擅长:python、mysql、java
<h2>编辑:根据您提供的示例数据更新答案</h2>
<h3>设置-注意我在<code>body</code>列中添加了带有“Other”的行:</h3>
<pre><code>import pandas as pd
import numpy as np
cars = [['Ford',15500.0,'crossover',68,2.5,'Gas','yes',2010,'Kuga','full'],
['Mercedes-Benz',20500.0,'sedan',173,1.8,'Gas','yes',2011,'E-Class','rear'],
['Mercedes-Benz',20500.0,"Other",173,1.8,'Gas','yes',2011,'E-Class','rear'],
['Ford',15500.0,"Other",68,2.5,'Gas','yes',2010,'Kuga','full']]
car_sales = pd.DataFrame(cars, columns=['car','price','body','mileage','engV','engType','registration','year','model','drive'])
</code></pre>
<h3>步骤1-将“Other”值替换为NaN(这允许您使用fillna函数):</h3>
<pre><code>car_sales["body"].replace("Other", np.nan, inplace=True)
</code></pre>
<h3>第2步-为每种车型创建值的字典映射,然后使用fillna填充值:</h3>
<pre><code>car_types = {"Mercedes-Benz":"sedan", "Ford":"crossover"}
car_sales["body"].fillna(car_sales["car"].map(car_types), inplace=True)
</code></pre>