String Basics

Common Operations

s = "Hello, World!"

# Case
s.lower()          # "hello, world!"
s.upper()          # "HELLO, WORLD!"
s.title()          # "Hello, World!"

# Whitespace
s.strip()          # Remove leading/trailing
s.lstrip()         # Remove leading
s.rstrip()         # Remove trailing

# Search
s.find("World")    # 7 (index) or -1
s.index("World")   # 7 (raises if not found)
"World" in s       # True
s.count("o")       # 2

# Replace
s.replace("o", "0")  # "Hell0, W0rld!"

# Split/Join
s.split(", ")      # ["Hello", "World!"]
", ".join(["a", "b"])  # "a, b"

Parsing

Splitting Data

line = "name,email,age"
parts = line.split(",")  # ["name", "email", "age"]

# With limit
"a,b,c,d".split(",", 2)  # ["a", "b", "c,d"]

Key-Value Parsing

line = "key=value"
key, value = line.split("=", 1)

Multi-line

text = """line1
line2
line3"""
lines = text.splitlines()  # ["line1", "line2", "line3"]

Regular Expressions

Basic Patterns

import re

text = "Contact: john@example.com or call 555-1234"

# Find first match
match = re.search(r'\d{3}-\d{4}', text)
if match:
    print(match.group())  # "555-1234"

# Find all matches
emails = re.findall(r'[\w.-]+@[\w.-]+\.\w+', text)

# Replace
clean = re.sub(r'\d', 'X', text)

# Split on pattern
parts = re.split(r'\s+', text)

Common Patterns

r'\d+'           # One or more digits
r'\w+'           # Word characters
r'\s+'           # Whitespace
r'[a-z]+'        # Lowercase letters
r'^Start'        # Starts with
r'end
Extraction
Between Delimiters
text = "Hello [World] and [Python]"
matches = re.findall(r'\[(.*?)\]', text)
# ["World", "Python"]
Capture Groups
text = "Name: John, Age: 30"
match = re.search(r'Name: (\w+), Age: (\d+)', text)
if match:
    name = match.group(1)  # "John"
    age = match.group(2)   # "30"
Extract Numbers
text = "Order 123 has 5 items for $99.99"
numbers = re.findall(r'\d+\.?\d*', text)
# ["123", "5", "99.99"]
Transformation
Clean Whitespace
text = "  too   many   spaces  "
clean = " ".join(text.split())  # "too many spaces"
Remove Characters
# Remove non-alphanumeric
clean = re.sub(r'[^\w\s]', '', text)

# Remove digits
clean = re.sub(r'\d', '', text)
Normalize
# Lowercase and strip
normalized = text.lower().strip()

# Remove accents
import unicodedata
def remove_accents(s):
    return ''.join(
        c for c in unicodedata.normalize('NFD', s)
        if unicodedata.category(c) != 'Mn'
    )
Format Conversion
CSV to Dict
import csv
from io import StringIO

csv_text = "name,age\nAlice,30\nBob,25"
reader = csv.DictReader(StringIO(csv_text))
for row in reader:
    print(row)  # {"name": "Alice", "age": "30"}
JSON Parsing
import json

text = '{"name": "Alice", "age": 30}'
data = json.loads(text)

# Back to string
text = json.dumps(data, indent=2)
Key-Value File
def parse_env(text):
    result = {}
    for line in text.splitlines():
        line = line.strip()
        if line and '=' in line:
            key, value = line.split('=', 1)
            result[key] = value
    return result
Validation
Email
def is_valid_email(email):
    pattern = r'^[\w.-]+@[\w.-]+\.\w+
URL
def is_valid_url(url):
    pattern = r'^https?://[\w.-]+\.[a-z]{2,}'
    return bool(re.match(pattern, url, re.IGNORECASE))
Phone
def is_valid_phone(phone):
    pattern = r'^\d{3}[-.]?\d{3}[-.]?\d{4}
Template Processing
F-strings
name = "Alice"
f"Hello, {name}!"
Template String
from string import Template

t = Template("Hello, $name!")
result = t.substitute(name="Alice")
Format Method
"Hello, {name}!".format(name="Alice")
"{0} and {1}".format("Alice", "Bob")
Encoding
UTF-8
# Encode string to bytes
b = "Hello".encode('utf-8')

# Decode bytes to string
s = b.decode('utf-8')
Handle Errors
# Replace invalid characters
s = bytes_data.decode('utf-8', errors='replace')
Conclusion
Text processing essentials:
String methods for basic operations

Regex for pattern matching

Proper parsing for structured data

Validation for input checking


Master these for effective text handling.

Next: Web Scraping Basics - Extracting web data          # Ends with
r'a|b'           # a or b
Extraction
Between Delimiters
__CODE_BLOCK_6__
Capture Groups
__CODE_BLOCK_7__
Extract Numbers
__CODE_BLOCK_8__
Transformation
Clean Whitespace
__CODE_BLOCK_9__
Remove Characters
__CODE_BLOCK_10__
Normalize
__CODE_BLOCK_11__
Format Conversion
CSV to Dict
__CODE_BLOCK_12__
JSON Parsing
__CODE_BLOCK_13__
Key-Value File
__CODE_BLOCK_14__
Validation
Email
__CODE_BLOCK_15__
URL
__CODE_BLOCK_16__
Phone
__CODE_BLOCK_17__
Template Processing
F-strings
__CODE_BLOCK_18__
Template String
__CODE_BLOCK_19__
Format Method
__CODE_BLOCK_20__
Encoding
UTF-8
__CODE_BLOCK_21__
Handle Errors
__CODE_BLOCK_22__
Conclusion
Text processing essentials:
String methods for basic operations

Regex for pattern matching

Proper parsing for structured data

Validation for input checking


Master these for effective text handling.

Next: Web Scraping Basics - Extracting web data
    return bool(re.match(pattern, email))
URL
__CODE_BLOCK_16__
Phone
__CODE_BLOCK_17__
Template Processing
F-strings
__CODE_BLOCK_18__
Template String
__CODE_BLOCK_19__
Format Method
__CODE_BLOCK_20__
Encoding
UTF-8
__CODE_BLOCK_21__
Handle Errors
__CODE_BLOCK_22__
Conclusion
Text processing essentials:
String methods for basic operations

Regex for pattern matching

Proper parsing for structured data

Validation for input checking


Master these for effective text handling.

Next: Web Scraping Basics - Extracting web data          # Ends with
r'a|b'           # a or b

Extraction

Between Delimiters

__CODE_BLOCK_6__

Capture Groups

__CODE_BLOCK_7__

Extract Numbers

__CODE_BLOCK_8__

Transformation

Clean Whitespace

__CODE_BLOCK_9__

Remove Characters

__CODE_BLOCK_10__

Normalize

__CODE_BLOCK_11__

Format Conversion

CSV to Dict

__CODE_BLOCK_12__

JSON Parsing

__CODE_BLOCK_13__

Key-Value File

__CODE_BLOCK_14__

Validation

Email

__CODE_BLOCK_15__

URL

__CODE_BLOCK_16__

Phone

__CODE_BLOCK_17__

Template Processing

F-strings

__CODE_BLOCK_18__

Template String

__CODE_BLOCK_19__

Format Method

__CODE_BLOCK_20__

Encoding

UTF-8

__CODE_BLOCK_21__

Handle Errors

__CODE_BLOCK_22__

Conclusion

Text processing essentials:

String methods for basic operations

Regex for pattern matching

Proper parsing for structured data

Validation for input checking

Master these for effective text handling.

Next: Web Scraping Basics - Extracting web data

return bool(re.match(pattern, phone))

Template Processing

F-strings

__CODE_BLOCK_18__

Template String

__CODE_BLOCK_19__

Format Method

__CODE_BLOCK_20__

Encoding

UTF-8

__CODE_BLOCK_21__

Handle Errors

__CODE_BLOCK_22__

Conclusion

Text processing essentials:

String methods for basic operations

Regex for pattern matching

Proper parsing for structured data

Validation for input checking

Master these for effective text handling.

Next: Web Scraping Basics - Extracting web data

# Ends with r'a|b' # a or b

Extraction

Between Delimiters

__CODE_BLOCK_6__

Capture Groups

__CODE_BLOCK_7__

Extract Numbers

__CODE_BLOCK_8__

Transformation

Clean Whitespace

__CODE_BLOCK_9__

Remove Characters

__CODE_BLOCK_10__

Normalize

__CODE_BLOCK_11__

Format Conversion

CSV to Dict

__CODE_BLOCK_12__

JSON Parsing

__CODE_BLOCK_13__

Key-Value File

__CODE_BLOCK_14__

Validation

Email

__CODE_BLOCK_15__

URL

__CODE_BLOCK_16__

Phone

__CODE_BLOCK_17__

Template Processing

F-strings

__CODE_BLOCK_18__

Template String

__CODE_BLOCK_19__

Format Method

__CODE_BLOCK_20__

Encoding

UTF-8

__CODE_BLOCK_21__

Handle Errors

__CODE_BLOCK_22__

Conclusion

Text processing essentials:

String methods for basic operations

Regex for pattern matching

Proper parsing for structured data

Validation for input checking

Master these for effective text handling.

Next: Web Scraping Basics - Extracting web data

return bool(re.match(pattern, email))

URL

__CODE_BLOCK_16__

Phone

__CODE_BLOCK_17__

Template Processing

F-strings

__CODE_BLOCK_18__

Template String

__CODE_BLOCK_19__

Format Method

__CODE_BLOCK_20__

Encoding

UTF-8

__CODE_BLOCK_21__

Handle Errors

__CODE_BLOCK_22__

Conclusion

Text processing essentials:

String methods for basic operations

Regex for pattern matching

Proper parsing for structured data

Validation for input checking

Master these for effective text handling.

Next: Web Scraping Basics - Extracting web data

# Ends with r'a|b' # a or b

Extraction

Between Delimiters

__CODE_BLOCK_6__

Capture Groups

__CODE_BLOCK_7__

Extract Numbers

__CODE_BLOCK_8__

Transformation

Clean Whitespace

__CODE_BLOCK_9__

Remove Characters

__CODE_BLOCK_10__

Normalize

__CODE_BLOCK_11__

Format Conversion

CSV to Dict

__CODE_BLOCK_12__

JSON Parsing

__CODE_BLOCK_13__

Key-Value File

__CODE_BLOCK_14__

Validation

Email

__CODE_BLOCK_15__

URL

__CODE_BLOCK_16__

Phone

__CODE_BLOCK_17__

Template Processing

F-strings

__CODE_BLOCK_18__

Template String

__CODE_BLOCK_19__

Format Method

__CODE_BLOCK_20__

Encoding

UTF-8

__CODE_BLOCK_21__

Handle Errors

__CODE_BLOCK_22__

Conclusion

Text processing essentials:

String methods for basic operations

Regex for pattern matching

Proper parsing for structured data

Validation for input checking

Master these for effective text handling.

Next: Web Scraping Basics - Extracting web data