Python’s IDLE (Integrated Development and Learning Environment) is a great tool for developing software in Python. In this tutorial, we will demonstrate how to import the NumPy library in Python’s IDLE.

**NumPy** is one of the most popular libraries for numerical computing in Python, and you will typically need to import it or parts of it to perform these types of calculations in your programs.

### Step 1: Install NumPy

Before importing NumPy, we need to ensure that it’s installed. NumPy is not included by default in Python, so it has to be installed separately. This can be achieved using Python’s package installer, pip. Execute the following command in your command prompt or terminal:

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pip install numpy |

If the above command is not working, try using:

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pip3 install numpy |

For more details on how to install NumPy, check out the official NumPy installation guide

### Step 2: Open Python’s IDLE

Once you complete the installation, open Python’s IDLE. The environment provides a simple and efficient workspace for Python development. You can access IDLE from the Python folder in your computer’s Start menu.

### Step 3: Import the NumPy Library

Now that you are in IDLE, you can import the NumPy library. You can do this by simply typing:

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import numpy |

Better yet, import it as np, which is the common convention. This is achieved by typing:

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import numpy as np |

### Step 4: Verify the Import

You can now verify whether NumPy has been successfully imported by performing some operations. For example, creating a NumPy array:

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array = np.array([1, 2, 3]) print(array) |

If the code runs without any errors, then you’ve successfully imported NumPy into your Python IDLE environment!

## The Full Code

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import numpy as np array = np.array([1, 2, 3]) print(array) |

## Output:

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[1 2 3] |

## Conclusion

Throughout this tutorial, we have demonstrated how to import **NumPy** in the Python IDLE environment. We’ve covered the installation of the library, the different ways NumPy can be imported into Python IDLE, and how to verify its import.

Understanding these simple steps can greatly assist you in setting up the Python IDLE workspace for any scientific calculations or data analysis tasks using NumPy. Happy coding!