{"id":16778651,"url":"https://github.com/ritvik19/vizard","last_synced_at":"2025-04-10T20:43:52.756Z","repository":{"id":65667022,"uuid":"339094681","full_name":"Ritvik19/vizard","owner":"Ritvik19","description":"Intuitive, Interactive, Easy and Quick Visualizations for Data Science Projects","archived":false,"fork":false,"pushed_at":"2022-12-25T06:57:14.000Z","size":70,"stargazers_count":8,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-09T14:50:01.160Z","etag":null,"topics":["data-analysis","data-science","data-visualization"],"latest_commit_sha":null,"homepage":"https://ritvik19.github.io/vizard/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Ritvik19.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-02-15T13:58:13.000Z","updated_at":"2024-01-23T07:25:41.000Z","dependencies_parsed_at":"2023-02-03T08:25:11.548Z","dependency_job_id":null,"html_url":"https://github.com/Ritvik19/vizard","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ritvik19%2Fvizard","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ritvik19%2Fvizard/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ritvik19%2Fvizard/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ritvik19%2Fvizard/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Ritvik19","download_url":"https://codeload.github.com/Ritvik19/vizard/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248055271,"owners_count":21040151,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","data-science","data-visualization"],"created_at":"2024-10-13T07:28:20.873Z","updated_at":"2025-04-10T20:43:52.729Z","avatar_url":"https://github.com/Ritvik19.png","language":"Python","readme":"# vizard\n\nIntuitive, Interactive, Easy and Quick Visualizations for Data Science Projects\n\n[![Downloads](https://pepy.tech/badge/vizard)](https://pepy.tech/project/vizard)\n[![Downloads](https://pepy.tech/badge/vizard/month)](https://pepy.tech/project/vizard)\n[![Downloads](https://pepy.tech/badge/vizard/week)](https://pepy.tech/project/vizard)\n\n## Installation\n\n    pip install vizard\n\nor\n\n    pip install git+https://github.com/Ritvik19/vizard.git\n\n## Documentation\n\n### Instantiate Vizard Object\n\nThe 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`\n\n    import vizard\n\n    class config:\n        PROBLEM_TYPE = 'regression' or 'classification' or 'unsupervised'\n        DEPENDENT_VARIABLE = 'target_variable'\n        CATEGORICAL_INDEPENDENT_VARIABLES = [categorical_features]\n        CONTINUOUS_INDEPENDENT_VARIABLES = [continuous features]\n        TEXT_VARIABLES = [text features]\n\n    viz = vizard.Vizard(df, config)\n    # for interactive plots use:\n    viz = vizard.VizardIn(df, config)\n\n### Exploratory Data Analysis\n\nAfter Instatiating the `Vizard` object, you can try different plots for EDA\n\n- Check Missing Values:\n\n      viz.check_missing()\n\n- Count of Missing Values:\n\n      viz.count_missing()\n\n- Count of Unique Values:\n\n      viz.count_unique()\n\n- Count of Missing Values by Group:\n\n      viz.count_missing_by_group(class_variable)\n\n- Count of Unique Values by Group:\n  viz.count_unique_by_group(class_variable)\n\n### Target Column Analysis\n\nBased on the type of problem, perform a univariate analysis of target column\n\n    viz.dependent_variable()\n\n### Segmented Univariate Analysis\n\nBased on the type of problem, preform segmented univariate analysis of all feature columns with respect to the target column\n\n- Categorical Variables\n\n        viz.categorical_variables()\n\n- Continuous Variables\n\n        viz.continuous_variables()\n\n- Text Variables\n\n        viz.wordcloud()\n\n        viz.wordcloud_by_group()\n\n        viz.wordcloud_freq()\n\n### Bivariate Analysis\n\nBased on the type of variables, perform bivariate analysis on all the feature columns\n\n- Pairwise Scatter\n\n        viz.pairwise_scatter()\n\n- Pairwise Violin\n\n        viz.pairwise_violin()\n\n- Pairwise Cross Tabs\n\n        viz.pairwise_crosstabs()\n\n### Trivariate Analysis\n\nBased on the type of variables, perform trivariate analysis on any of the feature columns\n\n- Trivariate Bubble (Continuous vs Continuous vs Continuous)\n\n        viz.trivariate_bubble(x, y, s)\n\n- Trivariate Scatter (Continuous vs Continuous vs Categorical)\n\n        viz.trivariate_scatter(x, y, c)\n\n- Trivariate Violin (Categorical vs Continuous vs Categorical)\n\n        viz.trivariate_violin(x, y, c)\n\n### Correlation Analysis\n\nBased on the type of variables, perform correaltion analysis on all the feature columns\n\n- Correlation Plot\n\n        viz.corr_plot()\n\n- Pair Plot\n\n        viz.pair_plot()\n\n- Chi Square Plot\n\n        viz.chi_sq_plot()\n\n## Save the plots to PDF using Viz2PDF\n\nYou can also save the plots to a pdf file in order to generate an EDA report\n\nThe `Viz2PDF` object takes in all your `Vizard` plots and creates a pdf report out of them\n\n```\nviz = vizard.Vizard(df, config)\nviz2pdf = vizard.Viz2PDF('viz_report.pdf')\n\nplots = [\n    viz.check_missing(),\n    viz.count_missing(),\n    viz.count_unique(),\n    viz.dependent_variable(),\n    viz.categorical_variables(),\n    viz.continuous_variables(),\n    viz.pairwise_scatter(),\n    viz.pairwise_violin(),\n    viz.pairwise_crosstabs(),\n]\nviz2pdf(plots)\n```\n\n## Usage\n\n1. [Classification Case](https://nbviewer.jupyter.org/github/Ritvik19/vizard-doc/blob/main/usage/Classification%20Case.ipynb)\n2. [Regression Case](https://nbviewer.jupyter.org/github/Ritvik19/vizard-doc/blob/main/usage/Regression%20Case.ipynb)\n3. [Text Classification Case](https://nbviewer.jupyter.org/github/Ritvik19/vizard-doc/blob/main/usage/Text%20Classification%20Case.ipynb)\n4. [Unsupervised Case](https://nbviewer.jupyter.org/github/Ritvik19/vizard-doc/blob/main/usage/Unsupervised%20Case.ipynb)\n5. [Classification Case (Interactive)](https://nbviewer.jupyter.org/github/Ritvik19/vizard-doc/blob/main/usage/Classification%20Interactive%20Case.ipynb)\n6. [Regression Case (Interactive)](https://nbviewer.jupyter.org/github/Ritvik19/vizard-doc/blob/main/usage/Regression%20Interactive%20Case.ipynb)\n7. [Unsupervised Case (Interactive)](https://nbviewer.jupyter.org/github/Ritvik19/vizard-doc/blob/main/usage/Unsupervised%20Interactive%20Case.ipynb)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fritvik19%2Fvizard","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fritvik19%2Fvizard","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fritvik19%2Fvizard/lists"}