First pre-release to test package installation.
Added
- Explore module (explore.py) for data exploration and visualization:
- get_corr() - Display the top n positive and negative correlations with a target variable in a DataFrame.
- get_outliers() - Detects and summarizes outliers for the specified numeric columns in a DataFrame, based on an IQR ratio.
- get_unique() - Print the unique values of all variables below a threshold n, including counts and percentages.
- plot_3d() - Create a 3D scatter plot using Plotly Express.
- plot_charts() - Display multiple bar plots and histograms for categorical and/or continuous variables in a DataFrame, with an option to dimension by the specified hue.
- plot_corr() - Plot the top n correlations of one variable against others in a DataFrame.
- plot_map_ca() - Plot longitude and latitude data on a geographic map of California.
- Clean module (clean.py) for data cleaning:
- convert_data_values() - Convert mixed data values (ex: GB, MB, KB) to a common unit of measurement.
- convert_dtypes() - Convert specified columns in a DataFrame to the desired data type.
- convert_time_values() - Convert time values in specified columns of a DataFrame to a target format.
- reduce_multicollinearity() - Reduce multicollinearity in a DataFrame by removing highly correlated features.
- split_outliers() - Split a DataFrame into two based on the presence of outliers.
- Model module (model.py) for model iteration and evaluation:
- create_pipeline() - Create a custom pipeline for data preprocessing and modeling.
- create_results_df() - Initialize the results_df DataFrame with the columns required for iterate_model.
- iterate_model() - Iterate and evaluate a model pipeline with specified parameters.
- plot_results() - Plot the results of model iterations and select the best metric.
- Tools module (tools.py) with helper functions:
- LogTransformer - Apply logarithmic transformation to numerical features.
- calc_pfi() - Calculate Permutation Feature Importance for a trained model.
- calc_vif() - Calculate the Variance Inflation Factor (VIF) for each feature.
- check_for_duplicates() - Check for duplicate items (ex: column names) across multiple lists.
- extract_coef() - Extract feature names and coefficients from a trained model.
- format_df() - Format columns of a DataFrame as either large or small numbers.
- log_transform() - Apply a log transformation to specified columns in a DataFrame.
- split_dataframe() - Split a DataFrame into categorical and numerical columns.
- thousand_dollars() - Format a number as currency with thousands separators on a matplotlib chart axis.
- thousands() - Format a number with thousands separators on a matplotlib chart axis.
- Documentation site (datawaza.com)
- API module documentation based on Sphinx and Readthedocs.io
- User Guide in the form of a Jupyter notebook with examples of every function
- Test cases via Doctest examples in each function, minimal coverage