Changing values in each row of a column based on values in other columns of the corresponding row (Python/Pandas)

Question

data = [{'a': 12, 'b': 2, 'c': 3, 'd': 'bat'},
        {'a': 'NaN', 'b': 20, 'c': 30, 'd': 'ball'},
        {'a': 4, 'b': 20, 'c': 30, 'd': 'pin'}]

df = pd.DataFrame(data)

I’m having a hard time figuring out how to replace the NaN values in column A with values in column b based on conditions set on columns c and d. For example, if I wanted to replace the NaN values in the a column with the values of column b (2 and 20, respectively) under circumstances where the value of c > 20 and ‘d’ = ‘ball.

Could someone help me with this?

I’ve tried a number of solutions with df.loc and df.mask that have not worked.

Asked By: Aleisha Khan

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Source

Answer 1

You can use df.apply for this

def fill_na(row: pd.Series):
    if pd.isna(row[0]):
        if row[2] > 20 and row[3] == "ball":
            row[0] = row[1]
    return row

df = df.apply(fill_na, axis=1)

As you don’t have rows that would satisfy your criteria, you can use this for testing

[{'a': np.NaN, 'b': 2, 'c': 3, 'd': 'bat'},
 {'a': 10, 'b': 20, 'c': 30, 'd': 'ball'},
 {'a': np.NaN, 'b': 20, 'c': 30, 'd': 'pin'},
 {'a': np.NaN, 'b': 15, 'c': 30, 'd': 'ball'}]

Answered By: Andrey

Answer 2

Try:

df['a'].loc[(df['a'].isna())&(df['c'] > 20) & (df['d'] == 'ball')] =df['b'].loc[(df['a'].isna())&(df['c'] > 20) & (df['d'] == 'ball')]

Answered By: jjk

Changing values in each row of a column based on values in other columns of the corresponding row (Python/Pandas)

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