Apply Lambda Function On Multiple Columns

Let's suppose that I have this DataFrame in pandas: year text_1 text_2 0 1999 ['Sunny', 'weather'] ['Foggy', 'weather'] 1 2005 ['Rainy, 'weather'

Solution 1:

Use DataFrame.applymap if need processing each value element wise:

df[['text_1', 'text_2']] = df[['text_1', 'text_2']].applymap(' '.join)
print (df)
   year         text_1          text_2
01999  Sunny weather   Foggy weather
12005  Rainy weather  Cloudy weather

Or combine DataFrame.apply with Series.str.join:

df[['text_1', 'text_2']] = df[['text_1', 'text_2']].apply(lambda x: x.str.join(' '))

Solution 2:

sample data

AB0[asdf, asf][eeee, tttt]

---

df['combined'] = df.apply(lambda x: [' '.join(i) for i in list(x[['A','B']])], axis=1)

Output

AB               combined
0[asdf, asf][eeee, tttt][asdf asf, eeee tttt]

---

Update

df[['A','B']] = df.apply(lambda x: pd.Series([' '.join(x['A']),' '.join(x['B'])]), axis=1)

Output

AB0  asdf asf  eeee tttt