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# Introduction
Though in trendy knowledge science you’ll primarily discover Jupyter notebooks, Pandas, and graphical dashboards, they don’t all the time provide the degree of management you may want. However, command-line instruments might not be as intuitive as you would like, however they’re highly effective, light-weight, and far quicker at executing the precise jobs they’re designed for.
For this text, I’ve tried to create a steadiness between utility, maturity, and energy. You’ll discover some classics which are almost unavoidable, together with extra trendy additions that fill gaps or optimize efficiency. You possibly can even name this a 2025 model of vital CLI instruments listing. For many who aren’t aware of CLI instruments however need to study, I’ve included a bonus part with assets within the conclusion, so scroll all the way in which down earlier than you begin together with these instruments in your workflow.
# 1. curl
curl is my go-to for making HTTP requests like GET, POST, or PUT; downloading information; and sending/receiving knowledge over protocols akin to HTTP or FTP. It’s perfect for retrieving knowledge from APIs or downloading datasets, and you may simply combine it with data-ingestion pipelines to drag JSON, CSV, or different payloads. One of the best factor about curl is that it’s pre-installed on most Unix programs, so you can begin utilizing it instantly. Nonetheless, its syntax (particularly round headers, physique payloads, and authentication) might be verbose and error-prone. If you end up interacting with extra advanced APIs, it’s possible you’ll want an easier-to-use wrapper or Python library, however figuring out curl remains to be a necessary plus for fast testing and debugging.
# 2. jq
jq is a light-weight JSON processor that permits you to question, filter, rework, and pretty-print JSON knowledge. With JSON being a dominant format for APIs, logs, and knowledge interchange, jq is indispensable for extracting and reshaping JSON in pipelines. It acts like “Pandas for JSON within the shell.” The largest benefit is that it supplies a concise language for coping with advanced JSON, however studying its syntax can take time, and very massive JSON information might require extra care with reminiscence administration.
# 3. csvkit
csvkit is a set of CSV-centric command-line utilities for remodeling, filtering, aggregating, becoming a member of, and exploring CSV information. You possibly can choose and reorder columns, subset rows, mix a number of information, convert from one format to a different, and even run SQL-like queries towards CSV knowledge. csvkit understands CSV quoting semantics and headers, making it safer than generic text-processing utilities for this format. Being Python-based means efficiency can lag on very massive datasets, and a few advanced queries could also be simpler in Pandas or SQL. When you want velocity and environment friendly reminiscence utilization, contemplate the csvtk toolkit.
# 4. qwk / sed
Hyperlink (sed): https://www.gnu.org/software program/sed/guide/sed.html
Basic Unix instruments like awk and sed stay irreplaceable for textual content manipulation. awk is highly effective for sample scanning, field-based transformations, and fast aggregations, whereas sed excels at textual content substitutions, deletions, and transformations. These instruments are quick and light-weight, making them good for fast pipeline work. Nonetheless, their syntax might be non-intuitive. As logic grows, readability suffers, and it’s possible you’ll migrate to a scripting language. Additionally, for nested or hierarchical knowledge (e.g., nested JSON), these instruments have restricted expressiveness.
# 5. parallel
GNU parallel hastens workflows by working a number of processes in parallel. Many knowledge duties are “mappable” throughout chunks of knowledge. Let’s say it’s a must to execute the identical transformation on tons of of information—parallel can unfold work throughout CPU cores, velocity up processing, and handle job management. It’s essential to, nevertheless, be conscious of I/O bottlenecks and system load, and quoting/escaping might be tough in advanced pipelines. For cluster-scale or distributed workloads, contemplate resource-aware schedulers (e.g., Spark, Dask, Kubernetes).
# 6. ripgrep (rg)
ripgrep (rg) is a quick recursive search instrument designed for velocity and effectivity. It respects .gitignore by default and ignores hidden or binary information, making it considerably quicker than conventional grep. It’s good for fast searches throughout codebases, log directories, or config information. As a result of it defaults to ignoring sure paths, it’s possible you’ll want to regulate flags to go looking all the pieces, and it isn’t all the time accessible by default on each platform.
# 7. datamash
datamash supplies numeric, textual, and statistical operations (sum, imply, median, group-by, and so forth.) straight within the shell through stdin or information. It’s light-weight and helpful for fast aggregations with out launching a heavier instrument like Python or R, which makes it perfect for shell-based ETL or exploratory evaluation. But it surely’s not designed for very massive datasets or advanced analytics, the place specialised instruments carry out higher. Additionally, grouping very excessive cardinalities might require substantial reminiscence.
# 8. htop
htop is an interactive system monitor and course of viewer that gives reside insights into CPU, reminiscence, and I/O utilization per course of. When working heavy pipelines or mannequin coaching, htop is extraordinarily helpful for monitoring useful resource consumption and figuring out bottlenecks. It’s extra user-friendly than conventional prime, however being interactive means it doesn’t match properly into automated scripts. It could even be lacking on minimal server setups, and it doesn’t exchange specialised efficiency instruments (profilers, metrics dashboards).
# 9. git
git is a distributed model management system important for monitoring adjustments to code, scripts, and small knowledge belongings. For reproducibility, collaboration, branching experiments, and rollback, git is the usual. It integrates with deployment pipelines, CI/CD instruments, and notebooks. Its downside is that it’s not meant for versioning massive binary knowledge, for which Git LFS, DVC, or specialised programs are higher suited. The branching and merging workflow additionally comes with a studying curve.
# 10. tmux / display screen
Terminal multiplexers like tmux and display screen allow you to run a number of terminal classes in a single window, detach and reattach classes, and resume work after an SSH disconnect. They’re important if it’s good to run lengthy experiments or pipelines remotely. Whereas tmux is really helpful attributable to its lively improvement and suppleness, its config and keybindings might be tough for newcomers, and minimal environments might not have it put in by default.
# Wrapping Up
When you’re getting began, I’d advocate mastering the “core 4”: curl, jq, awk/sed, and git. These are used in all places. Over time, you’ll uncover domain-specific CLIs like SQL shoppers, the DuckDB CLI, or Datasette to fit into your workflow. For additional studying, try the next assets:
- Information Science on the Command Line by Jeroen Janssens
- The Artwork of Command Line on GitHub
- Mark Pearl’s Bash Cheatsheet
- Communities just like the unix & command-line subreddits usually floor helpful methods and new instruments that may broaden your toolbox over time.
Kanwal Mehreen is a machine studying engineer and a technical author with a profound ardour for knowledge science and the intersection of AI with medication. She co-authored the book “Maximizing Productiveness with ChatGPT”. As a Google Technology Scholar 2022 for APAC, she champions range and educational excellence. She’s additionally acknowledged as a Teradata Variety in Tech Scholar, Mitacs Globalink Analysis Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having based FEMCodes to empower ladies in STEM fields.
