{"id":28560716,"url":"https://github.com/selva221724/edasql","last_synced_at":"2025-06-10T09:38:13.262Z","repository":{"id":46134474,"uuid":"424146101","full_name":"selva221724/edaSQL","owner":"selva221724","description":"edaSQL is a python library to bridge the SQL with Exploratory Data Analysis where you can connect to the Database and insert the queries. The query results can be passed to the EDA tool which can give greater insights to the user. 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This will solve many limitations in the SQL studios available in the market. Use the SQL Query language to get your Table Results. \n\n## Installation\nInstall dependency Packages before installing edaSQL\n```shell\npip install pyodbc\npip install ipython\n```\nOptional dependency for better visualization - [Jupyter Notebook](https://jupyter.org/install) \n```shell\npip install notebook\n```\n\n**Now Install using pip** . [Offical Python Package Here!!](https://pypi.org/project/edaSQL/)\n```shell\npip install edaSQL\n```\n\n(OR)\n\nClone this Repository. Run this from the root directory to install\n\n```shell\npython setup.py install\n```\n\n## Documentation\n\n\u003cimg src=\"https://blog.readthedocs.com/_static/logo-opengraph.png\"  width=\"20%\" height=\"20%\"\u003e\n\n[Read the detailed documentation in readthedocs.io](https://edasql.readthedocs.io/en/latest/) (still under the development) \n\n## License\nThe license for edaSQL is MIT license \n\n## Need help?\nStuck on your edaSQL code or problem? Any other questions? Don't\nhestitate to send me an email (selva221724@gmail.com).\n\n## edaSQL Jupyter NoteBook Tutorial\n\nAccess the sample Jupyter Notebook [here!!](https://github.com/selva221724/edaSQL/blob/main/example_notebook/SampleNoteBook_edaSQL.ipynb)\n\nAccess the Sample Data Used in this Repo\n- [CSV](https://github.com/selva221724/edaSQL/blob/main/sampleData/CSV/INX.csv)\n- [DataBase Backup](https://github.com/selva221724/edaSQL/blob/main/sampleData/DataBaseBackup/INX.bak) ( you can restore the DB in SQL Studio ) \n\n**edaSQL for DataFrame:** If you are using the CSV or Excel as a source , Read using the Pandas \u0026  start from the [**3. Data Overview**](#Chapter1)\n\n### Import Packages\n```python\nimport edaSQL\nimport pandas as pd\n```\n\n### 1. Connect to the DataBase\n```python\nedasql = edaSQL.SQL()\nedasql.connectToDataBase(server='your server name', \n                         database='your database', \n                         user='username', \n                         password='password',\n                         sqlDriver='ODBC Driver 17 for SQL Server')\n```\n\n\u003cimg src=\"https://raw.githubusercontent.com/selva221724/edaSQL/main/readme_src/notebook_results/db_connected.png\"\u003e\n\n### 2. Query Data \n```python\nsampleQuery = \"select  * from INX\"\ndata = pd.read_sql(sampleQuery, edasql.dbConnection)\n```\n\u003cimg src=\"https://raw.githubusercontent.com/selva221724/edaSQL/main/readme_src/notebook_results/data_sample.png\"\u003e\n\n\u003cdiv id=\"Chapter1\"\u003e\u003c/div\u003e\n\n### 3. Data Overview\n```python\ninsights =  edaSQL.EDA(dataFrame=data,HTMLDisplay=True)\ndataInsights =insights.dataInsights()\n```\n\n\u003cimg src=\"https://raw.githubusercontent.com/selva221724/edaSQL/main/readme_src/notebook_results/1.png\"\u003e\n\n```python\ndeepInsights = insights.deepInsights()\n```\n\u003cimg src=\"https://raw.githubusercontent.com/selva221724/edaSQL/main/readme_src/notebook_results/2.png\"\u003e\n\n### 4. Correlation\n```python\neda = edaSQL.EDA(dataFrame=data)\neda.pearsonCorrelation()\n```\n\u003cimg src=\"https://raw.githubusercontent.com/selva221724/edaSQL/main/readme_src/notebook_results/3.png\"\u003e\n\n```python\neda.spearmanCorrelation()\n```\n\u003cimg src=\"https://raw.githubusercontent.com/selva221724/edaSQL/main/readme_src/notebook_results/4.png\"\u003e\n\n```python\neda.kendallCorrelation()\n```\n\u003cimg src=\"https://raw.githubusercontent.com/selva221724/edaSQL/main/readme_src/notebook_results/5.png\"\u003e\n\n### 5. Missing Values\n\n```python\neda.missingValuesPlot(plot ='matrix')\n```\n\u003cimg src=\"https://raw.githubusercontent.com/selva221724/edaSQL/main/readme_src/notebook_results/6.png\"\u003e\n\n```python\neda.missingValuesPlot(plot ='bar')\n```\n\u003cimg src=\"https://raw.githubusercontent.com/selva221724/edaSQL/main/readme_src/notebook_results/7.png\"\u003e\n\n```python\neda.missingValuesPlot(plot ='heatmap')\n```\n\u003cimg src=\"https://raw.githubusercontent.com/selva221724/edaSQL/main/readme_src/notebook_results/8.png\"\u003e\n\n```python\neda.missingValuesPlot(plot ='dendrogram')\n```\n\u003cimg src=\"https://raw.githubusercontent.com/selva221724/edaSQL/main/readme_src/notebook_results/9.png\"\u003e\n\n### 6. Outliers \n\n```python\neda.outliersVisualization(plot = 'box')\n```\n\u003cimg src=\"https://raw.githubusercontent.com/selva221724/edaSQL/main/readme_src/notebook_results/10.png\"\u003e\n\n```python\neda.outliersVisualization(plot = 'scatter')\n```\n\u003cimg src=\"https://raw.githubusercontent.com/selva221724/edaSQL/main/readme_src/notebook_results/11.png\"\u003e\n\n```python\noutliers = eda.getOutliers()\n```\n\u003cimg src=\"https://raw.githubusercontent.com/selva221724/edaSQL/main/readme_src/notebook_results/12.png\"\u003e\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fselva221724%2Fedasql","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fselva221724%2Fedasql","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fselva221724%2Fedasql/lists"}