<p>一种解决方案是将整数值转换为<code>string</code>并添加<code>M</code>:</p>
<pre><code>results['Period'] = 'M' + results['Period'].astype(str)
</code></pre>
<p>或<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.map.html" rel="nofollow noreferrer">^{<cd3>}</a>更改了<code>dictionary</code>-键是<code>integers</code>:</p>
<pre><code>results['Period'] = results['Period'].map({x: 'M' + str(x) for x in range(1, 13)})
</code></pre>
<p><strong>细节</strong>:</p>
<pre><code>print ({x: 'M' + str(x) for x in range(1, 13)})
{1: 'M1', 2: 'M2', 3: 'M3', 4: 'M4', 5: 'M5', 6: 'M6',
7: 'M7', 8: 'M8', 9: 'M9', 10: 'M10', 11: 'M11', 12: 'M12'}
</code></pre>
<hr/>
<pre><code>print (results)
Year Period y yhat Contas Resultado
0 2017 M1 1.251556 1.251556 Devolucoes
1 2017 M2 0.210990 0.210990 Devolucoes
2 2017 M3 1.186015 1.186015 Devolucoes
3 2017 M4 0.253021 0.253021 Devolucoes
4 2017 M5 0.230574 0.230574 Devolucoes
5 2017 M6 0.236777 0.236777 Devolucoes
6 2017 M7 0.250967 0.250967 Devolucoes
7 2017 M8 0.252535 0.252535 Devolucoes
8 2017 M9 0.250966 0.250966 Devolucoes
9 2017 M10 0.220475 0.220475 Devolucoes
10 2017 M11 0.226277 0.226277 Devolucoes
11 2017 M12 0.237355 0.237355 Devolucoes
12 2018 M1 1.155845 1.155845 Devolucoes
</code></pre>