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3 DATA VISUALIZATION
Chapter Outline
3.1 Displaying the line graph 3.2 Bar Graph
3.3 Histogram
Introduction
It is said that a picture is worth a thousand words. Visualizing data through graphs, pie charts, histograms, and 3D plots
allows us to uncover patterns and trends in data that provide useful insights from the data. Using visual representations,
we can effectively communicate and interpret complex information. For example, line graphs are an excellent choice
for visualizing stock market trends over time. They allow us to identify patterns, track fluctuations, and make informed
decisions based on historical data. Pie charts provide an intuitive representation of demographic data, such as gender
distribution or class composition. Histograms can be employed to showcase the distribution of a single continuous
variable, such as the percentage marks in a class.
Python, a powerful and versatile programming language, offers a wide range of libraries for data visualization. Among
these is Matplotlib, a feature-rich toolkit offering an abundance of tools and functions to craft a diverse assortment of
visuals. Using these tools, we can draw various shapes (like circles, rectangles, and polygons), histograms, bar charts,
pie charts, scatter plots.
Throughout this chapter, we will explore how to create these graphs, and customize these plots by adding label, title,
and legend in plots, thus, empowering us to craft visually compelling and meaningful visualizations of data that bring
our data to life.
3.1 Displaying the line graph
3.1.1 Displaying a Point
It is said, "Every great dream begins with a small beginning." Let us begin our journey of data visualization by plotting
a single point. In Matplotlib, the pyplot module provides a simple interface for creating and customizing plots.
Below, we plot the point having coordinates (3, 2) as a circle in red color (Fig 3.1):
>>> import matplotlib.pyplot as plt
>>> plt.plot(3, 2, 'ro')
>>> plt.show()
90 Touchpad Informatics Practices-XII

