Multi step Form is a long form broken into multi steps. It is not a multi forms rather than multi steps in a single long form. The reason behind it, short form is more easy to complete than long form. It is more comfortable for users to fill the form step by step because their mind should process fewer fields at a time.
Now a days multi step form is more popular than regular form , as it is more user friendly rather than regular form. The multi step form is the best solution when there are too many fields in a from. So breaking large form into multiple step is more user friendly.
It is used to make long form, such as checkout page , registration form etc.,less intimidating and daunting.
So in this post, we will learn how to implement multi step form in vueJs. If you want to learn multi step form in Jquery read this below post.
Laravel 8 livewire form wizard example. In tutorial i will show you how to implement multi step form or form wizard using livewire package in laravel 8 app from scratch.
Follow the below given steps and easy implement multi step form or form wizard in laravel 8 app with livewire:
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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.
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 –
#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.
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.
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
A multi-cloud approach is nothing but leveraging two or more cloud platforms for meeting the various business requirements of an enterprise. The multi-cloud IT environment incorporates different clouds from multiple vendors and negates the dependence on a single public cloud service provider. Thus enterprises can choose specific services from multiple public clouds and reap the benefits of each.
Given its affordability and agility, most enterprises opt for a multi-cloud approach in cloud computing now. A 2018 survey on the public cloud services market points out that 81% of the respondents use services from two or more providers. Subsequently, the cloud computing services market has reported incredible growth in recent times. The worldwide public cloud services market is all set to reach $500 billion in the next four years, according to IDC.
By choosing multi-cloud solutions strategically, enterprises can optimize the benefits of cloud computing and aim for some key competitive advantages. They can avoid the lengthy and cumbersome processes involved in buying, installing and testing high-priced systems. The IaaS and PaaS solutions have become a windfall for the enterprise’s budget as it does not incur huge up-front capital expenditure.
However, cost optimization is still a challenge while facilitating a multi-cloud environment and a large number of enterprises end up overpaying with or without realizing it. The below-mentioned tips would help you ensure the money is spent wisely on cloud computing services.
Most organizations tend to get wrong with simple things which turn out to be the root cause for needless spending and resource wastage. The first step to cost optimization in your cloud strategy is to identify underutilized resources that you have been paying for.
Enterprises often continue to pay for resources that have been purchased earlier but are no longer useful. Identifying such unused and unattached resources and deactivating it on a regular basis brings you one step closer to cost optimization. If needed, you can deploy automated cloud management tools that are largely helpful in providing the analytics needed to optimize the cloud spending and cut costs on an ongoing basis.
Another key cost optimization strategy is to identify the idle computing instances and consolidate them into fewer instances. An idle computing instance may require a CPU utilization level of 1-5%, but you may be billed by the service provider for 100% for the same instance.
Every enterprise will have such non-production instances that constitute unnecessary storage space and lead to overpaying. Re-evaluating your resource allocations regularly and removing unnecessary storage may help you save money significantly. Resource allocation is not only a matter of CPU and memory but also it is linked to the storage, network, and various other factors.
The key to efficient cost reduction in cloud computing technology lies in proactive monitoring. A comprehensive view of the cloud usage helps enterprises to monitor and minimize unnecessary spending. You can make use of various mechanisms for monitoring computing demand.
For instance, you can use a heatmap to understand the highs and lows in computing visually. This heat map indicates the start and stop times which in turn lead to reduced costs. You can also deploy automated tools that help organizations to schedule instances to start and stop. By following a heatmap, you can understand whether it is safe to shut down servers on holidays or weekends.
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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! :)
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:
>>> 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 5
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)
>>> 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()
>>> 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)
>>> 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")
Selection and Non-Selection Glyphs
>>> p = figure(tools='box_select') >>> p. circle ('mpg', 'cyl', source=cds_df, selection_color='red', nonselection_alpha=0.1)
>>> from bokeh.models import HoverTool >>>hover= HoverTool(tooltips=None, mode='vline') >>> p3.add_tools(hover)
>>> 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')
>>> from bokeh.io import output_notebook, show >>> output_notebook()
>>> 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')
>>> from bokeh.embed import components >>> script, div= components(p)
>>> from bokeh.io import export_png >>> export_png(p, filename="plot.png")
>>> from bokeh.io import export_svgs >>> p. output_backend = "svg" >>> export_svgs(p,filename="plot.svg")
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')
>>> p.legend. border_line_color = "navy" >>> p.legend.background_fill_color = "white"
>>> p.legend.orientation = "horizontal" >>> p.legend.orientation = "vertical"
>>> 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)
>>> from bokeh.layouts import gridplot >>> rowl = [p1,p2] >>> row2 = [p3] >>> layout = gridplot([[p1, p2],[p3]])
>>> 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 Axes >>> p2.x_range = p1.x_range >>> p2.y_range = p1.y_range
>>> 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)
>>> show(p1) >>> show(layout) >>> save(p1)
Original article source at https://www.datacamp.com
#python #datavisualization #bokeh #cheatsheet