How to Install Matplotlib in Python (Windows, Mac & Linux)

Installing Matplotlib takes just one command in most cases. But if you’re hitting errors like ModuleNotFoundError, pip not recognized, or a blank plot that refuses to show up — you’re in the right place.

In this guide, I’ll walk you through every way to install Matplotlib: using pip, conda, a virtual environment, and your Linux package manager. I’ll cover Windows, macOS, and Linux, and I’ll also show you how to verify the install, run your first plot, fix common errors, and upgrade or uninstall it when needed.

What Is Matplotlib and Why Use It?

Matplotlib is a Python library for creating charts and visualizations — line graphs, bar charts, scatter plots, histograms, you name it. It’s one of the most-used libraries in data science, machine learning, and everyday data analysis.

If you’ve seen a Python-generated chart anywhere online, there’s a good chance it was built with Matplotlib.

It works well with NumPy, Pandas, and Jupyter Notebooks, making it a staple in the Python data stack.

Before You Install — Quick Checklist

Before running the install command, check these three things:

• Python is installed. Open your terminal and run python –version or python3 –version. You should see Python 3.8 or higher. If not, download it from python.org.
• pip is available. Run pip –version. If pip isn’t recognized, run python -m ensurepip –upgrade to install it.
• You’re in the right environment. If you’re using a virtual environment or Anaconda, make sure it’s activated first.

Install Matplotlib Using pip (Recommended)

For most users on any operating system, this is the command you want:

python -m pip install matplotlib

You can refer to the screenshot below to see the output.

I always recommend using python -m pip instead of just pip; it makes sure you’re installing into the exact Python environment you’re working in, not some other Python version silently installed on your machine.

To also upgrade pip before installing (good practice):

python -m pip install -U pip
python -m pip install -U matplotlib

You can refer to the screenshot below to see the output.

Install Matplotlib on Windows — Step by Step

Here’s the exact process on Windows 10 or 11:

Step 1: Open Command Prompt (search for cmd in the Start menu).

Step 2: Check your Python version:

python --version

Step 3: Upgrade pip:

python.exe -m pip install --upgrade pip

Step 4: Install Matplotlib:

python -m pip install matplotlib

Step 5: Verify it worked:

python -c "import matplotlib; print(matplotlib.__version__)"

You should see something like 3.10.1 printed in the terminal. If you do — you’re good to go.

Tip: If you get python is not recognized as an internal or external command, Python wasn’t added to PATH during installation. Reinstall Python and tick the “Add Python to PATH” checkbox on the first installer screen.

Install Matplotlib on macOS

macOS ships with its own system Python, but you don’t want to use that for your projects. I’d strongly recommend using a separate Python from python.org or Homebrew.

If you installed Python from python.org or Homebrew:

python3 -m pip install matplotlib

Verify the install:

python3 -c 'import matplotlib; print(matplotlib.__version__, matplotlib.__file__)'

This prints both the version and the file path, so you can confirm you’re using the right Python installation.

macOS tip: If you run which python3 and see /usr/bin/python3, that’s the system Python. That’s usually fine for installing packages, but for serious projects, use a virtual environment (covered below).

Install Matplotlib on Ubuntu/Linux

On Linux, you have two good options: pip or your system’s package manager.

Using pip (gets the latest version):

python3 -m pip install matplotlib

Using the system package manager (simpler, but may install an older version):

• Debian / Ubuntu:

sudo apt-get install python3-matplotlib

• Fedora:

sudo dnf install python3-matplotlib

• Red Hat:

sudo yum install python3-matplotlib

• Arch Linux:

sudo pacman -S python-matplotlib

I personally prefer pip for Linux when working on data projects, since the apt/dnf version can lag a few releases behind the latest Matplotlib.

Install Matplotlib with Conda/Anaconda

If you’re working in Anaconda or Miniconda, use conda to install Matplotlib so all dependencies are managed together properly.

From the Anaconda default channel:

conda install matplotlib

From the community-maintained conda-forge channel (recommended — usually more up to date):

conda install -c conda-forge matplotlib

After installation, verify it:

python -c "import matplotlib; print(matplotlib.__version__)"

Install Matplotlib in a Virtual Environment (Best Practice)

If you’re working on any real project, a virtual environment is the way to go. It keeps your project dependencies isolated so they don’t interfere with each other or your system Python.

Here’s the full workflow:

Step 1: Create the virtual environment

python -m venv myenv

Step 2: Activate it

On Windows:

myenv\Scripts\activate

On macOS/Linux:

source myenv/bin/activate

You’ll see (myenv) appear at the start of your terminal prompt — that tells you the environment is active.

Step 3: Install Matplotlib inside the environment

pip install matplotlib

Step 4: Verify

python -c "import matplotlib; print(matplotlib.__version__)"

Step 5: When you’re done, deactivate

deactivate

Every project I work on gets its own virtual environment. It saves a lot of headaches down the road with conflicting package versions.

How to Verify Matplotlib Is Installed

Run this in your terminal (not inside Python — just directly in the command line):

python -c "import matplotlib; print(matplotlib.__version__)"

If Matplotlib is installed, you’ll see the version number. Something like:

3.10.1

You can also check the install location at the same time:

python -c "import matplotlib; print(matplotlib.__version__, matplotlib.__file__)"

Inside a Python shell or Jupyter Notebook, you can also run:

import matplotlib
print(matplotlib.__version__)

Your First Matplotlib Plot (Test It Works)

Once Matplotlib is installed, the best way to confirm everything is working is to actually run a plot. Paste this into a .py file or Jupyter Notebook and run it:

import matplotlib.pyplot as plt

# Sample data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

# Create the plot
plt.plot(x, y, marker='o', color='steelblue', linewidth=2, markersize=8)

# Add labels and title
plt.title("My First Matplotlib Plot", fontsize=14)
plt.xlabel("X Axis")
plt.ylabel("Y Axis")

# Add grid lines for readability
plt.grid(True, linestyle='--', alpha=0.6)

# Display the plot
plt.show()

If a window pops up showing a clean line chart, Matplotlib is fully working. If the plot doesn’t appear, see the error fixes below.

Common Matplotlib Installation Errors & Fixes

This section is what most tutorials skip. Here are the errors I see most often, and how to actually fix them:

The single most common mistake: Running pip install matplotlib when you have multiple Python versions installed. Python might be 3.12, but pip might be pointing to an older Python 2.x. Always use python -m pip install matplotlib — this guarantees pip installs into the Python you’re actually using.

pip vs conda vs venv — Which Should You Use?

Here’s my quick take on when to use each method:

My recommendation: for any project beyond quick experimentation, use a virtual environment with pip. It’s the most reliable setup and works the same way across Windows, macOS, and Linux.

How to Upgrade or Uninstall Matplotlib

Upgrade to the latest version:

pip install --upgrade matplotlib

Check what version you have before upgrading:

pip show matplotlib

This shows you the version, install location, and dependencies, very useful for debugging.

Uninstall Matplotlib completely:

pip uninstall matplotlib

You’ll be asked to confirm. Press y and hit Enter.

If you need a specific version (for compatibility with an older project):

pip install matplotlib==3.8.0

FAQ

You may read:

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• Create Two Y Axes Bar Plot in Matplotlib

Bijay Kumar

Bijay Kumar is an experienced Python and AI professional who enjoys helping developers learn modern technologies through practical tutorials and examples. His expertise includes Python development, Machine Learning, Artificial Intelligence, automation, and data analysis using libraries like Pandas, NumPy, TensorFlow, Matplotlib, SciPy, and Scikit-Learn. At PythonGuides.com, he shares in-depth guides designed for both beginners and experienced developers. More about us.

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