Category: Programming

Problem Formulation and Solution Overview

This article will show you how 6 ways to remove List elements in Python.

To make it more interesting, we have the following running scenario:

Suppose you have a Christmas List containing everyone to buy a gift for. Once a gift is purchased, remove this person from the List. Once all gifts have been purchased, remove the entire List.

xmas_list = ['Anna', 'Elin', 'Inger', 'Asa', 'Sofie', 'Gunnel', 'Linn']

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Question: How would we write code to remove items from a Python List?

We can accomplish this task by one of the following options:

• Method 1: Use the del Keyword
• Method 2: Use remove() and a for Loop
• Method 3: Use slicing
• Method 4: Use pop()
• Method 5: Use List Comprehension
• Method 6: Use clear()

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Method 1: Use the del Keyword

This method uses Python’s del Keyword and highlights its ability to remove one List element and all List elements.

Remove One List Element

In this scenario, Asa's gift has been purchased and will be removed from the List.

xmas_list = ['Anna', 'Elin', 'Inger', 'Asa', 'Sofie', 'Gunnel', 'Linn']
del xmas_list[3]
print(xmas_list)

As shown on the highlighted line, Asa is removed from the List by using del, referencing xmas_list and specifying Asa’s location ([3]).

When xmas_list is output to the terminal, the following displays.

['Anna', 'Elin', 'Inger', 'Sofie', 'Gunnel', 'Linn']

Remove All List Elements

In this scenario, all gifts have been purchased, and all List elements will be removed.

xmas_list = ['Anna', 'Elin', 'Inger', 'Asa', 'Sofie', 'Gunnel', 'Linn']
del xmas_list
print(xmas_list)

As shown on the highlighted line, all elements of xmas_list are removed by using del and referencing xmas_list.

When xmas_list is output to the terminal, the following error is generated.

NameError: name 'xmas_list' is not defined

Note: This error is generated because the variable xmas_list no longer exists in memory.

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Method 2: Use remove() and a For Loop

This example uses the remove() function in conjunction with a for loop to remove one List element and all List elements.

Remove One List Element

In this scenario, Elin's gift has been purchased and will be removed from the List.

xmas_list = ['Anna', 'Elin', 'Inger', 'Asa', 'Sofie', 'Gunnel', 'Linn']
xmas_list.remove('Elin')
print(xmas_list)

As shown on the highlighted line, Elin is removed from the List using the remove() function and passing Elin’s name as an argument.

When xmas_list is output to the terminal, the following displays.

['Anna', 'Inger', 'Asa', 'Sofie', 'Gunnel', 'Linn']

Remove All List Elements

In this scenario, all gifts have been purchased, and all List elements will be removed.

xmas_list = ['Anna', 'Elin', 'Inger', 'Asa', 'Sofie', 'Gunnel', 'Linn']
for item in xmas_list.copy(): xmas_list.remove(item)
print(xmas_list)

As shown on the first highlighted line, a for loop is instantiated. This loop declares a shallow copy of the List to iterate.

On each iteration, the remove() function is called and passed the current name in xmas_list as an argument (see below) and removed.

For example:

Anna Elin Inger Asa Sofie Gunnel Linn

When xmas_list is output to the terminal, an empty List displays.

[]

Note: A shallow copy creates a reference to the original List. For further details, view the video below.

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Method 3: Use slicing

This method uses slicing to remove one List element and all List elements.

Remove One List Element

In this scenario, Anna's gift has been purchased and will be removed from the List.

xmas_list = ['Anna', 'Elin', 'Inger', 'Asa', 'Sofie', 'Gunnel', 'Linn']
xmas_list = xmas_list[1:]
print(xmas_list)

As shown on the highlighted line, Anna is removed from the List using slicing.

When xmas_list is output to the terminal, the following displays.

['Elin', 'Inger', 'Asa', 'Sofie', 'Gunnel', 'Linn']

Remove All List Elements

In this scenario, all gifts have been purchased, and all List elements will be removed.

xmas_list = ['Anna', 'Elin', 'Inger', 'Asa', 'Sofie', 'Gunnel', 'Linn']
xmas_list = []
print(xmas_list)

As shown on the highlighted line, all List elements are removed by declaring an empty List.

When xmas_list is output to the terminal, an empty List displays.

[]

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Method 4: Use pop()

This method uses the pop() function to remove one List element and all List elements.

Remove One List Element

In this scenario, Linn's gift has been purchased and will be removed from the List.

xmas_list = ['Anna', 'Elin', 'Inger', 'Asa', 'Sofie', 'Gunnel', 'Linn']
xmas_list.pop()
xmas_list.pop(2)
print(xmas_list)

As shown on the first highlighted line, the pop() method is appended to the xmas_list. This lets Python know to remove a List element from said List. Since no element is specified, the last element is removed (Linn).

On the second highlighted line, the pop() method is appended to the xmas_list and passed one (1) argument: the element to remove (2). This action removes Inger.

When xmas_list is output to the terminal, the following displays.

['Anna', 'Elin', 'Asa', 'Sofie', 'Gunnel']

Note: Both Linn and Inger are no longer in xmas_list.

Remove All List Elements

In this scenario, all gifts have been purchased, and all List elements will be removed.

xmas_list = ['Anna', 'Elin', 'Inger', 'Asa', 'Sofie', 'Gunnel', 'Linn'] for i in xmas_list.copy(): xmas_list.pop()
print(xmas_list)

As shown on the first highlighted line, a for loop is instantiated. This loop declares a shallow copy of the List to iterate.

On each iteration, the pop() method is called. Since no argument is passed, the last element is removed.

When xmas_list is output to the terminal, an empty List displays.

[]

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Method 5: Use List Comprehension

This method uses List Comprehension to remove all List elements that do not meet the specified criteria.

Remove One List Element

In this scenario, Gunnel's gift has been purchased and will be removed from the List.

xmas_list = ['Anna', 'Elin', 'Inger', 'Asa', 'Sofie', 'Gunnel', 'Linn']
xmas_list = [value for value in xmas_list if value != 'Gunnel']
print(xmas_list)

When xmas_list is output to the terminal, the following displays.

['Anna', 'Elin', 'Inger', 'Asa', 'Sofie', 'Linn']

Note: To remove all List elements, pass it empty brackets as shown follows: (xmas_list = []).

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Method 6: Use clear()

This method uses clear() to remove all List elements.

In this scenario, all gifts have been purchased, and all List elements will be removed.

xmas_list = ['Anna', 'Elin', 'Inger', 'Asa', 'Sofie', 'Gunnel', 'Linn']
xmas_list.clear()
print(xmas_list)

As shown on the highlighted line, all List elements are removed by appending the clear() function to xmas_list.

When xmas_list is output to the terminal, an empty List displays.

[]

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Summary

This article has provided six (6) ways to remove List elements to select the best fit for your coding requirements.

Good Luck & Happy Coding!

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Programming Humor

Programming is 10% science, 20% ingenuity, and 70% getting the ingenuity to work with the science.

~~~

• Question: Why do Java programmers wear glasses?
• Answer: Because they cannot C# …!

Feel free to check out our blog article with more coding jokes.

Summary: Use "given string".split() to split the given string by whitespace and store each word as an individual item in a list.
Minimal Example:
print("Welcome Finxter".split())
# OUTPUT: [‘Welcome’, ‘Finxter’]

Problem Formulation

Problem: Given a string, How will you split the string into a list of words using whitespace as a separator/delimiter?

Let’s understand the problem with the help of a few examples:

Example 1: Input: text = “Welcome to the world of Python” Explanation: Split the string into a list of words using a space ” ” as the delimiter to separate the words from the given string. Output: [‘Welcome’, ‘to’, ‘the’, ‘world’, ‘of’, ‘Python’] Example 2: Input: text = “””Item_1 Item_2 Item_3″”” print(text.split(‘\n’)) Explanation: Split the string into a list of words using a newline “\n” as the delimiter to separate the words from the given string. Output: [‘Item_1’, ‘Item_2’, ‘Item_3’] Example 3: text = “This is just a random text:\n New Line” Explanation: The given string contains a combination of whitespaces between the words, such as space, multiple-spaces, a tab and a new line character. All of these whitespace characters have to be considered as delimiters while separating the words from the given string and storing them as items in a list. Here’s how the output looks: Output: [‘This’, ‘is’, ‘just’, ‘a’, ‘random’, ‘text:’, ‘New’, ‘Line’]

So, we have two situations at hand. One, that has a single whitespace used as a delimiter and another that has multiple whitespace characters as delimiters in the same string. Let’s dive into the numerous ways of solving this problem.

Method 1: Using split()

split() is a built-in method in Python which splits the string at a given separator and returns a split list of substrings. Here’s a minimal example that demonstrates how the split function works – finxterx42'.split('x') will split the string with the character ‘x’ as the delimiter and return the following list as an output: ['fin', 'ter', '42']. The default separator, i.e., when no value is passed to the split function is considered as any whitespace character, i.e., it will take into account any whitespace such as ‘\n’, ” “, ‘\t’, etc.

Read more about the split() method in this blog tutorial: Python String split().

Approach: Thus to split a string based on a given whitespace delimiter, you can simply pass the specific whitespace character as a separator/delimiter to the split('whitespace_character') function.

Code:

# Example 1:
text = "Welcome to the world of Python"
print(text.split(' '))
# OUTPUT: ['Welcome', 'to', 'the', 'world', 'of', 'Python'] # Example 2:
text = """Item 1
Item 2
Item 3"""
print(text.split('\n'))
# OUTPUT: ['Item_1', 'Item_2', 'Item_3'] # Example 3: text = "This is just a\trandom text:\nNew Line"
print(text.split()) # OUTPUT: ['This', 'is', 'just', 'a', 'random', 'text:', 'New', 'Line']

Note that to separate the words in the third example we did specify any separator within the split() function. This is because when you don’t specify the separator, then Python will automatically consider that any whitespace character that occurs within the given string is a separator.

Method 2: Using regex

Another extremely handy way of separating a string with whitespace characters as separators is to use the regex library.

Approach 1: Import the regex library and use its split method as re.split('\s+', text) where ‘\s+’ returns a match whenever the string contains one or more whitespace characters. Therefore, whenever any whitespace character is encountered, the string will be separated at that point.

Code:

import re
# Example 1:
text = "Welcome to the world of Python"
print(re.split('\s+', text))
# OUTPUT: ['Welcome', 'to', 'the', 'world', 'of', 'Python'] # Example 2:
text = """Item_1
Item_2
Item_3"""
print(re.split('\s+', text))
# OUTPUT: ['Item_1', 'Item_2', 'Item_3'] # Example 3:
text = "This is just a\trandom text:\nNew Line"
print(re.split('\s+', text))
# OUTPUT: ['This', 'is', 'just', 'a', 'random', 'text:', 'New', 'Line']

Related Tutorial: Python Regex Split

Approach 2: Another way of using the regex library to solve this question is to use the findall() method of the regex library. Import the regex library and use re.findall(r'\S+', text) where the expression returns all the characters/words in a list that do not contain any whitespace character. This essentially means that whenever Python finds and segregates a string that has no whitespace in it. As soon as a whitespace character is found it considers that as a breakpoint, therefore the next word that has a continuous sequence of characters without the presence of any whitespace character is taken into account.

Here’s a graphical representation of the above explanaton:

Code:

import re
# Example 1:
text = "Welcome to the world of Python"
print(re.findall(r'\S+', text))
# OUTPUT: ['Welcome', 'to', 'the', 'world', 'of', 'Python'] # Example 2:
text = """Item_1
Item_2
Item_3"""
print(re.findall(r'\S+', text))
# OUTPUT: ['Item_1', 'Item_2', 'Item_3'] # Example 3:
text = "This is just a random text:\n New Line"
print(re.findall(r'\S+', text))
# OUTPUT: ['This', 'is', 'just', 'a', 'random', 'text:', 'New', 'Line']

Related Tutorial: Python re.findall() – Everything You Need to Know

Do you want to master the regex superpower? Check out my new book The Smartest Way to Learn Regular Expressions in Python with the innovative 3-step approach for active learning: (1) study a book chapter, (2) solve a code puzzle, and (3) watch an educational chapter video.

Conclusion

We have successfully solved the given problem using different approaches. I hope you enjoyed this article and it helps you in your Python coding journey. Please subscribe and stay tuned for more interesting articles!

Related Reads:
⦿ How To Split A String And Keep The Separators?
⦿ How To Cut A String In Python?
⦿ Python | Split String into Characters

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With this article, we’ll discover a new and fascinating world of bytes and strings, as well as ways to manipulate them, allocate memory arrays, and use array literals.

It’s part of our long-standing tradition to make this (and other) articles a faithful companion, or a supplement to the official Solidity documentation, starting with these docs for this article’s topics.

Types bytes and string

Besides the arrays we’ve already discussed, there are also some unique arrays, such as bytes and string arrays.

We have to note that the bytes type is very similar to bytes1[], however, the difference is that a bytes array is tightly packed in memory areas calldata and memory.

Furthermore, string is equal to bytes, but does not have a length property or support for index access.

Solidity doesn’t have string manipulation functions compared to other commonly used programming languages, but this can be worked around by including third-party string libraries.

With vanilla Solidity, we can concatenate two strings, e.g. string.concat(s1, s2), and compare two strings by using their keccak-256 hash, e.g.

keccak256(abi.encodePacked(s1)) == keccak256(abi.encodePacked(s2)).

Regarding the preferred use (we could consider this a design pattern), the bytes type is better than bytes1[], because bytes1[] is more expensive due to padding additional 31 bytes between the elements when used in memory.

The padding is absent in storage because of the tight packing used (docs).

Note: A rule of thumb says that bytes should be used for arbitrary-length raw byte data and string for arbitrary-length string data in UTF-8.

Note: If our data can be stored in a variable containing a number of bytes up to 32, it is better to use one of the value types bytes1 ... bytes32, due to their low cost.

To access a byte representation of a string s, we could use the following construct: bytes(s)[7] = 'x'; with regard to the string length, bytes(s).length, e.g.

// SPDX-License-Identifier: GPL-3.0 pragma solidity >=0.7.0 <0.9.0; /** * @title String modification * @dev Demonstrates how to modify a string represented as bytes. */
contract StringModification { string public s = "Some string"; function modifyString() public { bytes(s)[7]='Q'; }
}

Note: By using this approach, we’re accessing bytes of the UTF-8 representation, not the individual characters.

Functions bytes.concat() and string.concat()

Concatenation is a synonym for joining or gluing together.

Recommended Tutorial: String Concatenation in Solidity

String Concatenation

The function string.concat() enables us to concatenate any number of string values.

The result of using the string.concat() function is a single-string memory array containing the concatenated strings without any added spacing or padding.

If we’d like to use function parameters of other types that are not implicitly convertible to the string type, we first have to convert them to the string type.

Byte Concatenation

In the same manner, the bytes.concat() function enables us to concatenate any number of bytes or bytes1 ... bytes32 values.

The function result is a single bytes memory array containing the arguments without padding.

If we’d like to use string parameters or other types not implicitly convertible to bytes type, we first convert them to the bytes type.

Example

Let’s use an example to show how a function performs both string and bytes concatenation:

// SPDX-License-Identifier: GPL-3.0
pragma solidity ^0.8.12; contract C { string s = "Storage"; function f(bytes calldata bc, string memory sm, bytes16 b) public view { string memory concatString = string.concat(s, string(bc), "Literal", sm); assert((bytes(s).length + bc.length + 7 + bytes(sm).length) == bytes(concatString).length); bytes memory concatBytes = bytes.concat(bytes(s), bc, bc[:2], "Literal", bytes(sm), b); assert((bytes(s).length + bc.length + 2 + 7 + bytes(sm).length + b.length) == concatBytes.length); }
}

By calling bytes.concat(...) and string.concat(...) without arguments, a result is an empty array.

Allocating Memory Arrays

We can dynamically resize the storage arrays by adding elements via the .push() member function.

In contrast, memory arrays cannot be dynamically resized and the .push() member function is not available.

However, by using the alternative approach, we can create dynamic-length memory arrays by using the new operator. Just before using the new operator, we have to calculate the required size in advance or create a new, empty array and populate it by copying all elements.

Note: Following the same rule of default values, the elements of freshly allocated arrays are initialized with their default values (docs).

Here we have an example showing arrays a and b, initialized by either a constant size or a parameter-given size.

// SPDX-License-Identifier: GPL-3.0
pragma solidity >=0.4.16 <0.9.0; contract C { function f(uint len) public pure { uint[] memory a = new uint[](7); bytes memory b = new bytes(len); assert(a.length == 7); assert(b.length == len); a[6] = 8; }
}

Array Literals

Array literal is represented by a comma-separated list of any number of expressions, which are listed in square brackets, e.g. [1, a, f(3)].

The array literal type is determined in the following way:

1. The array literal is a statically-sized memory array, and its length is the number of expressions listed in the brackets;
2. The base type of the array is determined by the type of the first expression T in the list that satisfies the condition: all other expressions must be implicitly convertible to T. If it’s not possible to find such an expression, a type error is thrown;
3. Besides the convertibility condition (point 2.), one of the expressions must be of the T type.

The following example will clarify what the points above mean; the type of an array literal [1, 2, 3] is uint8[3] memory, because each of the expressions is of type uint8.

If we want to change the result to type uint[3] memory, we have to convert the first element to uint.

// SPDX-License-Identifier: GPL-3.0
pragma solidity >=0.4.16 <0.9.0; contract C { function f() public pure { g([uint(1), 2, 3]); } function g(uint[3] memory) public pure { // ... }
}

In contrast, the array literal [1, -2] is invalid because it doesn’t comply with point 2., stating that the first expression’s type is a target type T for implicit conversion of other expressions.

Since our first expression is of type uint8, and the second expression is of type int8 (including the negative numbers), the second expression cannot be implicitly converted to uint8.

To avoid a type error, we can declare our array literal as [int8(1), -1], forcing the first expression to be of compatible type int8.

In a more specific case of using, e.g. two-dimensional array literals, we’d step on a problem of fixed-size memory arrays that cannot be converted into each other, regardless of the compatibility of base types.

We can get around this problem by explicitly specifying a common base:

// SPDX-License-Identifier: GPL-3.0
pragma solidity >=0.4.16 <0.9.0; contract C { function f() public pure returns (uint24[2][4] memory) { uint24[2][4] memory x = [[uint24(0x1), 1], [0xffffff, 2], [uint24(0xff), 3], [uint24(0xffff), 4]]; // The following does not work, because some of the inner arrays are not of the right type. // uint[2][4] memory x = [[0x1, 1], [0xffffff, 2], [0xff, 3], [0xffff, 4]]; return x; }
}

We cannot assign fixed-size memory arrays to dynamically-sized memory arrays, as shown by the example:

// SPDX-License-Identifier: GPL-3.0
pragma solidity >=0.4.0 <0.9.0; // This will not compile.
contract C { function f() public { // The next line creates a type error because uint[3] memory // cannot be converted to uint[] memory. uint[] memory x = [uint(1), 3, 4]; }
}

To initialize dynamically-sized arrays, we’d have to resort to assigning the elements individually, as in the example:

// SPDX-License-Identifier: GPL-3.0
pragma solidity >=0.4.16 <0.9.0; contract C { function f() public pure { uint[] memory x = new uint[](3); x[0] = 1; x[1] = 3; x[2] = 4; }
}

Conclusion

In this article, we learned even more about reference types, in particular, bytes and string arrays and concatenation, memory array allocation, and array literals.

1. First, we explained the uniqueness of the arrays based on bytes and string types, and also touched on some of the similarities with the akin types.
2. Second, we’ve peeked into how to do string concatenation, comparison, and bytes concatenation.
3. Third, we discovered the specifics of allocating memory arrays and got introduced to the new operator.
4. Fourth, we got to know array literals with rules for determining the array literal base type. We also became aware of the invalid array literals and what can be done to make them valid.

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What’s Next?

This tutorial is part of our extended Solidity documentation with videos and more accessible examples and explanations. You can navigate the series here (all links open in a new tab):

Programming Challenge: Given a Pandas DataFrame or a Pandas Series object. How to convert them to a NumPy array?

In this short tutorial, you’ll learn (1) how to convert a 1D pandas Series to a NumPy array, and (2) how to convert a 2D pandas DataFrame to an array. Let’s get started with the first!

Convert Pandas Series to NumPy Array

First, let’s create a Pandas Series.

import pandas as pd # create dataframe df df = pd.Series([22,21,20,14], name= 'GSTitles', index= ['Nadal','Djokovic','Federer','Sampras'])
print(df)

Here’s the resulting Series df:

Nadal 22
Djokovic 21
Federer 20
Sampras 14
Name: GSTitles, dtype: int64

Now that we have our Pandas Series, you can convert this to a NumPy Array using the DataFrame.to_numpy() method.

Like so:

print(df.to_numpy())
# [22 21 20 14]

The resulting object is a NumPy array:

print(type(df.to_numpy()))
# <class 'numpy.ndarray'>

Attention: There is also the .values() method, but that is being deprecated now – when you look at the Pandas documentation, there is a warning “We recommend using DataFrame.to_numpy instead”.

With this method, only the values in the DataFrame or Series will return. The index labels will be removed.

Here’s how that’ll work:

print(df.values)
# [22 21 20 14]

This was a 1-dimensional array or a Series. Let’s move on to the 2D case next.

Convert DataFrame to NumPy Array

Question: Let’s try with a two-dimensional DataFrame — how to convert it to a NumPy array?

First, let’s print the dimension of the previous Series to confirm that it was, indeed, a 1D data structure:

print(df.ndim)
# 1

Next, you create a 2D DataFrame object:

import pandas as pd # Create a 2D DataFrame object
df2 = pd.DataFrame(data={'Nadal': [2, 14, 2, 4], 'Djokovic': [9, 2, 7, 3], 'Federer': [6, 1, 8, 5], 'Sampras': [2, 0, 7, 5]}, index=['AO', 'F', 'W', 'US']) print(df2)

Here’s the resulting DataFrame:

	Nadal	Djokovic	Federer	Sampras
AO	2	9	6	2
F	14	2	1	0
W	2	7	8	7
US	4	3	5	5

Now, let’s dive into the conversion of this DataFrame to a NumPy array by using the DataFrame.to_numpy() method.

# Convert this DataFrame to a NumPy array
print(df2.to_numpy())

The output shows a NumPy array from the 2D DataFrame — great!

[[ 2 9 6 2] [14 2 1 0] [ 2 7 8 7] [ 4 3 5 5]]

You can see that all indexing metadata has been stripped away from the resulting NumPy array!

Convert Specific Columns from DataFrame to NumPy Array

You can also convert specific columns of a Pandas DataFrame by accessing the columns using pandas indexing and calling the .to_numpy() method on the resulting view object.

Here’s an example:

print(df2[['Djokovic', 'Federer']].to_numpy())

The output:

[[9 6] [2 1] [7 8] [3 5]]

Summary

You can convert a Pandas DataFrame or a Pandas Series object to a NumPy array by means of the df.to_numpy() method. The indexing metadata will be removed.

You can also convert specific columns of a Pandas DataFrame by accessing the columns using pandas indexing and calling the .to_numpy() method on the resulting view object.

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Thanks for reading through the whole tutorial!

Question: What is the difference between return and break? When to use which?

Let’s first look at a short answer before we dive into a simple example to understand the differences and similarities between return and break.

Comparison

Both return and break are keywords in Python.

• The keyword return ends a function and passes a value to the caller.
• The keyword break ends a loop immediately without doing anything else. It can be used within or outside a function.

return	break
Used to end a function	Used to end a for or while loop
Passes an optional value to the caller of the function (e.g., return 'hello')	Doesn’t pass anything to the “outside”

While they serve a different purpose, i.e., ending a function vs ending a loop, there are some cases where they can be used interchangeably.

Similar Use Cases

The following use case shows why you may have confused both keywords return and break. In both cases, you can use them to end a loop inside a function and return to the outside.

Here’s the variant using return:

def f(): for i in range(10): print(i) if i>3: return f()

And here’s the variant using break:

def f(): for i in range(10): print(i) if i>3: break f()

Both code snippets do exactly the same—printing out the first 5 values 0, 1, 2, 3, and 4.

Output:

0
1
2
3
4

However, this is where the similarity between those two keywords ends. Let’s dive into a more common use case where they both perform different tasks in the code.

Different Use Cases

The following example uses both keywords break and return. It uses the keyword break to end the loop as soon as the loop variable i is greater than 3.

So the line print(i) is never executed after variable i reaches the value 4—the loop ends.

But the function doesn’t end because break only ends the loop and not the function. That’s why the statement print('hi') is still executed, and the return value of the function is 42 (which we also print in the final line).

def f(): for i in range(10): if i>3: break print(i) print('hi') return 42 print(f())

Output:

0
1
2
3
hi
42

Summary

The keyword return is different and more powerful than the keyword break because it allows you to specify an optional return value. But it can only be used in a function context and not outside a function.

• You use the keyword return to give back a value to the caller of the function or terminate the whole function.
• You use the keyword break to immediately stop a for or while loop.

Rule: Only if you want to exit a loop inside a function and this would also exit the whole function, you can use both keywords. In that case, I’d recommend using the keyword return instead of break because it gives you more degrees of freedom, i.e., specifying the return value. Plus, it is more explicit which improves the readability of the code.

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Thanks for reading over the whole tutorial—if you want to keep learning, feel free to join my email academy. It’s fun!

Recommended Video

Summary: Use the list("given string") to extract each character of the given string and store them as individual items in a list.
Minimal Example:
print(list("abc"))

Problem: Given a string; How will you split the string into a list of characters?

Example: Let’s visualize the problem with the help of an example:

input = “finxter” output = [‘f’, ‘i’, ‘n’, ‘x’, ‘t’, ‘e’, ‘r’]

Now that we have an overview of our problem let us dive into the solutions without further ado.

Method 1: Using The list Constructor

Approach: One of the simplest ways to solve the given problem is to use the list constructor and pass the given string into it as the input.

list() creates a new list object that contains items obtained by iterating over the input iterable. Since a string is an iterable formed by combining a group of characters, hence, iterating over it using the list constructor yields a single character at each iteration which represents individual items in the newly formed list.

Code:

text = "finxter"
print(list(text)) # ['f', 'i', 'n', 'x', 't', 'e', 'r']

Related Tutorial: Python list() — A Simple Guide with Video

Method 2: Using a List Comprehension

Another way to split the given string into characters would be to use a list comprehension such that the list comprehension returns a new list containing each character of the given string as individual items.

Code:

text = "finxter"
print([x for x in text]) # ['f', 'i', 'n', 'x', 't', 'e', 'r']

Prerequisite: To understand what happened in the above code, it is essential to know what a list comprehension does. In simple words, a list comprehension in Python is a compact way of creating lists. The simple formula is [expression + context], where the “expression” determines what to do with each list element. And the “context” determines what elements to select. The context can consist of an arbitrary number of for and if statements. To learn more about list comprehensions, head on to this detailed guide on list comprehensions.

Explanation: Well! Now that you know what list comprehensions are, let’s try to understand what the above code does. In our solution, the context variable x is used to extract each character from the given string by iterating across each character of the string one by one with the help of a for loop. This context variable x also happens to be the expression of our list comprehension as it stores the individual characters of the given string as separate items in the newly formed list.

Multi-line Solution: Another approach to formulating the above solution is to use a for loop. The idea is pretty similar; however, we will not be using a list comprehension in this case. Instead, we will use a for loop to iterate across individual characters of the given string and store them one by one in a new list with the help of the append method.

text = "finxter"
res = []
for i in text: res.append(i)
print(res) # ['f', 'i', 'n', 'x', 't', 'e', 'r']

Method 3: Using map and lambda

Yet another way of solving the given problem is to use a lambda function within the map function. Now, this is complex and certainly not the best fit solution to the given problem. However, it may (or may not ;P) be appropriate when you are handling really complex tasks. So, here’s how to use the two built-in Python functions to solve the given problem:

import re
text = "finxter"
print(list(map(lambda c: c, text))) # ['f', 'i', 'n', 'x', 't', 'e', 'r']

Explanation: The map() function is used to execute a specified function for each item of an iterable. In this case, the iterable is the given string and each character of the string represents an individual item within it. Now, all we need to do is to create a lambda function that simply returns the character passed to it as the input. That’s it! However, the map method will return a map object, so you must convert it to a list using the list() function. Silly! Isn’t it? Nevertheless, it works!

Conclusion

Hurrah! We have successfully solved the given problem using as many as three different ways. I hope you enjoyed this article and it helps you in your Python coding journey. Please subscribe and stay tuned for more interesting articles!

Related Reads:
⦿ How To Split A String And Keep The Separators?
⦿ How To Cut A String In Python?

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Do you need to create a function that returns a file but you don’t know how? No worries, in sixty seconds, you’ll know! Go!

A Python function can return any object such as a file object. To return a file, first open the file object within the function body, handle all possible errors if the file doesn’t exist, and return it to the caller of the function using the keyword operation return open(filename, mode='r').

Here’s a minimal example that tries to open a filename that was provided by the user via the input() function. If it fails, it prints an error message and asks for a different user input:

def open_file(): while True: filename = input('filename: ') try: return open(filename, mode='r') except: print('Error. Try again') f = open_file()
print(f.read()) 

If I type in the correct file right away, I get the following output when storing the previous code snippet in a file named code.py—the code reads itself (meta ):

filename: code.py
def open_file(): while True: filename = input('filename: ') try: return open(filename, mode='r') except: print('Error. Try again') f = open_file()
print(f.read())

Note that you can open the file in writing mode rather than reading mode by replacing the line with the return statement with the following line:

open(filename, mode='w')

A more Pythonic way, in my opinion, is to follow the single-responsibility pattern whereby a function should do only one thing. In that case, provide the relevant input values into the function like so:

def open_file(filename, mode): try: return open(filename, mode=mode) except: return None def ask_user(): f = open_file(input('filename: '), input('mode: ')) while not f: f = open_file(input('filename: '), input('mode: ')) return f f = ask_user() print(f.read()) 

Notice how the file handling of a single instance and the user input processing are separated into two functions. Each function does one thing only. Unix style.

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Programmer Humor

Q: How do you tell an introverted computer scientist from an extroverted computer scientist? A: An extroverted computer scientist looks at your shoes when he talks to you.