How to Remove Spaces from a String in Python

How to Remove Spaces from a String in Python
Hostman Team
Technical writer
Python
10.01.2025
Reading time: 8 min

Strings are one of the fundamental data types in Python, storing a sequence of characters. With strings, you can perform many operations: splitting, joining, replacing, comparing, and more.

Sometimes, it's necessary to format strings by removing unnecessary characters, such as spaces.

This article will cover the methods available in Python (version 3.10.12) for removing spaces from strings.

Removing Spaces from the Start and End

Often, we need to remove only extra spaces, such as those at the start or end of a string. Python provides several similar methods for this purpose:

  • strip() removes spaces from both the string's start and end.
  • lstrip() removes spaces only from the start.
  • rstrip() removes spaces only from the end.

Here’s an example of how to clean up a string by removing spaces at the edges:

text_before = "   This is a string with spaces   "

text_after = text_before.strip()
text_after_left = text_before.lstrip()
text_after_right = text_before.rstrip()

print(text_after)
print(text_after_left)
print(text_after_right)

Console output:

This is a string with spaces
This is a string with spaces   
   This is a string with spaces

Removing All Spaces

In some cases, you may need to remove all spaces entirely. This can be done by replacing every space character with an empty string:

text_before = "   This is a string with spaces   "

text_after = text_before.replace(' ', '')

print(text_after)

Console output:

Thisisastringwithspaces

Another way to remove all spaces is to use the translate() method. While less intuitive, it can be more flexible in certain scenarios:

text_before = "   This is a string with spaces   "

text_after = text_before.translate({ord(' '): None})

print(text_after)

Console output:

Thisisastringwithspaces

The translate() function takes a dictionary as an argument, where the keys are ASCII codes of characters to be replaced, and the values are the replacement characters. The ord() function converts a character to its corresponding ASCII code.

With translate(), you can replace multiple characters at once. For example:

text_before1 = "   This is a string with spaces   "
text_before2 = " 1 2 3 4 5 "
text_before3 = " { 'someData': 100, 'otherData': 'information' } "

space_table = str.maketrans({' ': None})

text_after1 = text_before1.translate(space_table)
text_after2 = text_before2.translate(space_table)
text_after3 = text_before3.translate(space_table)

print(text_after1)
print(text_after2)
print(text_after3)

Console output:

Thisisastringwithspaces
12345
{'someData':100,'otherData':'information'}

Removing Repeated Spaces

The simplest way to remove all repeated spaces in a string is to perform the following steps:

  1. Split the string using the split() function by spaces as delimiters, resulting in a list of substrings.
  2. Join the substrings from the list back into a single string using the join() function with a single space as the separator.

Here’s how this can be done:

text_before = "   This  is  a  string   with   spaces   "

# Split the string into substrings; spaces are the default delimiter
text_splitted = text_before.split()

# Join the substrings into a single string using a space as the delimiter
text_after = ' '.join(text_splitted)

print(text_after)

In the console, you’ll see the formatted string without extra spaces:

This is a string with spaces

You can write the same operations more concisely:

text_before = "   This  is  a  string   with   spaces   "
text_after = ' '.join(text_before.split())

print(text_after)

The console output will remain the same:

This is a string with spaces

Using this method, you can also replace spaces with any other character:

text_before = "   This  is  a  string   with   spaces   "
text_after = '_'.join(text_before.split())

print(text_after)

In this case, the console output will be:

This_is_a_string_with_spaces

Removing Spaces Using Regular Expressions

The methods shown earlier are effective for simple scenarios. However, strings often have more complex patterns, requiring advanced methods to remove spaces. A highly flexible way to handle string modifications is by using regular expressions.

Here’s an example:

import re  # Import the module for working with regular expressions

# A string containing sequences of two or more spaces, as well as some single spaces
text_before = "   This is a string    with   spaces   .   "
# Replace all sequences of two or more spaces with a single space
text_after = re.sub(r"\s+", " ", text_before)

print(text_after)

The console output will be a string where only single spaces remain:

This is a string with spaces .

This example introduces some problems:

  1. Multiple spaces before the period at the end are replaced with a single space. However, there should not be any space before the period.

  2. A sequence of spaces at the start of the string is replaced by a single space. However, there should not be any spaces at the beginning of the string.

We can resolve these issues by applying a sequence of transformations:

import re

text_before = "   This is a string    with   spaces   .   "

# Remove spaces at the start and end of the string using the OR operator (|)
text_after = re.sub(r"^\s*|\s*$", "", text_before)
# Replace all repeated spaces with a single space
text_after = re.sub(r"\s+", " ", text_after)
# Replace all periods surrounded by spaces with just a period
text_after = re.sub(r"\s*[.]\s*", ".", text_after)

print(text_after)

The console output will now contain a properly formatted string without unnecessary spaces:

This is a string with spaces.

Here:

  • \s: Matches any whitespace character (spaces, tabs, etc.).

  • +: Matches one or more repetitions of the preceding element.

  • *: Matches zero or more repetitions of the preceding element.

  • |: Represents a logical OR, allowing you to combine multiple conditions.

  • ^: Anchors the match at the beginning of the string.

  • $: Anchors the match at the end of the string.

When using regular expressions, it’s important to understand the potential structure of the strings being processed to design an appropriate solution. For example:

  • If the string may have periods surrounded by spaces, this must be handled explicitly.
  • The more complex the string patterns, the more intricate the logic for removing spaces becomes.

Ultimately, removing spaces from a string in Python often requires a custom solution tailored to the specific case.

Removing Spaces Using a Loop

For more complex string manipulation (in this case, removing spaces), you can manually check each character in a loop with multiple conditions. This approach offers more flexibility and control over the process.

In the simplest case, removing spaces inside a loop looks like this:

# Define a function for more complex string processing logic

def complexRemoval(string):
    after = ""

    for i in string:
        if not i.isspace():  # The isspace() function checks if the character is a space and returns a boolean result (True or False)
            after += i

    return after

text_before = "   This is a string    with   spaces   .   "
text_after = complexRemoval(text_before)

print(text_after)

The console output will contain all the characters of the original string, but without spaces:

Thisisastringwithspaces.

Clearly, this isn't the desired result, so we need to complicate the logic for removal.

To refine the logic, we can introduce a variable to track whether the previous character was a space:

def complexRemoval(string):
    after = ""
    wasSpace = True  # Variable to track if the previous character was a space

    for i in string:
        if not i.isspace():  # If the character is not a space
            if i == '.' and wasSpace:  # If we encounter a period and the previous character was a space, remove it
                after = after[:len(after) - 1]  # Remove the last character (space)
            after += i
            wasSpace = False
        elif not wasSpace:  # If it's a space but the previous character was not a space
            after += i
            wasSpace = True

    return after

# Test cases
print(complexRemoval("   This is a string    with   spaces   .   "))
print(complexRemoval("Lots    of different spaces blah blah blah .    Also a period   .   "))

The output in the console will now show perfectly formatted strings without unnecessary spaces:

This is a string with spaces.
Lots of different spaces blah blah blah. Also a period.

This method allows for more complex processing of spaces in strings, such as removing spaces before periods or handling sequences of spaces efficiently.

Conclusion

The Python programming language offers a specific set of built-in tools for string manipulation — for example, operations with space characters:

  • Removing spaces at the beginning of a string
  • Removing spaces at the end of a string
  • Removing spaces from both ends of a string
  • Removing all spaces in a string
  • Removing spaces from a string according to specific rules (using regular expressions)
  • Removing spaces according to unique rules (using iteration)

Each variant has its own set of methods — most of which we have covered in this guide.

If you want to build a web service using Python, you can rent a cloud server at competitive prices with Hostman.

Python
10.01.2025
Reading time: 8 min

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In this case, the syntax for dictionary comprehension is not much different from a simple iteration: objects = ['human', 'cat', 'alien', 'car'] # list of future dictionary keys states = ['walking', 'purring', 'hiding', 'driving'] # list of future dictionary values objects_states = {obj: state for obj, state in zip(objects, states)} # dictionary comprehension iterating over both lists print(objects_states) Console output: {'human': 'walking', 'cat': 'purring', 'alien': 'hiding', 'car': 'driving'} A natural question arises: what happens if one of the lists is shorter than the other? objects = ['human', 'cat', 'alien', 'car'] states = ['walking', 'purring'] objects_states = {obj: state for obj, state in zip(objects, states)} print(objects_states) The output will be: {'human': 'walking', 'cat': 'purring'} Thus, iteration in the dictionary comprehension stops at the shortest list. The code above can be written in a very compact form using the dict() constructor: objects = ['human', 'cat', 'alien', 'car'] states = ['walking', 'purring', 'hiding', 'driving'] objects_states = dict(zip(objects, states)) # create a dictionary from two lists without a for loop print(objects_states) The console output will be the same as in the previous examples: {'human': 'walking', 'cat': 'purring', 'alien': 'hiding', 'car': 'driving'} Creating a Dictionary with zip() and Conditional Logic In real-world applications, logic is often more complex than the simple examples shown earlier. Sometimes, you need to convert lists into dictionaries while applying specific conditions. For instance, some elements might need modification before inclusion in the dictionary or might not be included at all. This can be achieved using conditions in dictionary comprehensions. For example, we can exclude specific elements from the resulting dictionary: objects = ['human', 'cat', 'alien', 'car'] states = ['walking', 'purring', 'hiding', 'driving'] objects_states = {obj: state for obj, state in zip(objects, states) if obj != 'alien'} # Protect Earth from unknown extraterrestrial influence print(objects_states) Console output: {'human': 'walking', 'cat': 'purring', 'car': 'driving'} We can refine the selection criteria further by introducing multiple conditions: objects = ['human', 'cat', 'alien', 'car'] states = ['walking', 'purring', 'hiding', 'driving'] objects_states = {obj: state for obj, state in zip(objects, states) if obj != 'alien' if obj != 'cat'} # Exclude the alien and the cat—who might be a disguised visitor from another galaxy print(objects_states) Console output: {'human': 'walking', 'car': 'driving'} When using multiple if statements in a dictionary comprehension, they behave as if connected by a logical and operator. You can make dictionary generation even more flexible by combining if and else operators: objects = ['human', 'cat', 'alien', 'car'] states = ['walking', 'purring', 'hiding', 'driving'] # In this example, all string elements in the first list are longer than those in the second list, except for 'cat' objects_states = { obj: ('[SUSPICIOUS]' if len(obj) < len(state) else 'calmly ' + state) for obj, state in zip(objects, states) } # Mark the suspicious 'cat' appropriately and slightly modify other values print(objects_states) Console output: {'human': 'calmly walking', 'cat': '[SUSPICIOUS]', 'alien': 'calmly hiding', 'car': 'calmly driving'} Creating a Complex Dictionary from a Single List In the earlier examples, we created dictionaries from two separate lists. But what if the keys and values needed for the new dictionary are contained within a single list? In such cases, the logic of the dictionary comprehension needs to be adjusted: objects_and_states = [ 'human', 'walking', 'cat', 'purring', 'alien', 'hiding', 'car', 'driving' ] # Keys and values are stored sequentially in one list objects_states = { objects_and_states[i]: objects_and_states[i + 1] for i in range(0, len(objects_and_states), 2) } # The `range` function specifies the start, end, and step for iteration: range(START, STOP, STEP) print(objects_states) Console output: {'human': 'walking', 'cat': 'purring', 'alien': 'hiding', 'car': 'driving'} Sometimes, a list might contain nested dictionaries as elements. The values of these nested dictionaries can also be used to create a new dictionary. Here’s how the logic changes in such cases: objects = [ {'name': 'human', 'state': 'walking', 'location': 'street'}, {'name': 'cat', 'state': 'purring', 'location': 'windowsill'}, {'name': 'alien', 'state': 'hiding', 'location': 'spaceship'}, {'name': 'car', 'state': 'driving', 'location': 'highway'} ] objects_states = { obj['name']: obj['state'] for obj in objects } # Extract 'name' as key and 'state' as value print(objects_states) Console output: {'human': 'walking', 'cat': 'purring', 'alien': 'hiding', 'car': 'driving'} This approach enables handling more complex data structures, such as lists of dictionaries, by targeting specific key-value pairs from each nested dictionary. Converting a Dictionary to a List Converting a dictionary into a list in Python is a straightforward task, often better described as extracting data. From a single dictionary, you can derive several types of lists: A list of keys A list of values A list of key-value pairs Here’s how it can be done: objects_states = { 'human': 'walking', 'cat': 'purring', 'alien': 'hiding', 'car': 'driving' } # Convert dictionary components to lists using the `list()` function objects_keys = list(objects_states.keys()) # List of keys objects_values = list(objects_states.values()) # List of values objects_items = list(objects_states.items()) # List of key-value pairs print(objects_keys) print(objects_values) print(objects_items) Console output: ['human', 'cat', 'alien', 'car'] ['walking', 'purring', 'hiding', 'driving'] [('human', 'walking'), ('cat', 'purring'), ('alien', 'hiding'), ('car', 'driving')] Conclusion Lists and dictionaries are fundamental data structures in Python, each offering distinct ways of storing and accessing data. Dictionaries are more informative than lists, storing data as key-value pairs, whereas lists store values that are accessed by index. Converting a dictionary into a list is straightforward, requiring no additional data since you’re simply extracting keys, values, or their pairs. Converting a list into a dictionary, on the other hand, requires additional data or rules to map the list elements to dictionary keys and values. There are a few methods to convert a List to Dictionary Tool Key Values Syntax dict.fromkeys() Common new_dict = dict.fromkeys(old_list) Dictionary Comprehension Common new_dict = {new_key: 'any value' for new_key in old_list} Dict Comp + zip() Unique new_dict = {new_key: old_val for new_key, old_val in zip(list1, list2)} Dict Comp + zip() + if Unique new_dict = {new_key: old_val for new_key, old_val in zip(list1, list2) if ...} Dict Comp + zip() + if-else Unique new_dict = {new_key: (... if ... else ...) for new_key, old_val in zip(list1, list2)} Complex lists may require more intricate dictionary comprehension syntax. Techniques shown in this guide, such as using zip() and range() for iterations, help handle such cases. Converting a dictionary to a list is also possible in several ways, but it is much simpler. Tool Extracts Syntax list.keys() Keys list(old_dict.keys()) list.values() Values list(old_dict.values()) list.items() Key-Value Pairs list(old_dict.items()) Python offers flexible and efficient ways to convert structured data types between lists and dictionaries, enabling powerful manipulation and access.
13 January 2025 · 11 min to read

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