Pandas .fillna() 不填充 Python 3 中 DataFrame 中的值
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Pandas .fillna() not filling values in DataFrame in Python 3
提问by cenveoanalyst
I'm running Pandas in Python 3 and I noticed that the following:
我在 Python 3 中运行 Pandas，我注意到以下内容：
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)
Returns the following:
返回以下内容：
0 1
0 1 NaN
1 NaN 4
2 5 6
0 1
0 1 NaN
1 NaN 4
2 5 6
While the following:
而以下：
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)
Returns the following:
返回以下内容：
0 1
1 2
2 3
3 0
4 5
5 6
6 7
dtype: float64
So it's filling in Series values but not DataFrame values with 0 when I use .fillna(). Is this a problem with Python 3? Otherwise, what am I missing here to get 0s in place of null values in DataFrames? Thanks!
因此，当我使用 .fillna() 时，它使用 0 填充系列值而不是 DataFrame 值。这是 Python 3 的问题吗？否则，我在这里缺少什么来获取 0 代替 DataFrame 中的空值？谢谢！
回答by Alberto Bonsanto
As you can read in the documentation, the method fillna(newValue)returns another DataFramelike the previous one, but with the nanvalues replaced by the new value.
正如您在文档中所读到的，该方法fillna(newValue)返回另一个DataFrame与前一个类似的方法，但nan值被新值替换。
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
回答by ericmjl
It has to do with the way you're calling the fillna()function.
它与您调用fillna()函数的方式有关。
If you do inplace=True(see code below), they will be filled in place and overwrite your original data frame.
如果您这样做inplace=True（请参阅下面的代码），它们将被填充到位并覆盖您的原始数据框。
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]:
