The best pandas alternatives
The rest of Best Data Transformation Tools, in this site's order: each with what it costs, where it runs and its record score.
This is aimed at customer success executives and managers, renewals and retention teams, support leaders, CS operations, and onboarding teams.
Apache Beam suits data and software developers building batch or streaming pipelines for supported execution back ends.
It suits teams building and running data integration pipelines across varied environments.
The free desktop platform suits people building data workflows for personal use.
Cube may suit teams building internal business intelligence or embedded analytics, particularly those seeking governed definitions shared by…
It suits data teams that need governed metrics across analytics, embedded applications, or AI use cases.
Polars suits developers and data teams building code-based data transformation workflows across local, cloud, or on-premises environments.
LANSA is aimed at IT and development teams building enterprise business solutions, particularly teams working with IBM i.
Catalog is intended for data engineers and analysts, with a design intended for use across an organization without training.
jq suits developers and other command-line users who need to query or transform JSON streams.
OpenRefine suits people preparing or enriching datasets, including journalists, media professionals, galleries, libraries, archives, and museums.
It suits data teams that want to create, review, and run visual data preparation workflows using natural-language goals.
Tableau suits teams that need to build and share dashboards, visualizations, or data apps.
Bonobo suits Python developers building small-scale ETL jobs from simple, testable transformations.
csvkit may suit developers, desktop users, and science or research users who work with CSV data from the command line.
data.table suits R users working with tabular data and delimited files who are comfortable with a code-based workflow.
dplyr suits people learning data transformation in R, as well as users who work with data frames or tibbles.
Ibis suits Python practitioners, data platform teams, organizations, and library developers who want to work with dataframe operations across query…
