Julia – Working with Matplotlib’s Pyplot Class
Last Updated :
26 Apr, 2023
Data visualization is the process of representing the available data diagrammatically. There are packages that can be installed to visualize the data in Julia. Here, PyPlot class of the Matplotlib Module in Julia will be used for plotting.
To install the PyPlot package in Julia, the Matplotlib package of Python must be installed on the user’s device. Using the Pkg command we will install PyPlot in Julia Command-Line. Open the Julia terminal and type the following command:
using Pkg;
Pkg.add(“PyPlot”);
Line Chart
Two 1-D arrays will be provided as input to the plot() function to plot a simple line chart. Below is the Julia program to plot a Line chart:
Julia
using PyPlot;
arr = [2,5,6,8];
arr_2 = arr*3;
plot(arr,arr_2);
title("A simple Line Chart");
PyPlot.show();
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Output:
The graphs can be stylized using various attributes like line color, line width, line style, labels, etc. Some of the attributes are explained below:
Julia
using PyPlot;
arr = [2, 5, 6, 8];
arr_2 = arr * 3;
plot(arr, arr_2, linewidth = 5.0,
linestyle = "--", color = "green");
title("A simple Line Chart");
xlabel("This is X-Axis");
ylabel("This is Y-Axis");
PyPlot.show();
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Output:
Attributes:
- linewidth: It defines the width of the line. It only accepts float values.
- linestyle: This is used to define the style of the line we are plotting, there are different styles like ‘:’, ‘-.’, ‘–‘ etc.
- color: It is used to define the color of the line we are plotting.
- labels: This is used to define the labels for the x-axis and y-axis of the plot.
Bar Chart
A simple bar graph can be used to compare two things or represent changes over time or represent the relationship between two items. To plot a bar chart we will use the bar() function and pass the variables/values we want to plot. Below is the Julia program to plot a bar chart:
Julia
using PyPlot;
arr = range(start = 0, stop = 50, step = 2);
arr_2 = arr * 3;
bar(arr, arr_2, color = "red");
title("A simple Bar Chart");
xlabel("This is X-Axis");
ylabel("This is Y-Axis");
PyPlot.show();
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Output:
Explanation: Here as we can see, inside the bar() function alongside the two arrays we have also passed a keyworded argument color which is used to define the color of the bars of the bar chart.
Scatter Plot
It is used to reveal a pattern in the plotted data. To plot a scatter plot using the scatter() function. Below is the Julia program to plot a scatter plot:
Julia
using PyPlot;
x = [5, 7, 8, 7, 2, 17, 2, 9,
4, 11, 12, 9, 6]
y = [99, 86, 87, 88, 111, 86,
103, 87, 94, 78, 77, 85, 86]
scatter(x, y, color = "red");
title("A simple Scatter Plot");
xlabel("This is X-Axis");
ylabel("This is Y-Axis");
PyPlot.show();
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Output:
Explanation: Here also, inside of the scatter() function, the variables are passed alongside another argument color which is used to define the color of each dot of the scatter plot.
Pie Chart
A pie chart is a statistical graph used to represent proportions. The pie() function will be used to plot a Pie Chart in Julia. Below is the Julia program to plot a pie chart:
Julia
using PyPlot;
x = [2, 4, 3, 1, 7, 9, 3, 1, 4,
5, 6, 8, 10, 12, 4, 3, 9]
y = [99, 86, 87, 88, 111, 86, 103,
87, 94, 78, 77, 85, 86]
pie(x);
title("Pie Chart using PyPlot in Julia");
PyPlot.show();
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Output:
Below is the Julia program to add labels and legends in the pie chart:
Julia
using PyPlot;
fees = [5000, 7500, 3500, 4000, 6500]
gfg = ["DSA", "System Design", "Machine Learning",
"Data Science", "Blockchain"]
pie(fees, labels = gfg);
PyPlot.legend(bbox_to_anchor = (1.50, 1.00), loc = "upper right");
title("Pie Chart using PyPlot in Julia");
PyPlot.show();
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Output:
Attributes:
- bbox_to_anchor: Here a keyworded argument bbox_to_anchor has been used to position the Legend properly so that it doesn’t overlap the plot. It is available in the matplotlib library of Python and can be used here too.
- loc: This is used to mention the location of the legend we are showing with our Pie Chart.
Histogram
Histograms are mostly used for displaying the frequency of data. The bars are called bins. Taller the bins more the data falls under that range. The hist() function will be used to plot a Histogram. Below is the Julia program to plot a Histogram:
Julia
using PyPlot;
x = [2, 4, 3, 1, 7, 9, 3, 1,
4, 5, 6, 8, 10, 12, 4, 3, 9]
hist(x);
title("Histogram using PyPlot in Julia");
xlabel("This is X-Axis");
ylabel("This is Y-Axis");
PyPlot.show();
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Output:
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