# Matplotlib PyCharm

`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.

## 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:

### `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:

#### 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:

### `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:

### `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:

### `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:

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