How To Make Animated Website Footer Design Using HTML And CSS Step by Step Tutorial | Website Footer design in 15 minutes
In this video you will learn how to make a website footer using HTML and CSS. We will add animation in the footer title text and this footer design will be completely responsive. We will design this footer using HTML and CSS. Subscribe Easy Tutorial to watch more videos on Web Design Tutorial
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Hello Readers, welcome to my other blog, today in this blog I’m going to create a Responsive Footer by using HTML & CSS only. Earlier I have shared How to create a Responsive Navigation Menu and now it’s time to create a footer section.
As you can see on the image which is given on the webpage. There are various important topics there like About us, Our services and subscribes, some social media icons, and a contact section for easy connection. I want to tell you that it is fully responsive. Responsive means this program is fit in all screen devices like tablet, small screen laptop, or mobile devices.
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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.
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.
>>> 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
Have this Cheat Sheet at your fingertips
Original article source at https://www.datacamp.com
#matplotlib #cheatsheet #python
Demo Click Here: https://cutt.ly/2vFKuxe
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