data-science-analysis

name	data-science-analysis
description	Computes a numeric or categorical answer to a quantitative data-science question by cleaning and analyzing local data files (CSV, Excel, TSV, and scientific formats .npz/.fits/.h5) with pandas, numpy, and scipy. Use whenever a task ships its own dataset (in whatever local directory it provides) and asks you to derive a value via statistical, geospatial, temporal, ratio, percentage, growth-rate, correlation, signal-processing, orbital, or forecasting analysis. Spans domains including archeology (demographics, paleoclimate proxies, radiocarbon dating, historical conflicts); biomedical/bioinformatics (tumor histology, genes, proteins, peptides, genomic variants); environmental (water quality, bacterial/Enterococcus exceedance, rainfall correlations, environmental justice); legal/consumer (fraud, identity theft, consumer complaints by state/metro); wildfire (incidents, causes, suppression costs, acres burned, fatalities, geospatial intersections); and astronomy/heliophysics/space weather (satellite telemetry TLE/Swarm/SP3, indices OMNI/GOES, solar activity, atmospheric density, orbital mechanics, forecasting).
version	2

Data Science Analysis

A single, domain-agnostic workflow for answering quantitative questions from local datasets. Data files may live in any directory the task provides (e.g., input/, data/, the current working directory, or an explicit path in the prompt); first identify that data directory, then apply the workflow to it. The steps below apply to any domain (archeology, biomedical, environmental, legal, wildfire, astronomy/heliophysics/space weather, and beyond) and to any file type (tabular CSV/Excel or scientific array/binary formats like .npz, .fits, .h5). Domain-specific gotchas are flagged inline and detailed in references/domain-notes.md.

Core Principles

Principle	Why
Python for all arithmetic	Prevents LLM math errors
Read files directly in scripts	Never hardcode numbers from manual inspection—they drift and break
Consult the data dictionary first	Map every prompt term to exact columns/categories; never guess
Single consolidated script	For multi-step analysis, avoid many small .py files and inline python3 -c
Print intermediate results	Catches silent filtering/join errors before they reach the answer
Recompute derived metrics from raw	Never trust pre-calculated rates/percentages in source files
Round only at the final step	Intermediate rounding compounds error

Workflow

1. Discover & Profile

• Identify the data directory the task provides (e.g., input/, data/, the current working directory, or an explicit path given in the prompt), then glob all files under it (e.g., <data_dir>/**/*) — data files, data dictionaries, metadata, format specs (.fmt, .text, README), helper scripts. Avoid overly restrictive filename filters that might exclude the correct data. Restrict data access to this directory.
• Read data dictionaries / README / format specs first. Map every prompt term (e.g., "damaged", "generally unsafe", "tumor") to its exact column or category — never guess. Cross-reference discovered schemas against format specs to confirm column mappings and units.
• Inspect raw structure before loading: read the first (and last) 10–20 rows raw (header=None) to locate the true header row, metadata/summary/footer rows, delimiters, timestamp formats, and encoding quirks (BOM → utf-8-sig).
• For Excel, list sheet_names via pandas.ExcelFile; prioritize README/Legend/Metadata sheets, then preview each data sheet.
• For scientific/binary files (.npz, .fits, .h5), print all keys and array shapes to identify the correct arrays before use.
• Print exact column names and sample values; use names—not indices—for all downstream selection.

Run scripts/explore_input.py [data_dir] first to profile every file (pass the data directory; defaults to input/). See references/data-ingestion-and-cleaning.md for encoding, header-detection, Excel, and scientific/binary-format patterns.

2. Ingest Robustly

Write a single Python script. Apply these in the first execution to avoid retries:

• Match the tool to the task complexity: use lightweight numpy/csv for simple whitespace-delimited files and direct extraction; reserve pandas for tabular joins and richer processing. Avoid over-engineering (e.g., direct pairwise comparisons rather than full time-series propagation when they suffice).
• Handle encoding robustly: try utf-8-sig (BOM), fall back to latin-1/cp1252; strip non-breaking spaces.
• Strip whitespace from headers and string columns.
• Sanitize numeric strings (commas, currency symbols, quotes) before casting; coerce with pd.to_numeric(..., errors='coerce').
• Cast through float before int (int(float(x))) to handle decimal strings like "42.0".
• For large files, use memory-efficient loading (usecols, dtype, chunksize; Excel read_only=True).

3. Clean & Sanitize

• Missing/malformed placeholders & sentinel fills: map '-', 'NA', 'N/A', blanks, and sentinels (-999, -9999, and space-physics fills like 9.99E32, 999999.9, 99999.9, 9999, 999.9) to NaN before computing any statistic, correlation, or model—including them severely skews results.
• Censored values: handle detection-limit markers ('<0.1', '>100') explicitly before numeric conversion (see domain notes).
• Duplicates & scaled integers: drop duplicate timestamps, exclude non-data/metadata columns, and decode scaled-integer encodings before use.
• String normalization: .str.strip().str.lower() for categorical filters; include na=False in .str.contains()/.str.match() to avoid errors on mixed-type columns.
• Profile unique values in categorical columns before filtering to catch case/whitespace/typo variations.
• Verify data scales: confirm whether values are log-transformed before applying exponentiation; inverse-transform only when raw metrics are required.
• Prefer NaN-ignoring aggregations over dropping rows to avoid silent data loss.

Caution: Do not normalize typos or split concatenated strings unless the task asks for it—preserve exact literal counts for raw-unique-entry queries. See references/data-ingestion-and-cleaning.md.

4. Integrate & Join

• Identify linking keys (sample IDs, gene symbols, entity names, station IDs) across tables.
• Normalize join keys on both sides: strip whitespace, standardize case, zero-pad ID strings (never numeric-coerce IDs with leading zeros).
• Align granularities before merging (e.g., county → state, daily → monthly).
• Verify key overlap empirically (len(inner) vs len(left)) and log unmatched keys to detect silent drops.
• Apply exclusion criteria first (e.g., remove samples "not in the study") before extracting features.
• Prevent data leakage: when comparing a target entity to a background set, filter the target out of the background.

See references/analysis-patterns.md § Joins and references/domain-notes.md for entity normalization (MSA/County suffixes) and biomedical linking.

5. Filter

• Apply all spatial, temporal, and categorical filters before computing any aggregations.
• Translate qualitative constraints ('summer', 'preceding days', 'generally unsafe') into precise numeric/datetime bounds via the data dictionary.
• Group by stable identifiers (conflict ID, entity ID) rather than ambiguous names to prevent double-counting multi-row entries.
• Print intermediate row counts after each filter to verify the subset matches the prompt's scope. Ensure denominators reflect only the filtered subset.

6. Analyze

• Extremum / closest-value queries: sort by primary metric, then secondary tie-breaker; isolate all ties matching the primary condition before applying the tie-breaker. Print tie counts.
• Rates & ratios: align numerator and denominator to the exact same categorical/temporal/geographic scope. Distinguish pooled overall rate (grand totals) from average of periodic rates (mean of per-period rates)—compute exactly what the prompt asks. Guard against division by zero.
• Time-series: sort chronologically before any lag/rolling/diff. Filter to the exact prompt range (don't rely on global extrema); when merging multi-source series, resample to a common cadence and inner-join on timestamp. Verify temporal coverage from the actual data, not filenames. Handle gaps and interpolate missing periods; exclude partial-year data from temporal averages.
• Signal processing: detect peaks/troughs with scipy.signal.find_peaks rather than manual thresholding; rank top-N extrema by magnitude (not raw data); cluster events within a tolerance window to avoid overcounting. See references/analysis-patterns.md § Signal Processing.
• Spatial & orbital: validate coordinate ranges (detect swapped lat/lon); filter by attributes before computing distances; count distinct entities to prevent double-counting. For orbital/coordinate work, use domain libraries (astropy, sgp4/skyfield, pymap3d), normalize longitude conventions, and track units. See references/geospatial.md.
• Forecasting: use strict chronological train/test splits (never random); align lagged features and distinguish forecast-issuance from target dates; evaluate on date-aligned observed vs. predicted values; respect task-specified windows exactly. See references/analysis-patterns.md § Forecasting & Evaluation.
• Statistics: choose robust methods—Spearman for skewed/outlier data, log-transforms for right-skewed outcomes, non-parametric tests for distribution shifts. For causal questions, map variables to roles (treatment/outcome/confounder) and diagnose selection bias.
• Domain scales: align units before analysis (e.g., paleo BP counts backwards—"later" = minimum BP). See references/domain-notes.md.

See references/analysis-patterns.md for tie-breaking, rate, time-series, signal-processing, forecasting, and statistical patterns.

7. Validate

Print intermediate results at every stage:

• Frequency distributions of parsed values and pre/post-filter row counts.
• Matched subsets (city pairs, filtered rows) and tie-breaking subsets to confirm correct records.
• Boundary-straddling edge cases in range filters; pre/post-merge overlap sizes.
• Sanity checks (e.g., warn if numerator exceeds denominator).

8. Output

Save the final answer under the current artifact directory supplied by the QwenPaw Data runtime:

Direct chat:        artifacts/<session_id>/answer.json
TaskGraph node:     artifacts/<session_id>/<graph_id>/<node_id>/answer.json

{"answer": <value>}

• Always use the current session_id. Append graph_id/node_id only when the runtime explicitly supplies an active TaskGraph node; never invent IDs.
• Do not write answer files directly under the shared artifacts/ root.
• Ensure the current artifact directory exists before writing.
• Use the write_file tool (not shell echo/redirection) for proper JSON formatting.
• Match exact requested precision; omit units, thousands separators, markdown, and conversational filler.
• Obey negative constraints (e.g., "No explanation needed")—output only the direct answer value.
• In the text response, cite source files/sheets and summarize filtering/exclusion logic; list actual entities when counting or identifying specific items.

Environment Setup

pip install pandas openpyxl numpy scipy statsmodels

Add shapely (and optionally geopandas) for geospatial tasks; beautifulsoup4 for HTML tables; h5py/astropy for .h5/.fits files; astropy, sgp4/skyfield, pymap3d for orbital mechanics and coordinate conversions. If a specialized package fails to install, fall back to core numpy/scipy. Rely solely on local files—no web access.

Additional Resources

Resource	When to use
scripts/explore_input.py	Run at the start of every task to profile all files in the task's data directory (pass it as an argument; defaults to input/)
references/data-ingestion-and-cleaning.md	Encoding, header detection, type coercion, sentinel fills, censored values, Excel/HTML/scientific-binary parsing, join-key mismatches
references/analysis-patterns.md	Tie-breaking, joins, rate/ratio calculation, time-series, signal processing, forecasting, statistics, causal inference
references/geospatial.md	Coordinate validation, distance calculations, GPKG → shapely fallback, orbital mechanics & coordinate systems
references/domain-notes.md	Domain-specific knowledge (paleo/temporal scales, biomedical, environmental, legal, wildfire, astronomy/heliophysics/space weather)