Let's use the Bottle web framework with Bokeh to build custom Python web app bar charts. This tutorial works with either Python 2 or 3but Python 3 is strongly recommended for new applications. I used Python 3. In addition to Python throughout this tutorial we will also use the following application dependencies :. If you need help getting your development environment configured before running this code, take a look at this guide for setting up Python 3 and Bottle on Ubuntu All code in this blog post is available open source under the MIT license on GitHub under the bar-charts-bokeh-bottle-python-3 directory of the blog-code-examples repository.
Use the source code as you want to for your own projects. Create a new virtual environment for this project to isolate our dependencies using the following command in the terminal.
I usually run the venv command within a separate venvs directory where all my virtualenvs are store. Keep in mind that you need to activate the virtualenv in every new terminal window where you want to use the virtualenv to run the project. Bokeh and Bottle are installable into the now-activated virtualenv using pip.
Run this command to get the appropriate Bokeh and Bottle versions. Our required dependencies will be installed within our virtualenv after a brief download and installation period.
First we'll code a basic Bottle application and then we will add the bar charts to the rendered page. Create a folder for your project named bottle-bokeh-charts. Within bottle-bokeh-charts create a new file named app. The code shown above provides a short Bottle application with a single route, defined with the chart function. The last two lines in the allow us to run the Bottle application in debug mode on port Never use debug mode for production deployments!
WSGI servers like Gunicorn are built for handling real traffic and will be easier to configure without major security holes. Make sure your virtualenv is still activated and that you are in the base directory of your project where app. Run app. You should see a header message about the number of bugs found over the past 16 days.
However, there's no bar chart to accompany that message just yet. Our single Bottle route is in place but it is not very exciting. Time to create a nice-looking bar chart. We'll build on our basic Bottle app foundation using some new Python code to engage the Bokeh library.
Our bar chart will have "software bugs found" for its theme. The data will randomly generate each time the page is generated. In a real application you would of course likely have a more stable and useful data source.
Python Bokeh – Plotting Vertical Bar Graphs
Continue modifying app.Bokeh is a Python interactive data visualization. It targets modern web browsers for presentation providing elegant, concise construction of novel graphics with high-performance interactivity.
Bokeh can be used to plot vertical bar graphs. Plotting vertical bar graphs can be done using the vbar method of the plotting module. Example 1 : In this example we will be using the default values for plotting the graph. Output :. Example 2 : In this example we will be plotting verticle bars with different parameters.
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However, Pandas is not required to create anything shown here. Bokeh make it simple to create basic bar charts using the hbar and vbar glyphs methods. In the example below, we have the following sequence of simple 1-level factors:.
Note that passing the list of factors is a convenient shorthand notation for creating a FactorRange. The equivalent explicit notation is:. This more explicit form is useful when you want to customize the FactorRangee.
Next we can call vbar with the list of fruit name factors as the x coordinate, the bar height as the top coordinate, and optionally any width or other properties that we would like to set:. As usual, the data could also be put into a ColumnDataSource supplied as the source parameter to vbar instead of passing the data directly as parameters.
Later examples will demonstrate this. In the example below the fruit factors are sorted in increasing order according to their corresponding counts, causing the bars to be sorted:.
Often times we may want to have bars that are shaded some color. This can be accomplished in different ways. One way is to supply all the colors up front. This can be done by putting all the data, including the colors for each bar, in a ColumnDataSource. This is shown below:. Another way to shade the bars is to use a CategoricalColorMapper that colormaps the bars inside the browser. This can be passed to vbar in the same way as the column name in the previous example.
Putting everything together we obtain the same plot in a different way:. Another common operation or bar charts is to stack bars on top of one another. The example below shows the fruits data from above, but with the bars for each fruit type stacked instead of grouped:.
Note that behind the scenes, these functions work by stacking up the successive columns in separate calls to vbar or hbar. This kind of operation is akin the to dodge example above i.
Sometimes we may want to stack bars that have both positive and negative extents. The example below shows how it is possible to create such a stacked bar chart that is split by positive and negative values:. For stacked bar plots, Bokeh provides some special hover variables that are useful for common cases. When stacking bars, Bokeh automatically sets the name property for each layer in the stack to be the value of the stack column for that layer. It may also sometimes be desirable to have a different hover tool for each layer in the stack.
These can be used to customize different hover tools for each layer:. When creating bar charts, it is often desirable to visually display the data according to sub-groups. There are two basic methods that can be used, depending on your use case: using nested categorical coordinates, or applying visual dodges.
If the coordinates of a plot range and data have two or three levels, then Bokeh will automatically group the factors on the axis, including a hierarchical tick labeling with separators between the groups.
In the case of bar charts, this results in bars grouped together by the top-level factors. The example below shows this approach by creating a single column of coordinates that are each 2-tuples of the form fruit, year.
Accordingly, the plot groups the axes by fruit type, with a single call to vbar :. We can also apply a color mapping, similar to the earlier example. Recall that the factors are of the for fruit, year. Another method for achieving grouped bars is to explicitly specify a visual displacement for the bars.A mapping of event names to lists of CustomJS callbacks.
Typically, rather then modifying this property directly, callbacks should be added using the Model. A mapping of attribute names to lists of CustomJS callbacks, to be set up on BokehJS side when the document is created. No uniqueness guarantees or other conditions are enforced on any names that are provided, nor is the name used directly by Bokeh for any reason.
List of events that are subscribed to by Python callbacks. This is the set of events that will be communicated from BokehJS back to Python for this model. Or simply a convenient way to attach any necessary metadata to a model that can be accessed by CustomJS callbacks, etc. No uniqueness guarantees or other conditions are enforced on any tags that are provided, nor are the tags used directly by Bokeh for any reason.
Apply a set of theme values which will be used rather than defaults, but will not override application-set values. The passed-in dictionary may be kept around as-is and shared with other instances to save memory so neither the caller nor the HasProps instance should modify it. Collect the names of all DataSpec properties on this class. This method always traverses the class hierarchy and includes properties defined on any parent classes.
Collect a dict mapping the names of all DataSpec properties on this class to the associated properties. This is a convenience method that simplifies adding a CustomJS callback to update one Bokeh model property whenever another changes value. ValueError —. As a convenience, if the event name passed to this method is also the name of a property on the model, then it will be prefixed with "change:" automatically:. However, there are other kinds of events that can be useful to respond to, in addition to property change events.
For example to run a callback whenever data is streamed to a ColumnDataSourceuse the "stream" event on the source:. Find the PropertyDescriptor for a Bokeh property on a class, given the property name. Add a callback on this object to trigger when attr changes.
Generate Python Parameter values suitable for functions that are derived from the glyph. This method optionally traverses the class hierarchy and includes properties defined on any parent classes. Query the properties values of HasProps instances with a predicate. Returns all Models that this object has references to.
Query this object and all of its references for objects that match the given selector. Raises an error if more than one object is found. Returns single matching object, or None if nothing is found :param selector: :type selector: JSON-like. In the context of a Bokeh server application, incoming updates to properties will be annotated with the session that is doing the updating. This value is propagated through any subsequent change notifications that the update triggers.
The session can compare the event setter to itself, and suppress any updates that originate from itself. Results are returned as a dict from property name to value, or None if no theme overrides any values for this instance.
Creating Bar Chart Visuals with Bokeh, Bottle and Python 3
References to other objects are serialized as references just the object ID and type infoso the deserializer will need to separately have the full attributes of those other objects.
The Document this model is attached to can be None. Example import numpy as np from bokeh. Note No uniqueness guarantees or other conditions are enforced on any names that are provided, nor is the name used directly by Bokeh for any reason.
Note No uniqueness guarantees or other conditions are enforced on any tags that are provided, nor are the tags used directly by Bokeh for any reason.This article is the second part of my Bokeh love story.
The full story including Jupyter notebooks and all files is on my Github. No way your data story is full without visualizations, and bar charts are arguably one of the most loved types of categorical data representation.
We will get to the more complex ones in a jiffy. It is pretty straight-forward to draw bar charts with Bokeh. As usual, we need to specify a type of chart or chose a glyph and pass the data to the plotting function. Absolutely in the same fashion, we could create horizontal bar charts. The code is super-intuitive; we just have to remember we are working with horizontal bars. Our bar charts are rendered in a very simple manner, and they definitely can benefit from some added make-up.
For a list of some available palettes please visit Bokeh palettes documentation. In order to use any of them with Bokeh we need to import them specifically. This plot looks much friendlier then the ones we started with. Sometimes we need to plot a grouped bar chart. For example, we might need to group our health indicators for some countries. For that, we need to import a special procedure from the bokeh.
We could use a zillion of possible in-built methods to adjust the visual to our liking as well. Quite often we are not satisfied with a pre-set or a random palette, and we need to use some additional colormapping. And here we are — the first chart has some random colors, the second one is color factored:. Even though the first one looks funkier, the second one has a much more clear message when a color mapping statistics. We just need to import the Label class from the bokeh.
One just needs to know that Bokeh uses a separate layer for plotting, another one for labeling, etc. The beauty of Bokeh is that adding a whole set of labels is hardly a tad more difficult. We will simply need to use an instance of ColumnDataSource class for that and import from the bokeh.
There is quite a number of other interactive techniques that can really reshape your visualization and give your data-driven story a whole new dimension. Just to name a few — linking with panning, linking with brushing, hovering, etc. Bokeh is truly an endless source of inspiration. I can not praise enough its simplicity, smooth learning curve and wonderful interactive visuals one can render just in a few lines of code!!
Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Make learning your daily ritual. Take a look. Sign in. Interactive Bar Charts with Bokeh. Ilya Kvyatkovskiy Follow.
Styling Bar Charts For a list of some available palettes please visit Bokeh palettes documentation. Palettes and Gridplot Importing a pallette from bokeh.A subclass of Plot that simplifies plot creation with default axes, grids, tools, etc.
In addition to all the Figure property attributes, the following options are also accepted:. An optional argument to configure tooltips for the Figure. This argument accepts the same values as the HoverTool. If a hover tool is specified in the tools argument, this value will override that hover tools tooltips value. If no hover tool is specified in the tools argument, then passing tooltips here will cause one to be created and added.
Configure and add AnnularWedge glyphs to this Figure. No uniqueness guarantees or other conditions are enforced on any names that are provided, nor is the name used directly by Bokeh for any reason. Or simply a convenient way to attach any necessary metadata to a model that can be accessed by CustomJS callbacks, etc. No uniqueness guarantees or other conditions are enforced on any tags that are provided, nor are the tags used directly by Bokeh for any reason.
If the data is subsequently updated, the legend will automatically re-group. The grouping is performed in Python, before the Bokeh output is sent to a browser. If the date is subsequently updated, the legend will not automatically re-group. The legend entry is labeled with exactly the text supplied here. This can be done by explicitly creating a Glyph to use, or more simply by passing e.
Bokeh does not use this value in any way, but it may be useful for searching a Bokeh document to find a specific model. If not supplied, Bokeh will automatically construct an internal ColumnDataSource with default column names from the coordinates and other arguments that were passed-in as literal list or array values. If supplied, Bokeh will use the supplied data source to drive the glyph. In this case, literal list or arrays may not be used for coordinates or other arguments.
In the Bokeh guide there are examples of various bar charts that can be created. My question is if it's possible to add data labels to each individual bar of the chart? I searched online but could not find a clear answer. In my example I'm using vbar with the plotting interface, it is a little bit more low level then the Charts interface, but there might be a way to add it into the Bar chart. You can find more about labelset here: Bokeh annotations.
The bokeh. See the answers here and above for information on the stable bokeh. Yes, you can add labels to each bar of the chart. There are a few ways to do this. By default, your labels are tied to your data. But you can change what is displayed. Here are a few ways to do that using your example:. I used some logic to determine the new dataframe column. Of course you could use another column already in df it all depends on what data you're working.
All you really need here is to supply a new column to the dataframe. Another way to get a new dataframe column. Again, we just need to supply df a new column to use on our bar plot. I've done it two ways. Note that because I used strings it put them in alphabetical order on the chart. Bokeh has three main "interface levels". High level charts provides quick easy access but limited functionality; plotting which gives more options; models gives even more options.