{"id":5020,"date":"2019-05-04T10:40:42","date_gmt":"2020-04-22T06:37:46","guid":{"rendered":"https:\/\/vip.fxxkpython.com\/?p=5020"},"modified":"2021-08-31T03:22:02","modified_gmt":"2021-08-30T19:22:02","slug":"850a90e8b4","status":"publish","type":"post","link":"https:\/\/vip.fxxkpython.com\/?p=5020","title":{"rendered":"python\u722c\u866b28 | \u4f60\u722c\u4e0b\u7684\u6570\u636e\u4e0d\u5206\u6790\u4e00\u6ce2\u53ef\u5c31\u4e8f\u4e86\u554a\uff0c\u4f7f\u7528python\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316"},"content":{"rendered":"
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\u901a\u8fc7\u8fd9\u6bb5\u65f6\u95f4<\/p>\n

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\u5c0f\u5e05b\u6559\u4f60\u4ece\u6293\u5305\u5f00\u59cb<\/p>\n

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\u5230\u6570\u636e\u722c\u53d6<\/p>\n

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\u5230\u6570\u636e\u89e3\u6790<\/p>\n

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\u518d\u5230\u6570\u636e\u5b58\u50a8<\/p>\n

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\u76f8\u4fe1\u4f60\u5df2\u7ecf\u80fd\u6293\u53d6\u5927\u90e8\u5206\u4f60\u60f3\u722c\u53d6\u7684\u7f51\u7ad9\u6570\u636e\u4e86<\/p>\n

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\u606d\u559c\u606d\u559c<\/p>\n

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\"python\u722c\u866b28<\/p>\n

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\u4f46\u662f<\/p>\n

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\u6570\u636e\u6293\u53d6\u4e0b\u6765<\/p>\n

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\u8981\u597d\u597d\u5206\u6790\u4e00\u6ce2<\/p>\n

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\u6700\u597d\u7684\u65b9\u5f0f\u5c31\u662f\u628a\u6570\u636e\u8fdb\u884c\u53ef\u89c6\u5316<\/p>\n

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\u8fd9\u6837\u624d\u80fd\u76f4\u89c2<\/span>\u7684\u611f\u53d7\u5230\u6570\u636e\u7684\u9b45\u529b<\/p>\n

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\u4e0d\u8fc7\u6709\u4e00\u70b9<\/p>\n

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\u73b0\u5728\u5e02\u9762\u4e0a\u53ef\u4ee5\u4f7f\u7528 python \u7684\u53ef\u89c6\u5316\u5e93\u591a\u5982\u725b\u6bdb<\/p>\n

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\u5404\u6709\u5404\u7684\u4f18\u70b9<\/p>\n

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\u63a5\u4e0b\u6765\u5c0f\u5e05b\u628a\u81ea\u5df1\u5e38\u7528\u7684\u4e00\u4e9b\u53ef\u89c6\u5316\u6570\u636e\u5e93\u5206\u4eab\u7ed9\u4f60<\/p>\n

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\u597d\u4e0d\uff1f<\/p>\n

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\"python\u722c\u866b28<\/p>\n

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\u90a3\u4e48<\/p>\n

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\u63a5\u4e0b\u6765\u5c31\u662f<\/p>\n

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\u5b66\u4e60 python \u7684\u6b63\u786e\u59ff\u52bf<\/span><\/p>\n

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\"python\u722c\u866b28<\/p>\n

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\u5148\u6765\u8bf4\u8bf4\u4e00\u4e2a\u7ecf\u5178\u7684\u53ef\u89c6\u5316\u5e93<\/p>\n

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matplotlib<\/span><\/pre>\n
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\u5b83\u662f\u57fa\u4e8e\u00a0NumPy\u00a0\u7684\u4e00\u4e2a\u6570\u636e\u53ef\u89c6\u5316\u5de5\u5177\uff0c\u5185\u7f6e\u4e86\u975e\u5e38\u591a\u56fe\u7ed9\u6211\u4eec\u4f7f\u7528<\/p>\n
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\u63a5\u4e0b\u6765\u6211\u4eec\u5c31\u6765\u73a9\u73a9\u5427<\/p>\n
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\u9996\u5148\u4f60\u5f97\u53bb\u4e0b\u8f7d\u4e00\u4e0b\u8fd9\u4e2a\u5e93<\/p>\n
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python<\/span> -m pip install -U pip setuptools\n<\/span><\/span><\/code>python<\/span> -m pip install matplotlib<\/span><\/span><\/code><\/pre>\n<\/section>\n
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\u4e0b\u8f7d\u5b8c\u4e4b\u540e<\/p>\n
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\u5c31\u53ef\u4ee5\u6765\u73a9\u4ee3\u7801\u5566<\/p>\n
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\u753b\u753bsin\u548ccos\u7ebf<\/p>\n
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\n
import numpy as np\n<\/span><\/code>import  .pyplot as plt\n<\/span><\/code>\n<\/span><\/code>x = np.linspace(-np.pi, np.pi, 256)\n<\/span><\/code>\n<\/span><\/code>cos = np.cos(x)<\/span><\/code>sin = np.sin(x)\n<\/span><\/code>\n<\/span><\/code>plt.plot(x, cos, '--', linewidth=2)\n<\/span><\/code>plt.plot(x, sin)\n<\/span><\/code>\n<\/span><\/code>plt.show()<\/span><\/code><\/pre>\n<\/section>\n
\"python\u722c\u866b28<\/p>\n
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\u753b\u4e2a\u997c\u56fe<\/p>\n
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# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n<\/span><\/span><\/code>labels<\/span> = 'Frogs'<\/span>, 'Hogs'<\/span>, 'Dogs'<\/span>, 'Logs'\n<\/span><\/span><\/code>sizes = [15<\/span>, 30<\/span>, 45<\/span>, 10<\/span>]\n<\/span><\/code>explode = (0<\/span>, 0<\/span>.1<\/span>, 0<\/span>, 0<\/span>)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n<\/span><\/span><\/code>\n<\/span><\/code>fig1, ax1 = plt.subplots()\n<\/span><\/code>ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%'<\/span>,<\/span><\/code>        shadow=True, startangle=90<\/span>)\n<\/span><\/code>ax1.axis('equal'<\/span>)  # Equal aspect ratio ensures that pie is drawn as a circle.\n<\/span><\/span><\/code>\n<\/span><\/code>plt.show()<\/span><\/code><\/pre>\n<\/section>\n
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\"python\u722c\u866b28<\/p>\n
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\u753b\u753b\u76f4\u65b9\u56fe<\/p>\n
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import<\/span> numpy as<\/span> np\n<\/span><\/code>import<\/span> matplotlib.pyplot as<\/span> plt\n<\/span><\/code>\n<\/span><\/code>np.random.seed(0<\/span>)\n<\/span><\/code>\n<\/span><\/code>mu = 200\n<\/span><\/span><\/code>sigma = 25<\/span><\/span><\/code>x = np.random.normal(mu, sigma, size=100<\/span>)\n<\/span><\/code>\n<\/span><\/code>fig, (ax0, ax1) = plt.subplots(ncols=2<\/span>, figsize=(8<\/span>, 4<\/span>))\n<\/span><\/code>\n<\/span><\/code>ax0.hist(x, 20<\/span>, normed=1<\/span>, histtype='stepfilled'<\/span>, facecolor='g'<\/span>, alpha=0.75<\/span>)\n<\/span><\/code>ax0.set_title('stepfilled'<\/span>)\n<\/span><\/code>\n<\/span><\/code># Create a histogram by providing the bin edges (unequally spaced).\n<\/span><\/span><\/code>bins = [100<\/span>, 150<\/span>, 180<\/span>, 195<\/span>, 205<\/span>, 220<\/span>, 250<\/span>, 300<\/span>]\n<\/span><\/code>ax1.hist(x, bins, normed=1<\/span>, histtype='bar'<\/span>, rwidth=0.8<\/span>)<\/span><\/code>ax1.set_title('unequal bins'<\/span>)\n<\/span><\/code>\n<\/span><\/code>fig.tight_layout()\n<\/span><\/code>plt.show()<\/span><\/code><\/pre>\n<\/section>\n
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\"python\u722c\u866b28<\/p>\n
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\n
\u66f4\u591a\u5173\u4e8e\u00a0matplotlib \u7684\u6587\u6863\u53ef\u4ee5\u5230\u4ee5\u4e0b\u94fe\u63a5\u67e5\u770b<\/p>\n
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https:\/\/matplotlib.org\/2.0.2\/contents.html<\/p>\n
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\"python\u722c\u866b28<\/p>\n
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seaborn<\/span><\/h1>\n
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seaborn \u662f\u57fa\u4e8e\u00a0matplotlib \u7684\u5e93\uff0c\u6240\u4ee5\u6709\u66f4\u52a0\u9ad8\u7ea7\u7684\u63a5\u53e3\u7ed9\u6211\u4eec\u4f7f\u7528\uff0c\u76f8\u5bf9\u6765\u8bf4\u66f4\u52a0\u7b80\u5355\u4f7f\u7528\u4e00\u4e9b<\/span><\/p>\n
\u00a0<\/span><\/p>\n
\u753b\u4e2a\u6563\u70b9\u56fe<\/span><\/p>\n
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\n
import<\/span> numpy as<\/span> np\n<\/span><\/code>import<\/span> pandas as<\/span> pd\n<\/span><\/code>import<\/span> matplotlib.pyplot as<\/span> plt\n<\/span><\/code>import<\/span> seaborn as<\/span> sns\n<\/span><\/code>sns.set<\/span>(style=\"darkgrid\"<\/span>)<\/span><\/code>\n<\/span><\/code>\n<\/span><\/code>tips = sns.load_dataset(\"tips\"<\/span>)\n<\/span><\/code>sns.relplot(x=\"total_bill\"<\/span>, y=\"tip\"<\/span>, data<\/span>=tips);\n<\/span><\/code>plt.show()<\/span><\/code><\/pre>\n<\/section>\n
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\"python\u722c\u866b28<\/p>\n
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\u753b\u4e2a\u6298\u7ebf\u56fe<\/p>\n
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fmri = sns.load_dataset(\"fmri\"<\/span>)\n<\/span><\/code>sns.relplot(x=\"timepoint\"<\/span>, y=\"signal\"<\/span>, hue=\"event\"<\/span>, kind=\"line\"<\/span>, data=fmri);\n<\/span><\/code>plt.show()<\/span><\/code><\/pre>\n<\/section>\n
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\"python\u722c\u866b28<\/p>\n
\u00a0<\/span><\/p>\n
\u00a0<\/span><\/p>\n
\u753b\u4e2a\u76f4\u65b9\u56fe<\/span><\/span><\/p>\n
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\n<\/span><\/code>titanic = sns.load_dataset(\"titanic\"<\/span>)\n<\/span><\/code>sns.catplot(x=\"sex\"<\/span>, y=\"survived\"<\/span>, hue=\"class\"<\/span>, kind=\"bar\"<\/span>, data=titanic);\n<\/span><\/code>plt.show()<\/span><\/code>\n<\/span><\/code><\/pre>\n<\/section>\n
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\"python\u722c\u866b28<\/p>\n
\u00a0<\/span><\/p>\n
\u00a0<\/span><\/p>\n
\u66f4\u591a\u5173\u4e8e seaborn \u7684\u53ef\u4ee5\u770b\u770b\u4ee5\u4e0b\u94fe\u63a5<\/span><\/p>\n
\u00a0<\/span><\/p>\n
https:\/\/seaborn.pydata.org\/index.html<\/span><\/p>\n
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\"python\u722c\u866b28<\/p>\n
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pyecharts<\/span><\/h1>\n
\u00a0<\/span><\/p>\n
\u8fd9\u662f\u57fa\u4e8e\u767e\u5ea6\u5f00\u6e90\u7684\u6570\u636e\u53ef\u89c6\u5316\u7684 echarts \u7684\u5e93<\/span><\/p>\n
\u00a0<\/span><\/p>\n
echarts \u9047\u4e0a\u4e86 python \u4e4b\u540e<\/span><\/span><\/p>\n
\u00a0<\/span><\/span><\/p>\n
\u5c31\u50cf\u5de7\u514b\u529b\u9047\u4e0a\u4e86\u97f3\u4e50<\/span><\/span><\/p>\n
\u00a0<\/span><\/span><\/p>\n
\u4e1d\u6ed1\uff5e<\/span><\/span><\/p>\n
\u00a0<\/span><\/span><\/p>\n
\u7279\u522b\u662f\u5f53 pyechart \u7ed3\u5408\u00a0\u00a0Notebook \u7684\u65f6\u5019<\/p>\n
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\u7b80\u76f4\u4e0d\u80fd\u5728\u4e1d\u6ed1\u4e86<\/p>\n
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\u6765\u753b\u4e2a\u76f4\u65b9\u56fe<\/p>\n
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from<\/span> pyecharts.charts import<\/span> Bar\n<\/span><\/code>from<\/span> pyecharts import<\/span> options as<\/span> opts\n<\/span><\/code>\n<\/span><\/code>bar = (\n<\/span><\/code>    Bar()\n<\/span><\/code>    .add_xaxis([\"\u886c\u886b\"<\/span>, \"\u6bdb\u8863\"<\/span>, \"\u9886\u5e26\"<\/span>, \"\u88e4\u5b50\"<\/span>, \"\u98ce\u8863\"<\/span>, \"\u9ad8\u8ddf\u978b\"<\/span>, \"\u889c\u5b50\"<\/span>])\n<\/span><\/code>    .add_yaxis(\"\u5546\u5bb6A\"<\/span>, [114<\/span>, 55<\/span>, 27<\/span>, 101<\/span>, 125<\/span>, 27<\/span>, 105<\/span>])\n<\/span><\/code>    .add_yaxis(\"\u5546\u5bb6B\"<\/span>, [57<\/span>, 134<\/span>, 137<\/span>, 129<\/span>, 145<\/span>, 60<\/span>, 49<\/span>])\n<\/span><\/code>    .set_global_opts(title_opts=opts.TitleOpts(title=\"\u67d0\u5546\u573a\u9500\u552e\u60c5\u51b5\"<\/span>))\n<\/span><\/code>)\n<\/span><\/code>bar.render()<\/span><\/code><\/pre>\n<\/section>\n
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\"python\u722c\u866b28<\/p>\n
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\n
\u753b\u4e2a\u997c\u56fe<\/p>\n
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def<\/span> pie_base<\/span>()<\/span> -> Pie:\n<\/span><\/span><\/code>    c = (\n<\/span><\/code>        Pie()\n<\/span><\/code>        .add(\"\"<\/span>, [list(z) for<\/span> z in<\/span> zip(Faker.choose(), Faker.values())])\n<\/span><\/code>        .set_global_opts(title_opts=opts.TitleOpts(title=\"Pie-\u57fa\u672c\u793a\u4f8b\"<\/span>))\n<\/span><\/code>        .set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {c}\"<\/span>))\n<\/span><\/code>    )\n<\/span><\/code>    return<\/span> c\n<\/span><\/code>\n<\/span><\/code># \u9700\u8981\u5b89\u88c5 snapshot_selenium\n<\/span><\/span><\/code>make_snapshot(driver, pie_base().render(), \"pie.png\"<\/span>)<\/span><\/code><\/pre>\n<\/section>\n
\n
\n
\"python\u722c\u866b28<\/p>\n
\n
\n
\u518d\u6765\u753b\u4e2a\u8bcd\u4e91\u56fe<\/p>\n
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\n
words = [\n<\/span><\/code>    (\"Sam S Club\"<\/span>, 10000<\/span>),\n<\/span><\/code>    (\"Macys\"<\/span>, 6181<\/span>),\n<\/span><\/code>    (\"Amy Schumer\"<\/span>, 4386<\/span>),\n<\/span><\/code>    (\"Jurassic World\"<\/span>, 4055<\/span>),\n<\/span><\/code>    (\"Charter Communications\"<\/span>, 2467<\/span>),\n<\/span><\/code>    (\"Chick Fil A\"<\/span>, 2244<\/span>),\n<\/span><\/code>    (\"Planet Fitness\"<\/span>, 1868<\/span>),\n<\/span><\/code>    (\"Pitch Perfect\"<\/span>, 1484<\/span>),\n<\/span><\/code>    (\"Express\"<\/span>, 1112<\/span>),\n<\/span><\/code>    (\"Home\"<\/span>, 865<\/span>),\n<\/span><\/code>    (\"Johnny Depp\"<\/span>, 847<\/span>),\n<\/span><\/code>    (\"Lena Dunham\"<\/span>, 582<\/span>),\n<\/span><\/code>    (\"Lewis Hamilton\"<\/span>, 555<\/span>),\n<\/span><\/code>    (\"KXAN\"<\/span>, 550<\/span>),\n<\/span><\/code>    (\"Mary Ellen Mark\"<\/span>, 462<\/span>),\n<\/span><\/code>    (\"Farrah Abraham\"<\/span>, 366<\/span>),\n<\/span><\/code>    (\"Rita Ora\"<\/span>, 360<\/span>),\n<\/span><\/code>    (\"Serena Williams\"<\/span>, 282<\/span>),\n<\/span><\/code>    (\"NCAA baseball tournament\"<\/span>, 273<\/span>),\n<\/span><\/code>    (\"Point Break\"<\/span>, 265<\/span>),\n<\/span><\/code>]\n<\/span><\/code>\n<\/span><\/code>\n<\/span><\/code>def<\/span> wordcloud_base<\/span>()<\/span> -> WordCloud:\n<\/span><\/span><\/code>    c = (\n<\/span><\/code>        WordCloud()\n<\/span><\/code>        .add(\"\"<\/span>, words, word_size_range=[20<\/span>, 100<\/span>])\n<\/span><\/code>        .set_global_opts(title_opts=opts.TitleOpts(title=\"WordCloud-\u57fa\u672c\u793a\u4f8b\"<\/span>))\n<\/span><\/code>    )\n<\/span><\/code>    return<\/span> c\n<\/span><\/code>\n<\/span><\/code># \u9700\u8981\u5b89\u88c5 snapshot_selenium\n<\/span><\/span><\/code>make_snapshot(driver, wordcloud_base().render(), \"WordCloud.png\"<\/span>)<\/span><\/code><\/pre>\n<\/section>\n
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\"python\u722c\u866b28<\/p>\n
\n
\u662f\u4e0d\u662f\u5f88\u4e1d\u6ed1<\/p>\n
\n
\u66f4\u591a\u5173\u4e8e pyecharts \u7684\u53ef\u4ee5\u5230\u4ee5\u4e0b\u94fe\u63a5\u770b\u770b<\/p>\n
\n
https:\/\/pyecharts.org<\/p>\n
\n
\n
\u597d\u4e86<\/p>\n
\n
\u4ee5\u4e0a\u5c31\u662f\u5c0f\u5e05b\u5e38\u7528\u5230\u7684\u51e0\u4e2a\u53ef\u89c6\u5316\u6570\u636e\u5e93<\/p>\n
\n
\u5f53\u7136<\/p>\n
\n
\u8fd8\u6709\u5f88\u591a\u53ef\u89c6\u5316\u6570\u636e\u5e93<\/p>\n
\n
\u4e0d\u8fc7\u8fd9\u51e0\u4e2a\u7b97\u662f\u5f88\u53cb\u597d\u7684\u4e86<\/p>\n
\n
\u5e0c\u671b\u5bf9\u4f60\u6709\u7528<\/p>\n
\n
\u90a3\u4e48<\/p>\n
\n
\u6211\u4eec\u4e0b\u56de\u89c1<\/p>\n
\n
peace<\/span><\/p>\n
\n
\"python\u722c\u866b28<\/span><\/a><\/p>\n
\n
\u539f\u6587\u59cb\u53d1\u4e8e\u5fae\u4fe1\u516c\u4f17\u53f7\uff08\u5b66\u4e60python\u7684\u6b63\u786e\u59ff\u52bf\uff09\uff1apython\u722c\u866b28 | \u4f60\u722c\u4e0b\u7684\u6570\u636e\u4e0d\u5206\u6790\u4e00\u6ce2\u53ef\u5c31\u4e8f\u4e86\u554a\uff0c\u4f7f\u7528python\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316<\/a><\/p><\/blockquote>\n<\/div>\n","protected":false},"excerpt":{"rendered":"
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