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。

相关文章

  • pyqt5 使用cv2 显示图片,摄像头的实例

    pyqt5 使用cv2 显示图片,摄像头的实例

    今天小编就为大家分享一篇pyqt5 使用cv2 显示图片,摄像头的实例,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
    2019-06-06
  • python爬虫基础之urllib的使用

    python爬虫基础之urllib的使用

    这篇文章主要介绍了python爬虫基础之urllib的使用,帮助大家更好的理解和使用python,感兴趣的朋友可以了解下
    2020-12-12
  • python 重命名轴索引的方法

    python 重命名轴索引的方法

    今天小编就为大家分享一篇python 重命名轴索引的方法,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
    2018-11-11
  • Python的字符串示例讲解

    Python的字符串示例讲解

    本文重点讲解字符串的创建,字符串的定义和字符串的常见操作,对Python的字符串不熟悉的同学随小编一起学习吧!
    2021-04-04
  • Python使用CuPy模块实现高效数值计算

    Python使用CuPy模块实现高效数值计算

    CuPy是一个基于Python的GPU加速计算库,它提供了与NumPy相似的接口,可以在GPU上进行高效的数值计算,本文主要介绍一下CuPy的应用场景,并给出一些Python代码案例,需要的可以参考下
    2024-02-02
  • 批处理与python代码混合编程的方法

    批处理与python代码混合编程的方法

    这篇文章主要介绍了批处理与python代码混合编程的方法的相关资料,非常不错,具有参考价值,感兴趣的朋友一起学习吧
    2016-05-05
  • 基于Python制作打地鼠小游戏

    基于Python制作打地鼠小游戏

    这篇文章主要为大家介绍了如何利用Python实现打地鼠小游戏,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
    2022-03-03
  • 解决django的template中如果无法引用MEDIA_URL问题

    解决django的template中如果无法引用MEDIA_URL问题

    这篇文章主要介绍了解决django的template中如果无法引用MEDIA_URL问题,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
    2020-04-04
  • 给大家整理了19个pythonic的编程习惯(小结)

    给大家整理了19个pythonic的编程习惯(小结)

    这篇文章主要介绍了给大家整理了19个pythonic的编程习惯(小结),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
    2019-09-09
  • python 读txt文件,按‘,’分割每行数据操作

    python 读txt文件,按‘,’分割每行数据操作

    这篇文章主要介绍了python 读txt文件,按‘,’分割每行数据操作,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
    2020-07-07

最新评论