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