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假设我有这张桌子
Type | Killed | Survived
Dog 5 2
Dog 3 4
Cat 1 7
Dog nan 3
cow nan 2
[Type] = Dog缺少Killed值之一.
我想在[类型] =狗的[Killed]中归咎于平均值.
我的代码如下：
>搜索平均值
df [df [‘Type’] ==’Dog’].mean().round()
这将给我平均值(约2.25)
>估算均值(这是问题开始的地方)
df.loc [(df [‘Type’] ==’Dog’)& (df [‘Killed’])].fillna(2.25,inplace = True)
代码运行,但值不是估算,NaN值仍然存在.
我的问题是,我如何根据[Type] = Dog来估算[Killed]中的均值.
最佳答案 对我来说工作：
df.ix[df[‘Type’] == ‘Dog’, ‘Killed’] = df.ix[df[‘Type’] == ‘Dog’, ‘Killed’].fillna(2.25)
print (df)
Type Killed Survived
0 Dog 5.00 2
1 Dog 3.00 4
2 Cat 1.00 7
3 Dog 2.25 3
4 cow NaN 2
如果系列需要fillna – 因为2列被杀和幸存：
m = df[df[‘Type’] == ‘Dog’].mean().round()
print (m)
Killed 4.0
Survived 3.0
dtype: float64
df.ix[df[‘Type’] == ‘Dog’] = df.ix[df[‘Type’] == ‘Dog’].fillna(m)
print (df)
Type Killed Survived
0 Dog 5.0 2
1 Dog 3.0 4
2 Cat 1.0 7
3 Dog 4.0 3
4 cow NaN 2
如果需要fillna只在Killed列中：
#if dont need rounding, omit it
m = round(df.ix[df[‘Type’] == ‘Dog’, ‘Killed’].mean())
print (m)
4
df.ix[df[‘Type’] == ‘Dog’, ‘Killed’] = df.ix[df[‘Type’] == ‘Dog’, ‘Killed’].fillna(m)
print (df)
Type Killed Survived
0 Dog 5.0 2
1 Dog 3.0 8
2 Cat 1.0 7
3 Dog 4.0 3
4 cow NaN 2
您可以重用以下代码：
filtered = df.ix[df[‘Type’] == ‘Dog’, ‘Killed’]
print (filtered)
0 5.0
1 3.0
3 NaN
Name: Killed, dtype: float64
df.ix[df[‘Type’] == ‘Dog’, ‘Killed’] = filtered.fillna(filtered.mean())
print (df)
Type Killed Survived
0 Dog 5.0 2
1 Dog 3.0 8
2 Cat 1.0 7
3 Dog 4.0 3
4 cow NaN 2
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