Python Bokeh - Plotting Horizontal Bar Graphs Last Updated : 03 Jul, 2020 Comments Improve Suggest changes Like Article Like Report Bokeh is a Python interactive data visualization. It renders its plots using HTML and JavaScript. It targets modern web browsers for presentation providing elegant, concise construction of novel graphics with high-performance interactivity. Bokeh can be used to plot horizontal bar graphs. Plotting horizontal bar graphs can be done using the hbar() method of the plotting module. plotting.figure.hbar() Syntax : hbar(parameters) Parameters : y : y-coordinates of the center of the horizontal bars height : thickness of the horizontal bars right : x-coordinates of the right edges left : x-coordinates of the left edges, default is 0 fill_alpha : fill alpha value of the horizontal bars fill_color : fill color value of the horizontal bars hatch_alpha : hatch alpha value of the horizontal bars, default is 1 hatch_color : hatch color value of the horizontal bars, default is black hatch_extra : hatch extra value of the horizontal bars hatch_pattern : hatch pattern value of the horizontal bars hatch_scale : hatch scale value of the horizontal bars, default is 12 hatch_weight : hatch weight value of the horizontal bars, default is 1 line_alpha : percentage value of line alpha, default is 1 line_cap : value of line cap for the line, default is butt line_color : color of the line, default is black line_dash : value of line dash such as : solid dashed dotted dotdash dashdot default is solid line_dash_offset : value of line dash offset, default is 0 line_join : value of line join, default in bevel line_width : value of the width of the line, default is 1 name : user-supplied name for the model tags : user-supplied values for the model Other Parameters : alpha : sets all alpha keyword arguments at once color : sets all color keyword arguments at once legend_field : name of a column in the data source that should be used legend_group : name of a column in the data source that should be used legend_label : labels the legend entry muted : determines whether the glyph should be rendered as muted or not, default is False name : optional user-supplied name to attach to the renderer source : user-supplied data source view : view for filtering the data source visible : determines whether the glyph should be rendered or not, default is True x_range_name : name of an extra range to use for mapping x-coordinates y_range_name : name of an extra range to use for mapping y-coordinates level : specifies the render level order for this glyph Returns : an object of class GlyphRenderer Example 1 :In this example we will be using the default values for plotting the graph. Python3 # importing the modules from bokeh.plotting import figure, output_file, show # file to save the model output_file("gfg.html") # instantiating the figure object graph = figure(title = "Bokeh Horizontal Bar Graph") # y-coordinates to be plotted y = [1, 2, 3, 4, 5] # x-coordinates of the right edges right = [1, 2, 3, 4, 5] # height / thickness of the bars height = 0.5 # plotting the graph graph.hbar(y, right = right, height = height) # displaying the model show(graph) Output : Example 2 :In this example we will be plotting horizontal bars with different parameters. Python3 # importing the modules from bokeh.plotting import figure, output_file, show # file to save the model output_file("gfg.html") # instantiating the figure object graph = figure(title = "Bokeh Horizontal Bar Graph") # name of the x-axis graph.xaxis.axis_label = "x-axis" # name of the y-axis graph.yaxis.axis_label = "y-axis" # y-coordinates to be plotted y = [1, 2, 3, 4, 5] # x-coordinates of the right edges right = [1, 2, 3, 4, 5] # height / thickness of the bars height = [0.5, 0.4, 0.3, 0.2, 0.1] # color values of the bars fill_color = ["yellow", "pink", "blue", "green", "purple"] # plotting the graph graph.hbar(y, right = right, height = height, fill_color = fill_color) # displaying the model show(graph) Output : Comment More infoAdvertise with us Next Article What is Data Visualization and Why is It Important? Y Yash_R Follow Improve Article Tags : Data Visualization AI-ML-DS Python-Bokeh Python Bokeh-plotting-figure-class AI-ML-DS With Python +1 More Similar Reads Python - Data visualization tutorial Data visualization is a crucial aspect of data analysis, helping to transform analyzed data into meaningful insights through graphical representations. This comprehensive tutorial will guide you through the fundamentals of data visualization using Python. We'll explore various libraries, including M 7 min read What is Data Visualization and Why is It Important? Data visualization uses charts, graphs and maps to present information clearly and simply. It turns complex data into visuals that are easy to understand.With large amounts of data in every industry, visualization helps spot patterns and trends quickly, leading to faster and smarter decisions.Common 4 min read Data Visualization using Matplotlib in Python Matplotlib is a widely-used Python library used for creating static, animated and interactive data visualizations. It is built on the top of NumPy and it can easily handles large datasets for creating various types of plots such as line charts, bar charts, scatter plots, etc. These visualizations he 11 min read Data Visualization with Seaborn - Python Seaborn is a popular Python library for creating attractive statistical visualizations. Built on Matplotlib and integrated with Pandas, it simplifies complex plots like line charts, heatmaps and violin plots with minimal code.Creating Plots with SeabornSeaborn makes it easy to create clear and infor 9 min read Data Visualization with Pandas Pandas is a powerful open-source data analysis and manipulation library for Python. The library is particularly well-suited for handling labeled data such as tables with rows and columns. Pandas allows to create various graphs directly from your data using built-in functions. This tutorial covers Pa 6 min read Plotly for Data Visualization in Python Plotly is an open-source Python library designed to create interactive, visually appealing charts and graphs. It helps users to explore data through features like zooming, additional details and clicking for deeper insights. It handles the interactivity with JavaScript behind the scenes so that we c 12 min read Data Visualization using Plotnine and ggplot2 in Python Plotnine is a Python data visualization library built on the principles of the Grammar of Graphics, the same philosophy that powers ggplot2 in R. It allows users to create complex plots by layering components such as data, aesthetics and geometric objects.Installing Plotnine in PythonThe plotnine is 6 min read Introduction to Altair in Python Altair is a declarative statistical visualization library in Python, designed to make it easy to create clear and informative graphics with minimal code. Built on top of Vega-Lite, Altair focuses on simplicity, readability and efficiency, making it a favorite among data scientists and analysts.Why U 4 min read Python - Data visualization using Bokeh Bokeh is a data visualization library in Python that provides high-performance interactive charts and plots. Bokeh output can be obtained in various mediums like notebook, html and server. It is possible to embed bokeh plots in Django and flask apps. Bokeh provides two visualization interfaces to us 4 min read Pygal Introduction Python has become one of the most popular programming languages for data science because of its vast collection of libraries. In data science, data visualization plays a crucial role that helps us to make it easier to identify trends, patterns, and outliers in large data sets. Pygal is best suited f 5 min read Like