Tutorial Playlist
200 Lessons1. Introduction to Python
2. Features of Python
3. How to install python in windows
4. How to Install Python on macOS
5. Install Python on Linux
6. Hello World Program in Python
7. Python Variables
8. Global Variable in Python
9. Python Keywords and Identifiers
10. Assert Keyword in Python
11. Comments in Python
12. Escape Sequence in Python
13. Print In Python
14. Python-if-else-statement
15. Python for Loop
16. Nested for loop in Python
17. While Loop in Python
18. Python’s do-while Loop
19. Break in Python
20. Break Pass and Continue Statement in Python
21. Python Try Except
22. Data Types in Python
23. Float in Python
24. String Methods Python
25. List in Python
26. List Methods in Python
27. Tuples in Python
28. Dictionary in Python
29. Set in Python
30. Operators in Python
31. Boolean Operators in Python
32. Arithmetic Operators in Python
33. Assignment Operator in Python
34. Bitwise operators in Python
35. Identity Operator in Python
36. Operator Precedence in Python
37. Functions in Python
38. Lambda and Anonymous Function in Python
39. Range Function in Python
40. len() Function in Python
41. How to Use Lambda Functions in Python?
42. Random Function in Python
43. Python __init__() Function
44. String Split function in Python
45. Round function in Python
46. Find Function in Python
47. How to Call a Function in Python?
48. Python Functions Scope
49. Method Overloading in Python
50. Method Overriding in Python
51. Static Method in Python
52. Python List Index Method
53. Python Modules
54. Math Module in Python
55. Module and Package in Python
56. OS module in Python
57. Python Packages
58. OOPs Concepts in Python
59. Class in Python
60. Abstract Class in Python
61. Object in Python
62. Constructor in Python
63. Inheritance in Python
64. Multiple Inheritance in Python
65. Encapsulation in Python
66. Data Abstraction in Python
67. Opening and closing files in Python
68. How to open JSON file in Python
69. Read CSV Files in Python
70. How to Read a File in Python
71. How to Open a File in Python?
72. Python Write to File
73. JSON Python
74. Python JSON – How to Convert a String to JSON
Now Reading
75. Python JSON Encoding and Decoding
76. Exception Handling in Python
77. Recursion in Python
78. Python Decorators
79. Python Threading
80. Multithreading in Python
81. Multiprocеssing in Python
82. Python Regular Expressions
83. Enumerate() in Python
84. Map in Python
85. Filter in Python
86. Eval in Python
87. Difference Between List, Tuple, Set, and Dictionary in Python
88. List to String in Python
89. Linked List in Python
90. Length of list in Python
91. Reverse a List in Python
92. Python List remove() Method
93. How to Add Elements in a List in Python
94. How to Reverse a List in Python?
95. Difference Between List and Tuple in Python
96. List Slicing in Python
97. Sort in Python
98. Merge Sort in Python
99. Selection Sort in Python
100. Sort Array in Python
101. Sort Dictionary by Value in Python
102. Datetime Python
103. Random Number in Python
104. 2D Array in Python
105. Abs in Python
106. Advantages of Python
107. Anagram Program in Python
108. Append in Python
109. Applications of Python
110. Armstrong Number in Python
111. Assert in Python
112. Binary Search in Python
113. Binary to Decimal in Python
114. Bool in Python
115. Calculator Program in Python
116. chr in Python
117. Control Flow Statements in Python
118. Convert String to Datetime Python
119. Count in python
120. Counter in Python
121. Data Visualization in Python
122. Datetime in Python
123. Extend in Python
124. F-string in Python
125. Fibonacci Series in Python
126. Format in Python
127. GCD of Two Numbers in Python
128. How to Become a Python Developer
129. How to Run Python Program
130. In Which Year Was the Python Language Developed?
131. Indentation in Python
132. Index in Python
133. Interface in Python
134. Is Python Case Sensitive?
135. Isalpha in Python
136. Isinstance() in Python
137. Iterator in Python
138. Join in Python
139. Leap Year Program in Python
140. Lexicographical Order in Python
141. Literals in Python
142. Matplotlib
143. Matrix Multiplication in Python
144. Memory Management in Python
145. Modulus in Python
146. Mutable and Immutable in Python
147. Namespace and Scope in Python
148. OpenCV Python
149. Operator Overloading in Python
150. ord in Python
151. Palindrome in Python
152. Pass in Python
153. Pattern Program in Python
154. Perfect Number in Python
155. Permutation and Combination in Python
156. Prime Number Program in Python
157. Python Arrays
158. Python Automation Projects Ideas
159. Python Frameworks
160. Python Graphical User Interface GUI
161. Python IDE
162. Python input and output
163. Python Installation on Windows
164. Python Object-Oriented Programming
165. Python PIP
166. Python Seaborn
167. Python Slicing
168. type() function in Python
169. Queue in Python
170. Replace in Python
171. Reverse a Number in Python
172. Reverse a string in Python
173. Reverse String in Python
174. Stack in Python
175. scikit-learn
176. Selenium with Python
177. Self in Python
178. Sleep in Python
179. Speech Recognition in Python
180. Split in Python
181. Square Root in Python
182. String Comparison in Python
183. String Formatting in Python
184. String Slicing in Python
185. Strip in Python
186. Subprocess in Python
187. Substring in Python
188. Sum of Digits of a Number in Python
189. Sum of n Natural Numbers in Python
190. Sum of Prime Numbers in Python
191. Switch Case in Python
192. Python Program to Transpose a Matrix
193. Type Casting in Python
194. What are Lists in Python?
195. Ways to Define a Block of Code
196. What is Pygame
197. Why Python is Interpreted Language?
198. XOR in Python
199. Yield in Python
200. Zip in Python
In the data-driven landscape of today's technological world, ensuring seamless data interoperability is pivotal. Python, being a versatile and powerful programming language, has native functionalities to handle such conversions efficiently. One such conversion, which often stands crucial for developers and data scientists alike, is the transformation of a string to JSON Python format. In this tutorial, we shall unravel the intricacies of this process, focusing primarily on encoding and decoding methods tailored for professionals.
JSON, an acronym for JavaScript Object Notation, serves as a lightweight data-interchange format that is both human-readable and easy for machines to parse and generate. With built-in libraries, one can smoothly and reciprocally convert string to JSON Python. This tutorial is meticulously designed to walk you through encoding (serializing) and decoding (deserializing) processes in Python concerning JSON.
Encoding, often referred to as serializing, is the methodological process of converting a Python object into a JSON string. This ensures data is in a format suitable for easy sharing or storage. The most prevalent method in Python for encoding is json.dumps(). This function is responsible for serializing a Python object into a JSON-formatted string.
Before serializing, one must ascertain that the Python object is JSON serializable. Not all Python objects can be directly converted. Using JSON-formatted strings offers advantages such as being lightweight and easy to share, especially beneficial for API interactions and web-based applications.
Here is an example of encoding JSON:
import json
# Create a Python dictionary
data = {
"name": "Alice",
"age": 25,
"city": "Los Angeles"
}
# Encode the dictionary into a JSON-formatted string
json_str = json.dumps(data)
# Print the JSON string
print(json_str)
In the above example, we first import the json module, which provides functions for working with JSON data. Then, we create a Python dictionary called data containing key-value pairs. This is the data structure we want to encode into JSON.
We use the json.dumps(data) function to encode the data dictionary into a JSON-formatted string. This function serializes the Python data into a string that follows the JSON format. Finally, we print the json_str, which contains the JSON representation of the data dictionary.
Decoding, often termed deserializing, pertains to the conversion of a JSON string back into its original Python object form. This enables the Python program to process or read the encoded data. To achieve this reverse process, the json.loads Python function is your go-to. This function decodes the JSON string to render the original Python object.
While decoding, it's imperative to ensure the JSON string's correctness. Any malformation or error in the string can trigger exceptions. Deserializing helps in reconstituting stored or transmitted data, thereby ensuring the data's utility across different systems or after temporal storage.
Here is an example of decoding JSON:
import json
# JSON-formatted string
json_str = '{"name": "Alice", "age": 25, "city": "Los Angeles"}'
# Decode the JSON string into a Python dictionary
data = json.loads(json_str)
# Access values in the Python dictionary
name = data["name"]
age = data["age"]
city = data["city"]
# Print the decoded values
print("Name:", name)
print("Age:", age)
print("City:", city)
In the above code, we again import the json module and define a JSON-formatted string json_str that represents a dictionary in JSON format. We use the json.loads(json_str) function to decode the JSON string into a Python dictionary, and we store the result in the data variable.
We access individual values in the data dictionary using the keys "name," "age," and "city." Finally, we print the decoded values, which were originally stored in the JSON string.
json.loads() is a method provided by the json module in Python, and it stands for "load string." This method is specifically designed for parsing JSON strings and converting them into Python objects, usually dictionaries. It ensures that the JSON string is properly formatted and safe to parse.
Here is an example:
import json
# JSON string
json_str = '{"name": "Moon", "age": 28, "city": "Delhi"}'
# Convert JSON string to a Python dictionary
json_obj = json.loads(json_str)
# Access values in the JSON object
print("Name:", json_obj["name"])
print("Age:", json_obj["age"])
print("City:", json_obj["city"])
In the above code, we first import the json module, which as we already know provides the necessary functions for working with JSON data. We then define a JSON-formatted string json_str containing key-value pairs. We use json.loads(json_str) to parse the JSON string and convert it into a Python dictionary called json_obj. Finally, we access and print values from the json_obj dictionary using the keys "name," "age," and "city."
eval() is a built-in Python function that can evaluate a Python expression from a string.
You can use it to evaluate JSON-like strings, but it can be dangerous if used with untrusted input because it can execute arbitrary code.
Here is an example:
# JSON-like string
json_str = '{"name": "Moon", "age": 28, "city": "Kolkata"}'
# Convert JSON-like string to a Python dictionary using eval()
json_obj = eval(json_str)
# Access values in the dictionary
print("Name:", json_obj["name"])
print("Age:", json_obj["age"])
print("City:", json_obj["city"])
In the above example, we again define a JSON-like string json_str containing key-value pairs (similar to JSON format). Then, we use eval(json_str) to evaluate the string as a Python expression, effectively converting it into a Python dictionary called json_obj.
Like the first program, we again access and print values from the json_obj dictionary using the keys "name," "age," and "city."
ast.literal_eval() is part of the ast (Abstract Syntax Tree) module and is a safer alternative to eval(). It only evaluates literals and literal structures, so it's safe to use with untrusted input.
import ast
# JSON-like string
json_str = '{"name": "John", "age": 30, "city": "New York"}'
# Convert JSON-like string to a Python dictionary using ast.literal_eval()
json_obj = ast.literal_eval(json_str)
# Access values in the dictionary
print("Name:", json_obj["name"])
print("Age:", json_obj["age"])
print("City:", json_obj["city"])
In this example, we first import the ast module, which provides the ast.literal_eval() method for safe evaluation of literals. We define a JSON-like string json_str again containing key-value pairs. We use ast.literal_eval(json_str) to safely evaluate the string as a literal expression, resulting in a Python dictionary called json_obj.
Like the programs before, we access and print values from the json_obj dictionary using the keys "name," "age," and "city." in the same manner.
The json module in Python provides json.dumps() to encode a Python object (usually a dictionary) into a JSON string, and json.loads() to decode a JSON string back into a Python object. This is a common way to work with JSON data in Python when interacting with web APIs or handling configuration files.
Here is an example:
import json
# Python dictionary
data = {
"name": "Alice",
"age": 25,
"city": "Los Angeles"
}
# Encode the dictionary to a JSON string
json_str = json.dumps(data)
# Print the JSON string
print("JSON String:", json_str)
# Decode the JSON string back to a Python dictionary
decoded_data = json.loads(json_str)
# Access values in the decoded dictionary
print("Name:", decoded_data["name"])
print("Age:", decoded_data["age"])
print("City:", decoded_data["city"])
In the code above, we again import the json module for working with JSON data. We then define a Python dictionary called data containing key-value pairs. We use json.dumps(data) to encode the dictionary into a JSON-formatted string called json_str.
We print the JSON string, which represents the serialized version of the dictionary. Then we use json.loads(json_str) to decode the JSON string back into a Python dictionary called decoded_data.
Finally, we access and print values from the decoded_data dictionary using the keys "name," "age," and "city."
Mastering the art of encoding and decoding between strings and JSON in Python is fundamental, particularly for those vested in web services, data analytics, or any domain that relies heavily on data interchange. As we've explained, Python offers succinct and efficient methods for these transformations.
As the tech industry continually evolves, equipping oneself with such niche skills becomes indispensable. For professionals keen on further amplifying their coding prowess, upGrad provides an ensemble of upskilling courses designed in tandem with current industry requisites.
1. What entails the process of encoding in Python?
Encoding or serializing involves converting a Python object into a JSON formatted string using methods like json.dumps Python.
2. How can one revert a JSON string back to its Python form?
The json.loads function in Python facilitates the decoding or deserialization of a JSON string back to its Python object form.
3. Is direct conversion of list to JSON Python feasible?
Indeed, using the json.dumps method, Python lists can be serialized directly into JSON format.
4. Are there online platforms for string to JSON file Python conversions?
Numerous online platforms cater to string to JSON online conversions, but it's essential to remain cautious regarding data privacy when using third-party services.
5. How do you differentiate between creating a JSON string and saving it as a file in Python?
While json.dumps results in a JSON formatted string, to inscribe this string as a file in Python, one requires additional file writing functions.
PAVAN VADAPALLI
Director of Engineering
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working …Read More
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upGrad does not grant credit; credits are granted, accepted or transferred at the sole discretion of the relevant educational institution offering the diploma or degree. We advise you to enquire further regarding the suitability of this program for your academic, professional requirements and job prospects before enr...