The Honest Truth About Pandas Link to heading

If you’ve spent any serious time with dplyr, your first encounter with pandas probably went something like this:

You wrote a chain of .groupby().agg().reset_index() and thought: “Why do I need to reset the index? What even IS the index? Why is this so much harder than group_by() %>% summarise()?”

You’re not being dramatic. Pandas was designed in 2008, when single-threaded execution was the norm and in-place mutation was considered a feature, not a footgun. The API reflects those design decisions. For R users coming from the tidyverse, where immutability, method chaining, and declarative syntax are foundational, pandas feels like a step backward.

But here’s the good news: you don’t have to use pandas for your own data wrangling anymore.

What Is Polars? Link to heading

Polars is a DataFrame library written in Rust by Ritchie Vink, released in 2020 and reaching version 1.0 in mid-2024. It uses Apache Arrow as its in-memory columnar format, which means zero-copy data sharing with other Arrow-compatible tools.

The important part for you as a tidyverse user is what this translates to in practice:

  • Method chaining: polars expressions chain cleanly, reading top-to-bottom just like piped dplyr code

  • No index: there is no index in polars. No .reset_index(), no .set_index(), no MultiIndex nightmares. Rows are rows. Columns are columns. That’s it.

  • Lazy evaluation: you can build an entire query plan and let polars optimize it before executing. Think of it like how dbplyr translates your dplyr code into optimised SQL. Polars does something similar, but for in-memory data.

  • Automatic parallelism: operations run across all CPU cores by default, no configuration required

  • Strict typing: columns have explicit types, and polars tells you when something doesn’t match instead of silently coercing

The Rosetta Stone Link to heading

This is the section you’ll probably bookmark. Let’s compare the same operations across dplyr, pandas, and polars.

Filtering rows:

# dplyr
df %>% filter(logFC > 1, padj < 0.05)

# pandas
df.loc[(df['logFC'] > 1) & (df['padj'] < 0.05)]

# polars
df.filter((pl.col('logFC') > 1) & (pl.col('padj') < 0.05))

Selecting columns:

# dplyr
df %>% select(gene, logFC, padj)

# pandas
df[['gene', 'logFC', 'padj']]

# polars
df.select('gene', 'logFC', 'padj')

Grouped aggregation:

# dplyr
df %>% group_by(cluster) %>% summarise(mean_expr = mean(expression))

# pandas
df.groupby('cluster')['expression'].mean().reset_index()

# polars
df.group_by('cluster').agg(pl.col('expression').mean().alias('mean_expr'))

Notice the pattern? Polars reads almost identically to dplyr. The column selection uses pl.col() instead of bare column names, and you chain with dots instead of pipes, but the logic is the same. And crucially, there is no .reset_index() because there’s no index to reset.

A real bioinformatics example: chained operations

Here’s something you might actually do: filter your DEG results, sort by fold change, and grab the top hits.

# dplyr
df %>%
  filter(padj < 0.05, abs(logFC) > 1) %>%
  arrange(desc(abs(logFC))) %>%
  head(20)

# polars
df.filter(
    (pl.col('padj') < 0.05) & (pl.col('logFC').abs() > 1)
).sort(
    pl.col('logFC').abs(), descending=True
).head(20)

That reads exactly like the dplyr version. Filter, sort, take the top. No intermediate variables, no index gymnastics, no confusion about whether you’re looking at a copy or a view.

Lazy Evaluation: The Secret Weapon Link to heading

If you’ve used dbplyr in R, lazy evaluation will feel immediately familiar. Instead of executing operations the moment you write them, you build a query plan:

# Lazy mode: nothing executes until .collect()
result = (
    pl.scan_csv("cell_metadata.csv")
    .filter(pl.col("cell_type") == "T_cell")
    .group_by("patient_id")
    .agg(pl.col("nCount_RNA").mean())
    .sort("nCount_RNA", descending=True)
    .collect()  # NOW it executes
)

Before .collect() runs, polars examines your entire query plan and optimises it. If you only need two columns but the CSV has fifty, polars will only read those two from disk. It reorders operations, pushes filters down, and eliminates redundant steps, all automatically.

Pandas has no equivalent. Every line executes immediately, whether or not the result is needed.

Performance: Where It Actually Matters Link to heading

For a DEG table with 20,000 rows, you won’t notice a difference between pandas and polars. Both finish instantly.

But bioinformatics data gets big fast. Your scRNA-seq metadata table might have 500,000 rows and 30 columns. Your spatial transcriptomics dataset might be even larger. And if you’re doing any kind of meta-analysis across multiple datasets, you’re looking at millions of rows.

This is where polars pulls ahead dramatically. Benchmarks consistently show polars running common operations 10 to 100 times faster than pandas, with 30-70% lower memory usage. The performance gap widens with dataset size, which is exactly when performance matters most.

The memory efficiency deserves special attention. Pandas requires roughly 5 to 10 times as much RAM as the size of your dataset to carry out operations because it creates copies during transformations and stores intermediate results. Polars needs about 2 to 4 times the dataset size, thanks to its columnar Arrow format and in-place query optimization. If you’ve ever had a Jupyter kernel die mid-analysis because pandas ate all your RAM on a large cell-by-gene matrix, polars is the antidote.

Polars also handles larger-than-memory datasets through streaming execution, processing data in chunks without loading everything into RAM. Pandas simply crashes when data exceeds available memory.

The Ecosystem Question Link to heading

Here’s the honest caveat: scanpy expects pandas DataFrames. So does scikit-learn, seaborn, and most of the established Python data science stack.

But conversion is trivial:

# Polars to Pandas, when you need it
pandas_df = polars_df.to_pandas()

# Pandas to Polars, when you want speed
polars_df = pl.from_pandas(pandas_df)

The practical workflow is: use polars for your data wrangling, filtering, joining, and aggregation, all the stuff where you’d normally reach for dplyr. Convert to pandas at the boundary when handing data to scanpy or sklearn. This is no different from how you might use data.table for heavy lifting in R and then convert back to a tibble for ggplot.

My Recommendation Link to heading

If you’re a tidyverse user entering Python, start with polars for your data manipulation. Learn pandas too, because you’ll need it for ecosystem compatibility, but don’t force yourself to wrangle data in pandas when polars exists.

The mental model transfers directly: pl.col() is your column reference, polars .filter() maps to dplyr filter, and .group_by().agg() maps to group_by() %>% summarise(). Even the concept of lazy evaluation maps onto dbplyr patterns you might already use. The biggest adjustment is syntactic: dots instead of pipes, explicit pl.col() references instead of bare names, and that becomes second nature within a day or two.

The syntax will feel familiar. The performance will feel liberating. And you’ll stop wondering why Python data manipulation felt so much harder than R.

Next up: uv vs conda, why package management in Python no longer has to be painful.

See you there.