{"id":27658,"date":"2024-02-01T17:33:19","date_gmt":"2024-02-01T09:33:19","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=27658"},"modified":"2024-02-01T17:33:20","modified_gmt":"2024-02-01T09:33:20","slug":"%e4%b8%89%e5%88%86%e9%92%9f%e7%94%a8seaborn%e7%bb%98%e5%88%b6%e7%b2%be%e7%be%8e%e7%83%ad%e5%9b%be","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=27658","title":{"rendered":"\u4e09\u5206\u949f\u7528seaborn\u7ed8\u5236\u7cbe\u7f8e\u70ed\u56fe"},"content":{"rendered":"

\u5927\u5bb6\u597d\uff0c\u5c0f\u679c\u56de\u6765\u5566\uff01<\/p>\n

\u6700\u8fd1\uff0c\u5c0f\u679c\u6df1\u5165\u7814\u7a76\u4e86\u70ed\u56fe\uff08Heatmap\uff09\uff0c\u5b83\u662f\u6570\u636e\u53ef\u89c6\u5316\u9886\u57df\u7684\u4e00\u9897\u7480\u74a8\u660e\u661f\u3002\u5b83\u80fd\u591f\u4ee5\u76f4\u89c2\u3001\u751f\u52a8\u7684\u65b9\u5f0f\u5c55\u793a\u6570\u636e\u4e4b\u95f4\u7684\u5173\u7cfb\u548c\u6a21\u5f0f\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u5728\u6570\u636e\u7684\u6d77\u6d0b\u4e2d\u5bfb\u627e\u7ebf\u7d22\u3002<\/p>\n

\u5728\u6211\u4eec\u6df1\u5165\u63a2\u8ba8\u5982\u4f55\u4f7f\u7528Seaborn\u5e93\u6765\u7ed8\u5236\u70ed\u56fe\u4ee5\u53ca\u4e3a\u4ec0\u4e48\u9009\u62e9Seaborn\u4e4b\u524d\uff0c\u8ba9\u6211\u4eec\u5148\u6765\u4e86\u89e3\u4e00\u4e0b\u70ed\u56fe\u7684\u57fa\u672c\u6982\u5ff5\uff0c\u4ee5\u53ca\u5b83\u5728\u6570\u636e\u5206\u6790\u4e2d\u7684\u4e0d\u53ef\u6216\u7f3a\u4e4b\u5904\u3002\u8fd9\u4e2a\u65c5\u7a0b\u5c06\u5e26\u9886\u6211\u4eec\u8fdb\u5165\u6570\u636e\u7684\u795e\u5947\u4e16\u754c\uff0c\u4e00\u8d77\u6765\u63a2\u7d22\u5427\uff01<\/p>\n

\u70ed\u56fe\u7684\u57fa\u672c\u6982\u5ff5<\/strong><\/p>\n

\u70ed\u56fe\u7684\u6838\u5fc3\u601d\u60f3\u662f\u5c06\u6570\u636e\u96c6\u4e2d\u7684\u6570\u503c\u7528\u8272\u5f69\u8868\u793a\uff0c\u4ece\u800c\u5f62\u6210\u4e00\u4e2a\u77e9\u9635\u56fe\u3002\u901a\u5e38\uff0c\u70ed\u56fe\u5c06\u4e24\u4e2a\u7ef4\u5ea6\u7684\u6570\u636e\u4ee5\u77e9\u9635\u7684\u5f62\u5f0f\u5c55\u793a\uff0c\u5176\u4e2d\u4e00\u4e2a\u7ef4\u5ea6\u4ee3\u8868\u6570\u636e\u7684\u884c\uff0c\u53e6\u4e00\u4e2a\u4ee3\u8868\u5217\u3002\u6bcf\u4e2a\u5355\u5143\u683c\u7684\u989c\u8272\u6839\u636e\u6570\u636e\u503c\u7684\u5927\u5c0f\u800c\u53d8\u5316\uff0c\u4ece\u800c\u76f4\u89c2\u5730\u5448\u73b0\u4e86\u6570\u636e\u7684\u6a21\u5f0f\u3001\u5173\u8054\u548c\u5dee\u5f02\u3002<\/p>\n

\u4e3a\u4ec0\u4e48\u4f7f\u7528\u70ed\u56fe\uff1f<\/strong><\/p>\n

\u6570\u636e\u5173\u7cfb\u53ef\u89c6\u5316<\/strong>\uff1a\u70ed\u56fe\u80fd\u591f\u5e2e\u52a9\u6211\u4eec\u8fc5\u901f\u53d1\u73b0\u6570\u636e\u4e2d\u7684\u76f8\u5173\u6027\u548c\u8d8b\u52bf\u3002\u4f8b\u5982\uff0c\u5728\u751f\u7269\u5b66\u9886\u57df\uff0c\u53ef\u4ee5\u4f7f\u7528\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u5236\u4f5c\u70ed\u56fe\uff0c\u4ee5\u89c2\u5bdf\u54ea\u4e9b\u57fa\u56e0\u5728\u4e0d\u540c\u6761\u4ef6\u4e0b\u8868\u73b0\u51fa\u76f8\u4f3c\u7684\u6a21\u5f0f\uff0c\u4ece\u800c\u8bc6\u522b\u6f5c\u5728\u7684\u751f\u7269\u5b66\u5173\u7cfb<\/p>\n

\u6570\u636e\u964d\u7ef4<\/strong>\uff1a\u70ed\u56fe\u6709\u52a9\u4e8e\u5c06\u5927\u91cf\u6570\u636e\u538b\u7f29\u6210\u53ef\u7ba1\u7406\u7684\u56fe\u5f62\uff0c\u800c\u4e0d\u4f1a\u4e22\u5931\u91cd\u8981\u4fe1\u606f\u3002\u8fd9\u5bf9\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u96c6\u7684\u5feb\u901f\u53ef\u89c6\u5316\u548c\u7406\u89e3\u975e\u5e38\u6709\u7528\u3002<\/p>\n

\u8d8b\u52bf\u5206\u6790<\/strong>\uff1a\u70ed\u56fe\u53ef\u4ee5\u5e2e\u52a9\u60a8\u8bc6\u522b\u6570\u636e\u4e2d\u7684\u8d8b\u52bf\u548c\u5f02\u5e38\u3002\u901a\u8fc7\u89c2\u5bdf\u989c\u8272\u7684\u53d8\u5316\uff0c\u60a8\u53ef\u4ee5\u8bc6\u522b\u51fa\u6570\u636e\u4e2d\u7684\u9ad8\u70b9\u3001\u4f4e\u70b9\u548c\u5f02\u5e38\u503c\u3002<\/p>\n

\u6570\u636e\u805a\u7c7b<\/strong>\uff1a\u70ed\u56fe\u5e38\u7528\u4e8e\u805a\u7c7b\u5206\u6790\uff0c\u5c06\u76f8\u4f3c\u7684\u6570\u636e\u9879\u5206\u7ec4\u5728\u4e00\u8d77\u3002\u8fd9\u6709\u52a9\u4e8e\u7406\u89e3\u6570\u636e\u7684\u7ed3\u6784\u5e76\u53d1\u73b0\u6f5c\u5728\u7684\u7fa4\u7ec4\u3002<\/p>\n

\u4e3a\u4ec0\u4e48\u9009\u62e9Seaborn\u6765\u7ed8\u5236\u70ed\u56fe\uff1f<\/strong><\/p>\n

Seaborn\u662f\u4e00\u4e2a\u57fa\u4e8eMatplotlib\u7684Python\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u4e3a\u4ec0\u4e48\u6211\u4eec\u66f4\u613f\u610f\u9009\u62e9Seaborn\u6765\u7ed8\u5236\u70ed\u56fe\uff0c\u800c\u4e0d\u662f\u4f7f\u7528Matplotlib\u6216R\u5462\uff1f\u4ee5\u4e0b\u662fSeaborn\u5728\u70ed\u56fe\u7ed8\u5236\u4e2d\u7684\u4e00\u4e9b\u4f18\u70b9\uff1a<\/p>\n

\u7f8e\u89c2\u6027<\/strong>\uff1aSeaborn\u4ee5\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u8457\u79f0\uff0c\u56e0\u6b64\u60a8\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u5177\u6709\u5438\u5f15\u529b\u7684\u70ed\u56fe\uff0c\u65e0\u9700\u592a\u591a\u81ea\u5b9a\u4e49\u3002<\/p>\n

\u7b80\u5316API<\/strong>\uff1a\u76f8\u5bf9\u4e8eMatplotlib\uff0cSeaborn\u63d0\u4f9b\u4e86\u66f4\u7b80\u5355\u7684API\u6765\u521b\u5efa\u70ed\u56fe\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528\u4e00\u884c\u4ee3\u7801\u521b\u5efa\u4e00\u4e2a\u70ed\u56fe\uff0c\u800c\u65e0\u9700\u7f16\u5199\u5927\u91cf\u7684\u7ed8\u56fe\u4ee3\u7801\u3002<\/p>\n

\u7edf\u8ba1\u6570\u636e\u53ef\u89c6\u5316<\/strong>\uff1aSeaborn\u4e13\u6ce8\u4e8e\u7edf\u8ba1\u6570\u636e\u53ef\u89c6\u5316\uff0c\u56e0\u6b64\u5b83\u63d0\u4f9b\u4e86\u4e00\u4e9b\u9ad8\u7ea7\u529f\u80fd\uff0c\u5982\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u3001\u7ed8\u5236\u4e0d\u540c\u5b50\u96c6\u7684\u70ed\u56fe\u7b49\u3002<\/p>\n

\u5185\u7f6e\u989c\u8272\u6620\u5c04<\/strong>\uff1aSeaborn\u63d0\u4f9b\u4e86\u591a\u79cd\u5185\u7f6e\u7684\u989c\u8272\u6620\u5c04\uff0c\u4f7f\u60a8\u53ef\u4ee5\u8f7b\u677e\u9009\u62e9\u9002\u5408\u6570\u636e\u7684\u989c\u8272\uff0c\u800c\u4e0d\u5fc5\u81ea\u5df1\u5b9a\u4e49\u3002<\/p>\n

\u4e0ePandas\u96c6\u6210<\/strong>\uff1aSeaborn\u4e0ePandas\u975e\u5e38\u517c\u5bb9\uff0c\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u6570\u636e\u6846\u521b\u5efa\u70ed\u56fe\uff0c\u4e0d\u9700\u8981\u989d\u5916\u7684\u6570\u636e\u5904\u7406\u6b65\u9aa4\u3002<\/p>\n

\u6d3b\u8dc3\u7684\u793e\u533a\u652f\u6301<\/strong>\uff1aSeaborn\u6709\u4e00\u4e2a\u6d3b\u8dc3\u7684\u793e\u533a\uff0c\u63d0\u4f9b\u4e86\u5927\u91cf\u793a\u4f8b\u548c\u6587\u6863\uff0c\u4f7f\u5b66\u4e60\u548c\u4f7f\u7528\u53d8\u5f97\u66f4\u52a0\u5bb9\u6613\u3002<\/p>\n

\u4e0b\u9762\u8ba9\u6211\u4eec\u4f7f\u7528Seaborn\u8fdb\u884c\u793a\u8303\uff0c\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n

import seaborn as sns<\/p>\n

import pandas as pd<\/p>\n

import numpy as np<\/p>\n

import matplotlib.pyplot as plt<\/p>\n

# \u521b\u5efa\u4e00\u4e2a\u5305\u542b10\u884c10\u5217\u968f\u673a\u6570\u7684\u6570\u636e\u6846\uff0c\u5217\u540d\u4e3aa\u5230j<\/p>\n

df = pd.DataFrame(np.random.random((10, 10)), columns=[“a”, “b”, “c”, “d”, “e”, “f”, “g”, “h”, “i”, “j”])<\/p>\n

# \u7ed8\u5236\u4e00\u4e2a\u70ed\u529b\u56fe\uff0ccbar\u53c2\u6570\u8868\u793a\u4e0d\u663e\u793a\u989c\u8272\u6761<\/p>\n

sns.heatmap(df, cbar=False)<\/p>\n

plt.show()<\/p>\n

# \u7ed8\u5236\u4e00\u4e2a\u70ed\u529b\u56fe\uff0c\u4f7f\u7528\u84dd\u8272\u8c03\u8272\u677f\uff08cmap=”Blues”\uff09<\/p>\n

sns.heatmap(df, cmap=”Blues”)<\/p>\n

plt.show()<\/p>\n

# \u7ed8\u5236\u4e00\u4e2a\u70ed\u529b\u56fe\uff0c\u663e\u793a\u6bcf\u4e2a\u5355\u5143\u683c\u7684\u6570\u503c\uff0c\u8bbe\u7f6e\u6ce8\u91ca\u6587\u672c\u5b57\u4f53\u5927\u5c0f\u4e3a7<\/p>\n

sns.heatmap(df, annot=True, annot_kws={“size”: 7})<\/p>\n

plt.show()<\/p>\n

# \u7ed8\u5236\u4e00\u4e2a\u70ed\u529b\u56fe\uff0c\u589e\u52a0\u5355\u5143\u683c\u4e4b\u95f4\u7684\u7ebf\u5bbd\u4e3a2\uff0c\u7ebf\u7684\u989c\u8272\u4e3a\u9ec4\u8272<\/p>\n

sns.heatmap(df, linewidths=2, linecolor=’yellow’)<\/p>\n

plt.show() \u4fee\u6539\u8272\u8c03\u4e3a\u84dd\u8272\uff1a \u4e3a\u6bcf\u4e2a\u5355\u5143\u683c\u586b\u5145\u6570\u503c\uff1a \u4f7f\u7528\u81ea\u5b9a\u4e49\u7f51\u683c\u7ebf\u7ed8\u5236\u70ed\u56fe\uff1a \u5f53\u7136\u4e5f\u53ef\u4ee5\u7ed8\u5236\u597d\u770b\u7684\u805a\u7c7b\u70ed\u56fe\uff0c\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\u6240\u793a\uff1a
\nimport seaborn as sns<\/p>\n

import pandas as pd<\/p>\n

from matplotlib import pyplot as plt<\/p>\n

# \u5bfc\u5165\u6570\u636e\u96c6<\/p>\n

url = ‘https:\/\/raw.githubusercontent.com\/holtzy\/The-Python-Graph-Gallery\/master\/static\/data\/mtcars.csv’<\/p>\n

# \u4ece\u7ed9\u5b9aURL\u8bfb\u53d6CSV\u6587\u4ef6\u5e76\u521b\u5efa\u4e00\u4e2a\u6570\u636e\u6846<\/p>\n

df = pd.read_csv(url)<\/p>\n

# \u5c06’model’\u5217\u8bbe\u7f6e\u4e3a\u6570\u636e\u6846\u7684\u7d22\u5f15\uff0c\u4f5c\u4e3a\u884c\u6807\u7b7e<\/p>\n

df = df.set_index(‘model’)<\/p>\n

# \u51c6\u5907\u4e00\u4e2a\u989c\u8272\u5411\u91cf\uff0c\u6839\u636e’cyl’\u5217\u7684\u503c\u6620\u5c04\u989c\u8272<\/p>\n

my_palette = dict(zip(df.cyl.unique(), [“orange”, “yellow”, “brown”]))<\/p>\n

# \u521b\u5efa\u4e00\u4e2a\u5b57\u5178\uff0c\u5c06\u552f\u4e00\u7684’cyl’\u503c\u4e0e\u989c\u8272\u76f8\u5bf9\u5e94\uff0c\u7136\u540e\u901a\u8fc7’map’\u65b9\u6cd5\u5c06\u989c\u8272\u6620\u5c04\u5230\u6bcf\u884c<\/p>\n

row_colors = df.cyl.map(my_palette)<\/p>\n

# \u7ed8\u5236\u7c07\u72b6\u56fe<\/p>\n

sns.clustermap(df, metric=”correlation”, method=”single”, cmap=”Blues”, standard_scale=1, row_colors=row_colors)<\/p>\n

# \u4f7f\u7528Seaborn\u7684clustermap\u51fd\u6570\u7ed8\u5236\u805a\u7c7b\u70ed\u56fe\uff0c\u4f7f\u7528”correlation”\u4f5c\u4e3a\u76f8\u4f3c\u6027\u5ea6\u91cf\uff0c”single”\u4f5c\u4e3a\u805a\u7c7b\u65b9\u6cd5\uff0c”Blues”\u4f5c\u4e3a\u989c\u8272\u6620\u5c04\uff0c<\/p>\n

# “standard_scale=1″\u7528\u4e8e\u6807\u51c6\u5316\u6570\u636e\uff0crow_colors\u53c2\u6570\u5c06\u4e4b\u524d\u521b\u5efa\u7684\u989c\u8272\u5411\u91cf\u5e94\u7528\u4e8e\u884c<\/p>\n

plt.show()<\/p>\n

<\/p>\n

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