Advertisement

Active Hackathon

Hands-On Tutorial On ExploriPy: Effortless Target Based EDA Tool

In this article, we will explore ExploriPy to perform EDA on a dataset and derive useful insights.
ExploriPy

Exploratory Data Analysis is the initial step that should be performed on a dataset in order to know about the properties of the different attributes of the dataset. EDA gives us an idea of what all columns do data have, what are the values in these columns, what are the datatypes, etc. Other than that EDA also helps in visualizing the relationships between different columns/ attributes in the dataset.

Exploratory data analysis is an approach to analyze the datasets in order to summarize their main characteristics with both statistical and visual methods. In order to perform EDA in python, we generally use different libraries like pandas, NumPy, matplotlib, etc. to know about different properties of the dataset. Using all these libraries for EDA takes a lot of time and effort.

THE BELAMY

Sign up for your weekly dose of what's up in emerging technology.

ExploriPy is an open-source python library that can be used for EDA and make the whole process a lot easier and effortless. It automates the whole process of EDA and saves a lot of time which can be used in other tasks. It works in just a few lines of code so no prior hardcore coding experience is required to use ExploriPy.

In this article, we will explore ExploriPy to perform EDA on a dataset and derive useful insights.

Implementation of ExploriPy

Like any other library, we will start by installing ExploriPy using pip install ExploriPy.

  1. Importing required libraries

For loading the dataset we will use pandas so we need to import that and for EDA we will use ExploriPy so we will also import that.

from ExploriPy import EDA

import pandas as pd

  1. Loading the dataset

In this article, we will use a dataset of Advertisement Dataset of an MNC in which Sales is the Target Variable which is dependent on certain features like ‘TV’, ‘Radio’, etc.es of automobiles like ‘price’, ‘height’, ‘length’, etc. 

df = pd.read_csv(‘Advertisement.csv’)

df.head()

  1. Exploratory Data Analysis 

For Exploratory data analysis, we will use Exploripy. As we are using the advertising dataset and we know that sales are the target variable so we will pass it as the target variable.

ContinuousFeatures = [‘Radio’,’Newspaper’,’TV’]

analysis = EDA(df,title=’EDA for Sales Data’)

analysis.TargetAnalysis(‘Sales’)

After running the above-said commands we will be generating an EDA report with Sales as our target Variable.

Now let us explore different sections of the EDA report.

  • Home Page(Target Specific EDA)

This page shows what are the different attributes of the dataset along with their datatypes. Also, we can see here a pie-chart which clearly shows the distribution of the attributes in categorical or continuous variables. 

  • Null Values
ExploriPy Data Analysis

This segment of the report shows which attributes have missing data or null data and also represent it in the form of bar charts. The dataset we used has no missing data so it is showing as Null Percentage = 0%.

  • Statistics of Target Variable
ExploriPy Data Analysis

Here we can see the statistical properties of the target variable along with the number of records that it contains. Statistical properties contain Skewness and Kurtosis also.

  • Distribution of Data in Target Variable
ExploriPy Data Analysis

In this section, we can clearly see how our Sales data is distributed with the help of different visualizations namely Boxplot, KDE Plot, and histogram.

  • Distribution of Feature Variable
ExploriPy Data Analysis

This is the end of the report which shows the distribution of the feature variables.

Similarly, we can generate Target Specific EDA reports for different datasets.

Conclusion

In this article, we have created an EDA report using ExploriPy, a python library, we explored the different sections of the report corresponding to the distribution of the data and the spread of the data. ExploriPy is easy to use and creates reports fast which saves time.

More Great AIM Stories

Himanshu Sharma
An aspiring Data Scientist currently Pursuing MBA in Applied Data Science, with an Interest in the financial markets. I have experience in Data Analytics, Data Visualization, Machine Learning, Creating Dashboards and Writing articles related to Data Science.

Our Upcoming Events

Conference, Virtual
Genpact Analytics Career Day
3rd Sep

Conference, in-person (Bangalore)
Cypher 2022
21-23rd Sep

Conference, in-person (Bangalore)
Machine Learning Developers Summit (MLDS) 2023
19-20th Jan, 2023

Conference, in-person (Bangalore)
Data Engineering Summit (DES) 2023
21st Apr, 2023

Conference, in-person (Bangalore)
MachineCon 2023
23rd Jun, 2023

3 Ways to Join our Community

Discord Server

Stay Connected with a larger ecosystem of data science and ML Professionals

Telegram Channel

Discover special offers, top stories, upcoming events, and more.

Subscribe to our newsletter

Get the latest updates from AIM
MOST POPULAR