Installation

pip install -U klib

conda install -c conda-forge klib

Usage

importklibimportpandasaspddf=pd.DataFrame(data)# klib.describe - functions for visualizing datasets-klib.cat_plot(df)# returns a visualization of the number and frequency of categorical features-klib.corr_mat(df)# returns a color-encoded correlation matrix-klib.corr_plot(df)# returns a color-encoded heatmap, ideal for correlations-klib.corr_interactive_plot(df,split="neg").show()# returns an interactive correlation plot using plotly-klib.dist_plot(df)# returns a distribution plot for every numeric feature-klib.missingval_plot(df)# returns a figure containing information about missing values# klib.clean - functions for cleaning datasets-klib.data_cleaning(df)# performs datacleaning (drop duplicates & empty rows/cols, adjust dtypes,...)-klib.clean_column_names(df)# cleans and standardizes column names, also called inside data_cleaning()-klib.convert_datatypes(df)# converts existing to more efficient dtypes, also called inside data_cleaning()-klib.drop_missing(df)# drops missing values, also called in data_cleaning()-klib.mv_col_handling(df)# drops features with high ratio of missing vals based on informational content-klib.pool_duplicate_subsets(df)# pools subset of cols based on duplicates with min. loss of information

Examples

klib.missingval_plot(df)# default representation of missing values in a DataFrame, plenty of settings are available

klib.corr_plot(df,split='pos')# displaying only positive correlations, other settings include threshold, cmap...klib.corr_plot(df,split='neg')# displaying only negative correlations

klib.corr_plot(df,target='wine')# default representation of correlations with the feature column

klib.corr_interactive_plot(df,split="neg").show()# The interactive plot has the same parameters as the corr_plot, but with additional Plotly heatmap graph object kwargs.klib.corr_interactive_plot(df,split="neg",zmax=0)

#Since corr_interactive_plot returns a Graph Object Figure, it supports the update_layout chain method.klib.corr_interactive_plot(wine,split="neg").update_layout(template="simple_white")

klib.dist_plot(df)# default representation of a distribution plot, other settings include fill_range, histogram, ...

klib.cat_plot(data,top=4,bottom=4)# representation of the 4 most & least common values in each categorical column

Contributing

License