Formatting the Result
Indentation
Indentation helps in making JSON data more readable by adding spaces to show the hierarchy and structure of the data.
Using the indent Parameter:
The indent parameter in json.dumps() controls the number of spaces used for indentation.
- No Indentation (default): JSON output is in a compact form without extra spaces or line breaks.
- With Indentation: Adds newlines and spaces to format the JSON output.
Example:
import json
data = {
"name": "Alice",
"age": 30,
"city": "Paris",
"address": {
"street": "123 Main St",
"postal_code": "75000"
},
"hobbies": ["reading", "cycling"]
}
# Convert to JSON with indentation
json_string = json.dumps(data, indent=4)
print(json_string)
"""
Output:
{
"name": "Alice",
"age": 30,
"city": "Paris",
"address": {
"street": "123 Main St",
"postal_code": "75000"
},
"hobbies": [
"reading",
"cycling"
]
}
"""
Explanation:
- indent=4: Adds 4 spaces of indentation for each level of nesting.
- Indentation makes the JSON structure clear and easier to read.
Separators
The separators parameter allows you to customize the characters used to separate items and key-value pairs in the JSON output.
Using the separators Parameter:
- (item_separator, key_separator): Tuple that defines how items and key-value pairs are separated.
Example:
import json
data = {
"name": "Alice",
"age": 30,
"city": "Paris"
}
# Convert to JSON with custom separators
json_string = json.dumps(data, separators=(',', ': '))
print(json_string)
"""
Output:
{"name": "Alice", "age": 30, "city": "Paris"}
"""
Explanation:
- separators=(‘,’, ‘: ‘): Uses a comma followed by a space to separate items and a colon followed by a space to separate keys and values.
- Customizing separators can improve the readability or compactness of the JSON output.
Sorting Keys
The sort_keys parameter sorts the keys of JSON objects in alphabetical order.
Using the sort_keys Parameter:
Example:
import json
data = {
"name": "Alice",
"city": "Paris",
"age": 30
}
# Convert to JSON with sorted keys
json_string = json.dumps(data, sort_keys=True, indent=2)
print(json_string)
"""
Output:
{
"age": 30,
"city": "Paris",
"name": "Alice"
}
"""
Explanation:
- sort_keys=True: Sorts dictionary keys in alphabetical order.
- Useful for consistent and predictable JSON output, especially when debugging or comparing data.
Ensuring ASCII Characters
By default, json.dumps() escapes non-ASCII characters. You can control this behavior with the ensure_ascii parameter.
Using the ensure_ascii Parameter:
- ensure_ascii=True: Escapes non-ASCII characters, ensuring that the output is ASCII-only.
- ensure_ascii=False: Allows non-ASCII characters to be included in the output as-is.
Example:
import json
data = {
"name": "Alice",
"city": "München" # Non-ASCII character
}
# Convert to JSON with non-ASCII characters escaped
json_string = json.dumps(data, ensure_ascii=True)
print(json_string) # {"name": "Alice", "city": "München"}
"""
Output:
{"name": "Alice", "city": "München"}
"""
Explanation:
- ensure_ascii=True: Converts non-ASCII characters into escape sequences.
- ensure_ascii=False: Outputs non-ASCII characters directly, which is more human-readable but might not be suitable for all systems.
Compact vs. Pretty Printing
You can choose between compact JSON (no extra spaces) and pretty-printed JSON (with indentation and spaces).
Compact JSON:
import json
data = {
"name": "Alice",
"age": 30,
"city": "Paris"
}
# Compact JSON
json_string = json.dumps(data, separators=(',', ':'))
print(json_string)
"""
Output:
{"name":"Alice","age":30,"city":"Paris"}
"""
Pretty-Printed JSON:
import json
data = {
"name": "Alice",
"age": 30,
"city": "Paris"
}
# Pretty-printed JSON
json_string = json.dumps(data, indent=4, separators=(',', ': '))
print(json_string)
"""
Output:
{
"name": "Alice",
"age": 30,
"city": "Paris"
}
"""
Explanation:
- Compact JSON: Omits extra spaces to minimize size.
- Pretty-printed JSON: Includes indentation and spaces to improve readability.
Custom JSON Encoding
For custom data types or special formatting needs, you can use the default parameter to specify a function for encoding objects.
Example with Custom Encoder:
import json
from datetime import datetime
# Custom encoder function
def custom_encoder(obj):
if isinstance(obj, datetime):
return obj.isoformat()
raise TypeError("Type not serializable")
data = {
"name": "Alice",
"birthdate": datetime(1994, 7, 14)
}
# Convert to JSON with custom encoder
json_string = json.dumps(data, default=custom_encoder, indent=2)
print(json_string)
"""
Output:
{
"name": "Alice",
"birthdate": "1994-07-14T00:00:00"
}
"""
Explanation:
- default=custom_encoder: Uses the custom_encoder function to handle non-serializable objects, such as datetime.
- Allows for customized serialization of complex or non-standard data types.