---
title: "Pandas | Skills We Assign For | Azendo"
description: "Pandas for data preparation — memory limits, reproducibility, and the Azendo roles assigned for analysis work."
url: "https://azendo.co/skills/pandas/"
---

[Skills](https://azendo.co/skills/) AI and machine learning 

# Pandas.

Pandas is the standard Python library for tabular data manipulation — loading, cleaning, joining, reshaping and aggregating datasets in memory. It is where most analysis and most feature preparation actually happens.

## Where Pandas fits on a long engagement.

Pandas is where analytical work lives, and where it becomes unreproducible. A notebook with cells run out of order, a dataframe mutated in place four times, and a result nobody can regenerate is the normal state of analysis that was never intended to be repeated and then was.

It works in memory, which sets a real ceiling. Past that point the options are sampling, chunking, or pushing the work into the warehouse — and pushing it into the warehouse is usually correct, because the database is better at joins than a laptop is.

## What an assigned team does with Pandas.

Analysis becomes infrastructure without anyone deciding it should. A notebook written once to answer a question gets run monthly, then depended on, and eventually a business process rests on a file on somebody's machine.

Catching that early is a continuity benefit rather than a technical one. Where the work has genuinely become a pipeline, it should be rebuilt as one under [managed data services](https://azendo.co/services/data-engineering/) rather than left as a notebook with a calendar reminder.

## What we use Pandas for.

* Analysis that can be re-run Notebook work restructured into scripts that produce the same answer twice.
* Feature preparation for a model Cleaning and shaping that is version-controlled and applied identically at training and inference.
* Moving work to the warehouse Heavy joins pushed into SQL where they belong, leaving Pandas for the part that genuinely needs it.

## How Pandas capacity is assigned.

This work sits inside a [data science](https://azendo.co/services/data-science/) assignment rather than being scoped on its own.

## Roles we assign Pandas for

* [Data Scientist Data science](https://azendo.co/services/data-science/data-scientist/)
* [Machine Learning Engineer MLOps engineering](https://azendo.co/services/mlops-engineering/machine-learning-engineer/)

## Service lines it sits in

* [Data science](https://azendo.co/services/data-science/)
* [MLOps engineering](https://azendo.co/services/mlops-engineering/)

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in ai and machine learning

* [PyTorch — skill we assign for](https://azendo.co/skills/pytorch/)
* [TensorFlow — skill we assign for](https://azendo.co/skills/tensorflow/)
* [scikit-learn — skill we assign for](https://azendo.co/skills/scikit-learn/)
* [NumPy — skill we assign for](https://azendo.co/skills/numpy/)
* [embeddings — skill we assign for](https://azendo.co/skills/embeddings/)
* [function calling — skill we assign for](https://azendo.co/skills/function-calling/)
* [vLLM — skill we assign for](https://azendo.co/skills/vllm/)
* [MLflow — skill we assign for](https://azendo.co/skills/mlflow/)

## Tell us what your roadmap needs Pandas for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)