<p>如果我了解您想要的是什么…假设您想要的输出缺少<code>CURR_VOL</code>列:</p>
<pre><code># read your csv file
df = pd.read_csv(r'path\to\your\file.csv')
df['idx'] = df.groupby('IN_FID').cumcount()
# set index and unstack
new = df.set_index(['idx', 'IN_FID']).unstack(level=[0])
# list comprehension to create one column
new.columns = [f'{val}_{name}' for val, name in new.columns]
# output a new csv file
new.to_csv(r'some\path\to\new_file.csv')
ROUTE_NAME_0 ROUTE_NAME_1 CURR_VOL_0 CURR_VOL_1 NEAR_RANK_0 NEAR_RANK_1
IN_FID
1 test11 test12 test11 test12 test11 test12
2 test2 NaN test2 NaN test2 NaN
3 test3 test31 test3 test test3 test31
</code></pre>
<p>更有效的方法是使用<code>map</code>:</p>
<pre><code># group with astype(str)
df['idx'] = df.groupby('IN_FID').cumcount().astype(str)
# set index and unstack
new = df.set_index(['idx', 'IN_FID']).unstack(level=[0])
# more efficient using map
new.columns = new.columns.map('_'.join)
</code></pre>