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We have created 43 tutorial pages for you to learn more about NumPy. Starting with a basic introduction and ends up with creating and plotting random data sets, and working with NumPy functions:
What is NumPy? NumPy is a Python library used for working with arrays. It also has functions for working in domain of linear algebra, fourier transform, and matrices. NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely. NumPy stands for Numerical Python.
Array indexing is the same as accessing an array element. You can access an array element by referring to its index number. The indexes in NumPy arrays start with 0, meaning that the first element has index 0, and the second has index 1 etc.
Create a NumPy ndarray Object. NumPy is used to work with arrays. The array object in NumPy is called ndarray. We can create a NumPy ndarray object by using the array() function.
Example Get your own Python Server. Slice elements from index 1 to index 5 from the following array: import numpy as np. arr = np.array ( [1, 2, 3, 4, 5, 6, 7]) print(arr [1:5]) Try it Yourself ». Note: The result includes the start index, but excludes the end index.
Installation of NumPy. If you have Python and PIP already installed on a system, then installation of NumPy is very easy. Install it using this command:
The exercises are a mix of "multiple choice" and "fill in the blanks" questions. There are between 3 and 9 questions in each category. The answer can be found in the corresponding tutorial chapter. If you're stuck, or answer wrong, you can try again or hit the "Show Answer" button to see the correct answer.
In NumPy we work with arrays, and you can use the two methods from the above examples to make random arrays. Integers The randint() method takes a size parameter where you can specify the shape of an array.
ufuncs stands for "Universal Functions" and they are NumPy functions that operate on the ndarray object. Why use ufuncs? ufuncs are used to implement vectorization in NumPy which is way faster than iterating over elements.
Iterate on the elements of the following 3-D array: import numpy as np. arr = np.array ( [ [ [1, 2, 3], [4, 5, 6]], [ [7, 8, 9], [10, 11, 12]]]) for x in arr: print(x) Try it Yourself ». To return the actual values, the scalars, we have to iterate the arrays in each dimension.
