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Should I Learn Polars or Pandas First in 2025? A Data Analyst's Career Guide

Cowrie
Cowrie
Dev @ Bswen

Purpose

This post explains whether to learn polars or pandas first as a data analyst starting in 2025. I got confused hearing mixed opinions about pandas being outdated while polars was praised everywhere. I worried about choosing the wrong tool and hurting my career prospects.

The Problem

When I started learning Python for data analysis, I heard two conflicting stories:

  • Some people say pandas is unintuitive and should be avoided
  • Others praise polars as 5-30x faster with better syntax

The core question kept bothering me: Should I invest time in pandas if it’s becoming obsolete? But when I checked Reddit discussions, I saw a user early in their Python journey with the same worry.

Here’s what I found:

  • User concern: Worried about being rejected for interviews for using “modern” tools instead of industry standards
  • Career anxiety: Afraid to learn “the wrong tool” that won’t help get hired
  • Mixed advice: Getting confused about which tool to learn first

The Data Analysis Reality

I think the key reason this problem exists is:

  • Industry adoption: Pandas is still the lingua franca of data analysis in most companies
  • Production systems: Companies have years of pandas code in production
  • Interview expectations: Managers expect pandas proficiency in technical interviews

When I examined the Reddit thread, the top-voted comment confirmed this approach: “Managers want candidates who understand both and can make strategic decisions about which tool to use.”

How I Solved It

I tried analyzing my learning path logically:

Phase 1: Master pandas fundamentals

  • Dataframes, groupby, merge operations
  • Time series analysis basics
  • Data cleaning and preparation

Phase 2: Learn polars advantages

  • Polars syntax improvements
  • Performance gains (5-30x faster)
  • Memory efficiency (1/3 memory usage)

Phase 3: Strategic tool selection

  • Use pandas for compatibility with existing code
  • Use polars for performance-critical tasks

Here’s how the actual code compares:

Standard pandas workflow:

import pandas as pd


# Standard pandas workflow
df = pd.read_csv('data.csv')
result = df.groupby('category')['value'].agg(['mean', 'count'])
result = result[result['mean'] > 100]
print(result)

Polars workflow (same operation, faster):

import polars as pl


# Equivalent polars workflow
df = pl.read_csv('data.csv')
result = df.groupby('category').agg([
    pl.col('value').mean().alias('mean'),
    pl.col('value').count().alias('count')
]).filter(pl.col('mean') > 100)
print(result)

The syntax looks similar, but polars is significantly faster for large datasets:

  • Pandas: ~2-5 seconds on 10M rows
  • Polars: ~0.1-1 second on 10M rows (5-30x improvement)

Common Mistakes to Avoid

I found several data analysts making these errors:

  1. Assuming polars will replace pandas entirely

    • Misunderstanding adoption timelines
    • Companies won’t rewrite years of pandas code

  2. Learning polars first without pandas fundamentals

    • Confusion when reading existing code at work
    • Missing understanding of core data concepts

  3. Ignoring compatibility requirements

    • Team environments need pandas for collaboration
    • Legacy systems require pandas knowledge

  4. Overestimating immediate performance needs

    • Small datasets work fine with pandas
    • Premature optimization isn’t necessary

The Career Strategy

In my experience, the most effective approach is:

  • First: Build pandas fundamentals to meet industry expectations
  • Then: Add polars to your skill set for performance tasks
  • Finally: Learn to choose the right tool for each situation

Multiple comments in the Reddit thread confirmed companies still use pandas widely in production systems. One user said they got rejected in interviews for not knowing pandas fundamentals.

What About Performance?

Polars is genuinely faster. The community confirmed polars is 5-30x faster than pandas with 1/3 memory usage. But performance doesn’t matter if:

  1. You can’t get the job without pandas knowledge
  2. Your company’s existing codebase uses pandas
  3. Your team members all speak pandas

I think of it like learning SQL first, then learning NoSQL databases. You need the foundation before the specialized tools.

Implementation Plan

Here’s what I recommend for new data analysts:

Months 1-3: Pandas fundamentals

  • Complete pandas tutorials
  • Practice with real datasets
  • Understand core concepts like groupby, merge, time series

Months 4-6: Polars introduction

  • Learn polars syntax differences
  • Test performance on your datasets
  • Practice common operations

Months 7+: Strategic use

  • Learn when to choose each tool
  • Contribute to both types of projects
  • Stay updated on industry adoption

The Bottom Line

In this post, I showed why pandas should be learned first by data analysts starting in 2025. The key takeaway is that pandas meets current industry needs while polars adds future-proofing performance skills.

Start with pandas to build foundational skills and get hired, then add polars to handle performance-critical work. Understanding both makes you more valuable and versatile.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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