Pandas Visual Analysis Guide — A Faster way of Data Visualization
In the world of Data Science, Exploratory Data Analysis is the most important procedure of Examining the information to realize what the data is all about. Every Data speaks something. It depends upon you how much you can find the insights from the data.
To understand the Data, The Visualization techniques frequently produce various plots as well as graphs to realize what patterns, anomalies as well as outliers do information have.
It’s a crucial stage since it can help us examine the connection in between various characteristics inside themselves, additionally, it’s beneficial within examining the attributes of various characteristics of this dataset.
There are various coding methods available to Visualize your data & there is always an upgrade in the visualization techniques. In this upgrade, the python Community has introduced a new open-source library for interactive Data visualization named “pandas-visual-analysis”.
This library has been introduced to Visualize your data into a single line of code. It provides a user interface that can be utilized to create different types of plots & graphs by taking the different attributes of the dataset. Plotly is basically used to generate the graphs in this library so that the graphs can be highly interactive, easily downloadable & Visually appealing.
Here, we are going to see the real-time implementation of this library using jupyter notebook.
1. Installation: Let’s see the installation of this library. To install this library, you can use the pip command in your jupyter notebook. You can visit the below link to copy the exact library name. https://pypi.org/project/pandas-visual-analysis/ & after that, you can paste this library code into your jupyter notebook & run the cell.
You can refer to the below screenshot.
2. Importing the required libraries: Pandas is the primary library used for data analysis, Hence we are importing the pandas & just now installed the visual analysis library.
3. Importing the dataset: Now, to visualize the data, we are importing a dataset. Here I have used the Sonar dataset & Visualizing the dataframe.
4. Creating the Visualization using the Visual analysis library: For this visualization, I am just passing the complete dataframe which is holding the whole dataset into the instance of Visual Analysis library.
Note: — This visualization will create the 4-window as output. For demonstrating here, I have snipped both for your better understanding.
Let’s go for detailed Analysis of these window.
1. Statistical Analysis: The attached screenshot is the first window in the output. This will give you the statistical summary of the dataset. You can click on drop-down menu to analyse the different metrics like Mean, Median, Quartiles. Etc.
2. Scatter plot: The next window will show you the scatter plot of two columns. You can select the column of your choice from drop-down menu to visualize the scatter plot.
3. Histogram Plot: This will be the third window (below the Statistical Analysis) will provide you the visualization of single column data which gives you the clear idea about how the column data is being distributed. You can choose the column from the drop down-menu & can visualize the data distribution.
In the above visualization, you can see the data how it will appear after normalization if normalization required in any column
4. Distribution Plot: The Fourth window (below the Scatter plot) will provide you the visual analysis using Distribution plot.
Features of the Visual-Analysis library
1. It’s easy to use.
2. It provides you a user interface to select your data & Visualize it.
3. You can easily download the plotted graphs in your system. To download the graph, You can hover your Mouse on the graph, The First camera icon will give you the option to download the graph.
4. You can easily monitor your dataset & can make necessary changes as per the requirement by data selection from drop-down menus.
5. You can compare your dataset as well.
These features are just the basic features. Once you visit the below link,You will come to know about more features.You can also modify the colour, Size of the graph & also, you can pass the sample dataset. For more details, you can go to the below link.
Conclusion
In this blog, we have seen the practical implementation of pandas’ visual analysis library. We noticed just how readily we are able to produce an interface which can be utilized to produce graphs and plots in between various characteristics of the dataset and we additionally assessed the statistical qualities of the various numerical characteristics of the dataset.
