PySuricata

Build Status PyPI version Python versions License: MIT codecov Documentation Downloads LinkedIn

PySuricata Logo

Exploratory Data Analysis for Python, Built on Streaming Algorithms

Quick Start • Documentation • Examples

What It Does

PySuricata generates self-contained HTML reports from pandas or polars DataFrames. Reports include per-column statistics, histograms, correlation chips, missing value analysis, and outlier detection.

Data is processed in chunks using streaming algorithms, so memory usage stays bounded regardless of dataset size.

It also does two things a profiler usually does not: summarize() returns the same numbers as a versioned JSON payload with no HTML in the way, and pysuricata check compares a dataset against a stored baseline and exits non-zero when a threshold is crossed — so the same single pass can run in a notebook and in CI.

Quick Start

Installation

# using uv (recommended)
uv add pysuricata

# or using pip
pip install pysuricata

With polars support (optional):

uv add pysuricata[polars]
# or: pip install pysuricata[polars]

Generate a Report

import pandas as pd
from pysuricata import profile

url = "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv"
df = pd.read_csv(url)

report = profile(df)
report.save_html("titanic_report.html")

▶ See a live example report →

A PySuricata report: the dataset summary, the five columns that need a look, and a numeric column card with its histogram and bin controls

The examples below assume a df in scope. The Quick Start frame works, or anything of your own:

import numpy as np
import pandas as pd

rng = np.random.default_rng(0)
df = pd.DataFrame(
    {
        "age": rng.normal(30, 12, 800).round(1),
        "fare": rng.gamma(2, 20, 800).round(2),
        "sex": rng.choice(["male", "female"], 800),
        "booked": pd.date_range("2024-01-01", periods=800, freq="h"),
    }
)

Features

  • Streaming architecture — Data is processed in configurable chunks, keeping memory bounded. Useful for datasets that don't fit in RAM.
  • Pandas and Polars — Works natively with pandas.DataFrame, polars.DataFrame and polars.LazyFrame, plus Parquet files, DuckDB relations and Arrow batches.
  • Self-contained HTML — Single file with inline CSS, JS, and SVG charts. No external assets needed.
  • Configurable — Control chunk size, sample size, correlations and more with keyword options, a preset=, or a ProfileConfig.
  • Reproducible — Seeded random sampling produces deterministic results across runs.
  • Typed — Ships py.typed; summarize() returns a payload carrying a schema_version.
  • CLI tool — profile, summarize and check from the command line.

How It Works

PySuricata uses well-known streaming algorithms from the academic literature:

Algorithm Purpose Time Space
Welford/Pébay Exact mean, variance, skewness, kurtosis O(1) per value O(1)
KMV sketch Distinct count estimation (~2.2% error) O(log k) per value O(k)
Misra-Gries Top-k frequent values O(1) amortized O(k)
Reservoir sampling Uniform random sample for quantiles O(1) per value O(s)

k = sketch size (max_uniques, default 2048), s = sample size (numeric_sample_size, default 20 000)

KMV's relative standard error is 1/sqrt(k - 2), which is where the ~2.2% comes from. Approximate values are labelled approximate in the report and carry their error bound rather than being printed as exact integers.

All statistics are computed in a single pass over the data.

What's in a Report

Each column is analyzed based on its type:

  • Numeric — Mean, variance, skewness, kurtosis, quantiles, histogram, outlier detection (IQR, MAD, z-score), correlations
  • Categorical — Top values, distinct count, entropy, Gini impurity, string length statistics
  • DateTime — Temporal range, hour/day/month distributions, monotonicity detection
  • Boolean — True/false ratios, entropy, balance score

Plus dataset-level metrics: row/column counts, memory usage, missing value percentages, and duplicate row estimates.

Streaming Large Datasets

Process datasets larger than RAM by passing a generator:

import pandas as pd
from pysuricata import profile

def read_in_chunks():
    for i in range(100):
        yield pd.read_parquet(f"data/part-{i}.parquet")

report = profile(read_in_chunks())
report.save_html("large_report.html")

A Parquet path, a DuckDB relation or an Arrow source can be streamed directly, without loading the whole thing:

import duckdb
from pysuricata import profile
from pysuricata.sources import stream_duckdb, stream_parquet

report = profile(stream_parquet("data/events.parquet"))

relation = duckdb.connect("warehouse.db").sql("SELECT * FROM events")
report = profile(stream_duckdb(relation))

Statistics Only (No HTML)

Use summarize() for CI/CD quality checks. The payload carries a schema_version and is treated as a contract:

from pysuricata import summarize

stats = summarize(df)

assert stats["schema_version"] == 1
assert stats["dataset"]["missing_cells_pct"] < 5.0
assert stats["dataset"]["duplicate_rows_pct_est"] < 1.0

print(f"Mean age: {stats['columns']['age']['mean']:.1f}")

Comparing Two Datasets

compare() runs both through the same single pass and reports what moved:

from pysuricata import compare

last_week, this_week = df.iloc[:3], df.iloc[3:]
diff = compare(last_week, this_week).to_dict()

Configuration

Pass keyword options for the common cases:

from pysuricata import profile

report = profile(
    df,
    chunk_size=250_000,   # default 50_000
    sample=20_000,
    seed=42,
    correlations=True,
    title="My Analysis",
)

Or start from a preset — "fast" or "thorough":

from pysuricata import profile

report = profile(df, preset="fast")

For everything else, build a ProfileConfig. Keyword options and config= are mutually exclusive:

from pysuricata import profile, ProfileConfig

config = ProfileConfig()
config.compute.chunk_size = 250_000
config.compute.random_seed = 42
config.compute.corr_threshold = 0.5
config.render.title = "My Analysis"

report = profile(df, config=config)

See the Configuration Guide for all options.

CLI

# Generate an HTML report
pysuricata profile data.csv --output report.html

# Get JSON statistics
pysuricata summarize data.csv

# Compare against a stored baseline; exit non-zero when a threshold is crossed
pysuricata check data.csv --write-baseline baseline.json
pysuricata check data.csv --baseline baseline.json --max-missing-pct 5

check exits 0 on pass, 1 when a threshold is crossed, and 2 when the check could not run — so it drops into CI without a wrapper.

Documentation

Contributing

Contributions are welcome. See the Contributing Guide.

git clone https://github.com/alvarodiez20/pysuricata.git
cd pysuricata
uv sync --dev
uv run pytest

License

MIT License. See LICENSE for details.

Acknowledgments

Built using algorithms from:

  • Welford, B.P. (1962) — Streaming moments
  • Pébay, P. (2008) — Parallel merging of moments
  • Bar-Yossef, Z. et al. (2002) — KMV distinct count estimation
  • Misra, J. & Gries, D. (1982) — Streaming heavy hitters

Named after suricatas (meerkats) — small, vigilant animals that work cooperatively and thrive in harsh environments with limited resources.

Project links

Data verified by PyPI on Aug 17, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.2.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for alvarodiez2 from gravatar.com alvarodiez2

Credits

Author: Alvaro Diez

GitHub Statistics

Data verified by PyPI on Aug 17, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.10

Provides Extra

polars system

Tags

eda exploratory-data-analysis profiling data-quality pandas polars streaming

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pysuricata-0.1.1.tar.gz (861.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pysuricata-0.1.1-py3-none-any.whl (659.3 kB view details)

Uploaded Python 3

File details

Details for the file pysuricata-0.1.1.tar.gz.

File metadata

  • Download URL: pysuricata-0.1.1.tar.gz
  • Upload date:
  • Size: 861.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for pysuricata-0.1.1.tar.gz
Algorithm Hash digest
SHA256 2a3b8c7bf43d284d305dd0e18e47d2816238f8135b06a85ab0b501934115b3dc
MD5 2000a1881afb0a34ca72cabffa4c306a
BLAKE2b-256 f70abaf9f14f57c83ffa74dfef9dc4fb32c5f5d105b5311a8763ce697e9d9975

See more details on using hashes here.

Provenance

The following attestation bundles were made for pysuricata-0.1.1.tar.gz:

Publisher: cd.yml on alvarodiez20/pysuricata

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file pysuricata-0.1.1-py3-none-any.whl.

File metadata

  • Download URL: pysuricata-0.1.1-py3-none-any.whl
  • Upload date:
  • Size: 659.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for pysuricata-0.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 a2018f3767f2fc5edeb46610fe80633d447e393fc90d5c28b8bf549aaf6cc2d6
MD5 68b70a41430f6ad66f0585b92e3d1343
BLAKE2b-256 36cbf84323995b7cd68e5b0667425036bc0f8158917fa8b348bb85a8f2be624e

See more details on using hashes here.

Provenance

The following attestation bundles were made for pysuricata-0.1.1-py3-none-any.whl:

Publisher: cd.yml on alvarodiez20/pysuricata

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.2.0

2 files

0.1.5

2 files

0.1.4

2 files

0.1.3

2 files

0.1.2

2 files

This release

0.1.1 This release

2 files

0.1.0

2 files

0.0.73

2 files

0.0.72

2 files

0.0.71

2 files

0.0.70

2 files

0.0.68

2 files

0.0.66

2 files

0.0.65

2 files

0.0.64

2 files

0.0.63

2 files

0.0.62

2 files

0.0.61

2 files

0.0.60

2 files

0.0.59

2 files

0.0.58

2 files

0.0.57

2 files

0.0.56

2 files

0.0.55

2 files

0.0.54

2 files

0.0.53

2 files

0.0.52

2 files

0.0.51

2 files

0.0.50

2 files

0.0.49

2 files

0.0.48

2 files

0.0.47

2 files

0.0.46

2 files

0.0.45

2 files

0.0.44

2 files

0.0.43

2 files

0.0.42

2 files

0.0.41

2 files

0.0.40

2 files

0.0.39

2 files

0.0.38

2 files

0.0.37

2 files

0.0.36

2 files

0.0.35

2 files

0.0.34

2 files

0.0.33

2 files

0.0.32

2 files

0.0.31

2 files

0.0.30

2 files

0.0.29

2 files

0.0.28

2 files

0.0.27

2 files

0.0.26

2 files

0.0.25

2 files

0.0.24

2 files

0.0.23

2 files

0.0.22

2 files

0.0.21

2 files

0.0.20

2 files

0.0.19

2 files

0.0.18

2 files

0.0.17

2 files

0.0.16

2 files

0.0.15

2 files

0.0.14

2 files

0.0.13

2 files

0.0.12

2 files

0.0.11

2 files

0.0.10

2 files

0.0.9

2 files

0.0.8

2 files

0.0.7

2 files

0.0.6

2 files

0.0.5

2 files

0.0.4

2 files

0.0.3

2 files

0.0.1

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page