databroom 0.4

A cross-language DataFrame cleaning assistant with interactive GUI and one-click code export

Databroom

Clean messy DataFrames fast with CLI, GUI, and API – generate reproducible Python and R code in seconds.

Why Databroom?

Manual pandas approach:

# 15+ lines of repetitive code
import pandas as pd
import unicodedata

df = pd.read_csv("messy_data.csv")
# Remove empty columns
df = df.loc[:, df.isnull().mean() < 0.9]
# Clean column names
df.columns = df.columns.str.lower().str.replace(' ', '_')
# Remove accents from text values
def clean_text(text):
    if pd.isna(text): return text
    return ''.join(c for c in unicodedata.normalize('NFKD', str(text)) 
                   if not unicodedata.combining(c))
for col in df.select_dtypes(include=['object']).columns:
    df[col] = df[col].apply(clean_text)
df.to_csv("clean_data.csv", index=False)

Databroom approach:

# One-liner equivalent
databroom clean messy_data.csv --clean-all --output-file clean_data.csv

Installation

pip install databroom

Quick Start

Command Line Interface

# Full cleaning pipeline
databroom clean data.csv --clean-all --output-file cleaned.csv

# Clean only columns
databroom clean data.csv --clean-columns --output-file cleaned.csv

# Clean with code generation
databroom clean data.csv --clean-all --output-code script.py

# Generate R code
databroom clean data.csv --clean-all --output-code script.R --lang r

# Launch interactive GUI
databroom gui

Python API

from databroom.core.broom import Broom

# Load and clean data
broom = Broom.from_csv('data.csv')
cleaned = broom.clean_all()  # Smart clean everything

# Or use specific operations
cleaned = broom.clean_columns().clean_rows()

# Get cleaned DataFrame
df = cleaned.get_df()

Features

• Smart Operations: --clean-all, --clean-columns, --clean-rows, --promote-headers
• Structure Operations: Fix data format issues (promote headers, etc.)
• Advanced Options: Fine-tune with --no-snakecase, --empty-threshold, etc.
• Code Generation: Export Python/pandas or R/tidyverse scripts
• Pipeline Management: Save and load cleaning pipelines in JSON format (GUI, CLI, and API)
• Modular GUI: Component-based Streamlit interface with organized operations
• File Support: CSV, Excel, JSON input/output

Legacy operations (still supported)

• remove_empty_cols(), remove_empty_rows()
• standardize_column_names(), normalize_column_names()
• normalize_values(), standardize_values()

CLI Parameters

# Smart Operations
--clean-all              # Clean everything
--clean-columns          # Clean column names only  
--clean-rows            # Clean row data only

# Structure Operations
--promote-headers        # Convert data row to column headers
--promote-row-index 1    # Row index to promote (default: 0)
--keep-promoted-row      # Keep the promoted row in data

# Advanced Options
--no-snakecase          # Keep original text case
--no-remove-accents-vals # Keep accents in values
--empty-threshold 0.8   # Custom missing value threshold

# Output
--output-file clean.csv # Save cleaned data
--output-code script.py # Generate reproducible code
--lang python          # Code language (python/r)

Examples

Data Science Workflow

databroom clean survey.xlsx \
  --clean-all \
  --empty-threshold 0.7 \
  --output-file clean.csv \
  --output-code analysis.py
  --pipeline-file pipeline.json

R/Tidyverse Code Generation

databroom clean data.csv \
  --clean-all \
  --output-code analysis.R \
  --lang r

Batch Processing

for file in *.csv; do
  databroom clean "$file" --clean-columns --output-file "clean_$file"
done

GUI Interface

Launch the interactive web interface:

databroom gui
# Opens http://localhost:8501

Features:

• Drag & drop file upload
• Organized operations by category (Structure, Column, Row)
• Live preview of operations with step back/reset
• Interactive parameter tuning with configuration panels
• Real-time code generation (Python/R)
• One-click download of cleaned data and code

Method Chaining

from databroom.core.broom import Broom

# Method chaining with structure operations
result = (Broom.from_csv('messy_data.csv')
          .promote_headers(row_index=1)    # Convert row 1 to headers
          .clean_columns(empty_threshold=0.8)
          .clean_rows(snakecase=False)
          .get_df())

Code Generation

All operations automatically generate reproducible code:

# Generated Python code
import pandas as pd
from databroom.core.broom import Broom

broom_instance = Broom.from_csv("data.csv")
broom_instance = broom_instance.clean_all()
df_cleaned = broom_instance.pipeline.df

License

MIT License - see LICENSE file for details.