Suppose I have a dataframe:
C1 V1 C2 V2 Cond
1 2 3 4 X
5 6 7 8 Y
9 10 11 12 X
The statements should return: if Cond == X, pick C1 and v1, else pick C2 and V2.
The output dataframe is something like:
C V
1 2
7 8
9 10
** EDIT: To add one more requirement: the number of columns can change but follow some naming pattern. In this case select all columns with "1" in it, else with "2". I think the hard-coded solution might not work.
I try create more general solution with filter and numpy.where, for new column names use extract:
#if necessary sort columns
df = df.sort_index(axis=1)
#filter df by 1 and 2
df1 = df.filter(like='1')
df2 = df.filter(like='2')
print (df1)
C1 V1
0 1 2
1 5 6
2 9 10
print (df2)
C2 V2
0 3 4
1 7 8
2 11 12
#np.where need same shape of mask as df1 and df2
mask = pd.concat([df.Cond == 'X']*len(df1.columns), axis=1)
print (mask)
Cond Cond
0 True True
1 False False
2 True True
cols = df1.columns.str.extract('([A-Za-z])', expand=False)
print (cols)
Index(['C', 'V'], dtype='object')
print (np.where(mask, df1,df2))
Index(['C', 'V'], dtype='object')
[[ 1 2]
[ 7 8]
[ 9 10]]
print (pd.DataFrame(np.where(mask, df1, df2), index=df.index, columns=cols))
C V
0 1 2
1 7 8
2 9 10