"""
edaflow - A Python package for exploratory data analysis workflows
"""
from .analysis import (
check_null_columns,
analyze_categorical_columns,
convert_to_numeric,
visualize_categorical_values,
display_column_types,
impute_numerical_median,
impute_categorical_mode,
visualize_numerical_boxplots,
handle_outliers_median,
visualize_interactive_boxplots,
visualize_heatmap,
visualize_histograms,
visualize_scatter_matrix,
visualize_image_classes,
assess_image_quality,
analyze_image_features,
analyze_encoding_needs,
apply_smart_encoding,
apply_encoding,
apply_encoding_with_encoders,
summarize_eda_insights
)
from .display import optimize_display
# Make ML subpackage available
from . import ml
__version__ = "0.15.0"
__author__ = "Evan Low"
__email__ = "evan.low@illumetechnology.com"
[docs]
def hello():
"""
A sample hello function to test the package installation.
Returns:
str: A greeting message
"""
return "Hello from edaflow! Ready for exploratory data analysis and machine learning."
# Import main modules
# from .visualization import *
# from .preprocessing import *
# Export main functions
__all__ = [
'hello',
'optimize_display', # ⭐ New in v0.12.30: Universal dark mode compatibility
'check_null_columns',
'analyze_categorical_columns',
'convert_to_numeric',
'visualize_categorical_values',
'display_column_types',
'impute_numerical_median',
'impute_categorical_mode',
'visualize_numerical_boxplots',
'handle_outliers_median',
'visualize_interactive_boxplots',
'visualize_heatmap',
'visualize_histograms',
'visualize_scatter_matrix',
'visualize_image_classes',
'assess_image_quality',
'analyze_image_features',
'analyze_encoding_needs',
'apply_smart_encoding',
'apply_encoding', # ⭐ New in v0.12.33: Clean consistent API
'apply_encoding_with_encoders', # ⭐ New in v0.12.33: Explicit tuple return
'summarize_eda_insights',
'ml' # ⭐ New ML subpackage
]