Grammar of Graphics for Python: An Introduction to Plotline

A grammar of graphics is basically a tool that enables us to describe the components of a given graphic. Basically, what this allows us to see beyond the named graphics, (scatter plot, to name one) and to basically see the underlying statistics behind it. The grammar of graphics was originally introduced by Leland Wilkinson in the 1990s and was popularized by Hadley Wickham with ggplot.

 In Python, the Grammar of Graphics can be explored through libraries such as Plotline, which offers a systematic way to build plots by mapping data to visual properties.

Understanding the Grammar of Graphics

The Grammar of Graphics is a theoretical framework that breaks down the process of creating visualizations into a series of components. These components can be combined in various ways to construct a wide range of plots. The key elements of the Grammar of Graphics include:

First, we will see the three main components that are required to create a plot, and without these components, the plotnine would not be able to plot the graph. These are- 

• Data is the dataset that is used for plotting the plot.
• Aesthetics (aes) is the mapping between the data variables and the variables used by the plot such as x-axis, y-axis, color, fill, size, labels, alpha, shape, line width, line type.
• Geometric Objects (geoms) is the type of plot or a geometric object that we want to use such as point, line, histogram, bar, boxplot, etc.

There are various optional components that can make the plot more meaningful and presentable. These are:

• Facets allow the data to be divided into groups and each group is plotted separately.
• Statistical transformations compute the data before plotting it.
• Coordinates define the position of the object in a 2D plane.
• Themes define the presentation of the data such as font, color, etc.

Introducing Plotline

Plotline is a Python library that embodies the principles of the Grammar of Graphics, allowing users to create complex visualizations with ease. It provides a high-level interface for defining plots, making it accessible to both beginners and experienced data scientists.

Key Features of Plotline:

• Declarative Syntax: Plotline uses a declarative approach, where users specify what they want to achieve rather than how to do it.
• Flexibility: The library supports a wide range of plot types and customization options.
• Integration: Plotline integrates well with popular data manipulation libraries like Pandas, making it easy to visualize data directly from dataframes.

This grammar of graphics was first introduced in R, using ggplot and ggplot2. Considering its success in the past, it is also been introduced in Python as plotnine. 

Getting Started with Plotline

The "plotline" concept refers to the line-based representation of data within the Grammar of Graphics framework. Lines are used to connect data points and visualize trends, relationships, and patterns over continuous variables.

To begin using Plotline, you need to install the library. Although Plotline is a hypothetical library in this context, the installation process would typically involve using pip:

pip install plotnine 

Plotline allows you to create a variety of basic plots by defining the necessary components of the Grammar of Graphics.

Example 1:  Scatter Plot Example

import pandas as pd
from plotnine import * 


# load dataset 
dataset = pd.read_csv("dataset.csv") 

# ggplot is to plot the given data
(ggplot(dataset, aes(x = "area_0", y = "area_1"))+
    geom_point()
)

# aes contains parameters which work 
# as x-axis and y-axis for the given plot
# geom.point() makes the data entries as points

Output: 

Example 2: Scatter Plot with Additional Customizations

The provided code creates a scatter plot using the plotnine library, but with additional customizations.

import pandas as pd
from plotnine import * 


# load dataset 
dataset = pd.read_csv("dataset.csv") 

(ggplot(dataset, aes(x = "area_0", y = "area_1"))+
    geom_point(color = "label", alpha = 0.7,
               size = 0.5)
)

Output: 

Coordinate Systems and Faceting for Plotlines

In data visualization, the choice of coordinate systems and the use of faceting are crucial for effectively conveying the patterns and relationships in the data. These tools help enhance the interpretability and comparability of plots, making them more informative and accessible.

1. Coordinate Systems

A coordinate system is a framework that defines how data points are mapped onto a plot. Choosing the right coordinate system is essential as it affects the readability and interpretation of the visualization.

• Cartesian Coordinates: The Cartesian coordinate system is the most commonly used system for plotting data. It is defined by two perpendicular axes: the x-axis (horizontal) and the y-axis (vertical). Each data point on the plot corresponds to a pair of numerical values that determine its position along these axes.
• Polar Coordinates: The polar coordinate system represents data points based on a radius and an angle. Unlike the Cartesian system, which uses perpendicular axes, polar coordinates map points in a circular layout. Each point is defined by its distance from a central point (radius) and its angle relative to a fixed direction.

2. Faceting

Faceting, also known as trellising or small multiples, is a technique used to create multiple subplots within a single visualization. Each subplot represents a subset of the data, allowing for a more granular analysis and comparison across different categories or groups.

• The primary purpose of faceting is to enable comparative analysis across different subsets of data. By breaking down a dataset into smaller, manageable pieces, faceting allows viewers to observe variations and patterns that may not be evident in a single, aggregated plot.
• Highlighting Differences: Faceting is particularly useful for highlighting differences across categories, such as different demographic groups, time periods, or geographic regions. It makes it easier to identify outliers, trends, and correlations within specific segments of the data.

Benefits of Grammar of Graphics for Plotline

The Grammar of Graphics provides a coherent system for creating a wide range of visualizations by combining basic graphical elements. For plotlines, in particular, this approach offers several significant benefits:

• Structured and Systematic Approach: The Grammar of Graphics provides a structured methodology for creating plots by defining components such as data, aesthetics, geometric objects, scales, statistical transformations, and coordinate systems.
• Flexibility and Customization: GoG allows users to create highly customized plotlines by combining different components. Users can adjust the aesthetics (such as color, line type, and size) and choose the most suitable geometry (such as lines, points, or paths) for their specific data and visualization goals.
• Ease of Learning and Use: The Grammar of Graphics provides a unified syntax for creating various types of plots, including plotlines reducing the learning curve and allows for quicker adoption and application of advanced visualization techniques.
• Separation of Data and Aesthetics: One of the core principles of the Grammar of Graphics is the separation of data from aesthetics. This separation allows users to focus on the data's underlying patterns and trends when designing plotlines without being distracted by styling and formatting.
• Composability of Graphics: GoG allows users to compose complex visualizations from simple components. For plotlines, this means users can layer multiple lines, add trend lines, include statistical summaries, and combine different geometric objects within the same plot. This composability enhances the expressiveness of visualizations and allows users to convey multiple layers of information in a single plot.

Conclusion

Plotline, through its implementation of the Grammar of Graphics, provides a powerful and flexible framework for data visualization in Python. By breaking down plots into their fundamental components, Plotline offers users the ability to create complex and customized visualizations with ease.