Python 将嵌套的字典列表转换成Pandas数据框架

给定一个嵌套字典的列表，写一个Python程序，用它来创建一个Pandas数据框架。让我们来了解一下使用嵌套字典列表创建Pandas数据框架的逐步过程。

第1步：创建一个嵌套字典的列表。

# 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)

输出:

第2步：将dict值添加到行中。

# 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)

输出:

第三步：对数据框架进行透视并指定列名。

# 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)

输出:

以下是完整的代码。

# 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)

输出:

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