<p>你可以使用神奇的<code>pandas</code>包的力量:</p>
<ul>
<li>将其加载到<a href="https://pandas.pydata.org/pandas-docs/version/0.21/generated/pandas.DataFrame.html" rel="nofollow noreferrer">pandas DataFrame</a></li>
<li>应用此<a href="https://stackoverflow.com/a/27266225/771848">solution</a>来展开<code>tags</code>值:</li>
</ul>
<p>代码:</p>
<pre><code>import pandas as pd
data = [] # your list is here
df = pd.DataFrame(data)
# expand 'tags' column into multiple rows
tags = df.apply(lambda x: pd.Series(x['tags']), axis=1).stack().reset_index(level=1, drop=True)
tags.name = 'tags'
df = df.drop('tags', axis=1).join(tags)
print(df)
</code></pre>
<p>印刷品:</p>
^{pr2}$
<p>对于转储到CSV,可以使用<a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_csv.html" rel="nofollow noreferrer">^{<cd3>} method</a>。在</p>
<hr/>
<p>您还可以将“展开列”逻辑提取到单独的方法中并重用:</p>
<pre><code>def expand_column(df, column_name):
c = df.apply(lambda x: pd.Series(x[column_name]), axis=1).stack().reset_index(level=1, drop=True)
c.name = column_name
return df.drop(column_name, axis=1).join(c)
</code></pre>
<p>用法:</p>
<pre><code>df = pd.DataFrame(data)
df = expand_column(df, 'tags')
</code></pre>