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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?

Matplotlib plot with colored markers
Matplotlib plot with colored markers

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:

Different Line Color And Marker Color Plot image
Different Line Color And Marker Color Plot image

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! 🙂

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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:

Change Marker Style In Matplotlib
Matplotlib plot with different marker styles

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!

Change Marker Color In Matplotlib
Change Marker Color In Matplotlib

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? 😉

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!

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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:

An example Matplotlib plot with 3 lines drawn from 3 sets of data
An example Matplotlib plot with 3 lines drawn from 3 sets of data

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?

Change Marker Style In Matplotlib
Change Marker Style In Matplotlib

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!

Marker SymbolMarker Style
^Triangle Up
vTria. Down
<Triangle Left
>Tria. Right
1Tripod Down
2Tripod Up
3Tripod Left
4Tripod Right
pPentagon
sSquare
hHexagon
HRoated Hexagon
*Star
+Plus
Horizontal
DDiamond
dLean diamond
|Vertical Line
Available Marker Styles To Draw Using Matplotlib

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! 🙂

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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:

Matplotlib multiple lines same graph
Drawing Multiple Lines Using Matplotlib

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:

Change Line Style In Matplotlib by passing in the style as a parameter to the plot() function.
Change Line Style In Matplotlib by passing in the style as a parameter to the plot() function.

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:

Style SymbolStyle Type
Solid Line
Dashed Line
-.Dash Dot Line
:Dotted Line
4 line styles available for us to choose from in Matplotlib

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? 😉 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! 🙂

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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.

What Is GPT-3?
What Is 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!

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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! 😉

Python Init Function Example and its syntax
Python Init Function Example and its syntax explained in 5 minutes

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!

Know the difference between an expression Vs statement in Python

So until next time, have a nice day! 🙂

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How To Change Line Color In Matplotlib

When we draw the lines in Matplotlib, it uses default colors to draw the plot. However, what if we want to change the line color in Matplotlib to our liking? Is there a way we can do it? In this article, we will try and learn how to do just this.

How To Draw Lines In Matplotlib

But before we change the line color, we need to know how to draw the lines in Matplotlib. We need to know what functions to use and in what order to call them. But lucky enough, we already have an article that explains how to do just this.

So if we simply follow the linked article, we get a graph that looks like this:

Matplotlib multiple lines same graph
Matplotlib plot with multiple lines

And we got the above plot by running the following code:

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 just to help you better, let us go through the code once again.

The second line in the code is how we get a set of values between 1 to 10 stored into our array x.

Now, the next three lines of code is how we draw the lines using Matplotlib. The plot function is the one responsible to draw our lines. Since this function is called 3 times, we get 3 lines drawn.

Also, you can see that the plot function is taking x values as its input parameters. Now since we want 3 different lines, we cant pass the same value of x in all three plot() function calls. Therefore, we multiplied x with 1, 2 & 3 respectively for each of the three lines. So that is how we got the above Matplotlib plot with three lines.

But wait a second! How are the colors of these three lines set? How are the three lines drawn in different colors? Who made them do it?

Well the answer is, it was all done by Matplotlib. Because the default behavior of Matplotlib is to draw lines in different colors if color is not specified. So now you know it!

With this, let us now see how we can change the color of these lines.

How To Change Line Color In Matplotlib

Ok so how do we go about changing colors in Matplotlib? Can we get any help from Matplotlib here?

Luckily, yes! Since changing a line’s color is such a basic requirement that Matplotlib has included it as part of the plot() function itself!

Yes, you read that right. We can change the color of our line by using our plot() function itself. So far we have used plot() function by passing only parameter x & y. However, it also supports another parameter that we can use to set the color of the line it draws. So if we pass a third parameter for the color we want, it draws line using that color! Cool, isn’t it?!

So this begs us the next question. What type of values can we pass to specify a color to the plot() function?

Different Color Parameter Values Accepted By The Plot Function

The Matplotlib’s plot() function supports 3 different types of parameter values:

1. Predefined color set

One is a predefined set of values. This is shown in the following table:

charactercolor
‘b’blue
‘g’green
‘r’red
‘c’cyan
‘m’magenta
‘y’yellow
‘k’black
‘w’white

2. Hexadecimal Color Codes

Since the color drawn by Matplotlib is same as what we use in web development as well, we can use the same Hexademical color codes. So all the colors between #000000 to #FFFFFF becomes valid!

3. RGBA Color Codes

And the final type of color code it supports is the RGBA color code. Again, this is similar to what we see in our web development activity. Hence color codes like (0,1,0,1) become valid color codes again!

So now that we know the different color codes we can pass, let us modify our code to use them.

Now for the sake of simplicity, I will choose the first option. That is, use the values from Matplotlib’s predefined color set. So from the color set, we can see that there are 8 values to choose from. Since we have three lines to draw, let me pick these 3 colors – yellow, red & black.

So with our colors now selected, let us modify the code to add these values. Since we only need to modify three lines of code calling plot(), the final looks like this:

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

Since the three colors are represented by ‘y’, ‘r’ & ‘k’, I just plugged in those values to our plot() function call. So just like that, we get the below output plot with changed line colors:

Matplotlib Plot with changed line colors
Matplotlib Plot with changed line colors

So, there you have it! Since Matplotlib provided us with such a simple solution, this was pretty straight forward. But not all features will be as easy as this one. But we will tackle each one of these problems as and when they appear. So with that I will conclude this article over here. But if you still have any doubts about this, do let me know in the comments below. Until next time, ciao! 🙂

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Matplotlib Save Plot To File In PNG Or JPG Format

Matplotlib can save the plot to a file in PNG or JPG mage format. In this tutorial, we will learn how to get Matplotlib to save it’s output plot to such a file.

Why To Have Matplotlib Save Plot To File?

Matplotlib does a wonderful job in creating excellent plots and charts for us . However, the problem is it uses its own output window while displaying its results. While this is ok for us when learning things, it can’t be used in production. This is because in production we need these plots to be available in standard image formats. In other words, we want Matplotlib plot to be available as PNG or JPG file formats.

However, since this is such a basic requirement, Matplotlib already has a built-in function for this. So this function will enable us to use the Matplotlib plots even in production.

Understanding Matplotlib Savefig Function

The savefig function present in Matplotlib will help us in saving out output plot to an image file. This function has a very simple signature that looks like this:

savefig(fname, dpi=None, facecolor='w', edgecolor='w',
        orientation='portrait', papertype=None, format=None,
        transparent=False, bbox_inches=None, pad_inches=0.1,
        frameon=None, metadata=None)

Woah that’s a mouthful you say? Well, the function signature might be looking daunting, but trust me, this is a very simple function to use. Let us go by the arguments passed to this function one by one:

fname - This is an argument with the path and filename to the output file
dpi - This is the resolution of the output file in dots per inch
quality - This argument will help us define the quality of the image we want. Lower quality will result in smaller size of the output file and vice versa. It varies between 1(worst) to 95(best)
optimize - Bool variable. If set to true library will try to optimize the image

So in the above description, we have explained only the important parameters. But for the rest, you should check its official documentation. So in simple terms, the only parameters that we really want to use is the first parameter. It is the name and path of the output file. Rest of the other parameters are all optional.

So with this much knowledge, we can now start using this function. For easier understanding, let us just use the code and plot from our previous article. Currently, the code from our previous article looked 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])
plt.legend(['Blue=1x', 'Orange=2x', 'Green=3x'])

plt.grid()
plt.axis([0, 20, 0 , 40])
plt.xlabel('This is the X axis label')
plt.ylabel('This is the Y axis label')
plt.title('Dummy Plot')
plt.show()

And it’s resulting output plot looked like this:

Simple Matplotlib multiline plot with legends and labels

Up until now, we have been creating plots using Matplotlib that looked like above. However, so far we had not saved this plot to an image file. So In the next section we will learn how to instruct Matplotlib to save the plot to an image file.

Matplotlib Save Plot To File Example Code

As mentioned earlier, we can use Matplotlib to save the output plot to a file using its savefig() function. So in our code above, we simply need to call this function with appropriate arguments. However, to keep this example simple, we will not pass all the arguments. We will only pass the first argument that is the name and path of the resulting output file. But before that, we should also make one more little change to our current program.

Notice that in the current code, we use plt.show() to display the output plot. However, since we now want to save the plot instead of displaying it, we will remove this call. And in place of this, we will call our savefig() function.

So, the final code will look something 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])
plt.legend(['Blue=1x', 'Orange=2x', 'Green=3x'])

plt.grid()
plt.axis([0, 20, 0 , 40])
plt.xlabel('This is the X axis label')
plt.ylabel('This is the Y axis label')
plt.title('Dummy Plot')
#plt.show()      <------ Disable this line of code
plt.savefig('Matplotlib_save_plot.png')

With that simple change of code, we can finally find the output plot saved into a file called Matplotlib_save_plot.png. Here is how the final output plot of Matplotlib saved in the file looks like:

Final resulting image for Matplotlib save plot to file
Matplotlib plot saved to a PNG file

So that is it for this tutorial. Hope it was easy enough for you to understand how to save output plot to a file. If you still have any more queries about it, do let us know in the comments below. Until next time, ciao! 🙂

Categories
DATA MINING DATA SCIENCE DATA VISUALIZATION MACHINE LEARNING PROGRAMMING PYTHON TUTORIALS

Matplotlib Add Legend To An Existing Plot

In this tutorial, we will learn how to use Matplotlib to add legend to an existing plot. We can use Matplotlib to visualize data in different forms such as bar plots, charts, lines etc. However, none of these plots will be meaningful untill a legend is added to them. So, we need to first learn what a legend is. Why it is useful in a Matplotlib plot. And finally, we will learn how to write a simple Python program to achieve this.

What is a legend in Matplotlib plot?

A legend in a Matplotlib plot is a small infobox, whcih helps us in understanding what the plot is representing.

For example, let us take a look at an existing plot from our previous tutorial. It looks like this:

Matplotlib output chart with three lines in a 2D plot

We can see that it is a two dimensional plot. It’s axes labels also tells us what data is used to plot it. However, there is one thing that is still not clear. We are seeing three lines drawn in the plot above. But what does each of these lines represent? That is the question that a legend of a plot will answer.

Each of the above three lines for example could be representing the acceleration of a metro line. So the orange line in the plot could be representing an orange metro line train’s acceleration. Green line in the plot corresponds to a green metro line’s acceleration and so on.

So to represent this information on the plot, we need to make use of legends.

Now that we understand what a legend in a plot is, let us learn how to add one to the above plot.

How to add legend to an existing Matplotlib plot

So let us go back to the Python code from our previous tutorial. It looked 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])

plt.grid()
plt.axis([0, 20, 0 , 40])
plt.xlabel('This is the X axis label')
plt.ylabel('This is the Y axis label')
plt.title('Dummy Plot')
plt.show()

So we had added axes labels and title for the plot in our previous tutorials. It is now time to add legends to the plot above. From the code, we can see that the three lines where generated by multiplying their x-axis values by 1, 2 & 3 respectively. In other words, we got the blue line by multiplying x-axis values with 1. Similarly, orange line by multiplying x-axis values with 2. And finally, green line by multiplying with 3.

So our Matplotlib plot should have a legend that shows Blue=1x, Orange=2x and Green=3x. Do you agree?

Now, to draw a legend in the output of a Matplotlib plot, we will make use of a special function called legend(). Go through theMatplotlib legend function’s documentation. We can see that we can add legend to a plot by simply passing legend’s texts as a list argument to this function. So with this in mind, if we add a line of code like this:

plt.legend(['Blue=1x', 'Orange=2x', 'Green=3x'])

then we should be getting our desired output.

So, our final code to add legend to Matplotlib plot will look something 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])
plt.legend(['Blue=1x', 'Orange=2x', 'Green=3x'])

plt.grid()
plt.axis([0, 20, 0 , 40])
plt.xlabel('This is the X axis label')
plt.ylabel('This is the Y axis label')
plt.title('Dummy Plot')
plt.show()

Notice that we added our legend() function call right after plotting the three lines. The legend values in legend() is also passed in the same order as to how they are plotted. That is we are plotting the lines in the order blue, orange and green. So our legend function’s parameter also lists legends for blue, orange and green in that same order.

With this, our final output plot looks like this:

Matplotlib add legend to an existing plot
Matplotlib add legend to an existing plot

Conclusion

Using Matplotlib to add legend to an existing plot is not difficult. It is as simple as calling Matplotlib library’s legend() function. However, we have to ensure that we are passing the legend parameters in the correct order. So that is all for this tutorial. If you still have any questions about this, do let us know in the comment below. So until next time, ciao! 🙂

Categories
DATA MINING DATA SCIENCE DATA VISUALIZATION MACHINE LEARNING PROGRAMMING PYTHON TUTORIALS

Add Title To Matplotlib Plot Using Python

In this tutorial, we will learn how to add title to a Matplotlib plot using Python. The Matplotlib library gives us a special function that we can use to achieve this. We are going to learn more about this function and how we can use it. But before that, let us first understand what a title is? Why do we need it?

What Is A Title?

A title is a short heading describing the gist of a content. But in our case, we can simply say that a title in Matplotlib plot will give us a hint of what the plot is all about.

How To Add Title To Matplotlib Plot?

So now that we understand what a title is, let us see how we can add it to our Matplotlib plot.

But before we begin, let us first take a look at our last tutorial. In that tutorial, we had just learned how to add labels to the axes of the plot. The output plot of that tutorial looked like this:

Output plot of previous tutorial containing axes labels
Output plot of previous tutorial containing axes labels

Although the above plot looks complete, it is still missing one important element. We are still not able to make out what this plot is all about!

This is because the plot is not having a title. If there was a title in that plot, it would have told us what it is all about.

So now that we know the missing element, let us learn how to add it.

But first, let us get the original code from our previous article that gave us the above plot. It looks 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])

plt.grid()
plt.axis([0, 20, 0 , 40])
plt.xlabel('This is the X axis label')
plt.ylabel('This is the Y axis label')
plt.show()

So, with the above code, it is now time for us to learn about a new function of Matplotlib. This is the function that will help us add title to our plot. This function is part of the sub module pyplot of Matplotlib.

Introducing the Matplotlib title function

The function is simply called title(). The signature of this function looks like this:

matplotlib.pyplot.title(label, fontdict=None, loc='center', pad=None, **kwargs)

From the above function signature, we can see that it can accept a few arguments.

The first argument label will accept a string of text. So we can see that it is text that would get displayed as our title.

The second argument will accept the font information. So we can actually chose our font type here. We can also choose its size, style etc. Take a look at its documentation again to know more.

Next, the third parameter loc will help us in positioning our title. It can be set to one of the following positions: center, left or right. It’s default value is center.

Finally, we will take a look at it’s last parameter pad. It is used to add padding to the title text. So this padding is applied from the top of the axes.

With this much of information in our hand, we can now add our own title to the above plot. Since this plot is a dummy plot, let us use the title “Dummy Plot” for it. So with this, our code will now 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])

plt.grid()
plt.axis([0, 20, 0 , 40])
plt.xlabel('This is the X axis label')
plt.ylabel('This is the Y axis label')
plt.title('Dummy Plot')
plt.show()

From the above code, you can see that we have passed only one argument. The title of the plot with text ‘Dummy Plot’. So we have left all other parameter values to remain at its default values. With this, our new Matplotlib output plot looks like this:

Final Matplotlib output plot with added title
Final Matplotlib output plot with added title

This concludes our current tutorial. If you still have any more doubts on this, do let us know in the comment below. We will be happy to help.

Until next time, ciao! 🙂