擅长:python、mysql、java
<p>您也可以通过使用<code>pd.DateFrame.from_dict()</code>方法、<code>append()</code>方法和<code>ffill()</code>方法来实现这一点:</p>
<pre><code>test = {'a':32, 'b':21, 'c':92}
newdf=pd.DataFrame(test.values(),index=test.keys(),columns=['cost']).reset_index()
#OR(use any one of them to create dataframe named newdf)
newdf=pd.DataFrame.from_dict(test,orient='index',columns=['cost']).reset_index().rename(columns={'index':'result'})
</code></pre>
<p>最后:</p>
<pre><code>newdf=df.append(newdf,ignore_index=True).ffill()
</code></pre>
<p>现在,如果您打印<code>newdf</code>,您将获得所需的输出:</p>
<pre><code> date result cost
0 2021-03-01 a 30
1 2021-03-01 d 35
2 2021-03-01 j 98
3 2021-03-01 b 94
4 2021-03-01 a 32
5 2021-03-01 b 21
6 2021-03-01 c 92
</code></pre>