相思资源网

对pandas中to_dict的用法详解
站长资源 2024/11/18 佚名
71 1538
相思资源网 Design By www.200059.com

简介：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

Help on method to_dict in module pandas.core.frame:
to_dict(orient='dict') method of pandas.core.frame.DataFrame instance
 Convert DataFrame to dictionary.
 Parameters
 ----------
 orient : str {'dict', 'list', 'series', 'split', 'records', 'index'}
 Determines the type of the values of the dictionary.
 - dict (default) : dict like {column -> {index -> value}}
 - list : dict like {column -> [values]}
 - series : dict like {column -> Series(values)}
 - split : dict like
  {index -> [index], columns -> [columns], data -> [values]}
 - records : list like
  [{column -> value}, ... , {column -> value}]
 - index : dict like {index -> {column -> value}}
  .. versionadded:: 0.17.0
 Abbreviations are allowed. `s` indicates `series` and `sp`
 indicates `split`.
 Returns
 -------
 result : dict like {column -> {index -> value}}

1、选择参数orient='dict'

dict也是默认的参数，下面的data数据类型为DataFrame结构, 会形成 {column -> {index -> value}}这样的结构的字典，可以看成是一种双重字典结构

- 单独提取每列的值及其索引，然后组合成一个字典

- 再将上述的列属性作为关键字（key），值（values）为上述的字典

查询方式为 ：data_dict[key1][key2]

- data_dict 为参数选择orient='dict'时的数据名

- key1 为列属性的键值（外层）

- key2 为内层字典对应的键值

data 
Out[9]: 
 pclass age embarked   home.dest sex
1086 3rd 31.194181 UNKNOWN   UNKNOWN male
12 1st 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 2nd 41.000000 Cherbourg   New York, NY male
437 2nd 48.000000 Southampton Somerset / Bernardsville, NJ female
663 3rd 26.000000 Southampton   UNKNOWN male
669 3rd 19.000000 Southampton   England male
507 2nd 31.194181 Southampton  Petworth, Sussex male
In[10]: data_dict=data.to_dict(orient= 'dict')
In[11]: data_dict
Out[11]: 
{'age': {12: 31.19418104265403,
 437: 48.0,
 507: 31.19418104265403,
 562: 41.0,
 663: 26.0,
 669: 19.0,
 833: 32.0,
 1036: 31.19418104265403,
 1086: 31.19418104265403,
 1108: 31.19418104265403},
 'embarked': {12: 'Cherbourg',
 437: 'Southampton',
 507: 'Southampton',
 562: 'Cherbourg',
 663: 'Southampton',
 669: 'Southampton',
 833: 'Southampton',
 1036: 'UNKNOWN',
 1086: 'UNKNOWN',
 1108: 'UNKNOWN'},
 'home.dest': {12: 'Paris, France',
 437: 'Somerset / Bernardsville, NJ',
 507: 'Petworth, Sussex',
 562: 'New York, NY',
 663: 'UNKNOWN',
 669: 'England',
 833: 'Foresvik, Norway Portland, ND',
 1036: 'UNKNOWN',
 1086: 'UNKNOWN',
 1108: 'UNKNOWN'},
 'pclass': {12: '1st',
 437: '2nd',
 507: '2nd',
 562: '2nd',
 663: '3rd',
 669: '3rd',
 833: '3rd',
 1036: '3rd',
 1086: '3rd',
 1108: '3rd'},
 'sex': {12: 'female',
 437: 'female',
 507: 'male',
 562: 'male',
 663: 'male',
 669: 'male',
 833: 'male',
 1036: 'male',
 1086: 'male',
 1108: 'male'}}

2、当关键字orient=' list' 时

和1中比较相似，只不过内层变成了一个列表，结构为{column -> [values]}

查询方式为： data_list[keys][index]

data_list 为关键字orient='list' 时对应的数据名

keys 为列属性的键值，如本例中的'age' , ‘embarked'等

index 为整型索引，从0开始到最后

In[19]: data_list=data.to_dict(orient='list')
In[20]: data_list
Out[20]: 
{'age': [31.19418104265403,
 31.19418104265403,
 31.19418104265403,
 32.0,
 31.19418104265403,
 41.0,
 48.0,
 26.0,
 19.0,
 31.19418104265403],
 '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'],
 'pclass': ['3rd',
 '1st',
 '3rd',
 '3rd',
 '3rd',
 '2nd',
 '2nd',
 '3rd',
 '3rd',
 '2nd'],
 'sex': ['male',
 'female',
 'male',
 'male',
 'male',
 'male',
 'female',
 'male',
 'male',
 'male']}

3、关键字参数orient='series'

形成结构{column -> Series(values)}

调用格式为：data_series[key1][key2]或data_dict[key1]

data_series 为数据对应的名字

key1 为列属性的键值，如本例中的'age' , ‘embarked'等

key2 使用数据原始的索引（可选）

In[21]: data_series=data.to_dict(orient='series')
In[22]: data_series
Out[22]: 
{'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, 'pclass': 1086 3rd
 12 1st
 1036 3rd
 833 3rd
 1108 3rd
 562 2nd
 437 2nd
 663 3rd
 669 3rd
 507 2nd
 Name: pclass, 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、关键字参数orient='split'

形成{index -> [index], columns -> [columns], data -> [values]}的结构，是将数据、索引、属性名单独脱离出来构成字典

调用方式有 data_split[‘index'],data_split[‘data'],data_split[‘columns']

data_split=data.to_dict(orient='split')
data_split
Out[38]: 
{'columns': ['pclass', 'age', 'embarked', 'home.dest', 'sex'],
 'data': [['3rd', 31.19418104265403, 'UNKNOWN', 'UNKNOWN', 'male'],
 ['1st', 31.19418104265403, 'Cherbourg', 'Paris, France', 'female'],
 ['3rd', 31.19418104265403, 'UNKNOWN', 'UNKNOWN', 'male'],
 ['3rd', 32.0, 'Southampton', 'Foresvik, Norway Portland, ND', 'male'],
 ['3rd', 31.19418104265403, 'UNKNOWN', 'UNKNOWN', 'male'],
 ['2nd', 41.0, 'Cherbourg', 'New York, NY', 'male'],
 ['2nd', 48.0, 'Southampton', 'Somerset / Bernardsville, NJ', 'female'],
 ['3rd', 26.0, 'Southampton', 'UNKNOWN', 'male'],
 ['3rd', 19.0, 'Southampton', 'England', 'male'],
 ['2nd', 31.19418104265403, 'Southampton', 'Petworth, Sussex', 'male']],
 'index': [1086, 12, 1036, 833, 1108, 562, 437, 663, 669, 507]}

5、当关键字orient='records' 时

形成[{column -> value}, … , {column -> value}]的结构

整体构成一个列表，内层是将原始数据的每行提取出来形成字典

调用格式为data_records[index][key1]

data_records=data.to_dict(orient='records')
data_records
Out[41]: 
[{'age': 31.19418104265403,
 'embarked': 'UNKNOWN',
 'home.dest': 'UNKNOWN',
 'pclass': '3rd',
 'sex': 'male'},
 {'age': 31.19418104265403,
 'embarked': 'Cherbourg',
 'home.dest': 'Paris, France',
 'pclass': '1st',
 'sex': 'female'},
 {'age': 31.19418104265403,
 'embarked': 'UNKNOWN',
 'home.dest': 'UNKNOWN',
 'pclass': '3rd',
 'sex': 'male'},
 {'age': 32.0,
 'embarked': 'Southampton',
 'home.dest': 'Foresvik, Norway Portland, ND',
 'pclass': '3rd',
 'sex': 'male'},
 {'age': 31.19418104265403,
 'embarked': 'UNKNOWN',
 'home.dest': 'UNKNOWN',
 'pclass': '3rd',
 'sex': 'male'},
 {'age': 41.0,
 'embarked': 'Cherbourg',
 'home.dest': 'New York, NY',
 'pclass': '2nd',
 'sex': 'male'},
 {'age': 48.0,
 'embarked': 'Southampton',
 'home.dest': 'Somerset / Bernardsville, NJ',
 'pclass': '2nd',
 'sex': 'female'},
 {'age': 26.0,
 'embarked': 'Southampton',
 'home.dest': 'UNKNOWN',
 'pclass': '3rd',
 'sex': 'male'},
 {'age': 19.0,
 'embarked': 'Southampton',
 'home.dest': 'England',
 'pclass': '3rd',
 'sex': 'male'},
 {'age': 31.19418104265403,
 'embarked': 'Southampton',
 'home.dest': 'Petworth, Sussex',
 'pclass': '2nd',
 'sex': 'male'}]

6、当关键字orient='index' 时

形成{index -> {column -> value}}的结构，调用格式正好和'dict' 对应的反过来，请读者自己思考

data_index=data.to_dict(orient='index')
data_index
Out[43]: 
{12: {'age': 31.19418104265403,
 'embarked': 'Cherbourg',
 'home.dest': 'Paris, France',
 'pclass': '1st',
 'sex': 'female'},
 437: {'age': 48.0,
 'embarked': 'Southampton',
 'home.dest': 'Somerset / Bernardsville, NJ',
 'pclass': '2nd',
 'sex': 'female'},
 507: {'age': 31.19418104265403,
 'embarked': 'Southampton',
 'home.dest': 'Petworth, Sussex',
 'pclass': '2nd',
 'sex': 'male'},
 562: {'age': 41.0,
 'embarked': 'Cherbourg',
 'home.dest': 'New York, NY',
 'pclass': '2nd',
 'sex': 'male'},
 663: {'age': 26.0,
 'embarked': 'Southampton',
 'home.dest': 'UNKNOWN',
 'pclass': '3rd',
 'sex': 'male'},
 669: {'age': 19.0,
 'embarked': 'Southampton',
 'home.dest': 'England',
 'pclass': '3rd',
 'sex': 'male'},
 833: {'age': 32.0,
 'embarked': 'Southampton',
 'home.dest': 'Foresvik, Norway Portland, ND',
 'pclass': '3rd',
 'sex': 'male'},
 1036: {'age': 31.19418104265403,
 'embarked': 'UNKNOWN',
 'home.dest': 'UNKNOWN',
 'pclass': '3rd',
 'sex': 'male'},
 1086: {'age': 31.19418104265403,
 'embarked': 'UNKNOWN',
 'home.dest': 'UNKNOWN',
 'pclass': '3rd',
 'sex': 'male'},
 1108: {'age': 31.19418104265403,
 'embarked': 'UNKNOWN',
 'home.dest': 'UNKNOWN',
 'pclass': '3rd',
 'sex': 'male'}}

以上这篇对pandas中to_dict的用法详解就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。

标签：
pandas,to_dict
相思资源网 Design By www.200059.com
广告合作：本站广告合作请联系QQ：858582 申请时备注：广告合作（否则不回）
免责声明：本站文章均来自网站采集或用户投稿，网站不提供任何软件下载或自行开发的软件！ 如有用户或公司发现本站内容信息存在侵权行为，请邮件告知！ 858582#qq.com
相思资源网 Design By www.200059.com

评论“对pandas中to_dict的用法详解”

暂无对pandas中to_dict的用法详解的评论...
www.200059.com 相思资源网
139,976影音资源
144,792福利资源
21,817软件资源
631,128技术资源

友情链接

杰晶网络 DDR爱好者之家 桃源资源网 杰网资源 富贵资源网 南强小屋 铁雪资源网 幽灵资源网 万梅资源网 狼山资源网 白云岛资源网 昆仑资源网 相思资源网 明霞山资源网 内蒙古资源网 黑松山资源网 茶园资源网 饿虎岗资源网 大旗谷资源网 常春岛资源网 岱庙资源网 兴国资源网 快活林资源网 蝙蝠岛资源网 帝王谷资源网 白云城资源网 伏龙阁资源网 清风细雨楼 天枫庄资源网 圆月山庄资源网 无争山庄资源网 神水资源网 移花宫资源网 神剑山庄资源网 无为清净楼资源网 金钱帮资源网 丐帮资源网 华山资源网 极乐门资源网 小李飞刀资源网 凤求凰客栈 风云阁资源网 金狮镖局 鸳鸯亭资源网 千金楼资源网 更多链接
相思资源网 Design By www.200059.com