Data Tokenizing
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Tokenization definition:
Tokenization is the process of breaking down a piece of text into individual words or tokens. This is a common technique used in data engineering to prepare text data for analysis.
Tokenizing example using Python:
Here are some practical examples of tokenization in data engineering using Python-specific functions. Please note that you need to have the necessary Python libraries installed in your Python environment to run this code:
Using the split()
function: The split() function can be used to split a string into a list of words based on a delimiter, such as a space or a comma.
For example:
text = "This is a sample sentence."
tokens = text.split()
print(tokens)
This would output:
['This', 'is', 'a', 'sample', 'sentence.']
Using the word_tokenize() function from the NLTK library: The Natural Language Toolkit (NLTK) is a popular Python library for natural language processing. The word_tokenize() function from the NLTK library can be used to tokenize text data into individual words. For example:
import nltk
nltk.download('punkt')
from nltk.tokenize import word_tokenize
text = "This is a sample sentence."
tokens = word_tokenize(text)
print(tokens)
Will output:
['This', 'is', 'a', 'sample', 'sentence', '.']
Using regular expressions: Regular expressions can be used to define patterns for tokenizing text data. For example, the following code uses regular expressions to split a string into words based on whitespace and punctuation:
import re
text = "This is a sample sentence."
tokens = re.findall(r'\b\w+\b', text)
print(tokens)
This code would produce the following output in the terminal:
['This', 'is', 'a', 'sample', 'sentence']
Using the split()
function with a regular expression pattern: The split()
function can also be used with regular expression patterns to tokenize text data. For example:
import re
text = "This is a sample sentence."
tokens = re.split('\W+', text)
print(tokens)
This will yield:
['This', 'is', 'a', 'sample', 'sentence', '']
These are just a few examples of how tokenization can be used to prepare data for analysis and extract insights from text.
Align
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Clean or Cleanse
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Cluster
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Curate
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Denoise
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Denormalize
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Derive
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Discretize
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ETL
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Encode
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Filter
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Fragment
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Homogenize
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Impute
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Linearize
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Munge
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Normalize
Reduce
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Reshape
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Serialize
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Shred
Skew
Split
Standardize
Transform
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Wrangle
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