How to modify one column's null values based on another column?

Question

I have 12 columns of ingredients and 12 respective columns of measurements of those ingredients. For some rows the ingredients are specified but the measurements are not i.e. they are NA. I want to set up a condition such that if a certain ingredient entry is not NA and the corresponding entry is NA, set the corresponding measurement entry to 1.

cols1 = ["strIngredient1","strIngredient2","strIngredient3","strIngredient4","strIngredient5","strIngredient6",
        "strIngredient7","strIngredient8","strIngredient9","strIngredient10","strIngredient11","strIngredient12"]

cols2 = ["strMeasure1","strMeasure2","strMeasure3","strMeasure4","strMeasure5","strMeasure6","strMeasure7",
         "strMeasure8","strMeasure9","strMeasure10","strMeasure11","strMeasure12"]

Ynjxsjmh · Accepted Answer · 2022-04-30 18:03:23Z

1

if a certain ingredient entry is not NA and the corresponding entry is NA, set the corresponding entry to 1

If I understand correctly, you can use mask to replace values where the condition is True.

for col1, col2 in zip(cols1, cols2):
    df[col2] = df[col2].mask(df[col1].notna()&df[col2].isna(), 1)

answered Apr 30, 2022 at 18:03

Ynjxsjmh

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2 Comments

kapsha Over a year ago

Thank you so much! Another question. I tried using the condition: for col1, col2 in zip(cols1, cols2): if df[col1].notna()&df[col2].isna(): df[col2].isna().sum. but I got a value error saying ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Ynjxsjmh Over a year ago

@kapsha You are using Series in if condition, See this for more detail.

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