因此，我尝试用np.nan替换数据框中的None值，并注意到在此过程中，数据框中的float列的数据类型已更改为object即使它们不包含任何丢失的数据。
例如：
import pandas as pd
import numpy as np
data = pd.DataFrame({'A':np.nan,'B':1.096, 'C':1}, index=[0])
data.replace(to_replace={np.nan:None}, inplace=True)
在调用data.dtypes之前和之后，对replace的调用显示列B的数据类型从float更改为object，而C的数据类型保持为int。
如果我从原始数据中删除A列，则不会发生。
我想知道为什么会发生这种变化，以及如何避免这种影响。
答案 0 :(得分：0)
当您替换每列并从<div class="container">
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而不是replace调用pd.Series(...)时，效果很好。
除了注释pd.DataFrame(...)中所述，不能将其强制转换为浮点数（或int或任何数字-您宁愿使用NoneType()），因此它将被自动强制转换为{{1 }}。
NaN输出：
object答案 1 :(得分：0)
我已经遇到过很多次了，并且有一个解决方法。在使用astype（object）替换之前，它将保留dtype。我不得不将其用于合并问题，合并问题等。我不确定为什么以这种方式使用时会保留类型，但确实如此，一旦找到它就很有用。
data.info()
#<class 'pandas.core.frame.DataFrame'>
#Int64Index: 1 entries, 0 to 0
#Data columns (total 3 columns):
#A 0 non-null float64
#B 1 non-null float64
#C 1 non-null int64
#dtypes: float64(2), int64(1)
#memory usage: 32.0 bytes
import pandas as pd
import numpy as np
data = pd.DataFrame({'A':np.nan,'B':1.096, 'C':1}, index=[0])
data.replace(to_replace={np.nan:None}, inplace=True)
data.info()
#<class 'pandas.core.frame.DataFrame'>
#Int64Index: 1 entries, 0 to 0
#Data columns (total 3 columns):
#A 0 non-null object
#B 1 non-null object
#C 1 non-null int64
#dtypes: int64(1), object(2)
#memory usage: 32.0+ bytes
import pandas as pd
import numpy as np
data = pd.DataFrame({'A':np.nan,'B':1.096, 'C':1}, index=[0])
data.astype(object).replace(to_replace={np.nan:None}, inplace=True)
data.info()
#<class 'pandas.core.frame.DataFrame'>
#Int64Index: 1 entries, 0 to 0
#Data columns (total 3 columns):
#A 0 non-null float64
#B 1 non-null float64
#C 1 non-null int64
#dtypes: float64(2), int64(1)
#memory usage: 32.0 bytes