Js Node Against Javascript

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In short, Python is a generic language used for many applications, especially for machine learning . Node.js is a runtime environment for JavaScript that is typically used for server backends.

JavaScript the web owns, earning a spot alongside CSS and HTML as one of the building blocks of the Internet. Python, on the other hand, is incredibly popular outside of the web and is considered a scientific data royalty. What happens when we compare the most used language online with one of the most used languages ‚Äã‚Äãoffline? We start by previewing the two and then place them next to each other to see their differences.


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Python vs Node.js Comparison

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Node.js vs Python: typing-and- syntax

Python is easy to learn. It is highly recommended as a first language because it is so easy to learn while teaching programming basics and is a useful language if you are inexperienced or one. Professional Python

JavaScript, while very useful, is a bit more difficult to learn. JavaScript has advanced in many ways over the years, and it’s not easy to get acquainted with good coding practices in JavaScript. This is even more true when you consider Node.js, as development can be significantly slowed down by inexperienced developers. The errors in Python are a little more forgivable in comparison, and the project can go from the front without stop suddenly when someone made a small mistake.

Python is also built on readability, it’s built into Python’s DNA. For example, instead of braces to delimit blocks and lines of code, Python uses dashes. Python should be indented at work, so all code written in Python will be more readable and cleaner than code in a language that doesn’t use indentation. It is also much more forgiving in other ways, such as not using the semicolon.

JavaScript is also more difficult to debug than Python. JavaScript (and Node.js) tends to fail silently, which mean s if your code doesn’t return any traceback for an error, the code still isn’t working for some reason. It also has all the C language accessories such as braces and dots - commas. The only advantage is that programmers of C and C languages ‚Äã‚Äãmay be able to capture JavaScript faster than Python because the syntax of JavaScript is closer to C.

Python vs Node.js: performance < / h3>

When it comes to speed, both JavaScript Node.js and Python are very similar in form. They are both interpreted, dynamically typed, and high level languages. However, when benchmarked, Node.js is at the top of a mile. As for a screeching large number, Node.js is blazingly fast, leaving Python in the dust.

Some of that performance comes from the Node.js interpreter: Chrome’s V8 engine. This engine is built around speed and definitely shows, bringing JavaScript closer to compiled and typed languages ‚Äã‚Äãin terms of raw speed.

It is important to note however that Python does not take long. It is only slow compared to Node.js to crunch a lot. for most random applications the difference will be infinitely small and will continue to be indistinguishable until when it is not applied on a large scale. this mean s that if you try to manage the traffic like Google or Facebook are, or try counting of huge data sets, you probably will not waste a large part of the day in the course of running Python on Node. js.

JavaScript is fast and powerful when used with Node.js. However, that doesn’t mean he can do it all. JavaScript is happier on the web, and Node.js never changes. a lot. Node.js is built around JavaScript working as a back end for web applications and websites and not much. s an example of alc One of the applications where Node.js is the lag is delay in learning science and machine data

Science data

scientific data use different languages, but Python is at the top of their list. Indeed, Python is suitable the data science and learning communities of the machine and the data science camped around it. Although it is possible to create scientific data models in Node.js, it is preferable, because it there is less community built around it. to start working with machine learning in JavaScript you have to build everything from scratch as opposed to Python where other developers are building modules and frameworks from scratch. machine learning . actively

in Python you have a springboard to get into data science, and even if you choose not to use work done by others, the information about Python and data science is much richer. Python is currently one of the favorite tools for data science, so even finding information on how to apply it this way is not difficult. in comparing the two for the science data is like comparing a room and an electric drill. The two can turn a screw, but it will be much easier on your wrist.

While you could argue that Node.js is better for data science because it is more efficient when computing large numbers (and not you ’d wrong), the reason it doesn’t is not used is that, if speed is a requirement, most developers simply choose to skip JavaScript directly to C languages; which are even faster and still have communities built around them for data science and analytics like Python does.

However, there are a few tools available for machine learning with Node.js and JavaScript. The Tensorflow.js library provides the ability for people to experience machine learning right in their browser. However, there isn’t much more available yet. While it may pick up speed in the future, JavaScript is not the best choice for developing machine learning applications at this time.

on the web

With regard to the web, especially the front-end, JavaScript is in a position of power. It is a language designed for the dynamic building of applications and front end websites. However, we are not comparing JavaScript, we are comparing Node.js, which is designed to remove JavaScript from the front end and apply it to the back end. This is where Node.js and Python in direct competition, like Python is also used for back end systems.

The back end is the side of the internet you can’t see. It handles the raw information that we put in the sites, so if the front is a sink, then the back end is the pipes.

The first point goes to Node.js by default, it uses the same language as most front-end systems. If the whole stack is JavaScript, everything gets a bit smoother and fewer translation issues occur. If you are using Python for back end systems , chances are your front end will still be written in JavaScript. This mean s that you will have to exchange data between the two. And while Python may have prescribed resources to help with this transfer, if you custom make something you might run into some issues.

Node.js is also preferable for its speed and hasty performance, which is useful for real-time applications, such as instant messaging or chat. For this reason, it is also useful for high load applications or supplier applications where processing speed is important (such as booking a ticket).

Python still has a few advantages for the back end. Python is reliable and consistent. It’s also easier to use and set up, and more beginner- friendly . It is also preferable that its scientific background; whether your back end needs to run data science, machine learning applications , or needs to work with big data,then Python will work fine for you.

Ultimately , both languages ‚Äã‚Äãare very popular and in demand. If you are planning to work in a web domain, picking up JavaScript would probably be the best choice, even if you are planning to develop in Python. If you’re planning on doing something that’s not on the web, especially any sort of data science or artificial intelligence applications, or if you’re looking for a good first language to start with, Python is probably your best bet.

Js Node Against Javascript __del__: Questions

How can I make a time delay in Python?

5 answers

I would like to know how to put a time delay in a Python script.


Answer #1

import time
time.sleep(5)   # Delays for 5 seconds. You can also use a float value.

Here is another example where something is run approximately once a minute:

import time
while True:
    print("This prints once a minute.")
    time.sleep(60) # Delay for 1 minute (60 seconds).


Answer #2

You can use the sleep() function in the time module. It can take a float argument for sub-second resolution.

from time import sleep
sleep(0.1) # Time in seconds

How to delete a file or folder in Python?

5 answers

How do I delete a file or folder in Python?


Answer #1

Path objects from the Python 3.4+ pathlib module also expose these instance methods:

Js Node Against Javascript around: Questions

Removing white space around a saved image in matplotlib

2 answers

I need to take an image and save it after some process. The figure looks fine when I display it, but after saving the figure, I got some white space around the saved image. I have tried the "tight" option for savefig method, did not work either. The code:

  import matplotlib.image as mpimg
  import matplotlib.pyplot as plt

  fig = plt.figure(1)
  img = mpimg.imread(path)

  extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
  plt.savefig("1.png", bbox_inches=extent)


I am trying to draw a basic graph by using NetworkX on a figure and save it. I realized that without a graph it works, but when added a graph I get white space around the saved image;

import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import networkx as nx

G = nx.Graph()
pos = {1:[100,120], 2:[200,300], 3:[50,75]}

fig = plt.figure(1)
img = mpimg.imread("image.jpg")

nx.draw(G, pos=pos)

extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
plt.savefig("1.png", bbox_inches = extent)


Answer #1

You can remove the white space padding by setting bbox_inches="tight" in savefig:


You"ll have to put the argument to bbox_inches as a string, perhaps this is why it didn"t work earlier for you.

Possible duplicates:

Matplotlib plots: removing axis, legends and white spaces

How to set the margins for a matplotlib figure?

Reduce left and right margins in matplotlib plot


Answer #2

I cannot claim I know exactly why or how my “solution” works, but this is what I had to do when I wanted to plot the outline of a couple of aerofoil sections — without white margins — to a PDF file. (Note that I used matplotlib inside an IPython notebook, with the -pylab flag.)

plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, 
            hspace = 0, wspace = 0)
plt.savefig("filename.pdf", bbox_inches = "tight",
    pad_inches = 0)

I have tried to deactivate different parts of this, but this always lead to a white margin somewhere. You may even have modify this to keep fat lines near the limits of the figure from being shaved by the lack of margins.


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