<p>这可以通过多种方式实现:</p>
<ul>
<li><code>pivot</code></li>
<li><code>pivot_table</code></li>
<li><code>groupby</code></li>
</ul>
<p>但是,大多数的文件都需要刷一下才能输出您需要的格式。如果您不想寻找聚合函数并且希望输入这些条目,那么只有数字2可以工作。在</p>
<pre><code>def column_name(row):
return '{} {}'.format(row['Fiscal/Election date'].year, row['initial Political Party of Recipient'])
df['Fiscal/Election date'] = pd.to_datetime(df['Fiscal/Election date'])
df['Column Name'] = df.apply(column_name, axis=1)
</code></pre>
<p>1)<code>pivot_table</code></p>
^{pr2}$
<p>2)<code>pivot</code></p>
<pre><code>In [5]: (df[['Column Name', 'Monetary amount']]
...: .pivot(columns='Column Name', values='Monetary amount'))
Out[5]:
Column Name 2004 Liberal Party of Canada 2008 Liberal Party of Canada
0 800.0 NaN
1 1280.0 NaN
2 250.0 NaN
3 1000.0 NaN
4 NaN 800.0
</code></pre>
<p>3)<code>groupby</code></p>
<pre><code>In [6]: pd.DataFrame(df.groupby('Column Name')['Monetary amount'].sum()).transpo
...: se()
Out[6]:
Column Name 2004 Liberal Party of Canada 2008 Liberal Party of Canada
Monetary amount 3330 800
</code></pre>