对pandas中to_dict的用法详解

技术  /  管理员 发布于 7年前   386

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 sex1086 3rd 31.194181 UNKNOWN   UNKNOWN male12 1st 31.194181 Cherbourg   Paris, France female1036 3rd 31.194181 UNKNOWN   UNKNOWN male833 3rd 32.000000 Southampton Foresvik, Norway Portland, ND male1108 3rd 31.194181 UNKNOWN   UNKNOWN male562 2nd 41.000000 Cherbourg   New York, NY male437 2nd 48.000000 Southampton Somerset / Bernardsville, NJ female663 3rd 26.000000 Southampton   UNKNOWN male669 3rd 19.000000 Southampton   England male507 2nd 31.194181 Southampton  Petworth, Sussex maleIn[10]: data_dict=data.to_dict(orient= 'dict')In[11]: data_dictOut[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_listOut[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_seriesOut[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_splitOut[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_recordsOut[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_indexOut[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。