簡介：pandas 中的to_dict 可以對DataFrame類型的數據進行轉換
可以選擇六種的轉換類型，分別對應于參數 ‘dict', ‘list', ‘series', ‘split', ‘records', ‘index'，下面逐一介紹每種的用法
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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orient='dict'
dict也是默認的參數，下面的data數據類型為DataFrame結構, 會形成 {column -> {index -> value}}這樣的結構的字典，可以看成是一種雙重字典結構
- 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 為參數選擇orient='dict'時的數據名
- key1 為列屬性的鍵值（外層）
- key2 為內層字典對應的鍵值
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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、當關鍵字orient=' list' 時
和1中比較相似，只不過內層變成了一個列表，結構為{column -> [values]}
查詢方式為： data_list[keys][index]
data_list 為關鍵字orient='list' 時對應的數據名
keys 為列屬性的鍵值，如本例中的'age' , ‘embarked'等
index 為整型索引，從0開始到最后
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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、關鍵字參數orient='series'
形成結構{column -> Series(values)}
調用格式為：data_series[key1][key2]或data_dict[key1]
data_series 為數據對應的名字
key1 為列屬性的鍵值，如本例中的'age' , ‘embarked'等
key2 使用數據原始的索引（可選）
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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、關鍵字參數orient='split'
形成{index -> [index], columns -> [columns], data -> [values]}的結構，是將數據、索引、屬性名單獨脫離出來構成字典
調用方式有 data_split[‘index'],data_split[‘data'],data_split[‘columns']
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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、當關鍵字orient='records' 時
形成[{column -> value}, … , {column -> value}]的結構
整體構成一個列表，內層是將原始數據的每行提取出來形成字典
調用格式為data_records[index][key1]
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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、當關鍵字orient='index' 時
形成{index -> {column -> value}}的結構，調用格式正好和'dict' 對應的反過來，請讀者自己思考
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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'}} |
以上這篇對pandas中to_dict的用法詳解就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持服務器之家。
原文鏈接：https://blog.csdn.net/m0_37804518/article/details/78444110
