使用'numpy.where'函数替换多列中的值

Question

当满足特定条件时，我想更改多列中的NA值。下面给出了示例数据集。

Pool Area   Pool Quality    Pool Type   Pool Condition  Pool Finish
0   800             Good           A              Good        Gunite
1   400             Good           C              Good        Vinyl
2   485             Good           B              Good        Fibreglass
3   360             Poor           C              Poor        Vinyl
4   0               NaN            NaN            NaN         NaN
5   600             Best           A              Best        Gunite
6   500             Best           B              Best        Fibreglass
7   0               NaN            NaN            NaN         NaN
8   750             Best           A              Best        Gunite
9   900             Best           A              Best        Gunite
10  0               NaN            NaN            NaN         NaN
11  900             Best           A              Best        Gunite
12  400             Poor           C              Poor        Fibreglass
13  0               NaN            NaN            NaN         NaN

在上面的数据中，我想用"无池"替换NaN值，其中"池区域"列的值为"0"。

我知道我可以用np.其中函数来完成，我尝试了下面的代码。

df[['Pool Quality', 'Pool Type', 'Pool Condition', 'Pool Finish']] = np.where(df['Pool Area']==0, 'No Pool', df[['Pool Quality', 'Pool Type', 'Pool Condition', 'Pool Finish']])

它不起作用。

我单独尝试过，它很有效(参考下面的代码(。

df['Pool Quality'] = np.where(df['Pool Area']==0, 'No Pool', df['Pool Quality'])

但当我尝试一次处理多个列时，它不起作用。

下面是我得到的错误。

ValueError：操作数无法与形状(2919，((((2919,5(一起广播

注意：以上错误消息取自我的实际数据集，其中维度为2919行81列。

我不知道我的代码出了什么问题。请帮帮我。

Answer 1

使用布尔索引：

m = df['Pool Area'].eq(0)
df.loc[m] = df.loc[m].fillna('No Pool')
# or
# df[m] = df[m].fillna('No Pool')
# or to limit to given columns
# cols = ['Pool Quality', 'Pool Type', 'Pool Condition', 'Pool Finish']
# df.loc[m, cols] = df.loc[m, cols].fillna('No Pool')

更新的df:

Pool Area Pool Quality Pool Type Pool Condition Pool Finish
0         800         Good         A           Good      Gunite
1         400         Good         C           Good       Vinyl
2         485         Good         B           Good  Fibreglass
3         360         Poor         C           Poor       Vinyl
4           0      No Pool   No Pool        No Pool     No Pool
5         600         Best         A           Best      Gunite
6         500         Best         B           Best  Fibreglass
7           0      No Pool   No Pool        No Pool     No Pool
8         750         Best         A           Best      Gunite
9         900         Best         A           Best      Gunite
10          0      No Pool   No Pool        No Pool     No Pool
11        900         Best         A           Best      Gunite
12        400         Poor         C           Poor  Fibreglass
13          0      No Pool   No Pool        No Pool     No Pool