parse a dict from the csv file python

Question

I am reading a data from the csv file like :

import pandas as pd
data_1=pd.read_csv("sample.csv")
data_1.head(10)

It has two columns :

ID   detail
1    [{'a': 1, 'b': 1.85, 'c': 'aaaa', 'd': 6}, {'a': 2, 'b': 3.89, 'c': 'bbbb', 'd': 10}]

the detail column is not a json but it is a dict and I want to flatten the dict and want the result something like this :

ID  a   b     c     d
1   1   1.85  aaaa  6
1   2   3.89  bbbb  10

I always get a,b,c,d in the detail column and want to move the final results to a sql table.

Can someone please help me as how to solve it.

Answer 1

Use dictionary comprehension with ast.literal for convert strings repr to list of dicts and convert it to DataFrame, then use concat and convert first level of MultiIndex to ID column:

import ast

d = {i: pd.DataFrame(ast.literal_eval(d)) for i, d in df[['ID','detail']].to_numpy()}
#for oldier pandas version use .values
#d = {i: pd.DataFrame(ast.literal_eval(d)) for i, d in df[['ID','detail']].values)}
df = pd.concat(d).reset_index(level=1, drop=True).rename_axis('ID').reset_index()
print (df)
   ID  a     b     c   d
0   1  1  1.85  aaaa   6
1   1  2  3.89  bbbb  10

Or use lsit comprehension with DataFrame.assign for ID column, only necessary change order of columns - last column to first:

import ast

L = [pd.DataFrame(ast.literal_eval(d)).assign(ID=i) for i, d in df[['ID','detail']].to_numpy()]
#for oldier pandas versions use .values
#L = [pd.DataFrame(ast.literal_eval(d)).assign(ID=i) for i, d in df[['ID','detail']].values]
df = pd.concat(L, ignore_index=True)
df = df[df.columns[-1:].tolist() + df.columns[:-1].tolist()]
print (df)
   ID  a     b     c   d
0   1  1  1.85  aaaa   6
1   1  2  3.89  bbbb  10

EDIT:

For 2 IDs change second solution:

d = [pd.DataFrame(ast.literal_eval(d)).assign(ID1=i1, ID2=i2) for i1, i2, d in df[['ID1','ID2','detail']].to_numpy()]
df = pd.concat(d)
df = df[df.columns[-2:].tolist() + df.columns[:-2].tolist()]