statchat-app 1.0.0

Data cleaning, normalization, statistical analysis and AI-powered iterative adjustment

Stat Chat

Data Cleaning · Normalization · Statistical Analysis · PDF Reports

A Python tool with both a GUI (Tkinter) and CLI interface for analysing tabular data from CSV or Excel files.

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Installation

pip install -r requirements.txt

Tkinter ships with standard Python on Windows/macOS. On Linux: sudo apt install python3-tk

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Launch

GUI (default)

python main.py

CLI

python main.py --cli --input data.csv [options]

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GUI Walkthrough

1. Load CSV / Excel — opens a file picker; the Data Preview tab fills instantly.
2. Data Cleaning — tick any combination of:
   • Drop duplicate rows
   • Drop rows with null values
   • Fill nulls (mean / median / mode / zero)
3. Normalization — choose one method:
   • Z-score — standardise with custom target mean & std (default 0, 1)
   • Min-Max — scale to [0, 1]
   • Robust — median/IQR scaling (outlier-resistant)
4. Analysis Metrics — tick any combination:
   • Measures of Central Tendency (mean, median, mode)
   • Measures of Dispersion (std dev, variance, IQR, range, CV)
   • Shape stats (skewness, kurtosis)
   • Percentile Statistics (P5–P95)
   • Normality Tests (Shapiro-Wilk)
   • Correlation Matrix (heatmap in PDF)
   • ROC-AUC (enter the binary target column name)
5. Run Analysis — results appear in the Analysis Results tab.
6. Save Cleaned Data — exports as CSV, Excel, or JSON.
7. Export PDF Report — full styled report with tables and charts.

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CLI Reference

python main.py --cli --input FILE [options]

File I/O:
  --input, -i PATH          Input CSV or Excel file (required)
  --output, -o PATH         Save cleaned data here
  --output-format           csv | xlsx | json  (default: csv)
  --report, -r PATH         Save PDF report here

Cleaning:
  --drop-duplicates         Remove duplicate rows
  --drop-nulls              Drop rows containing any null
  --fill-nulls STRATEGY     mean | median | mode | zero

Normalization:
  --normalize METHOD        zscore | minmax | robust
  --norm-mean FLOAT         Z-score target mean  (default 0.0)
  --norm-std  FLOAT         Z-score target std   (default 1.0)

Analysis:
  --central-tendency        Mean, median, mode
  --dispersion              Std dev, variance, IQR, range
  --shape                   Skewness & kurtosis
  --percentiles             P5–P95
  --correlation             Pearson correlation matrix
  --roc-auc TARGET_COL      ROC-AUC vs a binary target column
  --all-metrics             Enable all metrics above

Example

python main.py --cli \
  --input sales.csv \
  --output cleaned_sales \
  --output-format xlsx \
  --drop-duplicates \
  --fill-nulls mean \
  --normalize zscore \
  --central-tendency \
  --dispersion \
  --shape \
  --correlation \
  --roc-auc churn \
  --report analysis_report.pdf

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Project Structure

statchat/
├── main.py               Entry point (GUI or CLI)
├── requirements.txt
├── core/
│   ├── loader.py         File I/O (CSV, Excel, JSON)
│   ├── cleaner.py        Cleaning & normalization
│   ├── analyzer.py       Statistical metrics
│   └── reporter.py       PDF report generation
├── gui/
│   └── app.py            Tkinter GUI
└── cli/
    └── runner.py         CLI runner

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Supported Metrics

Category	Metrics
Central Tendency	Mean, Median, Mode, Count
Dispersion	Std Dev, Variance, Range, IQR, Min, Max, CV
Shape	Skewness, Kurtosis
Percentiles	P5, P10, P25, P50, P75, P90, P95
Normality	Shapiro-Wilk statistic & p-value
Correlation	Pearson correlation matrix + heatmap
ROC-AUC	Per-feature AUC score + ROC curve plot