<p>正如锑指出的,听起来好像您的数据中偶尔会丢失一些值,而csv无法轻松处理这些值。我建议使用像pandas这样的库,它有一个<code>read_csv</code>函数,可以处理丢失的值。以这些数据为例:</p>
<pre><code>gene_id, ENSDARG00000104632, gene_version, 2, gene_name, RERG
gene_id, ENSDARG00000104632, gene_version, 2, transcript_id, ENSDART00000166186
gene_id, ENSDARG00000104632, gene_version, 2, transcript_id, ENSDART00000166186
gene_id, ENSDARG00000104632, gene_version, 2, transcript_id, ENSDART00000166186
gene_id, ENSDARG00000104632, gene_version, 2, transcript_id, ENSDART00000166186
gene_id, ENSDARG00000104632, gene_version, 2, transcript_id, ENSDART00000166186
gene_id, ENSDARG00000104632, gene_version, 2, transcript_id, ENSDART00000166186
gene_id, ENSDARG00000104632, gene_version, 2, transcript_id,
gene_id, ENSDARG00000104632, gene_version, , transcript_id,
gene_id, ENSDARG00000104632, gene_version, 2, transcript_id, ENSDART00000166186
</code></pre>
<p>其内容如下:</p>
^{pr2}$
<p>屈服:</p>
<pre><code> 1 5
0 ENSDARG00000104632 RERG
1 ENSDARG00000104632 ENSDART00000166186
2 ENSDARG00000104632 ENSDART00000166186
3 ENSDARG00000104632 ENSDART00000166186
4 ENSDARG00000104632 ENSDART00000166186
5 ENSDARG00000104632 ENSDART00000166186
6 ENSDARG00000104632 ENSDART00000166186
7 ENSDARG00000104632 NaN
8 ENSDARG00000104632 NaN
9 ENSDARG00000104632 ENSDART00000166186
</code></pre>
<p>如你所愿。在</p>
<p>但是,如果缺少数据(如本例中所示),则只需删除以下行:</p>
<pre><code>selected_data.dropna()
</code></pre>
<p>哪些输出:</p>
<pre><code> 1 5
1 ENSDARG00000104632 ENSDART00000166186
2 ENSDARG00000104632 ENSDART00000166186
3 ENSDARG00000104632 ENSDART00000166186
4 ENSDARG00000104632 ENSDART00000166186
5 ENSDARG00000104632 ENSDART00000166186
6 ENSDARG00000104632 ENSDART00000166186
9 ENSDARG00000104632 ENSDART00000166186
</code></pre>
<p>(但是,这可能不是您想要的。)</p>
<p>参考文献</p>
<p><a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html" rel="nofollow noreferrer">https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html</a></p>