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.
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
Either use replace or np.where on the values if they are strings:
df = df.replace('NaN', 0)
Or,
df[:] = np.where(df.eq('NaN'), 0, df)
Or, if they're actually NaNs (which, it seems is unlikely), then use 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):
df = df.apply(pd.to_numeric, errors='coerce').fillna(0, downcast='infer')
Thanks to piRSquared for this one!