Pandas fillna() 基于特定列属性

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

其中的价值之一Killed缺少[Type] = Dog.

我想将平均值归咎于[Killed] for [Type] = Dog.

我的代码如下：

1. 搜索平均值

df[df['Type'] == 'Dog'].mean().round()

这将给出平均值（大约 2.25）

2. 估算平均值（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，但值未插补，NaN 值仍然存在。

我的问题是，我如何将平均值归入[Killed]基于[Type] = Dog.

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

If need fillna http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.fillna.html by Series- 因为 2 列Killed and Survived:

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

如果需要只在列中填写Killed:

#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

您可以重用如下代码：

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