Text
Text is written or digitally encoded linguistic content represented as characters, words, tokens, or other textual units.
Data Without Labels
Text data is information represented as written language, including documents, messages, survey responses, and other textual content.
- How it is used
- Turns unstructured written feedback and other language into data for business insight and NLP tasks.
- How deep it goes
- Develops cleaning and vector-representation choices, while introducing generative AI only at a high level.
- Examples
- Uses product reviews, survey responses, transcribed calls, and context-dependent word meanings to illustrate sources and difficulties.
- What you can do after
- Apply it via Python and nltk to clean and parse text, then compare representations for analysis.
The Quick Python Book, Fourth Edition
Text is written language represented as a sequence of characters, including content stored in plain-text files.
- How it is used
- Supports reading, cleaning, segmenting, and extracting information from plain-text data files.
- How deep it goes
- Explains encoding, segmentation, normalization, and error handling, but stops short of comprehensive text-processing methods.
- Examples
- Uses tweets, Moby Dick, news stories, paragraph splitting, word extraction, and invalid-byte decoding examples.
- What you can do after
- Apply it via Python to read text, choose units, split paragraphs, normalize words, and manage decoding errors.
Read how The Quick Python Book, Fourth Edition explains text →
Pandas Workout: 200 exercises to make you a stronger data analyst
Textual data consists of character-based values representing words, phrases, or other written content rather than numeric quantities.
- How it is used
- Handles descriptive, categorical, and machine-learning text alongside numerical data through pandas Series operations.
- How deep it goes
- Explains storage types, the str accessor, returned Series, common methods, and custom functions without detailing string-class internals.
- Examples
- Uses usernames, product names, regions, business units, ticker symbols, company names, and split words such as test, 123, and 456.
- What you can do after
- Apply it via pandas' str accessor to measure, classify, search, replace, expand, and encode text in Series and data frames.
Read how Pandas Workout: 200 exercises to make you a stronger data analyst explains text →
Related concepts
- unstructured datakind of · Unstructured data is information that lacks a predefined schema or consistently organized fields.
- promptincludes · An input, typically containing instructions or context, supplied to an artificial-intelligence model to guide or elicit its output.
- generative modelingproduced by · Generative modeling is a machine-learning approach that learns patterns or a data distribution to generate new samples resembling the data.
- speech recognitionproduced by · Speech recognition is the computational conversion of spoken language from audio into a machine-readable representation, typically text.
- paragraph elementpart of · An HTML paragraph element is a p element representing a distinct block of related text.
- abstractionkind of · Abstraction is a simplified representation that exposes relevant properties or behavior while omitting lower-level details unnecessary for a given purpose.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.