# Tokenize

> Convert data into tokens or smaller units to simplify analysis or processing.

Source: https://dagster.io/glossary/data-tokenizing

## 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](https://dagster.io/glossary) 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:

```python
text = "This is a sample sentence."
tokens = text.split()
print(tokens)
```

This would output:

```plaintext
['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:

```python
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:

```plaintext
['This', 'is', 'a', 'sample', 'sentence', '.']
```

Using regular expressions: [Regular expressions](https://docs.python.org/3/library/re.html) 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:

```python
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:

```plaintext
['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:

```python
import re

text = "This is a sample sentence."
tokens = re.split('\W+', text)
print(tokens)
```

This will yield:

```plaintext
['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.