I want to join multiple columns of the same dataframe into a single column. The columns dosent have any labels.
input:
0 1 2 3 4
a b c d e
Result:
0
a,b,c,d,e
please help me with this.
I already tried data1['all'] = data[data.columns[1:]].apply(lambda x: ','.join(x.dropna().astype(str)),axis=1)
but I am not able to get the result as required.
Your solution working for me with small modification removed filering out first column and assign to same DataFrame:
data['all'] = data.apply(lambda x: ','.join(x.dropna().astype(str)),axis=1)
If no missing values and no numeric values:
data['all'] = data.apply(','.join,axis=1)
print (data)
0 1 2 3 4 all
0 a b c d e a,b,c,d,e
If need new one column DataFrame:
df = = data.apply(lambda x: ','.join(x.dropna().astype(str)),axis=1).to_frame('new')
#df = data.apply(','.join,axis=1).to_frame('new')
print (df)
new
0 a,b,c,d,e
You could avoid apply altogether and use Pandas' built in string methods ; in this case , string concatenate
#merge the other columns to '0' and specify a delimiter
df['0'].str.cat(df.loc[:,'1':],sep=',')
0 a,b,c,d,e
Name: 0, dtype: object
