Python presents one of a most famous plotting library referred to as Matplotlib. It is open-source, cross-platform for making 2D plots for from records in array. It is typically used for records visualization and represent thru the a number of graphs.
Matplotlib is at first conceived by using the John D. Hunter in 2003. The latest version of matplotlib is 2.2.0 released in January 2018.
Before start working with the matplotlib library, we need to install in our Python environment.
Installation of Matplotlib
Type the following command in your terminal and press enter.
pip install matplotlib
The above command will set up matplotlib library and its dependency bundle on Window working system.
Basic Concept of Matplotlib
A format incorporates the following parts. Let’s recognize these parts.
Figure: It is a entire parent which may also hold one or more axes (plots). We can assume of a Figure as a canvas that holds plots.
Axes: A Figure can incorporate a number of Axes. It consists of two or three (in the case of 3D) Axis objects. Each Axes is comprised of a title, an x-label, and a y-label.
Axis: Axises are the wide variety of line like objects and accountable for producing the plan limits.
Artist: An artist is the all which we see on the format like Text objects, Line2D objects, and collection objects. Most Artists are tied to Axes.
Introduction to pyplot
The matplotlib affords the pyplot package deal which is used to plot the design of given data. The matplotlib.pyplot is a set of command style features that make matplotlib work like MATLAB. The pyplot bundle consists of many features which used to create a figure, create a plotting location in a figure, decorates the plot with labels, plot some traces in a plotting area, etc.
We can plot a design with pyplot quickly. Let’s have a look at the following example.
Basic Example of plotting Graph
Here is the fundamental instance of producing a easy graph; the application is following:
from matplotlib import pyplot as plt #ploting our canvas plt.plot([1,2,3],[4,5,1]) #display the graph plt.show()
Ploting Different Type of Graphs
We can plot the a number diagram using the pyplot module. Let’s recognize the following examples.
- Line Graph
The line chart is used to show the information as a sequence of the line. It is easy to plot. Consider the following example.
from matplotlib import pyplot as plt x = [1,2,3] y = [10,11,12] plt.plot(x,y) plt.title("Line graph") plt.ylabel('Y axis') plt.xlabel('X axis') plt.show()
The line can be modified using the a range of functions. It makes the diagram greater attractive. Below is the example.
from matplotlib import pyplot as plt from matplotlib import style style.use('ggplot') x = [10, 12, 13] y = [8, 16, 6] x2 = [8, 15, 11] y2 = [6, 15, 7] plt.plot(x, y, 'b', label='line one', linewidth=5) plt.plot(x2, y2, 'r', label='line two', linewidth=5) plt.title('Epic Info') fig = plt.figure() plt.ylabel('Y axis') plt.xlabel('X axis') plt.show()
2. Bar Graph
Bar design is one of the most common graphs and it is used to signify the records related with the express variables. The bar() characteristic accepts three arguments – express variables, values, and color.
from matplotlib import pyplot as plt Names = ['Arun','James','Ricky','Patrick'] Marks = [51,87,45,67] plt.bar(Names,Marks,color = 'blue') plt.title('Result') plt.xlabel('Names') plt.ylabel('Marks') plt.show()
3. Pie Chart
A chart is a round layout which is divided into the sub-part or segment. It is used to characterize the share or proportional data the place each slice of pie represents a specific category. Let’s recognize the under example.
from matplotlib import pyplot as plt # Pie chart, where the slices will be ordered and plotted counter-clockwise: Aus_Players = 'Smith', 'Finch', 'Warner', 'Lumberchane' Runs = [42, 32, 18, 24] explode = (0.1, 0, 0, 0) # it "explode" the 1st slice fig1, ax1 = plt.subplots() ax1.pie(Runs, explode=explode, labels=Aus_Players, autopct='%1.1f%%', shadow=True, startangle=90) ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle. plt.show()
The histogram and bar format is quite similar however there is a minor difference them. A histogram is used to symbolize the distribution, and bar chart is used to evaluate the unique entities. A histogram is generally used to plot the frequency of a wide variety of values compared to a set of values ranges.
In the following example, we have taken the facts of the distinctive rating percentages of the student and plot the histogram with recognize to quantity of student. Let’s apprehend the following example.
from matplotlib import pyplot as plt from matplotlib import pyplot as plt percentage = [97,54,45,10, 20, 10, 30,97,50,71,40,49,40,74,95,80,65,82,70,65,55,70,75,60,52,44,43,42,45] number_of_student = [0,10,20,30,40,50,60,70,80,90,100] plt.hist(percentage, number_of_student, histtype='bar', rwidth=0.8) plt.xlabel('percentage') plt.ylabel('Number of people') plt.title('Histogram') plt.show()
Let’s understand another example.
Example – 2:
from matplotlib import pyplot as plt # Importing Numpy Library import numpy as np plt.style.use('fivethirtyeight') mu = 50 sigma = 7 x = np.random.normal(mu, sigma, size=200) fig, ax = plt.subplots() ax.hist(x, 20) ax.set_title('Historgram') ax.set_xlabel('bin range') ax.set_ylabel('frequency') fig.tight_layout() plt.show()
5. Scatter Plot
The scatter plot is used to evaluate the variable with admire to the other variables. It is described as how one variable affected the other variable. The facts is represented as a collection of points. Let’s apprehend the following example.
from matplotlib import pyplot as plt from matplotlib import style style.use('ggplot') x = [4,8,12] y = [19,11,7] x2 = [7,10,12] y2 = [8,18,24] plt.scatter(x, y) plt.scatter(x2, y2, color='g') plt.title('Epic Info') plt.ylabel('Y axis') plt.xlabel('X axis') plt.show()
Example – 2:
import matplotlib.pyplot as plt a = [2, 2.5, 3, 3.5, 4.5, 4.7, 5.0] b = [7.5, 8, 8.5, 9, 9.5, 10, 10.5] a1 = [9, 8.5, 9, 9.5, 10, 10.5, 12] b1 = [3, 3.5, 4.7, 4, 4.5, 5, 5.2] plt.scatter(a, b, label='high income low saving', color='b') plt.scatter(a1, b1, label='low income high savings', color='g') plt.xlabel('saving*100') plt.ylabel('income*1000') plt.title('Scatter Plot') plt.legend() plt.show()
In this tutorial, we have discussed all basic kinds of layout which used in statistics visualization. To research greater about graph, visit our matplotlib tutorial.