Many tools and processes go into modern front-end development: from component development to design and data fetching to testing and more. How does Stripe make these all work together?
Join Paul Asjes from Stripe’s engineering team for a talk on how to make all these components and processes work in a way that makes the modern front-end greater than the sum of its many parts.
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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.
Let’s briefly set the context first. We will briefly touch upon what React Native is and how it differs from earlier hybrid frameworks.
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:
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:
Due to these factors, React Native offers many more advantages compared to those earlier hybrid frameworks. We now review them.
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React Starter Kit is an opinionated boilerplate for web development built on top of Node.js, Express, GraphQL and React, containing modern web development tools such as Webpack, Babel and Browsersync. Helping you to stay productive following the best practices. A solid starting point for both professionals and newcomers to the industry.
master branch of React Starter Kit doesn't include a Flux implementation or any other advanced integrations. Nevertheless, we have some integrations available to you in feature branches that you can use either as a reference or merge into your project:
You can see status of most reasonable merge combination as PRs labeled as
If you think that any of these features should be on
master, or vice versa, some features should removed from the
master branch, please let us know. We love your feedback!
React Starter Kit
React Static Boilerplate
ASP.NET Core Starter Kit
|App type||Isomorphic (universal)||Single-page application||Single-page application|
|Libraries||React, History, Universal Router||React, History, Redux||React, History, Redux|
|Routes||Imperative (functional)||Declarative||Declarative, cross-stack|
|Libraries||Node.js, Express, Sequelize,|
|n/a||ASP.NET Core, EF Core,|
|Data API||GraphQL||n/a||Web API|
Anyone and everyone is welcome to contribute to this project. The best way to start is by checking our open issues, submit a new issue or feature request, participate in discussions, upvote or downvote the issues you like or dislike, send pull requests.
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.
Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.
>>> import numpy as np >>> x = np.linspace(0, 10, 100) >>> y = np.cos(x) >>> z = np.sin(x)
>>> 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'))
>>> 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)
>>> plt.savefig('foo.png') #Save figures >>> plt.savefig('foo.png', transparent=True) #Save transparent figures
>>> 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
>>> fig, ax = plt.subplots() >>> im = ax.imshow(img, #Colormapped or RGB arrays cmap= 'gist_earth', interpolation= 'nearest', vmin=-2, vmax=2) >>> axes2.pcolor(data2) #Pseudocolor plot of 2D array >>> axes2.pcolormesh(data) #Pseudocolor plot of 2D array >>> CS = plt.contour(Y,X,U) #Plot contours >>> axes2.contourf(data1) #Plot filled contours >>> axes2= ax.clabel(CS) #Label a contour plot
>>> 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
>>> ax1.hist(y) #Plot a histogram >>> ax3.boxplot(y) #Make a box and whisker plot >>> ax3.violinplot(z) #Make a violin plot
The basic steps to creating plots with matplotlib are:
1 Prepare Data
2 Create 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 6
>>> plt.cla() #Clear an axis >>> plt.clf(). #Clear the entire figure >>> plt.close(). #Close a window
>>> 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)
>>> 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"),)
>>> plt.title(r '$sigma_i=15$', fontsize=20)
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', length=10)
>>> 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 area
>>> 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
Original article source at https://www.datacamp.com
#matplotlib #cheatsheet #python
Since March 2020 reached 556 million monthly downloads have increased, It shows that React JS has been steadily growing. React.js also provides a desirable amount of pliancy and efficiency for developing innovative solutions with interactive user interfaces. It’s no surprise that an increasing number of businesses are adopting this technology. How do you select and recruit React.js developers who will propel your project forward? How much does a React developer make? We’ll bring you here all the details you need.
In context of software developer wage rates, the United States continues to lead. In high-tech cities like San Francisco and New York, average React developer salaries will hit $98K and $114per year, overall.
However, the need for React.js and React Native developer is outpacing local labour markets. As a result, many businesses have difficulty locating and recruiting them locally.
It’s no surprise that for US and European companies looking for professional and budget engineers, offshore regions like India are becoming especially interesting. This area has a large number of app development companies, a good rate with quality, and a good pool of React.js front-end developers.
As per Linkedin, the country’s IT industry employs over a million React specialists. Furthermore, for the same or less money than hiring a React.js programmer locally, you may recruit someone with much expertise and a broader technical stack.
React is a very strong framework. React.js makes use of a powerful synchronization method known as Virtual DOM, which compares the current page architecture to the expected page architecture and updates the appropriate components as long as the user input.
React is scalable. it utilises a single language, For server-client side, and mobile platform.
React is steady.React.js is completely adaptable, which means it seldom, if ever, updates the user interface. This enables legacy projects to be updated to the most new edition of React.js without having to change the codebase or make a few small changes.
React is adaptable. It can be conveniently paired with various state administrators (e.g., Redux, Flux, Alt or Reflux) and can be used to implement a number of architectural patterns.
Is there a market for React.js programmers?
The need for React.js developers is rising at an unparalleled rate. React.js is currently used by over one million websites around the world. React is used by Fortune 400+ businesses and popular companies such as Facebook, Twitter, Glassdoor and Cloudflare.
As you’ve seen, locating and Hire React js Developer and Hire React Native developer is a difficult challenge. You will have less challenges selecting the correct fit for your projects if you identify growing offshore locations (e.g. India) and take into consideration the details above.
If you want to make this process easier, You can visit our website for more, or else to write a email, we’ll help you to finding top rated React.js and React Native developers easier and with strives to create this operation
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We help businesses to scale up the process and achieve greater performance by providing the best react native app development services. Our skilled and experienced team’s apps have delivered all the expected results for our clients across the world.
To achieve growth for your business, hire react native app developers in India. You can count on us for all the technical services and support.
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