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I am working on a housing dataset. In a list of columns (Garage, Fireplace, etc), I have values called NA which just means that the particular house in question does not have that feature (Garage, Fireplace). It doesn't mean that the value is missing/unknown. However, Python interprets this as NaN, which is wrong. To come across this, I want to replace this value NA with XX to help Python distinguish it from NaN values. Because there is a whole list of them, I want use a for loop to accomplish this in a few lines of code:

na_data = ['Alley', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'FireplaceQu', 'GarageType',
           'GarageFinish', 'GarageQual', 'GarageCond', 'PoolQC', 'Fence', 'MiscFeature']

for i in range(len(na_data)):
    train[i] = train[i].fillna('XX')

I know this isn't the correct way of doing it as it is giving me a KeyError: 0. This is kinda like a pseudocode way of doing it to visualize what I'm trying to accomplish. What is the way to automate fillna('XX') on this list of columns?

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what you are looking for is replace().

And you don't need to write all the columns you can iterate over the columns name simply.

for col in train:
    train[col].replace("NA","XX",inplace=True)

You can do it on all the dataset in one line:

train.replace("NA","XX", inplace=True)

Or on specific columns:

for cols in na_data:
    train[col].replace("NA","XX",inplace=True)
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  • $\begingroup$ Yea but its not every column in train. It is a specific list of columns organized in na_data $\endgroup$ – Andros Adrianopolos Jul 3 at 8:12
  • $\begingroup$ I edited my response $\endgroup$ – vico Jul 3 at 8:16
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While replace is a valid approach, it can be inefficient and slow on a large scale - see this question.

You should instead use map to encode NA as XX - perhaps something like this:

na_data = ['Alley', ...,'Fence', 'MiscFeature']
for col in na_data:
   train[col]= train[col].map({'NA':'XX'})
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