Exploratory Data Analysis is the most crucial part, to begin with whenever we are working with a dataset. It allows us to analyze the data and let us explore the initial findings from data like how many rows and columns are there, what are the different columns, etc. EDA is an approach where we summarize the main characteristics of the data using different methods and mainly visualization.

EDA is an important and most crucial step if you are working with data. It takes up almost 30% of the total project timing to explore the data and find out what it is all about. EDA allows us and tells us how to preprocess the data before modeling. This is why EDA is most important but we can save this time by automating all the time taking EDA jobs and can use the time saved in modeling.

Pandasgui is an open-source python module/package which creates a GUI interface where we can analyze the pandas dataframe and use different functionalities in order to visualize and analyze data and perform exploratory data analysis.

In this article, we will explore Pandasgui and see how we can use it to automate the process of Exploratory Data Analysis and save our time and effort.

Installing Pandasgui

Like any other library, we can install pandasgui using pip.

pip install pandasgui

Loading dataset

A large variety of datasets are predefined in pandasgui we will use pandasgui to load one dataset named “IRIS” which is a very famous dataset and will explore it using the GUI interface of pandasgui. We will also import the “show” function which loads the dataset into the GUI.

from pandasgui.datasets import iris
#importing the show function
from pandasgui import show

#data-analysis #python #data-visualization #data-science #exploratory-data-analysis