No module named 'numpy'
You are missing numpy package in your Python environment!
You got error message about missing Python package. The error happens when you try to import Python module numpy. The Python complains that numpy module can't be importend because its path is not present in your current Python path.
When using Python srcipt in your terminal your output with error message might look like this:
python -c "import numpy"
Traceback (most recent call last):
File "<string>", line 1, in <module>
ModuleNotFoundError: No module named 'numpy'
Are you using Python notebook? Then your code cell with error message looks similar to:
import numpy
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Cell In[1], line 1
----> 1 import numpy
ModuleNotFoundError: No module named 'numpy'
3 ways to fix - No module named numpy
Don't worry. We will fix the issue. You need to install missing package. We will show you 3 ways of how it can be fixed:
- - install numpy using pip
- - install numpy using conda
- - automatic install of numpy in MLJAR Studio
Why there are three ways to fix this issue? It all depends on which package manager are you using. The most common package manager is pip. The common choice, especially in Windows environments, is conda manager. There are also other Python package managers, for example Poetry, but we don't cover them in this article.
The last way to fix issue, is to use MLJAR Studio. It is a notebook based programming environment that can automatically detect missing Python packages and install them.
How to check if package manager is pip or conda
Are you not sure what package manager are you using? Please try the following command and if you goet the conda version displayed it means that you are using conda package manager:
conda --version
conda 23.11.0
conda --version
Command 'conda' not found
Please try the below code cell if you are in Python notebook. If you get no error then you are using conda otherwise please use pip for installing new packages.
import sys
!conda list --prefix {sys.prefix}
Install numpy with pip
Please run the following command to install numpy with pip:
pip install numpy
If you are using virtual environment, please make sure that it is activated.
For Python notebook users, please use the following command:
import sys
!{sys.executable} -m pip install numpy
This will ensure that a new package is installed in Python from currently used notebook kernel. You will be able to import numpy package in your notebook.
It is worth to check the PyPi website of numpy. You can check there what is the current version and history of releases.
Install numpy with conda
The numpy package can be installed with the command:
conda install -c conda-forge numpy
If you are in Python notebook, please use the following code:
import sys
!conda install --yes --prefix {sys.prefix} -c conda-forge {entry.installName}
The above code cell ensures you that numpy is installed in the correct environment and will be accessable in your current notebook. Please check numpy conda-forge website to check latest version number and history of releases.
Automatic install of numpy in MLJAR Studio
MLJAR Studio is a notebook based programming environment. It offers a set of interactive Python code recipes to make coding super easy.
Each code recipe has information about required packages. If code recipe depends on numpy and it is not available in Python environment then you will get the below information.
Install packages
Please install below packages to use this code recipe.
Please wait a while for numpy package installation after clicking Install package button. You will see below message:
Install packages
Please install below packages to use this code recipe.
After successful installation you will get information that package is available and you can use code recipe.
Required packages
You can focus on coding rather than fighting your python environment thanks to automatic package management.
What is numpy?
NumPy is a fundamental open-source library for numerical computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. The core data structure in NumPy is the ndarray (n-dimensional array), which is a powerful container for homogeneous data. These arrays can be created from Python lists or tuples and can have any number of dimensions. NumPy arrays are highly efficient in terms of memory usage and computational performance, making them suitable for handling large datasets and performing complex mathematical operations.
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