CODEWITHSUNDEEP
pythonpandasdataframereplacenan
Josef
Josef

Reputation: 161

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.

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

Upvotes: 15

Views: 28765

Answers (3)

Ali Seify
Ali Seify

Reputation: 39

Don't use inplace=True and try this instead:

df.fillna(0)

Upvotes: 0

Gonçalo Peres
Gonçalo Peres

Reputation: 13582

@cs95's answer didn't work here.

Had to import numpy as np and use replace with np.Nan and inplace = True

import numpy as np

df.replace(np.NaN, 0, inplace=True)

Then all the columns got 0 instead of NaN.

Upvotes: 3

cs95
cs95

Reputation: 402253

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!

Upvotes: 32

Related Questions