I have a Pandas dataframe like :
pd.DataFrame({\'a\':[1,2], \'b\':[[{\'c\':1,\'d\':5},{\'c\':3, \'d\':7}],[{\'c\':10,\'d\':50}]]})
Out[2]:
a
You can use concat with list comprehension
:
df = pd.concat([pd.DataFrame(x) for x in df['b']], keys=df['a'])
.reset_index(level=1, drop=True).reset_index()
print (df)
a c d
0 1 1 5
1 1 3 7
2 2 10 50
EDIT:
If index is unique, then is possible use join for all columns:
df1 = pd.concat([pd.DataFrame(x) for x in df['b']], keys=df.index)
.reset_index(level=1,drop=True)
df = df.drop('b', axis=1).join(df1).reset_index(drop=True)
print (df)
a c d
0 1 1 5
1 1 3 7
2 2 10 50
I try simplify solution:
l = df['b'].str.len()
df1 = pd.DataFrame(np.concatenate(df['b']).tolist(), index=np.repeat(df.index, l))
df = df.drop('b', axis=1).join(df1).reset_index(drop=True)
print (df)
a c d
0 1 1 5
1 1 3 7
2 2 10 50