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What Sweetviz does
Sweetviz is an open-source Python library for automated EDA on pandas DataFrames. Its main functions create a report for one dataset, compare two datasets, or compare two groups within one dataset. Reports can be saved as standalone HTML or embedded in a notebook. The project is MIT-licensed.
“EDA in seconds” describes how little code it takes to generate a report, not a guarantee about runtime or a claim that analysis is complete. Processing time depends on the data, its types, the machine, and report complexity.
PyPI has a version-specific page for Sweetviz 2.3.3, but the project description also refers to an April 2026 update for 2.3.2. Check the package index or installed package rather than assuming which version is latest:
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python -m pip index versions sweetviz
python -m pip show sweetvizPyPI metadata lists Python classifiers from 3.7 through 3.11, while older embedded project text says Python 3.6 or later and pandas 0.25.3 or later. Because those compatibility statements differ, test the release you install in your own environment instead of treating the older text as current guidance. See the Sweetviz 2.3.3 package page.
Install Sweetviz in an isolated environment
A virtual environment helps keep Sweetviz and its dependencies separate from other Python projects.
-
Create an environment:
python -m venv .venv -
Activate it on macOS or Linux:
source .venv/bin/activateIn Windows PowerShell, use:
.venvScriptsActivate.ps1 -
Install Sweetviz and pandas:
python -m pip install -U pip python -m pip install sweetviz pandas -
Confirm the imported package and version:
python -c "import sweetviz as sv; print(sv.__version__)"
The python -m pip form runs pip for the selected Python interpreter, reducing the chance of installing into a different environment.
Generate a report for one DataFrame
Load a CSV with pandas, pass the DataFrame to analyze(), then save the report with show_html():
import pandas as pd
import sweetviz as sv
df = pd.read_csv("data.csv")
report = sv.analyze(df)
report.show_html("sweetviz_report.html")This writes sweetviz_report.html in the current working directory. Depending on the environment and display options, Sweetviz may also open it in a browser. The two-step pattern—create a report object, then render it—also applies to comparisons. The package documentation describes analyze(), compare(), compare_intra(), and the HTML and notebook display methods.
Focus the report on a target
For supervised-learning data, set target_feat to the exact name of a column in the DataFrame:
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report = sv.analyze(df, target_feat="target")
report.show_html("target_report.html")For example, a Titanic dataset with a Survived column can be analyzed with target_feat="Survived". Target analysis organizes the report around how the target varies with other features. It is descriptive: an apparent association does not establish causation, predictive value, or that the feature will be available and appropriate at prediction time.
Compare training and test data
Use compare() to inspect differences between two DataFrames. The optional target should be a column available in the data being compared:
train_df = pd.read_csv("train.csv")
test_df = pd.read_csv("test.csv")
report = sv.compare(
[train_df, "Training Data"],
[test_df, "Test Data"],
target_feat="target"
)
report.show_html("train_test_comparison.html")Before comparing, check that the schemas are compatible. Differences in distributions, missingness, unique-value counts, summaries, associations, or target behavior can be useful signals to investigate. They do not by themselves prove a split is valid or invalid: differences may be intentional, and visual similarity does not guarantee production stability.
A static report also cannot reliably rule out temporal leakage, duplicated entities across splits, or label contamination. Those checks require knowledge of how the data was collected and how the model will be used.
Compare two groups in one dataset
compare_intra() splits a DataFrame using a Boolean condition. The first name labels rows where the condition is true; the second labels rows where it is false:
report = sv.compare_intra(
df,
df["gender"] == "male",
["Male", "Female"],
target_feat="target"
)
report.show_html("group_comparison.html")The labels should match the condition and its complement; in this example, the false group is labeled Female only if the remaining rows are in fact female. The same pattern can compare converted and non-converted users, churned and retained customers, or other defined groups. These comparisons describe observed differences and do not show that group membership caused them.
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Display reports in HTML or a notebook
Control HTML output
show_html() accepts options for file path, browser launch, layout, and scale:
report.show_html(
filepath="report.html",
open_browser=False,
layout="vertical",
scale=0.8
)Use open_browser=False in scripts, remote servers, containers, and CI jobs where a browser may not be available. The documented layouts are widescreen and vertical; scaling can help when the report is difficult to read at its default size.
Embed a report in a notebook
In a notebook, call show_notebook():
report.show_notebook(
w="100%",
h=700,
scale=0.8,
layout="widescreen"
)Adjust width, height, scale, or layout if the embedded report is too large for the notebook cell. If rendering remains awkward, save the HTML and view it separately.
What to look for in the report
Sweetviz summarizes columns and their distributions, reports missing values and duplicate rows, and highlights frequent values. Its listed descriptive statistics include minimum, maximum, range, quartiles, mean, mode, standard deviation, sum, median absolute deviation, coefficient of variation, kurtosis, and skewness.
It also summarizes mixed-type associations using Pearson correlation for numerical pairs, the uncertainty coefficient for categorical pairs, and the correlation ratio for categorical–numerical pairs. These measures can help prioritize follow-up questions, but they do not capture every kind of dependence. Pearson correlation, for example, may miss nonlinear relationships. Treat a prominent score as a lead to investigate, not a final statistical conclusion.
Check the data before interpreting results
Automated profiling depends on the schema and values it receives. Review types and clean obvious issues before treating the charts as meaningful:
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Parse dates stored as text, and decide whether to derive useful date features.
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Check numeric columns that are really categories, such as codes where 1, 2, and 3 are labels rather than measurements.
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Exclude or separately handle identifiers, UUIDs, hashes, raw URLs, free-form text, and near-unique categories if their charts are not useful.
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Normalize missing-value markers such as
"N/A"where needed, and confirm Boolean fields represented as 0 and 1 are understood appropriately. -
Confirm the target’s name and type before analyzing it.
-
For very large data, start with a representative sample, remove unnecessary columns, and ensure the machine has enough memory. Sweetviz profiles pandas objects, so the data generally needs to be loaded into memory; no fixed maximum dataset size is established here.
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Limits, privacy, and common errors
Use it as a first-pass audit, not a verdict
A Sweetviz report does not determine whether an outlier is erroneous, whether a relationship is causal, whether a feature leaks information in a business process, or whether a dataset is fair or production-ready. It is not a complete cleaning pipeline, formal validation suite, or drift-monitoring system. Follow up with domain knowledge, targeted plots, data checks, and the tests your use case requires.
Protect report contents
A standalone HTML report is convenient to share, but it can contain personal information, rare categories, free text, internal fields, subgroup differences, or target labels. Inspect the report and apply your organization’s data-handling rules before emailing, attaching, or publishing it.
Fix import and display problems
-
ModuleNotFoundError: No module named 'sweetviz': the package may have been installed into a different interpreter or notebook kernel. Runpython -m pip install sweetvizin the active environment and check the import path withpython -c "import sweetviz; print(sweetviz.__file__)". In Jupyter,%pip install sweetvizinstalls into the active kernel; restart the kernel if needed. -
AttributeError: module 'sweetviz' has no attribute 'analyze': a local file namedsweetviz.pycan shadow the installed package. Rename it and remove stale.pycfiles or__pycache__entries.Recommended Free Tools
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Browser does not open: this is common in headless or remote environments. Save the report with
open_browser=Falseand retrieve the HTML through the environment’s usual file or artifact mechanism. -
Comparison schema mismatch: inspect shapes, column names, and dtypes in both DataFrames; resolve missing or extra columns, incompatible types, inconsistent missing-value conventions, or a target present in only one dataset before comparing.
-
Missing-glyph warnings for Asian characters: the project documentation notes reports of this display issue. It can be a font-rendering limitation rather than corrupted source data; use an environment with the required glyphs.
Sweetviz compared with other approaches
| Approach | Best fit | Trade-off |
|---|---|---|
| Sweetviz | Fast visual first-pass EDA on pandas data, especially target, train/test, or subgroup comparisons. | Does not replace domain-specific analysis, data validation, or production monitoring. |
| YData Profiling | Broader report-oriented profiling and data-quality diagnostics; its documentation covers pandas and Spark workflows. | Choose it when profiling breadth matters more than Sweetviz’s compact comparison workflow. |
| pandas with Matplotlib, Seaborn, or Plotly | Exact control over transformations, plots, aggregations, and statistical questions. | Requires more custom analysis than generating an automated profile. |
| Deepchecks | Systematic testing and validation of data and machine-learning models, including production-oriented monitoring use cases. | Addresses a different need from a quick local EDA report. |
Sweetviz’s package documentation also describes optional Comet integration for logging reports when configured with an API key. That workflow is not necessary for local HTML reporting; see the package description for details.
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