Amazing PandasGUI: Analyze Pandas dataframes with a Graphical User Interface
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.
Like any other library, we can install pandasgui using pip.
pip install pandasgui
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
Creating the Interface
Now we just need to call the show function by passing our dataset name as the parameter and it will launch a GUI where we can explore its different sections and try to explore different properties of the dataset.
Here you can see that the show function launches the GUI and we can clearly see different tabs containing different functionalities.
Let us analyze what are the different sections of this interface.
In this section, we can clearly analyze what are the different attributes and what values it contains. We can clearly analyze all the values and attributes. On this left side, we can also see the shape of the dataframe.
In this section, we can apply different filters to analyze the data. We can simply type in the query we want to run and apply the filter. You can check out this link to find out about queries in pandas.
This section is similar to the describe function of the pandas dataframe. It helps us analyze the statistical properties of the dataset.
This is the most important section, here we can clearly see different types of visualizations that we can create using the interface and save our effort of writing the code for each visualization. In the image above, I have created the scatter plot for sepal_length and sepal_width. Similarly, you can create different visualization by dragging and dropping column names in x, y, and other parameters.
In this section, we can analyze the dataset by applying different functions and changing the shape of the dataset. Two shape formats that are provided are ‘Pivot’ and ‘Melt’. We can drag and drops columns in different functions and analyze different shapes of the dataset accordingly.
These are the 5 sections that are provided by PandasGUI by which we can analyze the pandas dataframe and perform EDA on any given dataset. PandasGUI is a useful tool because it reduces the effort of writing the code again and again and saves time also.
Similarly, you can explore PandasGUI using different datasets. Try it and let me know your experiences in the responses to this article.
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