pandas 将数据帧中的 NaN 转换为零
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Converting NaN in dataframe to zero
提问by Josef
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.
我有字典并使用cars = pd.DataFrame.from_dict(cars_dict, orient='index') 创建了Pandas 并对索引进行了排序（按字母顺序排列的列
cars.sort_index(axis=1) 排序后我注意到DataFrame 有NaN我不确定 np.nan 是否真的值？ print(cars.isnull().any()) 和所有列都显示错误。
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将这些“NaN”值转换为零，这正是我想要做的，但它们都不起作用。我尝试过替换和填充方法，但没有任何效果下面是我的数据框示例..
speedtest size
toyota 65 NaN
honda 77 800
回答by cs95
Either use replaceor np.whereon the values if they are strings:
如果它们是字符串，则在值上使用replace或np.where：
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 NaN（这似乎不太可能），则使用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，请使用apply+ pd.to_numeric（稍慢但保证在任何情况下都能正常工作）：
df = df.apply(to_numeric, errors='coerce').fillna(0, downcast='infer')
Thanks to piRSquared for this one!
感谢 piRSquared 为这个！
