Pandas dict keys to columns [duplicate]

Question

I have a dataframe like this.

index          column1
e1             {u'c680': 5, u'c681': 1, u'c682': 2, u'c57...
e2             {u'c680': 6, u'c681': 2, u'c682': 1, u'c57...
e3             {u'c680': 2, u'c681': 4, u'c682': 2, u'c57...
e4             {u'c680': 4, u'c681': 2, u'c682': 3, u'c57...
e5             {u'c680': 3, u'c681': 5, u'c683': 3, u'c57...

Now I want to expand the dict in column1 to individual columns like below.

index          c680           c681        c682        c683
e1             5              1           2           0 
e2             6              2           1           0
e3             2              4           2           0
e4             4              2           3           0    
e5             3              5           0           3

Is there a pandas shortcut that can achieve this?

Answer 1

The best here is not use apply(pd.Series) because very slow, but DataFrame contructor with convert NaNs to 0 and then to ints:

df = pd.DataFrame({'column1': [{'c681': 1, 'c682': 2, 'c57': 4, 'c680': 5}, 
                               {'c681': 2, 'c682': 1, 'c57': 7, 'c680': 6}, 
                               {'c681': 4, 'c682': 2, 'c57': 8, 'c680': 2}, 
                               {'c681': 2, 'c682': 3, 'c57': 1, 'c680': 4}, 
                               {'c683': 3, 'c681': 5, 'c57': 0, 'c680': 3}]},
                                 index=['e1','e2','e3','e4','e5'])


print (df)
                                        column1
e1  {'c680': 5, 'c682': 2, 'c57': 4, 'c681': 1}
e2  {'c680': 6, 'c682': 1, 'c57': 7, 'c681': 2}
e3  {'c680': 2, 'c682': 2, 'c57': 8, 'c681': 4}
e4  {'c680': 4, 'c682': 3, 'c57': 1, 'c681': 2}
e5  {'c683': 3, 'c680': 3, 'c57': 0, 'c681': 5}


df = pd.DataFrame(df['column1'].values.tolist(), index=df.index).fillna(0).astype(int)
print (df)
    c57  c680  c681  c682  c683
e1    4     5     1     2     0
e2    7     6     2     1     0
e3    8     2     4     2     0
e4    1     4     2     3     0
e5    0     3     5     0     3

---

df = pd.concat([df] * 1000, ignore_index=True)

In [108]: %timeit (pd.DataFrame(df['column1'].values.tolist(), index=df.index))
100 loops, best of 3: 10.1 ms per loop

In [109]: %timeit (df['column1'].apply(pd.Series))
1 loop, best of 3: 1.14 s per loop