How to expand one column in Pandas to many columns?

半城伤御伤魂 提交于 2019-11-29 15:19:08

Not as fast as @jezrael's solution. But elegant :-)

apply with pd.Series

df.a.apply(pd.Series)

   0  1  2  3  4  5
0  0  1  2  3  4  5
1  0  1  2  3  4  5

or

df.a.apply(pd.Series, index=list('abcdef'))

   a  b  c  d  e  f
0  0  1  2  3  4  5
1  0  1  2  3  4  5

You can convert lists to numpy array by values and then use DataFrame constructor:

df = pd.DataFrame({'a':[[0,1,2,3,4,5],[0,1,2,3,4,5]]})
print (df)
                    a
0  [0, 1, 2, 3, 4, 5]
1  [0, 1, 2, 3, 4, 5]

df1 = pd.DataFrame(df['a'].values.tolist())
print (df1)
   0  1  2  3  4  5
0  0  1  2  3  4  5
1  0  1  2  3  4  5

cols = list('abcdef')
df1 = pd.DataFrame(df['a'].values.tolist(), columns=cols)
print (df1)
   a  b  c  d  e  f
0  0  1  2  3  4  5
1  0  1  2  3  4  5

If I understood your question correctly, you are looking for a transpose operation.

df = pd.DataFrame([1,2,3,4,5],columns='a')
# .T stands for transpose
print(df.T)
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