Reggie  Hudson

Reggie Hudson


Headless UI with React: Transition - Step by Step Guide

In this video, we’re going over transitions in React js using HeadlessUI.

Headless UI Documentation:

📚 Library(s) needed:
npm install tailwindcss
npm install @headlessui/react

🖥️ Source code:

💡 Have a video request?
Suggest it in the Dev Ascend Discord community server or leave it in the comments below!

🕐 Timestamps:
00:00 Introduction
00:39 Installation / Setup
01:46 Adding the Transition component
02:35 Overview of each transition prop
03:38 Working examples

#tailwindcss #transitions #react #devascend

#tailwindcss #react #css

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Headless UI with React: Transition - Step by Step Guide
Autumn  Blick

Autumn Blick


How native is React Native? | React Native vs Native App Development

If you are undertaking a mobile app development for your start-up or enterprise, you are likely wondering whether to use React Native. As a popular development framework, React Native helps you to develop near-native mobile apps. However, you are probably also wondering how close you can get to a native app by using React Native. How native is React Native?

In the article, we discuss the similarities between native mobile development and development using React Native. We also touch upon where they differ and how to bridge the gaps. Read on.

A brief introduction to React Native

Let’s briefly set the context first. We will briefly touch upon what React Native is and how it differs from earlier hybrid frameworks.

React Native is a popular JavaScript framework that Facebook has created. You can use this open-source framework to code natively rendering Android and iOS mobile apps. You can use it to develop web apps too.

Facebook has developed React Native based on React, its JavaScript library. The first release of React Native came in March 2015. At the time of writing this article, the latest stable release of React Native is 0.62.0, and it was released in March 2020.

Although relatively new, React Native has acquired a high degree of popularity. The “Stack Overflow Developer Survey 2019” report identifies it as the 8th most loved framework. Facebook, Walmart, and Bloomberg are some of the top companies that use React Native.

The popularity of React Native comes from its advantages. Some of its advantages are as follows:

  • Performance: It delivers optimal performance.
  • Cross-platform development: You can develop both Android and iOS apps with it. The reuse of code expedites development and reduces costs.
  • UI design: React Native enables you to design simple and responsive UI for your mobile app.
  • 3rd party plugins: This framework supports 3rd party plugins.
  • Developer community: A vibrant community of developers support React Native.

Why React Native is fundamentally different from earlier hybrid frameworks

Are you wondering whether React Native is just another of those hybrid frameworks like Ionic or Cordova? It’s not! React Native is fundamentally different from these earlier hybrid frameworks.

React Native is very close to native. Consider the following aspects as described on the React Native website:

  • Access to many native platforms features: The primitives of React Native render to native platform UI. This means that your React Native app will use many native platform APIs as native apps would do.
  • Near-native user experience: React Native provides several native components, and these are platform agnostic.
  • The ease of accessing native APIs: React Native uses a declarative UI paradigm. This enables React Native to interact easily with native platform APIs since React Native wraps existing native code.

Due to these factors, React Native offers many more advantages compared to those earlier hybrid frameworks. We now review them.

#android app #frontend #ios app #mobile app development #benefits of react native #is react native good for mobile app development #native vs #pros and cons of react native #react mobile development #react native development #react native experience #react native framework #react native ios vs android #react native pros and cons #react native vs android #react native vs native #react native vs native performance #react vs native #why react native #why use react native

Dylan  Iqbal

Dylan Iqbal


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:

Python Matplotlib cheat sheet

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 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 plt


>>> fig = plt.figure()
>>> fig2 = plt.figure(figsize=plt.figaspect(2.0))


>>> 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 figures

Show Plot


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 0

2D Data 

>>> fig, ax = plt.subplots()
>>> im = ax.imshow(img, #Colormapped or RGB arrays
      cmap= 'gist_earth', 
      interpolation= 'nearest',
>>> 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 plot

Vector 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 arrows

Data Distributions 

>>> ax1.hist(y) #Plot a histogram
>>> ax3.boxplot(y) #Make a box and whisker plot
>>> ax3.violinplot(z)  #Make a violin plot

Plot Anatomy & Workflow 

Plot Anatomy 




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],
          color= 'darkgreen',
          marker= '^' )
>>> ax.set_xlim(1, 6.5)
>>> plt.savefig('foo.png' ) #Step 5
>>> #Step 6

Close and Clear 

>>> plt.cla()  #Clear an axis
>>> plt.clf(). #Clear the entire figure
>>> plt.close(). #Close a window

Plotting 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' )


>>> fig, ax = plt.subplots()
>>> ax.scatter(x,y,marker= ".")
>>> ax.plot(x,y,marker= "o")


>>> 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,
           'Example Graph', 
            style= 'italic' )
>>> ax.annotate("Sine", 
xy=(8, 0),
xycoords= 'data', 
xytext=(10.5, 0),
textcoords= 'data', 
arrowprops=dict(arrowstyle= "->", 


>>> 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-axis


>>> 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 elements


>>> 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', 

Subplot Spacing 

>>> fig3.subplots_adjust(wspace=0.5,   #Adjust the spacing between subplots
>>> fig.tight_layout() #Fit subplot(s) in to the figure area

Axis 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 outward

Have this Cheat Sheet at your fingertips

Original article source at

#matplotlib #cheatsheet #python

A Beginner’s Guide to React

It’s nearly the end of 2019, and you think you might finally be ready to get started learning ReactJS. You hear it’s become the most popular front end JavaScript framework. You sit down at your computer, and are ready to give it a go. Starting off, you probably jump straight in with Facebook’s official React tutorial. After that maybe another tutorial on medium. You do some reading here and there, and if you are like me, you end up pretty confused. You hear terms like “props”, “state”, “virtual dom”, “ES6”, “babel”, “webpack”, “higher-order components”, “Redux”, and much more. Soon you realise that learning React is not as easy as you once imagined and either quit or confusedly persevere on.

Does this sound like you? Because this is exactly how I felt when I started learning React. All I wanted to do was set up a simple React app, and I was getting very confused. I thought React had a fairly difficult learning curve, and I was feeling pretty overwhelmed.

I soon realised that React was fairly easy to learn, but the way I went about learning it was difficult. The problem was I didn’t know how to learn it. Firstly, I was relatively new to the world of front end development and I didn’t know what I was doing. I was somewhat familiar with HTML and only used JavaScript a few times. That certainly did not help. There were technologies and information that I should have spent a little more time learning prior to React, that would have lowered the learning curve tremendously.

This is what I would have liked to have known before I began writing a single line of React code:


First, let’s nail out the basics. Before you start diving into React, you should probably have at least a little experience with each of the following:



- ES6 JavaScript

- NodeJS + NPM

If you are familiar with each of the above, then learning React is going to be a lot easier for you. React is big on JavaScript and HTML.

What is React

React is a JavaScript library built in 2013 by the Facebook development team. React wanted to make user interfaces more modular (or reusable) and easier to maintain. According to React’s website, it is used to “Build encapsulated components that manage their own state, then compose them to make complex UIs.”

Understand the Basic

React has 4 ideas that are key to getting started learning with React.


React apps have component based architectures. Conceptually, components are more like JavaScript Functions.They accept inputs(called “props”) and return React elements describing what should appear on screen. Probably a title, an author’s name, the date published, some text, some photos, like buttons, share buttons, etc. If you were building this blog in React, each of these would most likely be a component.

If you create a component for a share button, you can reuse that component to build other share buttons, or reuse it across multiple different kinds of articles. This is the idea with React. You are building components that then can be used and reused to build bigger components.

2. Props

Props is short for properties. Properties are how you pass information unidirectionally from parent to child components. I like to think of them as property attributes or parameters, since it is conceptually similar to passing arguments into a function, and syntactically similar to HTML attributes. Look at the example used previously. If this were a React component, the props would be what you are passing in as “src”, “alt”, “height”, and “width”. You can also pass in callback functions for the child to execute such as “onClick”.

3. State

Many React components will be stateful components. State is exactly what it sounds like. It’s the internal state of your component. Think of a checkbox on a web page. It can either be checked or unchecked. When the user clicks on the checkbox, it will check the box if it is unchecked, and when the user clicks it again it will uncheck the box. The checkbox is an example of a stateful component. In this example, the internal state of the checkbox would be a boolean that would either be checked true or checked false.

While many components have state, some are stateless. Just because lots of components have state doesn’t mean that every component needs to be stateful. Sometimes it makes sense to omit state from a component. Think of an image html tag.

**<img src=”smiley.gif” alt=”Smiley face” height=”42" width=”42">**

If this image tag would be an example of a stateless component. You are passing in parameters, but the image tag itself does not have an internal state that it needs to manage itself.

4. React lifecycle

React is much easier to understand if you have a basic idea behind the React component lifecycle. The React lifecycle describes when and how a component should mount, render, update, and unmount in the DOM. React has lifecycle hooks (React component methods) that help you manage state, props, and work with the lifecycle flow.

Image for post

Image for post

**React component lifecycle has three categories **— Mounting, Updating and Unmounting.

  1. The render_() _is the most used lifecycle method.
  • It is a pure function.
  • You cannot set state in render()

2. The componentDidMount() happens as soon as your component is mounted.

  • You can set state here but with caution.

3. The componentDidUpdate_() _happens as soon as the updating happens.

  • You can set state here but with caution.

4. The componentWillUnmount_() _happens just before the component unmounts and is destroyed.

  • This is a good place to cleanup all the data.
  • You cannot set state here.

5. The shouldComponentUpdate_() _can be used rarely.

  • It can be called if you need to tell React not to re-render for a certain state or prop change.
  • This needs to be used with caution only for certain performance optimizations.

6.The two new lifecycle methods are getDerivedStateFromProps() and getSnapshotBeforeUpdate().

  • They need to be used only occasionally.
  • Not many examples are out there for these two methods and they are still being discussed and will have more references in the future.

Note: You can read more about React’s lifecycle here

These are only the basics to get started.

#react-for-beginner #react-lifecycle #react #react-components #ui

A Comprehensive Guide to UI


UI or User Interface is the interface that is the access point where users interact with computers. It is also a way through which users can interact with a website or an application. UI design typically refers to graphical user interfaces but also includes others, such as voice-controlled ones, a keyboard, a mouse, and the appearance of a desktop.

UI design considers the look, feel, and interactivity of the product. Users judge the design on the basis of usability and likeability very swiftly, so a designer will focus on making each visual element look pleasurable and meaningful. The designer has to consider the color scheme, font imagery, spacing, responsiveness. Also, understanding the user’s context and mindset is crucial while making design decisions.

Types of user interfaces

The various types of user interfaces include:

  • graphical user interface (GUI
  • command line interface (CLI)
  • menu-driven user interface
  • touch user interface
  • voice user interface (VUI)
  • form-based user interface
  • natural language user interface

UI vs UX

Often confused a lot and understood one and the same thing terms UI and UX are related but not the same.

UX or User Experience describes the overall experience of the product and the UI only considers the appearance of the product. A UX designer’s work is to make the product usable and useful. UX means focusing on the whole user journey and the steps a user will take to attain a goal. UX designers will make wireframes without making any detailed design decisions for each wireframe. Once the wireframes are final they are handed over to UI designers to start adding emotions to it through design and animations.

UI is a part of UX which helps in making the user experience more pleasurable and user-centric. UI designer’s job is to make the product visually appealing and desirable. Where a UX designer will try to make a critical judgment on what feature to add and how to user will interact, a UI designer will make critical design decisions regarding those features. Like what should be the font, color scheme, and animations for the features and pages decided by the UX team.

Let’s take a scenario to see how UI designers and UX designers influence the same feature differently.

  • A UX designer will decide whether a page will have a top navigation bar, side navigation bar, or bottom. What links should be added to the bar and whether there will be a search bar in it or not.

  • A UI designer will decide what will be the color scheme of the navigation bar, whether to use icons or text in link buttons, what should be the font style, what animation to use when the user toggles navigation bar or switch between pages.

#scala #user interface (ui) #good design #principles of ui #ui #ui vs ux #user experience #user interface #ux #what is ui #what is ux

Garry Taylor

Garry Taylor


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. 

Python Bokeh Cheat Sheet

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:

  1. Prepare some data (Python lists, NumPy arrays, Pandas DataFrames and other sequences of values)
  2. Create a new plot
  3. Add renderers for your data, with visual customizations
  4. Specify where to generate the output
  5. Show or save the results
>>> from bokeh.plotting import figure
>>> from 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
>>> p.line(x, y, legend="Temp.", line_width=2) #Step 3
>>> output_file("lines.html") #Step 4
>>> show(p) #Step 5

1. 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 


Scatter Markers 
Bokeh Scatter Markers

>>>[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 

Bokeh 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]]),

Customized Glyphs

Selection and Non-Selection Glyphs 

Selection Glyphs

>>> p = figure(tools='box_select')
>>> p. circle ('mpg', 'cyl', source=cds_df,

Hover Glyphs

Hover Glyphs

>>> from bokeh.models import HoverTool
>>>hover= HoverTool(tooltips=None, mode='vline')
>>> p3.add_tools(hover)

Color Mapping 

Bokeh Colormapping Glyphs

>>> from bokeh.models import CategoricalColorMapper
>>> color_mapper = CategoricalColorMapper(
             factors= ['US', 'Asia', 'Europe'],
             palette= ['blue', 'red', 'green'])
>>>  p3. circle ('mpg', 'cyl', source=cds_df,
                 transform=color_mapper), legend='Origin')

4. Output & Export 


>>> from import output_notebook, show
>>> output_notebook()


Standalone HTML 

>>> from bokeh.embed import file_html
>>> from bokeh.resources import CON
>>> html = file_html(p, CON, "my_plot")

>>> from  import  output_file,  show
>>> output_file('my_bar_chart.html',  mode='cdn')


>>> from bokeh.embed import components
>>> script, div= components(p)


>>> from import export_png
>>> export_png(p, filename="plot.png")


>>> from 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


>>> from bokeh.layouts import row
>>>layout= row(p1,p2,p3)


>>> 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_range

Linked Brushing 

>>> p4 = figure(plot_width = 100, tools='box_select,lasso_select')
>>>'mpg', 'cyl' , source=cds_df)
>>> p5 = figure(plot_width = 200, tools='box_select,lasso_select')
>>>'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

#python #datavisualization #bokeh #cheatsheet