How to Fix ModuleNotFoundError: No Module Named ‘IPython’

ModuleNotFoundError: No module named 'IPython' means IPython is not installed for the Python that is running your code. The install command is python -m pip install ipython — note the package name is lowercase while the import is capitalised, which is the first thing that catches people. The second is that the error usually comes from a Jupyter kernel pointing at a different environment.

The fix#

python -m pip install ipython

from IPython.display import display, Markdown
import IPython

print(IPython.__version__)

The python -m prefix guarantees the install lands in the interpreter you just ran. A bare pip is a separate program that may belong to a different Python entirely, which is why “I already installed it” is such a common follow-up.

The capitalisation trap#

import ipython fails even when the package is correctly installed, because module names are case-sensitive. The error message quotes exactly what you asked for, so read it carefully: No module named 'ipython' with a lowercase i is a spelling mistake, not a missing package.

Where it usually comes from#

Most people never import IPython deliberately. It arrives as a dependency:

• Jupyter notebooks — IPython is the kernel
• IPython.display for showing images, HTML and Markdown in a notebook
• Debuggers such as ipdb
• Some plotting and progress-bar libraries that check for a notebook context

from IPython.display import display, HTML, Image, Markdown

display(Markdown("**bold text**"))
display(Image("chart.png"))

The Jupyter kernel mismatch#

This is the real cause in most reports. A notebook runs a kernel, and that kernel is a specific Python environment — often not the one your terminal uses.

Install from inside the notebook so there is no ambiguity:

import sys
!{sys.executable} -m pip install ipython

Then restart the kernel. Python caches imported modules, so a newly installed package is invisible until the kernel restarts.

To register an environment as a kernel properly:

source .venv/bin/activate
python -m pip install ipykernel
python -m ipykernel install --user --name myproject --display-name "Python (myproject)"

The new kernel then appears in Jupyter’s kernel menu, and selecting it means the notebook uses the same environment as your terminal.

jupyter kernelspec list      # what kernels exist and where they point

Virtual environments#

python -m venv .venv
source .venv/bin/activate        # .venv\Scripts\activate on Windows
python -m pip install ipython jupyter

A virtual environment cannot see globally installed packages. If IPython is installed system-wide and your project uses a venv, the import fails inside the venv — correctly, and by design.

Confirming which Python you are on#

import sys
print(sys.executable)
print(sys.version)

python -m pip show ipython

Run the first inside the failing context — the notebook cell, the script, the IDE — and compare with where pip installed the package. Any difference is your answer.

Writing code that works with and without it#

If a script might run in a notebook or a terminal, do not assume IPython exists:

try:
    from IPython.display import display
except ImportError:
    display = print


display("works either way")

To detect a notebook specifically:

def in_notebook():
    try:
        from IPython import get_ipython
        shell = get_ipython()
        return shell is not None and shell.__class__.__name__ == "ZMQInteractiveShell"
    except ImportError:
        return False

What IPython actually gives you#

Beyond notebooks, the ipython command is a much better interactive shell than the default:

ipython

In [1]: %timeit sum(range(1000))
In [2]: %run script.py
In [3]: len?          # documentation
In [4]: len??         # source, where available
In [5]: !ls           # run a shell command
In [6]: %debug        # post-mortem debugger after an error

Tab completion, syntax highlighting, sensible multi-line editing and the magic commands above make it worth installing even if you never touch a notebook.

Where to go next#

• Python virtual environments — the cause of most import mismatches.
• Fixing ModuleNotFoundError for pandas — the same diagnosis in more depth.
• Python and data science — where notebooks fit in.