vizard 1.3.0

Intuitive, Interactive, Easy and Quick Visualizations for Data Science Projects

vizard

Intuitive, Interactive, Easy and Quick Visualizations for Data Science Projects

Installation

pip install vizard

or

pip install git+https://github.com/Ritvik19/vizard.git

Documentation

Instantiate Vizard Object

The Vizard or VizardIn object holds the DataFrame along with its configurations including the PROBLEM_TYPE, DEPENDENT_VARIABLE, CATEGORICAL_INDEPENDENT_VARIABLES, CONTINUOUS_INDEPENDENT_VARIABLES, and TEXT_VARIABLES

import vizard

class config:
    PROBLEM_TYPE = 'regression' or 'classification' or 'unsupervised'
    DEPENDENT_VARIABLE = 'target_variable'
    CATEGORICAL_INDEPENDENT_VARIABLES = [categorical_features]
    CONTINUOUS_INDEPENDENT_VARIABLES = [continuous features]
    TEXT_VARIABLES = [text features]

viz = vizard.Vizard(df, config)
# for interactive plots use:
viz = vizard.VizardIn(df, config)

Exploratory Data Analysis

After Instatiating the Vizard object, you can try different plots for EDA

• Check Missing Values:

  viz.check_missing()
  

• Count of Missing Values:

  viz.count_missing()
  

• Count of Unique Values:

  viz.count_unique()
  

• Count of Missing Values by Group:

  viz.count_missing_by_group(class_variable)
  

• Count of Unique Values by Group: viz.count_unique_by_group(class_variable)

Target Column Analysis

Based on the type of problem, perform a univariate analysis of target column

viz.dependent_variable()

Segmented Univariate Analysis

Based on the type of problem, preform segmented univariate analysis of all feature columns with respect to the target column

• Categorical Variables

    viz.categorical_variables()
  

• Continuous Variables

    viz.continuous_variables()
  

• Text Variables

    viz.wordcloud()
  
    viz.wordcloud_by_group()
  
    viz.wordcloud_freq()
  

Bivariate Analysis

Based on the type of variables, perform bivariate analysis on all the feature columns

• Pairwise Scatter

    viz.pairwise_scatter()
  

• Pairwise Violin

    viz.pairwise_violin()
  

• Pairwise Cross Tabs

    viz.pairwise_crosstabs()
  

Trivariate Analysis

Based on the type of variables, perform trivariate analysis on any of the feature columns

• Trivariate Bubble (Continuous vs Continuous vs Continuous)

    viz.trivariate_bubble(x, y, s)
  

• Trivariate Scatter (Continuous vs Continuous vs Categorical)

    viz.trivariate_scatter(x, y, c)
  

• Trivariate Violin (Categorical vs Continuous vs Categorical)

    viz.trivariate_violin(x, y, c)
  

Correlation Analysis

Based on the type of variables, perform correaltion analysis on all the feature columns

• Correlation Plot

    viz.corr_plot()
  

• Pair Plot

    viz.pair_plot()
  

• Chi Square Plot

    viz.chi_sq_plot()
  

Save the plots to PDF using Viz2PDF

You can also save the plots to a pdf file in order to generate an EDA report

The Viz2PDF object takes in all your Vizard plots and creates a pdf report out of them

viz = vizard.Vizard(df, config)
viz2pdf = vizard.Viz2PDF('viz_report.pdf')

plots = [
    viz.check_missing(),
    viz.count_missing(),
    viz.count_unique(),
    viz.dependent_variable(),
    viz.categorical_variables(),
    viz.continuous_variables(),
    viz.pairwise_scatter(),
    viz.pairwise_violin(),
    viz.pairwise_crosstabs(),
]
viz2pdf(plots)

Usage

1. Classification Case
2. Regression Case
3. Text Classification Case
4. Unsupervised Case
5. Classification Case (Interactive)
6. Regression Case (Interactive)
7. Unsupervised Case (Interactive)