<p>您需要<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.str.split.html" rel="nofollow noreferrer">^{<cd1>}</a>通过<code>str[1]</code>选择第二个列表:</p>
<pre><code>csv_file['UpdatedMeasurement'] = csv_file['Measurement'].str.split('_', 1).str[1]
print (csv_file)
Measurement UpdatedMeasurement
0 COL_TOOL_QUALITY TOOL_QUALITY
1 COL_COMM_STATUS COMM_STATUS
2 COL_SEN_FW_HRTBT_STATUS SEN_FW_HRTBT_STATUS
3 COL_WNL_FW_HRTBT_STATUS WNL_FW_HRTBT_STATUS
4 COL_COMM_STATUS2 COMM_STATUS2
</code></pre>
<p>如果要使用自定义函数(如果<code>NaN</code>s失败),请使用<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.apply.html" rel="nofollow noreferrer">^{<cd4>}</a>或<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.map.html" rel="nofollow noreferrer">^{<cd5>}</a>:</p>
<pre><code>def update_measure(data):
return data.split('_', 1)[1]
csv_file['UpdatedMeasurement'] = csv_file['Measurement'].apply(update_measure)
#alternative solution
#csv_file['UpdatedMeasurement'] = csv_file['Measurement'].apply(update_measure)
#list comprehension solution
#csv_file['UpdatedMeasurement'] = [data.split('_', 1)[1] for data in csv_file['Measurement']]
</code></pre>