tidyviz 0.1.0

A Python package for tidying and visualizing survey data

TidyViz

A Python package for survey data cleaning and visualization.

Features

Data Cleaning

• Expand/collapse multiple choice responses
• Validate response ranges
• Detect missing data patterns
• Flag straight-lining and speeders
• Check logical consistency

Visualization

• Single choice bar charts
• Multiple choice bar charts
• Custom color palettes
• Survey-appropriate styling

Installation

pip install tidyviz

Development installation:

git clone https://github.com/pingfan-hu/tidyviz.git
cd tidyviz
pip install -e ".[dev]"

Quick Start

import pandas as pd
import tidyviz as tv

# Load survey data
df = pd.read_csv('survey.csv')

# Clean: Expand multiple choice column
df_expanded = tv.tidy.expand_multiple_choice(df, 'colors')

# Validate: Check response ranges
df_clean, invalid = tv.tidy.check_response_range(
    df, 'satisfaction', min_val=1, max_val=5
)

# Visualize: Plot single choice responses
tv.viz.set_survey_style(palette='categorical')
tv.viz.plot_single_choice(df, 'contact_method',
                          title='Preferred Contact',
                          show_percentages=True)

Documentation

Data Cleaning (tv.tidy)

Multiple Choice Handling

# Expand comma-separated values to binary columns
df_exp = tv.tidy.expand_multiple_choice(df, 'colors', sep=',')
# Creates: colors_Red, colors_Blue, colors_Green...

# Collapse binary columns back to comma-separated
df_col = tv.tidy.collapse_multiple_choice(df_exp, 'colors')

Response Validation

# Flag invalid responses
df, invalid_mask = tv.tidy.check_response_range(
    df, 'rating', min_val=1, max_val=5,
    handle_invalid='flag'
)

# Remove invalid responses
df_clean, _ = tv.tidy.check_response_range(
    df, 'rating', min_val=1, max_val=5,
    handle_invalid='remove'
)

Data Quality Checks

# Detect missing data patterns
info = tv.tidy.detect_missing_patterns(df)
# Returns: complete_rows, rows_with_missing, missing_rates

# Flag straight-liners (same response across questions)
flags = tv.tidy.flag_straight_liners(df, ['Q1', 'Q2', 'Q3'])

# Detect speeders (unusually fast completion)
flags = tv.tidy.detect_speeders(df, 'completion_time',
                                 method='iqr')

# Check logical consistency
rules = [{
    'name': 'age_check',
    'condition': lambda row: row['age'] >= 18
}]
df = tv.tidy.check_logical_consistency(df, rules)

Visualization (tv.viz)

Single Choice Questions

# Basic bar chart
tv.viz.plot_single_choice(df, 'method')

# With customization
tv.viz.plot_single_choice(
    df, 'method',
    title='Preferred Method',
    show_percentages=True,
    sort_by='count',  # or 'name'
    color_palette='sequential'
)

Multiple Choice Questions

# First expand the data
df_exp = tv.tidy.expand_multiple_choice(df, 'colors')
color_cols = [c for c in df_exp.columns if c.startswith('colors_')]

# Plot multiple choice
tv.viz.plot_multiple_choice(
    df_exp, color_cols,
    title='Favorite Colors',
    show_percentages=True,
    sort_by='count'
)

Styling

# Set global style
tv.viz.set_survey_style(
    style='default',  # or 'minimal', 'presentation'
    palette='categorical'  # or 'sequential', 'Set2', etc.
)

# Get color palette
colors = tv.viz.get_palette('categorical', n_colors=5)

Examples

See the examples/ directory for complete workflows:

• example_tidy.py - Data cleaning pipeline
• example_viz.py - Visualization examples

API Reference

tidyviz.tidy

Function	Description
expand_multiple_choice()	Convert comma-separated values to binary columns
collapse_multiple_choice()	Convert binary columns to comma-separated values
check_response_range()	Validate responses within expected range
detect_missing_patterns()	Analyze missing data patterns
flag_straight_liners()	Detect identical responses across questions
detect_speeders()	Identify unusually fast completion times
check_logical_consistency()	Validate custom logical rules

tidyviz.viz

Function	Description
plot_single_choice()	Bar chart for single-choice questions
plot_multiple_choice()	Bar chart for multiple-choice questions
set_survey_style()	Apply survey-appropriate styling
get_palette()	Get color palette for visualizations

Requirements

• Python ≥ 3.8
• pandas ≥ 1.3.0
• numpy ≥ 1.20.0
• matplotlib ≥ 3.4.0
• seaborn ≥ 0.11.0

Development

# Run tests
pytest

# Format code
black src/ tests/

# Lint code
flake8 src/ tests/

# Build package
python -m build

Author

Pingfan Hu

• Website: https://pingfanhu.com
• GitHub: @pingfan-hu
• Email: [e-mail]

License

MIT License - see LICENSE file for details.

Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

Citation

@software{tidyviz2025,
  title = {TidyViz: Survey Data Analysis for Python},
  author = {Hu, Pingfan},
  year = {2025},
  url = {https://github.com/pingfan-hu/tidyviz}
}