激情久久久_欧美视频区_成人av免费_不卡视频一二三区_欧美精品在欧美一区二区少妇_欧美一区二区三区的

腳本之家,腳本語言編程技術及教程分享平臺!
分類導航

Python|VBS|Ruby|Lua|perl|VBA|Golang|PowerShell|Erlang|autoit|Dos|bat|

服務器之家 - 腳本之家 - Python - 對pandas中to_dict的用法詳解

對pandas中to_dict的用法詳解

2021-03-01 00:07積跬步___至千里 Python

今天小編就為大家分享一篇對pandas中to_dict的用法詳解,具有很好的參考價值,希望對大家有所幫助。一起跟隨小編過來看看吧

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

?
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
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

?
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
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、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

?
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
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、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)

?
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
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、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']

?
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
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、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]

?
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
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、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

?
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
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

延伸 · 閱讀

精彩推薦
主站蜘蛛池模板: 草草久久久 | 精品69人人人人 | 国产喷白浆10p | 一级毛片手机在线观看 | av在线网站观看 | 成人免费精品视频 | 宅男噜噜噜66国产免费观看 | 91中文在线观看 | 久久精品探花 | 亚洲日本高清 | 日本不卡一区二区三区在线 | 免费看一级毛片欧美 | 久在线观看福利视频69 | 成人毛片网 | 97久久精品一区二区三区观看 | 精品一区二区三区在线观看视频 | 欧美亚洲黄色片 | 美女av在线免费观看 | 免费欧美| 97伦理| 亚洲成人福利电影 | 日韩在线激情 | 人人舔人人舔 | 91福利影视| 久久久久久久久久91 | 天使萌一区二区三区免费观看 | 27xxoo无遮挡动态视频 | 日韩精品免费一区二区三区 | 九九热久久免费视频 | 青青草成人影视 | 欧美视频国产 | 视频二区国产 | 娇喘在线 | 天堂二区| 日本黄肉网站在线观看 | 久久综合一区二区 | 伊人在线 | 国产一级做a | 国产成人精品一区在线播放 | 成年人免费视频大全 | 久草在线综合 |