1670946660
Step by Step Guide: Debugging Application in Kubernetes
What are Kubernetes Containers?
In order to debug application running in it, first, it is necessary to understand it and Docker containers. So, let’s start with a quick introduction:
- It is nothing but a container orchestration platform; it is an open-source platform for application scaling, management, and deployment. Automation application deployment, scaling, and power is the aim of it.
- Google developed it in 2014, and they have contributed now cloud-native computing foundation, which is managed by it currently. The reason everyone wants to use it is that it is more flexible.
- If you have more and more servers on that time, we have to challenge ourselves to manage all containers and how we get an idea about that and how we know which application is running on which server for reducing this complication we used it.
What are Docker Container?
- It is just a platform to build, ship and run its containers. It is a container orchestration open-source platform for Docker containers that is larger than Docker Swarm.
- Microservices connect with both. And both are used as the open-source platform. It is a tool designed to make it easier to create, deploy and run the application by using containers so that one can debug application running in it easily.
What are its key features?
The key features of Kubernetes are listed below:
Horizontal Scaling
It can scale horizontally and lets us deploy a pod and different containers. Your containers can be scaled when you have it configured automatically for debugging applications in it.
Self Healing
It has self-healing capabilities; also, it is the best feature for it. It automatically restarts the container.
Automated Scheduling
Automated Scheduling is a feature of Managed Kubernetes. Its scheduler is a critical part of the platform. Matchmaking a pod with a node Scheduler is responsible for that.
Load Balancing
Load distribution is load balancing; at the dispatch level, it is easy to implement.
Rollback and Rollout Automatically
It is also a useful feature for rollout and rollbacks any change according to the requirement. If something goes wrong, it will be helpful for rollback and for any change update it is useful for rollout also after performing debug application running in its process.
Storage Orchestration
It is also the best feature that we can mount the storage system according to our wish. Many more features are available in it that are mentioned in the above diagram.
What are its components and architecture?
It used a client-server architecture in diagram master play role as server and node play role as a client. It is a possible multi-master or server setup by which there is only a single master server that plays the role of controlling the client/node. The server and client consist of various components. That describes the following:
Master/Server Component
A primary and vital component of the master node is the following:
Kubernetes Scheduler
It iss a critical part of the platform. Matchmaking a pod with a node, Scheduler is responsible for that. It schedules on the best fit node after reading the requirements of the service.
Controller-Manager
For managing controller processes with dependencies on the underlying cloud provider, the cloud controller manager is responsible. For example, when a controller requires to check volume in the cloud infrastructure or load balancer, these are handled by the cloud controller manager. Some time needs to check if a node was terminated or set up routes, then this is also governed by them.
Controller Manager
Cloud control manager and Kube controller manager both are different from each other there also working differently while debugging applications in Kubernetes architecture.
etcd Cluster
It helps us to aggregate our available resources; basically, it is a collection of servers/hosts. It is only accessible for security reasons and from the API server. It stores the configuration details.
Client/Node Component
The vital components of the Client/Node node is the following:
Pod
A pod is a collection of containers. Containers can not run directly by it. Any container will share the same resources and the local network in the same pod; the container can easily communicate with each other container in a pod.
Kubelet
It is responsible for maintaining all pods in which contain the set of containers. It works to insure that pods and their containers are running in the right state, and all are healthy.
Debugging and Developing services locally in Kubernetes
Debugging on Kubernetes consists of different services. Each service is running in its container. Developing and to debug applications running in its clusters can be large and heavy, for this requires us to have a shell on a running container after then all your tools running in the remote body. Telepresence is a tool for debugging applications in it locally without any difficulty. Telepresence allows us to use custom tools like IDE and debugger. This document describes the telepresence used for debugging and developing services that are running on a cluster locally. The debugging and developing services need to install its cluster telepresence and must also be installed.
How to Develop and Debug existing services?
We make the program or debug a single service when developing an application on it. These services required other services to debug application running in it and testing. With the telepresence, Kube proxy uses the –swap-deployment option to swap an existing deployment. Swapping allows us to connect to the remote cluster and will enable us to run a service locally by debugging applications in it.
What are its benefits and limitations?
The below are the highlighted benefits and limitations:
Benefits of Debugging in Kubernetes
It is the best advantage that now developers can use other Kubernetes Security tools for debugging on it like in the Armador repo, use the telepresence tool as well as using Ksync & Squash to debug the application.
Limitations of Debugging in Kubernetes
- It is a standard part of the process and development lifecycle that Every developer debug locally. But when it comes to it, then this approach becomes more difficult.
- It has its orchestration mechanism and optimization methodologies when Developers can debug microservices hosted by cloud providers. But that methodology to debug application running in it made great. But it makes debugging applications in Kubernetes more difficult.
Conclusion
Kubernetes deployment is a concept of pods; pods are nothing but nodes which are nothing but servers where different content can be deployed in a pod. You can have a single container or multiple containers. Pods contain more containers. It can group containers that make up an application into logical units for easy management and discovery by this it identify how many nodes are there.
- Discover here about PostgreSQL Deployment in Kubernetes
- Click to know about Deploying Java Microservices Applications
Original article source at: https://www.xenonstack.com/
#kubernetes #debugging #application
What is GEEK
Buddha Community
1602964260
50+ Useful Kubernetes Tools for 2020 - Part 2
Introduction
Last year, we provided a list of Kubernetes tools that proved so popular we have decided to curate another list of some useful additions for working with the platform—among which are many tools that we personally use here at Caylent. Check out the original tools list here in case you missed it.
According to a recent survey done by Stackrox, the dominance Kubernetes enjoys in the market continues to be reinforced, with 86% of respondents using it for container orchestration.
And as you can see below, more and more companies are jumping into containerization for their apps. If you’re among them, here are some tools to aid you going forward as Kubernetes continues its rapid growth.
#blog #tools #amazon elastic kubernetes service #application security #aws kms #botkube #caylent #cli #container monitoring #container orchestration tools #container security #containers #continuous delivery #continuous deployment #continuous integration #contour #developers #development #developments #draft #eksctl #firewall #gcp #github #harbor #helm #helm charts #helm-2to3 #helm-aws-secret-plugin #helm-docs #helm-operator-get-started #helm-secrets #iam #json #k-rail #k3s #k3sup #k8s #keel.sh #keycloak #kiali #kiam #klum #knative #krew #ksniff #kube #kube-prod-runtime #kube-ps1 #kube-scan #kube-state-metrics #kube2iam #kubeapps #kubebuilder #kubeconfig #kubectl #kubectl-aws-secrets #kubefwd #kubernetes #kubernetes command line tool #kubernetes configuration #kubernetes deployment #kubernetes in development #kubernetes in production #kubernetes ingress #kubernetes interfaces #kubernetes monitoring #kubernetes networking #kubernetes observability #kubernetes plugins #kubernetes secrets #kubernetes security #kubernetes security best practices #kubernetes security vendors #kubernetes service discovery #kubernetic #kubesec #kubeterminal #kubeval #kudo #kuma #microsoft azure key vault #mozilla sops #octant #octarine #open source #palo alto kubernetes security #permission-manager #pgp #rafay #rakess #rancher #rook #secrets operations #serverless function #service mesh #shell-operator #snyk #snyk container #sonobuoy #strongdm #tcpdump #tenkai #testing #tigera #tilt #vert.x #wireshark #yaml
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Find the Best Restaurant Mobile App Development Company in Abu Dhbai
The era of mobile app development has completely changed the scenario for businesses in regions like Abu Dhabi. Restaurants and food delivery businesses are experiencing huge benefits via smart business applications. The invention and development of the food ordering app have helped all-scale businesses reach new customers and boost sales and profit.
As a result, many business owners are searching for the best restaurant mobile app development company in Abu Dhabi. If you are also searching for the same, this article is helpful for you. It will let you know the step-by-step process to hire the right team of restaurant mobile app developers.
Step-by-Step Process to Find the Best Restaurant App Development Company
Searching for the top mobile app development company in Abu Dhabi? Don't know the best way to search for professionals? Don't panic! Here is the step-by-step process to hire the best professionals.
#Step 1 – Know the Company's Culture
Knowing the organization's culture is very crucial before finalizing a food ordering app development company in Abu Dhabi. An organization's personality is shaped by its common beliefs, goals, practices, or company culture. So, digging into the company culture reveals the core beliefs of the organization, its objectives, and its development team.
Now, you might be wondering, how will you identify the company's culture? Well, you can take reference from the following sources –
- Social media posts
- App development process
- About us Page
- Client testimonials
#Step 2 - Refer to Clients' Reviews
Another best way to choose the On-demand app development firm for your restaurant business is to refer to the clients' reviews. Reviews are frequently available on the organization's website with a tag of "Reviews" or "Testimonials." It's important to read the reviews as they will help you determine how happy customers are with the company's app development process.
You can also assess a company's abilities through reviews and customer testimonials. They can let you know if the mobile app developers create a valuable app or not.
#Step 3 – Analyze the App Development Process
Regardless of the company's size or scope, adhering to the restaurant delivery app development process will ensure the success of your business application. Knowing the processes an app developer follows in designing and producing a top-notch app will help you know the working process. Organizations follow different app development approaches, so getting well-versed in the process is essential before finalizing any mobile app development company.
#Step 4 – Consider Previous Experience
Besides considering other factors, considering the previous experience of the developers is a must. You can obtain a broad sense of the developer's capacity to assist you in creating a unique mobile application for a restaurant business.
You can also find out if the developers' have contributed to the creation of other successful applications or not. It will help you know the working capacity of a particular developer or organization. Prior experience is essential to evaluating their work. For instance, whether they haven't previously produced an app similar to yours or not.
#Step 5 – Check for Their Technical Support
As you expect a working and successful restaurant mobile app for your business, checking on this factor is a must. A well-established organization is nothing without a good technical support team. So, ensure whatever restaurant mobile app development company you choose they must be well-equipped with a team of dedicated developers, designers, and testers.
Strong tech support from your mobile app developers will help you identify new bugs and fix them bugs on time. All this will ensure the application's success.
#Step 6 – Analyze Design Standards
Besides focusing on an organization's development, testing, and technical support, you should check the design standards. An appealing design is crucial in attracting new users and keeping the existing ones stick to your services. So, spend some time analyzing the design standards of an organization. Now, you might be wondering, how will you do it? Simple! By looking at the organization's portfolio.
Whether hiring an iPhone app development company or any other, these steps apply to all. So, don't miss these steps.
#Step 7 – Know Their Location
Finally, the last yet very crucial factor that will not only help you finalize the right person for your restaurant mobile app development but will also decide the mobile app development cost. So, you have to choose the location of the developers wisely, as it is a crucial factor in defining the cost.
Summing Up!!!
Restaurant mobile applications have taken the food industry to heights none have ever considered. As a result, the demand for restaurant mobile app development companies has risen greatly, which is why businesses find it difficult to finalize the right person. But, we hope that after referring to this article, it will now be easier to hire dedicated developers under the desired budget. So, begin the hiring process now and get a well-craft food ordering app in hand.
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Matplotlib Cheat Sheet: Plotting in Python
This Matplotlib cheat sheet introduces you to the basics that you need to plot your data with Python and includes code samples.
Data visualization and storytelling with your data are essential skills that every data scientist needs to communicate insights gained from analyses effectively to any audience out there.
For most beginners, the first package that they use to get in touch with data visualization and storytelling is, naturally, Matplotlib: it is a Python 2D plotting library that enables users to make publication-quality figures. But, what might be even more convincing is the fact that other packages, such as Pandas, intend to build more plotting integration with Matplotlib as time goes on.
However, what might slow down beginners is the fact that this package is pretty extensive. There is so much that you can do with it and it might be hard to still keep a structure when you're learning how to work with Matplotlib.
DataCamp has created a Matplotlib cheat sheet for those who might already know how to use the package to their advantage to make beautiful plots in Python, but that still want to keep a one-page reference handy. Of course, for those who don't know how to work with Matplotlib, this might be the extra push be convinced and to finally get started with data visualization in Python.
You'll see that this cheat sheet presents you with the six basic steps that you can go through to make beautiful plots.
Check out the infographic by clicking on the button below:
With this handy reference, you'll familiarize yourself in no time with the basics of Matplotlib: you'll learn how you can prepare your data, create a new plot, use some basic plotting routines to your advantage, add customizations to your plots, and save, show and close the plots that you make.
What might have looked difficult before will definitely be more clear once you start using this cheat sheet! Use it in combination with the Matplotlib Gallery, the documentation.
Matplotlib
Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.
Prepare the Data
1D Data
>>> import numpy as np
>>> x = np.linspace(0, 10, 100)
>>> y = np.cos(x)
>>> z = np.sin(x)2D Data or Images
>>> data = 2 * np.random.random((10, 10))
>>> data2 = 3 * np.random.random((10, 10))
>>> Y, X = np.mgrid[-3:3:100j, -3:3:100j]
>>> U = 1 X** 2 + Y
>>> V = 1 + X Y**2
>>> from matplotlib.cbook import get_sample_data
>>> img = np.load(get_sample_data('axes_grid/bivariate_normal.npy'))Create Plot
>>> import matplotlib.pyplot as pltFigure
>>> fig = plt.figure()
>>> fig2 = plt.figure(figsize=plt.figaspect(2.0))Axes
>>> fig.add_axes()
>>> ax1 = fig.add_subplot(221) #row-col-num
>>> ax3 = fig.add_subplot(212)
>>> fig3, axes = plt.subplots(nrows=2,ncols=2)
>>> fig4, axes2 = plt.subplots(ncols=3)Save Plot
>>> plt.savefig('foo.png') #Save figures
>>> plt.savefig('foo.png', transparent=True) #Save transparent figuresShow Plot
>>> plt.show()Plotting Routines
1D Data
>>> fig, ax = plt.subplots()
>>> lines = ax.plot(x,y) #Draw points with lines or markers connecting them
>>> ax.scatter(x,y) #Draw unconnected points, scaled or colored
>>> axes[0,0].bar([1,2,3],[3,4,5]) #Plot vertical rectangles (constant width)
>>> axes[1,0].barh([0.5,1,2.5],[0,1,2]) #Plot horiontal rectangles (constant height)
>>> axes[1,1].axhline(0.45) #Draw a horizontal line across axes
>>> axes[0,1].axvline(0.65) #Draw a vertical line across axes
>>> ax.fill(x,y,color='blue') #Draw filled polygons
>>> ax.fill_between(x,y,color='yellow') #Fill between y values and 02D Data
>>> fig, ax = plt.subplots()
>>> im = ax.imshow(img, #Colormapped or RGB arrays
cmap= 'gist_earth',
interpolation= 'nearest',
vmin=-2,
vmax=2)
>>> axes2[0].pcolor(data2) #Pseudocolor plot of 2D array
>>> axes2[0].pcolormesh(data) #Pseudocolor plot of 2D array
>>> CS = plt.contour(Y,X,U) #Plot contours
>>> axes2[2].contourf(data1) #Plot filled contours
>>> axes2[2]= ax.clabel(CS) #Label a contour plotVector Fields
>>> axes[0,1].arrow(0,0,0.5,0.5) #Add an arrow to the axes
>>> axes[1,1].quiver(y,z) #Plot a 2D field of arrows
>>> axes[0,1].streamplot(X,Y,U,V) #Plot a 2D field of arrowsData Distributions
>>> ax1.hist(y) #Plot a histogram
>>> ax3.boxplot(y) #Make a box and whisker plot
>>> ax3.violinplot(z) #Make a violin plotPlot Anatomy & Workflow
Plot Anatomy
y-axis
x-axis
Workflow
The basic steps to creating plots with matplotlib are:
1 Prepare Data
2 Create Plot
3 Plot
4 Customized Plot
5 Save Plot
6 Show Plot
>>> import matplotlib.pyplot as plt
>>> x = [1,2,3,4] #Step 1
>>> y = [10,20,25,30]
>>> fig = plt.figure() #Step 2
>>> ax = fig.add_subplot(111) #Step 3
>>> ax.plot(x, y, color= 'lightblue', linewidth=3) #Step 3, 4
>>> ax.scatter([2,4,6],
[5,15,25],
color= 'darkgreen',
marker= '^' )
>>> ax.set_xlim(1, 6.5)
>>> plt.savefig('foo.png' ) #Step 5
>>> plt.show() #Step 6Close and Clear
>>> plt.cla() #Clear an axis
>>> plt.clf(). #Clear the entire figure
>>> plt.close(). #Close a windowPlotting Customize Plot
Colors, Color Bars & Color Maps
>>> plt.plot(x, x, x, x**2, x, x** 3)
>>> ax.plot(x, y, alpha = 0.4)
>>> ax.plot(x, y, c= 'k')
>>> fig.colorbar(im, orientation= 'horizontal')
>>> im = ax.imshow(img,
cmap= 'seismic' )Markers
>>> fig, ax = plt.subplots()
>>> ax.scatter(x,y,marker= ".")
>>> ax.plot(x,y,marker= "o")Linestyles
>>> plt.plot(x,y,linewidth=4.0)
>>> plt.plot(x,y,ls= 'solid')
>>> plt.plot(x,y,ls= '--')
>>> plt.plot(x,y,'--' ,x**2,y**2,'-.' )
>>> plt.setp(lines,color= 'r',linewidth=4.0)Text & Annotations
>>> ax.text(1,
-2.1,
'Example Graph',
style= 'italic' )
>>> ax.annotate("Sine",
xy=(8, 0),
xycoords= 'data',
xytext=(10.5, 0),
textcoords= 'data',
arrowprops=dict(arrowstyle= "->",
connectionstyle="arc3"),)Mathtext
>>> plt.title(r '$sigma_i=15$', fontsize=20)Limits, Legends and Layouts
Limits & Autoscaling
>>> ax.margins(x=0.0,y=0.1) #Add padding to a plot
>>> ax.axis('equal') #Set the aspect ratio of the plot to 1
>>> ax.set(xlim=[0,10.5],ylim=[-1.5,1.5]) #Set limits for x-and y-axis
>>> ax.set_xlim(0,10.5) #Set limits for x-axisLegends
>>> ax.set(title= 'An Example Axes', #Set a title and x-and y-axis labels
ylabel= 'Y-Axis',
xlabel= 'X-Axis')
>>> ax.legend(loc= 'best') #No overlapping plot elementsTicks
>>> ax.xaxis.set(ticks=range(1,5), #Manually set x-ticks
ticklabels=[3,100, 12,"foo" ])
>>> ax.tick_params(axis= 'y', #Make y-ticks longer and go in and out
direction= 'inout',
length=10)Subplot Spacing
>>> fig3.subplots_adjust(wspace=0.5, #Adjust the spacing between subplots
hspace=0.3,
left=0.125,
right=0.9,
top=0.9,
bottom=0.1)
>>> fig.tight_layout() #Fit subplot(s) in to the figure areaAxis Spines
>>> ax1.spines[ 'top'].set_visible(False) #Make the top axis line for a plot invisible
>>> ax1.spines['bottom' ].set_position(( 'outward',10)) #Move the bottom axis line outwardHave this Cheat Sheet at your fingertips
Original article source at https://www.datacamp.com
#matplotlib #cheatsheet #python
1670946660
Step by Step Guide: Debugging Application in Kubernetes
What are Kubernetes Containers?
In order to debug application running in it, first, it is necessary to understand it and Docker containers. So, let’s start with a quick introduction:
- It is nothing but a container orchestration platform; it is an open-source platform for application scaling, management, and deployment. Automation application deployment, scaling, and power is the aim of it.
- Google developed it in 2014, and they have contributed now cloud-native computing foundation, which is managed by it currently. The reason everyone wants to use it is that it is more flexible.
- If you have more and more servers on that time, we have to challenge ourselves to manage all containers and how we get an idea about that and how we know which application is running on which server for reducing this complication we used it.
What are Docker Container?
- It is just a platform to build, ship and run its containers. It is a container orchestration open-source platform for Docker containers that is larger than Docker Swarm.
- Microservices connect with both. And both are used as the open-source platform. It is a tool designed to make it easier to create, deploy and run the application by using containers so that one can debug application running in it easily.
What are its key features?
The key features of Kubernetes are listed below:
Horizontal Scaling
It can scale horizontally and lets us deploy a pod and different containers. Your containers can be scaled when you have it configured automatically for debugging applications in it.
Self Healing
It has self-healing capabilities; also, it is the best feature for it. It automatically restarts the container.
Automated Scheduling
Automated Scheduling is a feature of Managed Kubernetes. Its scheduler is a critical part of the platform. Matchmaking a pod with a node Scheduler is responsible for that.
Load Balancing
Load distribution is load balancing; at the dispatch level, it is easy to implement.
Rollback and Rollout Automatically
It is also a useful feature for rollout and rollbacks any change according to the requirement. If something goes wrong, it will be helpful for rollback and for any change update it is useful for rollout also after performing debug application running in its process.
Storage Orchestration
It is also the best feature that we can mount the storage system according to our wish. Many more features are available in it that are mentioned in the above diagram.
What are its components and architecture?
It used a client-server architecture in diagram master play role as server and node play role as a client. It is a possible multi-master or server setup by which there is only a single master server that plays the role of controlling the client/node. The server and client consist of various components. That describes the following:
Master/Server Component
A primary and vital component of the master node is the following:
Kubernetes Scheduler
It iss a critical part of the platform. Matchmaking a pod with a node, Scheduler is responsible for that. It schedules on the best fit node after reading the requirements of the service.
Controller-Manager
For managing controller processes with dependencies on the underlying cloud provider, the cloud controller manager is responsible. For example, when a controller requires to check volume in the cloud infrastructure or load balancer, these are handled by the cloud controller manager. Some time needs to check if a node was terminated or set up routes, then this is also governed by them.
Controller Manager
Cloud control manager and Kube controller manager both are different from each other there also working differently while debugging applications in Kubernetes architecture.
etcd Cluster
It helps us to aggregate our available resources; basically, it is a collection of servers/hosts. It is only accessible for security reasons and from the API server. It stores the configuration details.
Client/Node Component
The vital components of the Client/Node node is the following:
Pod
A pod is a collection of containers. Containers can not run directly by it. Any container will share the same resources and the local network in the same pod; the container can easily communicate with each other container in a pod.
Kubelet
It is responsible for maintaining all pods in which contain the set of containers. It works to insure that pods and their containers are running in the right state, and all are healthy.
Debugging and Developing services locally in Kubernetes
Debugging on Kubernetes consists of different services. Each service is running in its container. Developing and to debug applications running in its clusters can be large and heavy, for this requires us to have a shell on a running container after then all your tools running in the remote body. Telepresence is a tool for debugging applications in it locally without any difficulty. Telepresence allows us to use custom tools like IDE and debugger. This document describes the telepresence used for debugging and developing services that are running on a cluster locally. The debugging and developing services need to install its cluster telepresence and must also be installed.
How to Develop and Debug existing services?
We make the program or debug a single service when developing an application on it. These services required other services to debug application running in it and testing. With the telepresence, Kube proxy uses the –swap-deployment option to swap an existing deployment. Swapping allows us to connect to the remote cluster and will enable us to run a service locally by debugging applications in it.
What are its benefits and limitations?
The below are the highlighted benefits and limitations:
Benefits of Debugging in Kubernetes
It is the best advantage that now developers can use other Kubernetes Security tools for debugging on it like in the Armador repo, use the telepresence tool as well as using Ksync & Squash to debug the application.
Limitations of Debugging in Kubernetes
- It is a standard part of the process and development lifecycle that Every developer debug locally. But when it comes to it, then this approach becomes more difficult.
- It has its orchestration mechanism and optimization methodologies when Developers can debug microservices hosted by cloud providers. But that methodology to debug application running in it made great. But it makes debugging applications in Kubernetes more difficult.
Conclusion
Kubernetes deployment is a concept of pods; pods are nothing but nodes which are nothing but servers where different content can be deployed in a pod. You can have a single container or multiple containers. Pods contain more containers. It can group containers that make up an application into logical units for easy management and discovery by this it identify how many nodes are there.
- Discover here about PostgreSQL Deployment in Kubernetes
- Click to know about Deploying Java Microservices Applications
Original article source at: https://www.xenonstack.com/
1653464648
Python Data Visualization: Bokeh Cheat Sheet
A handy cheat sheet for interactive plotting and statistical charts with Bokeh.
Bokeh distinguishes itself from other Python visualization libraries such as Matplotlib or Seaborn in the fact that it is an interactive visualization library that is ideal for anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.
Bokeh is also known for enabling high-performance visual presentation of large data sets in modern web browsers.
For data scientists, Bokeh is the ideal tool to build statistical charts quickly and easily; But there are also other advantages, such as the various output options and the fact that you can embed your visualizations in applications. And let's not forget that the wide variety of visualization customization options makes this Python library an indispensable tool for your data science toolbox.
Now, DataCamp has created a Bokeh cheat sheet for those who have already taken the course and that still want a handy one-page reference or for those who need an extra push to get started.
In short, you'll see that this cheat sheet not only presents you with the five steps that you can go through to make beautiful plots but will also introduce you to the basics of statistical charts.
In no time, this Bokeh cheat sheet will make you familiar with how you can prepare your data, create a new plot, add renderers for your data with custom visualizations, output your plot and save or show it. And the creation of basic statistical charts will hold no secrets for you any longer.
Boost your Python data visualizations now with the help of Bokeh! :)
Plotting With Bokeh
The Python interactive visualization library Bokeh enables high-performance visual presentation of large datasets in modern web browsers.
Bokeh's mid-level general-purpose bokeh. plotting interface is centered around two main components: data and glyphs.
The basic steps to creating plots with the bokeh. plotting interface are:
- Prepare some data (Python lists, NumPy arrays, Pandas DataFrames and other sequences of values)
- Create a new plot
- Add renderers for your data, with visual customizations
- Specify where to generate the output
- Show or save the results
>>> from bokeh.plotting import figure
>>> from bokeh.io import output_file, show
>>> x = [1, 2, 3, 4, 5] #Step 1
>>> y = [6, 7, 2, 4, 5]
>>> p = figure(title="simple line example", #Step 2
x_axis_label='x',
y_axis_label='y')
>>> p.line(x, y, legend="Temp.", line_width=2) #Step 3
>>> output_file("lines.html") #Step 4
>>> show(p) #Step 51. Data
Under the hood, your data is converted to Column Data Sources. You can also do this manually:
>>> import numpy as np
>>> import pandas as pd
>>> df = pd.OataFrame(np.array([[33.9,4,65, 'US'], [32.4, 4, 66, 'Asia'], [21.4, 4, 109, 'Europe']]),
columns= ['mpg', 'cyl', 'hp', 'origin'],
index=['Toyota', 'Fiat', 'Volvo'])
>>> from bokeh.models import ColumnOataSource
>>> cds_df = ColumnOataSource(df)2. Plotting
>>> from bokeh.plotting import figure
>>>p1= figure(plot_width=300, tools='pan,box_zoom')
>>> p2 = figure(plot_width=300, plot_height=300,
x_range=(0, 8), y_range=(0, 8))
>>> p3 = figure()3. Renderers & Visual Customizations
Glyphs
Scatter Markers 
>>> p1.circle(np.array([1,2,3]), np.array([3,2,1]), fill_color='white')
>>> p2.square(np.array([1.5,3.5,5.5]), [1,4,3],
color='blue', size=1)
Line Glyphs
>>> pl.line([1,2,3,4], [3,4,5,6], line_width=2)
>>> p2.multi_line(pd.DataFrame([[1,2,3],[5,6,7]]),
pd.DataFrame([[3,4,5],[3,2,1]]),
color="blue")
Customized Glyphs
Selection and Non-Selection Glyphs
>>> p = figure(tools='box_select')
>>> p. circle ('mpg', 'cyl', source=cds_df,
selection_color='red',
nonselection_alpha=0.1)
Hover Glyphs
>>> from bokeh.models import HoverTool
>>>hover= HoverTool(tooltips=None, mode='vline')
>>> p3.add_tools(hover)
Color Mapping
>>> from bokeh.models import CategoricalColorMapper
>>> color_mapper = CategoricalColorMapper(
factors= ['US', 'Asia', 'Europe'],
palette= ['blue', 'red', 'green'])
>>> p3. circle ('mpg', 'cyl', source=cds_df,
color=dict(field='origin',
transform=color_mapper), legend='Origin')
4. Output & Export
Notebook
>>> from bokeh.io import output_notebook, show
>>> output_notebook()
HTML
Standalone HTML
>>> from bokeh.embed import file_html
>>> from bokeh.resources import CON
>>> html = file_html(p, CON, "my_plot")
>>> from bokeh.io import output_file, show
>>> output_file('my_bar_chart.html', mode='cdn')Components
>>> from bokeh.embed import components
>>> script, div= components(p)PNG
>>> from bokeh.io import export_png
>>> export_png(p, filename="plot.png")SVG
>>> from bokeh.io import export_svgs
>>> p. output_backend = "svg"
>>> export_svgs(p,filename="plot.svg")Legend Location
Inside Plot Area
>>> p.legend.location = 'bottom left'
Outside Plot Area
>>> from bokeh.models import Legend
>>> r1 = p2.asterisk(np.array([1,2,3]), np.array([3,2,1])
>>> r2 = p2.line([1,2,3,4], [3,4,5,6])
>>> legend = Legend(items=[("One" ,[p1, r1]),("Two",[r2])], location=(0, -30))
>>> p.add_layout(legend, 'right')Legend Background & Border
>>> p.legend. border_line_color = "navy"
>>> p.legend.background_fill_color = "white"Legend Orientation
>>> p.legend.orientation = "horizontal"
>>> p.legend.orientation = "vertical"Rows & Columns Layout
Rows
>>> from bokeh.layouts import row
>>>layout= row(p1,p2,p3)
Columns
>>> from bokeh.layouts import columns
>>>layout= column(p1,p2,p3)Nesting Rows & Columns
>>>layout= row(column(p1,p2), p3)
Grid Layout
>>> from bokeh.layouts import gridplot
>>> rowl = [p1,p2]
>>> row2 = [p3]
>>> layout = gridplot([[p1, p2],[p3]])Tabbed Layout
>>> from bokeh.models.widgets import Panel, Tabs
>>> tab1 = Panel(child=p1, title="tab1")
>>> tab2 = Panel(child=p2, title="tab2")
>>> layout = Tabs(tabs=[tab1, tab2])Linked Plots
Linked Axes
Linked Axes
>>> p2.x_range = p1.x_range
>>> p2.y_range = p1.y_rangeLinked Brushing
>>> p4 = figure(plot_width = 100, tools='box_select,lasso_select')
>>> p4.circle('mpg', 'cyl' , source=cds_df)
>>> p5 = figure(plot_width = 200, tools='box_select,lasso_select')
>>> p5.circle('mpg', 'hp', source=cds df)
>>>layout= row(p4,p5)5. Show or Save Your Plots
>>> show(p1)
>>> show(layout)
>>> save(p1)Have this Cheat Sheet at your fingertips
Original article source at https://www.datacamp.com
#python #datavisualization #bokeh #cheatsheet