easy-insight 1.0.4

A simple library for easy exploratory data analysis

Easy Insight (One Click - Easy Exploratory Data Analysis)

Easy Insight is a simple library designed for exploratory data analysis (EDA) 📊. It provides an easy way to inspect and analyze datasets, helping you to quickly understand the structure and contents of your data 📈.

Features

• 🔍 Basic data inspection
• ❓ Missing values analysis
• 📊 Univariate analysis for numerical and categorical features
• 🔗 Bivariate analysis for understanding relationships between variables
• 🌐 Multivariate analysis for comprehensive insights

Installation ⚙️

You can install Easy Insight using Poetry or pip.

Using Poetry

1. Install Poetry if you haven't already:

   curl -sSL https://install.python-poetry.org | python3 -
   

2. Then run:

   poetry add easy-insight
   

Using pip

pip install easy-insight

Usage 🚀

Here's a quick example of how to use Easy Insight for exploratory data analysis on a DataFrame df:

import pandas as pd

from easy_insight.eda_tools.basic_data_inspection import DataInspector, DataTypeInspectionStrategy, SummaryStatisticsInspectionStrategy

from easy_insight.eda_tools.missing_values_analysis import SimpleMissingValuesAnalysis

from easy_insight.eda_tools.univariate_analysis import UnivariateAnalyzer, NumericalUnivariateAnalysis, CategoricalUnivariateAnalysis

from easy_insight.eda_tools.bivariate_analysis import BivariateAnalyzer, NumericalVsNumericalAnalysisStrategy, CategoricalVsNumericalAnalysisStrategy

from easy_insight.eda_tools.multivariate_analysis import SimpleMultivariateAnalysis

Load your DataFrame (example) 📂

df = pd.read_csv('your_dataset.csv')

Quick Automated EDA ⚡

For Quick automated EDA

from easy_insight.eda_tools.utility import quick_eda

quick_eda(df, perform_data_inspection=True, perform_missing_values_analysis=True,
          perform_univariate_analysis=True, perform_bivariate_analysis=True, perform_multivariate_analysis=True)

For Quick but Customized EDA 🛠️

Data Inspection

data_inspector = DataInspector(DataTypeInspectionStrategy())
data_inspector.evaluate_inspection(df)

Set strategy to Summary Statistics

data_inspector.set_strategy(SummaryStatisticsInspectionStrategy())
data_inspector.evaluate_inspection(df)

Missing Values Analysis❓

missing_values_analysis = SimpleMissingValuesAnalysis()
missing_values_analysis.analyze(df)

Univariate Analysis 📊

univariate_analyzer = UnivariateAnalyzer(NumericalUnivariateAnalysis())
numerical_columns = df.select_dtypes(include=[int, float]).columns
for feature in numerical_columns:
    univariate_analyzer.execute_analysis(df, feature=feature)

Bivariate Analysis 🔗

bivariate_analysis = BivariateAnalyzer(CategoricalVsNumericalAnalysisStrategy())
bivariate_analysis.execute_analysis(df, "department", "annual_salary")

Multivariate Analysis 🌐

multivariate_analysis = SimpleMultivariateAnalysis()
multivariate_analysis.analyze(df)

Contributing 🤝

Contributions are welcome! Please feel free to submit a pull request or open an issue for any suggestions or bugs you encounter.

License 📝

This project is licensed under the MIT License. See the LICENSE file for details.

Author ✍️

Durgesh Rathod - [e-mail]