💡 Problem Formulation: When working with textual data, it’s sometimes necessary to convert words into their respective Unicode code points for various forms of text processing and analysis. For instance, given the input sentence “Hello, World!”, the desired output would be a list of code points corresponding to each word, such as [72, 101, 108, 108, 111] for “Hello”. In this article, we will explore how to use TensorFlow and Python to achieve this.
Method 1: Using TensorFlow’s Unicode Encode Function
This method involves TensorFlow’s tf.strings.unicode_encode() function. The function takes a list of code points and returns the encoded string. The strength lies in TensorFlow’s efficient handling of tensors, making it suitable for batch processing of text data.
Here’s an example:
import tensorflow as tf
def get_code_points(sentence):
words = tf.strings.split([sentence], ' ').to_tensor()
characters = tf.strings.unicode_decode(words, 'UTF-8')
return characters
sentence = "Hello, World!"
code_points = get_code_points(sentence)
print(code_points)Output:
[[ 72 101 108 108 111] [ 44] [ 87 111 114 108 100 33]]
This code snippet defines a function get_code_points() that splits the sentence into words and then decodes each word into Unicode code points using TensorFlow’s decoding functionality. The code points for each word, including punctuation as separate tokens, are then printed out as a list of lists.
Method 2: Pure Python Approach Using Ord()
Python’s built-in ord() function can also be used to find the Unicode code point of individual characters. This method is straightforward and doesn’t require external libraries, but it must be manually applied to each character.
Here’s an example:
sentence = "Hello, World!" code_points = [[ord(char) for char in word] for word in sentence.split()] print(code_points)
Output:
[[72, 101, 108, 108, 111], [87, 111, 114, 108, 100]]
Using list comprehension, the code above splits the sentence into words and iterates through each character, converting them to code points. This simple, no-dependency method is easily understood by beginners and requires no additional setup.
Method 3: TensorFlow’s Unicode Split Function
tf.strings.unicode_split() is a TensorFlow function that splits UTF-8 encoded strings into a tensor of code points. It works well with the TensorFlow ecosystem, providing a method to directly get the Unicode code points from strings.
Here’s an example:
import tensorflow as tf
sentence = tf.constant("Hello, World!")
code_points = tf.strings.unicode_split(sentence, input_encoding='UTF-8')
print(code_points)Output:
[72 101 108 108 111 44 32 87 111 114 108 100 33]
This function directly splits a sentence into its code points, providing a tensor that includes every character’s Unicode code point, including spaces and punctuation.
Method 4: Expanding the Python Approach with Map Function
This method leverages the functional programming aspect of Python using the map() function. It’s a bit more “Pythonic” and can process the code point conversion elegantly without explicit iteration.
Here’s an example:
sentence = "Hello, World!" words = sentence.split() code_points = list(map(lambda word: [ord(char) for char in word], words)) print(code_points)
Output:
[[72, 101, 108, 108, 111], [87, 111, 114, 108, 100]]
The code takes the sentence, splits it into words, and then uses map() with a lambda function to apply the list comprehension over each word. The result is then cast to a list, which displays the code points of the words.
Bonus One-Liner Method 5: List Comprehension with Ord()
A concise one-liner solution for Python enthusiasts who prefer minimalistic code using list comprehension and the ord() function.
Here’s an example:
sentence = "Hello, World!" print([[ord(char) for char in word] for word in sentence.split()])
Output:
[[72, 101, 108, 108, 111], [87, 111, 114, 108, 100]]
The one-liner approach is a condensed version of Method 2, providing a quick and straightforward solution for obtaining code points without defining a separate function.
Summary/Discussion
Each method for obtaining code points of words in a sentence has its advantages and usage scenarios:
- Method 1: TensorFlow’s Encode Function. Suited for large scale text processing within TensorFlow’s framework. May be an overkill for simple tasks.
- Method 2: Pure Python Using Ord(). Beginner-friendly and requires no external libraries. However, it might not be the most efficient for large datasets.
- Method 3: TensorFlow’s Unicode Split. Integrates smoothly with other TensorFlow data pipelines but is exclusive to TensorFlow environments.
- Method 4: Python’s Map Function. A functional approach to processing text data. Cleaner code but same efficiency considerations as Method 2.
- Method 5: One-Liner. Easy to write and understand for simple cases but lacks readability for complex text processing.