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feat(examples): add Streamlit cleaning app recipe - #517
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Included review availability: This review used your included allowance. Your plan provides up to 1 included review per hour; 0 remain after this review. 📝 WalkthroughWalkthroughAdds a Streamlit app that loads sample data or an uploaded CSV, cleans it with FreshData, and displays cleaning metrics, data previews, a download, and a quality report. The examples index links to the app. ChangesStreamlit Data-Cleaning App
Priority: ⬇️ Low Estimated code review effort: 2 (Simple) | ~15 minutes Change: Feature Sequence Diagram(s)sequenceDiagram
actor User
participant StreamlitApp as streamlit_app.py
participant FreshData
User->>StreamlitApp: Upload CSV or use sample data
StreamlitApp->>StreamlitApp: Parse uploaded CSV
StreamlitApp->>FreshData: fd.clean(data, return_report=True)
FreshData-->>StreamlitApp: Cleaned data and report
StreamlitApp-->>User: Show metrics, previews, download, and report
Suggested reviewers: Merge Risk: ⚪ Minimal · up to The app provides the documented cleaning previews, metrics, report, and CSV download. No issue identified here needs resolution before merge. 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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Inline comments:
Review comments at @examples/integrations/streamlit_app.py:
- Line 76: Update the imputed-value count in the report action aggregation to
include automatic actions with `step == "missing"` as well as `"impute"`, and
count only fill actions by checking that `action.description` starts with
“filled ”; retain the positive-count filter.
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examples/README.mdexamples/integrations/streamlit_app.py
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JohnnyWilson16
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Thanks for contributing the Streamlit integration recipe, @satheesh142002! The recipe provides a clean, practical demo of interactive CSV cleaning with FreshData, side-by-side previews, metric cards, and a download button.
Before we can merge, please address the following issues:
1. Inaccurate "Columns Modified" Metric (Skipped Actions Counted)
Location: examples/integrations/streamlit_app.py:38-46
def count_modified_columns(report: object) -> int:
modified = set(report.columns_imputed)
modified.update(
action.column
for action in report.actions
if action.column is not None and action.count > 0
)
return len(modified)Issue: When FreshData protects identifier columns or sensitive columns from modification (e.g. semantic identifier protection), it records an action with count > 0 and status="skipped". Because count_modified_columns only checks action.count > 0, columns that were intentionally protected and left unchanged are counted as "modified". Additionally, completely dropped columns (report.columns_dropped) are missed because table-level drop actions have action.column is None.
Fix:
def count_modified_columns(report: Any) -> int:
"""Count columns modified by applied cleaning actions, imputations, or drops."""
modified = set(report.columns_imputed) | set(report.columns_dropped)
modified.update(
action.column
for action in report.actions
if action.column is not None
and action.count > 0
and getattr(action, "status", "automatic") in {"automatic", "approved"}
)
return len(modified)2. Fragile String Matching for Imputed Values
Location: examples/integrations/streamlit_app.py:75-81
imputed_values = sum(
action.count
for action in report.actions
if action.step in {"impute", "missing"}
and action.description.startswith("filled ")
and action.count > 0
)Issue: action.description.startswith("filled ") only matches standard fallback fills, but misses other FreshData imputation algorithms (e.g. MissForest logs step="impute" with description="imputed N missing value(s)...", timeseries logs "interpolated...").
Fix: Check action.step == "impute" or column membership in report.columns_imputed, and verify action.status:
imputed_values = sum(
action.count
for action in report.actions
if (action.step == "impute" or (action.step == "missing" and action.column in report.columns_imputed))
and getattr(action, "status", "automatic") in {"automatic", "approved"}
and action.count > 0
)3. ASCII Quality Report Layout Collapses in Streamlit
Location: examples/integrations/streamlit_app.py:101
with st.expander("Quality report"):
st.write(report.summary())Issue: st.write() renders plain text strings as Markdown prose. In Markdown, indentation and single newlines collapse into a single run-on paragraph, flattening the formatted ASCII report tree into an unreadable block.
Fix: Use st.code() or st.text() to preserve monospace layout, indentation, and line breaks:
with st.expander("Quality report"):
st.code(report.summary(), language="text")4. Interactive Sidebar Controls for Strategy and Duplicate Removal
Location: examples/integrations/streamlit_app.py:66
Issue: Currently fd.clean(raw_df, return_report=True) hardcodes default options (strategy="balanced", drop_duplicates=False). The demo dataset contains duplicate rows ("Noah Patel"), and FreshData outputs a warning that duplicates were not removed because drop_duplicates=False. As a result, the "Duplicates dropped" metric card always displays 0. Adding interactive sidebar controls showcases the power of an interactive Streamlit app.
Fix:
st.sidebar.header("Cleaning Settings")
strategy = st.sidebar.selectbox("Strategy", ["balanced", "conservative", "aggressive"], index=0)
drop_duplicates = st.sidebar.checkbox("Drop duplicate rows", value=True)
try:
cleaned_df, report = fd.clean(
raw_df,
strategy=strategy,
drop_duplicates=drop_duplicates,
return_report=True,
)5. Type Annotation & Entrypoint Hygiene
- In
count_modified_columns(report: object), change type annotation toreport: Any(importfrom typing import Any) to pass strict static typecheckers (objecthas no attributescolumns_imputed/actions). - Wrap top-level script execution in
def main() -> None:withif __name__ == "__main__": main()to avoid top-level side effects when importing functions.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
JohnnyWilson16
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JohnnyWilson16
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LGTM! Thanks for addressing the feedback so quickly, @satheesh142002.
All requested improvements are cleanly resolved:
- Accurate Metric Calculations:
count_modified_columnsaccounts for dropped columns and filters skipped/vetoed actions, andimputed_valuesinspects action step and imputation metadata rather than fragile string prefixes. - ASCII Tree Layout: Monospace formatting and tree indentation in the quality report are preserved using
st.code(..., language="text"). - Interactive Controls: Added sidebar controls for cleaning
strategyanddrop_duplicatestoggling. - Script Architecture: Added
main()entrypoint guard and updated type annotations.
Approved and ready to merge!
2 participants
What changed?
Added
streamlit_app.py, a standalone Streamlit app with CSV upload, fallback demo data, before/after previews, cleaning metrics, a quality report, and cleaned CSV download. Registered it inREADME.md.Why?
Provides an interactive way to explore FreshData cleaning. Fixes #511.
How was it tested?
tests(not applicable; this adds an example recipe)pytest -m "not online and not large"uvx ruff check examples/integrations/streamlit_app.pymypy src/freshdataAlso verified Python syntax with
python -m py_compileand checked the diff withgit diff --check.Any performance impact?
Any compatibility concerns?
Documentation updated?
docs[Unreleased]inCHANGELOG.mdSummary by CodeRabbit