Lux: Excellence In Data Analysis And Visualisation

Data is sacred in today’s world, and Python libraries like Lux make data analysis and visualisation so much easier.

Data analytics and visualisation are key components of any real-world application. The analysis of historical data as well as predictive evaluations is crucial and helps organisations make strategic decisions. Hidden patterns that emerge as a result of data engineering help companies to evaluate customer feedback, customer leads, social media messages, e-commerce shopping patterns, and so on.

The applications and use cases of data engineering include predictive maintenance, customer segmentation, fraud detection, demand forecasting, sentiment analysis, recommender systems, speech recognition, image classification, autonomous vehicles, drug discovery, medical diagnosis, energy consumption prediction, supply chain optimisation, financial risk modelling, anomaly detection, natural language processing, smart city analytics, climate modelling, etc.

Table 1 lists the free and open source libraries for data analytics that can be programmed in different languages depending upon the use case and application.

Table 1: Key libraries for data engineering, analytics and visualisation

Python offers a number of free and open source libraries that are dedicated to data visualisation and plotting. Table 2 lists these.

Table 2: Python-based libraries for data engineering and visualisation

Using the Lux library for advanced visualisation in minimal coding

The Lux library in Python has excellent features for data analytics, data engineering and visualisation. Coding is minimal, and no complex code and scripts need to be written. It also offers integration with the Pandas library in Python. Lux reduces the manual efforts required for plotting and data visualisation with the support of large datasets. This helps to develop and deploy large-scale real-time applications.

Lux integrates various features in Python including automatic visualisation suggestions, intelligent data profiling, minimal code usage, Pandas DataFrame integration, toggle visualisation mode, interactive visual widgets, chart recommendation engine, quick summary stats, outlier identification support, Jupyter notebook compatibility, built-in analytics insights, correlation detection tools, user intent specification, export visualisation options, attribute-based exploration, faceted visual comparisons, visual filtering interface, multi-chart generation, and many others. All these features enable highly effective visualisation in data engineering and analytics-based projects.

Lux integrates the following functions for plotting and advanced visualisation:

• Intent specification for statistical data analytics
• Metadata analysis
• Faceted chart generation
• Correlation analysis and views
• Relationship views
• Recommendation sections with statistics
• Querying and filtering

You can use the following code to install Lux in Google Colab:

!pip install lux-api
from google.colab import output
output.enable_custom_widget_manager()
LUX Python Code for Visualization of Sample Dataset in Script
import lux
import pandas as pd
# Sample Dataset for Visualization
data = {
    «Product»: [«Laptop», «Laptop», «Smart Watch», «Smart Watch», «Tablet», «Tablet», «IoT Gadget», «IoT Gadget»],
    «Month»: [«January», « July «, « January «, «July», « July «, «January», « January «, «July»],
    «Sales»: [220, 250, 220, 250, 90, 90, 165, 180],
    «Profit»: [30, 45, 50, 90, 15, 20, 20, 45],
    «Units_Sold”: [10, 19, 10, 23, 6, 9, 13, 15]
}
df = pd.DataFrame(data)
# Visualization with Display using Lux
df

For data visualisation and plotting, software developers need to use special libraries like Seaborn, Matplotlib, Plotly, Folium, etc. By using Lux in Python, there is no need to integrate dedicated plotting and visualisation libraries. Lux itself provides all the features needed for dynamic visualisation and auto identification of the attributes required for data analytics.