Python/Pandas Dataframe 将 0 替换为中值

Question

我有一个 python pandas 数据框，其中有几列，一列有 0 值。我想用此列的 median 或 mean 替换 0 值。

data 是我的数据框
artist_hotness 是列

mean_artist_hotness = data['artist_hotness'].dropna().mean()

if len(data.artist_hotness[ data.artist_hotness.isnull() ]) > 0:
data.artist_hotness.loc[ (data.artist_hotness.isnull()), 'artist_hotness'] = mean_artist_hotness

我试过了，但没用。

Answer 1

我认为您可以使用 mask and add parameter skipna=True to mean 而不是 dropna。如果需要替换 0 值或 data.artist_hotness.isnull() 如果需要替换 NaN 值，还需要将条件更改为 data.artist_hotness == 0：

import pandas as pd
import numpy as np

data = pd.DataFrame({'artist_hotness': [0,1,5,np.nan]})
print (data)
   artist_hotness
0             0.0
1             1.0
2             5.0
3             NaN

mean_artist_hotness = data['artist_hotness'].mean(skipna=True)
print (mean_artist_hotness)
2.0

data['artist_hotness']=data.artist_hotness.mask(data.artist_hotness == 0,mean_artist_hotness)
print (data)
   artist_hotness
0             2.0
1             1.0
2             5.0
3             NaN

或者使用 loc，但省略列名称：

data.loc[data.artist_hotness == 0, 'artist_hotness'] = mean_artist_hotness
print (data)
   artist_hotness
0             2.0
1             1.0
2             5.0
3             NaN

data.artist_hotness.loc[data.artist_hotness == 0, 'artist_hotness'] = mean_artist_hotness
print (data)

IndexingError: (0 True 1 False 2 False 3 False Name: artist_hotness, dtype: bool, 'artist_hotness')

另一个解决方案是 DataFrame.replace 指定列：

data=data.replace({'artist_hotness': {0: mean_artist_hotness}}) 
print (data)
    aa  artist_hotness
0  0.0             2.0
1  1.0             1.0
2  5.0             5.0
3  NaN             NaN

或者如果需要替换所有列中的所有 0 值：

import pandas as pd
import numpy as np

data = pd.DataFrame({'artist_hotness': [0,1,5,np.nan], 'aa': [0,1,5,np.nan]})
print (data)
    aa  artist_hotness
0  0.0             0.0
1  1.0             1.0
2  5.0             5.0
3  NaN             NaN

mean_artist_hotness = data['artist_hotness'].mean(skipna=True)
print (mean_artist_hotness)
2.0

data=data.replace(0,mean_artist_hotness) 
print (data)
    aa  artist_hotness
0  2.0             2.0
1  1.0             1.0
2  5.0             5.0
3  NaN             NaN

如果需要替换所有列中的 NaN 使用 DataFrame.fillna:

data=data.fillna(mean_artist_hotness) 
print (data)
    aa  artist_hotness
0  0.0             0.0
1  1.0             1.0
2  5.0             5.0
3  2.0             2.0

但如果仅在某些列中使用 Series.fillna:

data['artist_hotness'] = data.artist_hotness.fillna(mean_artist_hotness) 
print (data)
    aa  artist_hotness
0  0.0             0.0
1  1.0             1.0
2  5.0             5.0
3  NaN             2.0

Answer 2

使用pandasreplace方法：

df = pd.DataFrame({'a': [1,2,3,4,0,0,0,0], 'b': [2,3,4,6,0,5,3,8]}) 

df 
   a  b
0  1  2
1  2  3
2  3  4
3  4  6
4  0  0
5  0  5
6  0  3
7  0  8

df['a']=df['a'].replace(0,df['a'].mean())

df
   a  b
0  1  2
1  2  3
2  3  4
3  4  6
4  1  0
5  1  5
6  1  3
7  1  8

Answer 3

data['artist_hotness'] = data['artist_hotness'].map( lambda x : data.artist_hotness.mean() if x == 0 else x)

Answer 4

发现这些非常有用，虽然 mask 真的很慢（不知道为什么）。

我这样做了：

df.loc[ df['artist_hotness'] == 0 | np.isnan(df['artist_hotness']), 'artist_hotness' ] = df['artist_hotness'].median()

Answer 5

I think below code will solve your problem in one line.

    data['artist_hotness'] = data['artist_hotness'].replace(0, 
    data['artist_hotness'].mean())

Python/Pandas Dataframe 将 0 替换为中值

Python/Pandas Dataframe replace 0 with median value

python

mean

median

dataframe

pandas