Printers and other PC-connected devices depend on their drivers to perform all prior tasks, including Print, Scan, etc. Your Epson printer is one of these models, which may encounter the “Epson printer driver is unavailable” error.
Quick actions are required if your Epson driver is unavailable on your system. Such problems affect the print quality and printer functionalities and make your Epson printer model unresponsive. We’ll examine some solutions to fix the driver is unavailable printer Epson issue in this blog. First, find the cause and then fix it to restart the printing again.
So you gave the print command, and suddenly a pop–up says “Epson driver is unavailable” and stopped printing. The basic cause for this condition could be any of the below-mentioned reasons;
Before trying any solution to fix the Epson printer driver error, check and try some basics.
When your Epson driver is unavailable on your computer and can’t print anything, it could be either a driver issue or a Windows update problem. We’ll discuss all possible solutions to eliminate the continued error message. The below fixes helps you get back your Epson printer in the previous print mode. Read and try all fixes one by one. If one solution works for your Epson printer, you don’t need to try it next. However, if one couldn’t remove the driver is unavailable printer Epson error, try next and next and others. Now let’s get started with fixes quickly.
If you’ve just now downloaded the Epson printer driver and couldn’t install it, printer drivers may not be compatible with your current OS. Therefore, it’s best to check and download the correct and compatible drivers on your system.
A driver updater tool helps the fixed system’s all driver issues and then updates them. For example, here’s one tool you can use to fix the “Epson printer driver is unavailable” error in a single click.
This method has fixed multiple driver issues in Windows systems. Not only the printer but all connected device drivers help stay up-to-date automatically.
When you install built-in drivers for your Epson printer model, these drivers get outdated after some time and cause in “Driver is unavailable printer Epson” error where Windows Update can remove such cause.
The update takes a few minutes. Hence wait for some time. Then check the Epson driver error by printing a document. It’s best if you install drivers online instead of using a built-in driver because even if the Windows update fixes the “Epson driver is unavailable” error, you may encounter the error again after some time.
When your device driver gets outdated, the “driver is unavailable printer Epson” error starts appearing. To fix it, updating the driver is the best solution. You can use the automatic eSoftSafe Driver updater pro tool (mentioned in a fix 2) or follow the below steps to update drivers manually;
FROM DEVICE MANAGER –
REINSTALL FROM THE OFFICIAL EPSON SITE –
If the device manager couldn’t update your Epson printer drivers and Epson Printer Driver Is Unavailable message still appears on your PC screen, reinstall your drivers from the official online portal.
Troubleshooter in your Windows PC can help fix the Epson driver is unavailable error. Try this tool using the below steps;
Once you complete the troubleshooter scan, try to print a new document and check if Epson Printer Driver Is Unavailable error has removed.
If you face an Epson printer malfunction, you first go for technical assistance directly from the official tech support center. They would only supply you with an internet service, which will take a lot of time and will also provide very little or no support. With the aid of Epson Printer technical assistance, the errors in Epson printers are quickly handled. We provide our customers with the best Epson customer support. To overcome the challenges, we have a team of well-experienced and qualified experts.
Get More info: https://epsonprintersupports.com/
#epson printers support #epson printer support #epson printer support number #epson printer technical support number #epson printer technical support #epson printer customer service
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
The HP is an international brand and used by millions of users around the world to get amazing prints and printers are not the only things that HP is popular for. However, the HP printers are simply awesome and work amazingly for everyone. The users of the HP printers can choose the printers to form the wide range of the printers the HP offer for the users, but sometimes while Install HP Printer Driver, the users come across a fatal error 1603.
The HP Printer Install Error 1603 is the error that interrupts the users of the HP printers when they are installing software of the HP printers on the Windows OS and the Windows is trying to install more than one program at one time. The users have no option other than resolving the issue so that they can use the HP printer without any delay.
Given here are the quick steps in which you can resolve HP Error Code 1603 instantly.
The best way to eradicate this error code is to uninstall the driver. After a while, install it again to make it function well. Here are the steps to do so.
First of all, sign in the administrator mode, and then search for the Control panel.
When you open the control panel, you will look an option there named Add or delete programs. In this option, you’ve to look for the all over history of your printer.
When you select your entry, right click on it. Now it is the time to uninstall the driver. After uninstalling it, give a restart.
Now, look for your HP printer driver and install it again for the smooth work of the printer.
If you run a troubleshooter, then it will help you to resolve your issue immediately. The best is to run a Hardware Troubleshooter. Here are the steps to know how to run it.
On your computer, go to the control panel. There you will look an option to view for large icons.
Now, you have to for the troubleshooting. Open it and there is an option for hardware and sound. Select it.
When you open that folder, an option to setup a device will be there. Hit it. Then you will look a lot of instructions on your screen, follow all those to run a troubleshooter.
The next step is to run a printer driver in a simple mode
To run the printer fleetly, would like to} need the newest printer driver. Install it to your printer to run it effectively.
After putting in the updated printer driver, do a right click on the setup file. when doing therefore, you'll see the properties choice there.
When you click on the properties tab, AN choice of compatibility is there. Click thereon. enable it to run in a very compatibility manner. once you did it, it's higher to restart your system to implement all the specified changes. Now, check if the error is mounted or not.
You will see the safety tab on the device home screen. therein tab, AN choice of Add are there. Click thereon.
After clicking the Add choice, AN choice can seem, termed Name. Click thereon. At the time you click it, another choice of Add are there. Hit it afterwards click on the OK button.
When you click the ok button, AN choice of permissions can crop up. Hit the enable choice, afterwards, click Full management and in conclusion, click on the Advanced tab.
Now, click on all the choices that return up with a box that you simply ought to tick by touching thereon box.
This checking mark isn't for all the software users, if you access Windows XP, you'll be simply asked to hit the OK button.
When you do all the higher than procedures, a box can seem on your screen wherever it'll raise you to click affirmative.
The installer package that has a slip, you would like to double-click thereon.
If the problem is still there than the user needs to temporarily disable the services and then reinstall the HP software. When the HP software is installed once again, then enable all the services that user disabled before. The user needs to contact experts at HP Support Assistance Number +1-866-231-0111 if the problem is still there.
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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