对pandas中to_dict的用法详解
更新时间：2018年06月05日 10:03:39 作者：积跬步___至千里
今天小编就为大家分享一篇对pandas中to_dict的用法详解，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
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脚本之家。
您可能感兴趣的文章:
相关文章
这篇文章主要介绍了3分钟看懂Python后端必须知道的Django的信号机制，文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学习学习吧2020-07-07
这篇文章主要介绍了python文件的备份以及定位,实例分析了Python中返回一个返回值与多个返回值的方法,需要的朋友可以参考下2021-10-10
python Manager 之dict KeyError问题的解决
今天小编就为大家分享一篇python Manager 之dict KeyError问题的解决，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧2019-12-12
这篇文章主要介绍了详解django三种文件下载方式,小编觉得挺不错的，现在分享给大家，也给大家做个参考。一起跟随小编过来看看吧2018-04-04
这篇文章主要为大家详细介绍了Python+Pyqt实现简单GUI电子时钟，文中示例代码介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们可以参考一下2019-07-07
这篇文章主要介绍了python实现对服务器脚本敏感信息的加密解密功能，本文给大家介绍的非常详细，具有一定的参考借鉴价值,需要的朋友可以参考下2019-08-08
python web.py开发httpserver解决跨域问题实例解析
这篇文章主要介绍了python web.py开发httpserver解决跨域问题实例解析，分享了相关代码示例，小编觉得还是挺不错的，具有一定借鉴价值，需要的朋友可以参考下2018-02-02
这篇文章主要介绍了python多线程之间的同步，文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学习学习吧2019-04-04
这篇文章主要介绍了python3定位并识别图片验证码实现自动登录功能,本文给大家介绍的非常详细，对大家的学习或工作具有一定的参考借鉴价值，需要的朋友可以参考下2021-01-01
这篇文章主要为大家详细介绍了python实现三次样条插值，具有一定的参考价值，感兴趣的小伙伴们可以参考一下2018-12-12
最新评论