Replace NULL values in Pandas dataframe

Question

I'm pulling my hair out here. I need to replace null values in a pandas dataframe column. These are specifically null values not NaN values.

I've tried:

trainData['Embarked'].replace(trainData['Embarked'].isnull, embarkedMost, regex=True)

trainData['Embarked'].replace('', embarkedMost, regex=True)

trainData['Embarked'].replace('', embarkedMost, regex=True, inplace=True)

trainData['Embarked'].str.replace('', embarkedMost, regex=True)

trainData['Embarked'].isnull().replace(np.nan, embarkedMost, regex=True)

trainData['Embarked'].fillna(embarkedMost)

trainData['Embarked'].str.replace(np.Nan, embarkedMost, regex=True)

trainData['Embarked'].str.replace(pd.isnull, embarkedMost, regex=True)

trainData['Embarked'].replace(r'^\s+$', embarkedMost, regex=True, inplace=True)

Then:

trainData.to_csv(os.path.join(os.path.dirname(__file__), 'full.csv'), sep=',')

Whereafter I load the dataset into excel to check but none of these change the dataset though.

This provides me with the correct indices for empty values:

print(np.where(pd.isnull(trainData['Embarked'])))

I wanted to use apply with lambda but read that it is horribly inefficient.

Izak Joubert · Accepted Answer · 2019-03-13 09:41:17Z

2

Nevermind everyone

Solved with:

trainData['Embarked'] = trainData['Embarked'].fillna(embarkedMost)

I think I may have opened a csv in a different directory

answered Mar 13, 2019 at 9:41

Izak Joubert

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