Mastering Data Visualization Libraries: Python and Power BI in Action

An integral component of analytics and data science is data visualisation. It efficiently enables analysts and decision-makers to spot patterns, trends, and outliers by converting raw data into insightful knowledge. Python has a vast array of visualisation packages to assist you in achieving your objectives, whether you’re dealing with huge datasets, producing reports, or building interactive dashboards.

In this article, we will discuss some of the top Python visualisation libraries, their special qualities, and how Power BI may use them to improve business intelligence. Businesses currently use Power BI extensively as it offers seamless integration to several SQL databases and Azure Data Warehouse, as well as access control.

Note: I am not covering every visualization of each library in this article please check their official documentation for more information.

1. Matplotlib — The Classic Visualization Library

Matplotlib is the most fundamental plotting library in Python. It serves as the backbone for many other visualization libraries.

Features:

• Highly customizable
• Supports various plot types (line, bar, scatter, histograms, etc.)
• Can be used for publication-quality figures

Example:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.plot(x, y, label="Sine Wave")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.title("Basic Plot with Matplotlib")
plt.legend()
plt.show()

2. Seaborn — Statistical Data Visualization

Seaborn builds on Matplotlib, making it easier to create aesthetically pleasing and informative statistical graphics.

Features:

• Works seamlessly with Pandas Data Frames
• Provides high-level abstraction for statistical plots
• Beautiful default themes and colour palettes

Example:

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")
sns.boxplot(x="day", y="total_bill", data=tips)
plt.show()

3. Plotly — Interactive Web-Based Charts

Plotly is widely used for creating interactive visualizations and dashboards.

Features:

• Fully interactive with zooming, panning, and hover effects
• Supports 3D plotting and animations
• Ideal for web-based applications

Example:

import plotly.express as px

df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", size="petal_length")
fig.show()

4. Bokeh — Interactive Dashboards

Bokeh is designed for building dynamic and interactive visualizations, especially for web applications.

Features:

• Provides real-time streaming capabilities
• Easily integrates with Flask, Django, and Jupyter Notebooks
• Can export as standalone HTML files

Example:

from bokeh.plotting import figure, show
from bokeh.io import output_notebook

output_notebook()
p = figure(title="Simple Line Chart", x_axis_label="X", y_axis_label="Y")
p.line([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], legend_label="Trend", line_width=2)
show(p)

5. Altair — Declarative Statistical Visualization

Altair is a user-friendly visualization library based on the Vega-Lite grammar.

Features:

• Simple and intuitive syntax
• Great for exploratory data analysis (EDA)
• Supports interactivity and tooltips

Example:

import altair as alt
import pandas as pd

import altair as alt
import pandas as pd
df = pd.DataFrame({'x': range(10), 'y': [i**2 for i in range(10)]})
chart = alt.Chart(df).mark_line(point=True).encode(
    x=alt.X('x:Q', title="X-axis (Input Value)"),
    y=alt.Y('y:Q', title="Y-axis (Squared Value)"),
    tooltip=['x', 'y']
).properties(
    title="Interactive Line Chart with Tooltips",
    width=600,
    height=400
).interactive()  
chart.display()

6. Folium — Geospatial Data Visualization

Folium is the go-to library for mapping and geospatial data representation.

Features:

• Supports OpenStreetMap, Mapbox, and Leaflet.js
• Easy to overlay markers, heatmaps, and choropleths
• Works well with Pandas and GeoPandas

Example:

import folium

m = folium.Map(location=[51.5074, -0.1278], zoom_start=10)  # London
folium.Marker([51.5074, -0.1278], popup="London").add_to(m)
m

7. Power BI + Python — Advanced Business Intelligence

Power BI and Python together provide a powerful combination for data visualization and business intelligence. While Power BI offers an intuitive interface and seamless integration with enterprise databases, Python enhances it with advanced analytics, custom visualizations, and machine learning capabilities.

Why Use Python with Power BI?

• Advanced Analytics: Power BI provides built-in features, but Python allows deeper statistical analysis, machine learning, and predictive modelling.
• Custom Visualizations: Python enables users to go beyond Power BI’s default charts, offering libraries like Matplotlib, Seaborn, and Plotly for highly customized visuals.
• Seamless Azure Connectivity: Power BI easily integrates with Azure Data Warehouse, SQL Server, and other cloud-based databases, enabling real-time data analysis.
• Excel and Data Processing: Power BI supports Excel data integration, but Python allows complex data transformations, automations, and ETL processes.
• Scalability & Automation: Python scripts within Power BI can automate repetitive tasks and handle large-scale data processing efficiently.
• Enterprise-Level Security: With Power BI’s role-based access control, organizations can manage data access while utilizing Python for deep analytics.

Steps to Use Python in Power BI:

1. Open Power BI and go to Home > Get Data > Python Script
2. Write or paste your Python code
3. Load the data and visualize it

Example:

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")
sns.boxplot(x="day", y="total_bill", data=tips)
plt.show()

Why Use Python in Power BI?

• Custom Charts: Go beyond Power BI’s default visuals.
• Advanced Analytics: Leverage machine learning models and statistical analysis.
• Seamless Integration: Use Python scripts directly inside Power BI reports.

Choosing the Right Library

Final Thoughts

Each Python visualization library has unique strengths, making it suitable for different use cases. Whether you’re building simple reports, interactive dashboards, or geospatial maps, Python has a library for you.

The combination of Python and Power BI has transformed data visualization and business intelligence, enabling users to analyze, visualize, and interpret complex datasets with ease. Python’s ability to create highly customized, data-driven visualizations enhances Power BI’s real-time reporting and enterprise connectivity. As businesses continue to adopt data-driven decision-making, leveraging both Power BI’s intuitive analytics and Python’s computational power will lead to more advanced, insightful, and impactful data solutions. The synergy between these tools paves the way for the future of intelligent, context-aware, and scalable business intelligence applications.

I’d love to hear your feedback and suggestions! Share your thoughts in the comments or connect with me on LinkedIn.