How to convert string representation of list to a list

Diving into safe parsing

Routing Python literals via ast.literal_eval()

When you've got a string that looks like a python list, ast.literal_eval() is your best buddy. It knows Python literal structures like the alphabet and avoids functions or operators, lowering security risks. Here's how you utilize it:

import ast
mystring = "[1, 2, 3, 'four']"
# It's like listifying, but without wires and bolts!
mylist = ast.literal_eval(mystring.strip())
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Navigating JSON using json.loads()

For JSON formatted strings, or a string that slightly smells like JavaScript, json.loads() is your professional guide to convert that string to a python list. Complex data types, user inputs, no problem:

import json
json_string = '["one", 2, true, {"three": 4}]'
# JSON strings can now go on a Python vacation without jet lag!
new_list = json.loads(json_string)
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The tempting but dangerous eval()

New Python coders often get seduced by eval() due to its capabilities on parsing a wide range of Python expressions. However, remember that eval() is like a Muggle's Internet Explorer, easily fooled:

# Never use eval(), unless you like wild rides!
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The uncanny regex usage

A few brave coders even use regular expressions to extract list items. This might work in simple scenarios but can complicate easily, so handle with care:

import re
mystring = "['one', 'two', 'three']"
# It's like taking a stroll in regex park, watch your steps!
mylist = re.findall(r"'(.*?)'", mystring)
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Optimizing the Conversion Process

Tailoring parsing to your data's attire

Different data types call for different solutions. For simple Python literals, ast.literal_eval() is a comfy fit while json.loads() is ideal for a data party mix of different types.

The race between ast and json

If speed is your need, note that json.loads() generally outpaces ast.literal_eval() with JSON strings, while the latte swaggers with Python literal strings. It's like a Python cooking show, pick based on your menu.

Simplifying yet facilitating your code

In coding, you play the balancing act of making code functional, yet understandable. Clever use of keywords can make your script read like a gripping novel, not a sleep-inducing manual.

Embracing disgruntled cases and error handling

Data sanitization before parsing

Before unspooling the string with either the ast or json module, don't forget to clean it up! Verify your input to avoid any sour surprises.

Dancing smoothly with non-letter characters

Strings can contain various non-letter characters. ast.literal_eval() and json.loads() both handle these chaotically-precise characters with grace, making sure they stay intact in the output list:

import json
noisy_string = '["!@#", "$%^", "*&()"]'
# Converting noise to Python melody!
clean_list = json.loads(noisy_string)
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Staying good pals with all Python versions

json.loads() and ast.literal_eval() get along with all Python versions just fine. But with archaic versions, you may need to pull up a chair and do some tedious manual parsing or even fall back to regex:

# Good old the-compiler-is-my-oyster days!
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