import klib
import pandas as pd

df = 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

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