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.


Install dependency Packages before installing edaSQL

pip install pyodbc
pip install ipython

Optional dependency for better visualization – Jupyter Notebook

pip install notebook

Now Install using pip . Offical Python Package Here!!

pip install edaSQL


Clone this Repository. Run this from the root directory to install

python setup.py install


Read the detailed documentation in readthedocs.io (still under the development)


The license for edaSQL is MIT license

Need help?

Stuck on your edaSQL code or problem? Any other questions? Don’t
hestitate to send me an email ([email protected]).

edaSQL Jupyter NoteBook Tutorial

Access the sample Jupyter Notebook here!!

Access the Sample Data Used in this Repo

edaSQL for DataFrame: If you are using the CSV or Excel as a source , Read using the Pandas & start from the 3. Data Overview

Import Packages

import edaSQL
import pandas as pd

1. Connect to the DataBase

edasql = edaSQL.SQL()
edasql.connectToDataBase(server='your server name', 
                         database='your database', 
                         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)



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()


View Github