Data wrangling
Data wrangling is the process of cleaning, transforming, restructuring, organizing, and combining raw data into a form suitable for analysis or downstream processing.
Statistics Slam Dunk
Data wrangling is the process of cleaning, transforming, reshaping, filtering, summarizing, and combining data for analysis.
- How it is used
- Prepares web-sourced NBA data for exploration, visualization, statistical testing, and modeling.
- How deep it goes
- Demonstrates step-by-step dplyr and tidyr operations, including filtering, reshaping, derived variables, summaries, and joins.
- Examples
- Uses NBA box scores, payrolls, win shares, player seasons, and play-by-play records as messy source data.
- What you can do after
- Apply it via dplyr, tidyr, readr, and base R to turn raw CSV files into analysis-ready datasets.
Delivering Machine Learning Projects: From design to deployment
Data wrangling is the process of restructuring, cleaning, and transforming data into a form suitable for analysis or modeling.
- How it is used
- Frames the effort needed to turn scattered transactions into daily inventory or customer views during pre-sales planning.
- How deep it goes
- Identifies table fragmentation, scale, and uneven quality as challenges without specifying transformation steps or code.
- Examples
- Uses five years of Bike Shop transactions split between noisy legacy records and a cleaner recent source.
- What you can do after
- Evaluate/understand it as a delivery risk when estimating work from transaction samples.
Read how Delivering Machine Learning Projects: From design to deployment explains data wrangling →
Statistics Every Programmer Needs
Data wrangling is the process of transforming and preparing raw data into a clean, consistent, and analysis-ready format.
- How it is used
- Prepares imperfect datasets for statistical analysis, visualization, and models such as decision trees and random forests.
- How deep it goes
- Demonstrates filtering, missing-value handling, recoding, feature creation, and validation in Python, without developing general data-integration strategies.
- Examples
- Uses fourth-down NFL play data and East Baton Rouge Parish street-address records to demonstrate filtering, cleaning, extraction, and type conversion.
- What you can do after
- Apply via Python pandas and NumPy to produce validated, model-ready variables and analysis-specific subsets.
Read how Statistics Every Programmer Needs explains data wrangling →
D3.js in Action, Third Edition
Data wrangling is the process of transforming, cleaning, and reshaping data for subsequent use or analysis.
- How it is used
- Uses data wrangling to prepare datasets for analysis, prototyping, and custom visualization.
- How deep it goes
- Explains property access, type conversion, iteration, mapping, filtering, and finding, but does not cover all manipulation techniques.
- Examples
- Demonstrates employee records, numeric ID conversion, name extraction, conditional filtering, and unique-record lookup.
- What you can do after
- Apply it via JavaScript array methods to clean, reshape, and select records for visualization.
Read how D3.js in Action, Third Edition explains data wrangling →
The Complete Obsolete Guide to Generative AI
The process of transforming, cleaning, and structuring raw data into a consistent machine-readable form.
- How it is used
- Prepares raw business data for analysis and question-answering workflows.
- How deep it goes
- Defines wrangling as parsing data into consistent machine-readable form, then illustrates downstream querying rather than implementation details.
- Examples
- Uses an audio transcript PDF and an anonymized bank-customer dataset queried for wealth and investment prospects.
- What you can do after
- Apply it via GPT-based indexing and targeted queries over prepared documents or datasets.
Read how The Complete Obsolete Guide to Generative AI explains data wrangling →
Related concepts
- pandasused by · pandas is an open-source Python library that provides labeled data structures and tools for manipulating and analyzing tabular and other structured data.
- dplyrused by · dplyr is an R package that provides a consistent grammar and functions for transforming, grouping, and summarizing tabular data.
- distinct keywordused by · A distinct operation eliminates duplicate values, elements, or rows from a result, retaining one occurrence of each unique item.
- tidyrused by · tidyr is an R package for transforming, reshaping, and organizing data into tidy tabular form.
- comparedf() functionused by · The comparedf() function from arsenal compares two data frames or other data sets and returns a summary of their differences.
- group_by()uses · group_by() assigns grouping variables to tabular data so subsequent operations can be performed separately within each group.
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.