Pandas.fillna（）未在Python 3的数据帧中填充值

我在Python 3中运行Pandas，我注意到以下几点：

import pandas as pd
import numpy as np
from pandas import DataFrame
from numpy import nan

df = DataFrame([[1, nan], [nan, 4], [5, 6]])

print(df)

df2 = df
df2.fillna(0)

print(df2)

 0   1
0   1 NaN
1 NaN   4
2   5   6
    0   1
0   1 NaN
1 NaN   4
2   5   6

import pandas as pd
import numpy as np
from pandas import Series
from numpy import nan

sr1 = Series([1,2,3,nan,5,6,7])

sr1.fillna(0)

0    1
1    2
2    3
3    0
4    5
5    6
6    7
dtype: float64

返回以下内容：

import pandas as pd
import numpy as np
from pandas import DataFrame
from numpy import nan

df = DataFrame([[1, nan], [nan, 4], [5, 6]])

print(df)

df2 = df
df2.fillna(0)

print(df2)

 0   1
0   1 NaN
1 NaN   4
2   5   6
    0   1
0   1 NaN
1 NaN   4
2   5   6

import pandas as pd
import numpy as np
from pandas import Series
from numpy import nan

sr1 = Series([1,2,3,nan,5,6,7])

sr1.fillna(0)

0    1
1    2
2    3
3    0
4    5
5    6
6    7
dtype: float64

同时：

import pandas as pd
import numpy as np
from pandas import DataFrame
from numpy import nan

df = DataFrame([[1, nan], [nan, 4], [5, 6]])

print(df)

df2 = df
df2.fillna(0)

print(df2)

 0   1
0   1 NaN
1 NaN   4
2   5   6
    0   1
0   1 NaN
1 NaN   4
2   5   6

import pandas as pd
import numpy as np
from pandas import Series
from numpy import nan

sr1 = Series([1,2,3,nan,5,6,7])

sr1.fillna(0)

0    1
1    2
2    3
3    0
4    5
5    6
6    7
dtype: float64

返回以下内容：

import pandas as pd
import numpy as np
from pandas import DataFrame
from numpy import nan

df = DataFrame([[1, nan], [nan, 4], [5, 6]])

print(df)

df2 = df
df2.fillna(0)

print(df2)

 0   1
0   1 NaN
1 NaN   4
2   5   6
    0   1
0   1 NaN
1 NaN   4
2   5   6

import pandas as pd
import numpy as np
from pandas import Series
from numpy import nan

sr1 = Series([1,2,3,nan,5,6,7])

sr1.fillna(0)

0    1
1    2
2    3
3    0
4    5
5    6
6    7
dtype: float64

因此，当我使用.fillna（）时，它用0填充序列值，而不是数据帧值。这是Python3的问题吗？否则，我在这里缺少什么来在数据帧中用0代替空值？谢谢

如中所述，方法

fillna（newValue）

返回另一个

DataFrame

，与前一个类似，但新值替换了

nan

值

df = DataFrame([[1, nan], [nan, 2], [3, 2]])
df2 = df.fillna(0)

print(df2)
# Outputs
#   0 1
# 0 1 0
# 1 0 2
# 2 3 2

print(df)
# Outputs (The previous one isn't modified)
#   0   1
# 0 1   nan
# 1 nan 2
# 2 3   2

---

这与调用

fillna（）

函数的方式有关

如果您执行

inplace=True

（请参见下面的代码），它们将被填充到位并覆盖原始数据框

In [1]: paste
import pandas as pd
import numpy as np
from pandas import DataFrame
from numpy import nan

df = DataFrame([[1, nan], [nan, 4], [5, 6]])
## -- End pasted text --

In [2]: 

In [2]: df
Out[2]: 
    0   1
0   1 NaN
1 NaN   4
2   5   6

In [3]: df.fillna(0)
Out[3]: 
   0  1
0  1  0
1  0  4
2  5  6

In [4]: df2 = df

In [5]: df2.fillna(0)
Out[5]: 
   0  1
0  1  0
1  0  4
2  5  6

In [6]: df2  # note how this is unchanged.
Out[6]: 
    0   1
0   1 NaN
1 NaN   4
2   5   6

In [7]: df.fillna(0, inplace=True)  # this will replace the values.

In [8]: df
Out[8]: 
   0  1
0  1  0
1  0  4
2  5  6

In [9]: 

---

不是这里发生的事情，但可能会帮助某些人，如果数据类型不是某个数字，则不能将df.fillna与df.mean一起使用（用列平均值替换缺少的值）。听起来很明显，但df.mean（）本身仍然有效。