Exploratory Data Analytics tool for SQL
Project description
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 ipython
Optional dependency for better visualization - Jupyter Notebook
pip install notebook
Now 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 install
Documentation
Read the detailed documentation in readthedocs.io
edaSQL Code Sample
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',
user='username',
password='password')
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()
Jupyter NoteBook Tutorial
Project details
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Source Distribution
edaSQL-0.0.1.3.tar.gz
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