Python JSON lồng nhau
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JSON, hoặc Ký hiệu đối tượng JavaScript, là định dạng dựa trên văn bản mà con người có thể đọc được để trao đổi dữ liệu giữa máy chủ và ứng dụng web, như một giải pháp thay thế cho XML Show
API truy vấn bằng Python với đầu ra JSONTheo dõi blog trước đây của tôi có đề cập đến Rapidapi. com là một trong những nguồn dữ liệu để trích xuất dữ liệu OHLC (Mở, Cao, Thấp và Đóng giá) cho tiền điện tử, hãy xem cách truy vấn API trong Python Chúng tôi có thể sử dụng gói yêu cầu cho mục đích này. Để lấy dữ liệu dưới dạng đầu ra JSON, chúng tôi gọi yêu cầu **. lấy phương thức **. Biến phản hồi trong mã bên dưới, lưu trữ các giá trị được trả về và đầu ra JSON được phân tích cú pháp bằng phương thức json()  Yêu cầu. phương thức get có các giá trị tham số sau. yêu cầu. get(url, params={key. giá trị}, đối số) Tài liệu chi tiết có thể xem tại đây. Yêu cầu Python nhận Phương thức *❮ Mô-đun yêu cầu Tạo yêu cầu tới một trang web và trả lại mã trạng thái. yêu cầu nhập x =…*www. w3schools. com Nested-JSON là gì?Đối tượng JSON là tập hợp các cặp khóa và giá trị Ví dụ: { 'dữ liệu'. [ {'screen_ID’. '63', trong trường hợp này là 'data' và 'screen_ID' là khóa và '63' là giá trị. Khóa phải là chuỗi và giá trị phải là loại dữ liệu JSON hợp lệ (chuỗi, số, đối tượng, mảng, boolean hoặc null) Vậy Nested-JSON là gì? Yêu cầu API tới rapidapi. com tạo đầu ra JSON lồng nhau với các mảng Javascript, có cấu trúc như sau Đầu ra JSON cho Pandas DataframeMỗi đối tượng JSON lồng nhau có một đường dẫn truy cập duy nhất. Để lấy các khóa cấp một, chúng ta có thể sử dụng hàm json. phương thức phím ( ). Trong trường hợp này, nó trả về 'dữ liệu' là khóa cấp đầu tiên và có thể được nhìn thấy từ hình ảnh trên của đầu ra JSON **pd. json_normalize ** là một chức năng của gấu trúc có ích trong việc làm phẳng đầu ra JSON thành một dữ liệu có thể truy cập được. Vì vậy, sử dụng khóa cấp đầu tiên ở định dạng mã sau đây sẽ trả về một dữ liệu có thể truy cập được như bên dưới Tuyên bố vấn đề của chúng tôi ở đây là trích xuất các khóa và giá trị tương ứng của 'màu', 'ngày', 'cao', 'thấp', 'mở', 'perc_chg’, 'price', 'vol' sang định dạng bảng dữ liệu. Các giá trị này được chứa trong 'screen_data. data’( Để truy cập các trường lồng nhau, hãy nối các tên trường với một. (dấu chấm) làm dấu phân cách) của df, khung dữ liệu, sử dụng mã bên dưới, chúng tôi trích xuất cột này thành một dữ liệu có thể truy cập được Nhưng ở đây tất cả các giá trị được lưu trữ dưới dạng danh sách. Để chuyển đổi từng phần tử của danh sách này thành một hàng trong khung dữ liệu, chúng ta có thể sử dụng pd. khung dữ liệu. nổ tung. Điều này trả về các danh sách được phân tách dưới dạng các hàng của các cột tập hợp con; Bây giờ chúng ta có một từ điển trong mỗi hàng của dt có thể truy cập được, chúng ta có thể sử dụng pandas. Sê-ri để chuyển đổi từ điển sang định dạng sê-ri Pandas và áp dụng ** gấu trúc. Dataframe** để tiếp tục chuyển đổi nó thành loại dataframe “Việc trích xuất dữ liệu thông qua các yêu cầu API và quét web đặc biệt hữu ích để phân tích hoặc tự động hóa dịch vụ dữ liệu trên các ứng dụng dựa trên web. ” Bản thân định dạng JSON đã tương thích và các chức năng gấu trúc bổ sung thêm tính linh hoạt trong việc phân tích cú pháp và khả năng đọc của dữ liệu này thân thiện hơn với người dùng Như bạn có thể thấy trong ví dụ, một cặp khóa-giá trị duy nhất được phân tách bằng dấu hai chấm (. ) trong khi mỗi cặp khóa-giá trị được phân tách bằng dấu phẩy (,). Ở đây, “tên”, “hồ sơ”, “tuổi” và “vị trí” là các trường chính trong khi các giá trị tương ứng lần lượt là “Amit Pathak“, “Kỹ sư phần mềm“, “24”, “London, UK” JSON lồng nhau là một cấu trúc trong đó giá trị cho một hoặc nhiều trường có thể là một định dạng JSON khác. Ví dụ: hãy làm theo ví dụ dưới đây mà chúng tôi sẽ sử dụng để chuyển đổi sang định dạng CSV Một ví dụ về tệp JSON lồng nhau Một ví dụ JSON lồng nhau Trong ví dụ trên, trường khóa “bài viết” có giá trị là một định dạng JSON khác. JSON hỗ trợ nhiều tổ để tạo các tệp JSON phức tạp nếu được yêu cầu Chuyển đổi JSON sang CSV lồng nhauCông việc của chúng ta là convert file JSON sang định dạng CSV. Có thể có nhiều lý do tại sao chúng ta cần thực hiện chuyển đổi này. CSV dễ đọc khi được mở trong ứng dụng GUI bảng tính như Google Trang tính hoặc MS Excel. Chúng rất dễ làm việc với nhiệm vụ Phân tích dữ liệu. Đây cũng là định dạng được chấp nhận rộng rãi khi làm việc với dữ liệu dạng bảng vì nó dễ xem đối với con người, không giống như định dạng JSON Tiếp cận
Tệp đã sử dụng. mạo từ. tập tin json {
"article_id": 3214507,
"article_link": "http://sample.link",
"published_on": "17-Sep-2020",
"source": "moneycontrol",
"article": {
"title": "IT stocks to see a jump this month",
"category": "finance",
"image": "http://sample.img",
"sentiment": "neutral"
}
}Ví dụ. Chuyển đổi JSON sang CSV con trăn________ 195 ________ 196 {
"details": [
{
"id": "STU001",
"name": "Amit Pathak",
"age": 24,
"results": {
"school": 85,
"high_school": 75,
"graduation": 70
},
"education": {
"graduation": {
"major": "Computers",
"minor": "Sociology"
}
}
},
{
"id": "STU002",
"name": "Yash Kotian",
"age": 32,
"results": {
"school": 80,
"high_school": 58,
"graduation": 49
},
"education": {
"graduation": {
"major": "Biology",
"minor": "Chemistry"
}
}
},
{
"id": "STU003",
"name": "Aanchal Singh",
"age": 28,
"results": {
"school": 90,
"high_school": 70,
"graduation":65
},
"education": {
"graduation": {
"major": "Art",
"minor": "IT"
}
}
},
{
"id": "STU004",
"name": "Juhi Vadia",
"age": 23,
"results": {
"school": 95,
"high_school": 89,
"graduation": 83
},
"education": {
"graduation": {
"major": "IT",
"minor": "Social"
}
}
}
]
}97{
"details": [
{
"id": "STU001",
"name": "Amit Pathak",
"age": 24,
"results": {
"school": 85,
"high_school": 75,
"graduation": 70
},
"education": {
"graduation": {
"major": "Computers",
"minor": "Sociology"
}
}
},
{
"id": "STU002",
"name": "Yash Kotian",
"age": 32,
"results": {
"school": 80,
"high_school": 58,
"graduation": 49
},
"education": {
"graduation": {
"major": "Biology",
"minor": "Chemistry"
}
}
},
{
"id": "STU003",
"name": "Aanchal Singh",
"age": 28,
"results": {
"school": 90,
"high_school": 70,
"graduation":65
},
"education": {
"graduation": {
"major": "Art",
"minor": "IT"
}
}
},
{
"id": "STU004",
"name": "Juhi Vadia",
"age": 23,
"results": {
"school": 95,
"high_school": 89,
"graduation": 83
},
"education": {
"graduation": {
"major": "IT",
"minor": "Social"
}
}
}
]
}97{
"details": [
{
"id": "STU001",
"name": "Amit Pathak",
"age": 24,
"results": {
"school": 85,
"high_school": 75,
"graduation": 70
},
"education": {
"graduation": {
"major": "Computers",
"minor": "Sociology"
}
}
},
{
"id": "STU002",
"name": "Yash Kotian",
"age": 32,
"results": {
"school": 80,
"high_school": 58,
"graduation": 49
},
"education": {
"graduation": {
"major": "Biology",
"minor": "Chemistry"
}
}
},
{
"id": "STU003",
"name": "Aanchal Singh",
"age": 28,
"results": {
"school": 90,
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"graduation":65
},
"education": {
"graduation": {
"major": "Art",
"minor": "IT"
}
}
},
{
"id": "STU004",
"name": "Juhi Vadia",
"age": 23,
"results": {
"school": 95,
"high_school": 89,
"graduation": 83
},
"education": {
"graduation": {
"major": "IT",
"minor": "Social"
}
}
}
]
}99 {
"details": [
{
"id": "STU001",
"name": "Amit Pathak",
"age": 24,
"results": {
"school": 85,
"high_school": 75,
"graduation": 70
},
"education": {
"graduation": {
"major": "Computers",
"minor": "Sociology"
}
}
},
{
"id": "STU002",
"name": "Yash Kotian",
"age": 32,
"results": {
"school": 80,
"high_school": 58,
"graduation": 49
},
"education": {
"graduation": {
"major": "Biology",
"minor": "Chemistry"
}
}
},
{
"id": "STU003",
"name": "Aanchal Singh",
"age": 28,
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"school": 90,
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"graduation":65
},
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},
{
"id": "STU004",
"name": "Juhi Vadia",
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"graduation": 83
},
"education": {
"graduation": {
"major": "IT",
"minor": "Social"
}
}
}
]
}00_______201{
"details": [
{
"id": "STU001",
"name": "Amit Pathak",
"age": 24,
"results": {
"school": 85,
"high_school": 75,
"graduation": 70
},
"education": {
"graduation": {
"major": "Computers",
"minor": "Sociology"
}
}
},
{
"id": "STU002",
"name": "Yash Kotian",
"age": 32,
"results": {
"school": 80,
"high_school": 58,
"graduation": 49
},
"education": {
"graduation": {
"major": "Biology",
"minor": "Chemistry"
}
}
},
{
"id": "STU003",
"name": "Aanchal Singh",
"age": 28,
"results": {
"school": 90,
"high_school": 70,
"graduation":65
},
"education": {
"graduation": {
"major": "Art",
"minor": "IT"
}
}
},
{
"id": "STU004",
"name": "Juhi Vadia",
"age": 23,
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},
"education": {
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}
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]
}02{
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{
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}
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{
"id": "STU004",
"name": "Juhi Vadia",
"age": 23,
"results": {
"school": 95,
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},
"education": {
"graduation": {
"major": "IT",
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}
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}
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}0{
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{
"id": "STU001",
"name": "Amit Pathak",
"age": 24,
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"school": 85,
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},
"education": {
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},
{
"id": "STU004",
"name": "Juhi Vadia",
"age": 23,
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"school": 95,
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},
"education": {
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}1{
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{
"id": "STU001",
"name": "Amit Pathak",
"age": 24,
"results": {
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"education": {
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{
"id": "STU002",
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Điều tương tự cũng có thể đạt được thông qua việc sử dụng thư viện Pandas Python. Pandas là một thư viện python nguồn miễn phí được sử dụng để thao tác và phân tích dữ liệu. Nó thực hiện các hoạt động bằng cách chuyển đổi dữ liệu thành pandas. Định dạng khung dữ liệu. Nó cung cấp rất nhiều chức năng và hoạt động có thể được thực hiện trên khung dữ liệu Tiếp cận
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}5____19545đầu ra đầu ra bảng điều khiển python cho Code Block 2 Đầu ra CSV cho Code Block 2
Hai ví dụ trên là tốt khi chúng ta có một mức lồng duy nhất cho JSON nhưng khi mức lồng nhau tăng lên và có nhiều bản ghi hơn, các mã trên yêu cầu chỉnh sửa nhiều hơn. Chúng ta có thể xử lý JSON như vậy một cách dễ dàng bằng thư viện gấu trúc. Hãy để chúng tôi xem làm thế nào Chuyển đổi N-nested JSON sang CSVBất kỳ số lượng lồng và bản ghi nào trong JSON đều có thể được xử lý bằng mã tối thiểu bằng cách sử dụng phương thức “json_normalize()” trong gấu trúc. cú pháp
Tập tin đang sử dụng. chi tiết. tập tin json {
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}Ở đây, khóa "chi tiết" bao gồm một mảng gồm 4 phần tử, trong đó mỗi phần tử chứa 3 cấp đối tượng JSON lồng nhau. Khóa "chính" và "phụ" trong mỗi đối tượng này nằm trong một tổ cấp 3 Tiếp cận
Ví dụ. Chuyển đổi JSON n lồng nhau sang CSV con trăn________ 195 ________ 196 {
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}5____19545đầu ra
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