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# Introduction
There are quite a few instruments for processing datasets at present. All of them declare — after all they do — that they’re the very best and the precise alternative for you. However are they? There are two predominant necessities these instruments ought to fulfill: they need to simply carry out on a regular basis knowledge evaluation operations and accomplish that rapidly, even below the strain of huge datasets.
To find out the very best device amongst DuckDB, SQLite, and Pandas, we examined them below these circumstances.
First, we gave them solely on a regular basis analytical duties: summing values, grouping by classes, filtering with circumstances, and multi-field aggregations. This mirrored how analysts really work with actual datasets, in comparison with situations designed to showcase the very best traits of a device.
Second, we carried out these operations on a Kaggle dataset with over 1 million rows. It’s a sensible tipping level — sufficiently small to run on a single machine, but giant sufficient that reminiscence strain and question pace begin to reveal clear variations between instruments.
Let’s see how these assessments went.
# The Dataset We Used
// Dataset Overview
We used the Financial institution dataset from Kaggle. This dataset accommodates over 1 million rows, comprising 5 columns:
| Column Title | Description |
|---|---|
| Date | The date the transaction occurred |
| Area | The enterprise class or sort (RETAIL, RESTAURANT) |
| Location | Geographic area (Goa, Mathura) |
| Worth | Transaction worth |
| Transaction_count | The full variety of transactions on that day |
This dataset is generated utilizing Python. Whereas it might not totally resemble real-life knowledge, its dimension and construction are adequate to check and examine the efficiency variations between the instruments.
// Peeking Into the Knowledge with Pandas
We used Pandas to load the dataset right into a Jupyter pocket book and look at its normal construction, dimensions, and null values. Right here is the code.
import pandas as pd
df = pd.read_excel('bankdataset.xlsx')
print("Dataset form:", df.form)
df.head()
Right here is the output.
In order for you a fast reference to widespread operations when exploring datasets, take a look at this useful Pandas Cheat Sheet.
Earlier than benchmarking, let’s see the right way to arrange the surroundings.
# Setting Up a Honest Testing Surroundings
All three instruments — DuckDB, SQLite, and Pandas — had been arrange and run in the identical Jupyter Pocket book surroundings to make sure the check was truthful. This ensured that the circumstances throughout runtime and using reminiscence remained fixed all through.
First, we put in and loaded the required packages.
Listed below are the instruments we wanted:
- pandas: for normal
DataFrameoperations - duckdb: for SQL execution on a
DataFrame - sqlite3: for managing an embedded SQL database
- time: for capturing execution time
- memory_profiler: to measure reminiscence allocation
# Set up if any of them will not be in your surroundings
!pip set up duckdb --quiet
import pandas as pd
import duckdb
import sqlite3
import time
from memory_profiler import memory_usage
Now let’s put together the info in a format that may be shared throughout all three instruments.
// Loading Knowledge into Pandas
We’ll use Pandas to load the dataset as soon as, after which we’ll share or register it for DuckDB and SQLite.
df = pd.read_excel('bankdataset.xlsx')
df.head()
Right here is the output to validate.
// Registering Knowledge with DuckDB
DuckDB enables you to instantly entry Pandas DataFrames. You do not have to transform something—simply register and question. Right here is the code.
# Register DataFrame as a DuckDB desk
duckdb.register("bank_data", df)
# Question by way of DuckDB
duckdb.question("SELECT * FROM bank_data LIMIT 5").to_df()
Right here is the output.
// Making ready Knowledge for SQLite
Since SQLite does not learn Excel information instantly, we began by including the Pandas DataFrame to an in-memory database. After that, we used a easy question to look at the info format.
conn_sqlite = sqlite3.join(":reminiscence:")
df.to_sql("bank_data", conn_sqlite, index=False, if_exists="exchange")
pd.read_sql_query("SELECT * FROM bank_data LIMIT 5", conn_sqlite)
Right here is the output.
# How We Benchmarked the Instruments
We used the identical 4 queries on DuckDB, SQLite, and Pandas to check their efficiency. Every question was designed to deal with a typical analytical process that mirrors how knowledge evaluation is utilized in the actual world.
// Guaranteeing Constant Setup
The in-memory dataset was utilized by all three instruments.
- Pandas queried the
DataFrameinstantly - DuckDB executed SQL queries instantly towards the
DataFrame - SQLite saved a duplicate of the
DataFramein an in-memory database and ran SQL queries on it
This technique ensured that each one three instruments used the identical knowledge and operated with the identical system settings.
// Measuring Execution Time
To trace question period, Python’s time module wrapped every question in a easy begin/finish timer. Solely the question execution time was recorded; data-loading and preparation steps had been excluded.
// Monitoring Reminiscence Utilization
Together with processing time, reminiscence utilization signifies how properly every engine performs with giant datasets.
If desired, reminiscence utilization may be sampled instantly earlier than and after every question to estimate incremental RAM consumption.
// The Benchmark Queries
We examined every engine on the identical 4 on a regular basis analytical duties:
- Whole transaction worth: summing a numeric column
- Group by area: aggregating transaction counts per class
- Filter by location: filtering rows by a situation earlier than aggregation
- Group by area & location: multi-field aggregation with averages
# Benchmark Outcomes
// Question 1: Whole Transaction Worth
Right here we measure how Pandas, DuckDB, and SQLite carry out when summing the Worth column throughout the dataset.
// Pandas Efficiency
We calculate the full transaction worth utilizing .sum() on the Worth column. Right here is the code.
pandas_results = []
def pandas_q1():
return df['Value'].sum()
mem_before = memory_usage(-1)[0]
begin = time.time()
pandas_q1()
finish = time.time()
mem_after = memory_usage(-1)[0]
pandas_results.append({
"engine": "Pandas",
"question": "Whole transaction worth",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
pandas_results
Right here is the output.
// DuckDB Efficiency
We calculate the full transaction worth utilizing a full-column aggregation. Right here is the code.
duckdb_results = []
def duckdb_q1():
return duckdb.question("SELECT SUM(worth) FROM bank_data").to_df()
mem_before = memory_usage(-1)[0]
begin = time.time()
duckdb_q1()
finish = time.time()
mem_after = memory_usage(-1)[0]
duckdb_results.append({
"engine": "DuckDB",
"question": "Whole transaction worth",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
duckdb_results
Right here is the output.
// SQLite Efficiency
We calculate the full transaction worth by summing the worth column. Right here is the code.
sqlite_results = []
def sqlite_q1():
return pd.read_sql_query("SELECT SUM(worth) FROM bank_data", conn_sqlite)
mem_before = memory_usage(-1)[0]
begin = time.time()
sqlite_q1()
finish = time.time()
mem_after = memory_usage(-1)[0]
sqlite_results.append({
"engine": "SQLite",
"question": "Whole transaction worth",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
sqlite_results
Right here is the output.
// General Efficiency Evaluation
Now let’s examine execution time and reminiscence utilization. Right here is the code.
import matplotlib.pyplot as plt
all_q1 = pd.DataFrame(pandas_results + duckdb_results + sqlite_results)
fig, axes = plt.subplots(1, 2, figsize=(10,4))
all_q1.plot(x="engine", y="time", type="barh", ax=axes[0], legend=False, title="Execution Time (s)")
all_q1.plot(x="engine", y="reminiscence", type="barh", colour="salmon", ax=axes[1], legend=False, title="Reminiscence Utilization (MB)")
plt.tight_layout()
plt.present()
Right here is the output.
Pandas is by far the quickest and most memory-efficient right here, finishing virtually immediately with minimal RAM utilization. DuckDB is barely slower and makes use of extra reminiscence however stays environment friendly, whereas SQLite is each the slowest and the heaviest when it comes to reminiscence consumption.
// Question 2: Group by Area
Right here we measure how Pandas, DuckDB, and SQLite carry out when grouping transactions by Area and summing their counts.
// Pandas Efficiency
We calculate the full transaction rely per area utilizing .groupby() on the Area column.
def pandas_q2():
return df.groupby('Area')['Transaction_count'].sum()
mem_before = memory_usage(-1)[0]
begin = time.time()
pandas_q2()
finish = time.time()
mem_after = memory_usage(-1)[0]
pandas_results.append({
"engine": "Pandas",
"question": "Group by area",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
[p for p in pandas_results if p["query"] == "Group by area"]
Right here is the output.
// DuckDB Efficiency
We calculate the full transaction rely per area utilizing a SQL GROUP BY on the area column.
def duckdb_q2():
return duckdb.question("""
SELECT area, SUM(transaction_count)
FROM bank_data
GROUP BY area
""").to_df()
mem_before = memory_usage(-1)[0]
begin = time.time()
duckdb_q2()
finish = time.time()
mem_after = memory_usage(-1)[0]
duckdb_results.append({
"engine": "DuckDB",
"question": "Group by area",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
[p for p in duckdb_results if p["query"] == "Group by area"]
Right here is the output.
// SQLite Efficiency
We calculate the full transaction rely per area utilizing SQL GROUP BY on the in-memory desk.
def sqlite_q2():
return pd.read_sql_query("""
SELECT area, SUM(transaction_count) AS total_txn
FROM bank_data
GROUP BY area
""", conn_sqlite)
mem_before = memory_usage(-1)[0]
begin = time.time()
sqlite_q2()
finish = time.time()
mem_after = memory_usage(-1)[0]
sqlite_results.append({
"engine": "SQLite",
"question": "Group by area",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
[p for p in sqlite_results if p["query"] == "Group by area"]
Right here is the output.
// General Efficiency Evaluation
Now let’s examine execution time and reminiscence utilization. Right here is the code.
import pandas as pd
import matplotlib.pyplot as plt
groupby_results = [r for r in (pandas_results + duckdb_results + sqlite_results)
if "Group by" in r["query"]]
df_groupby = pd.DataFrame(groupby_results)
fig, axes = plt.subplots(1, 2, figsize=(10,4))
df_groupby.plot(x="engine", y="time", type="barh", ax=axes[0], legend=False, title="Execution Time (s)")
df_groupby.plot(x="engine", y="reminiscence", type="barh", colour="salmon", ax=axes[1], legend=False, title="Reminiscence Utilization (MB)")
plt.tight_layout()
plt.present()
Right here is the output.
DuckDB is quickest, Pandas trades a bit extra time for decrease reminiscence, whereas SQLite is each slowest and most memory-hungry.
// Question 3: Filter by Location (Goa)
Right here we measure how Pandas, DuckDB, and SQLite carry out when filtering the dataset for Location = 'Goa' and summing the transaction values.
// Pandas Efficiency
We filter rows for Location == 'Goa' and sum their values. Right here is the code.
def pandas_q3():
return df[df['Location'] == 'Goa']['Value'].sum()
mem_before = memory_usage(-1)[0]
begin = time.time()
pandas_q3()
finish = time.time()
mem_after = memory_usage(-1)[0]
pandas_results.append({
"engine": "Pandas",
"question": "Filter by location",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
[p for p in pandas_results if p["query"] == "Filter by location"]
Right here is the output.
// DuckDB Efficiency
We filter transactions for Location = 'Goa' and calculate their whole worth. Right here is the code.
def duckdb_q3():
return duckdb.question("""
SELECT SUM(worth)
FROM bank_data
WHERE location = 'Goa'
""").to_df()
mem_before = memory_usage(-1)[0]
begin = time.time()
duckdb_q3()
finish = time.time()
mem_after = memory_usage(-1)[0]
duckdb_results.append({
"engine": "DuckDB",
"question": "Filter by location",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
[p for p in duckdb_results if p["query"] == "Filter by location"]
Right here is the output.
// SQLite Efficiency
We filter transactions for Location = 'Goa' and sum their values. Right here is the code.
def sqlite_q3():
return pd.read_sql_query("""
SELECT SUM(worth) AS total_value
FROM bank_data
WHERE location = 'Goa'
""", conn_sqlite)
mem_before = memory_usage(-1)[0]
begin = time.time()
sqlite_q3()
finish = time.time()
mem_after = memory_usage(-1)[0]
sqlite_results.append({
"engine": "SQLite",
"question": "Filter by location",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
[p for p in sqlite_results if p["query"] == "Filter by location"]
Right here is the output.
// General Efficiency Evaluation
Now let’s examine execution time and reminiscence utilization. Right here is the code.
import pandas as pd
import matplotlib.pyplot as plt
filter_results = [r for r in (pandas_results + duckdb_results + sqlite_results)
if r["query"] == "Filter by location"]
df_filter = pd.DataFrame(filter_results)
fig, axes = plt.subplots(1, 2, figsize=(10, 4))
df_filter.plot(x="engine", y="time", type="barh", ax=axes[0], legend=False, title="Execution Time (s)")
df_filter.plot(x="engine", y="reminiscence", type="barh", colour="salmon", ax=axes[1], legend=False, title="Reminiscence Utilization (MB)")
plt.tight_layout()
plt.present()
Right here is the output.
DuckDB is the quickest and most effective; Pandas is slower with greater reminiscence utilization; and SQLite is the slowest however lighter on reminiscence.
// Question 4: Group by Area & Location
// Pandas Efficiency
We calculate the typical transaction worth grouped by each Area and Location. Right here is the code.
def pandas_q4():
return df.groupby(['Domain', 'Location'])['Value'].imply()
mem_before = memory_usage(-1)[0]
begin = time.time()
pandas_q4()
finish = time.time()
mem_after = memory_usage(-1)[0]
pandas_results.append({
"engine": "Pandas",
"question": "Group by area & location",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
[p for p in pandas_results if p["query"] == "Group by area & location"]
Right here is the output.
// DuckDB Efficiency
We calculate the typical transaction worth grouped by each area and location. Right here is the code.
def duckdb_q4():
return duckdb.question("""
SELECT area, location, AVG(worth) AS avg_value
FROM bank_data
GROUP BY area, location
""").to_df()
mem_before = memory_usage(-1)[0]
begin = time.time()
duckdb_q4()
finish = time.time()
mem_after = memory_usage(-1)[0]
duckdb_results.append({
"engine": "DuckDB",
"question": "Group by area & location",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
[p for p in duckdb_results if p["query"] == "Group by area & location"]
Right here is the output.
// SQLite Efficiency
We calculate the typical transaction worth grouped by each area and location. Right here is the code.
def sqlite_q4():
return pd.read_sql_query("""
SELECT area, location, AVG(worth) AS avg_value
FROM bank_data
GROUP BY area, location
""", conn_sqlite)
mem_before = memory_usage(-1)[0]
begin = time.time()
sqlite_q4()
finish = time.time()
mem_after = memory_usage(-1)[0]
sqlite_results.append({
"engine": "SQLite",
"question": "Group by area & location",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
[p for p in sqlite_results if p["query"] == "Group by area & location"]
Right here is the output.
// General Efficiency Evaluation
Now let’s examine execution time and reminiscence utilization. Right here is the code.
import pandas as pd
import matplotlib.pyplot as plt
gdl_results = [r for r in (pandas_results + duckdb_results + sqlite_results)
if r["query"] == "Group by area & location"]
df_gdl = pd.DataFrame(gdl_results)
fig, axes = plt.subplots(1, 2, figsize=(10, 4))
df_gdl.plot(x="engine", y="time", type="barh", ax=axes[0], legend=False,
title="Execution Time (s)")
df_gdl.plot(x="engine", y="reminiscence", type="barh", ax=axes[1], legend=False,
title="Reminiscence Utilization (MB)", colour="salmon")
plt.tight_layout()
plt.present()
Right here is the output.
DuckDB handles multi-field group-bys quickest with reasonable reminiscence use, Pandas is slower with very excessive reminiscence utilization, and SQLite is the slowest with substantial reminiscence consumption.
# Closing Comparability Throughout All Queries
We’ve in contrast these three engines towards one another when it comes to reminiscence and pace. Let’s test the execution time as soon as once more. Right here is the code.
import pandas as pd
import matplotlib.pyplot as plt
all_results = pd.DataFrame(pandas_results + duckdb_results + sqlite_results)
measure_order = [
"Total transaction value",
"Group by domain",
"Filter by location",
"Group by domain & location",
]
engine_colors = {"Pandas": "#1f77b4", "DuckDB": "#ff7f0e", "SQLite": "#2ca02c"}
fig, axes = plt.subplots(2, 2, figsize=(12, 8))
axes = axes.ravel()
for i, q in enumerate(measure_order):
d = all_results[all_results["query"] == q]
axes[i].barh(d["engine"], d["time"],
colour=[engine_colors[e] for e in d["engine"]])
for y, v in enumerate(d["time"]):
axes[i].textual content(v, y, f" {v:.3f}", va="heart")
axes[i].set_title(q, fontsize=10)
axes[i].set_xlabel("Seconds")
fig.suptitle("Per-Measure Comparability — Execution Time", fontsize=14)
plt.tight_layout()
plt.present()
Right here is the output.
This chart reveals that DuckDB persistently maintains the bottom execution occasions for nearly all queries, apart from the full transaction worth the place Pandas edges it out; SQLite is the slowest by a large margin throughout the board. Let’s test reminiscence subsequent. Right here is the code.
import pandas as pd
import matplotlib.pyplot as plt
all_results = pd.DataFrame(pandas_results + duckdb_results + sqlite_results)
measure_order = [
"Total transaction value",
"Group by domain",
"Filter by location",
"Group by domain & location",
]
engine_colors = {"Pandas": "#1f77b4", "DuckDB": "#ff7f0e", "SQLite": "#2ca02c"}
fig, axes = plt.subplots(2, 2, figsize=(12, 8))
axes = axes.ravel()
for i, q in enumerate(measure_order):
d = all_results[all_results["query"] == q]
axes[i].barh(d["engine"], d["memory"],
colour=[engine_colors[e] for e in d["engine"]])
for y, v in enumerate(d["memory"]):
axes[i].textual content(v, y, f" {v:.1f}", va="heart")
axes[i].set_title(q, fontsize=10)
axes[i].set_xlabel("MB")
fig.suptitle("Per-Measure Comparability — Reminiscence Utilization", fontsize=14)
plt.tight_layout()
plt.present()
Right here is the output.
This chart reveals that SQLite swings between being the very best and the worst in reminiscence utilization, Pandas is excessive with two greatest and two worst circumstances, whereas DuckDB stays persistently within the center throughout all queries. Because of this, DuckDB proves to be essentially the most balanced alternative total, delivering persistently quick efficiency with reasonable reminiscence utilization. Pandas reveals extremes—typically the quickest, typically the heaviest—whereas SQLite struggles with pace and sometimes finally ends up on the inefficient aspect for reminiscence.
Nate Rosidi is a knowledge scientist and in product technique. He is additionally an adjunct professor instructing analytics, and is the founding father of StrataScratch, a platform serving to knowledge scientists put together for his or her interviews with actual interview questions from prime firms. Nate writes on the newest developments within the profession market, provides interview recommendation, shares knowledge science initiatives, and covers every little thing SQL.
