PandasGUI: GUI interface for Pandas Library
Tags: python, datascience, machinelearning
Published: 2020-11-03 02:13:33

Over the recent few years owing to boom of Data Science Industry, [Pandas](https://pandas.pydata.org/) 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](https://github.com/adamerose/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 ->
#Installation
PandasGUI can be easily installed using [pip](https://pypi.org/project/pip/)
```
pip install pandasgui
```
*If you've not got pip already installed then watch following tutorial to see how you could do that -> *
{% youtube cm6WDGAzDPM %}
#Features
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 `import` statement.
```
import pandasgui
```
##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 ->

![Screen Shot 2020-11-03 at 2.52.03 pm](https://dev-to-uploads.s3.amazonaws.com/i/f2honwo4ogj9gmfjx1em.png)
 
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.**

##DataFrame Tab
This tab shows dataset which is read using `pandas` in tabular form.

![Screen Shot 2020-11-03 at 2.58.58 pm](https://dev-to-uploads.s3.amazonaws.com/i/dzfp8oijmt317e5lln6b.png)

##Filters Tab
Before using filters we need to drag DataFrame tab and leave at top. This will make dataset to show in filters tab. 

{% youtube liMdkWWfNUo %}

Different filters here can be applied like `==` `>` `<` to column names.

##Statistics Tab
Here you can see brief information Mean, Standard Deviation, Min or Max for different columns in dataset. 

![Screen Shot 2020-11-03 at 6.28.18 pm](https://dev-to-uploads.s3.amazonaws.com/i/41arhhy7btqw4ltq99a9.png)

##Grapher Tab
Here you can use columns in dataframe to make plots. There is option for many types of graphs like Histogram, Scatter and Bar etc.

![Screen Shot 2020-11-03 at 6.31.19 pm](https://dev-to-uploads.s3.amazonaws.com/i/ixp4qpyx3sachftdbddw.png)
 
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 `Name`.
* 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.

![Screen Shot 2020-11-03 at 6.36.48 pm](https://dev-to-uploads.s3.amazonaws.com/i/gyvhfmaem9zewcd7f852.png)


![Screen Shot 2020-11-03 at 6.38.42 pm](https://dev-to-uploads.s3.amazonaws.com/i/2zuuorotg04y7dee6dn9.png)
  
##Reshaper Tab

![Screen Shot 2020-11-03 at 6.40.55 pm](https://dev-to-uploads.s3.amazonaws.com/i/049b6gd6fwxu2nsre6nw.png)

So in this tab there are two functions which are being offered -> `Pivot` and `Melt`. These can be used in same way as grapher but this is for reshaping dataframe.

##Conclusion

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.

*If you liked this article and find it useful hit like button. Also check out my blog [Computer Science Hub](https://computersciencehub.io/).
If want to reach out to me then just DM me on [Linkedin](https://www.linkedin.com/in/gagandeep-singh2000/)*