data-analysis

Table of Contents

Introduction
Tool Strengths at a Glance
Setup
Syntax Comparison
Data Loading Performance
Query Optimization
GroupBy Performance
Memory Efficiency
Join Operations
Interoperability
Decision Matrix
Final Thoughts

Introduction
pandas has been the standard tool for working with tabular data in Python for over a decade. But as datasets grow larger and performance requirements increase, two modern alternatives have emerged: Polars, a DataFrame library written in Rust, and DuckDB, an embedded SQL database optimized for analytics.
Each tool excels in different scenarios:

Tool
Backend
Execution Model
Best For

pandas
C/Python
Eager, single-threaded
Small datasets, prototyping, ML integration

Polars
Rust
Lazy/Eager, multi-threaded
Large-scale analytics, data pipelines

DuckDB
C++
SQL-first, multi-threaded
SQL workflows, embedded analytics, file queries

This guide compares all three tools with practical examples, helping you choose the right one for your workflow.

💻 Get the Code: The complete source code and Jupyter notebook for this tutorial are available on GitHub. Clone it to follow along!

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Tool Strengths at a Glance
pandas
pandas is the original DataFrame library for Python that excels at interactive data exploration and integrates seamlessly with the ML ecosystem. Key capabilities include:

Direct compatibility with scikit-learn, statsmodels, and visualization libraries
Rich ecosystem of extensions (pandas-profiling, pandasql, etc.)
Mature time series functionality
Familiar syntax that most data scientists already know

Polars
Polars is a Rust-powered DataFrame library designed for speed that brings multi-threaded execution and query optimization to Python. Key capabilities include:

Speeds up operations by using all available CPU cores by default
Builds a query plan first, then executes only what’s needed
Streaming mode for processing datasets larger than RAM
Expressive method chaining with a pandas-like API

DuckDB
DuckDB is an embedded SQL database optimized for analytics that brings database-level query optimization to local files. Key capabilities include:

Native SQL syntax with full analytical query support
Queries CSV, Parquet, and JSON files directly without loading
Uses disk storage automatically when data exceeds available memory
Zero-configuration embedded database requiring no server setup

Setup
Install all three libraries:
pip install pandas polars duckdb

Generate sample data for benchmarking:
import pandas as pd
import numpy as np

np.random.seed(42)
n_rows = 5_000_000

data = {
"category": np.random.choice(["Electronics", "Clothing", "Food", "Books"], size=n_rows),
"region": np.random.choice(["North", "South", "East", "West"], size=n_rows),
"amount": np.random.rand(n_rows) * 1000,
"quantity": np.random.randint(1, 100, size=n_rows),
}

df_pandas = pd.DataFrame(data)
df_pandas.to_csv("sales_data.csv", index=False)
print(f"Created sales_data.csv with {n_rows:,} rows")

Created sales_data.csv with 5,000,000 rows

Syntax Comparison
All three tools can perform the same operations with different syntax. Here’s a side-by-side comparison of common tasks.
Filtering Rows
pandas:
Uses bracket notation with boolean conditions, which requires repeating the DataFrame name for each condition:
import pandas as pd

df_pd = pd.read_csv("sales_data.csv")
result_bracket = df_pd[(df_pd["amount"] > 500) & (df_pd["category"] == "Electronics")]
result_bracket.head()

category
region
amount
quantity

7
Electronics
West
662.803066
80

15
Electronics
North
826.004963
25

30
Electronics
North
766.081832
7

31
Electronics
West
772.084261
36

37
Electronics
East
527.967145
35

Alternatively, you can use the query() method, which provides cleaner SQL-like syntax:
result_query = df_pd.query("amount > 500 and category == 'Electronics'")

However, since query() is string-based, there’s no IDE autocomplete. Complex operations like string methods still require brackets:
result_str = df_pd[df_pd["category"].str.startswith("Elec")]
result_str.head()

category
region
amount
quantity

2
Electronics
North
450.941022
93

6
Electronics
West
475.843957
61

7
Electronics
West
662.803066
80

15
Electronics
North
826.004963
25

21
Electronics
South
292.399383
13

Polars:
Unlike pandas, Polars uses one syntax for all filters. The pl.col() expressions are type-safe with IDE autocomplete, and handle both simple comparisons and complex operations like string methods:
import polars as pl

df_pl = pl.read_csv("sales_data.csv")
result_pl = df_pl.filter(
(pl.col("amount") > 500) & (pl.col("category") == "Electronics")
)
result_pl.head()

category
region
amount
quantity

str
str
f64
i64

“Electronics”
“West”
662.803066
80

“Electronics”
“North”
826.004963
25

“Electronics”
“North”
766.081832
7

“Electronics”
“West”
772.084261
36

“Electronics”
“East”
527.967145
35

DuckDB:
Uses standard SQL with a WHERE clause, which is more readable by those who know SQL.
import duckdb

result_duckdb = duckdb.sql("""
SELECT * FROM 'sales_data.csv'
WHERE amount > 500 AND category = 'Electronics'
""").df()
result_duckdb.head()

category
region
amount
quantity

0
Electronics
West
662.803066
80

1
Electronics
North
826.004963
25

2
Electronics
North
766.081832
7

3
Electronics
West
772.084261
36

4
Electronics
East
527.967145
35

Selecting Columns
pandas:
Double brackets return a DataFrame with selected columns.
result_pd = df_pd[["category", "amount"]]
result_pd.head()

category
amount

0
Food
516.653322

1
Books
937.337226

2
Electronics
450.941022

3
Food
674.488081

4
Food
188.847906

Polars:
The select() method clearly communicates column selection intent.
result_pl = df_pl.select(["category", "amount"])
result_pl.head()

category
amount

str
f64

“Food”
516.653322

“Books”
937.337226

“Electronics”
450.941022

“Food”
674.488081

“Food”
188.847906

DuckDB:
SQL’s SELECT clause makes column selection intuitive for SQL users.
result_duckdb = duckdb.sql("""
SELECT category, amount FROM 'sales_data.csv'
""").df()
result_duckdb.head()

category
amount

0
Food
516.653322

1
Books
937.337226

2
Electronics
450.941022

3
Food
674.488081

4
Food
188.847906

GroupBy Aggregation
pandas:
Uses a dictionary to specify aggregations, but returns multi-level column headers that often require flattening before further use.
result_pd = df_pd.groupby("category").agg({
"amount": ["sum", "mean"],
"quantity": "sum"
})
result_pd.head()

amount

quantity

sum
mean
sum

Books
6.247506e+08
499.998897
62463285

Clothing
6.253924e+08
500.139837
62505224

Electronics
6.244453e+08
499.938189
62484265

Food
6.254034e+08
499.916417
62577943

Polars:
Uses explicit alias() calls for each aggregation, producing flat column names directly without post-processing.
result_pl = df_pl.group_by("category").agg([
pl.col("amount").sum().alias("amount_sum"),
pl.col("amount").mean().alias("amount_mean"),
pl.col("quantity").sum().alias("quantity_sum"),
])
result_pl.head()

category
amount_sum
amount_mean
quantity_sum

str
f64
f64
i64

“Clothing”
6.2539e8
500.139837
62505224

“Books”
6.2475e8
499.998897
62463285

“Electronics”
6.2445e8
499.938189
62484265

“Food”
6.2540e8
499.916417
62577943

DuckDB:
Standard SQL aggregation with column aliases produces clean, flat output ready for downstream use.
result_duckdb = duckdb.sql("""
SELECT
category,
SUM(amount) as amount_sum,
AVG(amount) as amount_mean,
SUM(quantity) as quantity_sum
FROM 'sales_data.csv'
GROUP BY category
""").df()
result_duckdb.head()

category
amount_sum
amount_mean
quantity_sum

0
Food
6.254034e+08
499.916417
62577943.0

1
Electronics
6.244453e+08
499.938189
62484265.0

2
Clothing
6.253924e+08
500.139837
62505224.0

3
Books
6.247506e+08
499.998897
62463285.0

Adding Columns
pandas:
The assign() method creates new columns with repeated DataFrame references like df_pd["amount"].
result_pd = df_pd.assign(
amount_with_tax=df_pd["amount"] * 1.1,
high_value=df_pd["amount"] > 500
)
result_pd.head()

category
region
amount
quantity
amount_with_tax
high_value

0
Food
South
516.653322
40
568.318654
True

1
Books
East
937.337226
45
1031.070948
True

2
Electronics
North
450.941022
93
496.035124
False

3
Food
East
674.488081
46
741.936889
True

4
Food
East
188.847906
98
207.732697
False

Polars:
The with_columns() method uses composable expressions that chain naturally without repeating the DataFrame name.
result_pl = df_pl.with_columns([
(pl.col("amount") * 1.1).alias("amount_with_tax"),
(pl.col("amount") > 500).alias("high_value")
])
result_pl.head()

category
region
amount
quantity
amount_with_tax
high_value

str
str
f64
i64
f64
bool

“Food”
“South”
516.653322
40
568.318654
true

“Books”
“East”
937.337226
45
1031.070948
true

“Electronics”
“North”
450.941022
93
496.035124
false

“Food”
“East”
674.488081
46
741.936889
true

“Food”
“East”
188.847906
98
207.732697
false

DuckDB:
SQL’s SELECT clause defines new columns directly in the query, keeping transformations readable.
result_duckdb = duckdb.sql("""
SELECT *,
amount * 1.1 as amount_with_tax,
amount > 500 as high_value
FROM df_pd
""").df()
result_duckdb.head()

category
region
amount
quantity
amount_with_tax
high_value

0
Food
South
516.653322
40
568.318654
True

1
Books
East
937.337226
45
1031.070948
True

2
Electronics
North
450.941022
93
496.035124
False

3
Food
East
674.488081
46
741.936889
True

4
Food
East
188.847906
98
207.732697
False

Conditional Logic
pandas:
Each additional condition in np.where() adds another nesting level. With three tiers, the final value is buried two levels deep:
import numpy as np

# Hard to read: "low" is nested inside two np.where() calls
result_np = df_pd.assign(
value_tier=np.where(
df_pd["amount"] > 700, "high",
np.where(df_pd["amount"] > 300, "medium", "low")
)
)
result_np[["category", "amount", "value_tier"]].head()

category
amount
value_tier

0
Food
516.653322
medium

1
Books
937.337226
high

2
Electronics
450.941022
medium

3
Food
674.488081
medium

4
Food
188.847906
low

For numeric binning, pd.cut() is cleaner:
result_pd = df_pd.assign(
value_tier=pd.cut(
df_pd["amount"],
bins=[-np.inf, 300, 700, np.inf],
labels=["low", "medium", "high"]
)
)
result_pd[["category", "amount", "value_tier"]].head()

category
amount
value_tier

0
Food
516.653322
medium

1
Books
937.337226
high

2
Electronics
450.941022
medium

3
Food
674.488081
medium

4
Food
188.847906
low

However, pd.cut() has drawbacks:

Only works for numeric ranges
Requires thinking in boundaries ([-inf, 300, 700, inf]) instead of conditions (amount > 700)
Needs numpy for open-ended bins

For non-numeric or mixed conditions, you’re back to np.where():
# "premium" if Electronics AND amount > 500 – pd.cut() can't do this
result = df_pd.assign(
tier=np.where(
(df_pd["category"] == "Electronics") & (df_pd["amount"] > 500),
"premium", "standard"
)
)
result.head()

category
region
amount
quantity
tier

0
Food
South
516.653322
40
standard

1
Books
East
937.337226
45
standard

2
Electronics
North
450.941022
93
standard

3
Food
East
674.488081
46
standard

4
Food
East
188.847906
98
standard

Polars:
The when().then().otherwise() chain solves both pandas problems: no nesting like np.where(), and works for any condition (not just numeric ranges like pd.cut()). The same syntax handles simple binning and complex mixed conditions:
result_pl = df_pl.with_columns(
pl.when(pl.col("amount") > 700).then(pl.lit("high"))
.when(pl.col("amount") > 300).then(pl.lit("medium"))
.otherwise(pl.lit("low"))
.alias("value_tier")
)
result_pl.select(["category", "amount", "value_tier"]).head()

category
amount
value_tier

str
f64
str

“Food”
516.653322
“medium”

“Books”
937.337226
“high”

“Electronics”
450.941022
“medium”

“Food”
674.488081
“medium”

“Food”
188.847906
“low”

DuckDB:
Standard SQL CASE WHEN syntax is more readable by those who know SQL.
result_duckdb = duckdb.sql("""
SELECT category, amount,
CASE
WHEN amount > 700 THEN 'high'
WHEN amount > 300 THEN 'medium'
ELSE 'low'
END as value_tier
FROM df_pd
""").df()
result_duckdb.head()

category
amount
value_tier

0
Food
516.653322
medium

1
Books
937.337226
high

2
Electronics
450.941022
medium

3
Food
674.488081
medium

4
Food
188.847906
low

Window Functions
pandas:
Uses groupby().transform() which requires repeating the groupby clause for each calculation.
result_pd = df_pd.assign(
category_avg=df_pd.groupby("category")["amount"].transform("mean"),
category_rank=df_pd.groupby("category")["amount"].rank(ascending=False)
)
result_pd[["category", "amount", "category_avg", "category_rank"]].head()

category
amount
category_avg
category_rank

0
Food
516.653322
499.916417
604342.0

1
Books
937.337226
499.998897
78423.0

2
Electronics
450.941022
499.938189
685881.0

3
Food
674.488081
499.916417
407088.0

4
Food
188.847906
499.916417
1015211.0

Polars:
The over() expression appends the partition to any calculation, avoiding repeated group definitions.
result_pl = df_pl.with_columns([
pl.col("amount").mean().over("category").alias("category_avg"),
pl.col("amount").rank(descending=True).over("category").alias("category_rank")
])
result_pl.select(["category", "amount", "category_avg", "category_rank"]).head()

category
amount
category_avg
category_rank

str
f64
f64
f64

“Food”
516.653322
499.916417
604342.0

“Books”
937.337226
499.998897
78423.0

“Electronics”
450.941022
499.938189
685881.0

“Food”
674.488081
499.916417
407088.0

“Food”
188.847906
499.916417
1015211.0

DuckDB:
SQL window functions with OVER (PARTITION BY …) are the industry standard for this type of calculation.
result_duckdb = duckdb.sql("""
SELECT category, amount,
AVG(amount) OVER (PARTITION BY category) as category_avg,
RANK() OVER (PARTITION BY category ORDER BY amount DESC) as category_rank
FROM df_pd
""").df()
result_duckdb.head()

category
amount
category_avg
category_rank

0
Clothing
513.807166
500.139837
608257

1
Clothing
513.806596
500.139837
608258

2
Clothing
513.806515
500.139837
608259

3
Clothing
513.806063
500.139837
608260

4
Clothing
513.806056
500.139837
608261

Data Loading Performance
pandas reads CSV files on a single CPU core. Polars and DuckDB use multi-threaded execution, distributing the work across all available cores to read different parts of the file simultaneously.
pandas
Single-threaded CSV parsing loads data sequentially.
┌─────────────────────────────────────────────┐
│ CPU Core 1 │
│ ┌─────────────────────────────────────────┐ │
│ │ Chunk 1 → Chunk 2 → Chunk 3 → … → End │ │
│ └─────────────────────────────────────────┘ │
│ CPU Core 2 [idle] │
│ CPU Core 3 [idle] │
│ CPU Core 4 [idle] │
└─────────────────────────────────────────────┘

pandas_time = %timeit -o pd.read_csv("sales_data.csv")

1.05 s ± 26.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Polars
Multi-threaded parsing distributes file reading across all available cores.
┌─────────────────────────────────────────────┐
│ CPU Core 1 ┌────────────────┐ │
│ │ ████████████ │ │
│ CPU Core 2 ┌────────────────┐ │
│ │ ████████████ │ │
│ CPU Core 3 ┌────────────────┐ │
│ │ ████████████ │ │
│ CPU Core 4 ┌────────────────┐ │
│ │ ████████████ │ │
└─────────────────────────────────────────────┘

polars_time = %timeit -o pl.read_csv("sales_data.csv")

137 ms ± 34 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

DuckDB
Similar to Polars, file reading is distributed across all available cores.
┌─────────────────────────────────────────────┐
│ CPU Core 1 ┌────────────────┐ │
│ │ ████████████ │ │
│ CPU Core 2 ┌────────────────┐ │
│ │ ████████████ │ │
│ CPU Core 3 ┌────────────────┐ │
│ │ ████████████ │ │
│ CPU Core 4 ┌────────────────┐ │
│ │ ████████████ │ │
└─────────────────────────────────────────────┘

duckdb_time = %timeit -o duckdb.sql("SELECT * FROM 'sales_data.csv'").df()

762 ms ± 77.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

print(f"Polars is {pandas_time.average / polars_time.average:.1f}× faster than pandas")
print(f"DuckDB is {pandas_time.average / duckdb_time.average:.1f}× faster than pandas")

Polars is 7.7× faster than pandas
DuckDB is 1.4× faster than pandas

While Polars leads with a 7.7× speedup in CSV reading, DuckDB’s 1.4× improvement shows parsing isn’t its focus. DuckDB shines when querying files directly or running complex analytical queries.
Query Optimization
pandas: No Optimization
pandas executes operations eagerly, creating intermediate DataFrames at each step. This wastes memory and prevents optimization.
┌─────────────────────────────────────────────────────────────┐
│ Step 1: Load ALL rows → 10M rows in memory │
│ Step 2: Filter (amount > 100) → 5M rows in memory │
│ Step 3: GroupBy → New DataFrame │
│ Step 4: Mean → Final result │
└─────────────────────────────────────────────────────────────┘
Memory: ████████████████████████████████ (high – stores all intermediates)

def pandas_query():
return (
pd.read_csv("sales_data.csv")
.query('amount > 100')
.groupby('category')['amount']
.mean()
)

pandas_opt_time = %timeit -o pandas_query()

1.46 s ± 88.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

This approach has three problems:

Full CSV load: All rows are read before filtering
No predicate pushdown: Rows are filtered after loading the entire file into memory
No projection pushdown: All columns are loaded, even unused ones

You can manually add usecols to load fewer columns:
def pandas_query_optimized():
return (
pd.read_csv("sales_data.csv", usecols=["category", "amount"])
.query('amount > 100')
.groupby('category')['amount']
.mean()
)

pandas_usecols_time = %timeit -o pandas_query_optimized()

1.06 s ± 48.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

This is faster, but has two drawbacks:

Manual tracking: You must specify columns yourself; change the query, update usecols
No row filtering: All rows still load before the filter applies

Polars and DuckDB handle both automatically by analyzing your query before execution.
Polars: Lazy Evaluation
Polars supports lazy evaluation, which builds a query plan and optimizes it before execution:
┌─────────────────────────────────────────────────────────────┐
│ Query Plan Built: │
│ scan_csv → filter → group_by → agg │
│ │
│ Optimizations Applied: │
│ • Predicate pushdown (filter during scan) │
│ • Projection pushdown (read only needed columns) │
│ • Multi-threaded execution (parallel across CPU cores) │
└─────────────────────────────────────────────────────────────┘
Memory: ████████ (low – no intermediate DataFrames)

query_pl = (
pl.scan_csv("sales_data.csv")
.filter(pl.col("amount") > 100)
.group_by("category")
.agg(pl.col("amount").mean().alias("avg_amount"))
)

# View the optimized query plan
print(query_pl.explain())

AGGREGATE[maintain_order: false]
[col("amount").mean().alias("avg_amount")] BY [col("category")]
FROM
Csv SCAN [sales_data.csv] [id: 4687118704]
PROJECT 2/4 COLUMNS
SELECTION: [(col("amount")) > (100.0)]

The query plan shows these optimizations:

Predicate pushdown: SELECTION filters during scan, not after loading
Projection pushdown: PROJECT 2/4 COLUMNS reads only what’s needed
Operation reordering: Aggregate runs on filtered data, not the full dataset

Execute the optimized query:
def polars_query():
return (
pl.scan_csv("sales_data.csv")
.filter(pl.col("amount") > 100)
.group_by("category")
.agg(pl.col("amount").mean().alias("avg_amount"))
.collect()
)

polars_opt_time = %timeit -o polars_query()

148 ms ± 32.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

DuckDB: SQL Optimizer
DuckDB’s SQL optimizer applies similar optimizations automatically:
┌─────────────────────────────────────────────────────────────┐
│ Query Plan Built: │
│ SQL → Parser → Optimizer → Execution Plan │
│ │
│ Optimizations Applied: │
│ • Predicate pushdown (WHERE during scan) │
│ • Projection pushdown (SELECT only needed columns) │
│ • Vectorized execution (process 1024 rows per batch) │
└─────────────────────────────────────────────────────────────┘
Memory: ████████ (low – streaming execution)

def duckdb_query():
return duckdb.sql("""
SELECT category, AVG(amount) as avg_amount
FROM 'sales_data.csv'
WHERE amount > 100
GROUP BY category
""").df()

duckdb_opt_time = %timeit -o duckdb_query()

245 ms ± 12.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Let’s compare the performance of the optimized queries:
print(f"Polars is {pandas_opt_time.average / polars_opt_time.average:.1f}× faster than pandas")
print(f"DuckDB is {pandas_opt_time.average / duckdb_opt_time.average:.1f}× faster than pandas")

Polars is 9.9× faster than pandas
DuckDB is 6.0× faster than pandas

Polars outperforms DuckDB (9.9× vs 6.0×) in this benchmark because its Rust-based engine handles the filter-then-aggregate pattern efficiently. DuckDB’s strength lies in complex SQL queries with joins and subqueries.
GroupBy Performance
Computing aggregates requires scanning every row, a workload that scales linearly with CPU cores. This makes groupby operations the clearest test of parallel execution.
Let’s load the data for the groupby benchmarks:
# Load data for fair comparison
df_pd = pd.read_csv("sales_data.csv")
df_pl = pl.read_csv("sales_data.csv")

pandas: Single-Threaded
pandas processes groupby operations on a single CPU core, which becomes a bottleneck on large datasets.
┌─────────────────────────────────────────────────────────────┐
│ CPU Core 1 │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ Group A → Group B → Group C → Group D → … → Aggregate │ │
│ └─────────────────────────────────────────────────────────┘ │
│ CPU Core 2 [idle] │
│ CPU Core 3 [idle] │
│ CPU Core 4 [idle] │
└─────────────────────────────────────────────────────────────┘

def pandas_groupby():
return df_pd.groupby("category")["amount"].mean()

pandas_groupby_time = %timeit -o pandas_groupby()

271 ms ± 135 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Polars: Multi-Threaded
Polars splits data across cores, computes partial aggregates in parallel, then merges the results.
┌─────────────────────────────────────────────────────────────┐
│ CPU Core 1 ┌──────────────┐ │
│ │ ████████████ │ → Partial Aggregate │
│ CPU Core 2 ┌──────────────┐ │
│ │ ████████████ │ → Partial Aggregate │
│ CPU Core 3 ┌──────────────┐ │
│ │ ████████████ │ → Partial Aggregate │
│ CPU Core 4 ┌──────────────┐ │
│ │ ████████████ │ → Partial Aggregate │
│ ↓ │
│ Final Merge → Result │
└─────────────────────────────────────────────────────────────┘

def polars_groupby():
return df_pl.group_by("category").agg(pl.col("amount").mean())

polars_groupby_time = %timeit -o polars_groupby()

31.1 ms ± 3.65 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

DuckDB: Multi-Threaded
Similar to Polars, DuckDB splits data across cores, computes partial aggregates in parallel, then merges the results.
┌─────────────────────────────────────────────────────────────┐
│ CPU Core 1 ┌──────────────┐ │
│ │ ████████████ │ → Partial Aggregate │
│ CPU Core 2 ┌──────────────┐ │
│ │ ████████████ │ → Partial Aggregate │
│ CPU Core 3 ┌──────────────┐ │
│ │ ████████████ │ → Partial Aggregate │
│ CPU Core 4 ┌──────────────┐ │
│ │ ████████████ │ → Partial Aggregate │
│ ↓ │
│ Final Merge → Result │
└─────────────────────────────────────────────────────────────┘

def duckdb_groupby():
return duckdb.sql("""
SELECT category, AVG(amount)
FROM df_pd
GROUP BY category
""").df()

duckdb_groupby_time = %timeit -o duckdb_groupby()

29 ms ± 3.33 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

print(f"Polars is {pandas_groupby_time.average / polars_groupby_time.average:.1f}× faster than pandas")
print(f"DuckDB is {pandas_groupby_time.average / duckdb_groupby_time.average:.1f}× faster than pandas")

Polars is 8.7× faster than pandas
DuckDB is 9.4× faster than pandas

DuckDB and Polars perform similarly (9.4× vs 8.7×), both leveraging parallel execution. DuckDB’s slight edge comes from late materialization and vector-at-a-time pipelined execution, which avoids creating intermediate results that Polars may still materialize for some operations.
Memory Efficiency
pandas: Full Memory Load
pandas loads the entire dataset into RAM:
┌─────────────────────────────────────────────────────────────┐
│ RAM │
│ ┌────────────────────────────────────────────────────────┐ │
│ │████████████████████████████████████████████████████████│ │
│ │██████████████████ ALL 10M ROWS ████████████████████████│ │
│ │████████████████████████████████████████████████████████│ │
│ └────────────────────────────────────────────────────────┘ │
│ Usage: 707,495 KB (entire dataset in memory) │
└─────────────────────────────────────────────────────────────┘

df_pd_mem = pd.read_csv("sales_data.csv")
pandas_mem = df_pd_mem.memory_usage(deep=True).sum() / 1e3
print(f"pandas memory usage: {pandas_mem:,.0f} KB")

pandas memory usage: 707,495 KB

For larger-than-RAM datasets, pandas throws an out-of-memory error.
Polars: Streaming Mode
Polars can process data in streaming mode, handling chunks without loading everything:
┌─────────────────────────────────────────────────────────────┐
│ RAM │
│ ┌────────────────────────────────────────────────────────┐ │
│ │█ │ │
│ │ (result only) │ │
│ │ │ │
│ └────────────────────────────────────────────────────────┘ │
│ Usage: 0.06 KB (streams chunks, keeps only result) │
└─────────────────────────────────────────────────────────────┘

result_pl_stream = (
pl.scan_csv("sales_data.csv")
.group_by("category")
.agg(pl.col("amount").mean())
.collect(streaming=True)
)

polars_mem = result_pl_stream.estimated_size() / 1e3
print(f"Polars result memory: {polars_mem:.2f} KB")

Polars result memory: 0.06 KB

For larger-than-RAM files, use sink_parquet instead of collect(). It writes results directly to disk as chunks are processed, never holding the full dataset in memory:
(
pl.scan_csv("sales_data.csv")
.filter(pl.col("amount") > 500)
.sink_parquet("filtered_sales.parquet")
)

DuckDB: Automatic Spill-to-Disk
DuckDB automatically writes intermediate results to temporary files when data exceeds available RAM:
┌─────────────────────────────────────────────────────────────┐
│ RAM Disk (if needed) │
│ ┌──────────────────────────┐ ┌──────────────────────┐ │
│ │█ │ │░░░░░░░░░░░░░░░░░░░░░░│ │
│ │ (up to 500MB) │ → │ (overflow here) │ │
│ │ │ │ │ │
│ └──────────────────────────┘ └──────────────────────┘ │
│ Usage: 0.42 KB (spills to disk when RAM full) │
└─────────────────────────────────────────────────────────────┘

# Configure memory limit and temp directory
duckdb.sql("SET memory_limit = '500MB'")
duckdb.sql("SET temp_directory = '/tmp/duckdb_temp'")

# DuckDB handles larger-than-RAM automatically
result_duckdb_mem = duckdb.sql("""
SELECT category, AVG(amount) as avg_amount
FROM 'sales_data.csv'
GROUP BY category
""").df()

duckdb_mem = result_duckdb_mem.memory_usage(deep=True).sum() / 1e3
print(f"DuckDB result memory: {duckdb_mem:.2f} KB")

DuckDB result memory: 0.42 KB

DuckDB’s out-of-core processing makes it ideal for embedded analytics where memory is limited.
print(f"pandas: {pandas_mem:,.0f} KB (full dataset)")
print(f"Polars: {polars_mem:.2f} KB (result only)")
print(f"DuckDB: {duckdb_mem:.2f} KB (result only)")
print(f"\nPolars uses {pandas_mem / polars_mem:,.0f}× less memory than pandas")
print(f"DuckDB uses {pandas_mem / duckdb_mem:,.0f}× less memory than pandas")

pandas: 707,495 KB (full dataset)
Polars: 0.06 KB (result only)
DuckDB: 0.42 KB (result only)

Polars uses 11,791,583× less memory than pandas
DuckDB uses 1,684,512× less memory than pandas

The million-fold reduction comes from streaming: Polars and DuckDB process data in chunks and only keep the 4-row result in memory, while pandas must hold all 10 million rows to compute the same aggregation.
Join Operations
Joining tables is one of the most common operations in data analysis. Let’s compare how each tool handles a left join between 1 million orders and 100K customers.
Let’s create two tables for join benchmarking:
# Create orders table (1M rows)
orders_pd = pd.DataFrame({
"order_id": range(1_000_000),
"customer_id": np.random.randint(1, 100_000, size=1_000_000),
"amount": np.random.rand(1_000_000) * 500
})

# Create customers table (100K rows)
customers_pd = pd.DataFrame({
"customer_id": range(100_000),
"region": np.random.choice(["North", "South", "East", "West"], size=100_000)
})

# Convert to Polars
orders_pl = pl.from_pandas(orders_pd)
customers_pl = pl.from_pandas(customers_pd)

pandas: Single-Threaded
pandas processes the join on a single CPU core.
┌─────────────────────────────────────────────┐
│ CPU Core 1 │
│ ┌─────────────────────────────────────────┐ │
│ │ Row 1 → Row 2 → Row 3 → … → Row 1M │ │
│ └─────────────────────────────────────────┘ │
│ CPU Core 2 [idle] │
│ CPU Core 3 [idle] │
│ CPU Core 4 [idle] │
└─────────────────────────────────────────────┘

def pandas_join():
return orders_pd.merge(customers_pd, on="customer_id", how="left")

pandas_join_time = %timeit -o pandas_join()

60.4 ms ± 6.98 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

Polars: Multi-Threaded
Polars distributes the join across all available CPU cores.
┌─────────────────────────────────────────────┐
│ CPU Core 1 ┌────────────────┐ │
│ │ ████████████ │ │
│ CPU Core 2 ┌────────────────┐ │
│ │ ████████████ │ │
│ CPU Core 3 ┌────────────────┐ │
│ │ ████████████ │ │
│ CPU Core 4 ┌────────────────┐ │
│ │ ████████████ │ │
└─────────────────────────────────────────────┘

def polars_join():
return orders_pl.join(customers_pl, on="customer_id", how="left")

polars_join_time = %timeit -o polars_join()

11.8 ms ± 6.42 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

DuckDB: Multi-Threaded
Similar to Polars, DuckDB distributes the join across all available CPU cores.
┌─────────────────────────────────────────────┐
│ CPU Core 1 ┌────────────────┐ │
│ │ ████████████ │ │
│ CPU Core 2 ┌────────────────┐ │
│ │ ████████████ │ │
│ CPU Core 3 ┌────────────────┐ │
│ │ ████████████ │ │
│ CPU Core 4 ┌────────────────┐ │
│ │ ████████████ │ │
└─────────────────────────────────────────────┘

def duckdb_join():
return duckdb.sql("""
SELECT o.*, c.region
FROM orders_pd o
LEFT JOIN customers_pd c ON o.customer_id = c.customer_id
""").df()

duckdb_join_time = %timeit -o duckdb_join()

55.7 ms ± 1.14 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

Let’s compare the performance of the joins:
print(f"Polars is {pandas_join_time.average / polars_join_time.average:.1f}× faster than pandas")
print(f"DuckDB is {pandas_join_time.average / duckdb_join_time.average:.1f}× faster than pandas")

Polars is 5.1× faster than pandas
DuckDB is 1.1× faster than pandas

Polars delivers a 5.1× speedup while DuckDB shows only 1.1× improvement. Both tools use multi-threading, but Polars’ join algorithm and native DataFrame output avoid the conversion overhead that DuckDB incurs when returning results via .df().
Interoperability
All three tools work together seamlessly. Use each tool for what it does best in a single pipeline.
pandas DataFrame to DuckDB
Query pandas DataFrames directly with SQL:
df = pd.DataFrame({
"product": ["A", "B", "C"],
"sales": [100, 200, 150]
})

# DuckDB queries pandas DataFrames by variable name
result = duckdb.sql("SELECT * FROM df WHERE sales > 120").df()
print(result)

product sales
0 B 200
1 C 150

Polars to pandas
Convert Polars DataFrames when ML libraries require pandas:
df_polars = pl.DataFrame({
"feature1": [1, 2, 3],
"feature2": [4, 5, 6],
"target": [0, 1, 0]
})

# Convert to pandas for scikit-learn
df_pandas = df_polars.to_pandas()
print(type(df_pandas))

<class 'pandas.core.frame.DataFrame'>

DuckDB to Polars
Get query results as Polars DataFrames:
result = duckdb.sql("""
SELECT category, SUM(amount) as total
FROM 'sales_data.csv'
GROUP BY category
""").pl()

print(type(result))
print(result)

<class 'polars.dataframe.frame.DataFrame'>
shape: (4, 2)
┌─────────────┬──────────┐
│ category ┆ total │
│ — ┆ — │
│ str ┆ f64 │
╞═════════════╪══════════╡
│ Electronics ┆ 6.2445e8 │
│ Food ┆ 6.2540e8 │
│ Clothing ┆ 6.2539e8 │
│ Books ┆ 6.2475e8 │
└─────────────┴──────────┘

Combined Pipeline Example
Each tool has a distinct strength: DuckDB optimizes SQL queries, Polars parallelizes transformations, and pandas integrates with ML libraries. Combine them in a single pipeline to leverage all three:
# Step 1: DuckDB for initial SQL query
aggregated = duckdb.sql("""
SELECT category, region,
SUM(amount) as total_amount,
COUNT(*) as order_count
FROM 'sales_data.csv'
GROUP BY category, region
""").pl()

# Step 2: Polars for additional transformations
enriched = (
aggregated
.with_columns([
(pl.col("total_amount") / pl.col("order_count")).alias("avg_order_value"),
pl.col("category").str.to_uppercase().alias("category_upper")
])
.filter(pl.col("order_count") > 100000)
)

# Step 3: Convert to pandas for visualization or ML
final_df = enriched.to_pandas()
print(final_df.head())

category region total_amount order_count avg_order_value category_upper
0 Food East 1.563586e+08 312918 499.679004 FOOD
1 Food North 1.563859e+08 312637 500.215456 FOOD
2 Clothing North 1.560532e+08 311891 500.345286 CLOTHING
3 Clothing East 1.565054e+08 312832 500.285907 CLOTHING
4 Food West 1.560994e+08 312662 499.259318 FOOD

📖 Related: For writing functions that work across pandas, Polars, and PySpark without conversion, see Unified DataFrame Functions.

Decision Matrix
No single tool wins in every scenario. Use these tables to choose the right tool for your workflow.
Performance Summary
Benchmark results from 10 million rows on a single machine:

Operation
pandas
Polars
DuckDB

CSV Read (10M rows)
1.05s
137ms
762ms

GroupBy
271ms
31ms
29ms

Join (1M rows)
60ms
12ms
56ms

Memory Usage
707 MB
0.06 KB (streaming)
0.42 KB (spill-to-disk)

Polars leads in CSV reading (7.7× faster than pandas) and joins (5× faster). DuckDB matches Polars in groupby performance and uses the least memory with automatic spill-to-disk.
Feature Comparison
Each tool makes different trade-offs between speed, memory, and ecosystem integration:

Feature
pandas
Polars
DuckDB

Multi-threading
No
Yes
Yes

Lazy evaluation
No
Yes
N/A (SQL)

Query optimization
No
Yes
Yes

Larger-than-RAM
No
Streaming
Spill-to-disk

SQL interface
No
Limited
Native

ML integration
Excellent
Good
Limited

pandas lacks the performance features that make Polars and DuckDB fast, but remains essential for ML workflows. Choose between Polars and DuckDB based on whether you prefer DataFrame chaining or SQL syntax.
Recommendations
The best tool depends on your data size, workflow preferences, and constraints:

Small data (<1M rows): Use pandas for simplicity
Large data (1M-100M rows): Use Polars or DuckDB for 5-10× speedup
SQL-preferred workflow: Use DuckDB
DataFrame-preferred workflow: Use Polars
Memory-constrained: Use Polars (streaming) or DuckDB (spill-to-disk)
ML pipeline integration: Use pandas (convert from Polars/DuckDB as needed)
Production data pipelines: Use Polars (DataFrame) or DuckDB (SQL) based on team preference

Final Thoughts
If your code is all written in pandas, you don’t need to rewrite it all. You can migrate where it matters:

Profile first: Find which pandas operations are slow
Replace with Polars: CSV reads, groupbys, and joins see the biggest gains
Add DuckDB: When SQL is cleaner than chained DataFrame operations

Keep pandas for final ML steps. Convert with df.to_pandas() when needed.
Related Tutorials

Polars vs. Pandas: A Fast, Multi-Core Alternative for DataFrames: Deep comparison of Polars and pandas performance and API differences
A Deep Dive into DuckDB for Data Scientists: Explore DuckDB’s SQL-native analytics with Python integration
Scaling Pandas Workflows with PySpark’s Pandas API: Scale existing pandas code to distributed processing with PySpark
Delta Lake: Transform pandas Prototypes into Production: Add ACID transactions and versioning to your pandas data pipelines

📚 Want to go deeper? Learning new techniques is the easy part. Knowing how to structure, test, and deploy them is what separates side projects from real work. My book shows you how to build data science projects that actually make it to production. Get the book →

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