擅长:python、mysql、java
<pre><code>import numpy as np
import pandas as pd
""" set1 set2 set3 set4
0 744110.0 507121.0 790001.0 785693.0
1 744107.0 507126.0 791002.0 788107.0
2 744208.0 535214.0 791103.0 788108.0
3 744210.0 534195.0 790116.0 784170.0
"""
df = pd.read_clipboard(sep='\s{2,}', engine='python', dtype = 'int')
df
</code></pre>
<p>对于第一个问题,可以在导入时设置数据类型。正如@user32185所提到的,<code>NaN</code>s在尝试转换为int时可能会导致问题</p>
^{pr2}$
<p>第二,我试了几件事,但效果最好:</p>
<pre><code>import numpy as np
df.iloc[np.where(df == 791103)]
</code></pre>
<p>输出:</p>
<pre><code> set3
2 791103
</code></pre>
<p>仅获取列名:</p>
<pre><code>df.iloc[np.where(df == 791103)].columns[0]
</code></pre>
<p>输出:</p>
<pre><code>'set3'
</code></pre>
<p>链接:</p>
<p><a href="https://stackoverflow.com/questions/21287624/convert-pandas-column-containing-nans-to-dtype-int">Convert Pandas column containing NaNs to dtype `int`</a></p>
<p><a href="https://chrisalbon.com/python/data_wrangling/pandas_create_column_using_conditional/" rel="nofollow noreferrer">https://chrisalbon.com/python/data_wrangling/pandas_create_column_using_conditional/</a></p>