You are right, such errors mostly caused by NaN representing empty cells. It is common to filter out such data, before applying your further operations, using this idiom on your dataframe df:
df_new = df[df['ColumnName'].notnull()]
Alternatively, it may be more handy to use fillna() method to impute (to replace) null values with something default. E.g. all null or NaN‘s can be replaced with the average value for its column
housing['LotArea'] = housing['LotArea'].fillna(housing.mean()['LotArea'])
or can be replaced with a value like empty string “” or another default value
housing['GarageCond']=housing['GarageCond'].fillna("")
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