How to create a bar plot with Plotly Express in Python

Plotly Express is a Python library that offers a simplified approach to generating interactive and visually captivating plots, such as bar plots, with minimal coding effort.

Bar plots are helpful in representing categorical data and facilitating comparisons between categories. With the aid of Plotly Express, developers can effortlessly create personalized bar plots, making it an invaluable tool for effective data visualization in various analytical tasks.

Features of the bar plot

Some of the key features of bar plot include:

Automatic plot generation: Plotly Express automatically generates bar plots from our data with minimal coding, saving time and effort.

Categorical data visualization: Bar plots in Plotly Express are specifically designed to visualize categorical data, making it easy to compare values across different categories.

Grouping and aggregation: Plotly Express allows us to group data by a specific variable and aggregate values within each group, enabling us to analyze and present summarized information.

Customizable bar appearance: We can control various visual aspects of the bars, including colors, transparency, bar width, and spacing, allowing us to match the plot aesthetics with our specific requirements or style.

Error bars: Plotly Express supports the addition of error bars to bar plots, providing a visual representation of uncertainties or confidence intervals associated with the data.

Faceting: With Plotly Express, we can easily create faceted bar plots, also known as grouped or clustered bar plots, to compare values across multiple dimensions or subcategories.

Annotations and labels: We can annotate our bar plot with text labels, providing additional information or insights. Labels can be positioned inside or outside the bars, depending on our preference.

Interactive features: Plotly Express generates interactive plots by default, allowing us to zoom, pan, and hover over bars to view specific data points or details. This interactivity enhances the exploratory and communicative aspects of our visualization.

Exporting and sharing: Once a bar plot is created, we can save it as an image file or share it as an interactive web-based visualization, making it easy to include in reports, presentations, or online platforms.

Syntax

The bar() function syntax typically follows this structure:

import plotly.express as px
fig = px.bar(df, x='x_column', y='y_column')
Syntax of the bar() function

Parameters

The bar function of Plotly Express offers a range of parameters that allow for flexible customization and fine-tuning of the resulting bar plot. Here are the key parameters:

  • data_frame: Specifies the DataFrame or data source containing the data for the plot.

  • x: Specifies the column name or values to be used on the x-axis.

  • y: Specifies the column name or values to be used on the y-axis.

  • color: Specifies the column name or values to be used for coloring the bars based on a specific category.

  • hover_name: Specifies the column name or values to be displayed when hovering over the bars.

  • animation_frame: Specifies the column name or values to create an animated bar plot based on different frames.

  • barmode: Specifies the bar mode, such as group, overlay, or relative’, to control the arrangement of bars in grouped or overlaid plots.

  • labels: Allows customization of the labels of the x-axis, y-axis, and legend.

  • title: Specifies the title of the plot.

  • width: Specifies the width of the bars.

  • opacity: Specifies the opacity of the bars.

  • orientation: Specifies the orientation of the bars, either 'v'vertical verticalor 'h'horizontalhorizontal.

  • category_orders: Allows specification of the order of categories on the x-axis or y-axis.

  • text: Specifies the column name or values to be displayed as text labels on or above the bars.

  • error_x and error_y: Specifies the column name or values representing the error bars in the x-direction or y-direction, respectively.

Return type

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

Implementation

In the following playground, we create a bar plot using a sample dataset called "tips" provided by Plotly Express. Used attributes (total_bill and day) defined as follows:

  • total_bill: This attribute represents the total bill amount for a given meal, including the cost of food, drinks, taxes, and any additional charges. It is a continuous numeric variable representing monetary values.

  • day: This attribute represents the day of the week on which a particular transaction or event occurred. It is a categorical variable that classifies the data into different days of the week: Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, and Saturday.

# Import necessary libraries
import plotly.express as px
import pandas as pd

# Load the dataset provided by Plotly Express
df = px.data.tips()

# Display the first five rows of the data
print(df.head())

# Create a bar plot using Plotly Express
fig = px.bar(df, x="day", y="total_bill", color="sex", barmode="group")

# Show the bar plot
fig.show()
Creating a bar plot of tips 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 density heatmap plot, and pandas as pd for handling data in a DataFrame.

  • Line 6: Loads a sample dataset called tips using the px.data.tips() function provided by Plotly Express. The dataset contains information about restaurant tips.

  • 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 inspect the data and verify its structure quickly.

  • Line 12: We create a bar plot using Plotly Express. The px.bar() function is used to generate the bar plot. We pass the DataFrame df (which contains the loaded dataset) as the first argument. We specify the column to be plotted on the x-axis using the x parameter set to day. The y parameter is set to total_bill, representing the column to be plotted on the y-axis. The color parameter is set to sex, allowing bars to be color-coded based on gender. The barmode parameter is set to "group" to create grouped bars.

  • Line 22: Display the plot using the fig.show() method, which shows the interactive plot.

Conclusion

Plotly Express provides a straightforward and efficient way to create bar plots in Python. With its high-level interface, developers can easily generate visually appealing and interactive bar plots with minimal code. Bar plots are valuable for visualizing categorical data and comparing values across different categories. Plotly Express offers various customization options, allowing users to tailor their plots to their specific requirements. Whether exploring data, presenting insights, or conveying information effectively, Plotly Express bar plots are a versatile solution for data visualization tasks.

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