{"id":3695,"date":"2022-03-18T12:14:41","date_gmt":"2022-03-18T04:14:41","guid":{"rendered":"https:\/\/egonlin.com\/?p=3695"},"modified":"2022-03-18T12:14:41","modified_gmt":"2022-03-18T04:14:41","slug":"%e7%ac%ac%e4%ba%8c%e8%8a%82%ef%bc%9aseaborn","status":"publish","type":"post","link":"https:\/\/egonlin.com\/?p=3695","title":{"rendered":"\u7b2c\u4e8c\u8282\uff1aSeaborn"},"content":{"rendered":"

1\u3001Seaborn<\/h2>\n

\u5728\u4e0a\u8282\u4e2d\u6211\u4eec\u5b66\u4e60\u4e86matplotlib\uff0c\u8fd9\u8282\u8bfe\u6211\u4eec\u6765\u770b\u770b\u53e6\u4e00\u4e2a\u53ef\u89c6\u5316\u7684\u6a21\u5757seaborn<\/code>\uff0c\u5b83\u662f\u57fa\u4e8ematplotlib\u7684\u66f4\u9ad8\u7ea7\u7684\u5f00\u6e90\u5e93\uff0c\u4e3b\u8981\u7528\u4f5c\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff0c\u89e3\u51b3\u4e86matplotlib\u7684\u4e24\u5927\u95ee\u9898\u3002\u6b63\u5982Michael Waskom\u6240\u8bf4\u7684\uff1aMatplotlib\u8bd5\u7740\u8ba9\u7b80\u5355\u7684\u4e8b\u60c5\u66f4\u52a0\u7b80\u5355\uff0c\u56f0\u96be\u7684\u4e8b\u60c5\u53d8\u5f97\u53ef\u80fd\uff0c\u90a3\u4e48Seaborn\u5c31\u662f\u8ba9\u56f0\u96be\u7684\u4e1c\u897f\u66f4\u52a0\u7b80\u5355\u3002<\/code><\/p>\n

\u4f7f\u7528matplotlib\u6700\u5927\u7684\u95ee\u9898\u5c31\u662f\u5b83\u9ed8\u8ba4\u7684\u5404\u79cd\u53c2\u6570\uff0c\u5728serborn\u5f53\u4e2d\u5219\u5b8c\u5168\u907f\u514d\u4e86\u8fd9\u4e9b\u95ee\u9898<\/p>\n

# \u5bfc\u5165\u6a21\u5757\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# \u521d\u59cb\u5316 Figure \u548c Axes \u5bf9\u8c61\nfig, ax = plt.subplots()\n\n# \u52a0\u8f7d\u6570\u636e\ntips = pd.read_csv("https:\/\/raw.githubusercontent.com\/mwaskom\/seaborn-data\/master\/tips.csv")\n\n# \u521b\u5efa violinplot\nax.violinplot(tips["total_bill"], vert=False)<\/code><\/pre>\n
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# \u5bfc\u5165\u6a21\u5757\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\n\n# \u52a0\u8f7d\u6570\u636e\ntips = pd.read_csv("https:\/\/raw.githubusercontent.com\/mwaskom\/seaborn-data\/master\/tips.csv")\n\n# \u521b\u5efa violinplot\nsns.violinplot(x = "total_bill", data=tips)\n\n# \u5c55\u793a\u56fe\u50cf\nplt.show()<\/code><\/pre>\n
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\u4f7f\u7528matplotlib\uff0c\u901a\u5e38\u90fd\u9700\u8981\u6211\u4eec\u81ea\u5df1\u6765\u589e\u52a0\u989c\u8272\u3001\u523b\u5ea6\u6807\u7b7e\u7b49\u4e00\u4e9b\u6837\u5f0f\u3002seaborn\u5c31\u662fmatplotlib\u7684\u5ef6\u4f38\uff0c\u5982\u679c\u4f1a\u7528matplotlib\uff0c\u90a3\u4e48\u4f7f\u7528seaborn\u4e5f\u6ca1\u95ee\u9898<\/p>\n
2\u3001\u52a0\u8f7d\u6570\u636e<\/h2>\n
\u4f7f\u7528seaborn\u4e0d\u4ec5\u53ef\u4ee5\u5c06\u81ea\u5df1\u672c\u5730\u7684\u6570\u636e\u7ed8\u5236\u6210\u56fe\u8868\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528\u5e93\u672c\u8eab\u63d0\u4f9b\u7684\u5185\u7f6e\u6570\u636e\u96c6<\/p>\n
2.1\u3001\u52a0\u8f7d\u5185\u7f6e\u6570\u636e\u96c6<\/h3>\n
\u53ef\u4ee5\u901a\u8fc7load_dataset()<\/code>\u6765\u4f7f\u7528\u5185\u7f6e\u7684seaborn\u6570\u636e\u96c6\uff0c\u9700\u8981\u67e5\u770b\u6240\u6709\u5185\u7f6e\u6570\u636e\u96c6\u53ef\u4ee5\u70b9\u51fbhttps:\/\/github.com\/mwaskom\/seaborn-data<\/a><\/p>\n
# \u5bfc\u5165\u6a21\u5757\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n# \u52a0\u8f7d\u9e22\u5c3e\u82b1\u6570\u636e\niris = sns.load_dataset("iris")\n\n# \u6784\u5efa iris plot\nsns.swarmplot(x="species", y="petal_length", data=iris)\n\n# \u5c55\u793a \u56fe\u50cf\nplt.show()<\/code><\/pre>\n
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2.2\u3001\u52a0\u8f7d\u672c\u5730\u6570\u636e\u96c6<\/h3>\n
\u6570\u636e\u53ef\u89c6\u5316\u5927\u90e8\u5206\u7684\u5e94\u7528\u573a\u666f\u90fd\u662f\u4f7f\u7528\u81ea\u5df1\u7684\u6570\u636e\u96c6\uff0cseaborn\u4e3b\u8981\u9002\u7528\u4e8e\u6211\u4eec\u5e73\u65f6\u5e38\u7528\u7684DataFrame\u6570\u7ec4\u3002\u5b83\u662f\u4e00\u79cd\u7c7b\u4f3c\u4e8e\u4e8c\u7ef4\u6570\u7ec4\u7684\u6570\u636e\u7ed3\u6784\u3002\nSeaborn\u5bf9DataFrame\u975e\u5e38\u53cb\u597d\u7684\u539f\u56e0\u662f\uff0c\u56e0\u4e3aDataFrame\u7684\u6807\u7b7e\u4f1a\u81ea\u52a8\u5bfc\u5165\u5230\u56fe\u8868\u5f53\u4e2d\u3002\u5728\u7b2c\u4e00\u4e2aSeaborn\u793a\u4f8b\u4e2d\u7ed8\u5236\u7684\u4e00\u4e2a\u5c0f\u63d0\u7434\u56fe\u50cf\uff0cx\u8f74\u4f1a\u81ea\u52a8\u6dfb\u52a0\u4e00\u4e2a\u6807\u7b7etotal_bill<\/code>\uff0c\u8fd9\u4e2a\u5728matplotlib\u4e2d\u5c31\u9700\u8981\u6211\u4eec\u81ea\u5df1\u624b\u52a8\u6dfb\u52a0\u4e86\u3002<\/code><\/pre>\n
# \u5bfc\u5165\u6a21\u5757\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport tushare as ts\n\ndata = ts.get_k_data('000001')\ndata = data.tail()\n\n# \u5efa\u7acb\u4e00\u4e2a factorplot\ng = sns.factorplot('high','low',data=data, kind="bar",palette="muted", legend=False)\n\n# \u5c55\u793a\u56fe\u50cf\nplt.show()<\/code><\/pre>\n
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3\u3001\u914d\u7f6e<\/h2>\n
3.1\u3001\u5728seaborn\u4e2d\u4f7f\u7528matplotlib\u7684\u9ed8\u8ba4\u503c<\/h3>\n
# Import Matplotlib\nimport matplotlib.pyplot as plt\n\n# \u67e5\u770b\u6240\u6709\u53ef\u7528\u7684\u6837\u5f0f\nplt.style.available\n\n# \u4f7f\u7528matplotlib\u7684\u9ed8\u8ba4\u503c\nplt.style.use("classic")<\/code><\/pre>\n
['bmh',\n 'classic',\n 'dark_background',\n 'fast',\n 'fivethirtyeight',\n 'ggplot',\n 'grayscale',\n 'seaborn-bright',\n 'seaborn-colorblind',\n 'seaborn-dark-palette',\n 'seaborn-dark',\n 'seaborn-darkgrid',\n 'seaborn-deep',\n 'seaborn-muted',\n 'seaborn-notebook',\n 'seaborn-paper',\n 'seaborn-pastel',\n 'seaborn-poster',\n 'seaborn-talk',\n 'seaborn-ticks',\n 'seaborn-white',\n 'seaborn-whitegrid',\n 'seaborn',\n 'Solarize_Light2',\n 'tableau-colorblind10',\n '_classic_test']<\/code><\/pre>\n
3.2\u3001\u5728matplotlib\u4e2d\u4f7f\u7528seaborn\u7684\u989c\u8272\u4f5c\u4e3a\u8272\u5f69<\/h3>\n
\u53ef\u4ee5\u4f7f\u7528color_palette\uff08\uff09\u6765\u5b9a\u4e49\u8981\u4f7f\u7528\u7684\u989c\u8272\u6620\u5c04\u548c\u53c2\u6570n_colors\u7684\u989c\u8272\u6570<\/p>\n
# \u5bfc\u5165\u76f8\u5173\u6a21\u5757\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib.colors import ListedColormap\n\n# \u5b9a\u4e49\u4e00\u4e2a\u53d8\u91cfN\nN = 500\n\n# \u6784\u5efa colormap\ncurrent_palette = sns.color_palette("muted", n_colors=5)\ncmap = ListedColormap(sns.color_palette(current_palette).as_hex())\n\n# \u521d\u59cb\u5316\u6570\u636e\ndata1 = np.random.randn(N)\ndata2 = np.random.randn(N)\n# \u4ea7\u751f\u968f\u673a\u6570\u6807\u7b7e\ncolors = np.random.randint(0,5,N)\n\n# \u521b\u5efa\u6563\u70b9\u56fe\u56fe\u8868\nplt.scatter(data1, data2, c=colors, cmap=cmap)\n\n# \u6dfb\u52a0\u989c\u8272\u6761\nplt.colorbar()\n\n# \u5c55\u793a\u56fe\u50cf\n# plt.show()<\/code><\/pre>\n
<\/code><\/pre>\n
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3.3\u3001\u5728seaborn\u4e2d\u65cb\u8f6c\u6807\u7b7e\u6587\u672c<\/h3>\n
# Import the necessary libraries\nimport matplotlib.pyplot as plt\nimport seaborn as sns \nimport numpy as np\nimport pandas as pd\n\n# \u521d\u59cb\u5316\u6570\u636e\nx = 10 ** np.arange(1, 10)\ny = x * 2\ndata = pd.DataFrame(data={'x': x, 'y': y})\n\n# \u521b\u5efa lmplot\ngrid = sns.lmplot('x', 'y', data, size=7, truncate=True, scatter_kws={"s": 100})\n\n# \u5728X\u8f74\u4e0a\u65cb\u8f6c\u6807\u7b7e\ngrid.set_xticklabels(rotation=90)\n\n# \u5c55\u793a\u56fe\u50cf\n# plt.show()<\/code><\/pre>\n
<\/code><\/pre>\n
\"\"<\/div><\/p>\n","protected":false},"excerpt":{"rendered":"
1\u3001Seaborn \u5728\u4e0a\u8282\u4e2d\u6211\u4eec\u5b66\u4e60\u4e86matplotlib\uff0c\u8fd9\u8282\u8bfe\u6211\u4eec\u6765\u770b\u770b\u53e6\u4e00\u4e2a\u53ef\u89c6\u5316\u7684\u6a21\u5757seaborn\uff0c […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[315,310],"tags":[],"_links":{"self":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/posts\/3695"}],"collection":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3695"}],"version-history":[{"count":0,"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/posts\/3695\/revisions"}],"wp:attachment":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3695"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3695"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3695"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}