将dataframe中的NaN转换为0
[英]Converting NaN in dataframe to zero
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.
我有字典，用cars = pd.DataFrame.from_dict(cars_dict, orient='index')创建了熊猫，并对索引进行排序(按字母顺序排列的cars中的列= cars.sort_index(axis=1))。nan值吗?print(cars.isnull().any())和所有列都显示为false。
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..
我尝试了不同的方法来将这些“NaN”值转换为零，这是我想要做的，但是没有工作。我尝试过替换和fillna方法，以下都是我的dataframe示例。
speedtest size
toyota 65 NaN
honda 77 800
1 个解决方案
#1
2
Either use replace or np.where on the values if they are strings:
使用replace或np。如果值是字符串，则在其中:
df = df.replace('NaN', 0)
Or,
或者,
df[:] = np.where(df.eq('NaN'), 0, df)
Or, if they're actually NaNs (which, it seems is unlikely), then use fillna:
或者，如果它们实际上是无元数(这似乎不太可能)，那么使用fillna:
df.fillna(0, inplace=True)
Or, to handle both situations at the same time, use apply + pd.to_numeric (slightly slower but guaranteed to work in any case):
或者，要同时处理这两种情况，使用apply + pd。to_numeric(稍微慢一点但保证在任何情况下工作):
df = df.apply(to_numeric, errors='coerce').fillna(0, downcast='infer')
Thanks to piRSquared for this one!
多亏了piRSquared给这个!
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