# Databroom
A DataFrame cleaning tool with CLI, GUI, and code generation capabilities.
## Why Databroom?
**Manual pandas approach:**
```python
# 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:**
```bash
# Single command
databroom clean messy_data.csv --clean-all --output-file clean_data.csv
```
## Installation
```bash
pip install databroom
```
## Quick Start
### Command Line Interface
```bash
# Clean everything (recommended)
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
```python
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`
- **Advanced Options**: Fine-tune with `--no-snakecase`, `--empty-threshold`, etc.
- **Code Generation**: Export Python/pandas or R/tidyverse scripts
- **Interactive GUI**: Streamlit-based web interface
- **File Support**: CSV, Excel, JSON input/output
## Available Operations
| Operation | Description |
|-----------|-------------|
| `clean_all()` | Complete cleaning: columns + rows with all operations |
| `clean_columns()` | Clean column names: snake_case + remove accents + remove empty |
| `clean_rows()` | Clean row data: snake_case + remove accents + remove empty |
### Legacy operations (still supported)
- `remove_empty_cols()`, `remove_empty_rows()`
- `standardize_column_names()`, `normalize_column_names()`
- `normalize_values()`, `standardize_values()`
## CLI Parameters
```bash
# Smart Operations
--clean-all # Clean everything
--clean-columns # Clean column names only
--clean-rows # Clean row data only
# 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
```bash
databroom clean survey.xlsx \
--clean-all \
--empty-threshold 0.7 \
--output-file clean.csv \
--output-code analysis.py
```
### R/Tidyverse Code Generation
```bash
databroom clean data.csv \
--clean-all \
--output-code analysis.R \
--lang r
```
### Batch Processing
```bash
for file in *.csv; do
databroom clean "$file" --clean-columns --output-file "clean_$file"
done
```
## GUI Interface
Launch the interactive web interface:
```bash
databroom gui
# Opens http://localhost:8501
```
Features:
- Drag & drop file upload
- Live preview of operations
- Interactive parameter tuning
- Real-time code generation
- One-click download
## Method Chaining
```python
from databroom.core.broom import Broom
result = (Broom.from_csv('messy_data.csv')
.clean_columns(empty_threshold=0.8)
.clean_rows(snakecase=False)
.get_df())
```
## Code Generation
All operations automatically generate reproducible code:
```python
# 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.
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"description": "# Databroom\r\n\r\nA DataFrame cleaning tool with CLI, GUI, and code generation capabilities.\r\n\r\n## Why Databroom?\r\n\r\n**Manual pandas approach:**\r\n```python\r\n# 15+ lines of repetitive code\r\nimport pandas as pd\r\nimport unicodedata\r\n\r\ndf = pd.read_csv(\"messy_data.csv\")\r\n# Remove empty columns\r\ndf = df.loc[:, df.isnull().mean() < 0.9]\r\n# Clean column names\r\ndf.columns = df.columns.str.lower().str.replace(' ', '_')\r\n# Remove accents from text values\r\ndef clean_text(text):\r\n if pd.isna(text): return text\r\n return ''.join(c for c in unicodedata.normalize('NFKD', str(text)) \r\n if not unicodedata.combining(c))\r\nfor col in df.select_dtypes(include=['object']).columns:\r\n df[col] = df[col].apply(clean_text)\r\ndf.to_csv(\"clean_data.csv\", index=False)\r\n```\r\n\r\n**Databroom approach:**\r\n```bash\r\n# Single command\r\ndatabroom clean messy_data.csv --clean-all --output-file clean_data.csv\r\n```\r\n\r\n## Installation\r\n\r\n```bash\r\npip install databroom\r\n```\r\n\r\n## Quick Start\r\n\r\n### Command Line Interface\r\n\r\n```bash\r\n# Clean everything (recommended)\r\ndatabroom clean data.csv --clean-all --output-file cleaned.csv\r\n\r\n# Clean only columns\r\ndatabroom clean data.csv --clean-columns --output-file cleaned.csv\r\n\r\n# Clean with code generation\r\ndatabroom clean data.csv --clean-all --output-code script.py\r\n\r\n# Generate R code\r\ndatabroom clean data.csv --clean-all --output-code script.R --lang r\r\n\r\n# Launch interactive GUI\r\ndatabroom gui\r\n```\r\n\r\n### Python API\r\n\r\n```python\r\nfrom databroom.core.broom import Broom\r\n\r\n# Load and clean data\r\nbroom = Broom.from_csv('data.csv')\r\ncleaned = broom.clean_all() # Smart clean everything\r\n\r\n# Or use specific operations\r\ncleaned = broom.clean_columns().clean_rows()\r\n\r\n# Get cleaned DataFrame\r\ndf = cleaned.get_df()\r\n```\r\n\r\n## Features\r\n\r\n- **Smart Operations**: `--clean-all`, `--clean-columns`, `--clean-rows`\r\n- **Advanced Options**: Fine-tune with `--no-snakecase`, `--empty-threshold`, etc.\r\n- **Code Generation**: Export Python/pandas or R/tidyverse scripts\r\n- **Interactive GUI**: Streamlit-based web interface\r\n- **File Support**: CSV, Excel, JSON input/output\r\n\r\n## Available Operations\r\n\r\n| Operation | Description |\r\n|-----------|-------------|\r\n| `clean_all()` | Complete cleaning: columns + rows with all operations |\r\n| `clean_columns()` | Clean column names: snake_case + remove accents + remove empty |\r\n| `clean_rows()` | Clean row data: snake_case + remove accents + remove empty |\r\n\r\n### Legacy operations (still supported)\r\n- `remove_empty_cols()`, `remove_empty_rows()`\r\n- `standardize_column_names()`, `normalize_column_names()`\r\n- `normalize_values()`, `standardize_values()`\r\n\r\n## CLI Parameters\r\n\r\n```bash\r\n# Smart Operations\r\n--clean-all # Clean everything\r\n--clean-columns # Clean column names only \r\n--clean-rows # Clean row data only\r\n\r\n# Advanced Options\r\n--no-snakecase # Keep original text case\r\n--no-remove-accents-vals # Keep accents in values\r\n--empty-threshold 0.8 # Custom missing value threshold\r\n\r\n# Output\r\n--output-file clean.csv # Save cleaned data\r\n--output-code script.py # Generate reproducible code\r\n--lang python # Code language (python/r)\r\n```\r\n\r\n## Examples\r\n\r\n### Data Science Workflow\r\n```bash\r\ndatabroom clean survey.xlsx \\\r\n --clean-all \\\r\n --empty-threshold 0.7 \\\r\n --output-file clean.csv \\\r\n --output-code analysis.py\r\n```\r\n\r\n### R/Tidyverse Code Generation\r\n```bash\r\ndatabroom clean data.csv \\\r\n --clean-all \\\r\n --output-code analysis.R \\\r\n --lang r\r\n```\r\n\r\n### Batch Processing\r\n```bash\r\nfor file in *.csv; do\r\n databroom clean \"$file\" --clean-columns --output-file \"clean_$file\"\r\ndone\r\n```\r\n\r\n## GUI Interface\r\n\r\nLaunch the interactive web interface:\r\n\r\n```bash\r\ndatabroom gui\r\n# Opens http://localhost:8501\r\n```\r\n\r\nFeatures:\r\n- Drag & drop file upload\r\n- Live preview of operations\r\n- Interactive parameter tuning\r\n- Real-time code generation\r\n- One-click download\r\n\r\n## Method Chaining\r\n\r\n```python\r\nfrom databroom.core.broom import Broom\r\n\r\nresult = (Broom.from_csv('messy_data.csv')\r\n .clean_columns(empty_threshold=0.8)\r\n .clean_rows(snakecase=False)\r\n .get_df())\r\n```\r\n\r\n## Code Generation\r\n\r\nAll operations automatically generate reproducible code:\r\n\r\n```python\r\n# Generated Python code\r\nimport pandas as pd\r\nfrom databroom.core.broom import Broom\r\n\r\nbroom_instance = Broom.from_csv(\"data.csv\")\r\nbroom_instance = broom_instance.clean_all()\r\ndf_cleaned = broom_instance.pipeline.df\r\n```\r\n\r\n## License\r\n\r\nMIT License - see LICENSE file for details.\r\n",
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