将数据帧中的NaN转换为零 [英] Converting NaN in dataframe to zero

问题描述

我有字典并使用创建了熊猫 汽车= pd.DataFrame.from_dict(cars_dict，orient ='index') 和 对索引进行排序(列按字母顺序
汽车= cars.sort_index(axis = 1) 排序后，我发现DataFrame具有NaN，我不确定 如果真的是np.nan值? print(cars.isnull().any())并且所有列都显示为false.

I have dictionary and created Pandas using cars = pd.DataFrame.from_dict(cars_dict, orient='index') and sorted the index (columns in alphabetical order
cars = cars.sort_index(axis=1) After sorting I noticed the DataFrame has NaN and I wasn't sure if the really np.nan values? print(cars.isnull().any()) and all column shows false.

我尝试了不同的方法将那些"NaN"值转换为零，这是我想要做的，但是没有一个在工作. 我尝试了replace和fillna方法，但没有任何效果 以下是我的数据框示例.

I have tried different method to convert those "NaN" values to zero which is what I want to do but non of them is working. I have tried replace and fillna methods and nothing works Below is sample of my dataframe..

            speedtest          size 
toyota       65                NaN 
honda        77                800 

推荐答案

如果值是字符串，则对它们使用replace或np.where:

Either use replace or np.where on the values if they are strings:

df = df.replace('NaN', 0)

或者，

df[:] = np.where(df.eq('NaN'), 0, df)

或者，如果它们实际上是NaN(似乎不太可能)，则使用fillna:

Or, if they're actually NaNs (which, it seems is unlikely), then use fillna:

df.fillna(0, inplace=True)

或者，要同时处理这两种情况，请使用apply + pd.to_numeric(速度稍慢，但可以保证在任何情况下都可以使用):

Or, to handle both situations at the same time, use apply + pd.to_numeric (slightly slower but guaranteed to work in any case):

df = df.apply(to_numeric, errors='coerce').fillna(0, downcast='infer')

感谢piRSquared！

Thanks to piRSquared for this one!

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