按条件重复数据框中的单元格值

Question

As the dataframe d1 below, A1 is correspond to B1 , A2 is correspond to B2 , and so on. I want to change B1-3 value by condition of: B or C = copy 2 times, D = copy 3 times, as the dataframe target .

d1 = DataFrame([{'A1': 'A', 'A2': 'A', 'A3': '', 'B1': '2', 'B2': '2', 'B3': ''},
                {'A1': 'A', 'A2': 'C', 'A3': '', 'B1': '2', 'B2': '2', 'B3': ''},
                {'A1': 'A', 'A2': 'B', 'A3': 'C', 'B1': '2', 'B2': '4', 'B3': '4'},
                {'A1': 'A', 'A2': 'C', 'A3': 'D', 'B1': '2', 'B2': '2', 'B3': '4'}])

d1
    A1  A2  A3  B1  B2  B3
0   A   A       2   2   
1   A   C       2   2   
2   A   B   C   2   4   4
3   A   C   D   2   2   4

target = DataFrame([{'A1': 'A', 'A2': 'A', 'A3': '', 'B1': '2', 'B2': '2', 'B3': ''},
                {'A1': 'A', 'A2': 'C', 'A3': '', 'B1': '2', 'B2': '22', 'B3': ''},
                {'A1': 'A', 'A2': 'B', 'A3': 'C', 'B1': '2', 'B2': '44', 'B3': '44'},
                {'A1': 'A', 'A2': 'C', 'A3': 'D', 'B1': '2', 'B2': '22', 'B3': '444'}])
target

    A1  A2  A3  B1  B2  B3
0   A   A       2   2   
1   A   C       2   22  
2   A   B   C   2   44  44
3   A   C   D   2   22  444 

And I've tried using np.where for the condition of B and C , but it's seems only apply on B to copy the value. Is there any methods to reach it.

Acol = ['A1','A2','A3']
Bcol = ['B1','B2','B3']
d1[Bcol] = np.where(d1[Acol] == ('B' or 'C'), d1[Bcol]+d1[Bcol], d1[Bcol])
d1

    A1  A2  A3  B1  B2  B3
0   A   A       2   2   
1   A   C       2   2   
2   A   B   C   2   44  4
3   A   C   D   2   2   4

Answer 1

I'd suggest storing the multiplier conditions for A, B, … in a dictionary and applying it like this:

multiplier_map={'':1,'A':1,'B':2,'C':2,'D':3}
for i in [1,2,3]:
    df['B{0}'.format(i)]=df['B{0}'.format(i)]*df['A{0}'.format(i)].map(multiplier_map)

Note that the multiplier_map also needs to contain an empty string as key.

Answer 2

Using np.select

for col in ('A1','A2','A3'):
    new_col = 'B'+col[-1]
    mask1 = df[col] == 'A'
    mask2 = (df[col] == 'B') | (df[col] == 'C')
    mask3 = df[col] == 'D'
    df[new_col] = df[new_col].astype('str')
    df[new_col] = np.select([mask1, mask2, mask3], [df[new_col], df[new_col]*2, df[new_col]*3], df[new_col])

Output:

    A1  A2  A3  B1  B2  B3
0   A   A       2   2   
1   A   C       2   22  
2   A   B   C   2   44  44
3   A   C   D   2   22  444

Answer 3

Maybe these four lines:

d1.loc[d1['A2'].eq('B') | d1['A2'].eq('C'), 'B2'] += d1.loc[d1['A2'].eq('B') | d1['A2'].eq('C'), 'B2']
d1.loc[d1['A2'].eq('D'), 'B2'] += d1.loc[d1['A2'].eq('D'), 'B2'] + d1.loc[d1['A2'].eq('D'), 'B2']
d1.loc[d1['A3'].eq('B') | d1['A3'].eq('C'), 'B3'] += d1.loc[d1['A3'].eq('B') | d1['A3'].eq('C'), 'B3']
d1.loc[d1['A3'].eq('D'), 'B3'] += d1.loc[d1['A3'].eq('D'), 'B3'] + d1.loc[d1['A3'].eq('D'), 'B3']

And now:

print(df)

Is:

  A1 A2 A3 B1  B2   B3
0  A  A     2   2     
1  A  C     2  22     
2  A  B  C  2  44   44
3  A  C  D  2  22  444

Answer 4

Try below:

d1['B1'] = np.where( d1['A1'].isin(['B' , 'C']), d1['B1'] * 2, np.where(d1['A1'].isin(['D']), d1['B1'] * 3, d1['B1']))
d1['B2'] = np.where( d1['A2'].isin(['B' , 'C']), d1['B2'] * 2, np.where(d1['A2'].isin(['D']), d1['B2'] * 3, d1['B2']))
d1['B3'] = np.where( d1['A2'].isin(['B' , 'C']), d1['B3'] * 2, np.where(d1['A3'].isin(['D']), d1['B3'] * 3, d1['B3']))