Michael
93e43fc0d9
Sidebar nav now groups tools under Data Review / Data Cleaners / Transformations / Automations via st.navigation, replacing the flat auto-discovered list. Tool display names switch to action-first phrasing (Find Duplicates, Fix Missing Values, Find Unusual Values, Standardize Formats, Clean Text, Quality Check, Map Columns, Combine Files, Automated Workflows) in EN + ES packs and on each page's H1. The Data Cleaners section follows the requested order: Missing Values → Outliers → Text Cleaner → Format Standardizer → Deduplicator → Quality Check. (Text Cleaner kept inside cleaners since the request didn't list it but the tool still ships.) Registry now carries a section field; helpers added: tools_in_section(), section_label(). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
104 lines
3.3 KiB
Python
104 lines
3.3 KiB
Python
"""DataTools Outlier Detector — stub page."""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import streamlit as st
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_project_root = Path(__file__).resolve().parent.parent.parent.parent
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if str(_project_root) not in sys.path:
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sys.path.insert(0, str(_project_root))
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from src.gui.components import (
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hide_streamlit_chrome,
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require_feature_or_render_upgrade,
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require_normalization_gate,
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)
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from src.license import FeatureFlag
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hide_streamlit_chrome()
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require_feature_or_render_upgrade(FeatureFlag.OUTLIER_DETECTOR)
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require_normalization_gate()
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# ---------------------------------------------------------------------------
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# Header
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# ---------------------------------------------------------------------------
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st.title("📊 Find Unusual Values")
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st.caption("Detect and handle outliers in numeric columns.")
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st.info("This tool is under development.")
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# ---------------------------------------------------------------------------
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# What this tool will do
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# ---------------------------------------------------------------------------
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st.markdown("""
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**Features:**
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- Z-score detection (configurable threshold)
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- IQR (interquartile range) detection
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- MAD (median absolute deviation) detection
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- Domain-rule violations (e.g., age < 0, price > $1M)
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- Visual outlier highlighting in data preview
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- Handling: flag only, remove, cap/winsorize to bounds
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""")
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st.divider()
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# ---------------------------------------------------------------------------
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# File upload (functional)
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# ---------------------------------------------------------------------------
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uploaded = st.file_uploader(
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"Upload CSV or Excel file",
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type=["csv", "tsv", "xlsx", "xls"],
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help="Upload a file to preview. Processing is not yet available.",
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key="outlier_file_upload",
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)
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if uploaded is not None:
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import pandas as pd
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try:
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if uploaded.name.endswith((".xlsx", ".xls")):
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df = pd.read_excel(uploaded)
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else:
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df = pd.read_csv(uploaded)
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st.subheader(f"Preview: {uploaded.name}")
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st.caption(f"{len(df)} rows, {len(df.columns)} columns")
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st.dataframe(df.head(10), use_container_width=True)
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except Exception as e:
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from src.core.errors import format_for_user
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st.error(
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f"**Could not read `{uploaded.name}`**\n\n"
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f"```\n{format_for_user(e)}\n```"
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)
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# ---------------------------------------------------------------------------
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# Placeholder options
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# ---------------------------------------------------------------------------
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st.subheader("Detection Method")
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st.selectbox("Method", ["Z-Score", "IQR (Interquartile Range)", "MAD (Median Absolute Deviation)"], disabled=True)
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st.slider("Z-Score threshold", 1.0, 5.0, 3.0, 0.1, disabled=True)
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st.slider("IQR multiplier", 1.0, 3.0, 1.5, 0.1, disabled=True)
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st.subheader("Handling")
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st.selectbox("Action", ["Flag only (add column)", "Remove outlier rows", "Cap / Winsorize to bounds"], disabled=True)
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st.divider()
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st.button("Detect Outliers", type="primary", use_container_width=True, disabled=True)
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# ---------------------------------------------------------------------------
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# Footer
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# ---------------------------------------------------------------------------
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st.divider()
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st.caption(
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"Runs locally. Your data never leaves this computer. "
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"| DataTools v3.0"
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)
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