Matplotlib PyCharm

Matplotlib PyCharm

  1. Install Matplotlib in PyCharm
  2. Manually Install Matplotlib in PyCharm
  3. Types of Plots With Matplotlib
  4. Conclusion

Matplotlib is a Python script module used to make 2D graphs and plots. With features to control line styles, font attributes, formatting axes, and other features.

It offers various graphs and plots, including error charts, bar charts, power spectra, and histograms.

Matplotlib is a multi-platform data visualization package created to deal with the larger SciPy stack and is based on NumPy arrays. One of the visualization’s biggest advantages is that it gives us visual access to vast volumes of data in forms that are simple to understand.

Install Matplotlib in PyCharm

To install the Matplotlib module in PyCharm IDE, run the following code in the IDE’s terminal.

C:\Users\Your Name>pip install matplotlib

Then import the Matplotlib module into your program in PyCharm IDE using the import keyword.

import matplotlib

Manually Install Matplotlib in PyCharm

To manually install the famous data visualization library matplotlib in your PyCharm IDE, follow the following steps.

  • Go to PyCharm IDE.
  • Create a Python project.
  • Go to File on the top bar and select Project from Settings.
  • Click on the Python Interpreter to reach the package section.
  • Click on the + symbol which indicates that install a new package or library.
  • Clicking on the + symbol will open a new window, now search for matplotlib and click on the suggested one.
  • Click Install Package.
  • Once installed, you will get the message package matplotlib installed successfully.
  • Now you can import it into your current project using the import keyword.

Types of Plots With Matplotlib

As we mentioned above, you can make many different plots and graphs with the help of the Matplotlib library. So, here we are going to discuss some of them.

Line Plot With Matplotlib

As its name describes, the line plot forms a straight line on the x and y-axis. You need to insert the parameters in an array form that will create a straight 2D line.

import sys
import matplotlib
%matplotlib inline

import matplotlib.pyplot as plt
import numpy as np

xpoints = np.array([2, 6])
ypoints = np.array([5, 12])

plt.plot(xpoints, ypoints)
plt.show()

Output:

line plot

Matplotlib Marker

A unique method of managing markers in Matplotlib graphs is called Matplotlib Marker. Graphs can be customized using marker functions because they come with various markers and other indicating icons.

The following are a few of the most commonly used markers.

Markers Description
. point
, pixel
o circle

Let’s understand all these with the help of examples.

for Point Marker

To form this, we use . here is the code:

import sys
import matplotlib
%matplotlib inline

import matplotlib.pyplot as plt
import numpy as np

ypoints = np.array([5, 13, 21, 25])

# for point marker

plt.plot(ypoints, marker = '.')
plt.show()

Output:

marker point

for Circle Marker

Use the following code to make a o (circle) marker:

import sys
import matplotlib
%matplotlib inline

import matplotlib.pyplot as plt
import numpy as np

ypoints = np.array([4, 9, 19, 26])

# for circle
plt.plot(ypoints, marker = 'o')
plt.show()

Output:

marker circle

Matplotlib Grid

The grid inside the graphic can be shown or hidden using the axes object’s grid() function.

Following is the code for the Matplotlib grid:

import sys
import matplotlib
%matplotlib inline

import numpy as np
import matplotlib.pyplot as plt

x = np.array([60, 65, 70, 75, 80, 85, 90, 95, 100, 105])
y = np.array([140, 150, 160, 170, 180, 190, 200, 210, 220, 230])

plt.plot(x, y)

plt.grid()

plt.show()

Output:

grid plot

Matplotlib Bar

To show the progress of values of different categories, you can use the bar() function.

import sys
import matplotlib
%matplotlib inline

import matplotlib.pyplot as plt
import numpy as np

x = np.array(["Maths", "Physics", "Chemistry", "Biology"])
y = np.array([80, 84, 75, 90])

plt.bar(x,y)
plt.show()

Output:

bar plot

Matplotlib Histogram

The histogram graph explains the frequency distribution in a given interval.

import sys
import matplotlib
%matplotlib inline

import matplotlib.pyplot as plt
import numpy as np

x = np.random.normal(140, 30, 270)

plt.hist(x)
plt.show()

Output:

Histogram plot

Conclusion

Matplotlib creates a 2D graph to visualize the inserted information. In this article, we tried to cover all the main graphs that can be plotted with the help of the Matplotlib module.

The compatibility of Matplotlib with a wide range of operating systems and graphics backends is one of its key advantages. You can rely on Matplotlib to function no matter what operating system you use or the output format you prefer.

Zeeshan Afridi avatar Zeeshan Afridi avatar

Zeeshan is a detail oriented software engineer that helps companies and individuals make their lives and easier with software solutions.

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