Chris  Miller

Chris Miller


How to Make Portfolio Website in Wordpress | Step By Step Tutorial

In this tutorial I will show you how to make a portfolio website in wordpress using Elementor. For this tutorial I will use Free version of Elementor and finally I will also show you how we can upgrade our website using Elementor Pro. Alright, Keep watching and enjoy!!

✅ Get Domain & Webhosting(60% off):

✅ Get Elementor Free:

✅ Get Elementor Pro:

✅ Download images and template:

Overview with Timestamps:
00:01 Overview of the tutorial
05:43 Get a domain and webhosting
15:01 Install Wordpress
17:25 Clean Up Wordpress

22:41 Install theme
23:58 Get Elementor page builder FREE

26:58 Create Homepage
27:27 Design Hero section
38:39 Design About section
49:50 Design Experience section
56:42 Design Portfolio section
1:05:52 Design Reviews section
1:12:19 Design Blog section
1:23:22 Check Responsiveness

1:25:11 Create Pages
1:25:54 Create Menu
1:27:29 Set Home as default page
1:28:08 Customize Header and Footer

1:35:12 Create Services Page
1:35:35 Create Contact Page
1:38:50 Create Contact Form

Finishing Touch
1:45:02 Link CV with download CV button
1:47:00 Customize single blog page for Elementor Free version
1:49:36 Customize Mobile menu

Work with Elementor Pro
1:53:24 Get Elementor Pro
1:57:26 Create Home page Pro version
2:07:33 Customize Single blog post page with Elementor Pro
2:13:37 Create Sticky Header
2:21:56 Customize Mobile menu with Elementor Pro

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How to Make Portfolio Website in Wordpress | Step By Step Tutorial
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

How WordPress Helps you Make Great Websites in 2020

Many sites are powered by a CMS (Content Management System) called WordPress. WordPress is an Open Source Software that offers free use of the product.

This is awesome for a low budget business or individual. To learn more about WordPress or there terms visit their website (

Before you launch, your WordPress site go through this checklist to get a better understanding

I will assume you have already purchased a domain name and had a hosting account set up with a service provider.

Many service providers are willing to go as far as installing WordPress for you now.

WordPress is great, but you must also learn about how websites work. Beginners should start by learning HTML (HyperText Markup Language) and CSS (Cascading Style Sheets).

These two computer languages basically cover how things are arranged and displayed on a page.

You should also understand how the FTP protocol is used for transferring files to your HTTP server.

Once you have got a good feel of HTML, CSS, HTTP, and FTP you will be ready to start exploring WordPress.

It allows you to change the look and feel of your website through a backside control panel.

You will find that WP is very robust and scalable.

It can provide a solution ranging from a single web page to an expansive multi-page website.

Another cool thing about WP is how it offers database solutions like pages, categories, comments, etc.

You can use it as a Blog or you can create static web pages. I am a huge fan. I have tried JOOMLA, but I still prefer WP.

There is a lot to learn when you are considering building a website.

I have been using WP for about 5 years and I am still learning. There is plenty of documentation available on the net.

If you are in need of a website, and you are starting this from scratch, I would recommend hiring a developer. But if you can accomplish learning this you will be able to build a good website solution for almost anyone.

#wordpress #wordpress-website #web-development #wordpress-website-building #wordpress-website-development #wordpress-tutorial #website #website-development

anita maity

anita maity


Responsive Personal Portfolio Website HTML CSS and JavaScript


#portfolio website html css #personal portfolio website tutorial #portfolio website #responsive personal portfolio website #portfolio website html css javascript #responsive portfolio website html css javascript

Create Portfolio website Using HTML, CSS & Bootstrap

Demo and Download Code

#personal portfolio website tutorial #portfolio website html css javascript #responsive portfolio website html css javascript #portfolio website #responsive web design #portfolio website mobile first

Nandini roy

Nandini roy


Responsive Personal Portfolio Website Using HTML CSS and JavaScript


#create a portfolio website with html css javascript #personal portfolio complete website using only html css javascript #responsive portfolio website html css javascript #responsive personal portfolio website #portfolio website #responsive website