Create dataframe from dictionary of list with variable length

Question

I have a dictionary of list which is like -

from collections import defaultdict
defaultdict(list,
            {'row1': ['Affinity'],
             'row2': ['Ahmc',
              'Garfield',
              'Medical Center'],
             'row3': ['Alamance','Macbeth'],
             'row4': [],
             'row5': ['Mayday']})

I want to convert this to a data frame. The output should look like-

ID  SYN1    SYN2    SYN3    SYN4    SYN5
row1    Affinity                
row2    Ahmc    Garfield    Medical Center      
row3    Alamance    Macbeth         
row4                    
row5    Mayday

Can you explain what "variable length of the values is varying" means? — rahlf23
– rahlf23, Commented Oct 15, 2018 at 14:36

jpp · Accepted Answer · 2018-10-15 14:38:08Z

8

collections.defaultdict is a subclass of dict.

So you can just use pd.DataFrame.from_dict:

res = pd.DataFrame.from_dict(dd, orient='index')
res.columns = [f'SYN{i+1}' for i in res]

print(res)

          SYN1      SYN2            SYN3
row1  Affinity      None            None
row2      Ahmc  Garfield  Medical Center
row3  Alamance   Macbeth            None
row4      None      None            None
row5    Mayday      None            None

answered Oct 15, 2018 at 14:38

jpp

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Comments

BENY · Accepted Answer · 2018-10-15 14:37:45Z

Yes you can using Series

df=pd.Series(d).apply(pd.Series).fillna('')
Out[55]: 
             0         1               2
row1  Affinity                          
row2      Ahmc  Garfield  Medical Center
row3  Alamance   Macbeth                
row4                                    
row5    Mayday

Or from dataframe constructor

df=pd.DataFrame(data=list(d.values()),index=d.keys())
Out[64]: 
             0         1               2
row1  Affinity      None            None
row2      Ahmc  Garfield  Medical Center
row3  Alamance   Macbeth            None
row4      None      None            None
row5    Mayday      None            None

Then we create the column

df.columns='SYN'+(df.columns+1).astype(str)
df
Out[67]: 
          SYN1      SYN2            SYN3
row1  Affinity      None            None
row2      Ahmc  Garfield  Medical Center
row3  Alamance   Macbeth            None
row4      None      None            None
row5    Mayday      None            None

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