Given a list of the nested dictionary, write a Python program to create a Pandas dataframe using it. Let’s understand the stepwise procedure to create a Pandas Dataframe using the list of nested dictionary.
Step #1: Creating a list of nested dictionary.
Python3
# importing pandasimport pandas as pd# List of nested dictionary initializationlist = [{ "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:
[{'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'}]
Step #2: Adding dict values to rows.
Python3
# rows list initializationrows = []# appending rowsfor data in list: data_row = data['Student'] time = data['Name'] for row in data_row: row['Name']= time rows.append(row)# using data framedf = pd.DataFrame(rows)print(df) |
Output:
Exam Grade Name
0 90 a Paras Jain
1 99 b Paras Jain
2 97 c Paras Jain
3 89 a Chunky Pandey
4 80 b Chunky Pandey
Step #3: Pivoting DataFrame and assigning column names.
Python3
# using pivot_tabledf = df.pivot_table(index ='Name', columns =['Grade'], values =['Exam']).reset_index()# Defining columnsdf.columns =['Name', 'Maths', 'Physics', 'Chemistry']# print dataframeprint(df) |
Output:
Name Maths Physics Chemistry
0 Chunky Pandey 89.0 80.0 NaN
1 Paras Jain 90.0 99.0 97.0
Below is the complete code:
Python3
# Python program to convert list of nested# dictionary into Pandas dataframe# importing pandasimport pandas as pd# List of list of dictionary initializationlist = [ { "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 initializationrows = []# appending rowsfor data in list: data_row = data['Student'] time = data['Name'] for row in data_row: row['Name']= time rows.append(row)# using data framedf = pd.DataFrame(rows)# using pivot_tabledf = df.pivot_table(index ='Name', columns =['Grade'], values =['Exam']).reset_index()# Defining columnsdf.columns =['Name', 'Maths', 'Physics', 'Chemistry']# print dataframeprint(df) |
Output:
Name Maths Physics Chemistry
0 Chunky Pandey 89 80 NaN
1 Paras Jain 90 99 97
