kesh-utils 0.1.8

Kesh Utils for Data science/EDA/Data preparation

Chart + Util = Chartil

During EDA/data preparation stage, I use few fixed chart types to analyse the relation among various features. Few are simple chart like univariate and some are complex 3D or even multiple features>3.

Over the period it became complex to maintain all relevant codes or repeat codes. Instead I developed a simple, single api to plot various type of relations which will hide all technical/code details from Data Science task and approch.

Using this approach I just need one api

from KUtils.eda import chartil

chartil.plot(dataframe, [list of columns]) or
chartil.plot(dataframe, [list of columns], {optional_settings})

Demo code:

Load UCI Dataset. Download From here

heart_disease_df = pd.read_csv('../input/uci/heart.csv')

heart_disease_df['age_bin'] = pd.cut(heart_disease_df['age'], [0, 32, 40, 50, 60, 70, 100], labels=['<32', '33-40','41-50','51-60','61-70', '71+']) heart_disease_df['sex'] = heart_disease_df['sex'].map({1:'Male', 0:'Female'})

Heatmap

chartil.plot(heart_disease_df, heart_disease_df.columns) # Send all column names ![Heatmap Numerical] (https://raw.githubusercontent.com/KeshavShetty/ds/master/Roughbook/misc_resources/heatmap1.png) chartil.plot(heart_disease_df, heart_disease_df.columns, optional_settings={'include_categorical':True} ) ![Heatmap With categorical] (https://raw.githubusercontent.com/KeshavShetty/ds/master/Roughbook/misc_resources/heatmap2.png) chartil.plot(heart_disease_df, heart_disease_df.columns, optional_settings={'include_categorical':True, 'sort_by_column':'trestbps'} ) ![Heatmap With categorical and ordered by a column] (https://raw.githubusercontent.com/KeshavShetty/ds/master/Roughbook/misc_resources/heatmap3.png)

Uni-categorical

chartil.plot(heart_disease_df, ['target']) # Barchart as count plot

Uni-Continuous

chartil.plot(heart_disease_df, ['age']) # boxplot

chartil.plot(heart_disease_df, ['age'], chart_type='barchart') # Force barchart on cntinuous by auto creating 10 equal bins

chartil.plot(heart_disease_df, ['age'], chart_type='barchart', optional_settings={'no_of_bins':5}) # Create custom number of bins

chartil.plot(heart_disease_df, ['age'], chart_type='distplot')

Uni-categorical with optional_settings

chartil.plot(heart_disease_df, ['age_bin']) # Barchart as count plot chartil.plot(heart_disease_df, ['age_bin'], optional_settings={'sort_by_value':True}) chartil.plot(heart_disease_df, ['age_bin'], optional_settings={'sort_by_value':True, 'limit_bars_count_to':5})

Bi Category vs Category (& Univariate Segmented)

chartil.plot(heart_disease_df, ['sex', 'target']) chartil.plot(heart_disease_df, ['sex', 'target'], chart_type='crosstab') chartil.plot(heart_disease_df, ['sex', 'target'], chart_type='stacked_barchart')

Bi Continuous vs Continuous

chartil.plot(heart_disease_df, ['chol', 'thalach']) # Scatter plot

Bi Continuous vs Category

chartil.plot(heart_disease_df, ['thalach', 'sex']) # Grouped box plot (Segmented univariate) chartil.plot(heart_disease_df, ['thalach', 'sex'], chart_type='distplot') # Distplot

Multi 3 Continuous

chartil.plot(heart_disease_df, ['chol', 'thalach', 'trestbps']) # Colored 3D scatter plot

Multi 3 Categorical

chartil.plot(heart_disease_df, ['age_bin', 'sex', 'target']) # Paired barchart

Multi 2 Continuous, 1 Category

chartil.plot(heart_disease_df, ['chol', 'thalach', 'target']) # Scatter plot with colored groups

Multi 1 Continuous, 2 Category

chartil.plot(heart_disease_df, ['thalach', 'sex', 'target']) # Grouped boxplot chartil.plot(heart_disease_df, ['thalach', 'sex', 'target'], chart_type='violinplot') # Grouped violin plot

Multi 3 Continuous, 1 category

chartil.plot(heart_disease_df, ['chol', 'thalach', 'trestbps', 'target']) # Group Color highlighted 3D plot

Multi 3 Continuous, 2 category

chartil.plot(heart_disease_df, ['sex','cp','target','thalach','trestbps']) # Paired scatter plot