{"id":3521,"date":"2026-03-09T13:00:06","date_gmt":"2026-03-09T05:00:06","guid":{"rendered":"http:\/\/xuebuwan.com\/wp\/archives\/3521"},"modified":"2026-03-09T16:37:04","modified_gmt":"2026-03-09T08:37:04","slug":"python%e6%8a%a5%e9%94%99%e6%b1%87%e6%80%bb%ef%bc%9a%e9%87%8f%e5%8c%96%e6%96%b0%e6%89%8b%e5%b8%b8%e8%a7%81%e9%97%ae%e9%a2%98","status":"publish","type":"post","link":"http:\/\/xuebuwan.com\/wp\/?p=3521","title":{"rendered":"Python\u62a5\u9519\u6c47\u603b\uff1a\u91cf\u5316\u65b0\u624b\u5e38\u89c1\u95ee\u9898"},"content":{"rendered":"<p>\u5b66\u4e60 Python \u91cf\u5316\u4ea4\u6613\u7684\u8fc7\u7a0b\u4e2d\uff0c\u62a5\u9519\u662f\u5fc5\u7ecf\u4e4b\u8def\u3002\u6bcf\u4e00\u4e2a\u62a5\u9519\u90fd\u662f\u4e00\u6b21\u5b66\u4e60\u673a\u4f1a\uff0c\u89e3\u51b3\u5f97\u591a\u4e86\uff0c\u81ea\u7136\u5c31\u6210\u4e86\u9ad8\u624b\u3002\u4eca\u5929\uff0c\u6211\u6574\u7406\u4e86\u65b0\u624b\u6700\u5e38\u9047\u5230\u7684 5 \u7c7b Python \u62a5\u9519\uff0c\u5e2e\u4f60\u4e00\u6b21\u6027\u89e3\u51b3\uff01<\/p>\n<h3>1. ModuleNotFoundError\uff1a\u6a21\u5757\u672a\u627e\u5230<\/h3>\n<p>\u8fd9\u662f\u65b0\u624b\u9047\u5230\u7684\u7b2c\u4e00\u4e2a&#8221;\u62e6\u8def\u864e&#8221;\u3002\u5f53\u4f60\u5bfc\u5165\u4e00\u4e2a\u672a\u5b89\u88c5\u7684\u5e93\u65f6\uff0cPython \u4f1a\u629b\u51fa\u8fd9\u4e2a\u9519\u8bef\u3002<\/p>\n<p><strong>\u9519\u8bef\u793a\u4f8b\uff1a<\/strong><\/p>\n<pre><code class=\"lang-python language-python python\">import akshare as ak\n# ModuleNotFoundError: No module named &#039;akshare&#039;<\/code><\/pre>\n<p><strong>\u539f\u56e0\u5206\u6790\uff1a<\/strong><\/p>\n<ul>\n<li>\u8be5\u6a21\u5757\u672a\u5b89\u88c5<\/li>\n<li>\u5b89\u88c5\u5728\u4e86\u9519\u8bef\u7684 Python \u73af\u5883<\/li>\n<li>\u865a\u62df\u73af\u5883\u672a\u6fc0\u6d3b<\/li>\n<\/ul>\n<p><strong>\u89e3\u51b3\u65b9\u6848\uff1a<\/strong><\/p>\n<pre><code class=\"lang-python language-python python\"># \u57fa\u7840\u5b89\u88c5\npip install \u6a21\u5757\u540d\n\n# \u6307\u5b9a\u56fd\u5185\u955c\u50cf\uff08\u66f4\u5feb\uff09\npip install akshare -i https:\/\/pypi.tuna.tsinghua.edu.cn\/simple\n\n# \u6307\u5b9a\u7248\u672c\u5b89\u88c5\npip install pandas==1.5.0<\/code><\/pre>\n<p><strong>\u9884\u9632\u63aa\u65bd\uff1a<\/strong>\n\u5efa\u8bae\u521b\u5efa\u9879\u76ee\u4f9d\u8d56\u6587\u4ef6 requirements.txt\uff1a<\/p>\n<pre><code class=\"lang-python language-python python\">pandas&gt;=1.5.0\nnumpy&gt;=1.20.0\nakshare&gt;=1.12.0\nbacktrader&gt;=1.9.78<\/code><\/pre>\n<p>\u7136\u540e\u4e00\u952e\u5b89\u88c5\uff1a<\/p>\n<pre><code class=\"lang-zsh language-zsh zsh\">pip install -r requirements.txt<\/code><\/pre>\n<p><strong>\u5e38\u89c1\u9677\u9631\uff1a<\/strong><\/p>\n<ul>\n<li>\u5b89\u88c5\u4e86\u4f46 Jupyter \u627e\u4e0d\u5230\uff1a\u91cd\u542f\u5185\u6838<\/li>\n<li>\u591a\u4e2a Python \u7248\u672c\uff1a\u4f7f\u7528 <code>python -m pip install<\/code> \u786e\u4fdd\u5b89\u88c5\u5230\u6b63\u786e\u73af\u5883<\/li>\n<li>\u6743\u9650\u95ee\u9898\uff1a\u6dfb\u52a0 <code>--user<\/code> \u53c2\u6570\u6216\u4f7f\u7528\u865a\u62df\u73af\u5883<\/li>\n<\/ul>\n<h3>2. KeyError\uff1a\u952e\u4e0d\u5b58\u5728<\/h3>\n<p>\u5904\u7406 DataFrame \u65f6\uff0c\u5982\u679c\u8bbf\u95ee\u4e0d\u5b58\u5728\u7684\u5217\u540d\uff0c\u4f1a\u62a5 KeyError\u3002<\/p>\n<p><strong>\u9519\u8bef\u793a\u4f8b\uff1a<\/strong><\/p>\n<pre><code class=\"lang-python language-python python\">import pandas as pd\n\ndf = pd.read_csv(&quot;stock_data.csv&quot;)\nprint(df[&quot;\u6536\u76d8&quot;])  # KeyError: &#039;\u6536\u76d8&#039;<\/code><\/pre>\n<p><strong>\u539f\u56e0\u5206\u6790\uff1a<\/strong><\/p>\n<ul>\n<li>\u5217\u540d\u6709\u62fc\u5199\u9519\u8bef<\/li>\n<li>\u5217\u540d\u5305\u542b\u7a7a\u683c\u6216\u7279\u6b8a\u5b57\u7b26<\/li>\n<li>\u5217\u540d\u5927\u5c0f\u5199\u4e0d\u5339\u914d<\/li>\n<li>\u6587\u4ef6\u7f16\u7801\u95ee\u9898\u5bfc\u81f4\u5217\u540d\u4e71\u7801<\/li>\n<\/ul>\n<p><strong>\u89e3\u51b3\u65b9\u6848\uff1a<\/strong><\/p>\n<pre><code class=\"lang-python language-python python\"># 1. \u5148\u67e5\u770b\u5b9e\u9645\u5217\u540d\nprint(df.columns.tolist())\n# \u8f93\u51fa\uff1a[&#039; \u4ee3\u7801 &#039;, &#039; \u540d\u79f0 &#039;, &#039; \u6536\u76d8 &#039;, &#039; \u6210\u4ea4\u91cf&#039;] \u6ce8\u610f\u6709\u7a7a\u683c\n\n# 2. \u53bb\u9664\u5217\u540d\u7a7a\u683c\ndf.columns = df.columns.str.strip()\n\n# 3. \u7edf\u4e00\u5217\u540d\u683c\u5f0f\ndf.columns = df.columns.str.lower()\n\n# 4. \u91cd\u547d\u540d\u5217\ndf = df.rename(columns={&quot;\u6536\u76d8&quot;: &quot;close&quot;, &quot;\u5f00\u76d8&quot;: &quot;open&quot;})\n\n# 5. \u5b89\u5168\u8bbf\u95ee\uff08\u4e0d\u5b58\u5728\u7684\u5217\u8fd4\u56de None\uff09\nprice = df.get(&quot;\u6536\u76d8&quot;, None)<\/code><\/pre>\n<p><strong>\u9884\u9632\u63aa\u65bd\uff1a<\/strong>\n\u8bfb\u53d6\u6570\u636e\u540e\u7acb\u5373\u6e05\u6d17\u5217\u540d\uff1a<\/p>\n<pre><code class=\"lang-python language-python python\">def clean_columns(df):\n    df.columns = df.columns.str.strip()\n    df.columns = df.columns.str.replace(&quot; &quot;, &quot;_&quot;)\n    return df\n\ndf = clean_columns(pd.read_csv(&quot;data.csv&quot;))<\/code><\/pre>\n<h3>3. TypeError\uff1a\u7c7b\u578b\u9519\u8bef<\/h3>\n<p>\u6570\u636e\u7c7b\u578b\u4e0d\u5339\u914d\u662f\u5e38\u89c1\u9519\u8bef\uff0c\u5c24\u5176\u5728\u6570\u636e\u8ba1\u7b97\u65f6\u3002<\/p>\n<p><strong>\u9519\u8bef\u793a\u4f8b\uff1a<\/strong><\/p>\n<pre><code class=\"lang-python language-python python\">df[&quot;\u6536\u76d8&quot;] = df[&quot;\u6536\u76d8&quot;] * 1.1\n# TypeError: can&#039;t multiply sequence by non-int of type &#039;float&#039;<\/code><\/pre>\n<p><strong>\u539f\u56e0\u5206\u6790\uff1a<\/strong><\/p>\n<ul>\n<li>\u5217\u6570\u636e\u7c7b\u578b\u662f\u5b57\u7b26\u4e32\u800c\u975e\u6570\u5b57<\/li>\n<li>\u6570\u636e\u4e2d\u5305\u542b\u7a7a\u503c\u6216\u975e\u6570\u5b57\u5b57\u7b26<\/li>\n<li>\u4ece CSV \u8bfb\u53d6\u65f6\u81ea\u52a8\u8bc6\u522b\u4e3a\u5bf9\u8c61\u7c7b\u578b<\/li>\n<\/ul>\n<p><strong>\u89e3\u51b3\u65b9\u6848\uff1a<\/strong><\/p>\n<pre><code class=\"lang-python language-python python\"># 1. \u67e5\u770b\u6570\u636e\u7c7b\u578b\nprint(df.dtypes)\nprint(df[&quot;\u6536\u76d8&quot;].dtype)  # \u8f93\u51fa\uff1aobject\n\n# 2. \u5f3a\u5236\u7c7b\u578b\u8f6c\u6362\ndf[&quot;\u6536\u76d8&quot;] = df[&quot;\u6536\u76d8&quot;].astype(float)\n\n# 3. \u5904\u7406\u65e0\u6cd5\u8f6c\u6362\u7684\u503c\ndf[&quot;\u6536\u76d8&quot;] = pd.to_numeric(df[&quot;\u6536\u76d8&quot;], errors=&quot;coerce&quot;)\n# errors=&quot;coerce&quot; \u4f1a\u5c06\u65e0\u6cd5\u8f6c\u6362\u7684\u503c\u8bbe\u4e3a NaN\n\n# 4. \u6e05\u7406\u6570\u636e\u540e\u8f6c\u6362\ndf[&quot;\u6536\u76d8&quot;] = df[&quot;\u6536\u76d8&quot;].str.replace(&quot;,&quot;, &quot;&quot;)  # \u53bb\u9664\u5343\u5206\u4f4d\u9017\u53f7\ndf[&quot;\u6536\u76d8&quot;] = df[&quot;\u6536\u76d8&quot;].astype(float)\n\n# 5. \u586b\u5145\u7a7a\u503c\ndf[&quot;\u6536\u76d8&quot;] = df[&quot;\u6536\u76d8&quot;].fillna(df[&quot;\u6536\u76d8&quot;].mean())<\/code><\/pre>\n<p><strong>\u9884\u9632\u63aa\u65bd\uff1a<\/strong>\n\u8bfb\u53d6\u6570\u636e\u65f6\u6307\u5b9a\u6570\u636e\u7c7b\u578b\uff1a<\/p>\n<pre><code class=\"lang-python language-python python\">df = pd.read_csv(&quot;data.csv&quot;, dtype={&quot;\u6536\u76d8&quot;: float, &quot;\u6210\u4ea4\u91cf&quot;: int})<\/code><\/pre>\n<h3>4. SettingWithCopyWarning\uff1a\u8b66\u544a\u63d0\u793a<\/h3>\n<p>\u8fd9\u4e0d\u662f\u9519\u8bef\uff0c\u4f46\u4f1a\u8ba9\u65b0\u624b\u56f0\u60d1\u3002\u5b83\u63d0\u793a\u4f60\u53ef\u80fd\u5728\u4fee\u6539 DataFrame \u7684\u526f\u672c\u800c\u975e\u539f\u6570\u636e\u3002<\/p>\n<p><strong>\u9519\u8bef\u793a\u4f8b\uff1a<\/strong><\/p>\n<pre><code class=\"lang-python language-python python\">df_subset = df[df[&quot;\u6536\u76d8&quot;] &gt; 10]\ndf_subset[&quot;\u65b0\u5217&quot;] = 1  # SettingWithCopyWarning<\/code><\/pre>\n<p><strong>\u539f\u56e0\u5206\u6790\uff1a<\/strong><\/p>\n<ul>\n<li>pandas \u4e0d\u786e\u5b9a\u4f60\u662f\u60f3\u4fee\u6539\u539f\u6570\u636e\u8fd8\u662f\u526f\u672c<\/li>\n<li>\u94fe\u5f0f\u7d22\u5f15\u53ef\u80fd\u5bfc\u81f4\u610f\u5916\u884c\u4e3a<\/li>\n<\/ul>\n<p><strong>\u89e3\u51b3\u65b9\u6848\uff1a<\/strong><\/p>\n<pre><code class=\"lang-python language-python python\"># \u65b9\u6cd5 1\uff1a\u4f7f\u7528 copy() \u660e\u786e\u521b\u5efa\u526f\u672c\ndf_subset = df[df[&quot;\u6536\u76d8&quot;] &gt; 10].copy()\ndf_subset[&quot;\u65b0\u5217&quot;] = 1  # \u4e0d\u518d\u8b66\u544a\n\n# \u65b9\u6cd5 2\uff1a\u4f7f\u7528 loc \u76f4\u63a5\u4fee\u6539\u539f\u6570\u636e\ndf.loc[df[&quot;\u6536\u76d8&quot;] &gt; 10, &quot;\u65b0\u5217&quot;] = 1\n\n# \u65b9\u6cd5 3\uff1a\u7981\u7528\u8b66\u544a\uff08\u4e0d\u63a8\u8350\uff09\npd.options.mode.chained_assignment = None<\/code><\/pre>\n<p><strong>\u6700\u4f73\u5b9e\u8df5\uff1a<\/strong><\/p>\n<pre><code class=\"lang-python language-python python\"># \u660e\u786e\u610f\u56fe\u662f\u5173\u952e\n# \u5982\u679c\u8981\u4fee\u6539\u539f\u6570\u636e\ndf.loc[\u6761\u4ef6\uff0c&quot;\u5217\u540d&quot;] = \u503c\n\n# \u5982\u679c\u8981\u521b\u5efa\u65b0 DataFrame\ndf_new = df[\u6761\u4ef6].copy()\ndf_new[&quot;\u5217\u540d&quot;] = \u503c<\/code><\/pre>\n<h3>5. FutureWarning\uff1a\u672a\u6765\u7248\u672c\u8b66\u544a<\/h3>\n<p>\u8fd9\u4e2a\u8b66\u544a\u63d0\u793a\u4f60\u4f7f\u7528\u7684 API \u5728\u672a\u6765\u7248\u672c\u4e2d\u53ef\u80fd\u4f1a\u53d8\u5316\u3002<\/p>\n<p><strong>\u9519\u8bef\u793a\u4f8b\uff1a<\/strong><\/p>\n<pre><code class=\"lang-python language-python python\"># FutureWarning: DataFrame.mean(axis=1) will change behavior\ndf.mean(axis=1)<\/code><\/pre>\n<p><strong>\u539f\u56e0\u5206\u6790\uff1a<\/strong><\/p>\n<ul>\n<li>pandas \u65b0\u7248\u672c API \u6709\u53d8\u5316<\/li>\n<li>\u67d0\u4e9b\u51fd\u6570\u53c2\u6570\u884c\u4e3a\u5c06\u8c03\u6574<\/li>\n<li>\u5e93\u7248\u672c\u4e0d\u5339\u914d<\/li>\n<\/ul>\n<p><strong>\u89e3\u51b3\u65b9\u6848\uff1a<\/strong><\/p>\n<pre><code class=\"lang-python language-python python\"># \u65b9\u6cd5 1\uff1a\u5347\u7ea7 pandas \u5230\u6700\u65b0\u7248\u672c\npip install --upgrade pandas\n\n# \u65b9\u6cd5 2\uff1a\u6839\u636e\u8b66\u544a\u4fe1\u606f\u8c03\u6574\u4ee3\u7801\n# \u65e7\u4ee3\u7801\ndf = df.append(new_row, ignore_index=True)\n\n# \u65b0\u4ee3\u7801\uff08pandas 2.0+\uff09\ndf = pd.concat([df, new_row.to_frame().T], ignore_index=True)\n\n# \u65b9\u6cd5 3\uff1a\u6682\u65f6\u5ffd\u7565\u8b66\u544a\uff08\u5f00\u53d1\u9636\u6bb5\u4e0d\u63a8\u8350\uff09\nimport warnings\nwarnings.simplefilter(action=&#039;ignore&#039;, category=FutureWarning)<\/code><\/pre>\n<p><strong>\u7248\u672c\u7ba1\u7406\u5efa\u8bae\uff1a<\/strong><\/p>\n<pre><code class=\"lang-python language-python python\"># \u5728\u4ee3\u7801\u5f00\u5934\u68c0\u67e5\u7248\u672c\nimport pandas as pd\nprint(f&quot;pandas \u7248\u672c\uff1a{pd.__version__}&quot;)\n\n# \u786e\u4fdd\u7248\u672c\u517c\u5bb9\nimport pandas as pd\nfrom packaging import version\n\nif version.parse(pd.__version__) &lt; version.parse(&quot;1.5.0&quot;):\n    print(&quot;\u8bf7\u5347\u7ea7 pandas \u5230 1.5.0 \u4ee5\u4e0a&quot;)<\/code><\/pre>\n<h3>\u5e38\u89c1\u5e93\u7248\u672c\u63a8\u8350<\/h3>\n<p>\u4ee5\u4e0b\u662f 2024 \u5e74\u91cf\u5316\u4ea4\u6613\u7684\u63a8\u8350\u7248\u672c\u7ec4\u5408\uff1a<\/p>\n<pre><code class=\"lang-python language-python python\"># \u6838\u5fc3\u6570\u636e\u5904\u7406\npandas&gt;=1.5.0,&lt;2.0.0\nnumpy&gt;=1.20.0,&lt;2.0.0\n\n# \u6570\u636e\u83b7\u53d6\nakshare&gt;=1.12.0\ntushare&gt;=1.2.80\n\n# \u56de\u6d4b\u6846\u67b6\nbacktrader&gt;=1.9.78\nzipline-reloaded&gt;=2.5.0\n\n# \u53ef\u89c6\u5316\nmatplotlib&gt;=3.5.0\nplotly&gt;=5.10.0\n\n# \u673a\u5668\u5b66\u4e60\uff08\u53ef\u9009\uff09\nscikit-learn&gt;=1.0.0<\/code><\/pre>\n<p><strong>\u7248\u672c\u7ba1\u7406\u5de5\u5177\uff1a<\/strong><\/p>\n<pre><code class=\"lang-python language-python python\"># \u4f7f\u7528 conda \u7ba1\u7406\u73af\u5883\nconda create -n quant python=3.9\nconda activate quant\n\n# \u4f7f\u7528 venv \u7ba1\u7406\u73af\u5883\npython -m venv quant_env\nsource quant_env\/bin\/activate  # Mac\/Linux\nquant_env\\Scripts\\activate     # Windows<\/code><\/pre>\n<h3>\u8c03\u8bd5\u6280\u5de7<\/h3>\n<p>\u6700\u540e\u5206\u4eab\u51e0\u4e2a\u5b9e\u7528\u8c03\u8bd5\u6280\u5de7\uff1a<\/p>\n<ol>\n<li>\u6253\u5370\u4e2d\u95f4\u53d8\u91cf\uff1aprint(df.head()) \u67e5\u770b\u6570\u636e\u72b6\u6001<\/li>\n<li>\u4f7f\u7528\u65ad\u70b9\uff1aimport pdb; pdb.set_trace() \u8fdb\u5165\u8c03\u8bd5\u6a21\u5f0f<\/li>\n<li>\u67e5\u770b\u5b8c\u6574\u9519\u8bef\u6808\uff1a\u4e0d\u8981\u53ea\u770b\u6700\u540e\u4e00\u884c\uff0c\u5411\u4e0a\u8ffd\u6eaf\u9519\u8bef\u6765\u6e90<\/li>\n<li>\u641c\u7d22\u5f15\u64ce\u662f\u4f60\u7684\u670b\u53cb\uff1a\u628a\u9519\u8bef\u4fe1\u606f\u590d\u5236\u5230 Google \u6216 Stack Overflow<\/li>\n<li>\u6700\u5c0f\u5316\u590d\u73b0\uff1a\u7528\u6700\u7b80\u5355\u7684\u4ee3\u7801\u590d\u73b0\u95ee\u9898\uff0c\u4fbf\u4e8e\u5b9a\u4f4d<\/li>\n<\/ol>\n<h3>\u7ed3\u8bed<\/h3>\n<p>\u62a5\u9519\u662f\u7f16\u7a0b\u7684\u4e00\u90e8\u5206\uff0c\u4e0d\u8981\u5bb3\u6015\u62a5\u9519\u3002\u6bcf\u4e00\u4e2a\u9519\u8bef\u4fe1\u606f\u90fd\u662f Python \u5728\u544a\u8bc9\u4f60\u95ee\u9898\u6240\u5728\uff0c\u8bfb\u61c2\u5b83\u3001\u89e3\u51b3\u5b83\uff0c\u4f60\u5c31\u8fdb\u6b65\u4e86\u3002<\/p>\n<p>\u8bb0\u4f4f\uff1a\u9ad8\u624b\u4e0d\u662f\u4e0d\u72af\u9519\u7684\u4eba\uff0c\u800c\u662f\u80fd\u5feb\u901f\u89e3\u51b3\u9519\u8bef\u7684\u4eba\u3002<\/p>\n<hr \/>\n<p><strong>\u514d\u8d23\u8bf4\u660e<\/strong>\uff1a\u672c\u6587\u4ec5\u4f9b\u6280\u672f\u5206\u4eab\u3002\u4ee3\u7801\u793a\u4f8b\u8bf7\u5728\u6d4b\u8bd5\u73af\u5883\u8fd0\u884c\uff0c\u751f\u4ea7\u73af\u5883\u8bf7\u6839\u636e\u5b9e\u9645\u60c5\u51b5\u8c03\u6574\u3002<\/p>","protected":false},"excerpt":{"rendered":"<p>\u5b66\u4e60 Python \u91cf\u5316\u4ea4\u6613\u7684\u8fc7\u7a0b\u4e2d\uff0c\u62a5\u9519\u662f\u5fc5\u7ecf\u4e4b\u8def\u3002\u6bcf\u4e00\u4e2a\u62a5\u9519\u90fd\u662f\u4e00\u6b21\u5b66\u4e60\u673a\u4f1a\uff0c\u89e3\u51b3\u5f97\u591a\u4e86\uff0c\u81ea\u7136\u5c31\u6210\u4e86\u9ad8\u624b\u3002 &#8230; <a title=\"Python\u62a5\u9519\u6c47\u603b\uff1a\u91cf\u5316\u65b0\u624b\u5e38\u89c1\u95ee\u9898\" class=\"read-more\" href=\"http:\/\/xuebuwan.com\/wp\/?p=3521\" aria-label=\"\u9605\u8bfb Python\u62a5\u9519\u6c47\u603b\uff1a\u91cf\u5316\u65b0\u624b\u5e38\u89c1\u95ee\u9898\">\u9605\u8bfb\u66f4\u591a<\/a><\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[18],"tags":[],"class_list":["post-3521","post","type-post","status-publish","format-standard","hentry","category-python-quant"],"_links":{"self":[{"href":"http:\/\/xuebuwan.com\/wp\/index.php?rest_route=\/wp\/v2\/posts\/3521","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/xuebuwan.com\/wp\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/xuebuwan.com\/wp\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/xuebuwan.com\/wp\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/xuebuwan.com\/wp\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3521"}],"version-history":[{"count":2,"href":"http:\/\/xuebuwan.com\/wp\/index.php?rest_route=\/wp\/v2\/posts\/3521\/revisions"}],"predecessor-version":[{"id":3565,"href":"http:\/\/xuebuwan.com\/wp\/index.php?rest_route=\/wp\/v2\/posts\/3521\/revisions\/3565"}],"wp:attachment":[{"href":"http:\/\/xuebuwan.com\/wp\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3521"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/xuebuwan.com\/wp\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3521"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/xuebuwan.com\/wp\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3521"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}