In this video, we will learn how to make async requests in python with aiohttp. This is just the basics and we only covered GET requests with aiohttp, if the video gets 100 likes I’ll make an in-depth guide on aiohttp.
Aiohttp docs: https://docs.aiohttp.org/
If you have any suggestions for future videos/doubts, leave it in the comments below. I might not be able to answer everyone but you can join my discord server for help: https://discord.gg/TXF3hBj
Queries solved in the video:
Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map
No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.
By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.
Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.
Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.
Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.
Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.
Simple to read and compose
Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.
The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.
Utilized by the best
Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.
Massive community support
Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.
Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.
Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.
The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.
Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.
The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.
#python development services #python development company #python app development #python development #python in web development #python software development
Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?
In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.
Swapping value in Python
Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead
>>> FirstName = "kalebu" >>> LastName = "Jordan" >>> FirstName, LastName = LastName, FirstName >>> print(FirstName, LastName) ('Jordan', 'kalebu')
#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development
Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.
In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.
Heres a solution
Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.
But How do we do it?
If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?
The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.
There’s a variety of hashing algorithms out there such as
#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips
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