擅长:python、mysql、java
<p>您需要先将列转换为浮点或整数,然后通过<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.astype.html" rel="nofollow noreferrer">^{<cd1>}</a>聚合<code>sum</code>,再通过<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.GroupBy.sum.html" rel="nofollow noreferrer">^{<cd3>}</a>:</p>
<p>在<code>groupby</code>中使用<code>Series</code>-的解决方案也用于<code>Series</code>-<code>article</code>列:</p>
<pre><code> df = (parsed_report_df['views'].astype(float)
.groupby(parsed_report_df['article']).sum()
.reset_index())
print (df)
article views
0 729910 445.0
1 730855 1.0
2 731449 2.0
</code></pre>
<p>另一个具有<code>views</code>列赋值后转换值的解决方案:</p>
<pre><code>parsed_report_df['views'] = parsed_report_df['views'].astype(float)
df = parsed_report_df.groupby('article', as_index=False)['views'].sum()
print (df)
article views
0 729910 445.0
1 730855 1.0
2 731449 2.0
</code></pre>