pandas 中的to_dict 可以对DataFrame类型的数据进行转换
可以选择六种的转换类型，分别对应于参数 ‘dict’, ‘list’, ‘series’, ‘split’, ‘records’, ‘index’，下面逐一介绍每种的用法
0、原始数据
import pandas as pd
data = pd.DataFrame([['3rd', 31.194181, 'UNKNOWN', 'UNKNOWN', 'male'],
['1rd', 31.194181, 'Cherbourg', 'Paris, France', 'female'],
['3rd', 31.194181, 'UNKNOWN', 'UNKNOWN', 'male'],
['3rd', 32.000000, 'Southampton', 'Foresvik, Norway Portland, ND', 'male'],
['3rd', 31.194181, 'UNKNOWN', 'UNKNOWN', 'male'],
['2rd', 41.000000, 'Cherbourg', 'New York, NY', 'male'],
['2rd', 48.000000, 'Southampton', 'Somerset / Bernardsville, NJ', 'female'],
['3rd', 26.000000, 'Southampton', 'UNKNOWN', 'male'],
['3rd', 19.000000, 'Southampton', 'England', 'male'],
['2rd', 31.194181, 'Southampton', 'Petworth, Sussex', 'male']],
index=['1086', '12', '1036', '833', '1108', '562', '437', '663', '669', '507'],
columns=['pclass', 'age', 'embarked', 'home.dest', 'sex'])
print(data)
输出结果：
pclass age embarked home.dest sex
1086 3rd 31.194181 UNKNOWN UNKNOWN male
12 1rd 31.194181 Cherbourg Paris, France female
1036 3rd 31.194181 UNKNOWN UNKNOWN male
833 3rd 32.000000 Southampton Foresvik, Norway Portland, ND male
1108 3rd 31.194181 UNKNOWN UNKNOWN male
562 2rd 41.000000 Cherbourg New York, NY male
437 2rd 48.000000 Southampton Somerset / Bernardsville, NJ female
663 3rd 26.000000 Southampton UNKNOWN male
669 3rd 19.000000 Southampton England male
507 2rd 31.194181 Southampton Petworth, Sussex male
1、选择参数orient=’dict’ ，可以看成是一种双重字典结构（columns是外层键，index是内层键）
形成结构{column -> {index -> value}}
data_dict = data.to_dict(orient='dict')
print(data_dict)
输出结果为：
{
'pclass': {
'1086': '3rd',
'12': '1rd',
'1036': '3rd',
'833': '3rd',
'1108': '3rd',
'562': '2rd',
'437': '2rd',
'663': '3rd',
'669': '3rd',
'507': '2rd'
},
'age': {
'1086': 31.194181,
'12': 31.194181,
'1036': 31.194181,
'833': 32.0,
'1108': 31.194181,
'562': 41.0,
'437': 48.0,
'663': 26.0,
'669': 19.0,
'507': 31.194181
},
'embarked': {
'1086': 'UNKNOWN',
'12': 'Cherbourg',
'1036': 'UNKNOWN',
'833': 'Southampton',
'1108': 'UNKNOWN',
'562': 'Cherbourg',
'437': 'Southampton',
'663': 'Southampton',
'669': 'Southampton',
'507': 'Southampton'
},
'home.dest': {
'1086': 'UNKNOWN',
'12': 'Paris, France',
'1036': 'UNKNOWN',
'833': 'Foresvik, Norway Portland, ND',
'1108': 'UNKNOWN',
'562': 'New York, NY',
'437': 'Somerset / Bernardsville, NJ',
'663': 'UNKNOWN',
'669': 'England',
'507': 'Petworth, Sussex'
},
'sex': {
'1086': 'male',
'12': 'female',
'1036': 'male',
'833': 'male',
'1108': 'male',
'562': 'male',
'437': 'female',
'663': 'male',
'669': 'male',
'507': 'male'
}
}
2、选择参数orient=’list’ ，也可以看成是一种双重字典结构，只不过内层变成了一个列表（columns是外层键，不显示index）
形成结构 {column -> [values]}
data_dict = data.to_dict(orient='list')
print(data_dict)
输出结果为：
{
'pclass': [
'3rd',
'1rd',
'3rd',
'3rd',
'3rd',
'2rd',
'2rd',
'3rd',
'3rd',
'2rd'
],
'age': [
31.194181,
31.194181,
31.194181,
32.0,
31.194181,
41.0,
48.0,
26.0,
19.0,
31.194181
],
'embarked': [
'UNKNOWN',
'Cherbourg',
'UNKNOWN',
'Southampton',
'UNKNOWN',
'Cherbourg',
'Southampton',
'Southampton',
'Southampton',
'Southampton'
],
'home.dest': [
'UNKNOWN',
'Paris, France',
'UNKNOWN',
'Foresvik, Norway Portland, ND',
'UNKNOWN',
'New York, NY',
'Somerset / Bernardsville, NJ',
'UNKNOWN',
'England',
'Petworth, Sussex'
],
'sex': [
'male',
'female',
'male',
'male',
'male',
'male',
'female',
'male',
'male',
'male'
]
}
3、选择参数orient=’series’
形成结构{column -> index(values)} ，还有数据类型、列名
data_dict = data.to_dict(orient='series')
print(data_dict)
输出结果为：
{'pclass':
1086 3rd
12 1rd
1036 3rd
833 3rd
1108 3rd
562 2rd
437 2rd
663 3rd
669 3rd
507 2rd
Name: pclass, dtype: object,
'age':
1086 31.194181
12 31.194181
1036 31.194181
833 32.000000
1108 31.194181
562 41.000000
437 48.000000
663 26.000000
669 19.000000
507 31.194181
Name: age, dtype: float64,
'embarked':
1086 UNKNOWN
12 Cherbourg
1036 UNKNOWN
833 Southampton
1108 UNKNOWN
562 Cherbourg
437 Southampton
663 Southampton
669 Southampton
507 Southampton
Name: embarked, dtype: object,
'home.dest':
1086 UNKNOWN
12 Paris, France
1036 UNKNOWN
833 Foresvik, Norway Portland, ND
1108 UNKNOWN
562 New York, NY
437 Somerset / Bernardsville, NJ
663 UNKNOWN
669 England
507 Petworth, Sussex
Name: home.dest, dtype: object,
'sex':
1086 male
12 female
1036 male
833 male
1108 male
562 male
437 female
663 male
669 male
507 male
Name: sex, dtype: object}
4、选择参数orient=’split’
形成结构{index -> [index], columns -> [columns], data -> [[values]，[values]，[values]……]}。总体分为三部分：index、columns、data
data_dict = data.to_dict(orient='split')
print(data_dict)
输出结果为：
{
'index': [
'1086',
'12',
'1036',
'833',
'1108',
'562',
'437',
'663',
'669',
'507'
],
'columns': [
'pclass',
'age',
'embarked',
'home.dest',
'sex'
],
'data': [
[
'3rd',
31.194181,
'UNKNOWN',
'UNKNOWN',
'male'
],
[
'1rd',
31.194181,
'Cherbourg',
'Paris, France',
'female'
],
[
'3rd',
31.194181,
'UNKNOWN',
'UNKNOWN',
'male'
],
[
'3rd',
32.0,
'Southampton',
'Foresvik, Norway Portland, ND',
'male'
],
[
'3rd',
31.194181,
'UNKNOWN',
'UNKNOWN',
'male'
],
[
'2rd',
41.0,
'Cherbourg',
'New York, NY',
'male'
],
[
'2rd',
48.0,
'Southampton',
'Somerset / Bernardsville, NJ',
'female'
],
[
'3rd',
26.0,
'Southampton',
'UNKNOWN',
'male'
],
[
'3rd',
19.0,
'Southampton',
'England',
'male'
],
[
'2rd',
31.194181,
'Southampton',
'Petworth, Sussex',
'male'
]
]
}
5、选择参数orient=’records’ （不显示index）
形成结构[{column -> value}, … , {column -> value}]，可以看作是一条数据一条数据组成的集合，不显示index
data_dict = data.to_dict(orient='records')
print(data_dict)
输出结果为：
[
{
'pclass': '3rd',
'age': 31.194181,
'embarked': 'UNKNOWN',
'home.dest': 'UNKNOWN',
'sex': 'male'
},
{
'pclass': '1rd',
'age': 31.194181,
'embarked': 'Cherbourg',
'home.dest': 'Paris, France',
'sex': 'female'
},
{
'pclass': '3rd',
'age': 31.194181,
'embarked': 'UNKNOWN',
'home.dest': 'UNKNOWN',
'sex': 'male'
},
{
'pclass': '3rd',
'age': 32.0,
'embarked': 'Southampton',
'home.dest': 'Foresvik, Norway Portland, ND',
'sex': 'male'
},
{
'pclass': '3rd',
'age': 31.194181,
'embarked': 'UNKNOWN',
'home.dest': 'UNKNOWN',
'sex': 'male'
},
{
'pclass': '2rd',
'age': 41.0,
'embarked': 'Cherbourg',
'home.dest': 'New York, NY',
'sex': 'male'
},
{
'pclass': '2rd',
'age': 48.0,
'embarked': 'Southampton',
'home.dest': 'Somerset / Bernardsville, NJ',
'sex': 'female'
},
{
'pclass': '3rd',
'age': 26.0,
'embarked': 'Southampton',
'home.dest': 'UNKNOWN',
'sex': 'male'
},
{
'pclass': '3rd',
'age': 19.0,
'embarked': 'Southampton',
'home.dest': 'England',
'sex': 'male'
},
{
'pclass': '2rd',
'age': 31.194181,
'embarked': 'Southampton',
'home.dest': 'Petworth, Sussex',
'sex': 'male'
}
]
6、选择参数orient=’index’
形成结构{index -> {column -> value}}，可以看作是一条数据一条数据组成的集合，但是每条数据之前显示index
data_dict = data.to_dict(orient='index')
print(data_dict)
输出结果为：
{
'1086': {
'pclass': '3rd',
'age': 31.194181,
'embarked': 'UNKNOWN',
'home.dest': 'UNKNOWN',
'sex': 'male'
},
'12': {
'pclass': '1rd',
'age': 31.194181,
'embarked': 'Cherbourg',
'home.dest': 'Paris, France',
'sex': 'female'
},
'1036': {
'pclass': '3rd',
'age': 31.194181,
'embarked': 'UNKNOWN',
'home.dest': 'UNKNOWN',
'sex': 'male'
},
'833': {
'pclass': '3rd',
'age': 32.0,
'embarked': 'Southampton',
'home.dest': 'Foresvik, Norway Portland, ND',
'sex': 'male'
},
'1108': {
'pclass': '3rd',
'age': 31.194181,
'embarked': 'UNKNOWN',
'home.dest': 'UNKNOWN',
'sex': 'male'
},
'562': {
'pclass': '2rd',
'age': 41.0,
'embarked': 'Cherbourg',
'home.dest': 'New York, NY',
'sex': 'male'
},
'437': {
'pclass': '2rd',
'age': 48.0,
'embarked': 'Southampton',
'home.dest': 'Somerset / Bernardsville, NJ',
'sex': 'female'
},
'663': {
'pclass': '3rd',
'age': 26.0,
'embarked': 'Southampton',
'home.dest': 'UNKNOWN',
'sex': 'male'
},
'669': {
'pclass': '3rd',
'age': 19.0,
'embarked': 'Southampton',
'home.dest': 'England',
'sex': 'male'
},
'507': {
'pclass': '2rd',
'age': 31.194181,
'embarked': 'Southampton',
'home.dest': 'Petworth, Sussex',
'sex': 'male'
}
}
参考资料:
https://blog.csdn.net/m0_37804518/article/details/78444110
来源：CSDN
作者：x²＋(y－√³x²)²＝1
链接：https://blog.csdn.net/qq_45083975/article/details/104502983