Planet Python

Last update: November 19, 2018 07:47 PM UTC

November 19, 2018

Simple is Better Than Complex

Launching our Community Forum

This is a short post just to announce today I’m releasing a community forum for the simpleisbetterthancomplex.com readers! And I want you to be part of it.

I decided to create this community forum for a couple of reasons. First of all, I receive many emails with questions, asking for advice and asking my opinion about specific topics. I’m happy to answer those emails whenever I can, but unfortunately, I can’t answer them all. And when I’m able to answer those emails, the conversations and discussions have a high potential to be useful to others. So why not have some of those discussions in an open forum?

With this community forum, I also want to have a place for questions that are not suitable for StackOverflow. For example, “what’s the best database to use with Django?” or “Apache or NGINX?”. This kind of questions, where there is no right or wrong answer, but can serve as a starting point for a good discussion and exchange of experience.

Another reason is to have a single place to organize the readers’ requests, suggestions, and ideas for future tutorials and videos. There is a specific category for tutorials requests where you can share your ideas and upvote other’s requests to help me prioritize.

And really, I just want this forum to be a safe and respectful place where other tech enthusiasts can get together to talk about tech stuff, share experiences and help each other.

If you want to be part of this community, join us at community.simpleisbetterthancomplex.com!

See you there!

November 19, 2018 05:00 PM UTC

PyCharm

Webinar: “Automating Build, Test and Release Workflows with tox” with Oliver Bestwalter

Python’s tox project is a critical tool for quality software production. Most of our users and customers know about it, but haven’t made the time to learn it.

This webinar’s for you. Oliver Bestwalter is one of the maintainers of tox, and as he discussed in a recent Test and Code interview, has many ideas on how to automate the build/release process.

• Thursday, December 13
• 4:00 PM – 5:00 PM CET (10:00 AM – 11:00 PM EST)
• Register here
• Aimed at intermediate Python developers

Agenda

We will look at what is necessary to automate all important workflows involved in building, testing and releasing software using tox.

We’ll cover how to use tox to …

• run static code analysis, automatic code formatting/fixing as a separate stage orchestrated by the pre-commit framework
• run tests with pytest
• measure and report test coverage
• build and upload packages to pypi/devpi/artifactory

All this can be run and debugged locally from the command line or programmatically.

These building blocks can then form a complete build, test and release pipeline to be run on CI systems like Travis-CI, Gitlab, Jenkins, Teamcity, etc.

If time permits, we’ll also look at how projects like tox and pytest are automating their own processes.

Speaking to You

Oliver is an engineer at Avira who fell in love with open source in the 1990s and with Python in 2006. He creates and helps to maintain test and automation tools helping developers and companies to produce better software more effectively.

Since 2011 he has been a Software Developer at Avira, helping a diverse range of product teams to improve their build, test and release processes. He strives to be a good open source citizen by helping to maintain and improve projects in the area of testing and automation. As part of this effort he spends 20% of his time at Avira working on open source projects. He also enjoys accompanying others on their journey, helping them to improve their skills, and acts as a coach and mentor at Avira and with the Python Academy. When he gets the chance (and can rustle up the courage) he also talks at conferences and meetups.

In 2016 he joined the tox project and is now one of the maintainers. Since 2017 he has been spending up to 20% of his time at Avira working on tox and other open source projects.

-PyCharm Team-
The Drive to Develop

November 19, 2018 04:00 PM UTC

Codementor

Graceful Data Ingestion with SQLAlchemy and Pandas

When the data size is not large enough to use distributed computing frameworks (like Apache Spark), processing data in a machine with pandas is an efficient way. But how to insert data with...

November 19, 2018 02:42 PM UTC

Real Python

Interactive Data Visualization in Python With Bokeh

Bokeh prides itself on being a library for interactive data visualization.

Unlike popular counterparts in the Python visualization space, like Matplotlib and Seaborn, Bokeh renders its graphics using HTML and JavaScript. This makes it a great candidate for building web-based dashboards and applications. However, it’s an equally powerful tool for exploring and understanding your data or creating beautiful custom charts for a project or report.

Using a number of examples on a real-world dataset, the goal of this tutorial is to get you up and running with Bokeh.

You’ll learn how to:

• Transform your data into visualizations, using Bokeh
• Customize and organize your visualizations
• Add interactivity to your visualizations

So let’s jump in.

From Data to Visualization

Building a visualization with Bokeh involves the following steps:

• Prepare the data
• Determine where the visualization will be rendered
• Set up the figure(s)
• Connect to and draw your data
• Organize the layout
• Preview and save your beautiful data creation

Let’s explore each step in more detail.

Prepare the Data

Any good data visualization starts with—you guessed it—data. If you need a quick refresher on handling data in Python, definitely check out the growing number of excellent Real Python tutorials on the subject.

This step commonly involves data handling libraries like Pandas and Numpy and is all about taking the required steps to transform it into a form that is best suited for your intended visualization.

Determine Where the Visualization Will Be Rendered

At this step, you’ll determine how you want to generate and ultimately view your visualization. In this tutorial, you’ll learn about two common options that Bokeh provides: generating a static HTML file and rendering your visualization inline in a Jupyter Notebook.

Set up the Figure(s)

From here, you’ll assemble your figure, preparing the canvas for your visualization. In this step, you can customize everything from the titles to the tick marks. You can also set up a suite of tools that can enable various user interactions with your visualization.

Connect to and Draw Your Data

Next, you’ll use Bokeh’s multitude of renderers to give shape to your data. Here, you have the flexibility to draw your data from scratch using the many available marker and shape options, all of which are easily customizable. This functionality gives you incredible creative freedom in representing your data.

Additionally, Bokeh has some built-in functionality for building things like stacked bar charts and plenty of examples for creating more advanced visualizations like network graphs and maps.

Organize the Layout

If you need more than one figure to express your data, Bokeh’s got you covered. Not only does Bokeh offer the standard grid-like layout options, but it also allows you to easily organize your visualizations into a tabbed layout in just a few lines of code.

In addition, your plots can be quickly linked together, so a selection on one will be reflected on any combination of the others.

Preview and Save Your Beautiful Data Creation

Finally, it’s time to see what you created.

Whether you’re viewing your visualization in a browser or notebook, you’ll be able to explore your visualization, examine your customizations, and play with any interactions that were added.

If you like what you see, you can save your visualization to an image file. Otherwise, you can revisit the steps above as needed to bring your data vision to reality.

That’s it! Those six steps are the building blocks for a tidy, flexible template that can be used to take your data from the table to the big screen:

"""Bokeh Visualization Template

This template is a general outline for turning your data into a 
visualization using Bokeh.
"""
# Data handling
import pandas as pd
import numpy as np

# Bokeh libraries
from bokeh.io import output_file, output_notebook
from bokeh.plotting import figure, show
from bokeh.models import ColumnDataSource
from bokeh.layouts import row, column, gridplot
from bokeh.models.widgets import Tabs, Panel

# Prepare the data

# Determine where the visualization will be rendered
output_file('filename.html')  # Render to static HTML, or 
output_notebook()  # Render inline in a Jupyter Notebook

# Set up the figure(s)
fig = figure()  # Instantiate a figure() object

# Connect to and draw the data

# Organize the layout

# Preview and save 
show(fig)  # See what I made, and save if I like it

Some common code snippets that are found in each step are previewed above, and you’ll see how to fill out the rest as you move through the rest of the tutorial!

Generating Your First Figure

There are multiple ways to output your visualization in Bokeh. In this tutorial, you’ll see these two options:

• output_file('filename.html') will write the visualization to a static HTML file.
• output_notebook() will render your visualization directly in a Jupyter Notebook.

It’s important to note that neither function will actually show you the visualization. That doesn’t happen until show() is called. However, they will ensure that, when show() is called, the visualization appears where you intend it to.

By calling both output_file() and output_notebook() in the same execution, the visualization will be rendered both to a static HTML file and inline in the notebook. However, if for whatever reason you run multiple output_file() commands in the same execution, only the last one will be used for rendering.

This is a great opportunity to give you your first glimpse at a default Bokeh figure() using output_file():

# Bokeh Libraries
from bokeh.io import output_file
from bokeh.plotting import figure, show

# The figure will be rendered in a static HTML file called output_file_test.html
output_file('output_file_test.html', 
            title='Empty Bokeh Figure')

# Set up a generic figure() object
fig = figure()

# See what it looks like
show(fig)

As you can see, a new browser window opened with a tab called Empty Bokeh Figure and an empty figure. Not shown is the file generated with the name output_file_test.html in your current working directory.

If you were to run the same code snippet with output_notebook() in place of output_file(), assuming you have a Jupyter Notebook fired up and ready to go, you will get the following:

# Bokeh Libraries
from bokeh.io import output_notebook
from bokeh.plotting import figure, show

# The figure will be right in my Jupyter Notebook
output_notebook()

# Set up a generic figure() object
fig = figure()

# See what it looks like
show(fig)

As you can see, the result is the same, just rendered in a different location.

More information about both output_file() and output_notebook() can be found in the Bokeh official docs.

# Import reset_output (only needed once) 
from bokeh.plotting import reset_output

# Use reset_output() between subsequent show() calls, as needed
reset_output()

Before moving on, you may have noticed that the default Bokeh figure comes pre-loaded with a toolbar. This is an important sneak preview into the interactive elements of Bokeh that come right out of the box. You’ll find out more about the toolbar and how to configure it in the Adding Interaction section at the end of this tutorial.

Getting Your Figure Ready for Data

Now that you know how to create and view a generic Bokeh figure either in a browser or Jupyter Notebook, it’s time to learn more about how to configure the figure() object.

The figure() object is not only the foundation of your data visualization but also the object that unlocks all of Bokeh’s available tools for visualizing data. The Bokeh figure is a subclass of the Bokeh Plot object, which provides many of the parameters that make it possible to configure the aesthetic elements of your figure.

To show you just a glimpse into the customization options available, let’s create the ugliest figure ever:

# Bokeh Libraries
from bokeh.io import output_notebook
from bokeh.plotting import figure, show

# The figure will be rendered inline in my Jupyter Notebook
output_notebook()

# Example figure
fig = figure(background_fill_color='gray',
             background_fill_alpha=0.5,
             border_fill_color='blue',
             border_fill_alpha=0.25,
             plot_height=300,
             plot_width=500,
             h_symmetry=True,
             x_axis_label='X Label',
             x_axis_type='datetime',
             x_axis_location='above',
             x_range=('2018-01-01', '2018-06-30'),
             y_axis_label='Y Label',
             y_axis_type='linear',
             y_axis_location='left',
             y_range=(0, 100),
             title='Example Figure',
             title_location='right',
             toolbar_location='below',
             tools='save')

# See what it looks like
show(fig)

Once the figure() object is instantiated, you can still configure it after the fact. Let’s say you want to get rid of the gridlines:

# Remove the gridlines from the figure() object
fig.grid.grid_line_color = None

# See what it looks like 
show(fig)

The gridline properties are accessible via the figure’s grid attribute. In this case, setting grid_line_color to None effectively removes the gridlines altogether. More details about figure attributes can be found below the fold in the Plot class documentation.

There is tons more I could touch on here, but don’t feel like you’re missing out. I’ll make sure to introduce different figure tweaks as the tutorial progresses. Here are some other helpful links on the topic:

• The Bokeh Plot Class is the superclass of the figure() object, from which figures inherit a lot of their attributes.
• The Figure Class documentation is a good place to find more detail about the arguments of the figure() object.

Here are a few specific customization options worth checking out:

• Text Properties covers all the attributes related to changing font styles, sizes, colors, and so forth.
• TickFormatters are built-in objects specifically for formatting your axes using Python-like string formatting syntax.

Sometimes, it isn’t clear how your figure needs to be customized until it actually has some data visualized in it, so next you’ll learn how to make that happen.

Drawing Data With Glyphs

An empty figure isn’t all that exciting, so let’s look at glyphs: the building blocks of Bokeh visualizations. A glyph is a vectorized graphical shape or marker that is used to represent your data, like a circle or square. More examples can be found in the Bokeh gallery. After you create your figure, you are given access to a bevy of configurable glyph methods.

Let’s start with a very basic example, drawing some points on an x-y coordinate grid:

# Bokeh Libraries
from bokeh.io import output_file
from bokeh.plotting import figure, show

# My x-y coordinate data
x = [1, 2, 1]
y = [1, 1, 2]

# Output the visualization directly in the notebook
output_file('first_glyphs.html', title='First Glyphs')

# Create a figure with no toolbar and axis ranges of [0,3]
fig = figure(title='My Coordinates',
             plot_height=300, plot_width=300,
             x_range=(0, 3), y_range=(0, 3),
             toolbar_location=None)

# Draw the coordinates as circles
fig.circle(x=x, y=y,
           color='green', size=10, alpha=0.5)

# Show plot
show(fig)

Once your figure is instantiated, you can see how it can be used to draw the x-y coordinate data using customized circle glyphs.

Here are a few categories of glyphs:

• Marker includes shapes like circles, diamonds, squares, and triangles and is effective for creating visualizations like scatter and bubble charts.

• Line covers things like single, step, and multi-line shapes that can be used to build line charts.

• Bar/Rectangle shapes can be used to create traditional or stacked bar (hbar) and column (vbar) charts as well as waterfall or gantt charts.

Information about the glyphs above, as well as others, can be found in Bokeh’s Reference Guide.

These glyphs can be combined as needed to fit your visualization needs. Let’s say I want to create a visualization that shows how many words I wrote per day to make this tutorial, with an overlaid trend line of the cumulative word count:

import numpy as np

# Bokeh libraries
from bokeh.io import output_notebook
from bokeh.plotting import figure, show

# My word count data
day_num = np.linspace(1, 10, 10)
daily_words = [450, 628, 488, 210, 287, 791, 508, 639, 397, 943]
cumulative_words = np.cumsum(daily_words)

# Output the visualization directly in the notebook
output_notebook()

# Create a figure with a datetime type x-axis
fig = figure(title='My Tutorial Progress',
             plot_height=400, plot_width=700,
             x_axis_label='Day Number', y_axis_label='Words Written',
             x_minor_ticks=2, y_range=(0, 6000),
             toolbar_location=None)

# The daily words will be represented as vertical bars (columns)
fig.vbar(x=day_num, bottom=0, top=daily_words, 
         color='blue', width=0.75, 
         legend='Daily')

# The cumulative sum will be a trend line
fig.line(x=day_num, y=cumulative_words, 
         color='gray', line_width=1,
         legend='Cumulative')

# Put the legend in the upper left corner
fig.legend.location = 'top_left'

# Let's check it out
show(fig)

To combine the columns and lines on the figure, they are simply created using the same figure() object.

Additionally, you can see above how seamlessly a legend can be created by setting the legend property for each glyph. The legend was then moved to the upper left corner of the plot by assigning 'top_left' to fig.legend.location.

You can check out much more info about styling legends. Teaser: they will show up again later in the tutorial when we start digging into interactive elements of the visualization.

A Quick Aside About Data

Anytime you are exploring a new visualization library, it’s a good idea to start with some data in a domain you are familiar with. The beauty of Bokeh is that nearly any idea you have should be possible. It’s just a matter of how you want to leverage the available tools to do so.

The remaining examples will use publicly available data from Kaggle, which has information about the National Basketball Association’s (NBA) 2017-18 season, specifically:

• 2017-18_playerBoxScore.csv: game-by-game snapshots of player statistics
• 2017-18_teamBoxScore.csv: game-by-game snapshots of team statistics
• 2017-18_standings.csv: daily team standings and rankings

This data has nothing to do with what I do for work, but I love basketball and enjoy thinking about ways to visualize the ever-growing amount of data associated with it.

If you don’t have data to play with from school or work, think about something you’re interested in and try to find some data related to that. It will go a long way in making both the learning and the creative process faster and more enjoyable!

To follow along with the examples in the tutorial, you can download the datasets from the links above and read them into a Pandas DataFrame using the following commands:

import pandas as pd

# Read the csv files
player_stats = pd.read_csv('2017-18_playerBoxScore.csv', parse_dates=['gmDate'])
team_stats = pd.read_csv('2017-18_teamBoxScore.csv', parse_dates=['gmDate'])
standings = pd.read_csv('2017-18_standings.csv', parse_dates=['stDate'])

This code snippet reads the data from the three CSV files and automatically interprets the date columns as datetime objects.

It’s now time to get your hands on some real data.

Using the ColumnDataSource Object

The examples above used Python lists and Numpy arrays to represent the data, and Bokeh is well equipped to handle these datatypes. However, when it comes to data in Python, you are most likely going to come across Python dictionaries and Pandas DataFrames, especially if you’re reading in data from a file or external data source.

Bokeh is well equipped to work with these more complex data structures and even has built-in functionality to handle them, namely the ColumnDataSource.

You may be asking yourself, “Why use a ColumnDataSource when Bokeh can interface with other data types directly?”

For one, whether you reference a list, array, dictionary, or DataFrame directly, Bokeh is going to turn it into a ColumnDataSource behind the scenes anyway. More importantly, the ColumnDataSource makes it much easier to implement Bokeh’s interactive affordances.

The ColumnDataSource is foundational in passing the data to the glyphs you are using to visualize. Its primary functionality is to map names to the columns of your data. This makes it easier for you to reference elements of your data when building your visualization. It also makes it easier for Bokeh to do the same when building your visualization.

The ColumnDataSource can interpret three types of data objects:

• Python dict: The keys are names associated with the respective value sequences (lists, arrays, and so forth).

• Pandas DataFrame: The columns of the DataFrame become the reference names for the ColumnDataSource.

• Pandas groupby: The columns of the ColumnDataSource reference the columns as seen by calling groupby.describe().

Let’s start by visualizing the race for first place in the NBA’s Western Conference in 2017-18 between the defending champion Golden State Warriors and the challenger Houston Rockets. The daily win-loss records of these two teams is stored in a DataFrame named west_top_2:

>>> west_top_2 = (standings[(standings['teamAbbr'] == 'HOU') | (standings['teamAbbr'] == 'GS')]
...               .loc[:, ['stDate', 'teamAbbr', 'gameWon']]
...               .sort_values(['teamAbbr','stDate']))
>>> west_top_2.head()
        stDate teamAbbr  gameWon
9   2017-10-17       GS        0
39  2017-10-18       GS        0
69  2017-10-19       GS        0
99  2017-10-20       GS        1
129 2017-10-21       GS        1

From here, you can load this DataFrame into two ColumnDataSource objects and visualize the race:

# Bokeh libraries
from bokeh.plotting import figure, show
from bokeh.io import output_file
from bokeh.models import ColumnDataSource

# Output to file
output_file('west-top-2-standings-race.html', 
            title='Western Conference Top 2 Teams Wins Race')

# Isolate the data for the Rockets and Warriors
rockets_data = west_top_2[west_top_2['teamAbbr'] == 'HOU']
warriors_data = west_top_2[west_top_2['teamAbbr'] == 'GS']

# Create a ColumnDataSource object for each team
rockets_cds = ColumnDataSource(rockets_data)
warriors_cds = ColumnDataSource(warriors_data)

# Create and configure the figure
fig = figure(x_axis_type='datetime',
             plot_height=300, plot_width=600,
             title='Western Conference Top 2 Teams Wins Race, 2017-18',
             x_axis_label='Date', y_axis_label='Wins',
             toolbar_location=None)

# Render the race as step lines
fig.step('stDate', 'gameWon', 
         color='#CE1141', legend='Rockets', 
         source=rockets_cds)
fig.step('stDate', 'gameWon', 
         color='#006BB6', legend='Warriors', 
         source=warriors_cds)

# Move the legend to the upper left corner
fig.legend.location = 'top_left'

# Show the plot
show(fig)

Notice how the respective ColumnDataSource objects are referenced when creating the two lines. You simply pass the original column names as input parameters and specify which ColumnDataSource to use via the source property.

The visualization shows the tight race throughout the season, with the Warriors building a pretty big cushion around the middle of the season. However, a bit of a late-season slide allowed the Rockets to catch up and ultimately surpass the defending champs to finish the season as the Western Conference number-one seed.

ColumnDataSource objects can do more than just serve as an easy way to reference DataFrame columns. The ColumnDataSource object has three built-in filters that can be used to create views on your data using a CDSView object:

• GroupFilter selects rows from a ColumnDataSource based on a categorical reference value
• IndexFilter filters the ColumnDataSource via a list of integer indices
• BooleanFilter allows you to use a list of boolean values, with True rows being selected

In the previous example, two ColumnDataSource objects were created, one each from a subset of the west_top_2 DataFrame. The next example will recreate the same output from one ColumnDataSource based on all of west_top_2 using a GroupFilter that creates a view on the data:

# Bokeh libraries
from bokeh.plotting import figure, show
from bokeh.io import output_file
from bokeh.models import ColumnDataSource, CDSView, GroupFilter

# Output to file
output_file('west-top-2-standings-race.html', 
            title='Western Conference Top 2 Teams Wins Race')

# Create a ColumnDataSource
west_cds = ColumnDataSource(west_top_2)

# Create views for each team
rockets_view = CDSView(source=west_cds,
                       filters=[GroupFilter(column_name='teamAbbr', group='HOU')])
warriors_view = CDSView(source=west_cds,
                        filters=[GroupFilter(column_name='teamAbbr', group='GS')])

# Create and configure the figure
west_fig = figure(x_axis_type='datetime',
                  plot_height=300, plot_width=600,
                  title='Western Conference Top 2 Teams Wins Race, 2017-18',
                  x_axis_label='Date', y_axis_label='Wins',
                  toolbar_location=None)

# Render the race as step lines
west_fig.step('stDate', 'gameWon',
              source=west_cds, view=rockets_view,
              color='#CE1141', legend='Rockets')
west_fig.step('stDate', 'gameWon',
              source=west_cds, view=warriors_view,
              color='#006BB6', legend='Warriors')

# Move the legend to the upper left corner
west_fig.legend.location = 'top_left'

# Show the plot
show(west_fig)

Notice how the GroupFilter is passed to CDSView in a list. This allows you to combine multiple filters together to isolate the data you need from the ColumnDataSource as needed.

For information about integrating data sources, check out the Bokeh user guide’s post on the ColumnDataSource and other source objects available.

The Western Conference ended up being an exciting race, but say you want to see if the Eastern Conference was just as tight. Not only that, but you’d like to view them in a single visualization. This is a perfect segue to the next topic: layouts.

Organizing Multiple Visualizations With Layouts

The Eastern Conference standings came down to two rivals in the Atlantic Division: the Boston Celtics and the Toronto Raptors. Before replicating the steps used to create west_top_2, let’s try to put the ColumnDataSource to the test one more time using what you learned above.

In this example, you’ll see how to feed an entire DataFrame into a ColumnDataSource and create views to isolate the relevant data:

# Bokeh libraries
from bokeh.plotting import figure, show
from bokeh.io import output_file
from bokeh.models import ColumnDataSource, CDSView, GroupFilter

# Output to file
output_file('east-top-2-standings-race.html', 
            title='Eastern Conference Top 2 Teams Wins Race')

# Create a ColumnDataSource
standings_cds = ColumnDataSource(standings)

# Create views for each team
celtics_view = CDSView(source=standings_cds,
                      filters=[GroupFilter(column_name='teamAbbr', 
                                           group='BOS')])
raptors_view = CDSView(source=standings_cds,
                      filters=[GroupFilter(column_name='teamAbbr', 
                                           group='TOR')])

# Create and configure the figure
east_fig = figure(x_axis_type='datetime',
           plot_height=300, plot_width=600,
           title='Eastern Conference Top 2 Teams Wins Race, 2017-18',
           x_axis_label='Date', y_axis_label='Wins',
           toolbar_location=None)

# Render the race as step lines
east_fig.step('stDate', 'gameWon', 
              color='#007A33', legend='Celtics',
              source=standings_cds, view=celtics_view)
east_fig.step('stDate', 'gameWon', 
              color='#CE1141', legend='Raptors',
              source=standings_cds, view=raptors_view)

# Move the legend to the upper left corner
east_fig.legend.location = 'top_left'

# Show the plot
show(east_fig)

The ColumnDataSource was able to isolate the relevant data within a 5,040-by-39 DataFrame without breaking a sweat, saving a few lines of Pandas code in the process.

Looking at the visualization, you can see that the Eastern Conference race was no slouch. After the Celtics roared out of the gate, the Raptors clawed all the way back to overtake their division rival and finish the regular season with five more wins.

With our two visualizations ready, it’s time to put them together.

Similar to the functionality of Matplotlib’s subplot, Bokeh offers the column, row, and gridplot functions in its bokeh.layouts module. These functions can more generally be classified as layouts.

The usage is very straightforward. If you want to put two visualizations in a vertical configuration, you can do so with the following:

# Bokeh library
from bokeh.plotting import figure, show
from bokeh.io import output_file
from bokeh.layouts import column

# Output to file
output_file('east-west-top-2-standings-race.html', 
            title='Conference Top 2 Teams Wins Race')

# Plot the two visualizations in a vertical configuration
show(column(west_fig, east_fig))

I’ll save you the two lines of code, but rest assured that swapping column for row in the snippet above will similarly configure the two plots in a horizontal configuration.

WARNING:bokeh.core.validation.check:W-1004 (BOTH_CHILD_AND_ROOT): Models should not be a document root...

# Bokeh libraries
from bokeh.plotting import figure, show
from bokeh.models import ColumnDataSource, CDSView, GroupFilter

# Create a ColumnDataSource
standings_cds = ColumnDataSource(standings)

# Create the views for each team
celtics_view = CDSView(source=standings_cds,
                      filters=[GroupFilter(column_name='teamAbbr', 
                                           group='BOS')])

raptors_view = CDSView(source=standings_cds,
                      filters=[GroupFilter(column_name='teamAbbr', 
                                           group='TOR')])

rockets_view = CDSView(source=standings_cds,
                      filters=[GroupFilter(column_name='teamAbbr', 
                                           group='HOU')])
warriors_view = CDSView(source=standings_cds,
                      filters=[GroupFilter(column_name='teamAbbr', 
                                           group='GS')])

# Create and configure the figure
east_fig = figure(x_axis_type='datetime',
                  plot_height=300,
                  x_axis_label='Date',
                  y_axis_label='Wins',
                  toolbar_location=None)

west_fig = figure(x_axis_type='datetime',
                  plot_height=300,
                  x_axis_label='Date',
                  y_axis_label='Wins',
                  toolbar_location=None)

# Configure the figures for each conference
east_fig.step('stDate', 'gameWon', 
              color='#007A33', legend='Celtics',
              source=standings_cds, view=celtics_view)
east_fig.step('stDate', 'gameWon', 
              color='#CE1141', legend='Raptors',
              source=standings_cds, view=raptors_view)

west_fig.step('stDate', 'gameWon', color='#CE1141', legend='Rockets',
              source=standings_cds, view=rockets_view)
west_fig.step('stDate', 'gameWon', color='#006BB6', legend='Warriors',
              source=standings_cds, view=warriors_view)

# Move the legend to the upper left corner
east_fig.legend.location = 'top_left'
west_fig.legend.location = 'top_left'

# Layout code snippet goes here!

Instead of using column or row, you may want to use a gridplot instead.

One key difference of gridplot is that it will automatically consolidate the toolbar across all of its children figures. The two visualizations above do not have a toolbar, but if they did, then each figure would have its own when using column or row. With that, it also has its own toolbar_location property, seen below set to 'right'.

Syntactically, you’ll also notice below that gridplot differs in that, instead of being passed a tuple as input, it requires a list of lists, where each sub-list represents a row in the grid:

# Bokeh libraries
from bokeh.io import output_file
from bokeh.layouts import gridplot

# Output to file
output_file('east-west-top-2-gridplot.html', 
            title='Conference Top 2 Teams Wins Race')

# Reduce the width of both figures
east_fig.plot_width = west_fig.plot_width = 300

# Edit the titles
east_fig.title.text = 'Eastern Conference'
west_fig.title.text = 'Western Conference'

# Configure the gridplot
east_west_gridplot = gridplot([[west_fig, east_fig]], 
                              toolbar_location='right')

# Plot the two visualizations in a horizontal configuration
show(east_west_gridplot)

Lastly, gridplot allows the passing of None values, which are interpreted as blank subplots. Therefore, if you wanted to leave a placeholder for two additional plots, then you could do something like this:

# Bokeh libraries
from bokeh.io import output_file
from bokeh.layouts import gridplot

# Output to file
output_file('east-west-top-2-gridplot.html', 
            title='Conference Top 2 Teams Wins Race')

# Reduce the width of both figures
east_fig.plot_width = west_fig.plot_width = 300

# Edit the titles
east_fig.title.text = 'Eastern Conference'
west_fig.title.text = 'Western Conference'

# Plot the two visualizations with placeholders
east_west_gridplot = gridplot([[west_fig, None], [None, east_fig]], 
                              toolbar_location='right')

# Plot the two visualizations in a horizontal configuration
show(east_west_gridplot)

If you’d rather toggle between both visualizations at their full size without having to squash them down to fit next to or on top of each other, a good option is a tabbed layout.

A tabbed layout consists of two Bokeh widget functions: Tab() and Panel() from the bokeh.models.widgets sub-module. Like using gridplot(), making a tabbed layout is pretty straightforward:

# Bokeh Library
from bokeh.io import output_file
from bokeh.models.widgets import Tabs, Panel

# Output to file
output_file('east-west-top-2-tabbed_layout.html', 
            title='Conference Top 2 Teams Wins Race')

# Increase the plot widths
east_fig.plot_width = west_fig.plot_width = 800

# Create two panels, one for each conference
east_panel = Panel(child=east_fig, title='Eastern Conference')
west_panel = Panel(child=west_fig, title='Western Conference')

# Assign the panels to Tabs
tabs = Tabs(tabs=[west_panel, east_panel])

# Show the tabbed layout
show(tabs)

The first step is to create a Panel() for each tab. That may sound a little confusing, but think of the Tabs() function as the mechanism that organizes the individual tabs created with Panel().

Each Panel() takes as input a child, which can either be a single figure() or a layout. (Remember that a layout is a general name for a column, row, or gridplot.) Once your panels are assembled, they can be passed as input to Tabs() in a list.

Now that you understand how to access, draw, and organize your data, it’s time to move on to the real magic of Bokeh: interaction! As always, check out Bokeh’s User Guide for more information on layouts.

Adding Interaction

The feature that sets Bokeh apart is its ability to easily implement interactivity in your visualization. Bokeh even goes as far as describing itself as an interactive visualization library:

Bokeh is an interactive visualization library that targets modern web browsers for presentation. (Source)

In this section, we’ll touch on five ways that you can add interactivity:

• Configuring the toolbar
• Selecting data points
• Adding hover actions
• Linking axes and selections
• Highlighting data using the legend

Implementing these interactive elements open up possibilities for exploring your data that static visualizations just can’t do by themselves.

Configuring the Toolbar

As you saw all the way back in Generating Your First Figure, the default Bokeh figure() comes with a toolbar right out of the box. The default toolbar comes with the following tools (from left to right):

• Pan
• Box Zoom
• Wheel Zoom
• Save
• Reset
• A link to Bokeh’s user guide for Configuring Plot Tools
• A link to the Bokeh homepage

The toolbar can be removed by passing toolbar_location=None when instantiating a figure() object, or relocated by passing any of 'above', 'below', 'left', or 'right'.

Additionally, the toolbar can be configured to include any combination of tools you desire. Bokeh offers 18 specific tools across five categories:

• Pan/Drag: box_select, box_zoom, lasso_select, pan, xpan, ypan, resize_select
• Click/Tap: poly_select, tap
• Scroll/Pinch: wheel_zoom, xwheel_zoom, ywheel_zoom
• Actions: undo, redo, reset, save
• Inspectors: crosshair, hover

To geek out on tools , make sure to visit Specifying Tools. Otherwise, they’ll be illustrated in covering the various interactions covered herein.

Selecting Data Points

Implementing selection behavior is as easy as adding a few specific keywords when declaring your glyphs.

The next example will create a scatter plot that relates a player’s total number of three-point shot attempts to the percentage made (for players with at least 100 three-point shot attempts).

The data can be aggregated from the player_stats DataFrame:

# Find players who took at least 1 three-point shot during the season
three_takers = player_stats[player_stats['play3PA'] > 0]

# Clean up the player names, placing them in a single column
three_takers['name'] = [f'{p["playFNm"]} {p["playLNm"]}' 
                        for _, p in three_takers.iterrows()]

# Aggregate the total three-point attempts and makes for each player
three_takers = (three_takers.groupby('name')
                            .sum()
                            .loc[:,['play3PA', 'play3PM']]
                            .sort_values('play3PA', ascending=False))

# Filter out anyone who didn't take at least 100 three-point shots
three_takers = three_takers[three_takers['play3PA'] >= 100].reset_index()

# Add a column with a calculated three-point percentage (made/attempted)
three_takers['pct3PM'] = three_takers['play3PM'] / three_takers['play3PA']

Here’s a sample of the resulting DataFrame:

>>> three_takers.sample(5)
                   name  play3PA  play3PM    pct3PM
229        Corey Brewer      110       31  0.281818
78           Marc Gasol      320      109  0.340625
126      Raymond Felton      230       81  0.352174
127  Kristaps Porziņģis      229       90  0.393013
66      Josh Richardson      336      127  0.377976

Let’s say you want to select a groups of players in the distribution, and in doing so mute the color of the glyphs representing the non-selected players:

# Bokeh Libraries
from bokeh.plotting import figure, show
from bokeh.io import output_file
from bokeh.models import ColumnDataSource, NumeralTickFormatter

# Output to file
output_file('three-point-att-vs-pct.html',
            title='Three-Point Attempts vs. Percentage')

# Store the data in a ColumnDataSource
three_takers_cds = ColumnDataSource(three_takers)

# Specify the selection tools to be made available
select_tools = ['box_select', 'lasso_select', 'poly_select', 'tap', 'reset']

# Create the figure
fig = figure(plot_height=400,
             plot_width=600,
             x_axis_label='Three-Point Shots Attempted',
             y_axis_label='Percentage Made',
             title='3PT Shots Attempted vs. Percentage Made (min. 100 3PA), 2017-18',
             toolbar_location='below',
             tools=select_tools)

# Format the y-axis tick labels as percentages
fig.yaxis[0].formatter = NumeralTickFormatter(format='00.0%')

# Add square representing each player
fig.square(x='play3PA',
           y='pct3PM',
           source=three_takers_cds,
           color='royalblue',
           selection_color='deepskyblue',
           nonselection_color='lightgray',
           nonselection_alpha=0.3)

# Visualize
show(fig)

First, specify the selection tools you want to make available. In the example above, 'box_select', 'lasso_select', 'poly_select', and 'tap' (plus a reset button) were specified in a list called select_tools. When the figure is instantiated, the toolbar is positioned 'below' the plot, and the list is passed to tools to make the tools selected above available.

Each player is initially represented by a royal blue square glyph, but the following configurations are set for when a player or group of players is selected:

• Turn the selected player(s) to deepskyblue
• Change all non-selected players’ glyphs to a lightgray color with 0.3 opacity

That’s it! With just a few quick additions, the visualization now looks like this:

For even more information about what you can do upon selection, check out Selected and Unselected Glyphs.

Adding Hover Actions

So the ability to select specific player data points that seem of interest in my scatter plot is implemented, but what if you want to quickly see what individual players a glyph represents? One option is to use Bokeh’s HoverTool() to show a tooltip when the cursor crosses paths with a glyph. All you need to do is append the following to the code snippet above:

# Bokeh Library
from bokeh.models import HoverTool

# Format the tooltip
tooltips = [
            ('Player','@name'),
            ('Three-Pointers Made', '@play3PM'),
            ('Three-Pointers Attempted', '@play3PA'),
            ('Three-Point Percentage','@pct3PM{00.0%}'),
           ]

# Add the HoverTool to the figure
fig.add_tools(HoverTool(tooltips=tooltips))

# Visualize
show(fig)

The HoverTool() is slightly different than the selection tools you saw above in that it has properties, specifically tooltips.

First, you can configure a formatted tooltip by creating a list of tuples containing a description and reference to the ColumnDataSource. This list was passed as input to the HoverTool() and then simply added to the figure using add_tools(). Here’s what happened:

Notice the addition of the Hover button to the toolbar, which can be toggled on and off.

If you want to even further emphasize the players on hover, Bokeh makes that possible with hover inspections. Here is a slightly modified version of the code snippet that added the tooltip:

# Format the tooltip
tooltips = [
            ('Player','@name'),
            ('Three-Pointers Made', '@play3PM'),
            ('Three-Pointers Attempted', '@play3PA'),
            ('Three-Point Percentage','@pct3PM{00.0%}'),
           ]

# Configure a renderer to be used upon hover
hover_glyph = fig.circle(x='play3PA', y='pct3PM', source=three_takers_cds,
                         size=15, alpha=0,
                         hover_fill_color='black', hover_alpha=0.5)

# Add the HoverTool to the figure
fig.add_tools(HoverTool(tooltips=tooltips, renderers=[hover_glyph]))

# Visualize
show(fig)

This is done by creating a completely new glyph, in this case circles instead of squares, and assigning it to hover_glyph. Note that the initial opacity is set to zero so that it is invisible until the cursor is touching it. The properties that appear upon hover are captured by setting hover_alpha to 0.5 along with the hover_fill_color.

Now you will see a small black circle appear over the original square when hovering over the various markers:

To further explore the capabilities of the HoverTool(), see the HoverTool and Hover Inspections guides.

Linking Axes and Selections

Linking is the process of syncing elements of different visualizations within a layout. For instance, maybe you want to link the axes of multiple plots to ensure that if you zoom in on one it is reflected on another. Let’s see how it is done.

For this example, the visualization will be able to pan to different segments of a team’s schedule and examine various game stats. Each stat will be represented by its own plot in a two-by-two gridplot() .

The data can be collected from the team_stats DataFrame, selecting the Philadelphia 76ers as the team of interest:

# Isolate relevant data
phi_gm_stats = (team_stats[(team_stats['teamAbbr'] == 'PHI') & 
                           (team_stats['seasTyp'] == 'Regular')]
                .loc[:, ['gmDate', 
                         'teamPTS', 
                         'teamTRB', 
                         'teamAST', 
                         'teamTO', 
                         'opptPTS',]]
                .sort_values('gmDate'))

# Add game number
phi_gm_stats['game_num'] = range(1, len(phi_gm_stats)+1)

# Derive a win_loss column
win_loss = []
for _, row in phi_gm_stats.iterrows():

    # If the 76ers score more points, it's a win
    if row['teamPTS'] > row['opptPTS']:
        win_loss.append('W')
    else:
        win_loss.append('L')

# Add the win_loss data to the DataFrame
phi_gm_stats['winLoss'] = win_loss

Here are the results of the 76ers’ first 5 games:

>>> phi_gm_stats.head()
        gmDate  teamPTS  teamTRB  teamAST  teamTO  opptPTS  game_num winLoss
10  2017-10-18      115       48       25      17      120         1       L
39  2017-10-20       92       47       20      17      102         2       L
52  2017-10-21       94       41       18      20      128         3       L
80  2017-10-23       97       49       25      21       86         4       W
113 2017-10-25      104       43       29      16      105         5       L

Start by importing the necessary Bokeh libraries, specifying the output parameters, and reading the data into a ColumnDataSource:

# Bokeh Libraries
from bokeh.plotting import figure, show
from bokeh.io import output_file
from bokeh.models import ColumnDataSource, CategoricalColorMapper, Div
from bokeh.layouts import gridplot, column

# Output to file
output_file('phi-gm-linked-stats.html',
                title='76ers Game Log')

# Store the data in a ColumnDataSource
gm_stats_cds = ColumnDataSource(phi_gm_stats)

Each game is represented by a column, and will be colored green if the result was a win and red for a loss. To accomplish this, Bokeh’s CategoricalColorMapper can be used to map the data values to specified colors:

# Create a CategoricalColorMapper that assigns a color to wins and losses
win_loss_mapper = CategoricalColorMapper(factors = ['W', 'L'], 
                                         palette=['green', 'red'])

For this use case, a list specifying the categorical data values to be mapped is passed to factors and a list with the intended colors to palette. For more on the CategoricalColorMapper, see the Colors section of Handling Categorical Data on Bokeh’s User Guide.

There are four stats to visualize in the two-by-two gridplot: points, assists, rebounds, and turnovers. In creating the four figures and configuring their respective charts, there is a lot of redundancy in the properties. So to streamline the code a for loop can be used:

# Create a dict with the stat name and its corresponding column in the data
stat_names = {'Points': 'teamPTS',
              'Assists': 'teamAST',
              'Rebounds': 'teamTRB',
              'Turnovers': 'teamTO',}

# The figure for each stat will be held in this dict
stat_figs = {}

# For each stat in the dict
for stat_label, stat_col in stat_names.items():

    # Create a figure
    fig = figure(y_axis_label=stat_label, 
                 plot_height=200, plot_width=400,
                 x_range=(1, 10), tools=['xpan', 'reset', 'save'])

    # Configure vbar
    fig.vbar(x='game_num', top=stat_col, source=gm_stats_cds, width=0.9, 
             color=dict(field='winLoss', transform=win_loss_mapper))

    # Add the figure to stat_figs dict
    stat_figs[stat_label] = fig

As you can see, the only parameters that needed to be adjusted were the y-axis-label of the figure and the data that will dictate top in the vbar. These values were easily stored in a dict that was iterated through to create the figures for each stat.

You can also see the implementation of the CategoricalColorMapper in the configuration of the vbar glyph. The color property is passed a dict with the field in the ColumnDataSource to be mapped and the name of the CategoricalColorMapper created above.

The initial view will only show the first 10 games of the 76ers’ season, so there needs to be a way to pan horizontally to navigate through the rest of the games in the season. Thus configuring the toolbar to have an xpan tool allows panning throughout the plot without having to worry about accidentally skewing the view along the vertical axis.

Now that the figures are created, gridplot can be setup by referencing the figures from the dict created above:

# Create layout
grid = gridplot([[stat_figs['Points'], stat_figs['Assists']], 
                [stat_figs['Rebounds'], stat_figs['Turnovers']]])

Linking the axes of the four plots is as simple as setting the x_range of each figure equal to one another:

# Link together the x-axes
stat_figs['Points'].x_range = \
    stat_figs['Assists'].x_range = \
    stat_figs['Rebounds'].x_range = \
    stat_figs['Turnovers'].x_range

To add a title bar to the visualization, you could have tried to do this on the points figure, but it would have been limited to the space of that figure. Therefore, a nice trick is to use Bokeh’s ability to interpret HTML to insert a Div element that contains the title information. Once that is created, simply combine that with the gridplot() in a column layout:

# Add a title for the entire visualization using Div
html = """<h3>Philadelphia 76ers Game Log</h3>
<b><i>2017-18 Regular Season</i>
<br>
</b><i>Wins in green, losses in red</i>
"""
sup_title = Div(text=html)

# Visualize
show(column(sup_title, grid))

Putting all the pieces together results in the following:

Similarly you can easily implement linked selections, where a selection on one plot will be reflected on others.

To see how this works, the next visualization will contain two scatter plots: one that shows the 76ers’ two-point versus three-point field goal percentage and the other showing the 76ers’ team points versus opponent points on a game-by-game basis.

The goal is to be able to select data points on the left-side scatter plot and quickly be able to recognize if the corresponding datapoint on the right scatter plot is a win or loss.

The DataFrame for this visualization is very similar to that from the first example:

# Isolate relevant data
phi_gm_stats_2 = (team_stats[(team_stats['teamAbbr'] == 'PHI') & 
                             (team_stats['seasTyp'] == 'Regular')]
                  .loc[:, ['gmDate', 
                           'team2P%', 
                           'team3P%', 
                           'teamPTS', 
                           'opptPTS']]
                  .sort_values('gmDate'))

# Add game number
phi_gm_stats_2['game_num'] = range(1, len(phi_gm_stats_2) + 1)

# Derive a win_loss column
win_loss = []
for _, row in phi_gm_stats_2.iterrows():

    # If the 76ers score more points, it's a win
    if row['teamPTS'] > row['opptPTS']:
        win_loss.append('W')
    else:
        win_loss.append('L')

# Add the win_loss data to the DataFrame
phi_gm_stats_2['winLoss'] = win_loss

Here’s what the data looks like:

>>> phi_gm_stats_2.head()
        gmDate  team2P%  team3P%  teamPTS  opptPTS  game_num winLoss
10  2017-10-18   0.4746   0.4286      115      120         1       L
39  2017-10-20   0.4167   0.3125       92      102         2       L
52  2017-10-21   0.4138   0.3333       94      128         3       L
80  2017-10-23   0.5098   0.3750       97       86         4       W
113 2017-10-25   0.5082   0.3333      104      105         5       L

The code to create the visualization is as follows:

# Bokeh Libraries
from bokeh.plotting import figure, show
from bokeh.io import output_file
from bokeh.models import ColumnDataSource, CategoricalColorMapper, NumeralTickFormatter
from bokeh.layouts import gridplot

# Output inline in the notebook
output_file('phi-gm-linked-selections.html',
            title='76ers Percentages vs. Win-Loss')

# Store the data in a ColumnDataSource
gm_stats_cds = ColumnDataSource(phi_gm_stats_2)

# Create a CategoricalColorMapper that assigns specific colors to wins and losses
win_loss_mapper = CategoricalColorMapper(factors = ['W', 'L'], palette=['Green', 'Red'])

# Specify the tools
toolList = ['lasso_select', 'tap', 'reset', 'save']

# Create a figure relating the percentages
pctFig = figure(title='2PT FG % vs 3PT FG %, 2017-18 Regular Season',
                plot_height=400, plot_width=400, tools=toolList,
                x_axis_label='2PT FG%', y_axis_label='3PT FG%')

# Draw with circle markers
pctFig.circle(x='team2P%', y='team3P%', source=gm_stats_cds, 
              size=12, color='black')

# Format the y-axis tick labels as percenages
pctFig.xaxis[0].formatter = NumeralTickFormatter(format='00.0%')
pctFig.yaxis[0].formatter = NumeralTickFormatter(format='00.0%')

# Create a figure relating the totals
totFig = figure(title='Team Points vs Opponent Points, 2017-18 Regular Season',
                plot_height=400, plot_width=400, tools=toolList,
                x_axis_label='Team Points', y_axis_label='Opponent Points')

# Draw with square markers
totFig.square(x='teamPTS', y='opptPTS', source=gm_stats_cds, size=10,
              color=dict(field='winLoss', transform=win_loss_mapper))

# Create layout
grid = gridplot([[pctFig, totFig]])

# Visualize
show(grid)

This is a great illustration of the power in using a ColumnDataSource. As long as the glyph renderers (in this case, the circle glyphs for the percentages, and square glyphs for the wins and losses) share the same ColumnDataSource, then the selections will be linked by default.

Here’s how it looks in action, where you can see selections made on either figure will be reflected on the other:

By selecting a random sample of data points in the upper right quadrant of the left scatter plot, those corresponding to both high two-point and three-point field goal percentage, the data points on the right scatter plot are highlighted.

Similarly, selecting data points on the right scatter plot that correspond to losses tend to be further to the lower left, lower shooting percentages, on the left scatter plot.

All the details on linking plots can be found at Linking Plots in the Bokeh User Guide.

Highlighting Data Using the Legend

That brings us to the final interactivity example in this tutorial: interactive legends.

In the Drawing Data With Glyphs section, you saw how easy it is to implement a legend when creating your plot. With the legend in place, adding interactivity is merely a matter of assigning a click_policy. Using a single line of code, you can quickly add the ability to either hide or mute data using the legend.

In this example, you’ll see two identical scatter plots comparing the game-by-game points and rebounds of LeBron James and Kevin Durant. The only difference will be that one will use a hide as its click_policy, while the other uses mute.

The first step is to configure the output and set up the data, creating a view for each player from the player_stats DataFrame:

# Bokeh Libraries
from bokeh.plotting import figure, show
from bokeh.io import output_file
from bokeh.models import ColumnDataSource, CDSView, GroupFilter
from bokeh.layouts import row

# Output inline in the notebook
output_file('lebron-vs-durant.html',
            title='LeBron James vs. Kevin Durant')

# Store the data in a ColumnDataSource
player_gm_stats = ColumnDataSource(player_stats)

# Create a view for each player
lebron_filters = [GroupFilter(column_name='playFNm', group='LeBron'),
                  GroupFilter(column_name='playLNm', group='James')]
lebron_view = CDSView(source=player_gm_stats,
                      filters=lebron_filters)

durant_filters = [GroupFilter(column_name='playFNm', group='Kevin'),
                  GroupFilter(column_name='playLNm', group='Durant')]
durant_view = CDSView(source=player_gm_stats,
                      filters=durant_filters)

Before creating the figures, the common parameters across the figure, markers, and data can be consolidated into dictionaries and reused. Not only does this save redundancy in the next step, but it provides an easy way to tweak these parameters later if need be:

# Consolidate the common keyword arguments in dicts
common_figure_kwargs = {
    'plot_width': 400,
    'x_axis_label': 'Points',
    'toolbar_location': None,
}
common_circle_kwargs = {
    'x': 'playPTS',
    'y': 'playTRB',
    'source': player_gm_stats,
    'size': 12,
    'alpha': 0.7,
}
common_lebron_kwargs = {
    'view': lebron_view,
    'color': '#002859',
    'legend': 'LeBron James'
}
common_durant_kwargs = {
    'view': durant_view,
    'color': '#FFC324',
    'legend': 'Kevin Durant'
}

Now that the various properties are set, the two scatter plots can be built in a much more concise fashion:

# Create the two figures and draw the data
hide_fig = figure(**common_figure_kwargs,
                  title='Click Legend to HIDE Data', 
                  y_axis_label='Rebounds')
hide_fig.circle(**common_circle_kwargs, **common_lebron_kwargs)
hide_fig.circle(**common_circle_kwargs, **common_durant_kwargs)

mute_fig = figure(**common_figure_kwargs, title='Click Legend to MUTE Data')
mute_fig.circle(**common_circle_kwargs, **common_lebron_kwargs,
                muted_alpha=0.1)
mute_fig.circle(**common_circle_kwargs, **common_durant_kwargs,
                muted_alpha=0.1)

Note that mute_fig has an extra parameter called muted_alpha. This parameter controls the opacity of the markers when mute is used as the click_policy.

Finally, the click_policy for each figure is set, and they are shown in a horizontal configuration:

# Add interactivity to the legend
hide_fig.legend.click_policy = 'hide'
mute_fig.legend.click_policy = 'mute'

# Visualize
show(row(hide_fig, mute_fig))

Once the legend is in place, all you have to do is assign either hide or mute to the figure’s click_policy property. This will automatically turn your basic legend into an interactive legend.

Also note that, specifically for mute, the additional property of muted_alpha was set in the respective circle glyphs for LeBron James and Kevin Durant. This dictates the visual effect driven by the legend interaction.

For more on all things interaction in Bokeh, Adding Interactions in the Bokeh User Guide is a great place to start.

Summary and Next Steps

Congratulations! You’ve made it to the end of this tutorial.

You should now have a great set of tools to start turning your data into beautiful interactive visualizations using Bokeh.

You learned how to:

• Configure your script to render to either a static HTML file or Jupyter Notebook
• Instantiate and customize the figure() object
• Build your visualization using glyphs
• Access and filter your data with the ColumnDataSource
• Organize multiple plots in grid and tabbed layouts
• Add different forms of interaction, including selections, hover actions, linking, and interactive legends

To explore even more of what Bokeh is capable of, the official Bokeh User Guide is an excellent place to dig into some more advanced topics. I’d also recommend checking out Bokeh’s Gallery for tons of examples and inspiration.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

November 19, 2018 02:00 PM UTC

Test and Code

53: Seven Databases in Seven Weeks - Luc Perkins

Luc Perkins joins the show to talk about "Seven Databases in Seven Weeks: A guide to modern databases and the NoSQL movement."

We discuss a bit about each database: Redis, Neo4J, CouchDB, MongoDB, HBase, Postgres, and DynamoDB.

Special Guest: Luc Perkins.

Sponsored By:

• PyCharm Professional: We have a special offer for you: any time before December 1, you can get an Individual PyCharm Professional 4-month subscription for free! If you value your time, you owe it to yourself to try PyCharm.

Support Test and Code

Links:

• Seven Databases in Seven Weeks, Second Edition: A Guide to Modern Databases and the NoSQL Movement
• PostgreSQL
• Redis
• Neo4j Graph Database
• CouchDB
• MongoDB
• HBase
• DynamoDB

November 19, 2018 06:30 AM UTC

gamingdirectional

Create Enemy Missile and Enemy Missile Manager

In this article we will create two new classes, enemy missile class and the enemy missile manager class. The enemy missile manager class will be called during each game loop by the enemy manager class to create new enemy missile as well as to update the position of those missiles and draw them on the game scene. First of all, we will create the enemy missile class which will be used by the enemy...

Source

Related posts:

Pygame tutorial 2 – Moving the object with keyboard Pygame’s Color class demo Create the spaceship in Rock Sweeper Group all the sprites together with Pygame How to move and flip the gaming character with Pygame Playing background music with Pygame Moving the sprite with Vector in Pygame How to detect boundary in Pygame Create player missile manager and player missile class in Pygame Draw Polygon with Pygame

November 19, 2018 06:13 AM UTC

Mike Driscoll

PyDev of the Week: Mike Müller

November 19, 2018 06:05 AM UTC

Podcast.__init__

Entity Extraction, Document Processing, And Knowledge Graphs For Investigative Journalists with Friedrich Lindenberg

Investigative reporters have a challenging task of identifying complex networks of people, places, and events gleaned from a mixed collection of sources. Turning those various documents, electronic records, and research into a searchable and actionable collection of facts is an interesting and difficult technical challenge. Friedrich Lindenberg created the Aleph project to address this issue and in this episode he explains how it works, why he built it, and how it is being used. He also discusses his hopes for the future of the project and other ways that the system could be used.

Summary

Investigative reporters have a challenging task of identifying complex networks of people, places, and events gleaned from a mixed collection of sources. Turning those various documents, electronic records, and research into a searchable and actionable collection of facts is an interesting and difficult technical challenge. Friedrich Lindenberg created the Aleph project to address this issue and in this episode he explains how it works, why he built it, and how it is being used. He also discusses his hopes for the future of the project and other ways that the system could be used.

Preface

• Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
• When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode today to get a $20 credit and launch a new server in under a minute.
• Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
• To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
• Join the community in the new Zulip chat workspace at podcastinit.com/chat
• Registration for PyCon US, the largest annual gathering across the community, is open now. Don’t forget to get your ticket and I’ll see you there!
• Your host as usual is Tobias Macey and today I’m interviewing Friedrich Lindenberg about Aleph, a tool to perform entity extraction across documents and structured data

Interview

• Introductions
• How did you get introduced to Python?
• Can you start by explaining what Aleph is and how the project got started?
• What is investigative journalism?
  • How does Aleph fit into their workflow?
  • What are some other tools that would be used alongside Aleph?
  • What are some ways that Aleph could be useful outside of investigative journalism?
• How is Aleph architected and how has it evolved since you first started working on it?
• What are the major components of Aleph?
  • What are the types of documents and data formats that Aleph supports?
• Can you describe the steps involved in entity extraction?
  • What are the most challenging aspects of identifying and resolving entities in the documents stored in Aleph?
• Can you describe the flow of data through the system from a document being uploaded through to it being displayed as part of a search query?
• What is involved in deploying and managing an installation of Aleph?
• What have been some of the most interesting or unexpected aspects of building Aleph?
• Are there any particularly noteworthy uses of Aleph that you are aware of?
• What are your plans for the future of Aleph?

Keep In Touch

• Website
• @pudo on Twitter
• pudo on GitHub

Picks

• Tobias
  • Mechanical Soup
• Friedrich
  • phonenumbers – because it’s useful
  • pyicu – super nerdy but amazing
  • sqlalchemy – my all-time favorite python package

Links

• Aleph
• Organized Crime and Corruption Reporting Project
• OCR (Optical Character Recognition)
• Jorge Luis Borges
• Buenos Aires
• Investigative Journalism
• Azerbaijan
• Signal
• Open Corporates
• Open Refine
• Money Laundering
• E-Discovery
• CSV
• SQL
• Entity Extraction (Named Entity Recognition)
• Apache Tika
• Polyglot
• SpaCy
  • Podcast.__init__ Episode
• LibreOffice
• Tesseract
• followthemoney
• Elasticsearch
• Knowledge Graph
• Neo4J
• Gephi
• Edward Snowden
• Document Cloud
• Overview Project
• Veracrypt
• Qubes OS
• I2 Analyst Notebook

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

November 19, 2018 12:28 AM UTC

Matt Layman

Building SaaS with Python on Twitch

I started streaming on Twitch. The stream covers how to build a Software as a Service (SaaS) with Python using Django. The stream runs at 9pm Eastern time on Wednesday most weeks. I show developers how to build a site that is more complex than a tutorial. We look at: Designing and creating pages for users How to create automated tests for the code Making background jobs that handle business processes Deploying code and infrastructure development During the stream, we’re working on College Conductor.

November 19, 2018 12:00 AM UTC

November 18, 2018

The Digital Cat

Clean architectures in Python: a step-by-step example

In 2015 I was introduced by my friend Roberto Ciatti to the concept of Clean Architecture, as it is called by Robert Martin. The well-known Uncle Bob talks a lot about this concept at conferences and wrote some very interesting posts about it. What he calls "Clean Architecture" is a way of structuring a software system, a set of consideration (more than strict rules) about the different layers and the role of the actors in it.

As he clearly states in a post aptly titled The Clean Architecture, the idea behind this design is not new, being built on a set of concepts that have been pushed by many software engineers over the last 3 decades. One of the first implementations may be found in the Boundary-Control-Entity model proposed by Ivar Jacobson in his masterpiece "Object-Oriented Software Engineering: A Use Case Driven Approach" published in 1992, but Martin lists other more recent versions of this architecture.

I will not repeat here what he had already explained better than I can do, so I will just point out some resources you may check to start exploring these concepts:

• The Clean Architecture a post by Robert Martin that concisely describes the goals of the architecture. It also lists resources that describe similar architectures.
• The Open Closed Principle a post by Robert Martin not strictly correlated with the Clean Architecture concept but important for the separation concept.
• Hakka Labs: Robert "Uncle Bob" Martin - Architecture: The Lost Years a video of Robert Martin from Hakka Labs.
• DDD & Testing Strategy by Lauri Taimila
• (upcoming) Clean Architecture by Robert Martin, published by Prentice Hall.

The purpose of this post is to show how to build a web service in Python from scratch using a clean architecture. One of the main advantages of this layered design is testability, so I will develop it following a TDD approach. The project was initially developed from scratch in around 3 hours of work. Given the toy nature of the project some choices have been made to simplify the resulting code. Whenever meaningful I will point out those simplifications and discuss them.

If you want to know more about TDD in Python read the posts in this category.

Project overview¶

The goal of the "Rent-o-matic" project (fans of Day of the Tentacle may get the reference) is to create a simple search engine on top of a dataset of objects which are described by some quantities. The search engine shall allow to set some filters to narrow the search.

The objects in the dataset are storage rooms for rent described by the following quantities:

• An unique identifier
• A size in square meters
• A renting price in Euro/day
• Latitude and longitude

As pushed by the clean architecture model, we are interested in separating the different layers of the system. The architecture is described by four layers, which however can be implemented by more than four actual code modules. I will give here a brief description of those layers.

Entities¶

This is the level in which the domain models are described. Since we work in Python, I will put here the class that represent my storage rooms, with the data contained in the database, and whichever data I think is useful to perform the core business processing.

It is very important to understand that the models in this layer are different from the usual models of framework like Django. These models are not connected with a storage system, so they cannot be directly saved or queried using methods of their classes. They may however contain helper methods that implement code related to the business rules.

Use cases¶

This layer contains the use cases implemented by the system. In this simple example there will be only one use case, which is the list of storage rooms according to the given filters. Here you would put for example a use case that shows the detail of a given storage room or every business process you want to implement, such as booking a storage room, filling it with goods, etc.

Interface Adapters¶

This layer corresponds to the boundary between the business logic and external systems and implements the APIs used to exchange data with them. Both the storage system and the user interface are external systems that need to exchange data with the use cases and this layer shall provide an interface for this data flow. In this project the presentation part of this layer is provided by a JSON serializer, on top of which an external web service may be built. The storage adapter shall define here the common API of the storage systems.

External interfaces¶

This part of the architecture is made by external systems that implement the interfaces defined in the previous layer. Here for example you will find a web server that implements (REST) entry points, which access the data provided by use cases through the JSON serializer. You will also find here the storage system implementation, for example a given database such as MongoDB.

API and shades of grey¶

The word API is of uttermost importance in a clean architecture. Every layer may be accessed by an API, that is a fixed collection of entry points (methods or objects). Here "fixed" means "the same among every implementation", obviously an API may change with time. Every presentation tool, for example, will access the same use cases, and the same methods, to obtain a set of domain models, which are the output of that particular use case. It is up to the presentation layer to format data according to the specific presentation media, for example HTML, PDF, images, etc. If you understand plugin-based architectures you already grasped the main concept of a separate, API-driven component (or layer).

The same concept is valid for the storage layer. Every storage implementation shall provide the same methods. When dealing with use cases you shall not be concerned with the actual system that stores data, it may be a MongoDB local installation, a cloud storage system or a trivial in-memory dictionary.

The separation between layers, and the content of each layer, is not always fixed and immutable. A well-designed system shall also cope with practical world issues such as performances, for example, or other specific needs. When designing an architecture it is very important to know "what is where and why", and this is even more important when you "bend" the rules. Many issues do not have a black-or-white answer, and many decisions are "shades of grey", that is it is up to you to justify why you put something in a given place.

Keep in mind however, that you should not break the structure of the clean architecture, in particular you shall be inflexible about the data flow (see the "Crossing boundaries" section in the original post of Robert Martin). If you break the data flow, you are basically invalidating the whole structure. Let me stress it again: never break the data flow. A simple example of breaking the data flow is to let a use case output a Python class instead of a representation of that class such as a JSON string.

Project structure¶

Let us take a look at the final structure of the project

The global structure of the package has been built with Cookiecutter, and I will run quickly through that part. The rentomatic directory contains the following subdirectories: domain, repositories, REST, serializers, use_cases. Those directories reflect the layered structure introduced in the previous section, and the structure of the tests directory mirrors this structure so that tests are easily found.

Source code¶

You can find the source code in this GitHub repository. Feel free to fork it and experiment, change it, and find better solutions to the problem I will discuss in this post. The source code contains tagged commits to allow you to follow the actual development as presented in the post. You can find the current tag in the Git tag: <tag name> label under the section titles. The label is actually a link to the tagged commit on GitHub, if you want to see the code without cloning it.

Project initialization¶

Git tag: step01¶

Update: this Cookiecutter package creates an environment like the one I am creating in this section. I will keep the following explanation so that you can see how to manage requirements and configurations, but for your next project consider using this automated tool.

I usually like maintaining a Python virtual environment inside the project, so I will create a temporary virtualenv to install cookiecutter, create the project, and remove the virtualenv. Cookiecutter is going to ask you some questions about you and the project, to provide an initial file structure. We are going to build our own testing environment, so it is safe to answer no to use_pytest. Since this is a demo project we are not going to need any publishing feature, so you can answer no to use_pypi_deployment_with_travis as well. The project does not have a command line interface, and you can safely create the author file and use any license.

virtualenv venv3 -p python3
source venv3/bin/activate
pip install cookiecutter
cookiecutter https://github.com/audreyr/cookiecutter-pypackage

Now answer the questions, then finish creating the project with the following code

deactivate
rm -fR venv3
cd rentomatic
virtualenv venv3 -p python3
source venv3/bin/activate

Get rid of the requirements_dev.txt file that Cookiecutter created for you. I usually store virtualenv requirements in different hierarchical files to separate production, development and testing environments, so create the requirements directory and the relative files

mkdir requirements
touch requirements/prod.txt
touch requirements/dev.txt
touch requirements/test.txt

The test.txt file will contain specific packages used to test the project. Since to test the project you also need to install the packages for the production environment the file will first include the production one.

-r prod.txt

pytest
tox
coverage
pytest-cov

The dev.txt file will contain packages used during the development process and shall install also test and production package

-r test.txt

pip
wheel
flake8
Sphinx

(taking advantage of the fact that test.txt already includes prod.txt).

Last, the main requirements.txt file of the project will just import requirements/prod.txt

-r prod.txt

Obviously you are free to find the project structure that better suits your need or preferences. This is the structure we are going to use in this project but nothing forces you to follow it in your personal projects.

This separation allows you to install a full-fledged development environment on your machine, while installing only testing tools in a testing environment like the Travis platform and to further reduce the amount of dependencies in the production case.

As you can see, I am not using version tags in the requirements files. This is because this project is not going to be run in a production environment, so we do not need to freeze the environment.

Remember at this point to install the development requirements in your virtualenv

$ pip install -r requirements/dev.txt

Miscellaneous configuration¶

The pytest testing library needs to be configured. This is the pytest.ini file that you can create in the root directory (where the setup.py file is located)

[pytest]
minversion = 2.0
norecursedirs = .git .tox venv* requirements*
python_files = test*.py

To run the tests during the development of the project just execute

$ py.test -sv

If you want to check the coverage, i.e. the amount of code which is run by your tests or "covered", execute

$ py.test --cov-report term-missing --cov=rentomatic

If you want to know more about test coverage check the official documentation of the Coverage.py and the pytest-cov packages.

I strongly suggest the use of the flake8 package to check that your Python code is PEP8 compliant. This is the flake8 configuration that you can put in your setup.cfg file

[flake8]
ignore = D203
exclude = .git, venv*, docs
max-complexity = 10

To check the compliance of your code with the PEP8 standard execute

$ flake8

Flake8 documentation is available here.

Note that every step in this post produces tested code and a of coverage of 100%. One of the benefits of a clean architecture is the separation between layers, and this guarantees a great degree of testability. Note however that in this tutorial, in particular in the REST sections, some tests have been omitted in favour of a simpler description of the architecture.

Domain models¶

Git tag: step02

Let us start with a simple definition of the StorageRoom model. As said before, the clean architecture models are very lightweight, or at least they are lighter than their counterparts in a framework.

Following the TDD methodology the first thing that I write are the tests. Create the tests/domain/test_storageroom.py and put this code inside it

import uuid
from rentomatic.domain.storageroom import StorageRoom


def test_storageroom_model_init():
    code = uuid.uuid4()
    storageroom = StorageRoom(code, size=200, price=10,
                              longitude=-0.09998975,
                              latitude=51.75436293)
    assert storageroom.code == code
    assert storageroom.size == 200
    assert storageroom.price == 10
    assert storageroom.longitude == -0.09998975
    assert storageroom.latitude == 51.75436293


def test_storageroom_model_from_dict():
    code = uuid.uuid4()
    storageroom = StorageRoom.from_dict(
        {
            'code': code,
            'size': 200,
            'price': 10,
            'longitude': -0.09998975,
            'latitude': 51.75436293
        }
    )
    assert storageroom.code == code
    assert storageroom.size == 200
    assert storageroom.price == 10
    assert storageroom.longitude == -0.09998975
    assert storageroom.latitude == 51.75436293

With these two tests we ensure that our model can be initialized with the correct values and that can be created from a dictionary. In this first version all the parameters of the model are required. Later we could want to make some of them optional, and in that case we will have to add the relevant tests.

Now let's write the StorageRoom class in the rentomatic/domain/storageroom.py file. Do not forget to create the __init__.py file in the subdirectories of the project, otherwise Python will not be able to import the modules.

from rentomatic.shared.domain_model import DomainModel


class StorageRoom(object):

    def __init__(self, code, size, price, latitude, longitude):
        self.code = code
        self.size = size
        self.price = price
        self.latitude = latitude
        self.longitude = longitude

    @classmethod
    def from_dict(cls, adict):
        room = StorageRoom(
            code=adict['code'],
            size=adict['size'],
            price=adict['price'],
            latitude=adict['latitude'],
            longitude=adict['longitude'],
        )

        return room


DomainModel.register(StorageRoom)

The model is very simple, and requires no further explanation. One of the benefits of a clean architecture is that each layer contains small pieces of code that, being isolated, shall perform simple tasks. In this case the model provides an initialization API and stores the information inside the class.

The from_dict method comes in handy when we have to create a model from data coming from another layer (such as the database layer or the query string of the REST layer).

One could be tempted to try to simplify the from_dict function, abstracting it and providing it through a Model class. Given that a certain level of abstraction and generalization is possible and desirable, the initialization part of the models shall probably deal with various different cases, and thus is better off being implemented directly in the class.

The DomainModel abstract base class is an easy way to categorize the model for future uses like checking if a class is a model in the system. For more information about this use of Abstract Base Classes in Python see this post.

Since we have a method creates an object form a dictionary it is useful to have a method that returns a dictionary version of the object. This allows us to easily write a comparison operator between objects, that we will use later in some tests.

The new tests in tests/domain/test_storageroom.py are

def test_storageroom_model_to_dict():
    storageroom_dict = {
        'code': uuid.uuid4(),
        'size': 200,
        'price': 10,
        'longitude': -0.09998975,
        'latitude': 51.75436293
    }

    storageroom = StorageRoom.from_dict(storageroom_dict)

    assert storageroom.to_dict() == storageroom_dict


def test_storageroom_model_comparison():
    storageroom_dict = {
        'code': uuid.uuid4(),
        'size': 200,
        'price': 10,
        'longitude': -0.09998975,
        'latitude': 51.75436293
    }
    storageroom1 = StorageRoom.from_dict(storageroom_dict)
    storageroom2 = StorageRoom.from_dict(storageroom_dict)

    assert storageroom1 == storageroom2

and the new methods of the object in rentomatic/domain/storageroom.py are

    def to_dict(self):
        return {
            'code': self.code,
            'size': self.size,
            'price': self.price,
            'latitude': self.latitude,
            'longitude': self.longitude,
        }

    def __eq__(self, other):
        return self.to_dict() == other.to_dict()

Serializers¶

Git tag: step03

Our model needs to be serialized if we want to return it as a result of an API call. The typical serialization format is JSON, as this is a broadly accepted standard for web-based API. The serializer is not part of the model, but is an external specialized class that receives the model instance and produces a representation of its structure and values.

To test the JSON serialization of our StorageRoom class put in the tests/serializers/test_storageroom_serializer.py file the following code

import datetime
import json
import uuid

import pytest

from rentomatic.serializers import storageroom_serializer as srs
from rentomatic.domain.storageroom import StorageRoom


def test_serialize_domain_storageroom():
    code = uuid.uuid4()

    room = StorageRoom(
        code=code,
        size=200,
        price=10,
        longitude=-0.09998975,
        latitude=51.75436293
    )

    expected_json = """
        {{
            "code": "{}",
            "size": 200,
            "price": 10,
            "longitude": -0.09998975,
            "latitude": 51.75436293
        }}
    """.format(code)

    json_storageroom = json.dumps(room, cls=srs.StorageRoomEncoder)

    assert json.loads(json_storageroom) == json.loads(expected_json)


def test_serialize_domain_storageruum_wrong_type():
    with pytest.raises(TypeError):
        json.dumps(datetime.datetime.now(), cls=srs.StorageRoomEncoder)

Put in the rentomatic/serializers/storageroom_serializer.py file the code that makes the test pass

import json


class StorageRoomEncoder(json.JSONEncoder):

    def default(self, o):
        try:
            to_serialize = {
                'code': str(o.code),
                'size': o.size,
                'price': o.price,
                "latitude": o.latitude,
                "longitude": o.longitude,
            }
            return to_serialize
        except AttributeError:
            return super().default(o)

Providing a class that inherits from json.JSONEncoder let us use the json.dumps(room, cls=StorageRoomEncoder) syntax to serialize the model.

There is a certain degree of repetition in the code we wrote, and this is the annoying part of a clean architecture. Since we want to isolate layers as much as possible and create lightweight classes we end up somehow repeating certain types of actions. For example the serialization code that assigns attributes of a StorageRoom to JSON attributes is very similar to that we use to create the object from a dictionary. Not exactly the same, obviously, but the two functions are very close.

Use cases (part 1)¶

Git tag: step04

It's time to implement the actual business logic our application wants to expose to the outside world. Use cases are the place where we implement classes that query the repository, apply business rules, logic, and whatever transformation we need for our data, and return the results.

With those requirements in mind, let us start to build a use case step by step. The simplest use case we can create is one that fetches all the storage rooms from the repository and returns them. Please note that we did not implement any repository layer yet, so our tests will mock it.

This is the skeleton for a basic test of a use case that lists all the storage rooms. Put this code in the tests/use_cases/test_storageroom_list_use_case.py

import uuid

import pytest
from unittest import mock

from rentomatic.domain.storageroom import StorageRoom
from rentomatic.use_cases import storageroom_use_cases as uc


@pytest.fixture
def domain_storagerooms():
    storageroom_1 = StorageRoom(
        code=uuid.uuid4(),
        size=215,
        price=39,
        longitude=-0.09998975,
        latitude=51.75436293,
    )

    storageroom_2 = StorageRoom(
        code=uuid.uuid4(),
        size=405,
        price=66,
        longitude=0.18228006,
        latitude=51.74640997,
    )

    storageroom_3 = StorageRoom(
        code=uuid.uuid4(),
        size=56,
        price=60,
        longitude=0.27891577,
        latitude=51.45994069,
    )

    storageroom_4 = StorageRoom(
        code=uuid.uuid4(),
        size=93,
        price=48,
        longitude=0.33894476,
        latitude=51.39916678,
    )

    return [storageroom_1, storageroom_2, storageroom_3, storageroom_4]


def test_storageroom_list_without_parameters(domain_storagerooms):
    repo = mock.Mock()
    repo.list.return_value = domain_storagerooms

    storageroom_list_use_case = uc.StorageRoomListUseCase(repo)
    result = storageroom_list_use_case.execute()

    repo.list.assert_called_with()
    assert result == domain_storagerooms

The test is straightforward. First we mock the repository so that is provides a list() method that returns the list of models we created above the test. Then we initialize the use case with the repo and execute it, collecting the result. The first thing we check is if the repository method was called without any parameter, and the second is the effective correctness of the result.

This is the implementation of the use case that makes the test pass. Put the code in the rentomatic/use_cases/storageroom_use_case.py

class StorageRoomListUseCase(object):

    def __init__(self, repo):
        self.repo = repo

    def execute(self):
        return self.repo.list()

With such an implementation of the use case, however, we will soon experience issues. For starters, we do not have a standard way to transport the call parameters, which means that we do not have a standard way to check for their correctness either. The second problem is that we miss a standard way to return the call results and consequently we lack a way to communicate if the call was successful of if it failed, and in the latter case what are the reasons of the failure. This applies also to the case of bad parameters discussed in the previous point.

We want thus to introduce some structures to wrap input and outputs of our use cases. Those structures are called request and response objects.

Requests and responses¶

Git tag: step05

Request and response objects are an important part of a clean architecture, as they transport call parameters, inputs and results from outside the application into the use cases layer.

More specifically, requests are objects created from incoming API calls, thus they shall deal with things like incorrect values, missing parameters, wrong formats, etc. Responses, on the other hand, have to contain the actual results of the API calls, but shall also be able to represent error cases and to deliver rich information on what happened.

The actual implementation of request and response objects is completely free, the clean architecture says nothing about them. The decision on how to pack and represent data is up to us.

For the moment we just need a StorageRoomListRequestObject that can be initialized without parameters, so let us create the file tests/use_cases/test_storageroom_list_request_objects.py and put there a test for this object.

from rentomatic.use_cases import request_objects as ro


def test_build_storageroom_list_request_object_without_parameters():
    req = ro.StorageRoomListRequestObject()

    assert bool(req) is True


def test_build_file_list_request_object_from_empty_dict():
    req = ro.StorageRoomListRequestObject.from_dict({})

    assert bool(req) is True

While at the moment this request object is basically empty, it will come in handy as soon as we start having parameters for the list use case. The code of the StorageRoomListRequestObject is the following and goes into the rentomatic/use_cases/request_objects.py file

class StorageRoomListRequestObject(object):
    @classmethod
    def from_dict(cls, adict):
        return StorageRoomListRequestObject()

    def __nonzero__(self):
        return True

The response object is also very simple, since for the moment we just need a successful response. Unlike the request, the response is not linked to any particular use case, so the test file can be named tests/shared/test_response_object.py

from rentomatic.shared import response_object as ro


def test_response_success_is_true():
    assert bool(ro.ResponseSuccess()) is True

and the actual response object is in the file rentomatic/shared/response_object.py

class ResponseSuccess(object):

    def __init__(self, value=None):
        self.value = value

    def __nonzero__(self):
        return True

    __bool__ = __nonzero__

Use cases (part 2)¶

Git tag: step06

Now that we have implemented the request and response object we can change the test code to include those structures. Change the tests/use_cases/test_storageroom_list_use_case.py to contain this code

import uuid

import pytest
from unittest import mock

from rentomatic.domain.storageroom import StorageRoom
from rentomatic.use_cases import request_objects as ro
from rentomatic.use_cases import storageroom_use_cases as uc


@pytest.fixture
def domain_storagerooms():
    storageroom_1 = StorageRoom(
        code=uuid.uuid4(),
        size=215,
        price=39,
        longitude=-0.09998975,
        latitude=51.75436293,
    )

    storageroom_2 = StorageRoom(
        code=uuid.uuid4(),
        size=405,
        price=66,
        longitude=0.18228006,
        latitude=51.74640997,
    )

    storageroom_3 = StorageRoom(
        code=uuid.uuid4(),
        size=56,
        price=60,
        longitude=0.27891577,
        latitude=51.45994069,
    )

    storageroom_4 = StorageRoom(
        code=uuid.uuid4(),
        size=93,
        price=48,
        longitude=0.33894476,
        latitude=51.39916678,
    )

    return [storageroom_1, storageroom_2, storageroom_3, storageroom_4]


def test_storageroom_list_without_parameters(domain_storagerooms):
    repo = mock.Mock()
    repo.list.return_value = domain_storagerooms

    storageroom_list_use_case = uc.StorageRoomListUseCase(repo)
    request_object = ro.StorageRoomListRequestObject.from_dict({})

    response_object = storageroom_list_use_case.execute(request_object)

    assert bool(response_object) is True
    repo.list.assert_called_with()

    assert response_object.value == domain_storagerooms

The new version of the rentomatic/use_case/storageroom_use_cases.py file is the following

from rentomatic.shared import response_object as ro


class StorageRoomListUseCase(object):

    def __init__(self, repo):
        self.repo = repo

    def execute(self, request_object):
        storage_rooms = self.repo.list()
        return ro.ResponseSuccess(storage_rooms)

Let us consider what we have achieved with our clean architecture up to this point. We have a very lightweight model that can be serialized to JSON and which is completely independent from other parts of the system. The code also contains a use case that, given a repository that exposes a given API, extracts all the models and returns them contained in a structured object.

We are missing some objects, however. For example, we have not implemented any unsuccessful response object or validated the incoming request object.

To explore these missing parts of the architecture let us improve the current use case to accept a filters parameter that represents some filters that we want to apply to the extracted list of models. This will generate some possible error conditions for the input, forcing us to introduce some validation for the incoming request object.

Requests and validation¶

Git tag: step07

I want to add a filters parameter to the request. Through that parameter the caller can add different filters by specifying a name and a value for each filter (for instance {'price_lt': 100} to get all results with a price lesser than 100).

The first thing to do is to change the request object, starting from the test. The new version of the tests/use_cases/test_storageroom_list_request_objects.py file is the following

from rentomatic.use_cases import request_objects as ro


def test_build_storageroom_list_request_object_without_parameters():
    req = ro.StorageRoomListRequestObject()

    assert req.filters is None
    assert bool(req) is True


def test_build_file_list_request_object_from_empty_dict():
    req = ro.StorageRoomListRequestObject.from_dict({})

    assert req.filters is None
    assert bool(req) is True


def test_build_storageroom_list_request_object_with_empty_filters():
    req = ro.StorageRoomListRequestObject(filters={})

    assert req.filters == {}
    assert bool(req) is True


def test_build_storageroom_list_request_object_from_dict_with_empty_filters():
    req = ro.StorageRoomListRequestObject.from_dict({'filters': {}})

    assert req.filters == {}
    assert bool(req) is True


def test_build_storageroom_list_request_object_with_filters():
    req = ro.StorageRoomListRequestObject(filters={'a': 1, 'b': 2})

    assert req.filters == {'a': 1, 'b': 2}
    assert bool(req) is True


def test_build_storageroom_list_request_object_from_dict_with_filters():
    req = ro.StorageRoomListRequestObject.from_dict({'filters': {'a': 1, 'b': 2}})

    assert req.filters == {'a': 1, 'b': 2}
    assert bool(req) is True


def test_build_storageroom_list_request_object_from_dict_with_invalid_filters():
    req = ro.StorageRoomListRequestObject.from_dict({'filters': 5})

    assert req.has_errors()
    assert req.errors[0]['parameter'] == 'filters'
    assert bool(req) is False

As you can see I added the assert req.filters is None check to the original two tests, then I added 5 tests to check if filters can be specified and to test the behaviour of the object with an invalid filter parameter.

To make the tests pass we have to change our StorageRoomListRequestObject class. There are obviously multiple possible solutions that you can come up with, and I recommend you to try to find your own. This is the one I usually employ. The file rentomatic/use_cases/request_object.py becomes

import collections


class InvalidRequestObject(object):

    def __init__(self):
        self.errors = []

    def add_error(self, parameter, message):
        self.errors.append({'parameter': parameter, 'message': message})

    def has_errors(self):
        return len(self.errors) > 0

    def __nonzero__(self):
        return False

    __bool__ = __nonzero__


class ValidRequestObject(object):

    @classmethod
    def from_dict(cls, adict):
        raise NotImplementedError

    def __nonzero__(self):
        return True

    __bool__ = __nonzero__


class StorageRoomListRequestObject(ValidRequestObject):

    def __init__(self, filters=None):
        self.filters = filters

    @classmethod
    def from_dict(cls, adict):
        invalid_req = InvalidRequestObject()

        if 'filters' in adict and not isinstance(adict['filters'], collections.Mapping):
            invalid_req.add_error('filters', 'Is not iterable')

        if invalid_req.has_errors():
            return invalid_req

        return StorageRoomListRequestObject(filters=adict.get('filters', None))

Let me review this new code bit by bit.

First of all, two helper objects have been introduced, ValidRequestObject and InvalidRequestObject. They are different because an invalid request shall contain the validation errors, but both can be converted to booleans.

Second, the StorageRoomListRequestObject accepts an optional filters parameter when instantiated. There are no validation checks in the __init__() method because this is considered to be an internal method that gets called when the parameters have already been validated.

Last, the from_dict() method performs the validation of the filters parameter, if it is present. I leverage the collections.Mapping abstract base class to check if the incoming parameter is a dictionary-like object and return either an InvalidRequestObject or a ValidRequestObject instance.

Since we can now tell bad requests from good ones we need to introduce a new type of response as well, to manage bad requests or other errors in the use case.

Responses and failures¶

Git tag: step08

What happens if the use case encounter an error? Use cases can encounter a wide set of errors: validation errors, as we just discussed in the previous section, but also business logic errors or errors that come from the repository layer. Whatever the error, the use case shall always return an object with a known structure (the response), so we need a new object that provides a good support for different types of failures.

As happened for the requests there is no unique way to provide such an object, and the following code is just one of the possible solutions.

The first thing to do is to expand the tests/shared/test_response_object.py file, adding tests for failures.

import pytest

from rentomatic.shared import response_object as res
from rentomatic.use_cases import request_objects as req


@pytest.fixture
def response_value():
    return {'key': ['value1', 'value2']}


@pytest.fixture
def response_type():
    return 'ResponseError'


@pytest.fixture
def response_message():
    return 'This is a response error'

This is some boilerplate code, basically pytest fixtures that we will use in the following tests.

def test_response_success_is_true(response_value):
    assert bool(res.ResponseSuccess(response_value)) is True


def test_response_failure_is_false(response_type, response_message):
    assert bool(res.ResponseFailure(response_type, response_message)) is False

Two basic tests to check that both the old ResponseSuccess and the new ResponseFailure objects behave consistently when converted to boolean.

def test_response_success_contains_value(response_value):
    response = res.ResponseSuccess(response_value)

    assert response.value == response_value

The ResponseSuccess object contains the call result in the value attribute.

def test_response_failure_has_type_and_message(response_type, response_message):
    response = res.ResponseFailure(response_type, response_message)

    assert response.type == response_type
    assert response.message == response_message


def test_response_failure_contains_value(response_type, response_message):
    response = res.ResponseFailure(response_type, response_message)

    assert response.value == {'type': response_type, 'message': response_message}

These two tests ensure that the ResponseFailure object provides the same interface provided by the successful one and that the type and message parameter are accessible.

def test_response_failure_initialization_with_exception():
    response = res.ResponseFailure(response_type, Exception('Just an error message'))

    assert bool(response) is False
    assert response.type == response_type
    assert response.message == "Exception: Just an error message"


def test_response_failure_from_invalid_request_object():
    response = res.ResponseFailure.build_from_invalid_request_object(req.InvalidRequestObject())

    assert bool(response) is False


def test_response_failure_from_invalid_request_object_with_errors():
    request_object = req.InvalidRequestObject()
    request_object.add_error('path', 'Is mandatory')
    request_object.add_error('path', "can't be blank")

    response = res.ResponseFailure.build_from_invalid_request_object(request_object)

    assert bool(response) is False
    assert response.type == res.ResponseFailure.PARAMETERS_ERROR
    assert response.message == "path: Is mandatory\npath: can't be blank"

We sometimes want to create responses from Python exceptions that can happen in the use case, so we test that ResponseFailure objects can be initialized with a generic exception.

And last we have the tests for the build_from_invalid_request_object() method that automate the initialization of the response from an invalid request. If the request contains errors (remember that the request validates itself), we need to put them into the response message.

The last test uses a class attribute to classify the error. The ResponseFailure class will contain three predefined errors that can happen when running the use case, namely RESOURCE_ERROR, PARAMETERS_ERROR, and SYSTEM_ERROR. This categorization is an attempt to capture the different types of issues that can happen when dealing with an external system through an API. RESOURCE_ERROR contains all those errors that are related to the resources contained in the repository, for instance when you cannot find an entry given its unique id. PARAMETERS_ERROR describes all those errors that occur when the request parameters are wrong or missing. SYSTEM_ERROR encompass the errors that happen in the underlying system at operating system level, such as a failure in a filesystem operation, or a network connection error while fetching data from the database.

The use case has the responsibility to manage the different error conditions arising from the Python code and to convert them into an error description made of one of the three types I just described and a message.

Let's write the ResponseFailure class that makes the tests pass. This can be the initial definition of the class. Put it in rentomatic/shared/response_object.py

class ResponseFailure(object):
    RESOURCE_ERROR = 'ResourceError'
    PARAMETERS_ERROR = 'ParametersError'
    SYSTEM_ERROR = 'SystemError'

    def __init__(self, type_, message):
        self.type = type_
        self.message = self._format_message(message)

    def _format_message(self, msg):
        if isinstance(msg, Exception):
            return "{}: {}".format(msg.__class__.__name__, "{}".format(msg))
        return msg

Through the _format_message() method we enable the class to accept both string messages and Python exceptions, which is very handy when dealing with external libraries that can raise exceptions we do not know or do not want to manage.

    @property
    def value(self):
        return {'type': self.type, 'message': self.message}

This property makes the class comply with the ResponseSuccess API, providing the value attribute, which is an aptly formatted dictionary.

    def __nonzero__(self):
        return False

    __bool__ = __nonzero__

    @classmethod
    def build_from_invalid_request_object(cls, invalid_request_object):
        message = "\n".join(["{}: {}".format(err['parameter'], err['message'])
                             for err in invalid_request_object.errors])
        return cls(cls.PARAMETERS_ERROR, message)

As explained before, the PARAMETERS_ERROR type encompasses all those errors that come from an invalid set of parameters, which is the case of this function, that shall be called whenever the request is wrong, which means that some parameters contain errors or are missing.

Since building failure responses is a common activity it is useful to have helper methods, so I add three tests for the building functions to the tests/shared/test_response_object.py file

def test_response_failure_build_resource_error():
    response = res.ResponseFailure.build_resource_error("test message")

    assert bool(response) is False
    assert response.type == res.ResponseFailure.RESOURCE_ERROR
    assert response.message == "test message"


def test_response_failure_build_parameters_error():
    response = res.ResponseFailure.build_parameters_error("test message")

    assert bool(response) is False
    assert response.type == res.ResponseFailure.PARAMETERS_ERROR
    assert response.message == "test message"


def test_response_failure_build_system_error():
    response = res.ResponseFailure.build_system_error("test message")

    assert bool(response) is False
    assert response.type == res.ResponseFailure.SYSTEM_ERROR
    assert response.message == "test message"

We add the relevant methods to the class and change the build_from_invalid_request_object() method to leverage the build_parameters_error() new method. Change the rentomatic/shared/response_object.py file to contain this code

    @classmethod
    def build_resource_error(cls, message=None):
        return cls(cls.RESOURCE_ERROR, message)

    @classmethod
    def build_system_error(cls, message=None):
        return cls(cls.SYSTEM_ERROR, message)

    @classmethod
    def build_parameters_error(cls, message=None):
        return cls(cls.PARAMETERS_ERROR, message)

    @classmethod
    def build_from_invalid_request_object(cls, invalid_request_object):
        message = "\n".join(["{}: {}".format(err['parameter'], err['message'])
                             for err in invalid_request_object.errors])
        return cls.build_parameters_error(message)

Use cases (part 3)¶

Git tag: step09

Our implementation of responses and requests is finally complete, so now we can implement the last version of our use case. The use case correctly returns a ResponseSuccess object but is still missing a proper validation of the incoming request.

Let's change the test in the tests/use_cases/test_storageroom_list_use_case.py file and add two more tests. The resulting set of tests (after the domain_storagerooms fixture) is the following

import pytest
from unittest import mock

from rentomatic.domain.storageroom import StorageRoom
from rentomatic.shared import response_object as res
from rentomatic.use_cases import request_objects as req
from rentomatic.use_cases import storageroom_use_cases as uc


@pytest.fixture
def domain_storagerooms():
    [...]


def test_storageroom_list_without_parameters(domain_storagerooms):
    repo = mock.Mock()
    repo.list.return_value = domain_storagerooms

    storageroom_list_use_case = uc.StorageRoomListUseCase(repo)
    request_object = req.StorageRoomListRequestObject.from_dict({})

    response_object = storageroom_list_use_case.execute(request_object)

    assert bool(response_object) is True
    repo.list.assert_called_with(filters=None)

    assert response_object.value == domain_storagerooms

This is the test we already wrote, but the assert_called_with() method is called with filters=None to reflect the added parameter. The import line has slightly changed as well, given that we are now importing both response_objects and request_objects. The domain_storagerooms fixture has not changed and has been omitted from the code snippet to keep it short.

def test_storageroom_list_with_filters(domain_storagerooms):
    repo = mock.Mock()
    repo.list.return_value = domain_storagerooms

    storageroom_list_use_case = uc.StorageRoomListUseCase(repo)
    qry_filters = {'a': 5}
    request_object = req.StorageRoomListRequestObject.from_dict({'filters': qry_filters})

    response_object = storageroom_list_use_case.execute(request_object)

    assert bool(response_object) is True
    repo.list.assert_called_with(filters=qry_filters)
    assert response_object.value == domain_storagerooms

This test checks that the value of the filters key in the dictionary used to create the request is actually used when calling the repository.

def test_storageroom_list_handles_generic_error():
    repo = mock.Mock()
    repo.list.side_effect = Exception('Just an error message')

    storageroom_list_use_case = uc.StorageRoomListUseCase(repo)
    request_object = req.StorageRoomListRequestObject.from_dict({})

    response_object = storageroom_list_use_case.execute(request_object)

    assert bool(response_object) is False
    assert response_object.value == {
        'type': res.ResponseFailure.SYSTEM_ERROR,
        'message': "Exception: Just an error message"
    }


def test_storageroom_list_handles_bad_request():
    repo = mock.Mock()

    storageroom_list_use_case = uc.StorageRoomListUseCase(repo)
    request_object = req.StorageRoomListRequestObject.from_dict({'filters': 5})

    response_object = storageroom_list_use_case.execute(request_object)

    assert bool(response_object) is False
    assert response_object.value == {
        'type': res.ResponseFailure.PARAMETERS_ERROR,
        'message': "filters: Is not iterable"
    }

This last two tests check the behaviour of the use case when the repository raises an exception or when the request is badly formatted.

Change the file rentomatic/use_cases/storageroom_use_cases.py to contain the new use case implementation that makes all the test pass

from rentomatic.shared import response_object as res


class StorageRoomListUseCase(object):

    def __init__(self, repo):
        self.repo = repo

    def execute(self, request_object):
        if not request_object:
            return res.ResponseFailure.build_from_invalid_request_object(request_object)

        try:
            storage_rooms = self.repo.list(filters=request_object.filters)
            return res.ResponseSuccess(storage_rooms)
        except Exception as exc:
            return res.ResponseFailure.build_system_error(
                "{}: {}".format(exc.__class__.__name__, "{}".format(exc)))

As you can see the first thing that the execute() method does is to check if the request is valid, otherwise returns a ResponseFailure build with the same request object. Then the actual business logic is implemented, calling the repository and returning a success response. If something goes wrong in this phase the exception is caught and returned as an aptly formatted ResponseFailure.

Intermezzo: refactoring¶

Git tag: step10

A clean architecture is not a framework, so it provides very few generic features, unlike products like for example Django, which provide models, ORM, and all sorts of structures and libraries. Nevertheless, some classes can be isolated from our code and provided as a library, so that we can reuse the code. In this section I will guide you through a refactoring of the code we already have, during which we will isolate common features for requests, responses, and use cases.

We already isolated the response object. We can move the test_valid_request_object_cannot_be_used from tests/use_cases/test_storageroom_list_request_objects.py to tests/shared/test_response_object.py since it tests a generic behaviour and not something related to the StorageRoom model and use cases.

Then we can move the InvalidRequestObject and ValidRequestObject classes from rentomatic/use_cases/request_objects.py to rentomatic/shared/request_object.py, making the necessary changes to the StorageRoomListRequestObject class that now inherits from an external class.

The use case is the class that undergoes the major changes. The UseCase class is tested by the following code in the tests/shared/test_use_case.py file

from unittest import mock

from rentomatic.shared import request_object as req, response_object as res
from rentomatic.shared import use_case as uc


def test_use_case_cannot_process_valid_requests():
    valid_request_object = mock.MagicMock()
    valid_request_object.__bool__.return_value = True

    use_case = uc.UseCase()
    response = use_case.execute(valid_request_object)

    assert not response
    assert response.type == res.ResponseFailure.SYSTEM_ERROR
    assert response.message == \
        'NotImplementedError: process_request() not implemented by UseCase class'

This test checks that the UseCase class cannot be actually used to process incoming requests.

def test_use_case_can_process_invalid_requests_and_returns_response_failure():
    invalid_request_object = req.InvalidRequestObject()
    invalid_request_object.add_error('someparam', 'somemessage')

    use_case = uc.UseCase()
    response = use_case.execute(invalid_request_object)

    assert not response
    assert response.type == res.ResponseFailure.PARAMETERS_ERROR
    assert response.message == 'someparam: somemessage'

This test runs the use case with an invalid request and check if the response is correct. Since the request is wrong the response type is PARAMETERS_ERROR, as this represents an issue in the request parameters.

def test_use_case_can_manage_generic_exception_from_process_request():
    use_case = uc.UseCase()

    class TestException(Exception):
        pass

    use_case.process_request = mock.Mock()
    use_case.process_request.side_effect = TestException('somemessage')
    response = use_case.execute(mock.Mock)

    assert not response
    assert response.type == res.ResponseFailure.SYSTEM_ERROR
    assert response.message == 'TestException: somemessage'

This test makes the use case raise an exception. This type of error is categorized as SYSTEM_ERROR, which is a generic name for an exception which is not related to request parameters or actual entities.

As you can see in this last test the idea is that of exposing the execute() method in the UseCase class and to call the process_request() method defined by each child class, which is the actual use case we are implementing.

The rentomatic/shared/use_case.py file contains the following code that makes the test pass

from rentomatic.shared import response_object as res


class UseCase(object):

    def execute(self, request_object):
        if not request_object:
            return res.ResponseFailure.build_from_invalid_request_object(request_object)
        try:
            return self.process_request(request_object)
        except Exception as exc:
            return res.ResponseFailure.build_system_error(
                "{}: {}".format(exc.__class__.__name__, "{}".format(exc)))

    def process_request(self, request_object):
        raise NotImplementedError(
            "process_request() not implemented by UseCase class")

While the rentomatic/use_cases/storageroom_use_cases.py now contains the following code

from rentomatic.shared import use_case as uc
from rentomatic.shared import response_object as res


class StorageRoomListUseCase(uc.UseCase):

    def __init__(self, repo):
        self.repo = repo

    def process_request(self, request_object):
        domain_storageroom = self.repo.list(filters=request_object.filters)
        return res.ResponseSuccess(domain_storageroom)

The repository layer¶

Git tag: step11

The repository layer is the one in which we run the data storage system. As you saw when we implemented the use case we access the data storage through an API, in this case the list() method of the repository. The level of abstraction provided by a repository level is higher than that provided by an ORM or by a tool like SQLAlchemy. The repository layer provides only the endpoints that the application needs, with an interface which is tailored on the specific business problems the application implements.

To clarify the matter in terms of concrete technologies, SQLAlchemy is a wonderful tool to abstract the access to an SQL database, so the internal implementation of the repository layer could use it to access a PostgreSQL database. But the external API of the layer is not that provided by SQLAlchemy. The API is a (usually reduced) set of functions that the use cases call to get the data, and indeed the internal implementation could also use raw SQL queries on a proprietary network interface. The repository does not even need to be based on a database. We can have a repository layer that fetches data from a REST service, for example, or that makes remote procedure calls through a RabbitMQ network.

A very important feature of the repository layer is that it always returns domain models, and this is in line with what framework ORMs usually do.

I will not deploy a real database in this post. I will address that part of the application in a future post, where there will be enough space to implement two different solutions and show how the repository API can mask the actual implementation.

Instead, I am going to create a very simple memory storage system with some predefined data. I think this is enough for the moment to demonstrate the repository concept.

The first thing to do is to write some tests that document the public API of the repository. The file containing the tests is tests/repository/test_memrepo.py.

First we add some data that we will be using in the tests. We import the domain model to check if the results of the API calls have the correct type

import pytest

from rentomatic.domain.storageroom import StorageRoom
from rentomatic.shared.domain_model import DomainModel

from rentomatic.repository import memrepo


@pytest.fixture
def storageroom_dicts():
    return [
        {
            'code': 'f853578c-fc0f-4e65-81b8-566c5dffa35a',
            'size': 215,
            'price': 39,
            'longitude': -0.09998975,
            'latitude': 51.75436293,
        },
        {
            'code': 'fe2c3195-aeff-487a-a08f-e0bdc0ec6e9a',
            'size': 405,
            'price': 66,
            'longitude': 0.18228006,
            'latitude': 51.74640997,
        },
        {
            'code': '913694c6-435a-4366-ba0d-da5334a611b2',
            'size': 56,
            'price': 60,
            'longitude': 0.27891577,
            'latitude': 51.45994069,
        },
        {
            'code': 'eed76e77-55c1-41ce-985d-ca49bf6c0585',
            'size': 93,
            'price': 48,
            'longitude': 0.33894476,
            'latitude': 51.39916678,
        }
    ]

Since the repository object will return domain models, we need a helper function to check the correctness of the results. The following function checks the length of the two lists, ensures that all the returned elements are domain models and compares the codes. Note that we can safely employ the isinstance() built-in function since DomainModel is an abstract base class and our models are registered (see the rentomatic/domain/storagerooms.py)

def _check_results(domain_models_list, data_list):
    assert len(domain_models_list) == len(data_list)
    assert all([isinstance(dm, DomainModel) for dm in domain_models_list])
    assert set([dm.code for dm in domain_models_list]
               ) == set([d['code'] for d in data_list])

We need to be able to initialize the repository with a list of dictionaries, and the list() method without any parameter shall return the same list of entries.

def test_repository_list_without_parameters(storageroom_dicts):
    repo = memrepo.MemRepo(storageroom_dicts)

    _check_results(
        repo.list(),
        storageroom_dicts
    )

The list() method shall accept a filters parameter, which is a dictionary. The dictionary keys shall be in the form <attribute>__<operator>, similar to the syntax used by the Django ORM. So to express that the price shall be less than 65 we can write filters={'price__lt': 60}.

A couple of error conditions shall be checked: using an unknown key shall raise a KeyError exception, and using a wrong operator shall raise a ValueError exception.

def test_repository_list_with_filters_unknown_key(storageroom_dicts):
    repo = memrepo.MemRepo(storageroom_dicts)

    with pytest.raises(KeyError):
        repo.list(filters={'name': 'aname'})


def test_repository_list_with_filters_unknown_operator(storageroom_dicts):
    repo = memrepo.MemRepo(storageroom_dicts)

    with pytest.raises(ValueError):
        repo.list(filters={'price__in': [20, 30]})

Let us then test that the filtering mechanism actually works. We want the default operator to be __eq, which means that if we do not put any operator an equality check shall be performed.

def test_repository_list_with_filters_price(storageroom_dicts):
    repo = memrepo.MemRepo(storageroom_dicts)

    _check_results(
        repo.list(filters={'price': 60}),
        [storageroom_dicts[2]]
    )


def test_repository_list_with_filters_price_eq(storageroom_dicts):
    repo = memrepo.MemRepo(storageroom_dicts)

    _check_results(
        repo.list(filters={'price__eq': 60}),
        [storageroom_dicts[2]]
    )


def test_repository_list_with_filters_price_lt(storageroom_dicts):
    repo = memrepo.MemRepo(storageroom_dicts)

    _check_results(
        repo.list(filters={'price__lt': 60}),
        [storageroom_dicts[0], storageroom_dicts[3]])


def test_repository_list_with_filters_price_gt(storageroom_dicts):
    repo = memrepo.MemRepo(storageroom_dicts)
    _check_results(
        repo.list(filters={'price__gt': 60}),
        [storageroom_dicts[1]]
    )


def test_repository_list_with_filters_size(storageroom_dicts):
    repo = memrepo.MemRepo(storageroom_dicts)

    _check_results(
        repo.list(filters={'size': 93}),
        [storageroom_dicts[3]]
    )


def test_repository_list_with_filters_size_eq(storageroom_dicts):
    repo = memrepo.MemRepo(storageroom_dicts)
    _check_results(
        repo.list(filters={'size__eq': 93}),
        [storageroom_dicts[3]]
    )


def test_repository_list_with_filters_size_lt(storageroom_dicts):
    repo = memrepo.MemRepo(storageroom_dicts)
    _check_results(
        repo.list(filters={'size__lt': 60}),
        [storageroom_dicts[2]]
    )


def test_repository_list_with_filters_size_gt(storageroom_dicts):
    repo = memrepo.MemRepo(storageroom_dicts)
    _check_results(
        repo.list(filters={'size__gt': 400}),
        [storageroom_dicts[1]]
    )


def test_repository_list_with_filters_code(storageroom_dicts):
    repo = memrepo.MemRepo(storageroom_dicts)

    _check_results(
        repo.list(filters={'code': '913694c6-435a-4366-ba0d-da5334a611b2'}),
        [storageroom_dicts[2]]
    )