What line of code will import matplotlib

Matplotlib is a popular Python library used for creating static, animated, and interactive visualizations. In this article, we will explore how to import matplotlib into a Python script or Jupyter notebook.

Importing matplotlib in Python script

To import matplotlib in a Python script, you can use the following line of code:

import matplotlib.pyplot as plt

Here, matplotlib.pyplot is a module within the matplotlib library that provides a MATLAB-like interface for creating plots. By importing matplotlib.pyplot as plt, you can access its functions and classes using the plt prefix.

Example 1:

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4], [5, 6, 7, 8])
plt.show()

Output:

What line of code will import matplotlib

In this example, we import matplotlib.pyplot as plt and use the plot function to create a simple line plot. The show function is then used to display the plot.

Example 2:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)

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

Output:

In this example, we also import the numpy library as np to create an array of x-values using np.linspace and y-values using np.sin. We then plot the sine curve using the plot function.

Importing matplotlib in Jupyter notebook

To import matplotlib in a Jupyter notebook, you can use the following line of code:

%matplotlib inline
import matplotlib.pyplot as plt

Here, %matplotlib inline is a magic command that allows plots to be displayed directly in the notebook, and matplotlib.pyplot is imported as plt in the same way as in a Python script.

Example 3:

%matplotlib inline
import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4], [5, 6, 7, 8])
plt.show()

In this example, we use the magic command %matplotlib inline to display the plot inline in the notebook.

Example 4:

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

x = np.linspace(0, 10, 100)
y = np.sin(x)

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

In this example, we import the numpy library as np and create a sine curve plot similar to Example 2.

Customizing matplotlib plots

Matplotlib provides a wide range of options for customizing plots, including setting plot styles, colors, labels, and formatting. Let’s explore some examples of customizing matplotlib plots using code.

Example 5: Setting plot title and labels

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4], [5, 6, 7, 8])
plt.title('Simple Line Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()

Output:

In this example, we add a title to the plot using the title function and set the x and y-axis labels using the xlabel and ylabel functions.

Example 6: Changing plot style and color

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4], [5, 6, 7, 8], linestyle='--', color='red')
plt.show()

Output:

In this example, we change the line style to a dashed line using the linestyle argument and the line color to red using the color argument.

Example 7: Adding grid lines to the plot

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4], [5, 6, 7, 8])
plt.grid(True)
plt.show()

Output:

In this example, we add grid lines to the plot using the grid function with the argument True.

Example 8: Changing the plot size

import matplotlib.pyplot as plt

plt.figure(figsize=(8, 6))
plt.plot([1, 2, 3, 4], [5, 6, 7, 8])
plt.show()

Output:

In this example, we change the plot size to 8×6 inches using the figure function with the figsize argument.

Example 9: Plotting multiple lines on the same plot

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4], [5, 6, 7, 8], label='Line 1')
plt.plot([1, 2, 3, 4], [8, 7, 6, 5], label='Line 2')
plt.legend()
plt.show()

Output:

In this example, we plot two lines on the same plot and add a legend using the legend function.

Saving matplotlib plots to a file

Matplotlib allows you to save plots to various file formats, such as PNG, PDF, and SVG. Let’s see how to save a matplotlib plot to a file using code.

Example 10: Saving plot as a PNG file

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4], [5, 6, 7, 8])
plt.savefig('plot.png')

In this example, we save the plot to a PNG file named plot.png using the savefig function.

Example 11: Saving plot as a PDF file

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4], [5, 6, 7, 8])
plt.savefig('plot.pdf')

In this example, we save the plot to a PDF file named plot.pdf.

Example 12: Saving plot as a SVG file

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4], [5, 6, 7, 8])
plt.savefig('plot.svg')

In this example, we save the plot to an SVG file named plot.svg.