SQL Bridge Tool to Exploratory Data Analysis
edaSQL is a library to link SQL to Exploratory Data Analysis and further more in the Data Engineering. This will solve many limitations in the SQL studios available in the market. Use the SQL Query language to get your Table Results.
Installation
Install dependency Packages before installing edaSQL
pip install pyodbc
pip install ipythonOptional dependency for better visualization - Jupyter Notebook
pip install notebookNow Install using pip . Offical Python Package Here!!
pip install edaSQL(OR)
Clone this Repository. Run this from the root directory to install
python setup.py installDocumentation
Read the detailed documentation in readthedocs.io
edaSQL Jupyter NoteBook Tutorial
Import Packages
import edaSQL
import pandas as pd1. Connect to the DataBase
edasql = edaSQL.SQL()
edasql.connectToDataBase(server='your server name',
database='your database',
user='username',
password='password',
sqlDriver='ODBC Driver 17 for SQL Server')
2. Query Data
sampleQuery = "select * from INX"
data = pd.read_sql(sampleQuery, edasql.dbConnection)
3. Data Overview
insights = edaSQL.EDA(dataFrame=data,HTMLDisplay=True)
dataInsights =insights.dataInsights()
deepInsights = insights.deepInsights()
4. Correlation
eda = edaSQL.EDA(dataFrame=data)
eda.pearsonCorrelation()
eda.spearmanCorrelation()
eda.kendallCorrelation()
5. Missing Values
eda.missingValuesPlot(plot ='matrix')
eda.missingValuesPlot(plot ='bar')
eda.missingValuesPlot(plot ='heatmap')
eda.missingValuesPlot(plot ='dendrogram')
6. Outliers
eda.outliersVisualization(plot = 'box')
eda.outliersVisualization(plot = 'scatter')
outliers = eda.getOutliers()
