Python | Convert list of nested dictionary into Pandas dataframe

Given a list of nested dictionary, write a Python program to create a Pandas dataframe using it. Let’s understand stepwise procedure to create Pandas Dataframe using list of nested dictionary.

Step #1: Creating a list of nested dictionary.

# importing pandas 
import pandas as pd 
  
# List of nested dictionary initialization 
list = [ 
        { 
        "Student": [{"Exam": 90, "Grade": "a"}, 
                    {"Exam": 99, "Grade": "b"}, 
                    {"Exam": 97, "Grade": "c"}, 
                   ], 
        "Name": "Paras Jain"
        }, 
        { 
        "Student": [{"Exam": 89, "Grade": "a"}, 
                    {"Exam": 80, "Grade": "b"} 
                   ], 
        "Name": "Chunky Pandey"
        } 
       ] 
  
#print(list) 

Output:

Step #2: Adding dict values to rows.

# rows list initialization 
rows = [] 
  
# appending rows 
for data in list: 
    data_row = data['Student'] 
    time = data['Name'] 
      
    for row in data_row: 
        row['Name']= time 
        rows.append(row) 
  
# using data frame 
df = pd.DataFrame(rows) 
  
# print(df) 

Output:

Step #3: Pivoting dataframe and assigning column names.

# using pivot_table 
df = df.pivot_table(index ='Name', columns =['Grade'], 
                        values =['Exam']).reset_index() 
  
# Defining columns 
df.columns =['Name', 'Maths', 'Physics', 'Chemistry'] 
  
# print dataframe 
print(df) 

Output:

Below is the complete code:

# Python program to convert list of nested  
# dictionary into Pandas dataframe 
  
# importing pandas 
import pandas as pd 
  
# List of list of dictionary initialization 
list = [ 
        { 
        "Student": [{"Exam": 90, "Grade": "a"}, 
                    {"Exam": 99, "Grade": "b"}, 
                    {"Exam": 97, "Grade": "c"}, 
                   ], 
        "Name": "Paras Jain"
        }, 
        { 
        "Student": [{"Exam": 89, "Grade": "a"}, 
                    {"Exam": 80, "Grade": "b"} 
                  ], 
        "Name": "Chunky Pandey"
        } 
       ] 
  
# rows list initialization 
rows = [] 
  
# appending rows 
for data in list: 
    data_row = data['Student'] 
    time = data['Name'] 
      
    for row in data_row: 
        row['Name']= time 
        rows.append(row) 
  
# using data frame 
df = pd.DataFrame(rows) 
  
# using pivot_table 
df = df.pivot_table(index ='Name', columns =['Grade'], 
                        values =['Exam']).reset_index() 
  
# Defining columns 
df.columns =['Name', 'Maths', 'Physics', 'Chemistry'] 
  
# print dataframe 
print(df) 

Output:

            Name  Maths  Physics  Chemistry
0  Chunky Pandey     89       80        NaN
1     Paras Jain     90       99         97

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