python中fillna_python – 基于特定列属性的Pandas fillna()

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