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There is no single Python function for parsing every string. Choose the method that matches the input: use split() or partition() for a simple delimiter, a type constructor for numeric text, and a format-specific parser such as json.loads() for structured data. Use regular expressions for pattern-shaped text and shlex.split() only for simple Unix-shell-like tokenization.
Choose a parsing method by input format
| Input | Method | Typical result |
|---|---|---|
| Text with a known delimiter | str.split() or str.partition() |
List of fields or a three-part tuple |
| Whitespace-separated words | str.split() with no argument |
List of words |
| Numeric text | int() or float() |
Integer or floating-point value |
| JSON text | json.loads() |
Python value such as a dictionary, list, string, number, boolean, or None |
| Text matching a pattern | re |
Matches or captured groups |
| Quoted Unix-shell-like tokens | shlex.split() |
List of tokens |
Use the parser for the actual format rather than stacking delimiter operations when input can contain quoting, nesting, or a defined grammar. The Python documentation describes the relevant behavior in its string methods reference, built-in functions reference, JSON documentation, regular-expression documentation, and shlex documentation.
Parse text separated by a delimiter
Use split() for all fields
When the delimiter is known and literal, pass it to split(). For example:
row = "Mira,42,engineer"
name, age, role = row.split(",")
print(name) # Mira
print(age) # 42
print(role) # engineerA specified separator splits at each occurrence by default. Repeated separators can produce empty fields: "a,,b".split(",") returns ["a", "", "b"]. If the number of fields is not what the format requires, unpacking raises ValueError; validate the input or catch that error when the string may be malformed.
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Use partition() when only the first separator matters
partition(sep) returns a three-item tuple: the text before the first separator, the separator itself, and the remaining text. The separator result tells you whether it was found.
text = "color=blue=green"
key, sep, value = text.partition("=")
if sep:
print(key) # color
print(value) # blue=green
else:
print("Missing '='")Unlike splitting on every occurrence, this keeps later separators in the remainder, which is useful for simple key-value strings.
Whitespace splitting is a different mode
Calling split() without an argument treats runs of whitespace as separators and omits empty fields at the edges:
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words = " red greentblue ".split()
print(words) # ['red', 'green', 'blue']This differs from split(" "), which uses one literal space and can create empty fields when spaces repeat or appear at the boundaries.
Trim boundary characters or remove an exact prefix
strip(chars) removes leading and trailing characters chosen from the supplied set; it does not remove that argument as one exact string. For an exact prefix or suffix, use removeprefix() or removesuffix() instead.
text = "...report.csv..."
print(text.strip(".")) # report.csv
print("prefix_report".removeprefix("prefix_")) # reportChoose the operation based on intent: trimming a set of boundary characters is not the same as removing a known boundary string.
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Convert numeric text into a number
Use int() or float() when the desired output is numeric, rather than leaving the value as a substring.
count = int("42")
ratio = float("3.14")
print(count + 1) # 43
print(ratio * 2) # 6.28Invalid numeric text raises ValueError. If the input comes from a user or external source, validate it or handle that exception at the point where you convert it.
Decode JSON with the JSON parser
For a string containing JSON, call json.loads(). It converts JSON syntax to corresponding Python values; it is not equivalent to splitting the text at commas or colons.
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import json
text = '{"active": true, "count": 3}'
record = json.loads(text)
print(record["active"]) # True
print(record["count"]) # 3Malformed JSON raises json.JSONDecodeError. Treat decoding as a validation boundary when input may be invalid. Python’s JSON documentation also warns that malicious JSON may consume considerable CPU and memory, so avoid parsing untrusted input without appropriate limits.
Use regular expressions for pattern-shaped text
Use the re module when the structure is naturally expressed as a pattern—for example, extracting a fixed identifier shape from a larger string. Raw strings are a practical way to write regex patterns because backslashes are not first interpreted as Python string escapes.
import re
text = "Order ID: AB-1234"
match = re.search(r"Order ID: ([A-Z]{2}-d{4})", text)
if match:
order_id = match.group(1)
else:
print("No order ID found")Regular expressions match patterns; they are not a substitute for a dedicated parser when the input has a richer grammar, such as nested structures.
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Tokenize simple Unix-shell-like text with shlex
Use shlex.split() when you need shell-like quote handling for a simple Unix-style command string:
import shlex
args = shlex.split('tool --label "two words"')
print(args) # ['tool', '--label', 'two words']This handles a limited shell-like syntax; it does not execute commands or parse every shell feature. It is not a portable Windows command-line parser. When launching a process, prefer passing an argument list to the process API rather than building a command string and treating shlex as a safety mechanism.
Handle malformed input at the boundary
Parsing is where assumptions about external text become concrete values. Decide which fields are required, check that separators or matches exist, and catch conversion or decoding errors where they can be handled meaningfully.
- For delimiter-separated fields, verify the expected field count before unpacking when input may vary.
- For
partition(), check whether the returned separator is empty. - For numeric conversion, handle
ValueErrorif the text is not guaranteed to be valid. - For JSON, handle
json.JSONDecodeErrorand consider resource limits for untrusted content. - For pattern extraction, account for a missing match before reading captured groups.
Documentation version context: the Python documentation reviewed for this guide surfaced Python 3.14.8 built-in and shlex references, Python 3.12.15 JSON documentation, and Python 3.11.17 regular-expression documentation. Check the documentation for the Python runtime you use when relying on version-specific behavior.
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