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This article mainly introduces how to use PandasGUI for exploratory data analysis. The introduction in this article is very detailed and has certain reference value. Interested friends must read it!

Pandasgui is an open source Python module that creates a GUI interface for Pandas where we can analyze data using Pandas functionality and use different features to visualize and analyze data and perform exploratory data analysis.

Exploratory data analysis is the most critical part, and whenever we work with a dataset, we analyze it first. It allows us to analyze the data and explore the initial results of the data, such as how many rows and columns there are, what the different columns are, and so on. EDA is a method where we use different methods, mainly visualizations, to summarize the main features of the data.

If you are working with data, EDA is an important and most critical step. Almost 30 percent of the time spent on the project was exploring the data and figuring out what it was really about. EDA allows us and tells us how to preprocess data before modeling. This is why EDA is the most important, but we can save time by automating all EDA work, and the saved time can be used in modeling.

In this article, we'll explore Pandasgui and learn how to use it to automate exploratory data analysis processes and save us time and effort.

Install Pandasgui

As with other libraries, we can install pandasgui using pip.

pip install pandasgui

Load data sets

A large number of datasets are predefined in Pandasgui, we will use Pandasgui to load a dataset named IRIS, which is a very famous dataset, and explore it using Pandasgui's GUI interface. 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

function introduction

Now we just need to call the show function by passing the dataset name as an argument, which will launch a GUI where we can explore different parts of it and try to explore different properties of the dataset.

show(iris)

Here, you can see the show function launch GUI, and we can clearly see the different tabs that contain different functions.

Let's analyze the different parts of this interface.

Dataframe

We can clearly analyze which are different attributes and what values they contain. We can clearly analyze all values and attributes. On the left, we can also see the shape of the dataframe.

Filters

In this section, we can apply different filters to analyze the data. We can simply enter the query we want to run and apply the filter.

Statistics

Similar to pandas dataframe describe function. It helps us analyze the statistical properties of the dataset.

Grapher

This is the most important part, where we can clearly see the different types of visualizations that we can create using the interface and save the effort of writing code for each visualization. In the image above, I created a scatterplot of sepallength and sepalwidth. Similarly, you can create different visualizations by dragging and dropping column names in x, y, and other parameters.

Reshaper

We can analyze the dataset by applying different functions and changing the shape of the dataset. The two shape formats provided are "pivot" and "melt." We can drag and drop columns in different functions and analyze the different shapes of the dataset accordingly.

That's all for "How to use Pandas GUI for exploratory data analysis." Thanks for reading! Hope to share the content to help everyone, more relevant knowledge, welcome to pay attention to the industry information channel!

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