24 23 \n", "Educational purposes 16 20 \n", "\n", "[5 rows x 24 columns]" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cols = find_cols(survey_df, ['use', 'python', 'most'])\n", "uses = survey_df['what do you use python for the most?']\n", "frameworks_uses = frameworks.apply(lambda col: pd.crosstab(index=uses, columns=col).iloc[:, 0])\n", "frameworks_uses.columns = [item.split(':')[0] for item in frameworks_uses.columns]\n", "frameworks_uses.head()" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "da_ml_frameworks_uses = frameworks_uses.loc[['Data analysis', 'Machine learning']]" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "fill": "tozeroy", "marker": { "color": "#386df9" }, "name": "Python 2", "type": "scatter", "uid": "8da0b1e6-ea75-11e8-92bd-d8c4973dfcaa", "x": [ "django", "flask", "tornado", "bottle", "web2py", "numpy / pandas / matplotlib / scipy and similar", "keras / theano / tensorflow / scikit-learn and similar", "pillow", "pyqt / pygtk / wxpython", "tkinter", "pygame", "cherrypy", "twisted", "pyramid", "requests", "asyncio", "kivy", "six", "aiohttp", "other - write in", "cloud platforms (google app engine, aws, rackspace, heroku and similar)", "jupyter notebook", "komodo editor", "komodo ide" ], "y": [ 424, 395, 73, 38, 49, 926, 397, 159, 200, 154, 94, 32, 31, 33, 376, 85, 44, 94, 46, 81, 279, 594, 27, 25 ] }, { "fill": "tozeroy", "marker": { "color": "#12c8e6" }, "name": "Python 3", "type": "scatter", "uid": "8da0b1e7-ea75-11e8-bfea-d8c4973dfcaa", "x": [ "django", "flask", "tornado", "bottle", "web2py", "numpy / pandas / matplotlib / scipy and similar", "keras / theano / tensorflow / scikit-learn and similar", "pillow", "pyqt / pygtk / wxpython", "tkinter", "pygame", "cherrypy", "twisted", "pyramid", "requests", "asyncio", "kivy", "six", "aiohttp", "other - write in", "cloud platforms (google app engine, aws, rackspace, heroku and similar)", "jupyter notebook", "komodo editor", "komodo ide" ], "y": [ 239, 186, 48, 19, 31, 462, 416, 90, 88, 76, 69, 15, 15, 12, 163, 40, 25, 37, 22, 33, 139, 297, 10, 7 ] } ], "layout": { "title": "Frameworks Usage among Data Analysis and Machine Learning Developers" } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# fill 的可选值为：['none', 'tozeroy', 'tozerox', 'tonexty', 'tonextx', 'toself', 'tonext']\n", "colors = [rgb2hex(i) for i in sns.color_palette('rainbow')[:2]]\n", "\n", "da = go.Scatter(x=da_ml_frameworks_uses.columns, y=da_ml_frameworks_uses.loc['Data analysis'], fill='tozeroy', marker={'color': colors[0]}, name='Python 2')\n", "ml = go.Scatter(x=da_ml_frameworks_uses.columns, y=da_ml_frameworks_uses.loc['Machine learning'], fill='tozeroy', marker={'color': colors[1]}, name='Python 3')\n", "\n", "data = [da, ml]\n", "layout = go.Layout(title='Frameworks Usage among Data Analysis and Machine Learning Developers')\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "fill": "tozeroy", "marker": { "color": "#386df9" }, "name": "Python 2", "type": "scatter", "uid": "8db63540-ea75-11e8-b245-d8c4973dfcaa", "x": [ "komodo ide", "komodo editor", "twisted", "cherrypy", "pyramid", "bottle", "kivy", "aiohttp", "web2py", "tornado", "other - write in", "asyncio", "pygame", "six", "tkinter", "pillow", "pyqt / pygtk / wxpython", "cloud platforms (google app engine, aws, rackspace, heroku and similar)", "requests", "flask", "keras / theano / tensorflow / scikit-learn and similar", "django", "jupyter notebook", "numpy / pandas / matplotlib / scipy and similar" ], "y": [ 25, 27, 31, 32, 33, 38, 44, 46, 49, 73, 81, 85, 94, 94, 154, 159, 200, 279, 376, 395, 397, 424, 594, 926 ] }, { "fill": "tozeroy", "marker": { "color": "#12c8e6" }, "name": "Python 3", "type": "scatter", "uid": "8db63541-ea75-11e8-8cbe-d8c4973dfcaa", "x": [ "komodo ide", "komodo editor", "twisted", "cherrypy", "pyramid", "bottle", "kivy", "aiohttp", "web2py", "tornado", "other - write in", "asyncio", "pygame", "six", "tkinter", "pillow", "pyqt / pygtk / wxpython", "cloud platforms (google app engine, aws, rackspace, heroku and similar)", "requests", "flask", "keras / theano / tensorflow / scikit-learn and similar", "django", "jupyter notebook", "numpy / pandas / matplotlib / scipy and similar" ], "y": [ 7, 10, 15, 15, 12, 19, 25, 22, 31, 48, 33, 40, 69, 37, 76, 90, 88, 139, 163, 186, 416, 239, 297, 462 ] } ], "layout": { "title": "Frameworks Usage among Data Analysis and Machine Learning Developers" } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sorted_da_ml_frameworks_uses = da_ml_frameworks_uses.sort_values(by='Data analysis', axis=1, ascending=True)\n", "colors = [rgb2hex(i) for i in sns.color_palette('rainbow')[:2]]\n", "\n", "da = go.Scatter(x=sorted_da_ml_frameworks_uses.columns, y=sorted_da_ml_frameworks_uses.loc['Data analysis'], fill='tozeroy', marker={'color': colors[0]}, name='Python 2')\n", "ml = go.Scatter(x=sorted_da_ml_frameworks_uses.columns, y=sorted_da_ml_frameworks_uses.loc['Machine learning'], fill='tozeroy', marker={'color': colors[1]}, name='Python 3')\n", "\n", "data = [da, ml]\n", "layout = go.Layout(title='Frameworks Usage among Data Analysis and Machine Learning Developers')\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": "#386df9" }, "name": "Data analysis", "orientation": "h", "type": "bar", "uid": "8dbf5d08-ea75-11e8-9fb2-d8c4973dfcaa", "x": [ 25, 27, 31, 32, 33, 38, 44, 46, 49, 73, 81, 85, 94, 94, 154, 159, 200, 279, 376, 395, 397, 424, 594, 926 ], "y": [ "komodo ide", "komodo editor", "twisted", "cherrypy", "pyramid", "bottle", "kivy", "aiohttp", "web2py", "tornado", "other - write in", "asyncio", "pygame", "six", "tkinter", "pillow", "pyqt / pygtk / wxpython", "cloud platforms (google app engine, aws, rackspace, heroku and similar)", "requests", "flask", "keras / theano / tensorflow / scikit-learn and similar", "django", "jupyter notebook", "numpy / pandas / matplotlib / scipy and similar" ] }, { "marker": { "color": "#12c8e6" }, "name": "Machine learning", "orientation": "h", "type": "bar", "uid": "8dbf5d09-ea75-11e8-8625-d8c4973dfcaa", "x": [ 7, 10, 15, 15, 12, 19, 25, 22, 31, 48, 33, 40, 69, 37, 76, 90, 88, 139, 163, 186, 416, 239, 297, 462 ], "y": [ "komodo ide", "komodo editor", "twisted", "cherrypy", "pyramid", "bottle", "kivy", "aiohttp", "web2py", "tornado", "other - write in", "asyncio", "pygame", "six", "tkinter", "pillow", "pyqt / pygtk / wxpython", "cloud platforms (google app engine, aws, rackspace, heroku and similar)", "requests", "flask", "keras / theano / tensorflow / scikit-learn and similar", "django", "jupyter notebook", "numpy / pandas / matplotlib / scipy and similar" ] } ], "layout": { "height": 1000, "title": "Frameworks Usage among Data Analysis and Machine Learning Developers", "yaxis": { "automargin": true } } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colors = [rgb2hex(i) for i in sns.color_palette('rainbow')[:2]]\n", "\n", "da = go.Bar(y=sorted_da_ml_frameworks_uses.columns, x=sorted_da_ml_frameworks_uses.loc['Data analysis'], marker={'color': colors[0]}, orientation='h', name='Data analysis')\n", "ml = go.Bar(y=sorted_da_ml_frameworks_uses.columns, x=sorted_da_ml_frameworks_uses.loc['Machine learning'], marker={'color': colors[1]}, orientation='h', name='Machine learning')\n", "\n", "data = [da, ml]\n", "layout = go.Layout(title='Frameworks Usage among Data Analysis and Machine Learning Developers', height=1000, yaxis={'automargin': True})\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 公司规模大小和是否使用 Python 3 的关系？" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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which version of python do you use the most?Python 2Python 3
how many people are in your project team?
2-7 people0.3311800.668820
8-12 people0.3588240.641176
21-40 people0.3731340.626866
13-20 people0.3823530.617647
More than 40 people0.4117650.588235
\n", "
" ], "text/plain": [ "which version of python do you use the most? Python 2 Python 3\n", "how many people are in your project team? \n", "2-7 people 0.331180 0.668820\n", "8-12 people 0.358824 0.641176\n", "21-40 people 0.373134 0.626866\n", "13-20 people 0.382353 0.617647\n", "More than 40 people 0.411765 0.588235" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cols = find_cols(survey_df, ['how', 'many', 'people', 'project'])\n", "team_scale = survey_df[cols[0]]\n", "team_pyver = pd.crosstab(team_scale, python_version)\n", "team_pyver = team_pyver.reindex(['2-7 people', '8-12 people', '13-20 people', '21-40 people', 'More than 40 people'])\n", "team_pyver_sorted = team_pyver.div(team_pyver.sum(axis=1), axis=0).sort_values(by='Python 3', ascending=False)\n", "team_pyver_sorted" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "line": { "width": 2 }, "marker": { "color": "#386df9" }, "mode": "lines+markers", "type": "scatter", "uid": "8dcc2e64-ea75-11e8-a624-d8c4973dfcaa", "x": [ "2-7 people", "8-12 people", "21-40 people", "13-20 people", "More than 40 people" ], "y": [ 0.6688199827734711, 0.6411764705882353, 0.6268656716417911, 0.6176470588235294, 0.5882352941176471 ] } ], "layout": { "title": "Team scale VS Use ratio of Python 3" } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "trace = go.Scatter(x=team_pyver_sorted.index, y=team_pyver_sorted['Python 3'], marker={'color': colors[0]}, mode='lines+markers', line={'width': 2})\n", "\n", "data = [trace]\n", "layout = go.Layout(title='Team scale VS Use ratio of Python 3')\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 开发者年龄和是否使用 Python 3 的关系？" ] }, { "cell_type": "code", "execution_count": 86, "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", "
which version of python do you use the most?Python 2Python 3
could you tell us your age range?
17 or younger0.1480360.851964
18-200.1608300.839170
21-290.2598820.740118
30-390.2872730.712727
40-490.2895350.710465
50-590.3470790.652921
60 or older0.2252250.774775
\n", "
" ], "text/plain": [ "which version of python do you use the most? Python 2 Python 3\n", "could you tell us your age range? \n", "17 or younger 0.148036 0.851964\n", "18-20 0.160830 0.839170\n", "21-29 0.259882 0.740118\n", "30-39 0.287273 0.712727\n", "40-49 0.289535 0.710465\n", "50-59 0.347079 0.652921\n", "60 or older 0.225225 0.774775" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cols = find_cols(survey_df, ['age', 'range'])\n", "age = survey_df[cols[0]]\n", "age_pyver = pd.crosstab(index=age, columns=python_version)\n", "age_pyver = age_pyver.div(age_pyver.sum(axis=1), axis=0)\n", "age_pyver" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "line": { "width": 2 }, "marker": { "color": "#386df9" }, "mode": "lines+markers", "name": "Python 3", "type": "scatter", "uid": "8dd8ff4a-ea75-11e8-bffa-d8c4973dfcaa", "x": [ "17 or younger", "18-20", "21-29", "30-39", "40-49", "50-59", "60 or older" ], "y": [ 0.851963746223565, 0.8391699092088197, 0.7401182695300342, 0.7127272727272728, 0.7104651162790697, 0.6529209621993127, 0.7747747747747747 ] } ], "layout": { "title": "The developers' age VS The use ratio of Python 3" } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "trace = go.Scatter(x=age_pyver.index, y=age_pyver['Python 3'], marker={'color': colors[0]}, mode='lines+markers', line={'width': 2}, name='Python 3')\n", "\n", "data = [trace]\n", "layout = go.Layout(title=\"The developers' age VS The use ratio of Python 3\")\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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could you tell us your age range?17 or younger18-2021-2930-3940-4950-5960 or older
what country do you live in?
Afghanistan3111000
Albania0484110
Algeria12154100
Andorra0010000
Antigua and Barbuda2000000
\n", "
" ], "text/plain": [ "could you tell us your age range? 17 or younger 18-20 21-29 30-39 40-49 \\\n", "what country do you live in? \n", "Afghanistan 3 1 1 1 0 \n", "Albania 0 4 8 4 1 \n", "Algeria 1 2 15 4 1 \n", "Andorra 0 0 1 0 0 \n", "Antigua and Barbuda 2 0 0 0 0 \n", "\n", "could you tell us your age range? 50-59 60 or older \n", "what country do you live in? \n", "Afghanistan 0 0 \n", "Albania 1 0 \n", "Algeria 0 0 \n", "Andorra 0 0 \n", "Antigua and Barbuda 0 0 " ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "country_age = pd.crosstab([survey_df['what country do you live in?'], survey_df['which version of python do you use the most?']], survey_df['could you tell us your age range?'])\n", "country_age_total = country_age.sum(level=0)\n", "country_age_total.head()" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": [ "#5247fc", "#2489f5", "#0ac0e8", "#3ae8d6", "#68fcc1", "#96fca7", "#c4e88a", "#f4c069", "#ff8947", "#ff4724" ] }, "type": "bar", "uid": "8de53482-ea75-11e8-9714-d8c4973dfcaa", "x": [ "United States", "United Kingdom", "Canada", "Germany", "Poland", "Mexico", "Italy", "Belgium", "Venezuela", "South Africa" ], "y": [ 56, 7, 6, 6, 2, 2, 2, 2, 2, 2 ] } ], "layout": { "title": "Top 10 countries of # of the developers whose age are 60+" } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sorted_country_age_total = country_age_total.sort_values(by='60 or older', ascending=False)\n", "colors = [rgb2hex(i) for i in sns.color_palette('rainbow', 10)]\n", "trace = go.Bar(x=sorted_country_age_total.index[:10], y=sorted_country_age_total.iloc[:10, -1], marker={'color': colors})\n", "\n", "data = [trace]\n", "layout = {'title': 'Top 10 countries of # of the developers whose age are 60+'}\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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could you tell us your age range?17 or younger18-2021-2930-3940-4950-5960 or older
what country do you live in?
United States0.0836210.0704910.2937110.2874910.1568760.0691090.038701
India0.0438100.2228570.5571430.1371430.0304760.0066670.001905
China0.0292600.0774530.6454390.2134250.0344230.0000000.000000
\n", "
" ], "text/plain": [ "could you tell us your age range? 17 or younger 18-20 21-29 \\\n", "what country do you live in? \n", "United States 0.083621 0.070491 0.293711 \n", "India 0.043810 0.222857 0.557143 \n", "China 0.029260 0.077453 0.645439 \n", "\n", "could you tell us your age range? 30-39 40-49 50-59 60 or older \n", "what country do you live in? \n", "United States 0.287491 0.156876 0.069109 0.038701 \n", "India 0.137143 0.030476 0.006667 0.001905 \n", "China 0.213425 0.034423 0.000000 0.000000 " ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "three_countries = country_age_total.loc[['United States', 'India', 'China']]\n", "three_countries = three_countries.div(three_countries.sum(axis=1), axis=0)\n", "three_countries" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": "#4062fa" }, "name": "17 or younger", "type": "bar", "uid": "8df7f80c-ea75-11e8-8fd2-d8c4973dfcaa", "x": [ "United States", "India", "China" ], "y": [ 0.08362128541810643, 0.04380952380952381, 0.029259896729776247 ] }, { "marker": { "color": "#00b5eb" }, "name": "18-20", "type": "bar", "uid": "8df7f80d-ea75-11e8-b693-d8c4973dfcaa", "x": [ "United States", "India", "China" ], "y": [ 0.07049067035245335, 0.22285714285714286, 0.0774526678141136 ] }, { "marker": { "color": "#40ecd4" }, "name": "21-29", "type": "bar", "uid": "8df7f80e-ea75-11e8-8f5c-d8c4973dfcaa", "x": [ "United States", "India", "China" ], "y": [ 0.29371112646855563, 0.5571428571428572, 0.6454388984509466 ] }, { "marker": { "color": "#80ffb4" }, "name": "30-39", "type": "bar", "uid": "8df7f80f-ea75-11e8-bd00-d8c4973dfcaa", "x": [ "United States", "India", "China" ], "y": [ 0.2874913614374568, 0.13714285714285715, 0.2134251290877797 ] }, { "marker": { "color": "#c0eb8d" }, "name": "40-49", "type": "bar", "uid": "8df7f810-ea75-11e8-9e8a-d8c4973dfcaa", "x": [ "United States", "India", "China" ], "y": [ 0.15687629578438148, 0.030476190476190476, 0.03442340791738382 ] }, { "marker": { "color": "#ffb360" }, "name": "50-59", "type": "bar", "uid": "8df81f68-ea75-11e8-943f-d8c4973dfcaa", "x": [ "United States", "India", "China" ], "y": [ 0.0691085003455425, 0.006666666666666667, 0 ] }, { "marker": { "color": "#ff5f30" }, "name": "60 or older", "type": "bar", "uid": "8df81f69-ea75-11e8-bcbc-d8c4973dfcaa", "x": [ "United States", "India", "China" ], "y": [ 0.038700760193503804, 0.0019047619047619048, 0 ] } ], "layout": { "title": "Age distribution of the developers who're from USA, India and China" } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colors = [rgb2hex(i) for i in sns.color_palette('rainbow', 7)]\n", "\n", "data = [go.Bar(x=three_countries.index, y=three_countries[c], marker={'color': colors[i]}, name=c)\n", " for i, c in enumerate(three_countries.columns)]\n", "layout = go.Layout(title=\"Age distribution of the developers who're from USA, India and China\")\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 使用 Python 3 和 Python 2 的开发者的国别分布？" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Italy\n", "1 United Kingdom\n", "2 France\n", "3 Spain\n", "4 Israel\n", "Name: what country do you live in?, dtype: object" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cols = find_cols(survey_df, ['country', 'live'])\n", "countries = survey_df[cols[0]]\n", "count_countries = countries.value_counts(ascending=False)\n", "countries.head()" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": [ "#5247fc", "#2489f5", "#0ac0e8", "#3ae8d6", "#68fcc1", "#96fca7", "#c4e88a", "#f4c069", "#ff8947", "#ff4724" ] }, "type": "bar", "uid": "8e0342d2-ea75-11e8-8aeb-d8c4973dfcaa", "x": [ "United States", "India", "China", "United Kingdom", "Germany", "Brazil", "Russia", "France", "Poland", "Canada" ], "y": [ 1638, 1343, 710, 521, 417, 383, 281, 261, 223, 219 ] } ], "layout": { "title": "Top 10 countries of # of the developers" } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 等同于 sns.countplot\n", "colors = [rgb2hex(i) for i in sns.color_palette('rainbow', 10)]\n", "\n", "trace = go.Bar(x=count_countries.index[:10], y=count_countries[:10], marker={'color': colors})\n", "\n", "data = [trace]\n", "layout = go.Layout(title='Top 10 countries of # of the developers')\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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which version of python do you use the most?Python 2Python 3
United States0.2729790.727021
India0.3038100.696190
China0.2547330.745267
United Kingdom0.2201260.779874
Germany0.2196380.780362
\n", "
" ], "text/plain": [ "which version of python do you use the most? Python 2 Python 3\n", "United States 0.272979 0.727021\n", "India 0.303810 0.696190\n", "China 0.254733 0.745267\n", "United Kingdom 0.220126 0.779874\n", "Germany 0.219638 0.780362" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "countries_pyver = pd.crosstab(index=countries, columns=python_version)\n", "top10_countries = countries_pyver.loc[countries.value_counts()[:10].index]\n", "top10_countries = top10_countries.div(top10_countries.sum(axis=1), axis=0)\n", "top10_countries.head()" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": "#386df9" }, "name": "Python 2", "type": "bar", "uid": "8e12d300-ea75-11e8-8de5-d8c4973dfcaa", "x": [ "United States", "India", "China", "United Kingdom", "Germany", "Brazil", "Russia", "France", "Poland", "Canada" ], "y": [ 0.27297857636489287, 0.3038095238095238, 0.2547332185886403, 0.22012578616352202, 0.21963824289405684, 0.20857142857142857, 0.1615720524017467, 0.27896995708154504, 0.19306930693069307, 0.19689119170984457 ] }, { "marker": { "color": "#12c8e6" }, "name": "Python 3", "type": "bar", "uid": "8e12d301-ea75-11e8-9b66-d8c4973dfcaa", "x": [ "United States", "India", "China", "United Kingdom", "Germany", "Brazil", "Russia", "France", "Poland", "Canada" ], "y": [ 0.7270214236351071, 0.6961904761904761, 0.7452667814113597, 0.779874213836478, 0.7803617571059431, 0.7914285714285715, 0.8384279475982532, 0.721030042918455, 0.806930693069307, 0.8031088082901554 ] } ], "layout": { "title": "Python 2 and Python 3 Usage among Different Countries" } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colors = [rgb2hex(i) for i in sns.color_palette('rainbow')[:2]]\n", "\n", "py2 = go.Bar(x=top10_countries.index, y=top10_countries['Python 2'], marker={'color': colors[0]}, name='Python 2')\n", "py3 = go.Bar(x=top10_countries.index, y=top10_countries['Python 3'], marker={'color': colors[1]}, name='Python 3')\n", "\n", "data = [py2, py3]\n", "layout = go.Layout(title='Python 2 and Python 3 Usage among Different Countries')\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [], "source": [ "countries_pyver_ratio = countries_pyver.div(countries_pyver.sum(axis=1), axis=0)" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": "#386df9" }, "name": "Python 2", "type": "bar", "uid": "8e234dd2-ea75-11e8-ba13-d8c4973dfcaa", "x": [ "United States", "India", "China", "United Kingdom", "Germany", "Brazil", "Russia", "France", "Poland", "Canada" ], "y": [ 0.27297857636489287, 0.3038095238095238, 0.2547332185886403, 0.22012578616352202, 0.21963824289405684, 0.20857142857142857, 0.1615720524017467, 0.27896995708154504, 0.19306930693069307, 0.19689119170984457 ] }, { "marker": { "color": "#12c8e6" }, "name": "Python 3", "type": "bar", "uid": "8e234dd3-ea75-11e8-b02f-d8c4973dfcaa", "x": [ "United States", "India", "China", "United Kingdom", "Germany", "Brazil", "Russia", "France", "Poland", "Canada" ], "y": [ 0.7270214236351071, 0.6961904761904761, 0.7452667814113597, 0.779874213836478, 0.7803617571059431, 0.7914285714285715, 0.8384279475982532, 0.721030042918455, 0.806930693069307, 0.8031088082901554 ] } ], "layout": { "title": "Python 2 and Python 3 Usage among Different Countries" } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 等同于 sns.distplot\n", "# 注意 hist_data, group_labels, colors 都必须是列表形式，一个元素表示一个数据集\n", "colors = [rgb2hex(i) for i in sns.color_palette('rainbow')[:2]]\n", "fig = ff.create_distplot(hist_data=[countries_pyver_ratio['Python 3']], group_labels=['Python 3'], bin_size=0.05, colors=[colors[0]])\n", "fig['layout'].update(title='Use ratio of Python 3 in the world')\n", "# go.FigureWidget(fig)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": "#386df9" }, "mode": "markers", "type": "scatter", "uid": "8e287dd8-ea75-11e8-bd6b-d8c4973dfcaa", "x": [ 0.3333333333333333, 0.8888888888888888, 0.6956521739130435, 1, 1, 0.7272727272727273, 1, 0.7518248175182481, 0.7272727272727273, 0.8, 0.5, 0.75, 0.7894736842105263, 0.6666666666666666, 0.7586206896551724, 0.746268656716418, 1, 1, 0.3333333333333333, 1, 0.7914285714285715, 0.6956521739130435, 1, 0.6666666666666666, 0.8031088082901554, 1, 1, 0.9130434782608695, 0.7452667814113597, 0.6666666666666666, 1, 0.6, 0.5, 0.65625, 0.5555555555555556, 0.5, 0.8135593220338984, 0.803921568627451, 1, 0.875, 0.6666666666666666, 0.7142857142857143, 0.75, 0.7333333333333333, 0.16666666666666666, 0.76, 0.721030042918455, 1, 1, 0.8648648648648649, 0.7803617571059431, 0.7692307692307693, 0.7924528301886793, 0.6666666666666666, 1, 1, 1, 0.7906976744186046, 0, 0.6961904761904761, 0.6530612244897959, 0.75, 0.5, 0.7837837837837838, 0.5222222222222223, 0.7288135593220338, 1, 0.9137931034482759, 0.75, 0.8333333333333334, 0.7777777777777778, 0.8333333333333334, 0.5, 1, 1, 0, 0.5384615384615384, 1, 0.75, 1, 0.76, 1, 0.5, 1, 0.5, 0.6455696202531646, 0.5, 1, 1, 1, 0.5263157894736842, 0.3333333333333333, 1, 0.7741935483870968, 0.7906976744186046, 0.6451612903225806, 0.6666666666666666, 0.7833333333333333, 0.5, 0.8387096774193549, 1, 0.8245614035087719, 1, 0.6666666666666666, 0.7142857142857143, 0.7741935483870968, 0.806930693069307, 0.5454545454545454, 1, 0.6619718309859155, 0.8384279475982532, 0.75, 1, 0.7142857142857143, 0, 0.6551724137931034, 0.7142857142857143, 0.8, 0.8125, 0.8863636363636364, 0.8837209302325582, 0.6991869918699187, 0.7142857142857143, 0.5, 0.8166666666666667, 0.6382978723404256, 1, 0.6896551724137931, 0, 0.625, 0.8, 1, 1, 0.6666666666666666, 0.7441860465116279, 1, 0.8247422680412371, 0.8260869565217391, 0.779874213836478, 0.7270214236351071, 0.5454545454545454, 1, 0, 0.6818181818181818, 0.7857142857142857, 1, 1, 0.8888888888888888 ], "y": [ "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3", "Python 3" ] } ], "layout": {} }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 等同于 sns.tripplot\n", "trace = go.Scatter(x=countries_pyver_ratio['Python 3'], y=['Python 3'] * len(countries_pyver_ratio), mode='markers', marker={'color': colors[0]})\n", "data = [trace]\n", "# go.FigureWidget(data)\n", "fig = go.Figure(data=data)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "box": { "visible": true }, "fillcolor": "#386df9", "line": { "color": "black" }, "meanline": { "visible": true }, "opacity": 0.6, "type": "violin", "uid": "8e2e9888-ea75-11e8-ab2d-d8c4973dfcaa", "x0": "Total Bill", "y": [ 0.3333333333333333, 0.8888888888888888, 0.6956521739130435, 1, 1, 0.7272727272727273, 1, 0.7518248175182481, 0.7272727272727273, 0.8, 0.5, 0.75, 0.7894736842105263, 0.6666666666666666, 0.7586206896551724, 0.746268656716418, 1, 1, 0.3333333333333333, 1, 0.7914285714285715, 0.6956521739130435, 1, 0.6666666666666666, 0.8031088082901554, 1, 1, 0.9130434782608695, 0.7452667814113597, 0.6666666666666666, 1, 0.6, 0.5, 0.65625, 0.5555555555555556, 0.5, 0.8135593220338984, 0.803921568627451, 1, 0.875, 0.6666666666666666, 0.7142857142857143, 0.75, 0.7333333333333333, 0.16666666666666666, 0.76, 0.721030042918455, 1, 1, 0.8648648648648649, 0.7803617571059431, 0.7692307692307693, 0.7924528301886793, 0.6666666666666666, 1, 1, 1, 0.7906976744186046, 0, 0.6961904761904761, 0.6530612244897959, 0.75, 0.5, 0.7837837837837838, 0.5222222222222223, 0.7288135593220338, 1, 0.9137931034482759, 0.75, 0.8333333333333334, 0.7777777777777778, 0.8333333333333334, 0.5, 1, 1, 0, 0.5384615384615384, 1, 0.75, 1, 0.76, 1, 0.5, 1, 0.5, 0.6455696202531646, 0.5, 1, 1, 1, 0.5263157894736842, 0.3333333333333333, 1, 0.7741935483870968, 0.7906976744186046, 0.6451612903225806, 0.6666666666666666, 0.7833333333333333, 0.5, 0.8387096774193549, 1, 0.8245614035087719, 1, 0.6666666666666666, 0.7142857142857143, 0.7741935483870968, 0.806930693069307, 0.5454545454545454, 1, 0.6619718309859155, 0.8384279475982532, 0.75, 1, 0.7142857142857143, 0, 0.6551724137931034, 0.7142857142857143, 0.8, 0.8125, 0.8863636363636364, 0.8837209302325582, 0.6991869918699187, 0.7142857142857143, 0.5, 0.8166666666666667, 0.6382978723404256, 1, 0.6896551724137931, 0, 0.625, 0.8, 1, 1, 0.6666666666666666, 0.7441860465116279, 1, 0.8247422680412371, 0.8260869565217391, 0.779874213836478, 0.7270214236351071, 0.5454545454545454, 1, 0, 0.6818181818181818, 0.7857142857142857, 1, 1, 0.8888888888888888 ] } ], "layout": { "title": "Use ratio of Python 3 in the world", "yaxis": { "zeroline": false } } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = {\n", " \"data\": [{\n", " \"type\": 'violin',\n", " \"y\": countries_pyver_ratio['Python 3'],\n", " \"box\": {\n", " \"visible\": True\n", " },\n", " \"line\": {\n", " \"color\": 'black'\n", " },\n", " \"meanline\": {\n", " \"visible\": True\n", " },\n", " \"fillcolor\": colors[0],\n", " \"opacity\": 0.6,\n", " \"x0\": 'Total Bill'\n", " }],\n", " \"layout\" : {\n", " \"title\": \"Use ratio of Python 3 in the world\",\n", " \"yaxis\": {\n", " \"zeroline\": False,\n", " }\n", " }\n", "}\n", "\n", "# go.FigureWidget(fig)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "box": { "visible": true }, "fillcolor": "#386df9", "line": { "color": "black" }, "meanline": { "visible": true }, "opacity": 0.6, "type": "violin", "uid": "8e3acd88-ea75-11e8-8f57-d8c4973dfcaa", "x": [ 0.3333333333333333, 0.8888888888888888, 0.6956521739130435, 1, 1, 0.7272727272727273, 1, 0.7518248175182481, 0.7272727272727273, 0.8, 0.5, 0.75, 0.7894736842105263, 0.6666666666666666, 0.7586206896551724, 0.746268656716418, 1, 1, 0.3333333333333333, 1, 0.7914285714285715, 0.6956521739130435, 1, 0.6666666666666666, 0.8031088082901554, 1, 1, 0.9130434782608695, 0.7452667814113597, 0.6666666666666666, 1, 0.6, 0.5, 0.65625, 0.5555555555555556, 0.5, 0.8135593220338984, 0.803921568627451, 1, 0.875, 0.6666666666666666, 0.7142857142857143, 0.75, 0.7333333333333333, 0.16666666666666666, 0.76, 0.721030042918455, 1, 1, 0.8648648648648649, 0.7803617571059431, 0.7692307692307693, 0.7924528301886793, 0.6666666666666666, 1, 1, 1, 0.7906976744186046, 0, 0.6961904761904761, 0.6530612244897959, 0.75, 0.5, 0.7837837837837838, 0.5222222222222223, 0.7288135593220338, 1, 0.9137931034482759, 0.75, 0.8333333333333334, 0.7777777777777778, 0.8333333333333334, 0.5, 1, 1, 0, 0.5384615384615384, 1, 0.75, 1, 0.76, 1, 0.5, 1, 0.5, 0.6455696202531646, 0.5, 1, 1, 1, 0.5263157894736842, 0.3333333333333333, 1, 0.7741935483870968, 0.7906976744186046, 0.6451612903225806, 0.6666666666666666, 0.7833333333333333, 0.5, 0.8387096774193549, 1, 0.8245614035087719, 1, 0.6666666666666666, 0.7142857142857143, 0.7741935483870968, 0.806930693069307, 0.5454545454545454, 1, 0.6619718309859155, 0.8384279475982532, 0.75, 1, 0.7142857142857143, 0, 0.6551724137931034, 0.7142857142857143, 0.8, 0.8125, 0.8863636363636364, 0.8837209302325582, 0.6991869918699187, 0.7142857142857143, 0.5, 0.8166666666666667, 0.6382978723404256, 1, 0.6896551724137931, 0, 0.625, 0.8, 1, 1, 0.6666666666666666, 0.7441860465116279, 1, 0.8247422680412371, 0.8260869565217391, 0.779874213836478, 0.7270214236351071, 0.5454545454545454, 1, 0, 0.6818181818181818, 0.7857142857142857, 1, 1, 0.8888888888888888 ] } ], "layout": { "title": "Use ratio of Python 3 in the world", "xaxis": { "zeroline": false } } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colors = [rgb2hex(i) for i in sns.color_palette('rainbow')[:2]]\n", "\n", "trace = go.Violin(x=countries_pyver_ratio['Python 3'], meanline={'visible': True}, box={'visible': True}, fillcolor=colors[0], opacity=0.6, line={'color': 'black'})\n", "layout = go.Layout(title=\"Use ratio of Python 3 in the world\", xaxis={'zeroline': False})\n", "\n", "data = [trace]\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "autocolorscale": false, "colorbar": { "ticksuffix": "%", "title": "Percent" }, "colorscale": "Bluered", "locationmode": "country names", "locations": [ "Afghanistan", "Albania", "Algeria", "Andorra", "Antigua and Barbuda", "Argentina", "Armenia", "Australia", "Austria", "Azerbaijan", "Bahamas, The", "Bahrain", "Bangladesh", "Barbados", "Belarus", "Belgium", "Belize", "Benin", "Bolivia", "Bosnia and Herzegovina", "Brazil", "Bulgaria", "Cambodia", "Cameroon", "Canada", "Cape Verde", "Chad", "Chile", "China", "Colombia", "Congo, Democratic Republic of the", "Costa Rica", "Cote d'Ivoire", "Croatia", "Cuba", "Cyprus", "Czech Republic", "Denmark", "Dominica", "Dominican Republic", "Ecuador", "Egypt", "El Salvador", "Estonia", "Ethiopia", "Finland", "France", "Gabon", "Gambia, The", "Georgia", "Germany", "Ghana", "Greece", "Guatemala", "Haiti", "Honduras", "Hong Kong", "Hungary", "Iceland", "India", "Indonesia", "Iran", "Iraq", "Ireland", "Israel", "Italy", "Jamaica", "Japan", "Jordan", "Kazakhstan", "Kenya", "Kuwait", "Kyrgyzstan", "Latvia", "Lebanon", "Libya", "Lithuania", "Luxembourg", "Macedonia", "Madagascar", "Malaysia", "Maldives", "Malta", "Mauritania", "Mauritius", "Mexico", "Moldova", "Monaco", "Mongolia", "Montenegro", "Morocco", "Myanmar", "Namibia", "Nepal", "Netherlands", "New Zealand", "Nicaragua", "Nigeria", "North Korea", "Norway", "Oman", "Pakistan", "Panama", "Paraguay", "Peru", "Philippines", "Poland", "Portugal", "Qatar", "Romania", "Russia", "Rwanda", "San Marino", "Saudi Arabia", "Senegal", "Serbia", "Singapore", "Slovakia", "Slovenia", "South Africa", "South Korea", "Spain", "Sri Lanka", "Sudan", "Sweden", "Switzerland", "Syria", "Taiwan", "Tajikistan", "Tanzania", "Thailand", "Timor-Leste", "Togo", "Tunisia", "Turkey", "Uganda", "Ukraine", "United Arab Emirates", "United Kingdom", "United States", "Uruguay", "Uzbekistan", "Vanuatu", "Venezuela", "Vietnam", "Yemen", "Zambia", "Zimbabwe" ], "marker": { "line": { "color": "rgb(180,180,180)", "width": 0.5 } }, "reversescale": true, "text": [ "Afghanistan", "Albania", "Algeria", "Andorra", "Antigua and Barbuda", "Argentina", "Armenia", "Australia", "Austria", "Azerbaijan", "Bahamas, The", "Bahrain", "Bangladesh", "Barbados", "Belarus", "Belgium", "Belize", "Benin", "Bolivia", "Bosnia and Herzegovina", "Brazil", "Bulgaria", "Cambodia", "Cameroon", "Canada", "Cape Verde", "Chad", "Chile", "China", "Colombia", "Congo, Democratic Republic of the", "Costa Rica", "Cote d'Ivoire", "Croatia", "Cuba", "Cyprus", "Czech Republic", "Denmark", "Dominica", "Dominican Republic", "Ecuador", "Egypt", "El Salvador", "Estonia", "Ethiopia", "Finland", "France", "Gabon", "Gambia, The", "Georgia", "Germany", "Ghana", "Greece", "Guatemala", "Haiti", "Honduras", "Hong Kong", "Hungary", "Iceland", "India", "Indonesia", "Iran", "Iraq", "Ireland", "Israel", "Italy", "Jamaica", "Japan", "Jordan", "Kazakhstan", "Kenya", "Kuwait", "Kyrgyzstan", "Latvia", "Lebanon", "Libya", "Lithuania", "Luxembourg", "Macedonia", "Madagascar", "Malaysia", "Maldives", "Malta", "Mauritania", "Mauritius", "Mexico", "Moldova", "Monaco", "Mongolia", "Montenegro", "Morocco", "Myanmar", "Namibia", "Nepal", "Netherlands", "New Zealand", "Nicaragua", "Nigeria", "North Korea", "Norway", "Oman", "Pakistan", "Panama", "Paraguay", "Peru", "Philippines", "Poland", "Portugal", "Qatar", "Romania", "Russia", "Rwanda", "San Marino", "Saudi Arabia", "Senegal", "Serbia", "Singapore", "Slovakia", "Slovenia", "South Africa", "South Korea", "Spain", "Sri Lanka", "Sudan", "Sweden", "Switzerland", "Syria", "Taiwan", "Tajikistan", "Tanzania", "Thailand", "Timor-Leste", "Togo", "Tunisia", "Turkey", "Uganda", "Ukraine", "United Arab Emirates", "United Kingdom", "United States", "Uruguay", "Uzbekistan", "Vanuatu", "Venezuela", "Vietnam", "Yemen", "Zambia", "Zimbabwe" ], "type": "choropleth", "uid": "8e4b2292-ea75-11e8-b090-d8c4973dfcaa", "z": [ 33.33333333333333, 88.88888888888889, 69.56521739130434, 100, 100, 72.72727272727273, 100, 75.18248175182481, 72.72727272727273, 80, 50, 75, 78.94736842105263, 66.66666666666666, 75.86206896551724, 74.6268656716418, 100, 100, 33.33333333333333, 100, 79.14285714285715, 69.56521739130434, 100, 66.66666666666666, 80.31088082901555, 100, 100, 91.30434782608695, 74.52667814113597, 66.66666666666666, 100, 60, 50, 65.625, 55.55555555555556, 50, 81.35593220338984, 80.3921568627451, 100, 87.5, 66.66666666666666, 71.42857142857143, 75, 73.33333333333333, 16.666666666666664, 76, 72.1030042918455, 100, 100, 86.48648648648648, 78.0361757105943, 76.92307692307693, 79.24528301886792, 66.66666666666666, 100, 100, 100, 79.06976744186046, 0, 69.61904761904762, 65.3061224489796, 75, 50, 78.37837837837837, 52.22222222222223, 72.88135593220339, 100, 91.37931034482759, 75, 83.33333333333334, 77.77777777777779, 83.33333333333334, 50, 100, 100, 0, 53.84615384615385, 100, 75, 100, 76, 100, 50, 100, 50, 64.55696202531645, 50, 100, 100, 100, 52.63157894736842, 33.33333333333333, 100, 77.41935483870968, 79.06976744186046, 64.51612903225806, 66.66666666666666, 78.33333333333333, 50, 83.87096774193549, 100, 82.45614035087719, 100, 66.66666666666666, 71.42857142857143, 77.41935483870968, 80.6930693069307, 54.54545454545454, 100, 66.19718309859155, 83.84279475982532, 75, 100, 71.42857142857143, 0, 65.51724137931035, 71.42857142857143, 80, 81.25, 88.63636363636364, 88.37209302325581, 69.91869918699187, 71.42857142857143, 50, 81.66666666666667, 63.829787234042556, 100, 68.96551724137932, 0, 62.5, 80, 100, 100, 66.66666666666666, 74.4186046511628, 100, 82.4742268041237, 82.6086956521739, 77.9874213836478, 72.70214236351072, 54.54545454545454, 100, 0, 68.18181818181817, 78.57142857142857, 100, 100, 88.88888888888889 ] } ], "layout": { "geo": { "projection": { "type": "equirectangular" }, "showcoastlines": false, "showframe": false }, "title": "Python 3 in the world" } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ['equirectangular', 'mercator', 'orthographic', 'natural earth', 'kavrayskiy7', 'miller', 'robinson', 'eckert4',\n", "# 'azimuthal equal area', 'azimuthal equidistant', 'conic\n", "# equal area', 'conic conformal', 'conic equidistant',\n", "# 'gnomonic', 'stereographic', 'mollweide', 'hammer',\n", "# 'transverse mercator', 'albers usa', 'winkel tripel',\n", "# 'aitoff', 'sinusoidal']\n", "data = [ dict(\n", " type = 'choropleth',\n", " locations = countries_pyver_ratio.index,\n", " locationmode = 'country names', \n", " z = countries_pyver_ratio['Python 3'] * 100,\n", " text = countries_pyver_ratio.index,\n", " colorscale = 'Bluered',\n", " autocolorscale = False,\n", " reversescale = True,\n", " marker = dict(\n", " line = dict (\n", " color = 'rgb(180,180,180)',\n", " width = 0.5\n", " ) ),\n", " colorbar = dict(\n", "# autotick = False,\n", " ticksuffix = '%',\n", " title = 'Percent'),\n", " ) ]\n", "\n", "layout = dict(\n", " title = 'Python 3 in the world',\n", " geo = dict(\n", " showframe = False,\n", " showcoastlines = False,\n", " projection = dict(\n", " type = 'equirectangular'\n", " ),\n", " )\n", ")\n", "\n", "\n", "# fig = go.Figure()\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "# py.iplot(fig)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 开发者中使用 IDE 的情况？" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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pycharm professional editionpycharm community editionsublime textvimatomvs codeeclipse + pydevaptanajupyter notebookintellij idea...netbeansspyderrodeogeditninja-idekomodo editorkomodo idewing idetextmateother - write in
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" ], "text/plain": [ " pycharm professional edition pycharm community edition sublime text vim \\\n", "0 NaN NaN NaN Vim \n", "1 NaN NaN NaN NaN \n", "2 PyCharm Professional Edition NaN Sublime Text Vim \n", "3 NaN NaN NaN NaN \n", "4 PyCharm Professional Edition NaN NaN NaN \n", "\n", " atom vs code eclipse + pydev aptana jupyter notebook intellij idea \\\n", "0 NaN NaN NaN NaN NaN NaN \n", "1 Atom NaN NaN NaN NaN NaN \n", "2 Atom NaN Eclipse + Pydev NaN NaN NaN \n", "3 NaN NaN NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN NaN NaN \n", "\n", " ... netbeans spyder rodeo gedit ninja-ide komodo editor \\\n", "0 ... NaN NaN NaN NaN NaN NaN \n", "1 ... NaN NaN NaN NaN NaN NaN \n", "2 ... NaN NaN NaN NaN NaN NaN \n", "3 ... NaN NaN NaN NaN NaN NaN \n", "4 ... NaN NaN NaN NaN NaN NaN \n", "\n", " komodo ide wing ide textmate other - write in \n", "0 NaN NaN NaN Other - Write In: \n", "1 NaN Wing IDE NaN NaN \n", "2 Komodo IDE NaN NaN NaN \n", "3 NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN \n", "\n", "[5 rows x 24 columns]" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cols = find_cols(survey_df, ['what', 'editor(s)/ide(s)'])\n", "editors = survey_df[cols]\n", "editors.columns = [item.split(':')[0] for item in editors.columns]\n", "editors.head()" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pycharm community edition 3061\n", "sublime text 2762\n", "vim 2468\n", "atom 2070\n", "pycharm professional edition 2069\n", "dtype: int64" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "count_editors = editors.count().sort_values(ascending=False)\n", "count_editors.head()" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": [ "#ff1f10", "#ff3e1f", "#ff5c2f", "#ff793e", "#ff964f", "#ffae5e", "#f0c46c", "#dcd67a", "#c6e789", "#b2f396", "#9efaa2", "#8bfeae", "#74feba", "#60fac5", "#4df3ce", "#38e7d7", "#22d6e0", "#0fc4e7", "#06aeed", "#1996f3", "#3079f7", "#445cfb", "#583efd", "#6c1fff" ] }, "orientation": "h", "type": "bar", "uid": "8e5c8626-ea75-11e8-81d2-d8c4973dfcaa", "x": [ 71, 132, 139, 163, 164, 185, 218, 349, 405, 548, 578, 611, 697, 751, 1048, 1581, 1740, 1820, 1854, 2069, 2070, 2468, 2762, 3061 ], "y": [ "rodeo", "ninja-ide", "textmate", "aptana", "komodo editor", "komodo ide", "wing ide", "netbeans", "other - write in", "gedit", "python tools for visual studio (ptvs)", "emacs", "intellij idea", "spyder", "eclipse + pydev", "vs code", "jupyter notebook", "idle", "notepad++", "pycharm professional edition", "atom", "vim", "sublime text", "pycharm community edition" ] } ], "layout": { "height": 1000, "margin": { "r": 10 }, "title": "What editor(s)/IDE(s) have you considered for use in your Python development?", "yaxis": { "automargin": true } } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colors = [rgb2hex(i) for i in sns.color_palette('rainbow', len(count_editors))]\n", "trace = go.Bar(y=count_editors.index[::-1], x=count_editors.values[::-1], marker={'color': colors[::-1]}, orientation='h')\n", "\n", "data = [trace]\n", "layout = go.Layout(\n", " title=\"What editor(s)/IDE(s) have you considered for use in your Python development?\",\n", " margin={'r': 10},\n", " height=1000,\n", " yaxis={'automargin': True}\n", ")\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": [ "#6c1fff", "#583efd", "#445cfb", "#3079f7", "#1996f3", "#06aeed", "#0fc4e7", "#22d6e0", "#38e7d7", "#4df3ce", "#60fac5", "#74feba", "#8bfeae", "#9efaa2", "#b2f396", "#c6e789", "#dcd67a", "#f0c46c", "#ffae5e", "#ff964f", "#ff793e", "#ff5c2f", "#ff3e1f", "#ff1f10" ] }, "orientation": "v", "type": "bar", "uid": "8e6511ae-ea75-11e8-96b8-d8c4973dfcaa", "x": [ "pycharm community edition", "sublime text", "vim", "atom", "pycharm professional edition", "notepad++", "idle", "jupyter notebook", "vs code", "eclipse + pydev", "spyder", "intellij idea", "emacs", "python tools for visual studio (ptvs)", "gedit", "other - write in", "netbeans", "wing ide", "komodo ide", "komodo editor", "aptana", "textmate", "ninja-ide", "rodeo" ], "y": [ 3061, 2762, 2468, 2070, 2069, 1854, 1820, 1740, 1581, 1048, 751, 697, 611, 578, 548, 405, 349, 218, 185, 164, 163, 139, 132, 71 ] } ], "layout": { "title": "What editor(s)/IDE(s) have you considered for use in your Python development?" } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colors = [rgb2hex(i) for i in sns.color_palette('rainbow', len(count_editors))]\n", "trace = go.Bar(x=count_editors.index, y=count_editors.values, marker={'color': colors}, orientation='v')\n", "\n", "data = [trace]\n", "layout = go.Layout(\n", " title=\"What editor(s)/IDE(s) have you considered for use in your Python development?\",\n", ")\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PyCharm Professional Edition 1339\n", "PyCharm Community Edition 1240\n", "Sublime Text 844\n", "Vim 775\n", "IDLE 708\n", "Name: what is the main editor you use for your current python development?, dtype: int64" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" } ], "source": [ "col = find_cols(survey_df, ['what', 'main', 'editor'])\n", "main_editor = survey_df[col[0]]\n", "count_main_editor = main_editor.value_counts(ascending=False)\n", "count_main_editor.head()" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": [ "#ff1f10", "#ff3e1f", "#ff5c2f", "#ff793e", "#ff964f", "#ffae5e", "#f0c46c", "#dcd67a", "#c6e789", "#b2f396", "#9efaa2", "#8bfeae", "#74feba", "#60fac5", "#4df3ce", "#38e7d7", "#22d6e0", "#0fc4e7", "#06aeed", "#1996f3", "#3079f7", "#445cfb", "#583efd", "#6c1fff" ] }, "orientation": "h", "type": "bar", "uid": "8e6f9922-ea75-11e8-8803-d8c4973dfcaa", "x": [ 2, 13, 21, 22, 28, 41, 46, 54, 61, 100, 136, 205, 206, 225, 235, 262, 382, 566, 601, 708, 775, 844, 1240, 1339 ], "y": [ "Rodeo", "TextMate", "Komodo Editor", "Ninja-IDE", "Komodo IDE", "Aptana", "NetBeans", "Wing IDE", "Gedit", "Python Tools for Visual Studio (PTVS)", "IntelliJ IDEA", "Spyder", "Other - Write In:", "Eclipse + Pydev", "Emacs", "Jupyter Notebook", "NotePad++", "Atom", "VS Code", "IDLE", "Vim", "Sublime Text", "PyCharm Community Edition", "PyCharm Professional Edition" ] } ], "layout": { "height": 1000, "margin": { "r": 10 }, "title": "What is the main editor you use for your current python development?", "yaxis": { "automargin": true } } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colors = [rgb2hex(i) for i in sns.color_palette('rainbow', len(count_main_editor))]\n", "trace = go.Bar(y=count_main_editor.index[::-1], x=count_main_editor.values[::-1], marker={'color': colors[::-1]}, orientation='h')\n", "\n", "data = [trace]\n", "layout = go.Layout(\n", " title=\"What is the main editor you use for your current python development?\",\n", " margin={'r': 10},\n", " height=1000,\n", " yaxis={'automargin': True}\n", ")\n", "# go.FigureWidget(data=data, layout=layout)\n", "fig = go.Figure(data=data, layout=layout)\n", "offline.iplot(fig, show_link=False)" ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": [ "#6c1fff", "#583efd", "#445cfb", "#3079f7", "#1996f3", "#06aeed", "#0fc4e7", "#22d6e0", "#38e7d7", "#4df3ce", "#60fac5", "#74feba", "#8bfeae", "#9efaa2", "#b2f396", "#c6e789", "#dcd67a", "#f0c46c", "#ffae5e", "#ff964f", "#ff793e", "#ff5c2f", "#ff3e1f", "#ff1f10" ] }, "orientation": "v", "type": "bar", "uid": "8e77d666-ea75-11e8-88c4-d8c4973dfcaa", "x": [ "PyCharm Professional Edition", "PyCharm Community Edition", "Sublime Text", "Vim", "IDLE", "VS Code", "Atom", "NotePad++", "Jupyter Notebook", "Emacs", "Eclipse + Pydev", "Other - Write In:", "Spyder", "IntelliJ IDEA", "Python Tools for Visual Studio (PTVS)", "Gedit", "Wing IDE", "NetBeans", "Aptana", "Komodo IDE", "Ninja-IDE", "Komodo Editor", "TextMate", "Rodeo" ], "y": [ 1339, 1240, 844, 775, 708, 601, 566, 382, 262, 235, 225, 206, 205, 136, 100, 61, 54, 46, 41, 28, 22, 21, 13, 2 ] } ], "layout": { "title": "What is the main editor you use for your current python development?" } }, "text/html": [ "
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