Categories

## Matplotlib Annotate Text In A Plot Using Python

In this article, we will learn how to annotate text in Matplotlib plot using Python. But if we only add annotation text to a plot, it will not look that great. Because we want that annotation to point to a particular location in the plot. Right? So we will also be looking at how we can add arrows to go with these annotations. By using arrows, we will be able to point it to a location on the plot as well.

Now does that sound good for you? Great! So then let u begin!

First thing first. Let us learn what an annotation is. Alright? Because knowing what an annotation does to a plot will help us know how to use it. Right? So let us start fro there.

## What Is Annotation?

Annotation is a piece of text that we can add to a plot to add give some explanation. So that is all there is to it.

But then you may ask why we want to text annotate a Matplotlib? After all, we already have a way to add text inside a plot in Matplotlib. Right? We have already seen how we can do that in our earlier article. So then why do we need annotation as well?

Well, there is a simple reason for that. So you see, using text( ) function provided by Matplotlib is fine for simple texts. But for larger texts, we want to go for annotations because we can use arrows here! So that way we can use larger texts but still point this text a point on the plot using arrows!

So that is the real benefit of using annotated text in Matplotlib plots rather than normal text( ) function. I hope this is clear to you by now!

So with that, let us see how we can text annotate a point in the Matplotlib plot.

## How To Use Matplotlib To Annotate Text In A Plot

So how do we add annotation to a Matplotlib plot? Well using the annotate( ) function from the pyplot module, of course!

So the pyplot module provides us with an annotate( ) function as well! So using this, we can add annotations to our plot. But how does the signature of this function look like? Let us take a look at it:

``annotate(text, xy, xytext, arrowprops)``

So this is the basic signature of the annotate( ) function. But this signature is not all of it. It is missing few more arguments. But they are all optional. So we have skipped what is not required.

So what do these arguments mean? To understand, let us take a look at them one by one:

``````text - This is the annotation text
xy - This is the x & y co-ordinates of the point on our plot to which we will be adding the annotation
xytext - This is the x & y co-ordinates where we want our annotated text to appear
arrowprops - This will define the properties of our arrow``````

I know this is all looking overwhelming. But it will become clear to you once we looked at an example code using that. Alright? So let us look at that right now!

## Matplotlib Annotate Text Example

So here is our example code:

``````import matplotlib.pyplot as plt
y = [8, 10, 11, 12, 10, 9, 10, 8, 7, 11, 10, 9]
plt.plot(y)
plt.annotate('this is the point I want to point!',
xy=(4, 10), xytext=(5, 10.75), arrowprops=dict(facecolor='red'))
plt.show()``````

As you can see, we are using a simple data set y with 12 values in it. We are then calling the normal plot( ) function to plot this data. But the real meat is in our 4th line where we are using our annotate( ) function.

So as you can see, we are passing a number of parameters here. The first is the text parameter which simply reads “this is the point I want to point!”.

The next is the xy parameter which is the point we want to annotate. This is followed by the xytext parameter which allows us to set the location where we wan our text to appear.

And finally we have the arrowprops parameter which allows us to set the arrow properties.

So using this code we get the following plot:

So there you have it. This is how you can use Python Matplotlib library to create a plot with annotated text. I hope it was easy enough for you. But if you have any questions, do let me know in the comments below and I will be happy to help.

So with that, I will end this tutorial now. Hope you have a nice day! ðŸ™‚

Categories

## How To Plot Bar Chart In Python Using Matplotlib

In this article, we will learn how to plot a Bar Chart in Python using Matplotlib library. In our earlier articles, we learnt how to plot a Histogram and line plots. So this is going to be a follow up on that. But this time with Bar Charts!

So are you ready to learn about it? Great! Then let us start right away!

## Plot Bar Chart In Python Using Matplotlib – The Basics

So to begin with, we will start with the basics. Alright? Because if we get our basics right, everything else will become very clear. I hope you agree with me! So here is the first basic thing to know about:

### What Is A Bar Chart?

A bar chart is a chart that uses rectangular bars whose length is equal to the value it represents. Now this bar can either be a vertical bar or a horizontal bar. Alright? So it can be used in either ways. But the main thing is that it’s width is the value that it represents!

So it is quite simple then. Right? But how are we going to plot a bar chart using Python? Any Guess? Yep, using our good old Matplotlib library!

So how does the code of a simple bar chart using Matplotlib look like? Let us take a look at it next!

## Example Plot Of A Bar Chart In Python Using Matplotlib

So here is our example code that shows us how we can do this:

``````import matplotlib.pyplot as plt
plt.bar([10, 12, 15], [18, 6, 24])
plt.show()``````

Okay. It looks pretty straight forward. So what is going on here?

As you can see in the first line, we are importing the standard plt module from the Matplotlib library. Now this module is like the Swiss army knife of the library. Because this is the module that has all the plotting functions.

So in this case, since we need to plot a bar chart, we will call the bar( ) function! Alright? So that is what we did in line 2.

``plt.bar([10, 12, 15], [18, 6, 24])``

But what are those two list values we are passing here? They are the X & Y co-ordinates. So the first list [10, 12, 15] gives the x-axis co-ordinate values. It is where the left margin of our bars will be drawn. On the other hand, the second list [18, 6, 24] gives us the height of the bars!

And finally in line3, we call the plt.show( ) function that will display our bar chart!

So how does our final Bar Chart look like? Well, take a look at it for yourself!

So there you go! That is how we plot a bar chart in Python using Matplotlib. It is quite easy to plot. Right? But if you still have any doubts, do let me know in the comments below. I will be more than happy to help! ðŸ™‚

Categories

## How To Plot Histogram In Python Using Matplotlib

So in this article, we will take a look at how we can plot histogram in Python using Matplotlib library. Now this is some thing quite different from the basics of line plotting we have seen so far. So I know you will need some time to get through it. So what I will do is to go through it in an easy to understand way. Alright?

So relax, take a cup of coffee if you want to. As we will now look into the plot of histogram in Python using Matplotlib!

## Plot Histogram In Python Using Matplotlib – The Basics

To get started, let us first learn a bit about what Histogram plot is. And then we will look at other questions. Like where it is used and how to draw it using Matplotlib. Okay? Great! So here we go!

### What is an Histogram?

A histogram is a way to display frequencies of some thing. So how does it look like? In simple words, it is drawn using bars.

Oh wait a second! So does that mean that it is a kind of bar graph? Yeah you are right. Kind of!

So what happens is, the data that you want to show in an histogram is grouped together. But it does not mean that they are grouped randomly. But instead, similar data items are grouped together. Alright? Does that make sense? So when you plot, you will be plotting these grouped data on the chart. Okay?

Now there is one other thing. In Matplotlib, we call these groups of data as bins.

### What are Histogram bins?

So a histogram bin is nothing but a group of similar data. That is all it is. So there is nothing really confusing about it!

Alright. So now that we know what a histogram bin in Matplotlib is, it is time for us to see an example of it. So how do we go about creating a plot of histogram in Python? Here is an example of it.

## Plot Histogram In Python Using Matplotlib – Example

So we all know that to start a plot of something, we need data. Right? So how do we get this data? Since histogram is used to plot a lot of data, we cannot create it by hand. So what do we do then? We will have to take help of a library. Of course!

And what better library than NumPy to get a set of random numbers. Right? So that is what we will do. We will use Numpy to generate a bunch of random numbers.

But how many random numbers shall we use? 10, 50 or 100? Naah! We can surely go more than that. Right? So how about using 1000 random numbers? ðŸ˜‰

So here is the piece of code we will use to generate 1000 random numbers using Numpy!

``````import numpy as np
y = np.random.randn(1000)``````

That is it! That is all the code we need to create 1000 random numbers using Numpy! So easy. Right?

So now that we have our data ready, let us see how we can plot it as a Histogram using Python’s Matplotlib.

So the code to plot a histogram using Matplotlib looks like this:

``````import matplotlib.pyplot as plt
import numpy as np

y = np.random.randn(1000)
plt.hist(y);
plt.show()``````

That’s it! We just import pyplot module and call it’s hist( ) function with our data. And the Matplotlib library does the rest. It will go ahead and plot a Histogram in Python for us!

This is very easy right? And that is the beauty of Matplotlib library. The modules and functions are so well written that you can create beautiful histogram plot in Python easily!

So then how does the final output plot of the Histogram look like? Well, you see it for yourself!

### Matplotlib Histogram Bins

Woah! What happened here? We gave it 1000 input data points right? What happened to all of it then? Well let me explain. Here is what Matplotlib has done.

It has taken our 1000 data input and grouped them together into 10 bins. And then it created the above histogram!

So why 10 bins? Why not 12 or 15 or any other number? Now that is a valid question for you to ask. So let me tell you why the number 10.

It is because that is the default number of bins Matplotlib will create for any number of input data you give to it. Okay? Does that make sense?

So in simple terms – Matplotlib took our 1000 data & grouped closer numbers together into 10 bins. It then went on to create the above histogram plot!

So that is all there is to it! But what if we want to have more than 10 bins? Well, we will come to that soon, but not now. Because it is going to need it’s own article that I will write next!

So see you in the next article!

Categories

## How To Use Different Line Color And Marker Color In Matplotlib

In this article, we will learn how to to set different line color and marker color in Matplotlib plot. But if have seen my earlier article, I showed you how we can set colors to markers. Right? So this will be a follow up on that article. Alright?

So what is the problem we are trying to solve here? Well you see, we want to have a plot with lines connecting markers. But then main thing is that we want different line color and the marker color in it! So how can we do that? Let me explain!

So first, let us take a look at our earlier plot. This is how it looked like, right?

So as you can see in the plot above, we had changed the color of the triangle markers to magenta color. Right? So what was the code we used to generate this plot? Let us take a look at it as well:

``````import matplotlib.pyplot as plt
x = range(1, 10)
plt.plot(x, [xi*1 for xi in x], '*')
plt.plot(x, [xi*2 for xi in x], '+')
plt.plot(x, [xi*3 for xi in x], 'm^')
plt.show()``````

So we used the color code ‘m‘ in the third plot to get the color. Right? But what is missing here? Well, you can see that there are no lines drawn. Correct? So how do we go about fixing that? And more importantly, how can we add lines with different colors?

Well, to do that we will need to use certain keywords in the plot( ) function! That is the key to solving this problem! Does that make sense? Great!

So then what keyword do we need to use? Well you see, Matplotlib gives us a lot of keywords to use when plotting. So there are special keys for setting line color as well as marker color!

But how do these keywords look like? Let me explain.

## Keywords To Use For Different Line Color And Marker Color In Matplotlib

There are three keywords we can use to set the color of a line and the marker. They are:

color or c – So by using a color or c keyword in our plot( ) function, we can set the line color of a plot.

markeredgecolor – By using this keyword, we will tell Matplotlib what color to use to draw the edges of our marker.

markerfacecolor – By using this keyword, we can tell Matplotlib what color to use for the face of our marker.

These are the 3 keywords than we can use to set different line color and marker color in Matplotlib. So now that we know what to use, let us next see how we can use it. Alright?

What better way than to use our previous plot and to change it’s color? Right? So let us do just that!

Let us change the color of our plot line to be Yellow while the triangles to be Red with a green border. Alright? So how will our plot then look like? Any guess?

Well, take a look at it yourself below:

Woah! That is nice, right? We now have total control over the colors we can use in our plots, right? So what is the code change we did to get this? Take a look at the code for yourself!

``````import matplotlib.pyplot as plt
x = range(1, 10)
plt.plot(x, [xi*1 for xi in x], '*')
plt.plot(x, [xi*2 for xi in x], '+')
plt.plot(x, [xi*3 for xi in x], color='yellow', marker='^', markeredgecolor='green', markerfacecolor='red')
plt.show()``````

So there is a tiny little change we have done to get this working. As said earlier, we simply used the keywords to set the color like we want. And it did work as we wanted. Right?

## Conclusion

So that is all there is to set different line color and marker color in Matplotlib. You just need to use the right keyword and it will work like a charm!

So with that, I will end this article now. But if you have any doubt about it, do let me know in the comment below. I will be more than happy to help!

So until next time, take care! ðŸ™‚

Categories

## How To Change Marker Color In Matplotlib

So in this article, we will learn how we can change the marker color in a Matplotlib plot. We will first see how we draw these markers and then see what we can do to change their colors.

Does that sound good? Great! Then let us start right away!

But before we start looking on how to change the Marker color of a plot, we need a plot. Right? But where do we get one?

Well, how about we make use of the plot we got from our previous article:

How To Change Marker Style In Matplotlib

That plot should be good enough. Right? So we will use just that!

Here is how the plot then looks like:

But what do we see here? We are seeing that each of the line marker in the plot already has different color. Right? But how did that happen? Who set the color for these markers?

Well the answer to that lies in the default behavior of the Matplotlib library. Because, even if we did not set those color, the library did it by itself. It made sure that each of the line markers got a different color.

That is cool! right? Because in that case, we will not have to worry about setting color ourselves. Isn’t it?

Well yes. That is true for most of the time. But there are times when we want to set the markers with a specific color. So having an option to change the marker color In Matplotlib is still needed. Right?

So then how can we do that? Well that is when the parameters of plot( ) function once again helps us! Here is how we can use it to change the marker color.

## Change Marker Color In Matplotlib

So before we look at how to change the marker color in Matplotlib, let us look at current code. The code that is responsible for the plot created above. This is how that code looks like:

``````import matplotlib.pyplot as plt
x = range(1, 10)
plt.plot(x, [xi*1 for xi in x], '*')
plt.plot(x, [xi*2 for xi in x], '+')
plt.plot(x, [xi*3 for xi in x], '^')
plt.show()``````

So using the above code we got three sets of markers in the above plot, right? And each set had a different color set to it. But what if I want the triangle in the first set to be in the color of magenta?

Well, luckily we can do that! How? By passing our desired color value to the plot( ) function. So the code for that will then look like this:

``````import matplotlib.pyplot as plt
x = range(1, 10)
plt.plot(x, [xi*1 for xi in x], '*')
plt.plot(x, [xi*2 for xi in x], '+')
plt.plot(x, [xi*3 for xi in x], 'm^')
plt.show()``````

Notice the addition of the color value “m” to our third plt.plot( ) function call? That is what will do the trick for us! Here the alue “m” stands for the color “magenta”. This tells the plot( ) function to draw the triangles using magenta color!

So then how does our final plot look like then? Take a look at it for yourself!

So what do you see?!

As you can see, the color of the triangles have changed from green to magenta. And that is what we wanted. Right? ðŸ˜‰

But then you must be asking what are all the available colors that you can use? Right? Well, they are the same set of colors that you used while changing line color in Matplotlib earlier! So it is quite easy then. Isn’t it?

So there you have it! That is how you change the marker color In Matplotlib. With this, I will end this article now. But if you have any questions, do let me know in the comments below.

So until next time, take care!

Categories

## How To Change Marker Style In Matplotlib

So in the previous article, we say how to change line style in Matplotlib. But in this article, we will take a look at how we can change the marker style in Matplotlib. So let us start with some idea about what these markers are in the first place. Shall we?

## What Are Markers In Matplotlib Plots?

So for us to learn what markers are in Matplotlib plots, let us take a look at our previous example. Alright? Show this is the plot that we had in our previous example:

So as you can see here, we have 3 lines drawn using Matplotlib. Right? But how did we get these lines in the first place? Any guess?

To understand that, we need to take a look at the piece of code that generated this plot. So here is what that code looks like:

``````import matplotlib.pyplot as plt
x = range(1, 10)
plt.plot(x, [xi*1 for xi in x], '-.')
plt.plot(x, [xi*2 for xi in x], '--')
plt.plot(x, [xi*3 for xi in x], ':')
plt.show()``````

So as you can see, the three lines we have plotted are drawn using these 3 lines of code:

``````plt.plot(x, [xi*1 for xi in x], '-.')
plt.plot(x, [xi*2 for xi in x], '--')
plt.plot(x, [xi*3 for xi in x], ':')``````

And what is the data set used to plot these lines? There are 3 sets of data and they are:

``````[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
[2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
[3, 6, 9, 12, 15, 18, 21, 24, 27, 30]``````

So as you can see, the three data sets shown above is what resulted in the 3 lines above.

But here is the thing. The lines that we have drawn in the plot are nothing but connections to these points. Right?

So in Matplotlib, we call these points as Markers and the lines joining them as Segments.

Aha! Now you know what these markers are, right? But there is another important thing you should notice here. The plot we have in the above pic is made up of both Markers & Segments. So the above Matplotlib plot has both Markers & Segments in it!

Woah..! We didn’t knew that the plot had markers in it right? But where is it then? Why can’t we see it?

Well it is because the markers in this plot are drawn using their default style and hence it is not clearly visible. Wait, what is the default style of a marker then?

### What Is The Default Style Of A Marker In Matplotlib Plot?

The default style of a Matplotlib Marker is to draw it as a point. And this is the reason why we are not able to see it. Because we are then connecting them by lines!

But then this begs us the next question:

### What can we do to make the markers in Matplotlib visible?

So how are we going to show clearly then? Well, the answer to that once again lies in the parameters we pass to the plot( ) function.

Wait, so how will that look like then?

Well, instead of using the plot( ) function to draw default markers and lines, we do something else. We will ask Matplotlib not to use default markers and also not to draw the lines!

So then, how will that code look like? Well, take a look at it yourself!

``````import matplotlib.pyplot as plt
x = range(1, 10)
plt.plot(x, [xi*1 for xi in x], '*')
plt.plot(x, [xi*2 for xi in x], '+')
plt.plot(x, [xi*3 for xi in x], '^')
plt.show()``````

So when we run this piece of code, what do you think will the plot look like? Any guess?

Woah! Where did this nice little plot come from?! How did we get those cool looking stars and triangles in the plot? Huh?!

### How To Change Marker Styles In Matplotlib

Well here the thing. When we used the marker style ‘*’, ‘+’ and ‘^’ symbol in our code, it got translated to that. Now that is one nice little feature that Matplotlib library is giving to us. Right?

So how do we know what all Marker styles are made available to us by Matplotlib then?

Well that is when this nifty table below will help you out! Take a look at it. It is showing you all the marker styles you can draw on your Python plot to make it look pretty!

And this is how you can get some nice little pretty plots using Matplotlib Python library. So I hope this was quite easy to understand for you. But if you still have any questions around it, do let me know. Because I will be more than happy to help! ðŸ™‚

Categories

## How To Change Line Style In Matplotlib

If you have been working with Matplotlib to plot lines, you might be looking for how you can style lines. Right? So if that is the case, they you have come to the right place. Because in this article, we will learn how to change line style in Matplotlib.

But before we do that, I hope you already know how to plot multiple lines on Matplotlib. But if you do not know, then take a look at the article linked above. That should get you going. Alright?

Okay then. With that, it is time for us to get started on changing the style of our lines now! Are you ready?

## Drawing Multiple Lines Using Matplotlib

So as I said in the earlier section, we first need to have the code to draw multiple lines. Here is that code we have from our earlier article:

``````import matplotlib.pyplot as plt
x = range(1, 10)
plt.plot(x, [xi*1 for xi in x])
plt.plot(x, [xi*2 for xi in x])
plt.plot(x, [xi*3 for xi in x])
plt.show()``````

So this is the same piece of code we have taken from our earlier article. When we run this code, we get the following output:

So as you can see from the above pic, we have 3 different lines plotted on the same graph. But what is even more important is that all these lines are in different color. That is good. Right?

But what we want to do now is to change the line style in this Matplotlib output. Correct? So how do we go abut doing that?

## How To Change Line Style In Matplotlib?

With the basic multi line plot ready, let us now see how we can change the style of these lines.

So how do we go about doing this? Any guess? I want you to think a little bit to see if you can make a guess before you look for the answer. Alright? Just take a minute or two to think it over before you read further.

Okay. Here is what we can do to change the line style that we draw using Matplotlib:

Just like we could change the color of our lines ourselves, we can also change the style of these lines. So how did we change the color of our lines using Matplotlib to what we want, earlier? Yes, we passed in a new parameter to our plot( ) function. Right?

So in the same way, we can change the line style in Matplotlib as well. By passing our desired style as a parameter to our plot( ) function!

So how does that look like? Well, we just need to pass in the style we want as another plot( ) function parameter. So we modify our code in the plt.plot( ) function as shown below:

``````plt.plot(x, [xi*1 for xi in x], '-.')
plt.plot(x, [xi*2 for xi in x], '--')
plt.plot(x, [xi*3 for xi in x], ':')``````

So what happens when you do that? You have just changed the line style of the plots. So here is how it now looks like:

Now that looks like a perfect plot, isn’t it? But this begs us to ask the next question:

### What Are The Options Available To Change The Line Style In Matplotlib

So there are 4 line styles that Matplotlib provides us to choose from. They are:

So if you choose from any one of these styles, you can get some pretty looking plots out there!

## Conclusion

So that is all there is to changing the line style of a Matplotlib plot! You just have to add a new parameter to the plot( ) function with the style information. That is it and then it just works!

Pretty easy way to achieve what we want then, right? ðŸ˜‰ That is the beauty of Matplotlib library. It has so many interesting features with such a simple API!

So then what does our final code look like? Here it is:

``````import matplotlib.pyplot as plt
x = range(1, 10)
plt.plot(x, [xi*1 for xi in x], '-.')
plt.plot(x, [xi*2 for xi in x], '--')
plt.plot(x, [xi*3 for xi in x], ':')
plt.show()``````

So with that, I will end this article now. But if you still have any questions, do let me know in the comment below. I will be more than happy to help! Alright?

So until next time, take care guys and gals out there! Have a great day! ðŸ™‚

Categories

## What Is GPT-3 By OpenAI? What Does It Do?

Are you wondering what is GPT-3, the word that is taking the tech world by storm off late? Are you also trying to see how this is going to change your own world? If you answered yes to any of these, then come join me. Together we will try to explore the world of GPT-3!

So with that info-commercial pitch out of the way, let me know get started on today’s topic. The topic of GPT-3. A new https://en.wikipedia.org/wiki/Natural_language_processingNatural Language Processing (NLP) model that is driving every one crazy these days!

## So What The Hell Is GPT-3 Anyway?

GPT-3 is as said earlier an NLP model. NLP models in simple terms are used to create AI tools that helps us in reading or writing contents.

But what is making GPT-3 special is the fact it has been trained on a large set of data. So the model created by it is so good that you can use it to create many tools. Now some of these tools that we can create can be for Q&A, auto generating contents etc.

But you may ask – What is the big deal in that? Didn’t we already have such models earlier as well? Well, in a sense yes. We did have a few model similar to GPT-3.

But what is setting GPT-3 apart is on how easy it is to create a useful application out of it. You do not have to train the model on a new data set for the most part! Because the model is generated using a variety of datasets.

So you may be wondering what those datasets are, right? Well, from what we know it has been trained on Wikipedia, Common Crawl, Web Text and similar contents.

But it is not just that! GPT-3 is also trained on datasets of different sizes. Each of these datasets having between 125M to 13B parameters!

So you can just imagine the amount of fine tuning gone into building this NLP model!

All in all, it is quite a revolutionary model that can help us create interesting applications on top of it.

There are already apps built and shared on Twitter that is leaving everyone dumb founded. These includes apps like auto frontend creator tool, Auto question answer tool etc. You can see them in action below:

## GPT-3 Examples

All in all, you can expect a flood of such interesting apps built on top of GPT-3 soon.

So here is to hoping that the future looks bright for all of us!

Categories

## Python Init Function Example, Syntax & Best Practices

In this article, we will take a look at the Python Init Function using an example. At first, we learn about it’s syntax and how we can use it. But once we are comfortable with it, we will then learn the best practices to use when we want to call it!

So, does this plan sound good? Yes? Then let’s do it! ðŸ˜‰

## What Is The Syntax Of Python Init Function?

So first, let us start with how the syntax of a Python Init function looks like. Now, for us to know about it, we will have to take a look at the Python’s official document. From this, we now get to know that the init function is a method of a class and it looks like:

``def __init__(self)``

From the above code, we can see that the init function takes at least one argument. This is called as ‘self‘ and it has got a special meaning to it. But before we learn about it, let us first know how Python’s Class works.

You see, in Python a Class can have many methods in it. But it will have one method in it for sure. It is called the init() method. Now this method will be present in a Python Class even if you have not declared it yourself!

So, in other words, init() is a function that you will always find in any Class of Python. If you declared it yourself, it is an explicit init() function. But if you did not, it will be an implicit init() function!

Alright, so we now that an init() function is always present in any Python Class, what does it do? We will answer this question next!

## Why do we need an init() function in Python?

So to answer this, we need you to have some knowledge about Object Oriented Programming (OOP). So if you are not so familiar with it, please go and learn about constructors in OOP. But for now, I will assume that you do know about OOP.

So as you know in OOP, we have a constructor method in every Class. So whenever we create an object using a Class, its constructor method will get called. Right?

In the same way, in Python the init function acts as a constructor. So it will get called whenever we create a Python’s Class object.

So this is why I said that the init() function is always present in a Python Class. So, the syntax of the init() that you saw above is how it looks like when it is implicit.

### What does the self parameter do?

In order to answer this, let us take a look at the syntax once again

``````class Test:
def __init__(self)``````

Here, we are looking at the code of an example Python class called Test. So here we can see that the init function has one argument called self.

Now, in order to for us to understand what this self argument does, let us first create an object of the Test class type.

``t = Test()``

So in the above code, we created a new object instance of the Test class. Now, as soon as this code runs, the init() function of the Test class will get called. But what is getting passed as the self argument here the variable t itself.

So, we now see that the self argument of the init() function points to the instance of the object itself!

Are you clear until now? Good! Then let us now look into an example of Python Init() function.

## Python Init Function Example

So, let us take a look at this Python Init function example code

``````class Point:
def __init__(self, x, y):
_x = x
_y = y

p = Point(1,2)``````

From the above code, we can see that we now have a new Class called Point. We also have an init() function that we have defined explicitly. This init() function takes in three arguments self, x & y.

Is it all clear? Good!

So we can quickly see the advantage of using init() function here. With the help of this function, we are able to set the initial values of x & y variables!

So when we created an instance of Point class using the code:

``p = Point(1,2)``

we were able to set the p object’s x & y values to 1 & 2 right away!

So, if things are clear until this point, we will then move on to the last part of this article.

## Best Practices With Python Init Function

So here is a list of best practices you should follow when writing an init function:

• Always use Init() function to initialize values to object parameters
• Reduce the number of parameters passed to the init() if possible. Do not over use it.
• Keep the init() function simple. Do not abuse it!

## Conclusion

So that is about it! That is all you need to know about Python init() function. We have discussed everything there is to know about Python’s init() function with an example. We also went through the best practice we should follow when making use of it.

So with this, I will end this article here. If you still have any more questions, please ask it in the comment section below!

So until next time, have a nice day! ðŸ™‚

Categories

## Buy Arduino Nano Online At A Low Price

In this article, we will learn how you can buy an Arduino Nano online. We will learn how to buy it online in India at a low price. But before we do that, let us first learn what an Arduino Nano is in the first place. So are you ready to dive in? Then let us go!

## What Is An Arduino Nano Board?

If you are still a beginner in the embedded world, chances are you may have not heard about Arduino Nano until now. So what is it?

Well, the Arduino Nano is a small computer board that one can buy to learn more about computers. It is a computer on a single PCB board that contains all the circuitry needed to run the software code on it.

Oh wait a second here! So we are learning that it is a single board computer, right? Cool! But then you may be wondering how does this Arduino Nano look like?

I am glad you asked this question! Here is a picture of an Arduino Nano board I will share with you for your pleasure. Take a look at it.

Woah! That is one weird looking single board computer out there! But what are all those pins coming out of it?! Looks scary isn’t it?

But don’t worry. Even though the Arduino Nano board looks like that, it is actually easy to use. There are many tutorials we have that will teach how to use it. So learning how to work with it will never be a problem at all.

So with this introduction to Arduino Nano, it is time for us to move on to know how we can buy it online.

## How To Buy Arduino Nano Online?

There are many sellers who are selling the Arduino Nano hardware board. They are selling these Arduino Nano boards in online stores as well as in the offline stores. However, when you try to buy the Arduino Nano board, you will realize that the price differs a lot from one seller to another!

Now this is happening because there they are all not original Arduino boards. You see, the thing is Arduino boards are all launched as open source hardware boards. So, their circuit as well as their board designs are all available for free for anyone to use.

So, what happened because of this is that there are many Arduino Nano clones as well. Since these Arduino clones are coming from China, they will be of cheaper cost.

### So in that case, who is the original manufacturer of Arduino Nano?

Arduino is the company that designs and manufactures the original Arduino boards. But as this company is from Italy, the products it makes are costlier than their clones.

So now this begs us with our next question which is:

### Who manufactures the Arduino clones?

Most of the Arduino clones manufacturers are from China. So since the manufacturing cost of these boards are cheaper in China as compared to Italy, the clones are always cheaper.

So here is the real reason then! This clearly tells us why we see so much difference in the price for the same Arduino Nano boards across different online websites!

So depending on how much you can spend, decide which Arduino Nano board you want to go for.

#### What is the difference between an original Arduino Nano Vs a Nano clone?

Now as I told earlier, both the original and the cloned Nano boards use the same circuit design. So nothing is different over here. But there is one difference between the two. And this difference is in terms of the quality of components used.

So if you want a higher quality board, you pay more and go for original Nano module. But on the other hand, if you want to spend less, just go for a Nano clone. It really doesn’t matter at all. Trust me!

## What Is The Price Of Arduino Nano Online In India?

If you are from India, you can buy the original Arduino Nano online at a low price of Rs.1449. But if you are OK to buy a cheap Arduino Nano clone, you can buy it at a low cost of Rs.200!

## What Is The Price Of Arduino Nano Online In The US?

In the US, you can buy an original Arduino Nano at a cost of \$22.00. But on the other hand, a cloned Arduino Nano will only cost you around \$9! It’s as cheap as that!

## What Is The Online Price Of Arduino Nano In The UK?

But if you are in the UK, you can buy the Nano board at just GBP20!

Have you looked at the new Arduino Portenta?

## Conclusion

So there you have it. We just looked into the price of an Arduino Nano in 3 different regions. I also explained to you why its price changes so much when you want to buy it online. It is because of the clones that has reduced the price of these boards.

I hope you have got what you were looking for in this article. If you still have any more questions on it, ask me right away and I will answer you.

I hope you will have fun working with them. These are the best things you can work on to gain good knowledge. It is also one the best hobby that will keep you busy for some time! So enjoy with your new toys! Until next time, ciao! ðŸ™‚