How to create a line plot with Plotly Express in Python

Plotly Express is a Python library that allows us to create line plots quickly and easily, with customizable parameters and an interactive interface.

A line plot is a type of data visualization that displays data points on a continuous axis, often representing time or a sequence of values. It connects individual data points with a line to form a continuous curve, making it easy to identify trends and patterns in the data. Line plots are a versatile and effective way of visualizing time series or sequential data.

Features of the line plot

Some of the key features of line plot include:

Customizable appearance: Plotly Express line plot enables users to customize the appearance of the plot, including the color and marker style of individual traces.

Interactive interface: The resulting visualization is interactive, allowing for zooming, panning, and hovering over data points for detailed information.

Multiple line plotting: Users can plot multiple lines on a single plot, making it easy to compare data and identify trends.

Animation: Plotly Express line plot allows for the creation of animations to visualize changes over time.

Ease of use: With just a few lines of code, we can quickly and easily create line plots with Plotly Express.

Syntax

The line function syntax typically follows this structure:

Parameters

The line function of Plotly Express provides a variety of parameters that enable users to customize and enhance their line plots. Here are the key parameters:

• data_frame: This contains the data to be plotted.

• x_variable: This is a string or list of strings specifying the column(s) of the data_frame to be plotted on the x-axis.

• y_variable: This is a string representing the column name or index of the variable to be plotted on the y-axis.

• color_variable: This is a string that represents either the column name or index of the variable to be used for color-coding the lines.

• line_group_variable: This is a string that represents either the column name or the variable to be used for grouping lines.

• title: This is a string representing the title of the plot.

• label_dict: This is a dictionary of string keys and string values representing the labels for x and y axes.

• plot_height: This is an integer value specifying the height of the plot in pixels.

• plot_width: This is an integer value specifying the width of the plot in pixels.

Return type

The px.line() function returns a Plotly figure object that can be displayed with fig.show(). The figure object contains all the information required to produce the line plot, including the data, layout, and style.

Implementation

In the following playground, we create a line plot using a sample dataset called "gapminder" provided by Plotly Express. Used attributes (gdpPercap, continent and year) defined as follows:

• gdpPercap: This attribute denotes the GDP per capita, representing the income per person in a specific location. It provides a measure of the economic prosperity or wealth of a country or region.

• continent: This attribute represents the continent to which a particular data point belongs. It is a categorical variable that classifies countries into different continents such as Africa, Americas, Asia, Europe, and Oceania.

• year: This attribute represents the specific year or period for which the data is recorded. It allows for analysis or visualization of variable changes over time, providing a temporal dimension to the dataset.

Explanation

The code above is explained in detail below:

• Lines 2–3: Import the required libraries for the code: plotly.express as px for creating the box plot, and pandas as pd for handling data in a DataFrame.

• Line 6: Loads the Plotly gapminder dataset using the px.data.gapminder() function and assigns it to the variable df. The df variable will now hold the dataset, allowing us to access and analyze its contents.

• Line 9: Prints the first five rows of the loaded dataset. The head() function retrieves the top rows of the DataFrame and print() displays the result in the console. It helps to quickly inspect the data and verify its structure.

• Line 12: We create a line plot using Plotly Express. The px.line() function is used to generate the line plot. We pass the DataFrame df (which contains the loaded dataset) as the data_frame parameter. We specify the column to be plotted on the x-axis using the x parameter set to year. The y parameter is set to gdpPercap, representing the column to be plotted on the y-axis. The color parameter is set to "continent", allowing different continents to be color-coded. Finally, we set the title parameter to "GDP per Capita Over Time" to give the plot a title.

• Line 15: Display the plot using the fig.show() method, which shows the interactive plot.

Conclusion

Line plots are particularly useful for visualizing trends and changes in data over time or across different categories. They are often used to display time series data, stock market trends, weather patterns, and other types of data that change over time.

With Plotly Express, we can easily create line plots that showcase these trends with customizable colors, markers, and labels. Additionally, the interactive features of Plotly Express allow users to zoom in on specific parts of the plot, hover over data points to view detailed information, and toggle between different views of the data. These capabilities make line plots drawn with Plotly Express a powerful tool for exploring and presenting complex data sets.