I’m working with large datasets in Python using Pandas (10M+ rows), and performance is becoming a bottleneck, especially during groupby and merge operations. I want to understand practical ways to optimize performance without moving to distributed frameworks like PySpark yet. Here’s a simplified version of what I’m doing:   import pandas as pd # Sample(Read More)

I’m working with large datasets in Python using Pandas (10M+ rows), and performance is becoming a bottleneck, especially during groupby and merge operations.

I want to understand practical ways to optimize performance without moving to distributed frameworks like PySpark yet.

Here’s a simplified version of what I’m doing:

 
import pandas as pd

# Sample large dataset
df = pd.read_csv(“large_data.csv”)

# Grouping operation
result = df.groupby(“category”)[“sales”].sum().reset_index()

# Merge with another dataset
df2 = pd.read_csv(“mapping.csv”)
final = result.merge(df2, on=“category”, how=“left”)

print(final.head())

 

I’ve looked into things like dtype optimization and indexing, but I’d like to know:

  • What are the most effective ways to speed this up?
  • Are there better alternatives within Python (like Polars or Dask) that are worth considering?
  • At what point should one realistically move away from Pandas?

Would appreciate insights from anyone who has handled similar scale problems.