Creating Line Plots with Custom Data¶

Matplotlib is a powerful plotting library in Python that allows you to create a wide range of static, animated, and interactive visualizations.

Matplotlib is widely used in data science and analytics to visualize data and gain insights. It integrates well with other libraries like NumPy and Pandas, making it a versatile tool for data visualization tasks.

Installing Matplotlib¶

Before we begin, we need to ensure that Matplotlib is installed in our Python environment. You can install it using pip:

In [ ]:

!pip install matplotlib

!pip install matplotlib

If you're using a virtual environment (which is recommended to avoid package conflicts), make sure it's activated before running the installation command.

Importing Matplotlib and Creating Data¶

First, we'll import the necessary module from Matplotlib and create some sample data to plot.

In [2]:

import matplotlib.pyplot as plt

import matplotlib.pyplot as plt

Now, let's create some data. We'll simulate the average monthly temperatures of two different cities over a year.

In [3]:

# Months of the year
months = range(1, 13)

# Average monthly temperatures for City A and City B
city_a_temps = [30.0, 32.5, 45.0, 55.5, 65.0, 75.0, 80.0, 78.0, 70.0, 58.0, 45.0, 35.0]
city_b_temps = [25.0, 28.0, 38.0, 50.0, 60.0, 70.0, 85.0, 83.0, 68.0, 55.0, 40.0, 30.0]

# Months of the year months = range(1, 13) # Average monthly temperatures for City A and City B city_a_temps = [30.0, 32.5, 45.0, 55.5, 65.0, 75.0, 80.0, 78.0, 70.0, 58.0, 45.0, 35.0] city_b_temps = [25.0, 28.0, 38.0, 50.0, 60.0, 70.0, 85.0, 83.0, 68.0, 55.0, 40.0, 30.0]

In this example:

• months represents the months from January to December.
• city_a_temps and city_b_temps are lists containing the average temperatures for City A and City B, respectively.

Creating a Simple Line Plot¶

Let's create a simple line plot to visualize the temperature data for City A.

In [4]:

plt.plot(months, city_a_temps)
plt.show()

plt.plot(months, city_a_temps) plt.show()

When you run this code, a window should pop up displaying the line plot.

• plt.plot(months, city_a_temps) plots the temperature data against the months.
• plt.show() displays the plot.

Adding Titles and Labels¶

Our current plot lacks context. Let's add a title and labels for the x-axis and y-axis.

In [5]:

plt.plot(months, city_a_temps)
plt.title('Average Monthly Temperatures in City A')
plt.xlabel('Month')
plt.ylabel('Temperature (°F)')
plt.show()

plt.plot(months, city_a_temps) plt.title('Average Monthly Temperatures in City A') plt.xlabel('Month') plt.ylabel('Temperature (°F)') plt.show()

• plt.title() adds a title to the plot.
• plt.xlabel() and plt.ylabel() label the x-axis and y-axis, respectively.

Plotting Multiple Lines¶

Now, let's plot the temperature data for both City A and City B on the same graph to compare them.

In [6]:

plt.plot(months, city_a_temps)
plt.plot(months, city_b_temps)
plt.title('Average Monthly Temperatures')
plt.xlabel('Month')
plt.ylabel('Temperature (°F)')
plt.show()

plt.plot(months, city_a_temps) plt.plot(months, city_b_temps) plt.title('Average Monthly Temperatures') plt.xlabel('Month') plt.ylabel('Temperature (°F)') plt.show()

This will display both lines on the same plot, but we still can't distinguish which line corresponds to which city.

Adding a Legend¶

To differentiate between the two lines, we'll add a legend.

In [7]:

plt.plot(months, city_a_temps, label='City A')
plt.plot(months, city_b_temps, label='City B')
plt.title('Average Monthly Temperatures')
plt.xlabel('Month')
plt.ylabel('Temperature (°F)')
plt.legend()
plt.show()

plt.plot(months, city_a_temps, label='City A') plt.plot(months, city_b_temps, label='City B') plt.title('Average Monthly Temperatures') plt.xlabel('Month') plt.ylabel('Temperature (°F)') plt.legend() plt.show()

• The label parameter in plt.plot() assigns a label to each line.
• plt.legend() displays the legend on the plot.

Customizing Line Styles and Colors¶

We can customize the appearance of the lines to make the plot more visually appealing.

In [8]:

plt.plot(months, city_a_temps, label='City A', color='blue', linestyle='--', marker='o')
plt.plot(months, city_b_temps, label='City B', color='red', linestyle='-', marker='s')
plt.title('Average Monthly Temperatures')
plt.xlabel('Month')
plt.ylabel('Temperature (°F)')
plt.legend()
plt.show()

plt.plot(months, city_a_temps, label='City A', color='blue', linestyle='--', marker='o') plt.plot(months, city_b_temps, label='City B', color='red', linestyle='-', marker='s') plt.title('Average Monthly Temperatures') plt.xlabel('Month') plt.ylabel('Temperature (°F)') plt.legend() plt.show()

• color changes the line color.
• linestyle changes the style of the line (e.g., solid '-', dashed '--').
• marker adds markers to data points (e.g., circle 'o', square 's').

Using Built-in Styles¶

Matplotlib comes with several built-in styles that can change the overall appearance of your plots.

In [9]:

print(plt.style.available)

print(plt.style.available)

['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']

This will print a list of available styles. Let's use the 'seaborn' style.

In [11]:

plt.style.use('seaborn-v0_8-bright')

plt.plot(months, city_a_temps, label='City A', marker='o')
plt.plot(months, city_b_temps, label='City B', marker='s')
plt.title('Average Monthly Temperatures')
plt.xlabel('Month')
plt.ylabel('Temperature (°F)')
plt.legend()
plt.show()

plt.style.use('seaborn-v0_8-bright') plt.plot(months, city_a_temps, label='City A', marker='o') plt.plot(months, city_b_temps, label='City B', marker='s') plt.title('Average Monthly Temperatures') plt.xlabel('Month') plt.ylabel('Temperature (°F)') plt.legend() plt.show()

• plt.style.use('seaborn') applies the Seaborn style to all subsequent plots.
• We've removed the explicit color and linestyle parameters to use the style's defaults.

Saving the Plot¶

To save the plot as an image file, use plt.savefig().

In [12]:

plt.style.use('seaborn-v0_8-bright')

plt.plot(months, city_a_temps, label='City A', marker='o')
plt.plot(months, city_b_temps, label='City B', marker='s')
plt.title('Average Monthly Temperatures')
plt.xlabel('Month')
plt.ylabel('Temperature (°F)')
plt.legend()
plt.tight_layout()  # Adjusts plot to ensure everything fits without overlap
plt.savefig('average_monthly_temperatures.png')
plt.show()

plt.style.use('seaborn-v0_8-bright') plt.plot(months, city_a_temps, label='City A', marker='o') plt.plot(months, city_b_temps, label='City B', marker='s') plt.title('Average Monthly Temperatures') plt.xlabel('Month') plt.ylabel('Temperature (°F)') plt.legend() plt.tight_layout() # Adjusts plot to ensure everything fits without overlap plt.savefig('average_monthly_temperatures.png') plt.show()

• plt.tight_layout() adjusts the padding of the plot.
• plt.savefig('filename.png') saves the current figure to a file.

Stay Up-to-Date

Sign up for the AI Learning Hub Newsletter to receive the latest updates, new tutorial releases, and valuable insights on AI, directly to your inbox.