Stop using Pandas .apply() for ML preprocessing: How I cut pipeline overhead by 35%
Posted by Separate_Action1216@reddit | Python | View on Reddit | 20 comments
Was working on preprocessing 50k+ records and hit a massive bottleneck: using loops and .apply() in Pandas. It’s fine for toy datasets, but once you scale, it slows down experimentation and validation cycles to a crawl.
Switching to strict vectorized operations (NumPy / scikit-learn) fixed it. The strategy:
- Swapped element-wise operations for contiguous array-level operations
- Reduced unnecessary data copying in memory
Result: \~35% faster preprocessing execution and much tighter iteration cycles.
Curious what others are doing before jumping to heavy distributed tools like Dask or Spark:
- Any go-to tricks for improving memory efficiency at this scale?
- How are you handling intermediate state caching in long pipelines?
EntertainmentOne7897@reddit
Choose one. Toy dataset and pandas or real dataset and anything but pandas.
Conclusion: why even start in pandas if there is a chance that it will go to production. polars, duckdb is out there for years now
Separate_Action1216@reddit (OP)
Hard to disagree that Polars or DuckDB are the right move for heavy production workloads. But 'why even start in pandas' ignores industry reality: it’s still the default for rapid EDA and the backbone of almost every legacy codebase you'll inherit. The point isn't to force Pandas into production at all costs it's that if a developer doesn't fundamentally understand vectorization and memory management, they'll just write the same slow, iterative bottlenecks in Polars too.
EntertainmentOne7897@reddit
Luckily my reality is that I havent seen pandas for a year now, we all switched to polars in the team
Separate_Action1216@reddit (OP)
That’s a great spot to be in working in a fully migrated Polars codebase is a massive quality of life upgrade. But for anyone stepping into a standard enterprise environment or inheriting older ML pipelines, knowing how to optimize and vectorize baseline Pandas before a migration gets approved is still a mandatory survival skill.
HolyInlandEmpire@reddit
Use polars and use proper select/filter/with_columns statements and lazy frames.
Stuck with pandas? Good news! Convert to polars, apply operations, convert back to pandas at the end of the processes.
Separate_Action1216@reddit (OP)
Lazy frames are definitely the way to go if you're building a fresh pipeline having the query optimizer handle execution order under the hood is fantastic. But the 'convert to Polars and back to Pandas' strategy is a trap for legacy codebases. The serialization overhead and memory duplication from casting between those structures can easily eat up the execution time you just saved. It’s usually better to either commit fully to the Polars engine end-to-end, or just drop down to raw NumPy vectors if you're locked into a Pandas environment.
HolyInlandEmpire@reddit
To be sure; fully committing is best. Having said that, if the conversion happens only one each way, rather than in a loop, it can still work pretty well until a complete migration can happen.
Separate_Action1216@reddit (OP)
Fair point. Incremental migration is usually the only realistic way to untangle a legacy pipeline anyway. The only major danger with the 'one-way in, one-way out' approach is the temporary memory spike. During that conversion handoff you're effectively holding both DataFrames in RAM simultaneously. As long as you aren't brushing up against your container's memory limits, it's a very solid bridge strategy while working towards a full migration.
aloobhujiyaay@reddit
Polars is another option here. Often faster than Pandas without needing distributed systems
Separate_Action1216@reddit (OP)
Agreed, Polars is a massive step up and perfectly bridges the gap before you actually need to reach for a distributed system like Dask. What I really appreciate about it is the Expression API it essentially forces developers out of that row-by-row .apply() habit and naturally pushes them into the exact vectorized mindset I was aiming for here.
Vhiet@reddit
Once datasets get bigger than memory, I switch to DuckDb.
Reaching for arrow is a nice intermediate step, but I just go straight to DuckDb and cut out the middleman these days. It also integrates beautifully to Postgres, which is my RDBMS of choice for persisting data models.
Separate_Action1216@reddit (OP)
DuckDB is an absolute powerhouse for out-of-core processing. I completely agree on the Postgres integration it’s my go-to RDBMS for persisting state in my backend systems as well, so that seamless handoff is a massive plus. While pushing strict vectorization keeps you operating in-memory much longer, DuckDB is the perfect architectural pivot once you inevitably hit that RAM ceiling.
ddofer@reddit
50k is typically toy
Separate_Action1216@reddit (OP)
True, 50k fits comfortably in memory and is small by production standards. But that’s exactly the point: if an inefficient .apply() loop is already bottlenecking a 'toy' dataset, it’s going to absolutely nuke a 5M record pipeline. Better to build the vectorization muscle at this scale before it becomes a catastrophic compute bill later.
v_a_n_d_e_l_a_y@reddit
People pushing polars miss the point.
Rewriting code to be vectorized is important in any library. Polars will see similar gains.
zzzthelastuser@reddit
Perhaps that's because most people in ML take vectorized processing pretty much as a given? I don't write manual python loops unless there is a really good reason to do so and I can't find a way around it.
It's not even complicated and in my opinion leads to code that is much easier to read, since it's closer to the math formulas.
Separate_Action1216@reddit (OP)
Fair point on the readability it definitely maps much closer to the actual linear algebra once you make the mental switch. But you'd be surprised how many people transition into data from traditional backend/software engineering and bring their 'for-loop' mindset with them. Pandas .apply() acts as a massive crutch for a lot of devs until they hit their first real production bottleneck and are forced to finally think in vectors.
lungben81@reddit
Yes. Vectorization can give you up to 100× the performance on python. Parallelism could give you a max speedup of the number of cores and a higher power demand.
Separate_Action1216@reddit (OP)
Spot on. A lot of people jump straight to multiprocessing to fix slow code, only to get killed by serialization overhead (IPC) and memory duplication. Pushing the workload down to C-level SIMD instructions via vectorization is almost always the cleaner, cheaper win before you even start worrying about scaling across cores.
Separate_Action1216@reddit (OP)
Exactly. Dropping Polars into a pipeline full of row-by-row iterations is just putting a band-aid on a broken architecture. The real performance unlock comes from forcing yourself to think in contiguous memory blocks and array-level operations, regardless of the wrapper.