JSON and regular expressions: Python Programming (Intermediate)

JSON: data as text

JSON (JavaScript Object Notation) is a way to write data as plain text. It looks almost like Python dicts and lists. When a weather app or a payment app talks to its server, the data usually travels as JSON.

python

import json
order = {"id": 101, "item": "Masala Dosa", "paid": True, "note": None}
text = json.dumps(order)
print(text)
print(type(text))
back = json.loads(text)
print(back["item"])

dumps: Python to JSON text. loads: JSON text to Python. Look at true and null.

Think of it like this: JSON is like a courier parcel. dumps packs your Python data into a flat box of text that any courier (any language: JavaScript, Java, Go) can carry. loads unpacks the box at the other end into Python data again.

Python	JSON
dict	object { }
list, tuple	array [ ]
str	string (always double quotes)
int, float	number
True / False	true / false
None	null

python

import json
student = {"name": "Asha", "marks": [82, 91], "scholarship": False}
print(json.dumps(student, indent=2))
print(json.dumps({"b": 1, "a": 2}, sort_keys=True))

indent makes it readable; sort_keys puts keys in A to Z order.

Careful: JSON is stricter than Python. Strings and keys need double quotes, keys are always strings, and a tuple comes back as a list. json.loads("{'name': 'Asha'}") fails with json.JSONDecodeError, which is a kind of ValueError, so you can catch it.

Regular expressions

A regular expression (regex) is a short pattern that describes text, such as "one or more digits". Write patterns as raw strings, r"...", so Python leaves every backslash for the re module to read.

Pattern	Matches
\d	one digit, 0 to 9
\w	one letter, digit or underscore
.	any one character
[6-9]	one character from 6 to 9
+	one or more of the thing before it
*	zero or more of the thing before it
?	zero or one: the thing before it is optional
{6}	exactly 6 of the thing before it
\b	a word boundary: the edge of a word or number

python

import re
text = "Call Ravi on 9876543210 or the office on 080-22334455."
m = re.search(r"[6-9]\d{9}", text)
print(m.group())
print(re.findall(r"\d+", text))
print(re.sub(r"\d", "X", "PIN 560001"))

search finds the first match, findall finds all, sub replaces.

re.search looks for the pattern anywhere and gives a match object (or None). re.findall returns a list of every match. re.sub replaces each match. re.fullmatch checks that the whole text fits the pattern, which is what you want for checking a form field.

python

import re
def valid_mobile(s):
    return re.fullmatch(r"[6-9]\d{9}", s) is not None

def valid_pin(s):
    return re.fullmatch(r"[1-9]\d{5}", s) is not None

print(valid_mobile("9876543210"), valid_mobile("1234567890"))
print(valid_pin("560001"), valid_pin("056001"))

Indian mobiles: 10 digits starting 6 to 9. PIN codes: 6 digits, not starting with 0.

Tip: Build patterns slowly and test them on a few good and bad examples. A pattern that is too loose accepts junk; one that is too strict rejects real numbers written with a +91 or spaces, so clean the input first.

JSON and regular expressions

Explanation

JSON: data as text

Regular expressions

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In real life: Cleaning a contact list for an SMS reminder

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