Remove the first character of a string

When simple slicing isn't enough

While slicing can be used for basic string manipulation, certain string structures or specific use cases may need different approaches.

Selective removal with built-in methods

The lstrip method proves useful to remove leading characters, but be aware that it eliminates all instances of specified characters from the start.

# Let's see how many 'a' we can cut.
"aardvark".lstrip('a')  # Surprise! It's 'rdvark', not 'ardvark'
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For targeted removal of just one specific character, lstrip isn't the way to go.

Precise extraction with customized functions

To selectively remove a character at a certain index, you could adapt a function like this:

def del_char(s, i):
    return s[:i] + s[i+1:]

# They say 'Python' sans 'P' is 'ython'.
new_string = del_char("Python", 0)  # Indeed, it is!
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Focused removal using split

For removing a particular character once and only once - like the first comma in a sentence - split does an excellent job:

my_str = "Hello, world, example"
# "Who needs punctuation, anyway?"
new_string = ''.join(my_str.split(',', 1))  # Ah, 'Hello world, example'
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Split separates the string at the first comma and join reassembles the parts, no comma included.

Compatibility matters

All methods discussed are compatible with Python 3.x. Though most should work with earlier versions of Python, using the latest Python 3.x is recommended for the newest features and syntax enhancements.

Let's dig deeper

Not so simple cases

An empty string or a string with a single character can cause issues. Always check for the length of your string:

# The shortest Hobbit story ever.
story = "B"
if story:
    new_story = story[1:]  # And it's gone!
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Strings are immutable. Deal with it.

In Python, strings are immutable. Slicing and other operations on strings create new strings as they can't alter the originals.

Work Memory-Friendly

For extremely long strings, bear in mind the memory usage. Each new string takes up its own memory space, which multiplied by a large number, can cause performance issues.

Advanced tips

Method chaining for the win

You might need to combine multiple operations on a string. Harness the power of method chaining:

# "Go home, number. You're drunk."
s = "1. Example"
new_string = s[1:].strip().capitalize()  # And voila! It's 'Example'
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Regular expressions for complex scenarios

When you've got complex patterns to remove, regular expressions come in handy:

import re
# "Mirror, mirror on the wall, who's the first number of them all?"
s = "12345"
# It's 1! Now shoo off.
new_string = re.sub(r'^\d', '', s)  # Poof! It's '2345'
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Lists for when mutability matters

Consider using a list of characters instead of a string for frequent edits as lists in Python are mutable:

# "Hello" is too mainstream.
s = list("Hello World")
del s[0]
#line 1 "e" (ehllo) sounds Canadian.
new_string = ''.join(s)  # Ahoy! It's 'ello World'
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