python - 基于特定列属性的 Pandas fillna()

假设我有这张 table

Type | Killed | Survived
Dog      5         2
Dog      3         4
Cat      1         7
Dog     nan        3
cow     nan        2

Killed 上的 [Type] = Dog 值之一缺失。

我想为 [Killed] 估算 [Type] = Dog 中的平均值。

我的代码如下:

搜索均值

df[df['Type'] == 'Dog'].mean().round()
这会给我平均值(大约 2.25)

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，但该值未插补，NaN 值仍然存在。

我的问题是，如何根据 [Killed] 估算 [Type] = Dog 中的平均值。

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

如果需要 fillna by Series - 因为有 2 列 Killed 和 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

如果只需要在 Killed 列中填充:

#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

关于python - 基于特定列属性的 Pandas fillna()，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/39242615/