如何对照另一个列检查pandas数据框列的值并在第三列中操作该值

Question

I have a pandas dataframe

dfcolour:

     A      B      C       D     E
0   red     0     redy     1    red
1   blue    1     bluey    2    bluey
2   green   0     greeny   0    greenz

I want to check the values in column E and compare them to columns A & C. If the value in E matches the value in A for the same row then I want to increment the value in B, if it matches the value in C for the same row, then I will increment the value in D, if it doesn't match either, I want to create 2 new columns F & G with F having the new string and G as an integer column being incremented starting at 0

The new dfcolour will look like:

     A      B      C       D     E       F        G
0   red     1     redy     1    red               0
1   blue    1     bluey    3    bluey             0
2   green   0     greeny   0    greenz   greenz   1

Is it possible to do this without iterating?

Code to create dataframe:

dfObject = pd.DataFrame()
dfObject.set_value(1, 'A', 'red')
dfObject.set_value(1, 'B', 0)
dfObject.set_value(1, 'C', 'redy')
dfObject.set_value(1, 'D', 1)
dfObject.set_value(1, 'E', 'red')
dfObject.set_value(2, 'A', 'blue')
dfObject.set_value(2, 'B', 1)
dfObject.set_value(2, 'C', 'bluey')
dfObject.set_value(1, 'D', 2)
dfObject.set_value(1, 'E', 'bluey')
dfObject.set_value(3, 'A', 'green')
dfObject.set_value(3, 'B', 0)
dfObject.set_value(3, 'C', 'greeny')
dfObject.set_value(1, 'D', 0)
dfObject.set_value(1, 'E', 'greenz')

Answer 1

You can create those conditions and use numpy.where to construct new columns:

AE = df.A == df.E
CE = df.C == df.E
df['B'] += AE          # if A == E, add one to B
df['D'] += CE          # if C == E, add one to D
df['F'] = pd.np.where(~(AE|CE), df.E, '')   # else create F
df['G'] = pd.np.where(~(AE|CE), 1, 0)       # else create G
​    
df
#      A    B        C  D        E       F  G
#0   red    1     redy  1      red          0
#1  blue    1    bluey  3    bluey          0
#2  green   0   greeny  0   greenz  greenz  1

Answer 2

Well sorry, my first idea was to do a kind of iteration: you can apply a function to the rows of the DataFrame , and return one or more columns. This is usually how I do it. It is still iterating, but kind of a "better" way to do than using iterrows .

def special_function(row):
    b = row['B']
    d = row['D']
    f = None
    g = 0
    if row['E'] == row['A']:
        b = b + 1
    elif row['E'] == row['C']:
        d = d + 1
    else:
        f = row['E']
        g = 1
    return pandas.Series({ 'B':b, 'D':d, 'F': f, 'G': g })

dfcolour[['B', 'D', 'F', 'G']] = dfcolour.apply(special_function, axis=1)