Draw a Circle Matplotlib Python

Want to know how Python is used for plotting and data visualization? Interested in learning one of the most normally used data visualization libraries in Python? If so, you're in the right place.

In this installment of a two-part tutorial, we'll learn how to use matplotlib, one of the most commonly used data visualization libraries in Python. Over the form of both articles, nosotros'll create dissimilar types of graphs, including:

• Line plots
• Histograms
• Bar plots
• Scatter plots
• Stack plots
• Pie charts

We'll besides see what different functions and modules are bachelor in matplotlib.

Here, we'll explore how to create only line plots and histograms with matplotlib. In addition to plotting graphs, nosotros'll also run across how to modify the default size of graphs and how to add together labels, titles, and legends to their axes.

Fix? Let's get started!

Installing the Matplotlib Library

The simplest way to install matplotlib is to employ the pip installer, which comes with most standard Python installations. Execute the following command from your preferred terminal:

pip install matplotlib        

If you're using the Anaconda distribution of Python, you can also employ the commands mentioned in the official Anaconda documentation to install the matplotlib library.

Importing Required Libraries: Numpy and MatplotLib.pyplot

Once we've installed matplotlib, the next step is to import the required libraries. The pyplot library of matplotlib is used to plot different types of graphs. We'll import it forth with the numpy library.

You'll encounter how exactly nosotros can employ these 2 libraries in a later section. For now, execute the following script to import them:

import matplotlib.pyplot as plt %matplotlib inline  import numpy equally np        

Since I'm using Jupyter Notebook to execute the scripts in this commodity, I have to execute the statement %matplotlib inline, which tells the IDE to plot the graphs inside its interface. If you're not using an IDE like this, you don't need to execute this argument.

Another important matter to note is that we renamed pyplot every bit plt when importing, since it'due south easier to type and is a standard nickname for pyplot. From now on in this article, we'll continue using this nickname.

Now, we have everything we need to start plotting different types of matplotlib graphs.

Irresolute Plot Size Using pyplot

To come across the default plot size of graphs drawn by plt, execute the post-obit script:

plot_size = plt.rcParams["figure.figsize"]  print(plot_size[0])  print(plot_size[1])        

In the script above, nosotros used the rcParams attribute of the plt module and pass in "figure.figsize" as a parameter, which returns a list containing the default width and superlative of the plot. The first index contains the width, and the second alphabetize contains peak. Both values are printed to the screen. You'll see 6 and 4 in the output, which means that the default width of the plot is 6 inches and the default height is 4 inches.

To change the plot size, execute the following script:

plot_size[0] = viii   plot_size[one] = 6   plt.rcParams["figure.figsize"] = plot_size        

In the script higher up, we inverse the width and pinnacle of the plot to viii and 6 inches, respectively.

Line Plots

The line plot is the simplest plot in the matplotlib library; information technology shows the relationship betwixt the values on the x- and y-axes in the form of a curve.

To create a line plot, you can apply the plot office of the plt module. The first argument to the plot office is the list of values that you want to display on the x-axis. The second argument is the list of values to be drawn on the y-axis. Take a wait at the following example:

plt.plot([-3,-2,-ane,0,i,ii,iii],[ix,4,ane,0,1,iv,nine]) plt.evidence()        

In the script above, we have six values in the list for the x-axis. On the y-axis, we have the squares of the 10 values. This means that the line plot will brandish the square part, every bit shown in the output. Note that the default plot colour for matplotlib graphs is blueish.

Information technology's important to mention that you need to call the show function of the plt module if you're using an editor other than Jupyter Notebook. In Jupyter, the show function is optional.

Producing Smooth Curves

Instead of manually inbound the values for the lists for the x and y-axis, nosotros can utilise the linspace function of the numpy library. This role takes iii arguments: the lower bound for the values to generate, the upper bound, and the number of every bit spaced points to render betwixt the lower and upper bounds. Wait at the following script:

ten = np.linspace(-fifteen, xiv, 30) y = np.power(x,3)  plt.plot(x, y, "rebeccapurple")  plt.testify()        

In the above script, we also fabricated employ of the power function of the numpy library to summate the cube of each element in the 10 array. In the output, yous'll come across the line for the cube role displayed in purple, since we specified 'rebeccapurple' as the tertiary parameter of the plot part.

Note for beginners: A function in programming performs specific operations. To pass data to a function, we employ arguments. The function then uses the arguments passed to it. For instance, in the plot part, the first parameter is the information to exist plotted on the x axis, the 2nd parameter is the data to be plotted on the y axis, and the third parameter is the color code. A color code of 'rebeccapurple' corresponds to a shade of purple.

Here's a nautical chart of other colors you can use:

The output looks like this:

Adding Labels, Titles, and Legends

To add labels to the x- and y-axes, you tin can utilize the xlabel and ylabel functions of the plt module. Similarly, to add together title, you lot tin apply championship function as shown beneath:

x = np.linspace(-15, 14, 30) y = np.ability(x,3)   plt.xlabel("input") plt.ylabel("output") plt.title("Cube Root") plt.plot(10, y, "deepskyblue")  plt.show()        

In the output, you should come across your new axis labels and title:

To add together legends to your plot, you'll accept to pass in a value for the label attribute of the plot role as shown beneath:

x = np.linspace(-15, 14, 30)   cube = np.power(x,3) square = np.ability(10,ii)   plt.xlabel("input") plt.ylabel("output") plt.title("Cube Root")   plt.plot(ten, cube, "rebeccapurple", characterization = "Cube")  plt.plot(x, foursquare , "deepskyblue", label = "Square")  plt.fable() plt.testify()        

In the script above, we have 2 plots: one for the square role and another for the cube function. To help distinguish the two, we tin can not only use different colors but besides include a legend that clearly labels which is which. In the script higher up, the fable for the cube plot has been aptly named Cube and volition be drawn in purple. The legend for the foursquare plot is named Foursquare and will be drawn in bluish. The output of the script in a higher place looks similar this:

Pro Tip: How to Improve Matplotlib Line Plots

Yous can also add together markers to the data points on a line plot. To practice so, yous need to pass a value for the marker parameter of the plot role as shown below:

x = np.linspace(-fifteen, xiv, thirty) x = np.linspace(-xv, xiv, 30)  cube = np.power(x,3) square = np.power(ten,two)  plt.xlabel("input") plt.ylabel("output") plt.title("Cube Root")  plt.plot(x, cube, "rebeccapurple", marker = "o", label = "Cube")  plt.plot(x, square , "deepskyblue", marking = "v", label = "Square")  plt.legend() plt.show()        

In the script above, we specified 'o' equally the value for the mark of the cube function; this will generate circles for the data points. Similarly, for the foursquare role, we specified '5' every bit the value for the marker; this uses an upside-down triangle for the points:

The codes to generate different types of markers in matplotlib can be found hither.

Histograms

A histogram shows the distribution of data in the form of data intervals called "bins." To plot a histogram, yous demand to telephone call the hist office of the plt module. The first argument is the data fix, the second is the bins, and the third is the type of histogram you want to plot. Y'all can also use the optional rwidth argument, which defines the width of each interval or "bin" in the histogram. Look at the post-obit example:

stock_prices = [23,21,43,32,45,34,56,23,67,89,23,21,43,32,45,34,56,23,67,89,23,21,43,32,45,34,56,23,67,89]   bins = [20,40,threescore,fourscore,100]  plt.hist(stock_prices, bins, color = "rebeccapurple", histtype="bar", rwidth=0.ix) plt.evidence()        

In the script above, we have imaginary data on the average stock prices of thirty companies. Nosotros define five bins for the information intervals. Side by side, we use the hist function to plot this data. The output looks like this:

Y'all can also create a horizontal histogram. To do so, you lot simply need to laissez passer in the value 'horizontal' as the value for the orientation parameter of the hist function:

stock_prices = [23,21,43,32,45,34,56,23,67,89,23,21,43,32,45,34,56,23,67,89,23,21,43,32,45,34,56,23,67,89]  bins = [20,forty,60,fourscore,100]  plt.hist(stock_prices, bins, color = "deepskyblue", histtype="bar", rwidth=0.ix, orientation = "horizontal") plt.bear witness()        

In the output, you'll encounter a horizontal histogram equally shown below:

Pro Tip: How to Amend Your Matplotlib Histogram

In improver to 1D histograms, you can also plot 2D histograms. To do and so, you need values for both the x and y axis of the 2nd histogram. The hist2d part is used to plot 2D histograms:

stock_prices = [23,21,43,32,45,34,56,23,67,89] years =[2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018]   plt.hist2d(stock_prices, years) plt.show()        

This script plots the stock prices confronting their years as shown below:

Hungry for More?

Here, nosotros looked at a brief introduction to plotting information in Python with simple graphs like line plots and histograms, along with their variants. In the 2d part of this series, you lot'll learn how to create bar plots, stack plots, besprinkle plots, and pie plots.

Want to learn more about Python for information science? Be sure to check out our Introduction to Python for Data Science online grade for a beginner'due south guide to Python programming.

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Source: https://learnpython.com/blog/python-drawing-intro-to-matplotlib/

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