pandas to_dict 的用法

冷不防 2023-10-07 11:56 6阅读 0赞

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\}\}  
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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'\}\}  
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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': \['male',  
'female',  
'male',  
'male',  
'male',  
'male',  
'female',  
'male',  
'male',  
'male'\]\}  
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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\}  
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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': \[1086, 12, 1036, 833, 1108, 562, 437, 663, 669, 507\]\}  
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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\[41\]:  
\[\{'age': 31.19418104265403,  
'embarked': 'UNKNOWN',  
'home.dest': 'UNKNOWN',  
'pclass': '3rd',  
'sex': 'male'\},  
\{'age': 31.19418104265403,  
'embarked': 'Cherbourg',  
'home.dest': 'Paris, France',  
'pclass': '1st',  
'sex': 'female'\},  
\{'age': 31.19418104265403,  
'embarked': 'UNKNOWN',  
'home.dest': 'UNKNOWN',  
'pclass': '3rd',  
'sex': 'male'\},  
\{'age': 32.0,  
'embarked': 'Southampton',  
'home.dest': 'Foresvik, Norway Portland, ND',  
'pclass': '3rd',  
'sex': 'male'\},  
\{'age': 31.19418104265403,  
'embarked': 'UNKNOWN',  
'home.dest': 'UNKNOWN',  
'pclass': '3rd',  
'sex': 'male'\},  
\{'age': 41.0,  
'embarked': 'Cherbourg',  
'home.dest': 'New York, NY',  
'pclass': '2nd',  
'sex': 'male'\},  
\{'age': 48.0,  
'embarked': 'Southampton',  
'home.dest': 'Somerset / Bernardsville, NJ',  
'pclass': '2nd',  
'sex': 'female'\},  
\{'age': 26.0,  
'embarked': 'Southampton',  
'home.dest': 'UNKNOWN',  
'pclass': '3rd',  
'sex': 'male'\},  
\{'age': 19.0,  
'embarked': 'Southampton',  
'home.dest': 'England',  
'pclass': '3rd',  
'sex': 'male'\},  
\{'age': 31.19418104265403,  
'embarked': 'Southampton',  
'home.dest': 'Petworth, Sussex',  
'pclass': '2nd',  
'sex': 'male'\}\]  
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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'\}\}  
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