Over the recent few years owing to boom of Data Science Industry, Pandas have become quite important tool specifically used for Exploratory Data Analysis.
Exploratory Data Analysis refers to the critical process of performing initial investigations on data so as to discover patterns,to spot anomalies,to test hypothesis and to check assumptions with the help of summary statistics and graphical representations.
Earlier Data Scientists were expected to have great Python programming skills. But now due to the development GUI based easy to use interfaces this anomaly is changing.
Recently PandasGUI have been released which provides a GUI Interface for accessing in built functions in Pandas. Developers of PandasGUI have wrapped Pandas into a clean GUI interface, which can be easily used for Data Analysis.
Here is a brief guide how to use it ->
PandasGUI can be easily installed using pip
pip install pandasgui
Let’s see what features does PandasGUI have by using an example.
Importing library into workspace
Common way to import libraries into Python workspace is by using
Opening GUI interface
For opening GUI interface just pass
dataset as a parameter to
show function which can be imported by using
from pandasgui import show. This will open up GUI interface showing dataset in tabular form. Let’s see how this work by taking
titanic(inbuilt dataset in pandasgui) as an example.
import pandas as pd from pandasgui import show from pandasgui.datasets import show gui = show(titanic)
This will open up following GUI ->
As is clear from above picture there is a vertical column on left hand side.This contains information about dataset. Along with this there’re five tabs – DataFrame, Filters, Statistics, Grapher and Reshaper.
This tab shows dataset which is read using
pandas in tabular form.
Before using filters we need to drag DataFrame tab and leave at top. This will make dataset to show in filters tab.
Different filters here can be applied like
< to column names.
Here you can see brief information Mean, Standard Deviation, Min or Max for different columns in dataset.
Here you can use columns in dataframe to make plots. There is option for many types of graphs like Histogram, Scatter and Bar etc.
Any plot can easily be made by following these steps ->
- Click on graph name you want to make, this will involve some function which will show default variables required for making that graph under
- Just drag and drop column names on to default variables(as per you want to make graph). Then click on
Finish. This will run and graph would pop up.
So in this tab there are two functions which are being offered ->
Melt. These can be used in same way as grapher but this is for reshaping dataframe.
In this article, we looked at a GUI-based tool for analyzing pandas dataframes. This tool has a number of interesting features like filtering, sorting, visualizing, and even aggregating, which we saw in detail with an example dataset. As this library is just released and still is in developing phase so there will be more and more features which would be added to this in future.