Combine all column elements except two particular columns

col != ['SourceFile','Label']

df['FileDescription']=df[col].apply(lambda row:'_'.join(row.values.astype(str)),axis=1)

I want to combine the elements in all columns except two columns, ‘SourceFile’ and ‘Label’. I tried the above code. Which resulted in value error. There is so many columns. So I can’t use

col=['SourceFile','AggregationType','APP14Flags0','APP14Flags1','Application','ArchivedFileName','Artist',.....]
df['FileDescription']=df[col].apply(lambda row:'_'.join(row.values.astype(str)),axis=1)

Answer

col != ['SourceFile','Label'] is syntactically wrong and it gives NameError not the ValueError. First get the columns you don’t want and convert it to set.

col = set(['SourceFile','Label'])

Now get all columns as set:

allCols = set(df.columns.to_list())

Finally take the set difference and assign back as a list:

cols = list(set.difference(allCols, col))

Now you can use aggregate method:

df[col].astype(str).agg('_'.join)

See the sample execution:

df
     0    1    2    3    4    5     6     7     8     9
0  0.0  1.0  2.0  3.0  4.0  5.0   6.0   7.0   8.0   9.0
1  1.0  2.0  3.0  4.0  5.0  6.0   7.0   8.0   9.0  10.0
2  2.0  3.0  4.0  5.0  6.0  7.0   8.0   9.0  10.0  11.0
3  3.0  4.0  5.0  6.0  7.0  8.0   9.0  10.0  11.0  12.0
4  4.0  5.0  6.0  7.0  8.0  9.0  10.0  11.0  12.0  13.0

col= set([0])
allCols = set(df.columns.to_list())

col = list(set.difference(allCols, col))
df[col].astype(str).agg('_'.join, axis=1)
0        1.0_2.0_3.0_4.0_5.0_6.0_7.0_8.0_9.0
1       2.0_3.0_4.0_5.0_6.0_7.0_8.0_9.0_10.0
2      3.0_4.0_5.0_6.0_7.0_8.0_9.0_10.0_11.0
3     4.0_5.0_6.0_7.0_8.0_9.0_10.0_11.0_12.0
4    5.0_6.0_7.0_8.0_9.0_10.0_11.0_12.0_13.0
dtype: object