簡介：pandas 中的to_dict 可以對DataFrame類型的數(shù)據(jù)進行轉(zhuǎn)換

可以選擇六種的轉(zhuǎn)換類型，分別對應(yīng)于參數(shù) ‘dict', ‘list', ‘series', ‘split', ‘records', ‘index'，下面逐一介紹每種的用法

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、選擇參數(shù)orient='dict'

dict也是默認(rèn)的參數(shù)，下面的data數(shù)據(jù)類型為DataFrame結(jié)構(gòu), 會形成 {column -> {index -> value}}這樣的結(jié)構(gòu)的字典，可以看成是一種雙重字典結(jié)構(gòu)

- 單獨提取每列的值及其索引，然后組合成一個字典

- 再將上述的列屬性作為關(guān)鍵字（key），值（values）為上述的字典

查詢方式為 ：data_dict[key1][key2]

- data_dict 為參數(shù)選擇orient='dict'時的數(shù)據(jù)名

- key1 為列屬性的鍵值（外層）

- key2 為內(nèi)層字典對應(yīng)的鍵值

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'}}