Create Line Charts in Python with Matplotlib

Learn how to build a line chart in Python using Matplotlib, from preparing data to plotting values, labeling axes, and visualizing trends step by step.

1. Step-by-Step Demonstration

Now we will create our first line chart using Python and Matplotlib.

We will use the university enrollment dataset introduced earlier.

Step 1: Install Matplotlib

Matplotlib is a Python library used to create charts and graphs.

Open your terminal or command prompt and execute:

pip install matplotlib

Wait until the installation is completed.

Note: If you are using Jupyter Notebook, you can run !pip install matplotlib in a notebook cell.

Step 2: Import the required library

Before using Matplotlib, we need to import it.

import matplotlib.pyplot as plt

Understand the code:

• import loads a Python library.
• matplotlib is the library name.
• pyplot provides functions for creating charts.
• as plt gives us a short name to use instead of writing matplotlib.pyplot every time.

For example, we can write:

plt.show()

Instead of writing the full library name.

Step 3: Prepare the dataset

We will store the months and enrollment values in two Python lists.

months = [
    "January", "February", "March", "April",
    "May", "June", "July", "August",
    "September", "October", "November", "December"
]

students = [
    50, 65, 60, 80, 75, 100,
    110, 95, 120, 130, 125, 150
]

Understand the code:

• months stores the names of the months.
• students stores the number of enrolled students.
• Each position in the first list corresponds to the same position in the second list.

For example:

Important: Both lists must contain the same number of items.

Step 4: Create a basic line chart

We will use plt.plot() to create our first line chart.

import matplotlib.pyplot as plt

months = [
    "January", "February", "March", "April",
    "May", "June", "July", "August",
    "September", "October", "November", "December"
]

students = [
    50, 65, 60, 80, 75, 100,
    110, 95, 120, 130, 125, 150
]

plt.plot(months, students)

plt.show()

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Understand plt.plot():

plt.plot(months, students)

What happens?

1. Python reads the month names.
2. It reads the corresponding enrollment values.
3. Matplotlib places the values on the chart.
4. It connects the points with lines.
5. plt.show() displays the chart.

Step 5: Add a chart title

A chart title tells viewers what the chart represents.

Use plt.title().

plt.plot(months, students)
plt.title("Monthly Student Enrollment")plt.show()

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Step 6: Add X-axis and Y-axis labels

We should describe what each axis represents.

Use:

• plt.xlabel() for the horizontal axis.
• plt.ylabel() for the vertical axis.

plt.plot(months, students)
plt.title("Monthly Student Enrollment")
plt.xlabel("Months")
plt.ylabel("Number of Students")
plt.show()

Expected chart information:

• Title: Monthly Student Enrollment
• X-axis: Months
• Y-axis: Number of Students

Step 7: Display the chart

The function plt.show() displays the completed chart.

It is normally placed at the end of the chart code.

plt.show()

Without this command, a chart may not appear in some Python environments.

Step 8: Add markers to show individual data points

A line connects data points, but we may also want to display the individual points clearly.

We can use the marker parameter.

plt.plot(months, students, marker="o")
plt.title("Monthly Student Enrollment")
plt.xlabel("Months")
plt.ylabel("Number of Students")
plt.show()

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Here:

• marker="o" displays circular markers.
• Each circle represents one month’s enrollment.

Other marker examples:

Step 9: Change line colors and styles

We can customize the appearance of the line.

plt.plot(    months,    students,    color="green",    linestyle="--",    marker="o")
plt.title("Monthly Student Enrollment")
plt.xlabel("Months")
plt.ylabel("Number of Students")plt.show()

Important parameters:

Common line styles:

Common colors include:

"red", "blue", "green", "orange", "black" and "purple".

Step 10: Add gridlines

Gridlines make it easier to read numerical values.

Use plt.grid().

plt.plot(months, students, marker="o")
plt.title("Monthly Student Enrollment")
plt.xlabel("Months")
plt.ylabel("Number of Students")
plt.grid(True)plt.show()

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Here:

• plt.grid(True) displays gridlines.
• Gridlines help us compare the position of each point with the axis values.

Step 11: Add a legend

A legend identifies the data shown by a line.

We can use label inside plt.plot() and plt.legend() to display it.

plt.plot(    months,    students,    marker="o",    color="blue",    label="University Enrollment")
plt.title("Monthly Student Enrollment")
plt.xlabel("Months")
plt.ylabel("Number of Students")
plt.legend()plt.grid(True)plt.show()

Understand:

• label gives a name to the line.
• plt.legend() displays that name on the chart.

A legend becomes especially important when a chart contains multiple lines.

Step 12: Display values using annotations

Sometimes we want to show the exact numerical value beside each point.

We can use plt.annotate().

plt.plot(months, students, marker="o")

for i in range(len(months)):
    plt.annotate(
        students[i],
        (months[i], students[i])
    )

plt.title("Monthly Student Enrollment")
plt.xlabel("Months")
plt.ylabel("Number of Students")
plt.grid(True)

plt.show()

Understand the new code:

• len(months) returns the number of months.
• range() generates positions from zero to eleven.
• for repeats the annotation operation for every month.
• students[i] gets the enrollment value at position i.
• (months[i], students[i]) identifies the location of the point.

Result: Every data point displays its corresponding enrollment value.

Complete example: Your first professional line chart

Now combine the concepts learned in this section.

import matplotlib.pyplot as plt

# Step 1: Prepare data
months = [
    "January", "February", "March", "April",
    "May", "June", "July", "August",
    "September", "October", "November", "December"
]

students = [
    50, 65, 60, 80, 75, 100,
    110, 95, 120, 130, 125, 150
]

# Step 2: Create line chart
plt.plot(
    months,
    students,
    color="blue",
    linestyle="-",
    linewidth=2,
    marker="o",
    markersize=7,
    label="Student Enrollment"
)

# Step 3: Add chart information
plt.title("Monthly Student Enrollment")
plt.xlabel("Month")
plt.ylabel("Number of Students")

# Step 4: Add data labels
for i in range(len(months)):
    plt.annotate(
        students[i],
        (months[i], students[i]),
        textcoords="offset points",
        xytext=(0, 8),
        ha="center"
    )

# Step 5: Add gridlines and legend
plt.grid(True)
plt.legend()

# Step 6: Display chart
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

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What have we learned?

We have created a line chart with:

• A title.
• X-axis and Y-axis labels.
• A blue connecting line.
• Circular markers.
• Data labels.
• Gridlines.
• A legend.
• Rotated month names.